Stand-alone and Hybrid Wind Energy Systems: Technology, Energy Storage and Applications (Woodhead Publishing Series in Energy)

Stand-alone and hybrid wind energy systems

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Woodhead Publishing Series in Energy: Number 6

Stand-alone and hybrid wind energy systems Technology, energy storage and applications

Edited by J. K. Kaldellis

Oxford

Cambridge

© Woodhead Publishing Limited, 2010

New Delhi

Published by Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, UK www.woodheadpublishing.com Woodhead Publishing India Private Limited, G-2, Vardaan House, 7/28 Ansari Road, Daryaganj, New Delhi – 110002, India www.woodheadpublishingindia.com Published in North America by CRC Press LLC, 6000 Broken Sound Parkway, NW, Suite 300, Boca Raton, FL 33487, USA First published 2010, Woodhead Publishing Limited and CRC Press LLC © Woodhead Publishing Limited, 2010 The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN 978-1-84569-527-9 (book) Woodhead Publishing ISBN 978-1-84569-962-8 (e-book) CRC Press ISBN 978-1-4398-0143-7 CRC Press order number: N10031 The publishers’ policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp which is processed using acid-free and elemental chlorine-free practices. Furthermore, the publishers ensure that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by Toppan Best-set Premedia Limited Printed by TJ International Limited, Padstow, Cornwall, UK

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Contents

Contributor contact details Woodhead Publishing Series in Energy Preface

Part I

1

1.1 1.2 1.3 1.4 1.5 1.6 1.7 2

2.1 2.2 2.3 2.4

Fundamental science and engineering of stand-alone and hybrid wind energy systems and energy storage technology Overview of stand-alone and hybrid wind energy systems J. K. Kaldellis, TEI of Piraeus, Greece Introduction Description of a wind-based stand-alone energy system Description of a stand-alone hybrid energy system Energy storage opportunities of stand-alone hybrid energy systems Applications of stand-alone and hybrid energy systems The future of stand-alone hybrid energy systems References Overview of energy storage technologies for renewable energy systems D. P. Zafirakis, TEI of Piraeus, Greece Introduction Description of a typical energy storage system (ESS) Application range of energy storage systems (ESSs): category of generation Application range of energy storage systems (ESSs): category of transmission and distribution

xiii xvii xix

1

3 3 4 7 11 13 24 26

29 29 32 42 44 v

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vi

Contents

2.5

Application range of energy storage systems (ESSs): category of customer service Application range of energy storage systems (ESSs): requirements of electricity applications Contemporary energy storage systems (ESSs) Mechanical energy storage Chemical energy storage Electrical energy storage Comparison of energy storage systems (ESSs) Future trends References

2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 3

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 4

4.1 4.2 4.3 4.4 4.5 4.6 4.7

Design and performance optimisation of stand-alone and hybrid wind energy systems E. Kondili, TEI of Piraeus, Greece Introduction: scope and objectives of the chapter Energy systems modelling Synthesis, design and operation of a hybrid energy system Hybrid energy systems optimisation techniques Software tools for the simulation and optimisation of hybrid energy systems Summary of optimisation techniques Future trends References and further reading Feasibility assessment for stand-alone and hybrid wind energy systems J. K. Kaldellis, TEI of Piraeus, Greece Introduction First installation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Maintenance and operation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Cost-benefit analysis of a typical stand-alone hybrid electricity generation wind-based (HEW) system Reliability impact-loss of load cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Electricity generation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Socio-environmental impacts of stand-alone hybrid electricity generation wind-based (HEW) systems

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46 47 49 50 53 61 63 72 74

81 81 82 87 91 94 96 97 98

102 102 104 107 109 112 114 115

Contents 4.8 4.9

4.10 4.11

Analysis of case studies of stand-alone hybrid electricity generation wind-based (HEW) systems Sensitivity analysis of the financial behaviour of stand-alone hybrid electricity generation wind-based (HEW) systems Conclusions References

vii

121

145 155 156

Part II Development of stand-alone and hybrid wind energy systems and energy storage technology

163

5

Stand-alone wind energy systems D. Wood, University of Newcastle, Australia and P. Freere, Monash University, Australia Introduction Stand-alone wind energy systems Small wind turbine technology Control and electronics Stand-alone power systems Further aspects of system sizing Conclusions References

165

Hybrid wind–diesel energy systems G. Bhuvaneswari and R. Balasubramanian, Indian Institute of Technology (Delhi), India Introduction Overview of wind–diesel generation system Wind turbine sizing in a hybrid wind–diesel scheme Wind–diesel systems: design considerations Components of a hybrid wind–diesel system Control strategies for wind–diesel generation systems Modelling and simulation of wind–diesel systems Conclusions Future trends References

191

Hybrid wind–photovoltaic energy systems G. Notton, University of Corsica, France Introduction Renewable energy resources and their potential

216

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 6

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 7 7.1 7.2

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165 166 170 177 183 185 188 189

191 192 194 195 197 199 207 211 213 214

216 216

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7.3

Design and configuration of a wind–photovoltaic (PV) hybrid energy system Modelling and simulation of a wind–photovoltaic (PV) hybrid energy system Sizing and optimization of a wind–photovoltaic (PV) hybrid energy system Wind–photovoltaic (PV) hybrid energy system: case studies Future trends Conclusions References Nomenclature

7.4 7.5 7.6 7.7 7.8 7.9 7.10 8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 9

9.1 9.2 9.3 9.4

9.5 9.6

226 227 239 242 247 248 248 251

Hybrid wind–hydrogen energy systems T. Tsoutsos, Technical University of Crete, Greece Introduction Design of wind electrolysis production systems Design of hydrogen storage systems Optimization of wind–hydrogen power systems Environmental impact assessment of wind–hydrogen systems Market potential and barriers for wind–hydrogen systems Future trends Sources of further information and advice References Abbreviations

254

Hybrid wind–hydropower energy systems O. A. Jaramillo, O. Rodríguez-Hernández and A. Fuentes-Toledo, Universidad Nacional Autónoma de México, Mexico Introduction The need to couple wind–hydropower systems (WHPS) Different types of wind–hydropower systems (WHPS) Research and development of wind–hydropower systems (WHPS) (modelling/simulation and evaluation experience) Benefits and limitations of wind–hydropower systems (WHPS) Different operational policies and techniques for isolated grids

282

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254 255 260 263 267 272 274 279 279 281

282 283 284

302 310 314

Contents 9.7 9.8 9.9 9.10 9.11 10

10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 10.11 10.12 10.13 10.14 10.15 10.16 10.17 10.18 10.19 10.20 10.21 11

11.1 11.2 11.3 11.4 11.5

Environmental impacts of wind–hydropower systems (WHPS) The economics of wind–hydropower systems (WHPS) Conclusions Acknowledgements References Electro-chemical energy storage technologies for wind energy systems M. Skyllas-Kazacos, University of New South Wales, Australia Introduction Off-grid or remote power systems Wind–diesel grids Large grid-connected wind farms Energy storage Fundamentals of electrochemical cells Types of electrochemical energy storage technologies Electrochemical capacitors (EC) Fuel cells Lead–acid battery Nickel–metal hydride batteries Li ion battery Metal–air battery Sodium–sulphur (NaS) battery The zero emissions battery research activity (ZEBRA) battery Flow batteries Zn/Br battery All-vanadium redox battery (G1 VB) Vanadium bromide redox battery (G2 V/Br) Summary References Flywheel energy storage technologies for wind energy systems A. Ruddell, STFC Rutherford Appleton Laboratory, UK Introduction Flywheel design and construction Features and limitations of flywheel storage technology Technology status of flywheel storage technology Application of flywheel storage technology

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315 316 318 319 320

323

323 324 326 328 329 329 335 335 336 339 343 344 346 347 350 352 354 357 361 362 363

366

366 368 375 377 383

x

Contents

11.6 11.7

Sources of further information and advice References

12

Compressed air energy storage technologies for wind energy systems A. Cavallo, Princeton, USA Introduction Current status and future progress of compressed air energy storage (CAES) Texas: the Ridge Energy wind compressed air energy storage (CAES) study Wind integration issues Discussion and conclusions References and notes

12.1 12.2 12.3 12.4 12.5 12.6

Part III Applications of stand-alone and hybrid wind energy systems and energy storage technology 13

13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 13.10 13.11 13.12 14

14.1 14.2 14.3

Integration of renewable energy systems into remote micro-grids J. A. Carta, University of Las Palmas de Gran Canaria, Spain Introduction Hybrid micro-grid options General comments on the technological components of isolated micro-grids Architectures for stand-alone hybrid micro-grids Control and monitoring of hybrid micro-grids Design and construction of hybrid micro-grids Modelling and simulation of hybrid micro-grids Optimising integration of hybrid micro-grids Advantages and limitations of hybrid micro-grids Future trends Sources of further information and advice References Integration of stand-alone and hybrid wind energy systems into buildings K. A. Kavadias, TEI of Piraeus, Greece Introduction Building sector characteristics An overview of energy consumption in buildings

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390 390

393 393 396 403 404 418 419

423

425

425 427 429 435 437 442 450 457 461 462 463 464

475 475 477 478

Contents 14.4 14.5 14.6 14.7 14.8 14.9 15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9

European Union facts about hybrid energy systems in buildings Description of hybrid energy systems in buildings Sizing procedure for hybrid energy systems in buildings Operational modes of hybrid energy systems in buildings System performance and optimization of hybrid energy systems in buildings References and further reading

xi

481 482 489 495 496 504

Hybrid wind energy systems for desalination E. Kondili, TEI of Piraeus, Greece Introduction: the water scarcity problem Desalination processes and plants Energy requirements of desalination processes Integrated systems of renewable energy sources (RES) with desalination plants Environmental impacts of renewable energy sources (RES)-based desalination plants Economic considerations in renewable energy sources (RES)-based desalination Future trends Sources of further information and advice References

506

Index

536

© Woodhead Publishing Limited, 2010

506 507 512 516 525 527 531 532 533

Contributor contact details

(* = main contact)

Chapter 3 and 15

Editor and Chapters 1 and 4

E. Kondili Mechanical Engineering Department TEI of Piraeus 250 P. Ralli and Thivon Avenue 122 44 Athens Greece E-mail: [email protected]

J. K. Kaldellis Laboratory of Soft Energy Applications and Environmental Protection TEI of Piraeus PO Box 41046 12201 Athens Greece E-mail: [email protected]

Chapter 2 D. P. Zafirakis Laboratory of Soft Energy Applications and Environmental Protection TEI of Piraeus PO Box 41046 12201 Athens Greece E-mail: [email protected]

Chapter 5 David Wood* School of Engineering University of Newcastle NSW 2308 Australia E-mail: David.Wood@newcastle. edu.au Peter Freere Department of Electrical and Computer System Engineering Monash University Victoria 3800 Australia

xiii © Woodhead Publishing Limited, 2010

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Contributor contact details

Chapter 6

Chapter 9

Dr G. Bhuvaneswari* Department of Electrical Engineering Indian Institute of Technology New Delhi-110016 India E-mail: [email protected]

O. A. Jaramillo* Centro de Investigación en Energía Universidad Nacional Autónoma de México Privada Xochicalco s/n Temixco Morelos CP 62580 México E-mail: [email protected]

Dr R. Balasubramanian Centre for Energy Studies Indian Institute of Technology New Delhi-110016 India E-mail: [email protected]

Chapter 7 G. Notton Department of Renewable Energy University of Corsica – UMR CNRS 6134 Scientific Centre of Vignola Route des Sanguinaires F20 000 Ajaccio France E-mail: [email protected]

Chapter 8 T. Tsoutsos Laboratory of Renewable and Sustainable Energy (ReSEL) Department of Environmental Engineering Technical University of Crete University Campus GR 73100 Chania Greece E-mail: theocharis.tsoutsos@ enveng.tuc.gr

O. Rodríguez-Hernández and A. Fuentes-Toledo Universidad Nacional Autónoma de México Privada Xochicalco s/n Temixco Morelos CP 62580 México E-mail:[email protected] [email protected]

Chapter 10 M. Skyllas-Kazacos School of Chemical Sciences and Engineering Faculty of Engineering University of New South Wales Sydney NSW 2052 Australia E-mail: [email protected]

Chapter 11 A. Ruddell STFC Rutherford Appleton Laboratory Harwell Science and Innovation Campus Didcot OX11 0QX UK E-mail: [email protected]

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Contributor contact details

xv

Chapter 12

Chapter 14

A. Cavallo 289 Western Way Princeton NJ 08540 5336 USA E-mail: [email protected]

K. A. Kavadias Laboratory of Soft Energy Applications and Environmental Protection TEI of Piraeus PO Box 41046 Athens 12201 Greece E-mail: [email protected]

Chapter 13 J. A. Carta Department of Mechanical Engineering University of Las Palmas de Gran Canaria Engineering Building 35017 Las Palmas de Gran Canaria Spain E-mail: [email protected]

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Woodhead Publishing Series in Energy

1

Generating power at high efficiency: Combined cycle technology for sustainable energy production Eric Jeffs

2

Advanced separation techniques for nuclear fuel reprocessing and radioactive waste treatment Edited by Kenneth L. Nash and Gregg J. Lumetta

3

Bioalcohol production: Biochemical conversion of lignocellulosic biomass Edited by Keith Waldron

4

Understanding and mitigating ageing in nuclear power plants: Materials and operational aspects of plant life management (PLiM) Edited by Philip G. Tipping

5

Advanced power plant materials, design and technology Edited by Dermot Roddy

6

Stand-alone and hybrid wind energy systems: Technology, energy storage and applications Edited by J. K. Kaldellis

7

Biodiesel science and technology: From soil to oil Jan C. J. Bart, Natale Palmeri and Stefano Cavallaro

8

Developments and innovation in carbon dioxide (CO2) capture and storage technology Volume 1: Carbon dioxide (CO2) capture, transport and industrial applications Edited by M. Mercedes Maroto-Valer

9

Geologic repositories for safe disposal of nuclear materials Edited by Joonhong Ahn and Mick Apted xvii © Woodhead Publishing Limited, 2010

xviii

Woodhead Publishing Series in Energy

10

Wind energy systems: Optimising design and construction for safe and reliable operation Edited by John Dalsgaard Sørensen and Jens Nørkær Sørensen

11

Solid oxide fuel cell technology: Principles, performance and operations Kevin Huang and John Bannister Goodenough

12

Handbook of advanced radioactive waste conditioning technologies Edited by Michael I. Ojovan

13

Nuclear reactor safety systems Edited by Dan Gabriel Cacuci

14

Materials for energy efficiency and thermal comfort in buildings Edited by Matthew R. Hall

15

Handbook of biofuels production: Processes and technology Edited by Rafael Luque, Juan Campelo and James Clark

16

Developments and innovation in carbon dioxide (CO2) capture and storage technology Volume 2: Carbon dioxide (CO2) storage and utilisation Edited by M. Mercedes Maroto-Valer

17

Oxy-fuel combustion for fossil-fuel power plants: Developments and applications for advanced CO2 capture Edited by Ligang Zheng

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Preface

There is a growing awareness of the necessity to develop and implement innovative energy solutions that will be cost-effective, cover the required energy demand and also minimize potential environmental impacts. Standalone and hybrid wind-based energy systems are a very promising solution with excellent prospects to cover the energy needs of specific areas in an efficient and sustainable way. The development of this book has been stimulated by the continuously increased interest in the stand-alone and hybrid wind-based energy systems. In order to cover the scientific and practical issues of these systems, the book’s chapters deal with the state of the art of the technology, design, operation, feasibility and applications of hybrid wind-based energy systems. The chapters have been written by expert authors from academia and industry, who are highly experienced in their respective fields, and I would like to take the opportunity to sincerely thank all the contributors to the book and to express my hope that the result of our concerted efforts will be rewarding. More specifically, the book is based on a comprehensive synthesis of the aforementioned subjects in holistic chapters that are described below. Stand-alone wind-based hybrid energy systems are an attractive solution supplying clean electricity to autonomous consumers and allowing them to be independent from oil price fluctuations and any political conflicts that might affect the energy sector. Hence, taking into consideration the increased development of these systems and their significant future prospects, Chapter 1 presents an overview of the stand-alone and hybrid wind energy systems. Subsequently, Chapter 2 is dedicated to the presentation of contemporary energy storage technologies, also emphasizing the critical role of energy storage for further stand-alone system expansion. Accordingly, the scope of Chapter 3 is to analyse and describe the concepts and the parameters that may affect the design and optimization of wind-based hybrid energy systems. For this purpose a short review of the methods and techniques usually employed in energy systems optimization is included. xix © Woodhead Publishing Limited, 2010

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Preface

The first part of the book is completed with the investigation of the financial behaviour of hybrid energy systems on the basis of an integrated cost–benefit analysis. In this context, methods and tools for the estimation of the corresponding pay-back period, the financial efficiency, the net present value and the internal rate of return of a hybrid energy system are described. In addition, the reliability, social and environmental benefits of wind-based stand-alone and hybrid energy systems are also investigated. The second part of the book demonstrates the development of standalone and hybrid energy systems in view of the evolution of the corresponding energy storage technologies. In this context, Chapter 5 deals with the stand-alone power systems containing small wind generators. Emphasis is placed on the control and the electronics system as well as on the assessment of the energy yield and the estimation of loads to be powered. Chapter 6 deals with certain important issues concerning wind–diesel systems, such as the circumstances under which such a wind–diesel hybrid energy system may be installed, the system overview, design considerations, selection of generator ratings and control schemes especially under varying wind velocities and fluctuating load conditions. Subsequently, Chapter 7 includes a brief presentation of a photovoltaic-assisted wind-based standalone system, while special attention is given to the complementarity of wind and solar resources. Chapter 8 focuses on wind–hydrogen stand-alone energy systems, underlining the fact that there is a potential of generating relatively inexpensive hydrogen from the exploitation of wind energy via electrolysis. This hydrogen quantity can be used in appropriate fuel cells in order to produce electricity during high demand and low wind speed time periods. The corresponding environmental benefits and impacts are also discussed. Accordingly, in Chapter 9 a number of useful concepts are presented to help the understanding of the different factors involved in coupling water and wind as complementary energy systems. Different wind–hydro energy systems are described, including water pumping. Chapter 10 analyses in depth the existing electro-chemical energy storage technologies, including sodium sulphur, zinc bromine and vanadium redox flow battery technologies. Actually, special attention is paid in order to demonstrate the abilities of the flow batteries, since they separate power output and storage time thus offering great flexibility, particularly in applications requiring several hours of storage. This is not the case for flywheel energy storage technologies presented in Chapter 11, which are mainly applied to power smoothing in wind energy systems. More precisely, Chapter 11 provides an overview of flywheel storage technology, including the rotor design and construction, the power interface using flywheels and a description of commercial products, specifications, and capital and running costs. Part II ends

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with Chapter 12, describing the compressed air energy storage technologies for wind energy systems. Recognising the importance of the implementation of socially and environmentally beneficial energy systems, the last part of the book describes some of the most interesting and financially attractive applications of windbased stand-alone hybrid energy systems in selected sectors of economy. More specifically, Chapter 13 describes micro-grids comprising hybrid energy systems, loads and energy storage systems as a sustainable energy solution for remote areas in the world. Accordingly, the capability of standalone hybrid energy systems, when properly sized, to handle the electrification requirements of numerous isolated consumers worldwide, including country houses and remote farms is examined in Chapter 14. Chapter 15 describes the technology and implementation of water desalination systems, based on wind energy exploitation, for fresh water supply and highlights another valuable contribution of wind-based stand-alone and hybrid energy systems in the solution of imperative social problems. J. K. Kaldellis

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1 Overview of stand-alone and hybrid wind energy systems J. K. KALDELLIS, TEI of Piraeus, Greece

Abstract: This chapter introduces the reader to the definition and development of stand-alone and hybrid energy systems. Emphasis is given to the description of wind-based stand-alone hybrid energy systems as well as to the use of energy storage for the support of such configurations. Accordingly, the most established applications of similar systems are presented, including some representative real-life examples. Future prospects of such systems are discussed. Key words: stand-alone system, hybrid system, wind power, energy storage, remote consumer.

1.1

Introduction

At the beginning of the twenty-first century, almost every inhabitant of the industrialized world has access to a constant electricity supply and thus electricity may be viewed as a significant aspect of contemporary societies, similar to fresh water and clean air. Nevertheless, this is not the case for the planet’s entire population. According to official statistics (European Commission, 1999), almost two billion people worldwide have no direct access to electrical networks with 500 000 of them living in the European Union and other financially developed countries. Afar from decision centres and having limited political influence, isolated consumers are usually abandoned, facing a dramatically insufficient infrastructure (Jensen, 2000; Kaldellis et al., 2001a). In this context, autonomous stand-alone wind-power systems have proven to be one of the most interesting and environmentally friendly technological solutions for the electrification of remote consumers, especially in the presence of high wind potential (Kaldellis, 2002, 2004). Small wind turbines are able to produce an annual total of only few MW h which, although limited in absolute numbers, makes a considerable difference in upgrading living standards in the remote areas of our planet. The required investment cost, however, may be quite high, especially in cases of medium quality wind potential regions and no-load rejection operational conditions, i.e. the entire load demand must be met. One of the most expensive components of a stand-alone system is the energy storage device, necessary to guarantee the required system reliability. Thus, in cases of 3 © Woodhead Publishing Limited, 2010

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Stand-alone and hybrid wind energy systems

increased system autonomy the energy storage contribution to the initial or the total operational cost is found to be dominant (Kaldellis, 2003, 2008a). In addition, energy storage systems are usually land-intensive, need a lot of maintenance and often need to be replaced every specific time period, thus increasing the operational cost of the system. To avoid oversizing of energy storage configurations, wind-based stand-alone systems are augmented with another available energy source, such as solar energy, hydropower or biomass. Such a stand-alone hybrid energy system is an option worth considering (Muselli et al., 1999; Kaldellis and Kavadias, 2001; Kaldellis et al., 2006a). Recapitulating, stand-alone wind energy systems are electricity-generating systems, based on the operation of one or more wind turbines, being also remote (not connected) from the central electrical grids. In this context, import or export of electricity is not permitted, but there are occasions where a stand-alone system can be connected to an existing electrical network, e.g. emergency status (Bueno and Carta, 2006). Accordingly, hybrid energy systems incorporate two or more electricity generation options, based either on the exploitation of renewable energy sources (RES) or on small thermal power units, e.g. diesel-electric generators or even micro-turbines. Note, however, that with regards to the case currently studied, the first electricity generation option is by definition wind energy.

1.2

Description of a wind-based stand-alone energy system

A typical wind energy stand-alone system (see Fig. 1.1) includes: • one or more (usually small) wind converters of No kW; • an appropriate energy storage device, e.g. a lead–acid battery storage array, able to guarantee ho hours of autonomy, or equivalently with energy storage capacity Qmax and maximum permitted discharge capacity Qmin; • an AC/DC rectifier of Nr kW in case the energy storage installation operates on DC current; • a charge controller of Nc kW; • a UPS (uninterruptible power supply) of Np kW in order to guarantee high quality AC electricity generation; • a DC/AC inverter of Np kW.

1.2.1 Wind turbine The rated power of the selected wind-turbine(s) depends on the system electricity demand, the available wind potential and the operational

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Overview of stand-alone and hybrid wind energy systems

5

UPS AC/DC rectifier

Charge controller

Inverter Control panel

Wind turbine

AC load

Battery bank

1.1 Typical wind energy stand-alone system.

characteristics of the machine (Vlachou et al., 1999). Keep also in mind that the wind-turbine output curves are given at standard-day conditions, without air humidity. Thus, in real-day conditions, the output of the turbine depends (Kaldellis et al., 2004) on the wind speed value V at hub height, the manufacturer’s power curve NW = NW* (V) at standard day conditions and the air density ρ at the installation area, thus: NW (V) =

ρ * (V) NW 1.2215

1.1

Finally, note that the air density value depends on the ambient temperature and pressure as well as on the corresponding air humidity (Houghton and Brock, 1980).

1.2.2 Energy storage There are several different energy storage alternatives, such as flywheels, hydraulic storage, pumped hydro, battery storage and even fuel cells (Kaldellis and Zafirakis, 2007; Kaldellis et al., 2009a), with lead-acid batteries being one of the most widely applied solutions adopted in similar size applications. The operational principle of an energy storage installation in this kind of systems is based on the accumulation of available energy surplus in order for it to be used during periods of inadequate wind. More precisely, the energy storage size is given in units of the time-period that the storage can cover the average load without the contribution of other power sources. Hence, the energy storage system used is defined by the installation’s hours of energy autonomy ho, the corresponding operational characteristics, e.g. the output voltage Ub for battery storage systems, the maximum permitted depth of discharge DODL and the overall efficiency of the energy storage branch. Note that the latter includes the energy

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Stand-alone and hybrid wind energy systems

storage process (e.g. rectifier and charge controller losses), the standing losses owing to the energy storage self-discharge, the losses of the line connecting the storage branch apparatus and finally any electricity generation losses (e.g. inverter).

1.2.3 System electronic devices To ensure smooth operation for the remote consumer under investigation an AC/DC rectifier of nominal power Nr related to the wind turbine rated power No is necessary to convert the incoming three-phase AC voltage UAC from the wind turbine excess power to a nominal UDC corresponding to the DC current accepted by the system charge controller. Note that in cases of pumped hydro (Kaldellis et al., 2001b) or small compressed air energy storage (CAES) (Zafirakis and Kaldellis, 2009) systems this transformation is not required. The output of the AC/DC rectifier enters a DC/DC charge controller of Nc rated power that charges the system batteries with a charging voltage Ucc, slightly higher than the respective of the batteries Ub and feeds any existing DC loads of the installation. The corresponding charge rate Rch depends on the charge voltage and the battery charge current, while the discharge rate is defined by the battery voltage and the corresponding discharge current. Finally, any excess energy is forwarded to other low-priority loads or is directly rejected into a water-heating dump load by the controller, if no other low-priority loads exist. The energy storage electricity production branch is based on either an appropriate DC/AC inverter converting the DC output of the batteries into standard 50 Hz current of operational voltage 220/380 V or a small hydro turbine (in the case of pumped hydro). Several other electricity options are described in the next chapters of this book. The maximum power Np of the inverter (hydro turbine) should be capable of meeting the AC consumption peak load demand, including a future increase margin (e.g. 30%), while its efficiency strongly varies with the load demand. In fact, during partial load operation remarkable efficiency decrease is encountered. In Fig. 1.2 one may find a typical inverter efficiency curve for its entire operational range. Finally, UPS of rated power Np, frequency 50 Hz and operational voltage 220/380 V, is optionally applied in cases that the system load requires specific operational conditions and the existing wind turbine cannot provide the required quality of power for the consumer devices. The UPS autonomy time δt (e.g. δt ≈ 1–2 min) should be also adequate to facilitate the other power devices (e.g. battery-inverter, diesel electric generator) in meeting the consumption load on occasions of sudden low wind energy production.

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Overview of stand-alone and hybrid wind energy systems

7

1.00

Efficiency (h)

0.98 0.96 0.94 0.92 0.90 0.88 0.86

0

1000

2000

3000

4000

5000

6000

Electrical load (W)

1.2 Typical inverter efficiency curve.

1.2.4 Operational modes During the long-term operation of the proposed stand-alone system, the following situations may appear: The power demand ND is less than the power output NW of the wind turbine, (NW > ND). In this case the energy surplus (ΔN = NW − ND) is stored via the rectifier and the energy charge controller. If the energy storage facility is full (Q = Qmax), the residual energy is forwarded to low-priority loads. • The power demand is greater than the power output of the wind turbine (NW < ND), which is not zero, i.e. NW ≠ 0. In similar situations, the energy deficit (ΔN = ND − NW) is covered by the energy storage system via the DC/DC converter and the DC/AC inverter. During this operational condition, special emphasis is laid on the two electricity production subsystems management plan. • There is no energy production (e.g. low wind speed, machine not available), i.e. NW = 0. In this case, all the energy demand is fulfilled by the energy storage–DC/DC controller–DC/AC inverter subsystem, under the condition that Q > Qmin. In this case and the previous one, when the energy storage system is near the energy storage bottom limit, an electricity-demand management plan should be applied; otherwise the load would be rejected. •

1.3

Description of a stand-alone hybrid energy system

Stand-alone systems based on RES exploitation have proved both interesting and environmentally friendly technological solutions for the electrification of remote consumers. However, the first installation cost is quite high

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(Kaldellis and Kavadias, 2007) while in some occasions the life-cycle cost is also high (Kaldellis and Kavadias, 2006). As already mentioned, to limit the relatively high operational cost and to increase the system reliability several authors suggest the reinforcement of the stand-alone solution with the parallel exploitation of more than one RES, i.e. installation of stand-alone hybrid energy systems based on the available renewable potential of each candidate region. Actually, a hybrid energy system incorporates two or more electricity generation options based either on pure RES or utilizing also a small thermal power unit (e.g. diesel-electric generator or a small gas-turbine) along with an appropriate energy storage bank and the corresponding electronic devices. In this context, a hybrid energy system combines the potential of more than one RES, i.e. wind/solar/hydro-power or even biomass, while the utilization of geothermal and wave energy is also expected in the near future. The main advantages of RES-based hybrid energy systems include the following: • • •

•

•

Increased reliability of the hybrid energy installation, since it is based on more than one electricity generation source. Reduction of the energy storage capacity, especially in cases where the different RES utilized present complementary behaviour. Limited operation and maintenance (O&M) cost, especially in cases where the installation of photovoltaic (PV) panels replaces classic energy storage devices, such as the lead–acid batteries. Optimum environmental behaviour, especially in cases where the hybrid energy system does not use any fossil fuel (exclusively RES-based hybrid energy systems). Minimum levelized life-cycle electricity generation cost, not dependent on the fossil fuels price time evolution, especially in cases where the hybrid energy system is based on optimum design techniques.

On the other hand, the hybrid energy systems also present some disadvantages: •

•

In most cases the hybrid energy system is over-sized, since the system designers try to make each system component able to cover the load demand without the contribution of the other participating energy sources. This aspect can, however, be resolved by using new sizing algorithms. The first installation cost is rather high, although the long-term cost is normally low. This high installation cost discourages some potential investors.

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•

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The application of different technologies introduces a degree of complication (especially in the electronic control devices and in the O&M procedures) to the stand-alone installation, a serious problem especially for remote consumers. The introduction of thermal units (e.g. diesel-electric generators) as well as the utilization of batteries are both related to environmental impacts, thus decreasing the environmentally friendly attributes of these RESbased systems.

On the basis of the existing information in the international literature one may find several wind-based hybrid energy configurations, e.g.: • • • • • • •

wind–diesel systems; wind–PV-based systems; wind–hydro installations; wind–biomass-based installation; wind–PV and diesel-based systems; wind–hydro and diesel-based installations; wind–hydrogen/fuel cell hybrid energy systems.

Similar power stations are able to cover the electricity needs of single remote consumers up to isolated villages and remote islands, with minimum fossil fuel consumption. Accordingly, a typical wind-based stand-alone hybrid energy system (see Fig. 1.3), is based on the following:

UPS AC/DC rectifier

Charge controller

Inverter Control panel

Wind turbine

PV array/ small hydro/ small biomass thermal power station

Battery bank

1.3 Typical hybrid wind-based stand-alone system.

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• One or more (usually small) wind converters of No kW. • A PV array of z panels (N+ maximum/peak power of every panel) properly connected to feed the charge controller with the voltage and the power required, or a small hydro turbine able to meet the remote consumer load demand, or even a small thermal power station based either on biomass (biogas or biofuel) or consuming fossil fuels. • An appropriate energy storage device, e.g. a lead–acid battery storage array, able to guarantee ho hours of autonomy, or equivalently with energy storage capacity Qmax and maximum discharge capacity Qmin. • An AC/DC rectifier of Nr kW in case that the energy storage installation operates on DC current. • A charge controller of Nc kW. • An optional UPS of Np kW in order to guarantee high-quality AC electricity generation. • A DC/AC inverter of Np kW, in the case of AC load demand. During the long-term operation of a typical wind-based stand-alone hybrid energy system (a wind–PV system is selected here as the working example), the following situations may appear: •

The power demand ND of the consumption is less than the power output (including any transformation losses) NW of the wind turbine (NW > ND). In that case the energy surplus (ΔN = NW − ND) is stored via the rectifier and the battery charge controller along with the energy production of the PV generator, NPV. If the battery is full (Q = Qmax), the residual energy is forwarded to low-priority loads. • The power demand is greater than the power output of the wind turbine (NW < ND) but less than the sum of power (including any transformation losses) of the PV station and the wind converter, i.e. NW + NPV > ND. In this case the remaining load demand is covered by the PV station via the DC/AC inverter. Any energy surplus from the PV station is stored in the battery via the charge controller. If the battery is full (Q = Qmax), the residual energy is forwarded again to low-priority loads. • The power demand is greater than the power output of the two renewable stations, i.e. NW + NPV < ND, where NW + NPV ≠ 0. In similar situations the energy deficit (ΔN = ND − (NW + NPV)) is covered (along with the corresponding losses) by the batteries via the DC/DC controller and the DC/AC inverter. During this operational condition, special emphasis is laid on the management plan of the three electricity production subsystems. • There is no renewable energy production (e.g. low wind speed, machine not available and zero solar irradiance), i.e. NW + NPV = 0. In that case, all the energy demand is covered by the battery–DC/DC controller–DC/ AC inverter subsystem under the condition that Q > Qmin. In this case

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and the previous one, when the battery capacity is near the bottom limit, an electricity demand management plan should be applied, otherwise the load would be rejected. Recapitulating, the scope of a stand-alone system is to meet the electricity demand of a remote consumer at a rational cost and under a given loss of load (or reliability level) constraint. Depending on the importance and on the installation to be served one may demand no-load rejection (i.e. the load should be fulfilled at any case) operation or may permit a maximum (pre-defined) number of hours without electricity load coverage.

1.4

Energy storage opportunities of stand-alone hybrid energy systems

As already mentioned, interest in the use of wind energy has grown significantly over the past years, mainly as a reaction to concerns about the environmental impact from the use of fossil and nuclear fuels, along with oil and natural gas price instability in the international market. In contrast, renewable energy sources and especially wind energy have demonstrated their independence from economic fluctuations (Kaldellis and Zervos, 2002), while in most cases an initial cost reduction over the course of time is encountered. However, because of the stochastic behaviour of wind, wind generation cannot provide firm capacity to an electrical power system (Kaldellis, 2008b). Additionally, these fluctuations can – in some cases – cause problems to a distribution network related to stability, harmonics or flicker. Such issues pose serious obstacles to the extensive establishment of windonly power systems for the electrification of remote consumers and small (weak) power grids (Kaldellis, 2001). However, an energy storage system, when sized appropriately, can match (see Fig. 1.4) the stochastic wind power production to a generally variable and hardly predictable system demand, greatly limiting the energy production cost (e.g. generating capacity savings). In this context, the vast majority of stand-alone and hybrid energy systems use several energy storage devices in order to store wind energy during high wind and low consumption periods and provide electrical energy during low wind and high load demand periods. The next chapter provides an overview of the main features of the most commonly applied energy storage solutions for wind-based stand-alone and hybrid energy systems, including among others: • •

lead-acid batteries; pumped hydro;

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Stand-alone and hybrid wind energy systems Energy production (W h) Energy consumption (W h)

3500

Battery capacity (A h)

6000

Energy (W h)

2500

5000 2000 4000 1500 3000 1000

2000

500

Battery capacity (A h)

7000

3000

1000

0

0 1

10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163

Hours (h)

1.4 Stand-alone wind-based power system energy balance for a high wind potential area. Energy storage impact.

• •

Compressed air energy storage (CAES); flywheels.

In this context, the main advantages of the incorporation of energy storage systems include: • •

• •

• • • • •

exploitation of otherwise wasted amounts of energy (e.g. rejected amounts of wind energy can be stored); increase of energy autonomy/independence and promotion of the distributed generation concept through maximum exploitation of the local RES potential; increased reliability of energy supply (since an extra power source is available); increased energy efficiency and reduced emissions through the optimum energy management of a given electricity system (e.g. operation of thermal units at their optimum point); elimination of peak demands and deferral of electricity capacity increase; higher utilization and decongestion of transmission lines; abatement of risk entailed by the fuel price volatility provided that RES potential is used efficiently with the contribution of storage; high quality of power delivered to end-users; reduced life-cycle electricity generation costs.

On the other hand, the main disadvantages of these systems are the following:

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• • • •

high initial cost required in most cases; inherent transformation and other types of losses; considerably lower energy densities than fossil fuels; introduction of environmental (especially for bulk energy storage such as pumped-hydro) and safety concerns (e.g. toxic wastes in the case of certain battery types); • additional energy use in the first place in order to build/construct a new energy storage system/device (may, however, ameliorate an entire standalone system energy payback through maximum exploitation of RES energy production); • anticipation of advances in other scientific and technological fields required for some energy storage technologies to develop.

1.5

Applications of stand-alone and hybrid energy systems

Stand-alone hybrid energy systems can be used in several cases worldwide. One of the most common applications includes installations in remote and stony areas, where the corresponding grid connection cost is very high. Keep in mind that the minimum grid extension cost for low-voltage lines exceeds 10 000g per km of grid line, while this value may be quite higher in cases of difficult access situations. On top of these, there are numerous cases where technical constraints hinder remote consumers from connecting to an electrical network. This is the case for isolated small islands or installations existing in entirely remote locations, far from any electrical grid. In all these cases the solution adopted up to now is based on the utilization of small diesel-electric generators, consuming expensive and heavy polluting diesel oil. On the other hand, the installation of a properly sized wind-based hybrid energy system may fulfil energy demand, at the same time improving the living standards of the remote communities. In this context, some common applications of stand-alone wind-based hybrid energy systems may include telecommunication (T/C) stations, small desalination systems, water pumping installations, isolated farms, winter or summer shelters and small or grid-isolated communities (e.g. remote islands). Applicability of these types of systems is demonstrated by the discussion following, considering also some typical installations worldwide.

1.5.1 T/C stations Among the alternatives of electricity generation for T/C stations, T/C providers themselves (Motorola, 2007) identify wind energy as an energy solution of minimum operating expense and negligible environmental footprint,

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1.5 Small wind turbine adjusted to the mast of a T/C relay.

suitable for coastal locations or hilly areas with appreciable wind potential. Small wind turbines may even be adjusted on the relay mast (Fig. 1.5) as a supplement to the diesel energy option, while wind-based stand-alone systems occupy comparatively larger wind turbines, installed near the mast area, able to minimize the fuel consumption of diesel generators used as back-up suppliers only. In any case given, however, a battery bank of the appropriate capacity is also necessary. Depending on the local area characteristics, reduction of the battery bank size may be achieved through the incorporation of a PV array. Introducing PV power to the system may complement wind energy generation while also eliminating oversizing of storage and further reducing oil fuel consumption, especially during the summer months, i.e. when the air-conditioning needs of the station increase. Energy efficiency programmes run by T/C companies, e.g. in Portugal, use wind micro-generating systems country-wide (Fig. 1.5), reducing fuel consumption and emissions by a considerable 15–20%, while in other cases the employment of higher power output wind turbines minimizes oil use and its impacts. For example, three very remote base stations were installed in Kenya in 2005, based on pilot wind–diesel hybrid energy systems. The systems consisted of a 7.5 kW turbine on a 24 m tower, sealed batteries and an inverter, with the results obtained showing excellent reliability and diesel fuel savings of 70–95% (Fig. 1.6). Other examples include the installations shown in Fig. 1.7 to 1.9. In Fig. 1.7, a similar but comparatively larger wind– diesel installation located at Osmussaar, Estonia, comprises a 30 kW wind turbine, two ordinary diesel generator sets of 32 kW each and a battery bank of 250 A h, while Fig. 1.8 and 1.9, show two wind–PV systems found

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1.6 Remote cell phone base station at Laisamis, Kenya.

1.7 T/C station at Osmussaar, Estonia, powered by a wind-based system.

in Turkey, with the system of Fig. 1.8 incorporating two wind turbines of 5 kW each, PV panels of 4 kWp and an appropriate battery bank system.

1.5.2 Small desalination systems The scarcity of potable water resources often found in remote island regions may be resolved by wind-based installations, provided of course that wind conditions in the area of interest are favourable. Generally speaking

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1.8 T/C station powered by a wind–PV based installation in Turkey.

1.9 T/C station powered by a wind–PV based installation at Cesme-Izmir, Turkey.

however, coastal areas usually have considerable wind potential, thus, the purpose of applying wind energy solutions becomes two-fold, serving both the electricity and the potable water needs of these remote consumers. There are several studies (Habali and Saleh, 1994; Kiranoudis et al., 1997;

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Miranda and Infield, 2003) regarding the implementation of wind energy in both seawater and brackish water desalination plants of membrane and distillation technologies, with special interest laid on the techniques of reverse osmosis first – being arguably the most efficient (Tzen and Morris, 2003) – and mechanical vapour compression secondly. Note, however, that as underlined in Tzen et al. (2002), non-steady power supply, i.e. a common defect of wind energy in stand-alone units, forces desalination plants to operate in non-optimal conditions, and therefore considerable energy storage capacity, usually batteries, is necessary to serve small-scale stand-alone systems. Information on several applications and studies may be obtained from Tzen and Morris (2003) and Kalogirou (2005), while the contribution of PV power has a sound effect on these types of wind-based hybrid energy systems as well (Petersen et al., 1979). A typical installation is the wind-driven desalination plant on the island of Rugen, Germany, operating since 1995 (Fig. 1.10). Based on the technique of reverse osmosis, the average daily production amounts to approximately 13 m3 of drinking water, from seawater with a salt concentration of 36 g/kg and with a mean annual wind speed of 7 m/s, using a 300 kW wind turbine. Some additional examples of wind-based installations are given in the following:

1.10 Wind-driven desalination plant on the island of Rugen, using a Tacke TW 300 wind turbine and a pressurized evaporation plant.

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• • •

Stand-alone and hybrid wind energy systems A seawater desalination plant at Pozo Izquierdo, Gran Canaria (Spain) with a potential for a production of 50 m3/d, based on the technique of mechanical vapour compression and the operation of a 230 kW wind turbine (Ehmann and Cendagorta, 1996). The Syros island (Greece) seawater plant, using reverse osmosis and a 500 kW wind turbine, able to produce 900 m3 on a daily basis. A seawater reverse osmosis unit on the island of Drenec, France, driven by a wind turbine of 10 kW rated power (Peral et al., 1991). Two reserve osmosis desalination plants supplied by a 6 kW wind energy converter and a 2.5 kW solar generator in Mexico (Petersen et al., 1979).

Of special interest is the concept of a floating wind-based desalination plant, already in operation in the area of the Aegean Sea, Cyclades, Greece. The system is based on the energy production of a 30 kW wind turbine and is able to produce potable water at the rate of 70 m3/day while its main advantages are the exploitation of high-quality sea wind potential and the ability to serve vicinal island regions, as described in detail in Chapter 15.

1.5.3 Water pumping According to Smulders (1996), wind water pumping embraces a number of potential applications, including domestic water supply, community water supply, cattle watering and irrigation. Several wind water pumping installations may be encountered in remote areas (e.g. in isolated farms, see Fig. 1.11) where infrastructure is poor and water supply is used to cover additional needs, on top of domestic ones. Actually, the importance of serving the water needs of remote communities is well illustrated by the fact that even though there is increasing water consumption in both the domestic and the industrial sector, agriculture – especially in the developing countries (Mohsen and Akash, 1998; Sadrul Islam et al., 2000) – is still the dominant water user, absorbing almost three-quarters of the global water resources (IWMI, 2006). In this context, national programmes such as the one of India (Purohit, 2007), although not achieving the targets expected (initial estimations of 400 000 installations in India) have promoted the use of wind pumps throughout the developing world and have led to the development of small-scale markets for multi-bladed and low-rated speed wind turbines. Given the complementarity, however, between increased water needs and high solar potential available during the summer months, a shift has been noted during the recent years to the PV pumping concept, encouraged also by the gradual cost reduction of contemporary PV modules. Nevertheless, as in previous applications, wind-based hybrid energy systems incorporating

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1.11 Wind turbine used for water pumping in an off-grid farm.

1.12 Wind-based hybrid water pumping unit with the incorporation of PV panels.

PV power as well are also an option (Fig 1.12). A similar pilot hybrid energy system is operated by the Soft Energy Applications & Environmental Protection laboratory in Greece (Fig. 1.13), where a 2 kW wind turbine along with 610 Wp of PV power and an appropriate lead–acid battery bank

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Solar collector

PV panels Wind turbine

Lamps Data logger stylitis-41

Charge controller

Control panel Water pump

PC Battery bank

Water reservoir

24V DC circuit Data circuit Water circuit

1.13 Experimental hybrid wind-based stand-alone unit in the Soft Energy Applications and Environmental Protection Lab.

are able of elevating water quantity of 23 m3/day from a ground depth of 30 m (Kaldellis et al., 2009b). More details on wind water pumping installations may be found in Chapter 9 as well as in several studies, e.g. Smulders and de Jongh (1994).

1.5.4 Domestic to community level electrification Apart from the special applications presented in Sections 1.5.1–1.5.3, windbased stand-alone systems may apply to satisfy domestic electrification demands, from the level of an isolated farm or a winter/summer shelter to the level of an entire remote community that do not benefit from an electricity grid. In all these cases, the presence of the appropriate energy storage system is essential to ensure energy autonomy and minimize any dependence on fuel imports. In this context, as already implied, the existence of

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numerous energy storage technologies (from small to large scale) enables the application of most systems; nevertheless detailed study of all problem parameters must be undertaken in order to select the most appropriate solution. Local demand patterns, energy storage characteristics, local RES energy potential, and the possibility of the implementation of energy management plans and cost issues should all be considered when designing a wind-based stand-alone system. In most cases, single consumer needs are satisfied by the incorporation of a typical battery system (e.g. lead–acid batteries), the back-up use of a diesel generator and the possibility of introducing PV panels as well; while to serve a remote community, larger-scale energy storage technologies may be used, such as pumped hydro, hydrogen storage and fuel cells, etc., often supported by the contribution of local thermal power stations (Kaldellis and Zafirakis, 2007; Kaldellis, 2008a). Of special interest are the isolated communities of remote island areas where either the absence of an electrical grid or the existence of a weak micro-grid forces the system to operate on the use of dominant oil power, which allows a minimum contribution of RES power, and wind power in pacticular. To overcome these problems at both the micro-level of a single consumer (isolated farms, winter/summer shelters) and the level of an entire remote community, constant research and development of windbased stand-alone systems is necessary. Relative to this, one may encounter several research studies addressing the problem of energy satisfaction on the basis of the local wind potential (Bueno and Carta, 2006; McDowall, 2006; Kaldellis et al., 2006b; Zafirakis and Kaldellis, 2009), while some typical applications are given in the following. Figure 1.14 shows two wind-based hybrid energy systems used to cover the electricity needs of two small remote villages in Chile. The first system in Isla Tac Village consists of 2 × 7 kW wind turbines, flooded batteries, 2 × 4.5 kW inverters and one 16 kW back-up gas generator, while the second system (Villa Las Araucarias) is based on a 7 kW wind turbine, a battery of 33.6 kW h capacity, a 4.5 kW inverter and a 4.5 kW back-up gas generator. Figures 1.15 and 1.16 show two wind–PV–diesel systems, the first in Mexico and the second in Mongolia. The system of San Juanico in Mexico, supplying the local fishing community of 400 people, uses 70 kW of wind power, 17 kWp of PV power and an 80 kW diesel generator able to cover any RES deficit, even in the extreme case of a wind and solar power blackout. Relatively smaller is the system of Fig. 1.16, located in Mongolia, where two wind turbines of 5 kW each are employed, in collaboration with PV panels of 1500 Wp and a diesel generator set of 10 kW. Note that in all these cases, three-bladed wind turbines are employed, opposite to the multibladed machines used for water pumping. On the other hand, wind energy

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(b)

1.14 Wind-based hybrid system covering the needs of the remote communities of (a) Isla Tac Village, Chile and (b) Villa Las Araucarias, Chile.

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1.15 Wind-based hybrid system including PV power used for the electrification of a remote fishing community at San Juanico, Mexico.

1.16 Wind-based hybrid system including PV power used for the electrification of a remote fishing community in Mongolia.

surplus in remote island areas may be used for desalination purposes as well, such as in the case of the El Hierro island project, where the 100% RES plan based on the implementation of wind pumped-hydro system will exploit any wind energy surplus for seawater desalination as well.

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1.17 Wind-solar hybrid street lamps for city lighting in China.

Finally, applications on the community level may also include other types of stand-alone wind-based systems, such as the city road lighting system in China (Fig. 1.17) where wind–solar hybrid street lamps use a 400 W smallscale wind turbine, PV cells of 150 Wp and a battery of 200 A h, gradually replace conventional street lighting with multiple environmental and financial gains.

1.6

The future of stand-alone hybrid energy systems

The continuous instability of fossil fuel prices, the forthcoming depletion of oil and natural gas reserves and the serious environmental degradation due to the over-exploitation of existing energy reserves are among the main reasons supporting the establishment of wind-based applications. On top of these, the continuous augmentation of the electrical power demand strongly questioning the reliability of large electrical networks, and the existence of considerable numbers of remote consumers claiming the coverage of their vital energy and clean water needs, certainly encourage further development of stand-alone hybrid energy power systems. Finally, the modular type of most hybrid power stations limits the corresponding initial capital to be invested while the life-cycle cost of the installation may be considerably reduced via an optimum sizing technique (Kaldellis and Kavadias, 2006, 2007; Kaldellis et al., 2006b).

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In this context, one of the most important markets of the wind-based stand-alone systems is the one comprising more than 1 000 000 remote consumers and farmers of Europe and North. America (see, for example, Figs 1.18 and 1.19), who are either located far from the existing electrical grids

1.18 Wind-based hybrid system including PV panels, used for the electrification of a farm in Netherlands.

1.19 Small wind turbine used for the electrification of a farm in Kansas, USA.

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or have no possibility of being connected with large power systems due to technical constraints. In the same category one may include the number of small communities, such as those in small remote islands, where the implementation of hybrid power stations may create several stand-alone micro-grids. The prospects of stand-alone wind-based hybrid energy systems are even more encouraging if one takes into consideration the fact that more than two billion people live in countries under development, where even small amounts of electricity could make a big difference in the living standards of local inhabitants. In this context, the proposed hybrid energy systems are able to at least face the fundamental needs of all these people on the basis of the existing wind and solar potential, requiring a minimum first installation cost. On top of these applications one should not disregard the continuous increasing demand for covering the electrical requirements of remote T/C stations or remote shelters, while similar wind-based hybrid power stations can contribute substantially to supporting small desalination and water pumping installations. Recapitulating, stand-alone wind-based hybrid energy systems are an attractive electricity generation solution in areas with high and medium– high wind potential and are able to provide a viable techno-economic answer for the problems related to oil price fluctuations, also supplying clean electricity to autonomous consumers and allowing them to be independent from the energy sector political conflicts. Finally, in terms of technology a similar hybrid power system has low maintenance needs, can be easily adapted to the specific consumer load profile and may respect the individual character of every specific community.

1.7

References

Bueno, C., Carta, J.A., 2006. Wind powered pumped hydro storage systems, a means of increasing the penetration of renewable energy in the Canary Islands. Renewable and Sustainable Energy Reviews, 10, 312–340. Ehmann, H., Cendagorta, M., 1996. PRODESAL – Development and pilot operation of the first wind powered reverse osmosis seawater desalination plant. In: Mediterranean Conference on Renewable Energy Sources for Water Production, European Commission, EURORED Network, CRES, EDS, Santorini, Greece, 10–12 June. European Commission, 1999. Wind Energy. The Facts. A Plan for Action in Europe. Habali, S.M., Saleh, I.A., 1994. Design of stand-alone brackish water desalination wind energy system for Jordan. Solar Energy, 52, 525–532. Houghton, E.L., Brock, A.E., 1980. Aerodynamics for Engineering Students. Edward Arnold Ltd, London. IWMI (International Water Management Institute), 2006. Insights from the Comprehensive Assessment of Water Management in Agriculture. In: Stockholm World Water Week, Stockholm, Sweden.

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Jensen, Th.L., 2000. Renewable energy on small islands. Forum for Energy & Development, Copenhagen, Denmark. Kaldellis, J.K., 2001. Evaluating the maximum wind energy penetration limit for weak electrical grids. In: European Wind Energy Conference, Bella Centre, Copenhagen, 2–6 July. Kaldellis, J.K., 2002. Minimum stand-alone wind power system cost solution for typical Aegean Sea islands. Wind Engineering Journal, 26, 241–255. Kaldellis, J.K., 2003. An integrated feasibility analysis of a stand-alone wind power system, including no-energy fulfillment cost. Wind Energy Journal, 6, 355–364. Kaldellis, J.K., 2004. Parametric investigation concerning dimensions of a standalone wind power system. Journal of Applied Energy, 77, 35–50. Kaldellis, J.K., 2008a. Integrated electrification solution for autonomous electrical networks on the basis of RES and energy storage configurations. Energy Conversion and Management Journal, 49, 3708–3720. Kaldellis, J.K., 2008b. Maximum wind potential exploitation in autonomous electrical networks on the basis of stochastic analysis. Journal of Wind Engineering & Industrial Aerodynamics, 9, 1412–1424. Kaldellis, J.K., Kavadias, K.A., 2001. Optimal wind-hydro solution for Aegean Sea islands electricity demand fulfillment. Journal of Applied Energy, 70, 333–354. Kaldellis, J.K., Kavadias, K.A., 2006. Optimum sizing of a stand-alone wind–diesel system on the basis of life cycle cost analysis. In: European Wind Energy Conference and Exhibition, Athens, Greece, 27 February–2 March. Kaldellis, J.K., Kavadias, K.A., 2007. Cost–benefit analysis of remote consumers’ electrification on the basis of hybrid wind–diesel power stations. Energy Policy Journal, 35, 1525–1538. Kaldellis, J.K., Zafirakis, D., 2007. Optimum energy storage techniques for the improvement of renewable energy sources-based electricity generation economic efficiency. Energy Journal, 32, 295–2305. Kaldellis, J.K., Zervos, A., 2002. Wind power: a sustainable energy solution for the world development. In: Energy–2002 International Conference, Athens, Greece. Kaldellis, J.K., Vlachou, D., Kavadias, K., 2001a. An integrated renewable energy solution for very small Aegean Sea islands. In: Renewable Energies for Islands – Towards 100% RES Supply International Conference, Chania, Greece, June 14–16. Kaldellis, J.K., Kavadias, K., Christinakis, E., 2001b. Evaluation of the wind-hydro energy solution for remote islands. Journal of Energy Conversion and Management, 42, 1105–1120. Kaldellis, J.K., Kavadias, K.A., Korbakis, G., Vlachou, D.S., 2004. The impact of local ambient conditions on the energy production of contemporary wind power stations. In: 7th Hellenic Conference in Meteorology, Climatology and Atmospheric Physics, Univ. of Cyprus, Nicosia, Cyprus, September 27–29. Kaldellis, J.K., Kostas, P., Filios, A., 2006a. Minimization of the energy storage requirements of a stand-alone wind power installation by means of photovoltaic panels. Wind Energy International Journal, 9, 383–397. Kaldellis, J.K., Kondili, E., Filios, A., 2006b. Sizing a hybrid wind–diesel stand-alone system on the basis of minimum long-term electricity production cost. Applied Energy Journal, 83, 1384–1403.

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Kaldellis, J.K., Zafirakis, D., Kavadias, K., 2009a. Techno-economic comparison of energy storage systems for island autonomous electrical networks. Journal of Renewable and Sustainable Energy Reviews, 13, 378–392. Kaldellis, J.K., Spyropoulos, G.C., Kavadias, K.A., Koronaki, I.P., 2009b. Experimental validation of autonomous PV-based water pumping system optimum sizing. Renewable Energy Journal, 34, 1106–1113. Kalogirou, S.A., 2005. Seawater desalination using renewable energy sources. Progress in Energy and Combustion Science, 31, 242–281. Kiranoudis, C.T., Voros, N.G., Maroulis, Z.B., 1997. Wind energy exploitation for reverse osmosis desalination plants. Desalination, 109, 195–209. McDowall, J., 2006. Integrating energy storage with wind power in weak electricity grids. Journal of Power Sources, 162, 959–964. Miranda, M.S., Infield, D., 2003. A wind-powered seawater reverse osmosis system without batteries. Desalination, 153, 9–16. Mohsen, M.S., Akash, B.A., 1998. Potentials of wind energy development for water pumping in Jordan. Renewable Energy, 14, 441–446. Motorola, 2007. White Paper on the Alternatives for Powering Telecommunications Base Stations. Muselli, M., Notton, G., Louche, A., 1999. Design of hybrid-photovoltaic power generator, with optimization of energy management. Solar Energy Journal, 65, 143–157. Peral, A., Contreras, G.A., Navarro, T., 1991. IDM-Project: Results of one year’s operation. In: Seminar on New Technologies for the Use of RE Sources in Water Desalination, Athens, Greece, 26–28 September. Petersen, G., Fries, S., Mohn, J., Muller, A., 1979. Wind and solar powered reverse osmosis desalination units-description of two demonstration projects. Desalination, 31, 501–509. Purohit, P., 2007. Financial evaluation of renewable energy technologies for irrigation water pumping in India. Energy Policy, 35, 3134–3144. Sadrul Islam, A.K.M, Islam, M.Q., Hussain, S.S., 2000. Wind power utilization for water pumping in Bangladesh. In: World Renewable Energy Congress VI, Brighton, UK, 3–7 July. Smulders, P.T., 1996. Wind water pumping: the forgotten option. Energy for Sustainable Development, 2, 8–13. Smulders, P.T., de Jongh, J., 1994. Wind water pumping: status, prospects and barriers. Renewable Energy, 5, 587–594. Tzen, E., Morris, R., 2003. Renewable energy sources for desalination. Solar Energy, 75, 375–379. Tzen, E., Theofilloyianakos, D., Sigalas, M., Karamanis, K., 2002. Design and development of a hybrid autonomous system for seawater desalination. In: PV in Europe. From PV Technology to Energy Solutions Conference, Rome, Italy, 7–11 October. Vlachou, D., Messaritakis, G., Kaldellis, J.K., 1999. Presentation and energy production analysis of commercial wind turbines. In: European Wind Energy Conference and Exhibition, Nice, France, March 1–5. Zafirakis, D., Kaldellis, J.K., 2009. Economic evaluation of the dual mode CAES solution for increased wind energy contribution in autonomous island networks. Energy Policy, 37, 1958–1969.

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2 Overview of energy storage technologies for renewable energy systems D. P. ZAFIRAKIS, TEI of Piraeus, Greece

Abstract: This chapter presents a range of contemporary energy storage systems (ESSs). The introduction emphasizes distributed generation and renewable energy sources (RES), also designating the critical role of energy storage for further RES penetration. Topics such as the description of a typical ESS, the range of applications served by energy storage technologies, the presentation of each technology and a comparison of different systems, are all extensively discussed, along with a short description of future trends at the end of the chapter. Key words: distributed generation, renewable energy sources, energy storage.

2.1

Introduction

To satisfy the ever-increasing demand for electricity consumption (EIA, 2007), promote the protection of the environment (Stern, 2006; IPCC, 2007) and foster energy self-sustained communities (Scheer, 2006), constant research and development (R&D) into renewable energy sources (RES) technologies is required. Scarcity of fossil fuels, price volatility of oil and natural gas imports (Geman and Ohana, 2009) and the establishment of environmental policies via legislative measures (Soleille, 2006) prompt electricity production from RES, while plans concerning their exploitation on an international level (European Commission, 2001) are found in accordance with the shift attempted from conventional, fossil fuel-based electricity generation to cleaner and more sustainable power production methods (Little, 2005). In this context, distributed generation is at the centre of attention (Bayod-Rújula, 2009; Chicco and Mancarella, 2009), as if power generation has come full circle (Strachan, 2004), i.e. centralized power generation is gradually being abandoned so that cleaner and more agile systems can emerge. Higher energy efficiencies and the avoidance of transmission and distribution losses, enhanced flexibility of the local electricity networks and increased levels of reliability/security of supply, together with the fact that the problem of pollution is brought about to its actual extent, may be reckoned among the advantages of decentralized energy production (Pepermans et al., 2005). Restructuring of current power generation systems, however, requires the use of alternative energy technologies such as 29 © Woodhead Publishing Limited, 2010

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combined cycle gas turbines, internal combustion engines, micro-turbines, Stirling engines, and RES (Ackermann et al., 2001), with past predictions claiming that by 2010, 60% of RES installations should be decentralized (Grubb, 1995). According to the above, sound stimuli do exist for the adoption of RES technologies on a broader scale, encompassing both the need for the introduction of more sustainable power production methods and the trend noted towards the re-establishment of the distributed generation concept. For RES technologies to meet the challenge, however, there are certain issues that must be addressed. Although comprising mature technologies with considerable progress over the years (Charters, 2001), RES are still on the sidelines of conventional generation methods. Acting as complementary power sources has allowed RES technologies to obscure some of their inherent handicaps. However, operation under the conditions set by fossil fuel plants and centralized generation networks condemns RES technologies to restrictions and limitations (Papathanassiou and Boulaxis, 2006; Georgilakis, 2008). As a result, impacts may be encountered on the economic efficiency of these systems (Kaldellis et al., 2004), also hindering the shift to alternative patterns of electricity generation previously discussed. For example, integration of wind energy, with wind turbines being admittedly the most mature RES technology, is one of the most common issues regarding further penetration of RES into various generation mixtures (Maddaloni et al., 2009). Fluctuating and/or intermittent wind energy production, owing to wind speed variability, is in most cases found to be unable to adjust to the profile of electricity demand, while the impacts (being more or less severe depending on the level of wind power penetration and the scale-characteristics of the electrical system examined) on power quality, power systems dynamics, transmission planning, etc. (Georgilakis, 2008) also reflect the need for developing new integration strategies. To confront the problems encountered at the level of both centralized generation networks and future distributed generation schemes, the idea of introducing energy storage constantly gains ground (IRES, 2006, 2007). Regarding large-scale conventional electricity networks, the requirement for an even geographical spreading of wind power to avoid overloading the network at certain points may be bypassed by the implementation of energy storage (mostly bulk systems) used to absorb the shock of wind energy overproduction and also fill the gap of energy production in times of low wind speeds or wind energy black-outs (Korpaas et al., 2003). The same is valid for RES-based distributed generation systems (either connected to the distribution network or isolated), where both power quality issues become more important – owing to the difficulty of small-scale networks to handle disturbances equally well – and the efficient management of RES energy production becomes critical in the absence of other major conven-

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tional power contributors (Weisser and Garcia, 2005). Finally, it must not be neglected that energy storage also applies at the remote consumer level where RES-based stand-alone systems are rather common for the satisfaction of electricity needs (Nema et al., 2009). In this context, the benefits stemming from the adoption of energy storage systems (ESSs) may be summarized as the exploitation of otherwise wasted amounts of energy (e.g. rejected amounts of wind energy can be stored), the increased reliability of energy supply (since an extra power source is available) and the improved operation of the power system and existing power units (e.g. operation of conventional units at optimum point). However, storage technologies are treated with some scepticism, arising from the high initial cost of the system and the inherent transformation losses. What should be stressed is that via the utilization of ESSs, maximum exploitation of RES is possible across the entire range of applications, i.e. from the remote consumer level (stand-alone systems such as wind-battery or photovoltaic (PV)-battery) to the service of large-scale wind parks, while the value of introducing energy storage in the various stages of the conventional electricity chain should also be acknowledged (Makansi and Abboud, 2002). More specifically, the utilization of ESSs allows base-load arbitrage (Bathurst and Strbac, 2003; Walawalkar et al., 2007) and abatement of risk entailed by the fuel price volatility (both owing to the proper design and operation of fossil fuel power stations since oversizing is not necessary), supports higher utilization and decongestion of the transmission lines (where reinforcement was previously required) (Cavallo, 2007), ensures the stability of the distribution system and provides high quality of power delivered to end-users (see also Fig. 2.1).

Challenges Low utilization

Congestion

Security

Dirty power

Generation

Transmission

Distribution

Services

Stability

Power quality

Fuel price

Volatility

Fuel

(t)

Energy storage Hedge risk

Baseload arbitrage Higher utilization Benefits

2.1 Benefits stemming from the adoption of energy storage (Makansi and Abboud, 2002).

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Transition to RES and distribution generation

2.2 The critical role of energy storage; from central to distributed generation.

In conclusion, energy storage is faced with two coexisting challenges (Fig. 2.2): the first is to improve the operation of already existing conventional centralized power networks and the second is to signal the shift to the era of RES-based and distributed electricity generation. During this transition, ESSs should prove sufficiently flexible so as to serve both purposes and should most importantly designate the ability of RES technologies to overcome any inherent shortcomings. For this to be realized, the number of available technologies covering a broad range of applications comprises a critical factor itself. Contemporary technologies include pumped hydro storage (PHS), compressed air energy storage (CAES), fuel cells and hydrogen storage (FC-HS), flywheels, supercapacitors (SCs), superconducting magnetic energy storage (SMES) and various battery systems.

2.2

Description of a typical energy storage system (ESS)

Before proceeding to the presentation of each system in detail, some basic definitions concerning ESSs in general need to be established. More precisely, the main components of a typical ESS, its operating principles, energy flows and main characteristics, should all be discussed.

2.2.1 Description of a typical ESS’s main components Figure 2.3 shows a typical energy storage configuration comprising an energy source, either fuel- or RES-powered (or on the electrical grid), the

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Data communication Energy flows

Control panel Source interface

PCSout

PCSin

AC interface

Energy demand

Energy storage

Load ESS

2.3 Typical energy storage configuration.

power conversion components (including the energy source and the AC load interface, as well as the main power conversion system (PCS)), the control devices and, finally, the energy storage media and demand side. The electricity generated (either DC or AC) passes through the necessary conversion stages (source interface and PCSin) in order to be stored mechanically, chemically, or in an appropriate form of electricity. Whenever an energy deficit appears on the demand side, the required amount of energy is drawn from storage in order, eventually, to be converted to AC (PCSout and AC interface). Note that the source and AC interfaces, along with the control devices and other auxiliary subsystems, are better known as balance of the system (BOS) components. In the case of other main devices participating in the energy storage configuration (e.g. water pumps, air compressors, gas turbines), common PCSs, meaning a rectifier and an inverter, can also be included in the BOS. In this context, the breakdown of the various system components that may be encountered in an energy storage installation is given in Table 2.1 (Butler et al., 2002). As already explained, the energy source may be either the electrical grid or a single/multiple number of power units, either interconnected or standalone, single-source or hybrid, having AC or DC outputs. However, since most electrical appliances require AC, it is common for the PCSout and load interface to feed consumption with AC rather than DC. In either case (whether for an AC or a DC interface), the equipment required for the source and load interfaces includes current and voltage sensors, fuses, isolation switches, transformers, filters and surge arrestors (among other items). There are four common types of PCS, divided according to their connection arrangement (Atcitty et al., 1998):

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Table 2.1 Main components of a typical ESS (Butler et al., 2002) Energy storage system

Electromechanical Electrochemical Electrical

Interfaces to AC load and source

New lines Voltage transformers Protection devices

Power conversion system

AC switchgear/disconnect Rectifier/inverter DC switchgear/disconnect Protection devices

Auxiliary systems and accessories

Electrical parts (interconnects, protection devices, chargers) Mechanical parts (racking support, water/heating/air and fluid pumping systems, safety equipment, refrigeration systems, vacuum)

Monitors and controls

Monitors/diagnostics (storage media, power conversion, subsystems) Controls (storage media, protection devices, power conversion, subsystems)

Facilities

Foundation and structure Lighting Grounding/cabling Heating, ventilation, air-conditioning (HVAC)

• • • •

grid connected parallel configuration; grid connected series configuration; stand-alone parallel hybrid configuration; and stand-alone series hybrid configuration.

In the case of a grid parallel connection PCS, the ESS (e.g. electricity storage) is connected to the load in parallel with the utility/power source, via a bidirectional inverter (inverter/rectifier) and a transformer; a converter is optional, and a controller should also be considered. Grid connected series configurations are permanently on-line, and consist of a PCS that is used non-stop and which includes a rectifier, an inverter, an optional DC/DC or AC/DC converter and a bypass switch. The purpose of a PCS in a hybrid installation – usually including an oilbased generator, a RES installation (e.g. a wind turbine) and storage – is to serve the load through the coordination of all participating energy sources. Parallel connection means that the participating engine generator is directly connected to the AC load, while in a series connection the generator is first connected to the DC bus of the PCS via a rectifier. Furthermore, in a series connection, the load is primarily satisfied by storage, which is

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charged whenever energy from the RES installation is available. An oilbased generator, on the other hand, feeds storage only when the maximum depth of discharge condition is violated (i.e. the electricity storage device has insufficient charge from RES generation to meet the operational load requirements). This is not the case for parallel connections, where either the engine generator remains off-line or operates at its optimum point, being supported by the operation of other energy sources and storage (for more information see Atcitty et al., 1998). In addition, controllers are used to manage the operation of all participating components and to ensure that the desired result is obtained. This is carried out by functions such as control of current supplied to the consumption, power management via voltage or power regulation, communication via signals with the various device drivers, and the simultaneous reading of several operation parameters (e.g. the storage system state of charge, charging and discharging rates, and many other parameters).

2.2.2 Operation principle and energy flows of a typical ESS

Peaks Mid merit

Response tr time

Without storage With storage Storage period tst

tdis Discharging period

Charging period tch

Charging

Frequency & voltage control via storage

Baseload

Electricity demand

The principle of operation of an ESS is the storage of energy during times of low energy demand (provided that an energy surplus is available), and the delivery of stored energy during times of high energy consumption, i.e. when energy production is not sufficient. A simplified profile of charging and discharging for a typical ESS is given in Fig. 2.4. According to the common patterns of energy demand, during late evening/night-time, consumption is kept at baseload levels and therefore any excess energy may be used to charge the ESS (charging period tch). Conversely, electrical loads

6am

Midday

period

6pm

Midnight

2.4 Energy generation management and frequency–voltage control through energy storage (based on Boyes, 2000; Makansi and Abboud, 2002).

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gradually increase as day comes, and the entire energy production is used to cover demand (storage period tst for the ESS). Depending on its size and the destination of the ESS utilized, certain amounts of stored energy are used to supplement energy production (discharging period tdis) and cover parts of, or the entire energy deficit (meaning the extra electrical demand in comparison to either the baseload or the mid-merit levels) that appears as the electricity demand tends to peak (usually during the middle of the day and during the early evening). In this context, special attention must be given to the response time of energy storage tr, in order for scheduling to be accurate. The daily cycle is completed by the gradual reduction of electricity demand and the subsequent recharging of the storage unit, partly or entirely discharged during the previous time period. Management of the power injections used to regulate frequency and voltage are also among the duties of an ESS. This rather abstract description of how an ESS operates demonstrates that there are a variety of potential applications for the respective systems to be used in. Below, it will be shown that for the entire range of applications (from network scale to stand-alone, and from large-scale energy storage to voltage and frequency regulation) the qualities and characteristics of the various ESSs both vary and overlap. However, before proceeding to the presentation of the main characteristics and applications of ESSs, a short analysis of the energy flows in a typical ESS is necessary. Figure 2.5 shows the Sankey diagram of a typical ESS. The input energy delivered to the ESS during its charging phase is reduced owing to distribution and conversion losses. Distribution losses occur during the transfer of energy from the original energy source to the ESS, while conversion losses (usually the most critical losses) derive from

Energy storage system Conversion Self-discharge losses or idling Conversion losses losses Distribution losses

h=

W hout W hin

h ≈ hin · hout Distribution losses

Input engergy

ESS energy losses

Output energy

2.5 Energy flows in an ESS (based on Denholm and Kulcinski, 2003).

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the conversion of electrical energy to the form of energy required to charge the ESS. Analogous to these are the losses during the discharging phase, i.e. when energy is drawn from the ESS and converted to electricity so as to feed the connected load. Additional losses, although minor in most cases, include self-discharge or idling losses, and take place during the standby or off-duty mode of the system. Usually, distribution losses are disregarded and it is the conversion and idling losses that are referred to as energy storage losses. The cycle efficiency during the charging–discharging cycle of an ESS, being one of its most common energy storage characteristics, is defined as the ratio of the ESS energy output to the ESS energy input, i.e. η = W hout /W hin. This, of course, takes into account all intermediate losses, and only by assuming that idling losses are negligible is η = ηin · ηout valid, with ηin being the charging stage efficiency and ηout the discharging stage efficiency. Otherwise, the self-discharge or idling losses (in the form of power losses Ploss) and the cyclic period T have to be taken into account, in order for the cycle efficiency to be estimated (Kondoh et al., 2000), i.e.

η=

ηout

−1

ηin −1 + T ⋅ Es ⋅ ( Eout ⋅ τ s )

where Es and Eout are the amount of stored energy and energy output respectively, while τs represents the Es/Ploss ratio.

2.2.3 Main characteristics of a typical ESS Apart from cycle energy efficiency, performance of an ESS is determined by a number of qualities and technical characteristics. A definition of the main characteristics used to evaluate and classify the various ESSs is provided below. Rated or available power, energy to power ratio The power rating of an ESS, meaning the size of the power conversion subsystems, usually results from the maximum power requirements of the electrical load on the generation side (the discharging part) and the most frequently appearing excess power on the input side (the charging part). Since energy is the product of time and average power (over a period of time), influence of the power drain on the energy storage capacity of the system is apparent, determining also the energy to power ratio, i.e. the amount of time the system can deliver full rated power. Similarly, the current and voltage requirements of the load and the energy source determine the size of the interface equipment at both the charging and the discharging side of the system.

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Energy storage capacity, useful energy storage capacity The energy storage capacity is the actual parameter determining the size of storage, and it can be decided based on the power and autonomy period requirements as well as on the system’s efficiency and ability to perform deep discharging. Physical and cost constraints may keep the storage size below the initial theoretical estimations. Additionally, since 100% discharging is not usually an option, the term useful energy capacity is introduced, precisely to point out that part of the energy store cannot be used for electricity generation. Discharging time, reaction time, storage duration Discharging and reaction time comprise two very critical parameters for ESSs. Although the time of discharge (or autonomy) may be thought of as a dependent variable, interacting with the available energy storage capacity and the system rated power, reaction (or response) time is inherent to the system. ESSs with short reaction times (or ramping times) may be asked to provide electricity instantly (black start capability), while systems with comparatively longer reaction times only adjust to scheduled generation patterns that allow for a time interval between start-up and electricity production. Note also that the rate of discharge is directly related to the efficiency of recovering energy stores, i.e. exceeding a certain rate limit means that efficiency is considerably reduced. The opposite is valid in the case that the rate minimizes and the energy stores are left idle for long periods of time, i.e. discharge losses occurring decrease the available energy stores. This of course is directly related to another time parameter, which is the storage duration (or cyclic period), clearly configured by the system losses during off-duty periods. In case these losses are important, there is no rationale in leaving the system idle for long periods of time. Efficiency, energy ratio (ER) and energy payback There are several expressions used to evaluate the energy performance of an ESS, of which standard terms include cycle efficiency, round-trip efficiency, energy ratio (ER) and energy payback period. Cycle efficiency takes into account the ratio between the energy output and the energy input of the storage system, i.e. η = W hout/W hin, also including storage losses during standby mode. That means that a full cycle is considered: from charging to discharging. Round-trip efficiency (also known as ‘from AC to AC’ efficiency) on the other hand also includes transmission losses (see also Fig. 2.5), which are considerable in cases of bulk energy storage where it is

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common for storage to be installed at a respectable distance from both the load and the energy source. ER suggests the exact inverse expression of the cycle efficiency ER = W hin /W hout, while when referring to the respective net ER, ERnet, transmission losses should also be taken into account (i.e. the respective ERtr), as in the case of the round-trip efficiency ERnet = ER · ERtr. Finally, the energy payback period suggesting an alternative evaluation standard compares the life-cycle (LC) electricity output of the energy storage installation with the respective energy required for its construction, installation, maintenance and decommissioning (LC embodied energy). Although from a system point of view it seems that the additional losses and the additional LC energy requirements introduced by the ESS increase the energy payback of the entire system (since there is no additional energy production by the ESS), the opposite may also be valid. More precisely, if instead of system energy production, the respective useful energy generation is used (Kaldellis et al., 2009a), i.e. any energy curtailments or wasted energy is excluded from the original energy payback estimation that does not consider the operation of an ESS, then it is possible for the energy payback to present a considerable reduction as soon as the ESS is added. Self-discharge, parasitic losses, ageing mechanisms, maximum depth of discharge Self-discharge, ageing mechanisms and depth of discharge suggest inherent characteristics of an ESS that strongly influence all other system parameters. As discussed earlier, losses occurring during the time that amounts of energy remain stored, namely self-discharge or idling losses, determine the maximum storage duration and thus delimit the system’s application range. Energy expenses are required to compensate for self-discharge losses and/ or sustain certain conditions of operation required for some ESSs (such as maintaining a rather high or a rather low temperature environment, creating and maintaining vacuum, etc.). These energy expenses are known as parasitic losses and common examples include the trickle charging of batteries, where attention must be paid so that the rate of supplied energy does not overcome the respective self-discharge, leading to system overcharging. Ageing mechanism is a term commonly met in batteries and applies to any chemical or mechanical reason leading to system failure. These mechanisms may suggest long-term gradual degradation and/or abrupt causes that, along with stress factors, configure the system service period expectancy (Ruetschi, 2004). Also already addressed, the maximum depth of discharge, DODmax, determines the maximum exploitable energy storage capacity of a system in order for smooth operation to be guaranteed. Relative to this, the state

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of charge, SOC, of the ESS should at all times exceed the respective minimum value, SOCmin, corresponding to the term 1 − DODmax, while it should be mentioned that both charging and discharging efficiency are considerably affected once certain SOC values are exceeded (Gergaud, 2002). Energy and power density Another set of parameters distinguishing ESSs involves energy and power density, expressed either in relation to the mass or the volume of the system. Power or energy density estimation is provided by the ratio of energy storage capacity or rated power to the system volume or mass. Specific system boundaries are critical and may be limited to storage media only, or expanded so as to include power conversion subsystems and source/load interfaces (Kondoh et al., 2000). In this context, floor space requirements are of critical interest for the installation of an ESS. The space to be occupied by the system may in certain cases be extremely large or, in others, the volume required may not be available (e.g. when natural caverns are used as storage). Alternatively, the term footprint requirements may be used (Butler et al., 2002), especially when space and volume requirements imply strong disorder of the local environment. Both energy and mass densities determine the portability requirements of a given storage device, for which, depending on its size and weight, transportation issues may prove very important (Rydh and Sandén, 2005a). Influence on the environment Although destined to support RES and increase energy efficiency, ESSs themselves have environmental impacts and also contribute towards greenhouse gas (GHG) emissions. Serious environmental considerations include disposal of toxic waste and chemical solutions (Morrow, 2001), disturbance of the local environment in case of considerable civil works required, conventional fuel use, maintenance of strong magnetic fields, and others. On top of these, embodied energy, as previously discussed, is also a matter of concern (Denholm and Kulcinski, 2004; Rydh and Sandén, 2005a), while considerable GHG emissions should also be expected, owing to the activities involved especially during construction or manufacturing stages. Note also that for certain systems (although not as significant), GHG emissions are produced during the operational period as well (Denholm and Kulcinski, 2004). Nevertheless, to obtain a solid result concerning the environmental performance of a given ESS, a comparison with other supplementary energy options should be carried out at all times, while the fact that potentially waste energy is recovered should also be considered (Weisser, 2007).

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Lifetime, number of cycles, duty cycle requirements The lifetime (or service period) is either expressed in years under a certain cycling rate, or in number of cycles, where a cycle is the period during which the system is fully charged and discharged. The number of operating cycles and the lifetime of an ESS system strongly depend on the characteristics of discharge, meaning at which depth discharges are carried out, with deep discharges implying reduction of the system lifetime. Furthermore, distribution and periodicity are important characteristics of the duty cycle requirements. Based on the uniform distribution or not of cycling over time, the efficiency of various storage subsystems may be either positively or negatively influenced, while ageing mechanisms and stress factors previously addressed also play a vital role in the life time expectancy of an ESS (Ruetschi, 2004). System costs The capital cost of an ESS, CESS, comprises the capital cost of the storage device, CSTOR, the capital cost of the PCSs, CPCS (charging and discharging side), and the capital cost of the various BOS components, CBOS (Schoenung and Hassenzahl, 2003). The capital cost of the storage device is usually expressed in relation to its energy storage capacity (c/kW h), while the respective power conversion cost is expressed in relation to the PCS rated power (c/kW). Finally, the BOS component’s cost is either proportional to the system or given as a fixed value. As may easily be concluded, the capital cost, being directly dependent on the size of the ESS, is largely influenced by all previous parameters determining the system dimensions. Nevertheless, it must be underlined that, due to economies of scale, costs per kW h and costs per kW for the same ESS may present considerable variation depending on the system size. On the other hand, to obtain a common comparison basis for all ESSs, the introduction of the discharge efficiency (ESA, 2009a) and the maximum depth of discharge in the cost evaluation provides the cost per unit of useful energy, while by also considering the number of cycles, the cost per unit of useful energy and per cycle of operation may be given: CESS · (Eused · Ncycles)−1, where Eused = Estor · ηout · DODmax. For a detailed cost analysis, however, usually on a life-cycle basis, the fixed and variable maintenance and operation (M&O) costs of the system are also required (Kaldellis and Zafirakis, 2007; Kaldellis et al., 2009b). Other features In addition to the characteristics so far presented, other features of an ESS, although not easily quantified, can in certain cases be critical to its adoption.

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In this context, parameters such as the maturity of the technology, its adaptation to the location of interest, any operational constraints and the supplier’s profile may prove crucial for the feasibility of the project. Some technologies may be considered mature while others are still in the development stage; depending on the local environment, energy status and socioeconomic standards of a given site, an ESS may just as easily prove suitable or inappropriate to install. Furthermore, the operational constraints of certain systems (e.g. thermal behaviour, landscape requirements, fuel supply) and the profile of the main suppliers (e.g. guarantees and service provided) may also affect decision making.

2.3

Application range of energy storage systems (ESSs): category of generation

As has already been seen, energy storage is faced with two challenges (Fig. 2.2): support of conventional centralized generation on the one hand and promotion of RES-based distributed generation on the other. To cope with these challenges, ESSs are increasingly expected to provide services to a number of applications. Based on both the flexibility of energy storage due to the numerous technologies available, and on the fact that technological developments in the field are ongoing, the grounds for application of energy storage are constantly expanding. Different classification areas for the various recognized applications include the following. Taking into account the power and energy requirements of the demand side, one may distinguish the areas of energy management, bridging power and power quality-reliability (or enhanced power quality) (ESA, 2009b). Energy management encompasses the applications concerned with harmonizing the energy generation and demand profiles, i.e. for demand to coincide with generation via the implementation of storage, and requires the use of bulk ESSs with considerable energy storage capacity, storage duration and discharge time. Bridging power, on the other hand, is less demanding as far as the capacity requirements are concerned and involves applications where the discharge duration is kept within a timescale of minutes. Finally, power quality and reliability applications refer to the provision of considerable power within periods of seconds, so as to ensure that power disturbances are eliminated. A second demarcation (Butler et al., 2002), according to the service area, includes generation, transmission and distribution and customer service (or energy service), whereas the renewable support is often disengaged from utility applications in order to emphasize the critical role of ESS in the promotion of RES. Besides, it should be noted that regardless of the identification of an ESS to serve a given application, multifunctional capabilities of ESSs, i.e. the ability of a system to serve more than a single purpose, should also be taken

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Renewable energy support (grid connected, DG and stand-alone applications) ESS Transmission & distribution (system stability, voltage regulation, facility deferral) ESS

Generation category (rapid reserve, area & frequency control and commodity storage) ESS

ESS

ESS

Customer service (energy management, peak shaving, power quality & reliability)

2.6 Application areas of ESSs.

into account. Of the two classifications seen, classification of applications according to service purpose is the most commonly used. In this context, services provided by ESSs are summarized in Fig. 2.6, while definitions and classifications for each application are given below.

2.3.1 Rapid or spinning reserve or contingency reserve In order for utilities to compensate for the possible failure of a system power generator, common practice suggests the employment of thermal units as back-up power. This is achieved either by the operation of existing thermal units under their rated power, or by the installation of new back-up units (combustion turbines), dedicated to covering any energy deficit appearing. In both cases, thermal units and combustion turbines ought to operate in reserve mode, thus presenting increased fuel consumption and fast wear. To avoid the results of this tactic, energy storage may substitute thermal units and provide the required amounts of energy, on the presumption that the response time of the candidate ESS is analogous to the system expectations. Furthermore, an additional benefit that may accrue from the adoption of energy storage in that case is the potential reduction of generating capacity, since a certain number of generators may be allowed to operate at optimum point levels, i.e. fewer units are required for the same power output.

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2.3.2 Area control and frequency responsive reserve Although large-scale networks have the ability to assimilate the imbalance between generation and load demand (based on the operation of many generators, as well as on the existence of excess energy areas from which energy can be drawn, and high-demand neighbouring areas to which excess energy may be directed towards), the same is not valid for small-scale, island-mode networks. In the absence of energy trade-off with vicinal network areas, the implementation of energy storage may prove quite useful by accepting any energy surplus and by covering any energy deficit, thus balancing energy generation with energy demand. On the other hand, load fluctuations exceeding given limitations in an interconnected system entail significant frequency changes that may result in the damaging of electrical appliances at the consumer end and utility equipment at the generation side. With the introduction of energy storage, the counterbalancing of load fluctuations and the regulation of frequency become possible.

2.3.3 Commodity storage or load levelling or arbitrage Commodity storage, or load levelling or arbitrage, is clearly one of the most important applications for ESSs. The satisfaction of peak demand has always been a major issue for electricity utilities. Such as in the case of spinning reserve, supplementary energy is usually provided by the operation of an extra combustion generator, determined by considerably low utilization rates and therefore increased costs of operation. During off-peak times, on the other hand, the operation of base-load thermal power units at a minimum given load implies the appearance of energy surplus in case that demand is lower. Using this energy surplus to charge an ESS and allow discharge during peak hours entails certain profits for utilities, depending, however, on the amount of energy provided and the contract terms agreed. Note also that according to the local network demand profile, commodity storage may involve cycling on both a diurnal and seasonal basis, while avoidance of installing extra generation capacity should also be taken into account.

2.4

Application range of energy storage systems (ESSs): category of transmission and distribution

2.4.1 Transmission system stability What actually happens during system instability periods is that the system generators fail to synchronize with the rest of the system. This means that there is a difference between the phase-angle of the generator and that of

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the demand side which, if too large for the system to handle, may even cause the system to collapse. Since load disturbances are the original cause of transmission instabilities, smoothing the load via the use of energy storage may lead to the avoidance of similar events and thus ensure synchronous network operation.

2.4.2 Transmission voltage regulation To obtain uniform voltage across the entire transmission line, the injection of reactive power is necessary. Hence, to deal with voltage differences, capacitors are used that provide the required reactive power. Using an energy storage device, provision of reactive power is possible at all times (charging phase, discharging phase and standby mode), and thus it is common for a utility to employ an ESS for a certain purpose and also use it as a reactive power provider (i.e. multifunctional use of the ESS).

2.4.3 Transmission facility deferral Transmission facility deferral is another major application area for ESSs. Utilities, faced with a constant increase in electricity demand, are obliged to provide sufficient capacity of transmission lines. This, in turn, may result in the low utilization of new lines and transformers, making this solution cost-ineffective. Until the new capacity to be installed is close to the electricity demand of the area and the utilization rate is sufficient to justify the investment, transmission capacity may be replaced by the use of energy storage, serving the peak demand periods which would otherwise be responsible for the upgrading of transmission lines. Apart from deferring the upgrading of transmission facilities, the implementation of energy storage may also extend the life of already existing transmission lines, through the avoidance of overloading (i.e. avoiding the overheating of transmission lines which otherwise reduces the lines’ in-service lifetime). Finally, one side benefit not easily quantified is that, owing to the trade-off between the ESS and transmission lines, the utilization rate of the transmission network increases and thus greater utilization of the respective investment is also realized.

2.4.4 Distribution facility deferral As in the case of transmission deferral, the purchase and installation of new distribution equipment may be deferred until local electricity demand requirements match aggregate distribution capacity. In the meantime, ESSs may better adjust to the satisfaction of peak demands exceeding the existing distribution capacity. It should be noted that ESSs introduced into a system

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for this reason are not necessarily retired when the new lines are installed; on the contrary, until such a time that the cumulative new capacity is also exceeded, and provided that the service period of the ESS has not yet ended, the system may be used for other utility purposes (i.e. multifunctional use of the ESS).

2.5

Application range of energy storage systems (ESSs): category of customer service

2.5.1 Energy management or peak shaving or demand charge reduction Energy management concerns the elimination of high load demand in order for utility customers to avoid the imposition of monthly utility fees concerned with the highest peak. Peak shaving via the implementation of energy storage is used in order to prevent the appearance of a high peak that leads to the imposition of analogous fees. An ESS is employed onsite, and is kept in charging and standby mode during the interval between two consecutive peaks; attention should be paid to the system sizing, so as to adjust to the given demand profile and cope with any forthcoming peak.

2.5.2 Power quality and reliability Harmonic distortions, voltage sags, spikes and failures may cause serious problems to numerous vulnerable electronic devices. In order for customers to protect this kind of device from similar phenomena, an appropriate ESS may be used to replace the grid power supply until the power quality of the supply network is restored.

2.5.3 Renewable energy Integration with RES is clearly the most important challenge facing ESSs at the moment. As has already been seen, a shift to RES-based distributed generation, operation of stand-alone systems and the establishment of RES technologies as baseload units in conventional power generation reflect the wide range of applications for ESSs. In this context, the role of ESSs is twofold. On the one hand, energy storage may be used for power firming, and on the other hand it may be used to match RES production with peak demand. In the first case, the provision of firm power capacity through energy storage is achieved by the provision of supplementary power by the ESS whenever RES production drops below the guaranteed power output. In this way the incorporation of an energy system equates RES technologies with other power generation technologies, and thus eliminates the

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inherent disadvantage of stochastic energy generation. Additionally, by using energy storage, any excess RES energy production on a diurnal basis may be used to cover the respective peak demand and thus take advantage of the higher rates offered by the utilities. Finally, the use of ESSs in standalone applications may be further expanded, since developments and cost reduction in both fields of RES technologies and energy storage may eventually lead to the abandonment of diesel hybrid schemes, in areas where the RES potential is considerable.

2.6

Application range of energy storage systems (ESSs): requirements of electricity applications

Figures 2.7 and 2.8 list the requirements for a number of different applications concerning main characteristics that candidate ESSs need to cope with. More specifically, Fig. 2.7 shows the power output and discharge time requirements of ten different applications, also providing a preliminary estimation of the system energy storage capacities. As can be seen from the figure, the applications of commodity storage, rapid reserve, area control and frequency responsive reserve, and transmission system stability demand the highest power output. Among these, commodity storage requires considerably higher discharge duration, i.e. significant energy storage capacity, 1000 10 h

Commodity storage Distribution deferral

Discharge duration (min)

100

Customer energy management

10

Transmission deferral

Rapid reserve

Transmission voltage regulation

Area control & frequency responsive reserve

1h

Renewable energy management 1 min

1

0.1 Power quality & reliability Transmission system stability

0.01

1s

100 ms 0.001 10 kW

100 kW

1 MW

10 MW

100 MW

Power output

2.7 Power output and discharge period requirements of electricity applications (based on Butler et al., 2002).

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>1000 Area control & frequency responsive reserve(LP)

Annual duty cycle requirements

1000

HP: high portability importance LP: low portability importance NP: negligible portability importance VP: variable portability importance

Transmission system stability(LP) Transmission voltage regulation(HP) Commodity storage(NP)

100

Transmission deferral(HP) Distribution deferral(HP)

Rapid reserve(LP)

10

1

Power quality & reliability(HP) Customer energy management(VP) Renewable energy management(HP)

Low

Medium

High

Floor space (importance)

2.8 Duty cycle, portability and space requirements of electricity applications (based on Butler et al., 2002).

while transmission system stability requires the employment of ESSs with fast response and rather short discharge times (of the scale of milliseconds to seconds). Distribution and transmission deferral are both described by respectable discharge duration, while in the case of distribution, less power is required. Customer energy management and transmission voltage regulation present similar discharge time requirements (tens of minutes) with voltage regulation presuming power ratings at the level of transmission system stability, and customer energy management ranging from a few kilowatts to the megawatt scale. Power quality and reliability applications are found in the same scale of power (i.e. from a few kilowatts to the megawatt scale) but correspond to discharge duration at the scale of milliseconds to tens of seconds. Finally, it is interesting to see the broad range of applications introduced with the support of RES technologies, reflecting the challenge with which ESSs are faced. In Fig. 2.8, the various applications are examined with regards to duty cycle, floor space and portability requirements. Area control and frequencyresponsive reserve applications require the ESS to charge and discharge thousands of times. On the other hand, issues of portability and especially of floor area are not of great importance. The opposite is true for the major-

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Table 2.2 Alternatives of ESS for common electrical applications (Butler et al., 2002) Application

Technology & non-technology alternatives

Rapid reserve

Centralized: thermal fossil, combustion gas turbines, small diesel generators, spot purchases Distributed: diesel generators, microturbines

Area control & frequencyresponsive reserve

Intermediate cycling and peaking plants, combustion turbines

Commodity storage

Flexible AC transmission system, cogeneration

Transmission system stability

Auto-transformer, flexible AC transmission system

Transmission voltage regulation

Capacitor banks

Transmission facility deferral

Diesel generators, oil coolers for transformers, superconducting cables

Distribution facility deferral

Diesel generators, oil coolers for transformers, superconducting cables

Energy management

Thermal storage (passive solar, chiller), diesel generators, microturbines

Power quality & reliability

Diesel generators, static and rotary uninterruptible power supply (UPS), dynamic voltage restorers

Renewable energy management

Diesel generators

ity of applications, where both portability and footprint issues should be considered. Less frequent cycling is required for rapid reserve applications, while only in the case of commodity storage can portability be granted as negligible. Finally, what is interesting to see is the systems and technologies currently used to serve the numerous electrical applications presented; put in a different way, there is a wide range of systems and technologies that ESSs have to compete with to establish their role in electricity markets (Table 2.2).

2.7

Contemporary energy storage systems (ESSs)

ESSs used for electricity generation purposes are usually classified according to their form of energy storage. In this context, there are three main categories that should be considered:

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Stand-alone and hybrid wind energy systems mechanical storage, including flywheels, PHS and CAES; chemical storage, including all batteries, flow batteries and FC-HS; electrical storage, including SCs and SMES.

2.8

Mechanical energy storage

2.8.1 PHS PHS systems should be considered as the most mature bulk energy storage technology with more than 100 GW of installed capacity worldwide (ASCE, 1993). In a PHS system, energy surplus appearing in times of low demand, either deriving from the electrical grid or any given generation unit (such as a wind park), is exploited to pump water into an elevated (upper) storage reservoir (see Fig. 2.9). During peak demand, water is released from the upper reservoir and water turbines operate to ‘feed’ a connected electric generator. As a result, the system is able to cover an existing power deficit by using the appropriate amount of previously stored energy. In another version, water turbines can be replaced by reversible hydraulic machines working either way (in pumping and turbine mode), often supported by an independent pump unit (Fig. 2.9). Operating two water pipes is, in many cases, thought to be unnecessary; nevertheless, parallel operation (with simultaneous storage and generation) is also an option. The cycle efficiency of a typical PHS ranges between 65% and 77% (Papantonis, 1995), while the main drawback of such systems is their high capital cost, directly related to the need for the construction of at least two reservoirs, preferably close to consumption. If natural reservoirs are

2

5 7 6

3

H 4 P

1

1. Wind park 2. Electricity grid 3. Lower reservoir 4. Pumping system 5. Upper reservoir 6. Reversible hydr. machines 7. Electricity consumption

2.9 Pumped hydro storage configuration.

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employed instead, the feasibility of the project is in most cases guaranteed, while large-scale projects are usually more attractive (Bindner, 1999). Lack of suitable sites, on the other hand, sets some serious restrictions, and environmental impacts caused to the surroundings during installation are also a matter of concern. With this in mind, the open sea (Fujihara et al., 1998) and underground caverns (Chen and Berman, 1981) may serve equally well as lower reservoirs. Such systems are able to take up load within a few minutes, and are determined by a high rate of extracted energy, while PHSs in general are suitable for energy management, spinning reserve and frequency control applications. As a result, pumped hydro is thought to be essential for the support of wind parks operating in island mode networks (Bueno and Carta, 2006; Kaldellis et al., 2006; Anagnostopoulos and Papantonis, 2008; Katsaprakakis et al., 2008), where wind energy curtailments are considerable.

2.8.2 CAES In a CAES system (see Fig. 2.10), off-peak power is taken from the grid or other generation source and is used to pressurize air into an underground cavern (with air pressures reaching 80 bars). During times of peak demand, the required amount of air is released from the cavern, heated with natural gas and then supplied in the form of gases to a gas turbine where expansion takes place, as in a typical Brayton/Joule cycle. This is actually the main benefit of a CAES system: the fact that the stages of compression and generation are separated from one another. Consequently, what can be as much

11 10

T

C

M 2

7

3 5 P/H 4

1

6 C.C.

G

1. Wind park 8 2. Motor 3. Air compressor 4. Air storage cavern 9 5. Preheater 6. Combustion chamber 7. Gas turbine 8. Generator 9. Natural gas tank 10. Electricity grid 11. Electricity consumption

2.10 Compressed air energy storage configuration.

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as 66% of fuel consumption needed to drive the compressor in a typical Brayton/Joule cycle, is not needed in the case of a CAES cycle. As a result, in a CAES system, the entire power of the gas turbine is available for consumption. In this context, during a charging/discharging cycle, 1 kW h of generated electricity requires approximately 0.75 kW h of electricity for the compressor and 4500 kJ of fuel for combustion (Denholm and Kulcinski, 2004). The amount of fuel required is the main subject of controversy over the unconditional acceptance of such systems. In an effort to disengage CAES from the natural gas factor, one concept supports the use of biofuels (Denholm, 2006), while another interesting approach is the socalled ‘advanced adiabatic CAES’ where no fuel is used (Bullough et al., 2004). CAES, like PHS, demands favourable sites and geological formations, suitable for underground storage. The storage media most commonly used are rock caverns, salt caverns, porous media reservoirs and buried pipes for small subsurface CAES units (Bradshaw, 2000; Dayan et al., 2004). In terms of energy capacity, CAES is thought to be the only reliable alternative option for PHS. Since the losses recognized are not appreciable, the storage period is considerable. Among the advantages of CAES are its fast ramp rate (two to three times faster than conventional units), its stable heat rate at low capacity, and the considerably lower emissions (compared with simple and combined cycle units) (Bradshaw, 2000). Note that because of their potential to operate at partial load with satisfying fuel consumption, CAES systems are more suitable for load control applications. Additionally, the flexibility of CAES systems to serve as both base load plants (Cavallo, 1997; Denholm, 2006; Greenblatt et al., 2007) and peak following units (Lund et al., 2009) provides considerable opportunities for improved management of wind energy generation (Cavallo, 1997; Denholm, 2006; Cavallo, 2007; Salgi and Lund, 2008).

2.8.3 Flywheels By adjusting any voltage sags and surges as well as instantaneous interruptions, flywheels (Hull, 2004; Suzuki et al., 2005; Bolund et al., 2007) aim mainly to ensure short duration power quality as well as to provide a reliable option for UPS applications. In this context, the use of flywheels for wind power regulation is a common application for such systems (Davies et al., 1988; Infield, 1994; Carrillo et al., 2009). In a flywheel ESS (Fig. 2.11), kinetic energy is stored by causing a disk or rotor to spin on its axis. When short-term back-up power is required, the flywheel takes advantage of the rotor’s inertia and the kinetic energy stored is converted into electricity. A modern flywheel consists of a rotating mass (a rim attached to a shaft) supported by bearings and connected to a motor/generator. During the motor’s

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6 3

53

2

1. RES and/or grid 2. Source interface ± rectifier 3. Vacuum pump 4. Flywheel (with motor/generator) 5. Inverter & AC interface 6. Electricity consumption

1

2.11 Flywheel energy storage configuration.

operation, electrical energy is provided to the stator and the torque produced increases the kinetic energy of the rotor. During discharge, the system operates in the opposite way. The amount of energy stored in a flywheel is directly proportional to the rotor’s mass moment of inertia and the square of its rotational speed. Taking this into consideration, flywheels can be classified in two main categories: low-speed and high-speed. A high-speed flywheel is able to rotate at speeds greater than 100,000 rpm, therefore implying a significant increase of its energy density. To accomplish such rotational speeds, air drag and bearing losses should be minimized, while the rim’s tensile strength should be maximized. For the first goal to be achieved, the flywheel along with the motor/generator must be placed inside a vacuum chamber to avoid the deceleration effects caused by air (hence a vacuum pump is required; see Fig. 2.11), while the use of active magnetic, passive magnetic and superconducting magnetic bearings is recommended to avoid friction losses (Koshizuka, 2006). Some of the key features describing flywheels are their high power density, relatively low maintenance needs (considering that a flywheel consists of kinetic components), their high cycling rate, deep discharges and very high self-discharge rate.

2.9

Chemical energy storage

2.9.1 Battery energy storage Batteries are the most widely adopted energy storage technology, traditionally used in many RES-based stand-alone applications. The numerous battery types existing, each with its own special characteristics, cover a wide range of applications, from power quality to energy management. The

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3

1. RES and/or grid 2. Source interface ± rectifier 3. Battery bank 4. Inverter & AC interface 5. Electricity consumption

5 2

1

2.12 Battery energy storage configuration.

technologies considered here include ‘mature’ lead–acid (L/A) and nickel– cadmium (Ni-Cd) batteries, and advanced sodium–sulphur (Na-S) as well as metal–air and lithium-ion (Li-ion) batteries, which have lately become commercially available. A typical battery system comprises a battery stack, where electrical energy is converted to chemical energy, and vice versa (battery cells connected in series and in parallel to obtain the desired levels of voltage and current output), the PCS, meaning the energy source and load interfaces together with an AC/DC rectifier on the production side and an inverter on the demand side, and, finally, the implementation of any control systems used to coordinate system operation (see also Fig. 2.12). Batteries are usually determined by their efficiency, depth of discharge, number of cycles, operating temperature, energy density and self-discharge, while one of their main technological advantages is the absence of kinetic parts limiting maintenance requirements. L/A L/A batteries comprise two electrodes, the negative made from lead and the positive made from lead dioxide, separated by an electrolyte (dilute H2SO4), assigned to electrically isolate the two electrodes and provide the sulphate ions for the discharge reactions. There are two main types of L/A batteries: flooded and valve regulated. Flooded batteries demand periodic maintenance (water refilling is required) and present moderate energy densities (∼25 W h/kg), whereas valve regulated batteries are maintenance free and are determined by higher energy density values (up to 50 W h/kg) and deeper discharges. On the other hand, the lifespan of flooded batteries

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is much higher than that of the respective valve regulated (1000 and 300 cycles respectively); nevertheless, the service period in both cases is among the lowest for any energy storage. Overall, L/A batteries can be described as a mature technology with known performance characteristics and a reliable market background (Paul, 1994; Parker, 2001; Razelli, 2003; Perrin et al., 2005). Their self-discharge is considerable among batteries (though amongst the lowest in energy storage) while low maintenance requirements is one of their benefits. On the other hand, their low energy density, limited service period, environmentally unfriendly content and the recommended low depth of discharge are the drawbacks of this particular technology. Ni-Cd Ni-Cd batteries can also be considered a mature technology. The main structure of such batteries contains a nickel hydroxide positive electrode plate, a cadmium hydroxide negative electrode plate, a separator, and an alkaline electrolyte (usually KOH). Although their energy density is higher than that of corresponding L/A batteries, self-discharge is more significant. Deep discharges and a considerable service period, counterbalancing the respective high capital cost, are the technology’s most positive features; low efficiency rates and environmental concerns regarding cadmium toxicity (Rudnik and Nikiel, 2007) are the technology’s most negative features. Na-S In an Na-S battery the positive (liquid sulphur) and the negative (liquid sodium) electrode are separated by a solid beta-alumina ceramic electrolyte, allowing only the positive sodium ions to pass. During discharge, positive ions of sodium pass through the electrolyte and electrons flow in the external circuit of the battery producing voltage. When combined with sulphur, sodium ions form sodium polysulphides. The opposite process occurs during charging periods. For the battery to operate, however, a temperature of 300 °C is necessary, meaning that a heat supply should also be considered for a Na-S system. Because of the material’s high energy potential, Na-S batteries demonstrate proportional energy densities, both gravimetric and volumetric, while because of the existence of beta-alumina (with zero electron conductivity) there is no self-discharge. In addition, both efficiency and depth of discharge for such batteries are quite high (Oshima et al., 2004; Rydh and Sanden, 2005a,b; Wen et al., 2008); however, the use of Na-S may not be able to satisfy certain application requirements as the need to maintain high temperature levels sets a serious obstacle.

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Li-ion In an Li-ion battery (Ritchie and Howard, 2006) the positive electrode is a lithiated metal oxide (LiCoO2, LiMO2) and the negative electrode is made of graphitic carbon. The electrolyte consists of lithium salts dissolved in organic carbonates. During the charging stage, the atoms of lithium in the cathode ionize. These ions move through the electrolyte to the negative electrode where they combine with external electrons and finally end up between the carbon layers as lithium atoms. The reverse process occurs during discharge. The main advantages of this technology are the high energy density with a potential for yet higher capacities, the high efficiency value (∼95%), and the respectable number of cycles combined with deep discharges (Rydh and Sanden, 2005a,b). Additional advantages include a low self-discharge rate and analogous maintenance needs. The limitations set at present are the required protection circuits to maintain voltage and current within safety limits, the immaturity of the technology and, most importantly, the capital cost. As a result, Li-ion batteries are currently used in small portable equipment (Megahed and Ebner, 1995) and are expected to expand their application range in the next few years. Metal–air batteries In metal–air batteries (Blurton and Sammells, 1979), common metals releasing electrons when oxidized, preferably of high energy density, are used for the anode (e.g. zinc or aluminium) (Chakkaravarthy et al., 1981), while for the cathode a porous carbon structure or metal mesh with a proper catalyst are typical. KOH in liquid form or a solid polymer membrane saturated with KOH is used as the electrolyte. Energy density of the scale of hundreds of W h/kg is typical of the technology; nevertheless, difficult recharging conditions limit the system performance to a maximum of 50% efficiency and to a maximum of a few hundred cycles for the system life time. Selfdischarge is negligible, while system cost is in the range of 100c/kW h.

2.9.2 Flow batteries Flow batteries are a relatively novel technology that stores energy by means of a reversible chemical reaction. The main characteristic determining the operation of such systems is that energy is stored in two liquid electrolyte solutions. Given this, energy capacity and system rated power are independent from one another. The storage capacity depends exclusively on the quantity of the electrolytes used, while the power rating is determined by the active area of the cell stack. The flow battery system shown in Fig. 2.13 is formed by a number of electrochemical cells, each one having two com-

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1. RES and/or grid 2. Source interface ± rectifier 3. Electrolyte pumps 4. Electrolyte tanks 5. Electrochemical cells 6. Inverter & AC interface 7. Electricity consumption

6 4 4 3

57

7

3

2

1

2.13 Flow battery energy storage configuration.

partments (one for each electrolyte), being separated by an ion-exchange membrane. The two electrolytes are pumped from the tanks, through the cell stack and across the membrane. When passing through the membrane, the one electrolyte is oxidized and the other is reduced, producing current available to the external circuit. The pumps needed to circulate the electrolytes bring some losses while the use of aggressive chemical solutions is an additional concern. As already mentioned, the energy capacity of these systems depends on the size of the electrolytic tanks. Apparently, increasing the quantities of electrolytes used could lead to the service of large energy storage applications, theoretically in areas where only PHS and CAES may apply. Hence, novel flow battery technology may be encountered in a number of applications, also including the service of both large-scale and stand-alone RES-based installations (Joerissen et al., 2004; Dufo-López et al., 2009). Present technologies are determined by the electrolytes used and are described below. Vanadium redox battery In a vanadium redox battery (VRB), energy is stored by using vanadium redox couples (V2+/V3+ in the negative and V4+/V5+ in the positive half-cells) (Sum and Skyllas-Kazacos, 1985). The couples are stored in mild sulphuric acid solutions. During a charge–discharge cycle, H+ ions are exchanged

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between the two electrolyte tanks, through a hydrogen-ion permeable polymer membrane. Some of the advantages that VRB holds are its high efficiency, deep discharges and service period that may even exceed 10,000 cycles (Lotspeich, 2002). Purchase costs show great variation, proportional to the given capacity, and appear quite low when referring to larger systems. Polysulphide bromide battery The polysulphide bromide battery (PSB) is a regenerative fuel cell technology that stores energy by means of a reversible electrochemical reaction between two salt solution electrolytes (sodium bromide and sodium polysulphide). The two electrolytes are placed close together inside a cell and remain separated by a polymer membrane. The membrane only allows positive sodium ions to pass, producing voltage across the membrane. The roundtrip efficiency is lower than in VRB (Divya and Østergaard, 2009; Lotspeich, 2002) and the energy density is around 20–30 W h/l. In standby mode, with the electrolytes charged, the system can respond in milliseconds, while the technology potential concerning lifetime and power-energy capacity extends to over 15 years and to a scale of tens of megawatt hours, respectively. Zinc–bromine battery Energy storage in zinc–bromine (Zn-Br) batteries (Singh and Jonshagen, 1991) is based on the reaction occurring between two common chemicals, zinc and bromine. The negative electrode is made up of zinc and the positive from bromine. The electrolyte is usually aqueous with zinc bromide salt dissolved in water. During charge, zinc is plated from the electrolyte solution onto the negative electrode surfaces, inside the cell stacks. The bromine produced on the positive electrodes surfaces is stored afterwards on the bottom of the positive electrolyte tank in the form of a chemically complex organic phase. When the Zn-Br battery is completely discharged, all the metallic zinc formed on the negative electrode surface is dissolved into the electrolyte and is again available for the next charge. Moderate cycle life combined with relatively high energy density (65–84 W h/kg), deep discharges and negligible self-discharge are the main system characteristics (Lotspeich, 2002).

2.9.3 Fuel cells and hydrogen storage (FC-HS) Fuel cells consist of two electrodes surrounding an electrolyte. Oxygen passes over one electrode and hydrogen over the other, generating electric-

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ity, water and heat. In principle, a fuel cell operates like a battery. However, a fuel cell does not require recharging; as long as fuel is supplied to the cell, electricity is produced. Thus, the restrictions imposed on storage capacity are determined by the fuel tank size. The energy that a fuel cell produces is directly dependent on the fuel cell type, the operation temperature, and the catalyst used to improve the chemical reaction’s performance. In this context, the main characteristics of different fuel cell types are listed in Table 2.3 (EG&G, 2004), while a typical FC-HS configuration is shown in Fig. 2.14. The main disadvantage of this technology is the cycle efficiency which, by including the hydrogen production stage (electrolysis is currently considered), is estimated to be in the range of 30–40%. The losses are detected during the electrolysis to produce hydrogen, during the storage stage, and finally during the electricity generation process via the fuel cell. The fuel cell component alone, however, may for certain types reach efficiencies of 60% (in cases of high-temperature fuel cells, i.e. molten carbonate fuel cells (MCFC), solid oxide fuel cells (SOFC) or alkaline fuel cells (AFC)). Among the technology advantages that one may encounter are the high energy density due to the use of hydrogen, the low energy cost, the negligible selfdischarge and the wide range of applications, including interaction with RES (Bauen and Hart, 2000; Sørensen, 2000; Agbossou et al., 2001; Duic and Da Graça Carvalho, 2004; Chen et al., 2007). In fact, it has been argued that intermittent RES are the ideal power source for the production of hydrogen (Moriarty and Honnery, 2007).

1. RES and/or grid 2. Electrolysis unit 3. Compressor 4. Hydrogen storage 5. Fuel cell system 6. Electricity consumption

5

4

6 3 2 1

2.14 Fuel cell and hydrogen energy storage configuration.

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Electrolyte

Polymer membrane

Potassium hydroxide (KOH)

Concentrated phosphoric acid

Melted Li/K carbonate

Solid oxide ceramic

Fuel cell type

Polymer electrolyte membrane (PEMFC)

Alkaline (AFC)

Phosphoric acid (PAFC)

Molten carbonate (MCFC)

Solid oxide (SOFC)

Hydrogen, natural gas

Hydrogen, natural gas

Hydrogen, natural gas

Pure hydrogen

Hydrogen, methanol

Fuels used

Table 2.3 Different types of fuel cells and main characteristics (FCWAY, 2006)

700–1000

600–800

130–220

70–220

Up to 120

Operating temperature (°C)

Power plants, combined heat and power (up to 10 MW)

Power plants, (up to 2 MW)

Small power plants (up to 1 MW)

Space vehicles, land vehicles, submarines (up to 12 kW)

Cars, buses, portable (up to 250 kW)

Applications range

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Electrical energy storage

2.10.1 SMES In a SMES system (Xue et al., 2006), energy is stored in the magnetic field produced when direct current flows through a superconducting coil. For this to happen, the superconducting material of the coil must be cooled adequately so as to show no resistance to the flow of current, enabling the unit to store energy in the magnetic field. The superconducting material currently used is a nobium–titanium alloy, operating at liquid helium temperature. The recommended operating temperature for most systems ranges from 50 to 77 K, while the amount of energy stored is proportional to the inductance of the coil and the electrical current’s square. A typical system is given in Fig. 2.15, including the superconducting coil, the power conditioning systems and the refrigeration unit. SMES systems have the ability of fast response. They may be deeply discharged without any influence on either their operational efficiency or service period, and their lack of moving parts and impressive efficiency value (>90%) are some additional advantages. SMES systems do face some problems related to the stability of the superconducting coil: superconductivity appears to be quite sensitive to temperature variations and critical magnetic field. The main drawback of the technology, however, is the enormous amount of power needed to keep the coil at such low temperatures, combined with the high overall cost for the employment of such a unit (especially in the case of micro-SMES systems). By providing power on a scale of MW with a bridging time of seconds, SMES systems are designed for the treatment of voltage sags and frequency instabilities, e.g. when the output power of a wind farm fluctuates with the wind, the SMES can be 4

5

3

6 2

1. RES and/or grid 2. Source interface ± rectifier 3. Refrigerator unit 4. Superconducting coil containment 5. Inverter & AC interface 6. Electricity consumption

1

2.15 Superconducting magnetic energy storage configuration.

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controlled to exchange the active power with the AC power grid, and the power fluctuation of the wind farm can be smoothed out (Shi et al., 2008; Ngamroo et al., 2009).

2.10.2 SCs The operational principle of SCs is based on that of conventional capacitors, i.e. when direct current applies, energy storage occurs in an existing electrical field. What separates SCs from common devices is their remarkably increased capacitance. Representative capacitance is around 5 F/cm2 while the corresponding value of conventional capacitors does not exceed 40 μF/cm2. In terms of structure, SCs are strongly reminiscent of batteries, with two electrodes being immersed in an electrolyte solution and kept at distance via a separator. The electrodes are made up of a high surface area porous material and the electrolyte can be either aqueous or organic: differences between the two include a higher energy density in the case of organic electrolytes (Morimoto et al., 1996) but lower costs and a wider temperature range in the case of aqueous electrolytes. A typical configuration is given in Fig. 2.16. Note that, in contrast with SMES and flywheel configurations, SCs do not require any additional power supply (i.e. for a refrigerator unit or vacuum pump). System advantages include the highest of power densities, fast charge and discharge rates, low current leakage, a considerable service period, thousands of cycles per year at deep discharge, operational stability within a wide range of temperatures and high energy efficiency. On the other hand, 4 3

1. RES and/or grid 2. Source interface ± rectifier 3. Supercapacitor 4. Inverter & AC interface 5. Electricity consumption

5 2

1

2.16 Supercapacitor energy storage configuration.

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although energy density is higher than in common capacitors, it is still among the lowest in energy storage, while cost issues are also a serious constraint for such systems. SCs are destined for power quality applications, also contributing to hybrid storage configurations for the support of RES systems (Khan and Iqbal, 2005; Onar et al., 2006).

2.11

Comparison of energy storage systems (ESSs)

Each of the most established energy storage technologies have a number of advantages and disadvantages that designate the respective range of applications. To provide further insights into and quantification of each system’s main characteristics, a series of comparison charts is presented (see Figs 2.17 to 2.26 below). It must be emphasized that the data used for the compilation of the charts derive from numerous sources, i.e. Boyes, 2000; Kondoh et al., 2000; Cavallo, 2001; Dell and Rand, 2001; Schoenung, 2001; Butler et al., 2002; Makansi and Abboud, 2002; Beurskens and de Noord, 2003; Hubert et al., 2003; Denholm and Kulcinski, 2004; Eyer et al., 2004; Gonzalez et al., 2004; Swanbarton, 2004; Thackeray, 2004; Rydh and Sandén, 2005a,b; Sauer, 2006; Baker, 2008; Hall and Bain, 2008; Ibrahim et al., 2008; Chen et al., 2009; Divya and Østergaard, 2009; ESA, 2009a; Hadjipaschalis et al., 2009; Kaldellis et al., 2009b. Thus, a wide range of data is presented; however, a certain level of uncertainty concerning the validity of information should always be considered, especially since constant development in the field means that data values can change over short periods of time. Available power 1 kW

1 year

Discharge time

1 month 1 week ½ day

10 MW

100 kW

0.01 kW/kW h

1 GW

(1)

(5)

(3)

10 GW (2)

(4)

1 hour

1 kW/kW h

100 kW/kW h

1 min

(1): PHS (2): CAES (3): FA-HS (4): Flow batteries (5): Batteries (6): SMES (7): Flywheels & SCs

1 sec (6) (7) 10 ms kW h

×101

×102

MW h

×101

×102

GW h

×101

×102

TW h

Installed storage capacity

2.17 Power, discharge time and energy capacity ratings (based on Sauer, 2006).

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2.11.1 Energy storage capacity vs discharge time Figure 2.17 provides a classification of ESSs in terms of energy storage capacity and rated power. More specifically, energy storage capacity is plotted against discharge time, i.e. the period over which the ESS discharges at its rated power, thus the system rated power is given as well, while the power to energy ratio (kW/kW h) is also available. Systems found on the upper right side of the chart (where discharge time and energy storage capacity are considerable), such as PHS, CAES and FC-HS, are ideal for the applications of commodity storage, rapid reserve and area controlfrequency responsive reserve. By contrast, systems found in the lower left side of the chart (where the power to energy ratio is high and the discharge time requirements are low), such as flywheels, SCs and SMES, are suitable for power quality/reliability and transmission system stability applications. Batteries cover a wide range of applications, from power quality to the early stages of energy management, with flow batteries being more appropriate for transmission and distribution deferral. Note that in the chart, a concentrated point of view is provided, considering battery technology as a whole. More information about the performance of actual systems may be obtained from the updated database (November 2008) of the Electricity Storage Association (ESA, 2009a); see also Fig. 2.18. In this context, Na-S comprises the battery technology with the highest discharge time, not influenced by 102 A 10

E

B

Discharge time (hours)

I 1

K F

C

0.1 10–2

10–3

D A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S

F: Li-ion I: VRB K: Zn-Br M: Flywheels N: SC

M

N

10–4 10–4

10–3

10–2

0.1

1

10

102

103

Rated power (MW)

2.18 Power and discharge time ratings for cumulative installed capacity until 2008 (based on ESA, 2009a).

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the rated power output, while the opposite is true for the discharge time of L/A, Ni-Cd and Li-ion batteries. Na-S and L/A demonstrate similar power outputs (up to a scale of tens to hundreds of megawatts) with Li-ion showing the most moderate available power among battery systems (up to hundreds of kilowatts). Ni-Cd batteries on the other hand cover a wide range of power, from a few kilowatts to tens of megawatts. Finally, the power outputs of VRB and Zn-Br are not really affected by variation of discharge time, while VRB extends its power range back to the scale of a few kilowatts as well, in the interstage between customer energy management and power quality applications. Furthermore, SCs apply in conditions of high-rated power (even at the megawatt scale) and minimum discharge time (in a scale of seconds) with flywheels having the ability to satisfy both high-power applications for short duration (high-power flywheels) and considerable time applications at moderate power output (long duration flywheels).

2.11.2 Self-discharge vs recommended storage duration

Days Hours Seconds Minutes

Recommended storage duration

Months

As already discussed, self-discharge is used to express the losses of a storage system during off-duty periods and thus determines the maximum permitted storage duration. In Fig. 2.19, self-discharge of the ESSs under examination is plotted against the recommended storage period. The importance of self-discharge is divided into four areas: negligible and low, for both benign

E

G

A,H

A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S F: Li-ion G: FC-HS

B,I, K,O

F

H: Metal–air I: VRB K: Zn-Br L : SMES M: Flywheels N: SC O: PSB

C

Towards energy management

L D Towards power quality

Negligible

N M

Low

Considerable

Self-discharge importance

2.19 Recommended storage duration vs. self-discharge.

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and very small self-discharge (i.e from 0% to ∼5% per month); considerable, in cases of 5–30%; and high if self-discharge losses exceed 30%. As can be seen, the relation between importance of self-discharge and recommended storage period is evident. Na-S and metal–air batteries, along with bulk energy storage including PHS, CAES and flow batteries, experience zero (in the case of Na-S) or minimum losses, while SCs and flywheels are very much limited by their inherent self-discharge (flywheels may fully discharge over the period of a day). Having a limited storage period in turn excludes these systems from certain applications, like spinning reserve where the periodicity of cycling is very low and where long time intervals between two consecutive cycles are expected. By contrast, provided that other requirements are satisfied as well, these systems may be suitable for power quality applications, where the cycling periodicity is high (with annual duty cycle requirements reaching 1000 cycles/year). On the other hand, bulk ESSs are essential for energy management applications, such as rapid reserve and commodity storage, while depending on their specific features they may serve other purposes as well (e.g. provision of area control-frequency responsive reserve).

2.11.3 Energy and power densities Another aspect of ESSs is covered by an investigation of energy and power density. Figure 2.20 provides both mass and volume energy density, while Fig. 2.21 shows the respective available power densities. From Fig. 2.20, it can be seen that most of the chemical storage media are favoured with high values for both mass and volume energy density, while mechanical and electrical energy storage technologies are determined by considerably lower values. Among these, only flywheels extend to 90 W h/l, also presenting the highest mass density, owing to the use of composite materials. On the other hand, to store considerable amounts of energy in an SC would require enormous system size, while metal–air and fuel cells imply minimum footprint impact and negligible portability concerns. Whereas electrical and small-scale mechanical systems present moderate energy densities, the opposite is valid for both mass and volume power density (Fig. 2.21). Indeed, SCs are determined by remarkably high values, followed by the technologies of SMES and flywheels, while against energy density rates, chemical systems (namely batteries) are not as efficient in terms of power extraction per unit mass or per unit volume.

2.11.4 Service period and number of cycles The lifetime and total number of cycles are also critical for the adoption of an ESS. For this purpose, Fig. 2.22 compares service period (in years) with

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10 4

10

A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S F: Li-ion G: FC-HS

3

Mass energy density (W h/kg)

250 150 120 90

G H E F

Towards negligible portability issues K

70 50

D

B

M

30

C

I O

10

Towards minimum footprint impact

L

1

A

N

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H: Metal–air I: VRB K: Zn-Br L: SMEs M: Flywheels N: SC O: PSB

0.1

1

10

30

50

70

90

120

150

250

10 3

10 4

Volume energy density (W h/l)

2.20 Mass and volume energy density.

5000

Mass power density (W/kg)

2000

C: L/A D: Ni-Cd E: Na-S F: Li-ion

G: FC-HS L: SMES M: Flywheels N: SC N

1200 L 900

M G

600

Towards negligible portability issues

300

E D

C

Towards minimum footprint impact

F

100 10

100

1000

10 4

Volume power density (W/l)

2.21 Mass and volume power density.

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Service period (years)

50 40

A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S F: Li-ion G: FC-HS

H: Metal–air I: VRB K: Zn-Br L: SMES M: Flywheels N: SC O: PSB

A Fewer than 1000 cycles/year

30

B

L

20 16

D

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O E

12 G

M

F

K

8 4

H

C

More than 1000 cycles/year

0 10

100

1000

2000

4000

6000 8000

10 4

10 5

10 6

Number of cycles (@80%DOD)

2.22 Service period vs. number of cycles.

the respective number of cycles. Although chemical energy storage has high energy and power density, most systems are less than 15 years old. By contrast, mechanical and electrical storage (apart from flywheels) may exceed 20 years of service period, with bulk ESSs even reaching 40–60 years. Furthermore, chemical storage, excluding PSB technology, is also limited by the number of cycles, with most of the systems found on the left side of the 1000 cycles per year curve (between 150 and 350 cycles per year on average). Flywheels, SCs and SMES may be fully charged and discharged between 2500 and 3500 times during a year on average, while metal–air batteries show the least attractive lifetime characteristics. Lifetime limitation is also the main disadvantage of L/A batteries, also affecting the life cycle cost of these systems. Overall, by adjusting the results of Fig. 2.8 to the duty cycle requirements of Fig. 2.22, the most suitable ESS for each application can be accrued.

2.11.5 Energy and power costs The capital cost of a system is mainly a synthesis of the energy cost (per unit of storage capacity) and the power cost (per unit of power output). On top of this, BOS components also entail a capital cost while in order to obtain a LC evaluation of the investment, additional data is necessary, such as the fixed and variable M&O cost for the ESS. The above information is

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Overview of energy storage technologies for RES A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S F: Li-ion G: FC-HS

10 5 Toward power quality applications

10 4 L

Energy cost (?/kW h)

10 3

D N

800

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H: Metal–air I: VRB K: Zn-Br L: SMES M: Flywheels N: SC O: PSB

F

M

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K,O I

400 200

Toward energy management applications

E

C

100 A

H

10

B G

1.0 10

100

200

400

600

800

1000

10 4

10 5

Power cost (?/kW)

2.23 Energy and power cost.

installation-specific and considerable uncertainty underlies the provision of any relative data. Further, economies of scale and numerous suppliers also influence the configuration of the capital cost. In this context, in Fig. 2.23, the energy and power costs of each system are given. ESSs found in the direction of power cost reduction are thought to be suitable for applications where high-power provision is required for short periods of time, while towards the direction of energy cost reduction one may encounter the ESSs being most appropriate for energy management applications (long discharge duration and considerable power). Bulk energy storage including PHS, CAES and FC-HS are determined by the lowest energy costs, while electrical storage, flywheels and metal-air batteries are kept under 400c/kW.

2.11.6 Useful energy, power extraction response and cycle efficiency By considering energy efficiency during discharge and the maximum recommended depth of discharge of an ESS, the actual useful energy extracted from the system can be obtained. Using the information available, the product of these two aforementioned parameters is plotted against the

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VRB 80% Zn-Br 70%

Towards instant high power injections

Towards higher utilization of energy storage capacity

90%

PHS

10

PSB SMES

Na-S Ni-CD

60%

L/A 50%

1 Flywheels

CAES FC-HS

0.1

SC

40% Metal Air

30% 100 KW h

1 MW h 10 MW h 100 MW h 1 GW h 100 GW h +100 GW h

100

1/4 cycle

Energy storage capacity

Rated power Output (MW)

Maximum depth of discharge × efficiency

100%

0.01 1 cycle

Response time

2.24 Useful energy and power extraction response.

energy storage capacity ratings of certain ESSs (see Fig. 2.24). In this context, systems used for power quality applications where storage capacity is already limited are not currently evaluated. Instead, they are used in the second half of Fig. 2.24 where power rating is compared with system response time. In the left half of the figure, although electrolysis is excluded from the output efficiency, FC-HS still presents the lowest utilization of energy storage capacity among all the ESSs. The need to maintain air pressure inside the storage cavern for CAES, and the inability of L/A batteries to perform rather deep discharges, explain the fact that both leave almost 40% of their energy capacity unexploited. By contrast, flow batteries and PHS allow more than 70% of their capacity to be extracted, with VRB approaching 90% of energy utilization. SMES are thought to provide the highest power output in the shortest time, while flywheels require the entire cycle duration to take up load. Cycle efficiencies of ESSs are provided in Fig. 2.25. Flywheels and electrical storage systems, along with Na-S and Li-ion batteries, clearly exceed 80% while FC-HS and metal-air batteries drop below 50%. The rest of the technologies present efficiency rates ranging from 60 to 85%.

2.11.7 Environmental and safety concerns The environmental impact caused by the implementation and operation of an ESS is a parameter that is hard to quantify, though still of high significance for the realization of such projects. In terms of magnitude, bulk energy storage entails the most considerable impacts. PHS requires the construction of dams and tunnels, the manufacturing of equipment and the

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Cycle efficiency

100%

80%

60%

40%

SC

Flywheels

SMES

Metal–air

Li-ion

Ni-Cd

Zn-Br

PSB

VRB

L/A

Na-S

FC-HS

CAES

PHS

20%

2.25 Cycle efficiency.

utilization of water resources, while CAES demands cavern formation, the installation of the plant and the natural gas infrastructure as well as consumption of fuel and the production of air emissions. On the other hand, certain chemical storage systems entail the production of toxic wastes (e.g. lead and cadmium disposal) and the production of excessive heat to the surroundings (e.g. Na-S batteries). Safety concerns are encountered in the case of flywheel operation, where the containment structures should be compact enough to withstand a possible burst, or in the case of SMES, where the development of intense magnetic fields may affect the staff employed, or in the case of FC-HS, where high pressure hydrogen storage also implies risks. Although hard to quantify, a measure of the environmental performance of ESSs may be provided by the common energy payback period (Denholm and Kulcinski, 2003). In this context, it is interesting to see that the contribution of energy storage remarkably reduces the energy payback period of RES installations alone via the recovery of otherwise wasted amounts of energy (Kaldellis et al., 2009a), while the respective energy payback period of the entire system, i.e. with the ESS, needs further investigation.

2.11.8 Maturity ESSs can be classified into three main categories according to their maturity, i.e. systems in developing stages (from concept to demonstration), systems already developed (from demonstration to commercial use), and mature systems. L/A batteries and PHS can be found in the category of mature systems, while FC-HS and metal–air batteries are included in the

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Stand-alone and hybrid wind energy systems Demonstration stage SC

Concept stage SC & flywheels

SC SMES

Flywheels & metal–air

PSB & NaS VRB & ZnBr

Flywheels & metal–air

Mature commercial plants Flywheels & SMES

SMES

Adv. capacitors

L/A PHS& CAES

Flywheels & L/A

L/A

MW × 100 First commercial service Second plant in service Third plant in Preconstruction & Available for service commercial order licensing period Cost of Fourth plant in mature plants service Development Fifth plant in period Estimates Actual costs service Simplified estimation, insufficient data Design & construction period Finalized cost estimate

MW × 10 KW × 100 to MW × 1 Up to kW ×10 Seconds Hours

2.26 Maturity levels, novel concepts and cost distribution.

developing stage category. The rest of the technologies are consequently classified in the developed technologies category. Nevertheless, R&D is constant and new concepts do not allow strict classification in any of the three categories. For example, high-speed flywheels and SCs of tens of megawatts are still in the design or prototype stage. In this context, the maturity curve given in Fig. 2.26 depicts the cost distribution over the various development stages of a typical ESS and presents the stage of development where different technology types can be encountered.

2.12

Future trends

As has already been shown, the application range of most ESSs is delimited by a number of constraints. In this context, the need for large-scale penetration of energy storage, so as to facilitate the oncoming shift to distributed power generation, makes R&D in the field imperative. Energy storage developers and researchers constantly come up with new ideas (Baker, 2008; Hall and Bain, 2008) that aim to both improve the performance of such systems and reduce high procurement costs. Nevertheless, in the case of most ESSs, technological advances relate to incremental changes rather than fundamental steps, meaning that a lot is expected from material science, engineering, processing and fabrication rather than from a thorough review of existing concepts. A summary of future trends concerning the three main categories of ESSs, i.e. mechanical, chemical and electrical, is given below.

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2.12.1 Mechanical energy storage Development expectations in PHSs are identical to those encountered in the field of turbo-machinery and civil works. Cost reductions in power electronics will encourage the introduction of adjustable speed units and the employment of two-stage machines, while promotion of seawater and underground reservoirs may limit the construction work required. R&D in CAES includes above-ground air storage, adiabatic CAES, small subsurface CAES systems, buried pipe systems, as well as the use of alternative fuels such as biogas, low-british thermal units (BTU) gas, No. 2 fuel oil, and mixtures of hydrogen and natural gas (Nakhamkin, 2007). Flywheels, on the other hand, turn to the use of high strength composite materials (Tzeng et al., 2006) and superconducting magnetic bearings (Koshizuka, 2006) that may allow higher speeds and analogous energy densities.

2.12.2 Chemical energy storage Attention is presently being given to lithium ion batteries, where developments related to lithium–sulphur and lithium–sulphide technologies are expected to bring remarkable increases of energy density. On the other hand, advances in electrodes, plates, seals, membranes and electrolytes are a common expectation for all battery technologies, while cell packaging, state-of-charge estimation and thermal management are also issues of major concern (Jin et al., 2003). For flow batteries, apart from the interest shown concerning energy density and costs, emphasis is currently being given to increase power density through the development of new electrodes, membranes and electrolytes (Hall and Bain, 2008). In the case of FC-HS, worldwide R&D expenditures during 2006 reached $829M (Fuel Cells Bulletin, 2008), reflecting the urgency of shareholders to promote the hydrogen economy. At the same time, the thematic areas of research set by HY-CO (HY-CO, 2009) include hydrogen production, solid hydrogen storage, PEM fuel cell stack/systems, high temperature fuel cells (MCFC and SOFC) and socio-economic aspects.

2.12.3 Electrical energy storage With regard to SCs, emphasis is presently being given to the areas of electrodes, electrolytes and packaging (Zhang et al., 2009), directly related to the development of nanostructured carbons and controlled porosity polymers, the use of ionic liquid electrolytes, and the employment of monolithic electrodes, respectively. On the other hand, for SMES to obtain a dominant position in the area of power quality, constraints deriving from the crystal nature of high-temperature superconductors must be addressed, while the

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need to improve the management of critical currents and magnetic fields should also be considered (Minami et al., 2001).

2.13

References

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3 Design and performance optimisation of stand-alone and hybrid wind energy systems E. KONDILI, TEI of Piraeus, Greece

Abstract: Optimisation considerations in a hybrid energy system include the configuration of such a system, the sizing of the individual components and the percentage of load that will be covered by renewable energy sources (RES), depending on the needs of the specific site and various economic and environmental constraints. Therefore, there is a wide optimisation scope when dealing with hybrid energy systems, in their configuration and design as well as in their operation. The specific objectives of the present chapter are to analyse and describe the concepts and the parameters affecting the design and optimisation of hybrid energy systems. The main directions of the present chapter are to (i) identify the parameters determining the optimal design of each individual component as a part of a larger system; (ii) highlight the objectives and constraints governing the optimisation of hybrid energy systems; (iii) give a short review of the methods and techniques typically employed in the optimisation process; and (iv) provide an insight into the future prospects of hybrid energy systems optimisation. Key words: hybrid energy systems optimisation, component sizing, energy systems modelling.

3.1

Introduction: scope and objectives of the chapter

Hybrid energy systems can offer a valuable means of supplying electricity to remote areas. Future success of these systems relies on the continuous research, development and demonstration of renewable energy technologies, featuring improved operating performance, cost reduction and improved reliability. In most cases there is a range of different combinations of components that may be considered for a specific area, each combination exhibiting different technical and economic characteristics, i.e. the configuration of a hybrid energy system is itself an optimisation problem. Furthermore, the most favourable system configuration does not necessarily consist of the partial optima of the individual components and this poses another interesting optimisation problem. Many ideas have been proposed for approaching the optimal operation of hybrid energy systems. However, it should be noted that there is no globally accepted approach or solution to the problem. On the contrary, the problem is very complex, and the site-specific characteristics (e.g. 81 © Woodhead Publishing Limited, 2010

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geographical location) and technical details of a system affect the application and success of any given solution. The aim of this chapter is to describe the problems of design optimisation for hybrid energy systems. More specifically, the purpose of this chapter is to: •

•

• • •

emphasise the role of energy systems modeling, and highlight the basic energy systems problems that may be approached through model development; identify the parameters that determine the design of each individual component and, specifically, each component as a part of a complete system; define the facets of hybrid energy system optimisation and enumerate potential objectives and constraints governing optimisation; give a short review of the methods and techniques usually employed to optimise energy systems; highlight the research and development trends and future prospects of hybrid energy system optimisation.

3.2

Energy systems modelling

3.2.1 Scope and type of energy models Energy systems models are the mathematical models that are developed in order to represent as reliably as possible various energy-related problems. Lately, these models have become a common means to identify and solve such problems. This trend has been encouraged by the development of robust solution algorithms and capable computing systems. Depending upon the case under consideration, the related mathematical models may concern a wide variety of problems, such as synthesis, design, operation and optimisation, as will be described in more detail later in the chapter. The size and complexity of the model are closely related to the level of detail of the analysis required, the data availability and the scale of the problem. For instance, the model required could be for a specific unit in a plant only, or, on a much larger scale, for use in energy supply planning of a remote geographical area or even a whole country. Figure 3.1 indicates the type of energy models that may be encountered, as well as their implementation and use. Energy models can be developed for the efficient forecasting, planning, design, operation and optimisation of all energy systems. In a recent review (Jebaraj and Iniyan, 2006) in which a wide variety of energy models were analysed, factors such as gross income, gross output, profit, energy quantity, gross national product (GNP)/energy ratio, energy performance and energy production were cited as possible criteria for energy system optimisation.

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Design and performance optimisation Problem/model

Comments

Forecasting model

Use of forecasting techniques to predict future energy demand magnitudes

Energy planning models

Planning of energy balance for a long-term horizon in a specific area

Energy supply and demand models

Modelling of each supply source and demand pattern

Behaviour of energy equipment

Analysis of single equipment for energy efficiency calculations

Behaviour of entire energy system

Analysis of an entire energy system describing its physical characteristics and laws

Energy systems optimisation models

One or more optimisation criteria determine the optimal solution of a specific energy system

Exploitation/use of energy models

83

• Conventional energy systems

System synthesis/configuration

• RES

Detailed system and components design

• Hybrid energy systems

Simulation of systems operation System optimisation

3.1 Types and uses of energy models.

It should also be noted that technology, efficiency, supply, demand, employment and resource availability have been used as constraints in these models. Different mathematical programming models may be fully applied to the optimisation of hybrid energy systems at any time, using efficiency and cost factors as critical parameters in formulating an objective approach to optimisation. As mathematical programming models are developed, it is interesting to study in detail the type of optimisation criteria that can be employed in different problematic scenarios (Ostergaard, 2009). These criteria may express economic functions such as net present value, total cost, annualised cost and profit; or alternatively a well-defined performance measure based on the operation of the energy system; or even the consideration of sustainability issues. Furthermore, the physical, various technical and resource limitations of the system under consideration may be very simply

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embedded in the model. In fact, the possibility of accommodating environmental constraints and costs makes energy modelling a promising approach in the search for solutions to complex optimisation problems.

3.2.2 Synthesis, design and operation energy models As in many other engineering systems, a wide range of synthesis, design and operation problems may be encountered and solved with the use of energy models (Table 3.1). It is interesting here to mention that all these problems may also be approached with other techniques, such as scenario analysis and simulation. The use of integrated energy models assumes that the problem can be

Table 3.1 Definition of specific problem types Problem

Input/data

Expected output

Synthesis/ configuration problem

• System requirements • Resource availability • Alternative considerations • Basic system specifications

• Which components will be included in the system • How these components are linked to each other

Design/sizing problem

• System configuration (results of the synthesis problem) • Detailed system requirements • Basic data (properties, costs, etc.)

• Size and type of each component • Implied investment cost

Operation analysis

• System design • Operating mode • Requirements, technical data • Cost data

Calculation of all the system magnitudes (flows, efficiencies, operating costs, etc.)

Optimisation: It may concern any of the above problems, i.e.

• Criteria to be optimised • Operational, technical and environmental constraints

The one optimal solution of the problem, i.e. the solution that optimises the criteria provided that all the constraints are satisfied

• the optimal configuration • the optimal sizing and design • the optimal operation of a system

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described reliably with mathematical relationships, and that its complexity can be encompassed by the available algorithms. The synthesis problem In the synthesis problem the system’s configuration will be determined, i.e. which units will be allocated in the system, and where. This is a very crucial aspect in the design, since there are usually many alternative possibilities related to which individual components will be included in a hybrid energy system.

For example, the synthesis problem of a given hybrid energy system would be to determine: • • • •

the type of renewable energy system (RES) to be included (i.e. wind, photo voltaic (PV), hydro); the number and capacity of RES units to be installed (if used); whether a back-up diesel generator would be included in the system; whether energy storage would be integrated into the system.

In the development of mathematical models for the synthesis problem, integer (binary) variables are defined for this type of decision, i.e. whether or not a component will be included in the system, and how it will be integrated. In modelling terms, this usually leads to an optimisation problem with integer variables, possibly of a non-linear nature. Therefore, synthesis problems usually require the use of mixed-integer linear programming (MILP) or mixed-integer non-linear programming (MINLP) mathematical models. The design problem The design problem addresses the size and number of system components. For example, a hybrid energy system consisting of various components (proposed by the configuration solution to the synthesis problem) may be designed in order to cover a specific demand. The mathematical models expressing the design problem will include as variables the number and size of each individual component, as well as their interconnections. Owing to the frequent interactions between the design and synthesis problems, the two are often faced simultaneously. Therefore, both the structure and main dimensions of the system are often decided together.

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For example, the design problem of a hybrid energy system would be to determine the size of each given component of the system, so that • a specific load would be satisfied; • the capital costs would be limited; • the environmental impacts would be reduced; • other important variables would be accounted for.

The design problem may also be formulated as an optimisation problem, with a variety of optimisation criteria such as cost minimisation, variables reflecting the size of each component and constraints describing the physics of the system as well as potential limits of the system under consideration. The operation problem Another approach that is extensively used in hybrid energy system design and operation is systems simulation. In this case a model is once again developed, but this specific model attempts to simulate the real system by iterative solutions. Various alternative scenaria are evaluated with parameter values input in sequential iterations, such that the solutions improve incrementally upon each new evaluation. Usually, operational models are simpler than the synthesis – design models and so simulation problems are usually easier to solve. However, the optimal solution cannot necessarily be guaranteed. In modelling an operational hybrid energy system, a wide spectrum of calculation problems may arise, for instance, from imprecision in performance measure estimations, or due to the inherent complexity involved in planning an entire system configuration. This operation problem is critical, given that there are many alternative modes of operating a system, resulting in various performance efficiencies, and satisfying different operational constraints. In each of the above cases, the variables, parameters, constraints and optimisation criteria are different. The following sections will highlight the close relationship between the synthesis, design and operation problems and optimisation models.

3.2.3 Optimisation models The use of optimisation models has vastly improved systems engineering. It has evolved from a methodology of solely academic interest into one that has many interesting practical implementations, and continues to make a

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significant impact on the industry (Biegler Lorenz and Grossman, 2004; Grossman and Biegler Lorenz, 2004), where optimal solutions to systems engineering problems translate into larger savings in money, energy, labour, water usage, time, etc., and increased competitiveness. The scope for optimisation is wide and may include the plant, the process, a piece of equipment, or any intermediate scale between them. Optimisation of an energy system may focus on a complex combination of units, on individual units, on individual pieces of equipment, or on subsystems within a piece of equipment. There are currently many different types of problems that can be solved through the use of optimisation models, depending on the scope and complexity of the system under consideration. The classification of problem types is in most cases independent of the solution methods being applied, i.e. very different problems may be solved with the same solution method and vice versa. The process of optimisation involves the development of an appropriate mathematical model coupled with an algorithm to solve the representative problem, a process which is usually implemented through optimisation software. The advantages of such optimisation models includes, for example, the possibility of embedding all the characteristics of a system into one problem, with the possibility of determining the optimal solution to that problem virtually guaranteed (if, indeed the optimal solution exists). However, the development of a complete mathematical model that can take all parameters into account is not always easy, and the reliability of the solution obtained depends upon the completeness and the quality of the model. Furthermore, sometimes the model may become so complex that its solution may in turn become equally complex and expensive to realise (for example, in the case of large number of integer variables or in highly non-linear systems). According to the definitions provided above, the synthesis, design and operation problems can easily be approached as optimisation problems, as determined by appropriate variables, optimisation criteria and systems constraints. Since design problems tend to rely more heavily on the predictions of process models, which are non-linear in nature, these problems can be approached as non-linear programming (NLP) and MINLP problems. In operation problems the time-dependent requirements and activities related to planning are most important and so these problems can be approached as linear programming (LP) and MILP problems.

3.3

Synthesis, design and operation of a hybrid energy system

As has already been detailed in other chapters of this book, a hybrid energy system may consist of wind turbines, PV panels, micro-hydro, biomass

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Anemometer

Wind turbine 2 kW

Thermometer PV array 610 Wp

Pyranometer

Electronic lamps Upper reservoir

Data logger Flowmeter PC

DC current 24 V Data signal Water circuit

Charge controller

Control panel

Thermometer Battery 24 V

Lower reservoir Water pump

3.2 Hybrid renewable energy system with wind–PV–hydro energy storage system (Kaldellis et al., 2009e).

power generator systems, and conventional diesel generators, potentially combined with energy storage systems. A general schematic representation of a hybrid energy system is shown in Figs 3.2 and 3.3 (Kaldellis, 2008; Kaldellis et al., 2009e).

3.3.1 Local conditions and system selection The selection of the system components depends on the local conditions of the area. Because of the stochastic behaviour of most renewable energy sources, the major considerations involved in the design of a hybrid energy system must include the reliability of the power supply under varying weather conditions and the projected cost of energy. The steps to be followed in an integrated synthesis–design project of a hybrid system (Fig. 3.4) can be summarized as follows (Diaf et al., 2007): 1. Estimation and evaluation of the renewable energy potential. 2. Decision on the hybrid system configuration based on a pre-feasibility analysis. 3. Decision on the specific set of components. 4. Hybrid system model development. 5. Sizing and economic optimisation of the system. 6. Simulation results and operational analysis of the resulting system.

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Design and performance optimisation Local applications

Wind farm

Control panel Consumption

PV station ESS

3.3 Hybrid renewable energy system with wind–PV–diesel energy storage components (Kaldellis, 2008).

Evaluation of local conditions

System configuration

Hybrid system model development

Sizing of system components

Simulation and operational analysis

3.4 Basic steps for energy system development.

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In order to calculate the performance of an existing system, or to predict the energy consumption or energy generation of a system in the design stage, appropriate weather data are required. Pre-feasibility studies based on weather data determine the availability and magnitude of resources (e.g. wind speed and solar irradiation) which can then be coupled to load requirements for the specific site. The reliability of information concerning sitespecific climate conditions determines to a large extent the quality of the proposed solution. After a pre-feasibility study, the selection of proper sizing of equipment can be made based on the local climate conditions (weather data and wind), required load and capacity.

3.3.2 Optimal design of hybrid energy systems Many different approaches have been followed in the industry in order to apply the basic steps outlined in Fig. 3.4. Sizing considerations for individual components of a hybrid energy system have been considered by many researchers, with many different approaches applied which mainly rely on simulation and scenario analysis. However, the development and implementation of optimisation models for the design stage has not been a common approach to the problem. Furthermore, in most cases the optimal sizing is thought to be a problem to be considered simultaneously with the selection of the set of system components (configuration/synthesis), and the optimal number and type of units in terms of technical and economic concepts. The unit sizing of the integrated hybrid energy system plays an important role in configuring its reliability and economy. A sizing optimisation method can help to guarantee the lowest investment with full use of a PV array, wind turbine and battery bank, so that the hybrid system can work at the optimum condition in terms of financial investment and system reliability. As has already been mentioned, there is no generic model and solution method for the optimal design and sizing of hybrid energy systems. Various tools have been developed and applied to that effect. A review of simulation and design models that have been used can be found in Bernal-Agustin and Dufo-Lopez (2009), although recent works are not cited. The most widely studied hybrid energy systems are PV–diesel, PV–wind, PV–wind– diesel and wind–diesel hybrid systems. Various research groups approach the design problem using either their own simulation and optimisation software tools (Kaldellis, 2008; Kaldellis et al., 2008, 2009a,b,c,d,e), or using commercial software such as HOMER and Hybrid2 (National Renewable Energy Laboratory, 2005). Most studies concern the design and economic aspects of these systems and deal less with control issues. Environmental considerations have also been

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taken into account in these studies, while some specific case studies are included, usually concerning the application of hybrid energy systems in remote areas. There is a continuously increasing interest in developing models for hybrid energy systems (Deshmukh and Deshmukh, 2008), and of the various techniques considered for the optimal sizing of hybrid energy systems, we could mention: • • • • •

linear programming; probabilistic approach; iterative techniques; dynamic programming; multiobjective optimisation.

Linear programming techniques minimise the average production cost of electricity while meeting the load requirements in a reliable manner, and take environmental factors into consideration both in the design and operation phases.

3.4

Hybrid energy system optimisation techniques

3.4.1 Criteria for hybrid energy system optimisation In order to select an optimum combination for a hybrid system to meet the load demand, evaluation must be carried out on the basis of power reliability and system life-cycle cost. The optimum combination can make the best compromise between the two main objectives: power reliability and system cost. Power reliability analysis Because intermittent resource characteristics (wind strength, solar irradiation) strongly influence energy production, a power reliability analysis is usually a required step in any system design process. There are a number of methods used to calculate the reliability of a hybrid energy system, including: • •

•

loss of load probability (LOLP): power failure time period divided by a given period of time (generally one year); loss of power supply probability (LPSP): probability that an insufficient power supply will result when the hybrid system is unable to satisfy the load demand; unmet load (UL): non-served load divided by the total load of a period of time (normally one year).

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The most popular method is the LPSP method. The LPSP is the probability that an insufficient power supply will result when the hybrid energy system is not able to satisfy the load demand. The design of a reliable stand-alone hybrid solar–wind system (integrated with energy storage) can be pursued by using the LPSP as the key design parameter. Two approaches exist for the application of the LPSP in designing a stand-alone hybrid solar–wind system. The first one is based on chronological simulations. This approach is computationally intensive and requires the availability of data spanning a sufficient period of time. The second approach uses probabilistic techniques to incorporate the fluctuating nature of the resource and the load, thus eliminating the need for time-series data. System cost analysis Several economic criteria exist for analysing system costs, such as the net present cost (NPC), levelised cost of energy (LCE) and life-cycle cost. The NPC is defined as the total present value of a time-series of cash flows, which includes the initial cost of all the system components, the cost of any component replacements that occur within the project lifetime, and the cost of maintenance, i.e. investment costs plus the discounted present values of all future costs during the lifetime of the system. The system’s lifetime is usually considered to be the life of the elements that have the longest lifespan. The LCE is widely used in the design and optimisation of energy systems. It reflects the cost of generating energy (usually electricity) for a particular system and it is defined as the ratio of the total annualised cost of the system to the annual electricity delivered by the system. This includes all the costs over the system’s lifetime from initial investment and capital costs, to operations and maintenance (e.g. fuel) and financing costs. An excellent analysis of all the economic issues that may be considered for a hybrid energy system is provided in Chapter 4 on feasibility assessment.

3.4.2 Economic and techno-economic optimisation of hybrid energy systems As previously noted, the synthesis, design and operation problems of a hybrid energy system may be expressed as optimisation problems, where certain criteria need to be optimised (minimised or maximised) subject to a set of operational, technical and/or environmental constraints stemming from the physical and operational characteristics of the system. At a general level, hybrid energy systems may be designed from an economic perspective or from a techno-economical perspective; but within

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these two approaches, several subdivisions can be found. Economic optimisation criteria may include: • • •

total energy systems costs; capacity costs; and societal costs.

Techno-operational optimisation criteria may include: • fuel savings; • CO2 emissions; • reserve/backup capacity; • elimination of excess power generation. The potential optimisation criteria that may be considered for a particular hybrid energy system could, for example, include: • • • •

minimisation of the LCE; maximisation of the uilisation rate of the RES; minimisation of diesel generator fuel consumption; minimisation of diesel generator start/stop frequency.

There is no generally accepted economic design criterion according to which systems are universally analysed, optimised and designed. Numerous papers have been written about the optimum economic designs of PV and/ or wind and/or diesel systems with energy storage in batteries. Usually, the optimum design is carried out minimising the NPC or by minimising the LCE. Diaf et al. (2007, 2008) present an application of a hybrid PV–wind– battery system in Corsica (France) which minimises the LCE. BernalAgustın and Dufo-Lopez (2009) carried out the optimisation of hybrid PV–Diesel–Battery systems by means of genetic algorithms (GA). Shaahid and El-Amin (2009) used the HOMER software for economic optimisation (minimisation of the NPC) of a PV–diesel–battery system to supply a shopping centre located in Dhahran (Saudi Arabia). Yang et al. (2008) present a method for the optimisation of hybrid PV– wind–battery systems which minimise the LCE. The optimisation was carried out by trying component combinations: • • • • •

changing changing changing changing changing

the the the the the

number of PV modules; orientation of PV modules; rated power of the wind turbine; tower height of the wind turbine; and capacity of the battery bank.

Multicriteria decision analysis may be elaborated (Shaahid and El-Amin, 2009); however, generally applicable optimisation models have not yet been

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developed, representing an area in need of future research. In BernalAgustin and Dufo-Lopez (2009) there is a complete review of optimisation routes, and an evaluation of the software systems that have been developed for the simulation and optimisation of hybrid energy systems. The use of improved software tools should prove very useful to the future development of multicriteria optimisation models.

3.5

Software tools for the simulation and optimisation of hybrid energy systems

Several software tools (simulation programs) are available for designing hybrid energy systems. In this section, three of these systems are presented; namely HOMER, Hybrid2 and HOGA. Table 3.2 shows the capabilities of each system.

3.5.1 HOMER software system The Hybrid Optimisation Model for Electric Renewables (HOMER) software system is a public domain program produced by National Renewable Energy Laboratory (NREL) (2005). It is a time-step simulator using hourly load and environmental data inputs for renewable energy system assessment; it facilitates the optimisation of renewable energy systems based on NPC for a given set of constraints and sensitivity variables. HOMER has been used extensively in previous renewable energy system case studies and in renewable energy system validation tests. Although simulations can take a long time depending on the number of variables used, its operation is straightforward. The program’s limitation is that it does not enable the user to intuitively select the appropriate components for a system, as the algorithms and calculations are not visible or accessible.

Table 3.2 Characteristics of hybrid simulation and optimisation tools (indicatively) (Diaf et al., 2007)

Free download and use PV, diesel, batteries Wind Mini hydro Simulation Economic optimisation Multi-objective optimisation, GAs

HOMER

Hybrid2

HOGA

x x x x x x

x x x x x

x x x x x

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Input information to be provided to HOMER includes: • • • • • •

electrical loads (load data); renewable resources (e.g. solar radiation data); component technical details/costs; constraints; controls; type of dispatch strategy, etc.

HOMER designs an optimal power system to serve the desired loads, performing hundreds or thousands of hourly simulations (to ensure the best possible match between supply and demand). The software performs automatic sensitivity analyses to account for the sensitivity of key parameters to the system design, such as resource availability or component costs.

3.5.2 Hybrid2 software system The Hybrid2 software package is a user-friendly tool which can perform detailed, long-term performance and economic analysis on a wide variety of hybrid power systems. Hybrid2 is a probabilistic/time-series computer model, which uses time-series data for loads, wind speed, solar insolation (solar irradiation) and temperature to predict the performance of the hybrid power system selected by the user. Variations in wind speed and load within each time frame are factored into the performance predictions. The code does not consider short-term system fluctuations caused by system dynamics or component transients. Hybrid2 was designed to study a wide variety of hybrid power systems including three types of electrical loads, multiple wind turbines of different types, photovoltaics, multiple diesel generators, battery storage and four other types of power conversion devices. An economic analysis tool is also included, that calculates the economic value of the project using many economic and performance parameters. The Hybrid2 code employs a graphical user interface (GUI) and a glossary of terms commonly associated with hybrid power systems and comes packaged with tools to assist the user in designing hybrid power systems. Each piece of equipment held in its library is commercially available and uses the manufacturers’ specifications. In addition, the library includes sample power systems and template projects. Two levels of output are provided, a summary and a detailed timestep-by-time-step description of power flows. A graphical results interface (GRI) allows for easy and in-depth review of the detailed simulation results.

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3.5.3 The HOGA software system Hybrid Optimisation by Genetic Algorithms (HOGA) is a simulation and optimisation program developed in C++ by the Electric Engineering Department of the University of Zaragoza (Spain). It can be used for hybrid energy systems that generate electricity (either direct current (DC) or alternating current (AC)), produce hydrogen or that are applied to water pumping loads (either separately or in combination). Optimisation is achieved by minimising total system costs throughout the whole of its useful lifespan, based on NPC calculations. However, the programme allows for multi-objective optimisation, where additional variables may also be minimised: CO2 emissions or unmet load (energy not served), for example. The hybrid system may comprise the following elements: photovoltaic (PV) panels, wind turbines, hydraulic turbine, fuel cell, hydrogen tank and electrolyser, as well as batteries, battery charge regulator, inverters (DC/ AC converter), rectifier (AC/DC converter), and AC generator (the latter based on conventional or renewable fuel sources, e.g. biogas). All elements may be present simultaneously, and the user may decide to include only some of them as part of the desired system. The software was initially developed for remote systems (i.e. off-grid systems). However, the program allows AC electricity to be bought from sold to the grid (surplus unused energy), or surplus hydrogen produced in the electrolyser and stored in the tank. Simulations are also possible for feasibility studies of zero-consumption renewable energy facilities connected to the electrical grid. Optimisation is available throughout the program both for different element combinations as well as for system control strategies. When the number of possible combinations of components and control strategies is too high, requiring excessive time to enumerate all the possible combinations, a GA technique can help the designer to obtain a good combination (the optimal or a combination near the optimal), in a reasonable run time. The optimisation carried out by means of GAs can be mono-objective or multi-objective.

3.6

Summary of optimisation techniques

Various optimisation techniques such as graphical construction methods, probabilistic approaches and iterative techniques have been recommended by researchers to guarantee the lowest investment with full use of the corresponding energy systems. For example, an interesting review of the most commonly applied optimisation techniques for hybrid systems is given in Zhou et al. (2009). The

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Table 3.3 Simple summary of the relative merits and demerits of different optimisation methodologies (Diaf et al., 2007) Optimisation techniques

Advantages

Graphic construction method

Probabilistic approach

Disadvantages Only two parameters can be included in the optimisation process

Eliminate the need for time-series data

Iterative technique

Cannot represent the dynamic changing performance of the system Usually results in increased computational efforts and suboptimal solutions

Artificial intelligence methods

Find the global optimum system configuration with relative computational simplicity

Multi-objective design

Can optimise simultaneously at least two conflict objectives

results of this review are presented in Table 3.3, along with the advantages and disadvantages of each employed method. In any case, the scope for further research in the elaboration of such techniques will have a significant impact on the improvement and competitiveness of hybrid energy systems.

3.7

Future trends

Hybrid energy systems represent a very promising sustainable solution for power generation in stand-alone applications. Research and development carried out in these emerging technologies will certainly result in reducing the cost of the systems, despite the complex procedure involved in the design and optimisation of these systems. Optimum resource allocation, based on load demand and renewable resource forecasting, also promises to significantly reduce the total operating cost of the system. In addition to

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mere cost minimisation criteria alone, it is increasingly important to consider other relevant factors such as minimisation of emissions, or maximisation of systems reliability. The optimisation of the configuration, design and operation of hybrid energy systems is supported by advanced models that describe the systems realistically. Further research into the development of generic mathematical models will facilitate the development and application of reliable and easily accessible multi-objective optimisation tools, such as software programs. The development of a generic, validated and complete methodology for the synthesis and the design of hybrid energy systems – incorporating appropriate planning and standardised models to take into account the characteristics of the location, the suitable hybrid energy system, and all the potential operational and performance scenarios for its application – would significantly improve the implementation of these technologies. The application of modern control techniques (such as a centralised system controller) would further improve the operational performance and energy management of these modular hybrid energy systems, allowing the utilisation of the renewable resource to be optimised.

3.8

References and further reading

Bernal-Agustin, J., Dufo-Lopez, R. 2009, Simulation and optimisation of standalone hybrid renewable energy systems, Renewable and Sustainable Energy Reviews 13, 2111–2118. Biegler Lorenz, T., Grossmann, I.E. 2004, Retrospective on optimization, Computers and Chemical Engineering 28, 1169–1192. Cai, Y.P., Huang, G.H., Yang, Z.F., Lin, Q.G., Bass, B., Tan, Q. 2008, Development of an optimization model for energy systems planning in the Region of Waterloo, International Journal of Energy Research 32(11), 988–1005. Celik, A.N. 2003, Techno-economic analysis of autonomous PV–wind hybrid energy systems using different sizing methods, Energy Conversion and Management 44, 1951–1968. Deshmukh, M.K., Deshmukh, S.S. 2008, Modelling of hybrid renewable energy systems, Renewable and Sustainable Energy Reviews 12(1), 235–249. Deshmukh, S.S., Boehm, R.F. 2008, Review of modelling details related to renewably powered hydrogen systems, Renewable and Sustainable Energy Reviews 12(9), 2301–2330. Diaf, S., Diaf, D., Belhamel, M., Haddadi, M., Louche, A. 2007, A methodology for optimal sizing of autonomous hybrid PV/wind system, Energy Policy 35, 5708–5718. Diaf, S., Nottn, G., Belhamel, M., Haddadi, M., Louche, A. 2008, Design and techno-economical optimisation for hybrid PV/wind system under various meteorological conditions, Applied Energy 85, 968–987. Ekren, O., Ekren, B., Ozerdem, B. 2009, Break–even analysis and size optimisation of a PV/Wind hybrid energy conversion system with battery storage – a case study, Applied Energy 86, 1043–1054.

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Fung, C.C., Ho, S.C.Y., Nayar, C.V. 1993, Optimisation of a hybrid energy system using simulated annealing technique. Proceedings of the IEEE Region 10 Conference on Computer, Communication, Control and Power Engineering, TENCON 1993, pp. 235–238. Fung, C.C., Iyer, V., Maynard, C. 1998a, Computational intelligence techniques for short term generation scheduling in a hybrid energy system. In S PRICAI’98: Topics in Artificial Intelligence, pp. 272–281. Springer, Berlin/Heidelberg. Available online at: http://www.springerlink.com Fung, C.C., Iyer, V., Maynard, C. 1998b, Short-term generation scheduling of a remote area hybrid energy system using computational intelligence techniques. Proceedings of the IEEE Conference on Power Electronic Drives and Energy Systems for Industrial Growth, 1(1–3), 365–370. Grossmann, I.E., Biegler Lorenz, T.B. 2004, Part II. Future perspective on optimization, Computers and Chemical Engineering 28, 1193–1218. Gupta, A., Saini, R.P., Sharma, M.P. 2006a, Modelling of hybrid energy system for off grid electrification of clusters of villages. Proceedings of the IEEE Conference on Power Electronics, Drives and Energy Systems, (PEDES) 2006, pp. 1–5. Gupta, A., Saini, R.P., Sharma, M.P. 2006b, Optimised application of hybrid renewable energy system in rural electrification. Proceeding of the IEEE Indian Conference on Power Electronics (IICPE) 2006, pp. 337–340. Gupta, A., Saini, R.P., Sharma, M.P. 2007, Design of an optimal hybrid energy system model for remote rural area power generation. Proceedings of the IEEE Conference on Electrical (ICEE) 2007, pp. 1–6. Gupta, A., Saini, R.P., Sharma, M.P. 2008a, Computerized modelling of hybrid energy system – Part I: Problem formulation and model development. Proceedings of the IEEE Conference on Electrical and Computer Engineering (ICECE) 2008, pp. 7–12. Gupta, A., Saini, R.P., Sharma, M.P. 2008b, Computerized modelling of hybrid energy system – Part II: Combined dispatch strategies and solution algorithm. Proceedings of the IEEE Conference on Electrical and Computer Engineering (ICECE) 2008, pp. 13–18. Haque, R.U., Iqbal, M.T., Quaicoe, J.E. 2006, Sizing, dynamic modelling and power electronics of a hybrid energy system. Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, CCECE, 2006, pp. 1135–1138. Hokaoglu, F.O., Gerek, O., Kurban, M. 2009, A novel hybrid (wind-photovoltaic) system sizing procedure, Solar Energy 83, 11, 2019–2028. Jebaraj, S., Iniyan, S. 2006, Review of energy models, Renewable and sustainable energy reviews 10, 281–311. Kaldellis, J.K. 2008, Integrated electrification solution for autonomous electrical networks on the basis of RES and energy storage configurations, Energy Conversion and Management 49, 3708–3720. Kaldellis, J.K., Zafirakis, D., Kavadias, K., Kondili, E. 2008, An optimum sizing methodology for combined photovoltaic-energy storage electricity generation configurations, ASME Journal of Solar Energy Engineering, SOL-07-1177 131(2), 021010(1–12). Kaldellis, J.K., Simotas, M., Zafirakis, D., Kondili, E. 2009a, Optimum autonomous photovoltaic solution for the Greek islands on the basis of energy pay-back analysis, Journal of Cleaner Production 17, 1311–1323.

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Kaldellis, J.K., Zafirakis, D., Kondili, E. 2009b, Optimum autonomous stand-alone photovoltaic system design on the basis of energy pay-back analysis, Energy 34, 1187–1198. Kaldellis, J.K., Zafirakis, D., Kondili, E. 2009c, Optimum sizing of photovoltaicenergy storage systems for autonomous small islands, International Journal of Electrical Power & Energy Systems 32(1), 24–38. Kaldellis, J.K., Zafirakis, D., Kaldelli, E.K., Kavadias, K. 2009d, Cost benefit analysis of a photovoltaic-energy storage electrification solution for remote islands, Renewable Energy 34, 1299–1311. Kaldellis, J.K., Spyropoulos, G.C., Kavadias, K.A., Koronaki, I.P. 2009e, Experimental validation of autonomous PV-based water pumping system optimum sizing, Renewable Energy 34(4), 1106–1113. Kélouwani, S., Agbossou, K., Chahine, R. 2005, Model for energy conversion in renewable energy system with hydrogen storage, Journal of Power Sources 140(2), 392–399. Lagorse, J., Paire, D., Miraoui, A. 2009, Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery. Renewable Energy 34(3), 683–691. Li, C-H., Zhu, X-J., Cao, G-Yi, Sui, S., Hu, M-Ruo, 2009, Dynamic modelling and sizing optimization of stand-alone photovoltaic power systems using hybrid energy storage technology, Renewable Energy 34(3), 815–826. Maclay, J.D., Brouwer, J., Samuelsen, G.S. 2007, Dynamic modeling of hybrid energy storage systems coupled to photovoltaic generation in residential applications, Journal of Power Sources 163(2), 916–925. Manolakos, D., Papadakis, G., Papantonis, D., Kyritsis, S. 2001, A simulationoptimisation programme for designing hybrid energy systems for supplying electricity and fresh water through desalination to remote areas: Case study: the Merssini village, Donoussa island, Aegean Sea, Greece, Energy 26(7), 679–704. Mellit, A., Kalogirou, S.A., Hontoria, L., Shaari, S. 2009, Artificial intelligence techniques for sizing photovoltaic systems: a review, Renewable and Sustainable Energy Reviews 13(2), 406–419. National Renewable Energy Laboratory, 2005, HOMER Getting Started Guide Version 2.1, NREL. Nema, P., Nema, R.K., Rangnekar, S. 2009, A current and future state of art development of hybrid energy system using wind and PV-solar: a review, Renewable and Sustainable Energy Reviews, 13(8), 2096–2103. Onar, O.C., Uzunoglu, M., Alam, M.S. 2006, Dynamic modeling, design and simulation of a wind/fuel cell/ultra-capacitor-based hybrid power generation system, Journal of Power Sources 161(1), 707–722. Onar, O.C., Uzunoglu, M., Alam, M.S. 2008, Modeling, control and simulation of an autonomous wind turbine/photovoltaic/fuel cell/ultra-capacitor hybrid power system, Journal of Power Sources 185(2), 1273–1283. Ostergaard, P.A. 2009, Reviewing optimisation criteria for energy systems analyses of renewable energy integration, Energy 34(9), 1236–1245. Razak, J., Sopian, K., Nopiah, Z., Zaharim, A., Ali, Y. 2007, Optimization of renewable energy hybrid system by minimizing excess capacity, International Journal of Energy 1, 77–81.

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Shaahid, S.M., El-Amin, I. 2009, Techno-economic evaluation of off-grid hybrid photovoltaic–diesel–battery power systems for rural electrification in Saudi Arabia – A way forward for sustainable development, Renewable and Sustainable Energy Reviews 13(3), 625–633. Tuborache, T., Morega, Al. 2008, Optimum design of a wind/PV/diesel/batteries hybrid systems, 2nd International Conference on Moderm Power Systems MPS2008. Uzunoglu, M., Onar, O.C, Alam, M.S. 2009, Modeling, control and simulation of a PV/FC/UC based hybrid power generation system for stand-alone applications, Renewable Energy 34(3), 509–520. Vieira, F., Ramos, H.M. 2009, Optimization of operational planning for wind/hydro hybrid water supply systems, Renewable Energy 34(3), 928–936. Yang, H-X., Zhou, W., Lu, L., Fang, Z-H. 2008, Optimal sizing method for standalone hybrid solar–wind system with LPSP technology by using genetic algorithm, Solar Energy 82, 354–367. Zhou, W., Yang, H-X., Fang, Z-H. 2008, Battery behavior prediction and battery working states analysis of a hybrid solar–wind power generation system, Renewable Energy 33, 1413–1423. Zhou, W., Lou, C-Z., Li, Z-S., Lu, L., Yang, H-X. 2009, Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems, Applied Energy 2009, 87(2), 380–389. http://www.ceere.org/rerl/projects/software/hybrid2 (accessed on 08 October 2009). http://www.hoga-renewable.es.tl/ (accessed on 08 October 2009).

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4 Feasibility assessment for stand-alone and hybrid wind energy systems J. K. KALDELLIS, TEI of Piraeus, Greece

Abstract: The present chapter investigates the financial behaviour of hybrid electricity generation wind-based (HEW) systems, taking into account the existing information concerning the initial investment and the maintenance and operation cost of similar small power stations. In this context, an integrated cost–benefit analysis provided leads to the estimation of the payback period, the financial efficiency, the net present value (NPV) and the internal rate of return (IRR) for several HEW installations examined. Subsequently, the reliability impact on the total cost function is investigated, while emphasis is also laid on the socioenvironmental benefits of stand-alone HEW systems. Furthermore, in order to obtain a realistic comparison between the existing fossil fuel-based solutions and the proposed HEW installation, the electricity generation cost of the wind-based hybrid station is estimated. Finally, in an attempt to demonstrate the clear competitive advantages of the proposed HEW solution, selected representative case studies are currently examined, while a detailed sensitivity analysis concerning the financial behaviour of stand-alone HEW systems also provided completes the present study. Key words: hybrid wind energy systems, feasibility assessment, cost– benefit analysis, sensitivity analysis, electricity generation cost.

4.1

Introduction

Hybrid electricity generation wind-based (HEW) systems significantly contribute to the protection of the environment and potentially reduce the dependence of remote consumers on oil imports. However, when considering their annual energy yield, their main disadvantage is the relatively high initial cost, although the efficiency of most renewable energy source (RES)based electricity generation techniques has continually increased, while their cost has remarkably reduced during the past years. It is still reducing at a rate faster than that of any other energy production technology (Masakazu et al., 2003; Albrecht, 2007; Blanco, 2009). Meanwhile, far from decision centres and having limited political influence, isolated consumers are often faced with serious problems, owing to insufficient local infrastructure (Jensen, 2000). In this context, autonomous HEW systems have proven to be one of the most promising and environmentally friendly technological solutions for the electrification of remote 102 © Woodhead Publishing Limited, 2010

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consumers, especially in the presence of considerable wind potential (Kaldellis, 2004). However, one important technical problem of similar installations results from the highly fluctuating power output of wind turbines, which is generally incompatible with the demand of typical domestic or commercial users (Kaldellis et al., 2005a). To face this problem, an appropriate energy-storage device is usually incorporated, which significantly increases the initial cost of stand-alone installations (Kaldellis et al., 2009a). Alternatively, one may operate a small diesel-electric generator. However, diesel-electric generator sets used up to now, while being relatively inexpensive to purchase, are generally expensive to operate and maintain, especially at partial-load levels (Hunter and Elliot, 1994). In fact, in the absence of energy storage, a high start–stop cycling frequency being evident leads to increased wear of the diesel engine and, therefore, to increased maintenance needs (Beyer et al., 1995). In an attempt to obtain a realistic and environmentally friendly solution, the idea of using a HEW system is hereby examined, considering also any other available RES for the area at each investigation. For example, complementarity between solar and wind energy resources significantly reduces both the inevitable diesel engine operation and energy storage requirements (Kaldellis et al., 2006a). In this context, the main parameters of the proposed hybrid system dimensions should first be determined (Kaldellis, 2002a; Kaldellis and Vlachos, 2005; Kaldellis et al., 2007), in accordance with electricity consumption requirements and the characteristics of the local renewable energy potential. Accordingly, this chapter investigates the financial behaviour of HEW systems, taking into account the existing information concerning the initial investment and the maintenance and operation (M&O) cost of similar small power stations, in view of the remarkable financial support for similar renewable energy applications from government and international resources. More precisely, the next section analyses the first installation cost of a typical stand-alone HEW system, which is followed by an investigation of both the fixed and the variable M&O costs. Furthermore, Section 4.4 presents an integrated cost–benefit analysis, leading to the estimation of the major parameters of the installation, such as the pay-back period, the financial efficiency, the net present value (NPV) and the internal rate of return (IRR). Subsequently, the reliability impact on the total cost function is investigated, while emphasis is also laid on the socio-environmental benefits of stand-alone HEW systems. Furthermore, in order to obtain a realistic comparison between the existing fossil fuel-based solutions and the proposed HEW installation, the electricity generation cost of the windbased hybrid station is estimated. In an attempt to demonstrate the clear competitive advantages of the proposed HEW solution, selected representative case studies are currently examined, while a detailed sensitivity

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analysis concerning the financial behaviour of stand-alone HEW systems also provided completes the present study.

4.2

First installation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system

As already mentioned a typical stand-alone HEW system (Fig. 4.1) is based on the following: • •

One or more (usually small) wind converters of No kW. A complementary RES-based generator (e.g. small hydro turbine, photovoltaic (PV) generator). For example, in the current analysis one may

V (m/s)

(°C)

t (h)

rα (kg/m3)

t (h)

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hin

En (kW h)

N (kW)

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P/Po

t (h)

24 t (h)

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t (h)

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Diesel

Charge controller

hx = 0.95

Inverter Po

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nrec = 0.95 he = 0.95 Battery

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Q (A h)

U (V) 13 12 11 10 G (W/m

2)

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(°C)

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t (h)

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25

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Ew (kW h)

Eλ (kW h)

U (V) c

(°C)

t (h)

t (h)

h/ hp c

(°C)

t (h)

4.1 Proposed autonomous HEW system.

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P/Pe

00

24 t (h)

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•

•

• • • • •

105

utilize a PV array of z panels (N+ maximum/peak power of every panel) properly connected to feed the charge controller with the voltage and the power required in order to meet the remote consumer load demand. Extensive financial analysis regarding wind–hydro hybrid stations may be found in the following studies (Kaldellis and Kavadias, 2001; Kaldellis et al., 2001, 2006b; Kaldellis, 2007). Optionally one may also utilize a small thermal power engine (dieselelectric generator or a mini gas turbine) based either on biomass (biogas or biofuel) or fossil fuel consumption (Zafirakis and Kaldellis, 2009). An appropriate energy storage device, e.g. a lead-acid battery storage array, able to guarantee ho typical hours of energy autonomy, or equivalently with energy storage capacity Qmax and discharge capacity limit Qmin, or equivalently maximum depth of discharge DODL. An AC/DC rectifier of Nr kW in case that the energy storage installation needs DC current. A charge controller of Nc kW. An optional UPS (uninterruptible power supply) of Np kW in order to guarantee high quality AC electricity generation. A DC/AC inverter of Np kW. Other auxiliary equipment and the non-active part of the installation, including supporting structures, power conditioning devices and cabling.

In order to proceed to the financial evaluation of a similar installation one should take into consideration that the entire investment cost of a HEW energy system (after n years of operation) is a combination (Kaldellis and Gavras, 2000; Kaldellis et al., 2005b) of the initial installation cost and the corresponding M&O cost, considering also the investment residual value. More precisely, as already seen in previous works (Kodossakis and Kaldellis, 1997; Kaldellis, 2002b), the initial investment cost IC0 includes the market (ex-works) price of the installation components (i.e. wind turbine, ICWT; PV-panels, ICPV; battery, ICbat; diesel-electric generator, ICd, and electronic devices ICel, including inverter, UPS, rectifier and charge controller cost) and the corresponding balance of the plant cost, expressed as a fraction f of the wind turbine market price. Thus one may write: IC0 = ICWT + ICPV + ICd + ICbat + ICel + f · ICWT

4.1

According to the analysis of Kaldellis and Kavadias (2007) the ex-works price ICWT of a small wind turbine (rated power No ≤ 100 kW) may be expressed using the following relation: ⎛ a ⎞ IC WT = ⎜ + c⎟ ⋅ N o ⎝ b + N ox ⎠

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4.2

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where the corresponding coefficients may be approached for the European market as: a = 8.7 × 105 (C/kW) b = 621 x = 2.05 c = 700 (C/kW) taking into consideration that the specific cost (C/kW) of small wind turbines is usually higher (1000–1500C/kW) than the one of big commercial contemporary machines (600–800C/kW). The values existing in the market present significant variation due to the numerous small manufacturers producing a large variety of products (Clausen and Wood, 1999; Refocus, 2002). Accordingly, the ex-works price of contemporary PV panels may be estimated (Haas, 2002; Hoffmann, 2006) using equation (4.3), i.e.: ICPV = ζ · Pr · z · N+

4.3

where ζ is a function of z (i.e. ζ = ζ(z)), expressing the scale economies for increased number of PV panels utilized. In the present case ζ may be taken equal to 1. Subsequently Pr is the specific buy-cost (Kaldellis et al., 2009b) of a PV panel (generally Pr = Pr(z.N+)) expressed in C/kWp; see also Fig. 4.2. If a small internal combustion engine is utilized (mainly as back-up option) the diesel-electric generator (rated power Nd) ex-works price ICd is given as:

Pr (1000 × ?/kWp)

ICd = φ · Nd

4.4

12 11 10 9 8 7 6 5 4 3 2 1 0 1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04 NO(kWp)

4.2 Specific price of existing PV installations.

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with φ = 150–250 (C/kW), depending also on the technology applied. Small gas turbines are also available; however, their utilization is not very widespread for remote installations. If the installation storage system is a lead–acid battery bank (rated storage capacity Qmax) the corresponding purchase cost ICbat may be approximated (for 24 V, DODL = 75%) by the following relation (Kaldellis, 2002c): ω ICbat = ξ · Q1− max

4.5

with ξ = 5.04 (C/A h) and ω = 0.078. Subsequently, the electronic devices’ (including inverter Np, UPS Np, rectifier No, charge controller No), ex-works cost ICelec is given as: ICel = λ · N 1−τ p + B · No

4.6

where the following values (λ = 483 (C/kW), τ = 0.083, B = 380 (C/kW)) describe the data of the European market at a specific time period. Finally, the balance of the plant cost f · ICWT of the hybrid power station is strongly case-dependent (Kaldellis, 2002b, 2003a) thus the corresponding coefficient f takes values between 0.15 and 0.50, while a typical value of f = 0.30 may be adopted in most cases. Recapitulating, the initial installation cost of a stand-alone HEW system is a function of the wind turbine rated power No, the rated power of the complementary RES generator (for example the number of PV panels z), the energy storage capacity Qmax, the annual fossil fuel consumption Mf as well as of the peak load demand of the installation Np, i.e.: IC0 = IC0 (No, z, Qmax, Mf, Np)

4.7

Finally, in several countries an initial investment cost subsidy is available for RES-based environmentally friendly applications. Actually, γ is the subsidy percentage taking values between 30% and 50%. This is the case for Greece, where according to the current development law (e.g. 3299/04) or the corresponding National Operational Competitiveness Program (Kaldellis, 2002b; Kaldellis et al., 2005b) there is a remarkable financial support of similar environmentally friendly energy production technologies; see also Section 4.7.

4.3

Maintenance and operation cost of a typical stand-alone hybrid electricity generation windbased (HEW) system

During long-term operation, the M&O cost can be split (Kaldellis et al., 1989; Kaldellis, 2003a) into the fixed FCn and the variable VCn maintenance

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cost. In the present analysis, the fixed M&O cost also considers the fuel cost consumed by the diesel-electric generator. Generally speaking, the annual fixed M&O cost can be expressed (Kaldellis, 2000) as a fraction m of the initial capital invested, including also an annual inflation rate equal to gm describing the annual changes of labour cost and the corresponding spare parts, embracing also any lubricant’s consumption. Subsequently, the fuel consumption cost results by the annual diesel-oil quantity consumed Mf, the current fuel price cf and the oil price annual escalation rate ef. Thus one gets: n n−1 ⎡ 1 + gm ⎛ 1 + gm ⎞ 2 1 + gm ⎞ 1 + gm ⎞ ⎤ FC n = m ⋅ IC o ⋅ ⎢ + +…+ ⎛ +⎛ ⎝ 1+ i ⎠ ⎝ 1 + i ⎠ ⎥⎦ ⎣ 1+ i ⎝ 1+ i ⎠ n−1 n ⎡ 1 + ef ⎛ 1 + ef ⎞ 2 1 + ef ⎞ 1 + ef ⎞ ⎤ + cf ⋅ Mf ⋅ ⎢ + +…+ ⎛ +⎛ 4.8 ⎝ 1+ i ⎠ ⎝ 1 + i ⎠ ⎥⎦ ⎣ 1+ i ⎝ 1+ i ⎠

where i is the return on investment index. The variable M&O cost VCn mainly depends (Kaldellis, 2002c) on the replacement of ko major parts of the installation, which have a shorter lifetime nk than the complete installation. Using the symbol rk for the replacement cost coefficient of each ko major part (battery, diesel-electric generator, inverter, charger, etc.) the VCn term can be expressed as: k = ko ⎧l =lk l ⋅n − l ⋅n ⎫ VC n = IC o ⋅ ∑ rk ⋅ ⎨ ∑ [(1 + gk ) ⋅ (1 − ρk )] k ⋅ (1 + i )( k ) ⎬ ⎩ l =1 ⎭ k =1

4.9

where lk is the integer part of the following equation, i.e.: ⎛ n − 1⎞ lk = ⎜ ⎝ nk ⎟⎠

4.10

while gk and ρk describe the mean annual change of the price and the corresponding technological improvement level for the kth major component of the system. Taking into account that one may introduce the parameter hk defined as: 1 + hk = (1 + gk) · (1 − ρk)

4.11

equation (4.9) reads equivalently as: ⎡ l =lk ⎛ 1 + hk ⎞ l⋅nk ⎤ VC n = IC o ⋅ ∑ rk ⎢ ∑ ⎝ ⎠ ⎥ k =1 ⎣ l =1 1 + i ⎦ k = ko

4.12

In the present analysis one may take into account the diesel-electric generator, the battery bank and the electronics (e.g. inverter and charger) replacement every nd, nb and ne years respectively (e.g. nd ≈ 4–6, nb ≈ 5–7 and ne ≈ 10 years). Applying equation (4.12), one finally gets:

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Feasibility assessment for stand-alone and HEW systems VCn = IC0 · Ψ

109 4.13

with:

Ψ = 0 for n ≤ nd = 5 n 1 + hd ⎞ d Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠

for nd + 1 ≤ n ≤ nb = 7

n n 1 + hd ⎞ d 1 + hb ⎞ b + rb ⋅ ⎛ Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠

for nb + 1 ≤ n ≤ 2 nd = 10

2n n n n 1 + hd ⎞ d 1 + hb ⎞ b 1 + he ⎞ e 1 + hd ⎞ d + rd ⋅ ⎛ + re ⋅ ⎛ + rb ⋅ ⎛ Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ for 2 nd + 1 ≤ n ≤ 2nb = 14 n 2n n 2n n 1 + hd ⎞ d 1 + hd ⎞ d 1 + hb ⎞ b 1 + hb ⎞ b 1 + he ⎞ e + rb ⋅ ⎛ + re ⋅ ⎛ + rd ⋅ ⎛ + rb ⋅ ⎛ Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ for 2 nb + 1 ≤ n ≤ 3nd = 15

⎡ 1 + hd ⎞ nd ⎛ 1 + hd ⎞ 2 nd ⎛ 1 + hd ⎞ 3 nd ⎤ ⎡⎛ 1 + hb ⎞ nb ⎛ 1 + hb ⎞ 2 nb ⎤ + + r + Ψ = rd ⋅ ⎢⎛ ⋅ b ⎢⎝ 1 + i ⎠ + ⎝ 1 + i ⎠ ⎥ ⎝ 1 + i ⎠ ⎥⎦ ⎝ 1+ i ⎠ ⎣⎝ 1 + i ⎠ ⎣ ⎦ 1 + he ⎞ + re ⋅ ⎛ ⎝ 1+ i ⎠

ne

for 3nd + 1 ≤ n ≤ 4 nd = 20

4.14

where rd · IC0 is the diesel-electric generator, rb · IC0 is the battery and re · IC0 is the electronic (inverter, charger, etc.) replacement cost in present values, while hd, hb and he describe the diesel-electric generator/battery/electronics purchase cost-technology improvement mean annual change (technologyinflation rate); see also equation (4.11).

4.4

Cost-benefit analysis of a typical stand-alone hybrid electricity generation wind-based (HEW) system

Using the above analysis and considering that the proposed stand-alone HEW system is going to operate for n years, one may estimate the corresponding total operational cost by combining the initial cost and the fixed and variable maintenance cost in present (constant) values, i.e.: Cn = IC0 · (1 − γ) + FCn + VCn − Yn

4.15

In this context, using the analysis of Sections 4.2 and 4.3 the total cost of the hybrid power station during n years of operation is given as: x n − 1 cf ⋅ Mf yn − 1 ⎡ ⎤ Cn = ICo ⋅ ⎢(1 − γ ) + m ⋅ x ⋅ + ⋅ y⋅ + Ψ ⎥ − Yn − x − 1 y 1 IC ⎣ ⎦ o

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4.16

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where: x=

1 + gm 1+ i

4.17

y=

1 + ef 1+ i

4.18

and

Similarly, Yn represents the residual (salvage) value of the investment, attributed to amounts recoverable at the nth year of the hybrid system life (e.g. value of land or buildings, scrap or second hand value of equipment), along with the experience gained and the corresponding technological know-how. On the other hand, the total income Rn over an n year period – resulting from the operation of a stand-alone hybrid power station – is mainly attributed to the energy production E. This energy yield is normally used to cover the remote consumer electricity needs, while in some cases a portion of energy (surplus) may be sold to the national electrical grid. In order to avoid complicated mathematical equations in the present analysis it is assumed that the entire energy yield is absorbed by the remote consumer, thus any taxation impact is neglected (Kavadias et al., 2000). Details for the economic evaluation of grid connected applications may be found in (Kaldellis et al., 2002, 2005b; Colle et al., 2004; Mondol et al., 2009). Thus, the present value of the total HEW station income (operating for n years) is given as: j=n

1+ e ⎤ Rn = E ⋅ co ⋅ ∑ ⎡⎢ ⎥ ⎣ j =1 1 + i ⎦

j

n−1 n ⎡1+ e ⎛ 1+ e⎞2 1+ e⎞ 1+ e⎞ ⎤ = E ⋅ co ⋅ ⎢ + +⎛ +…+ ⎛ ⎝ 1+ i ⎠ ⎝ 1 + i ⎠ ⎥⎦ ⎣ 1+ i ⎝ 1+ i ⎠

4.19

where co is the current energy effective cost coefficient (C/kW h) usually expressed either as electricity price or as electricity generation cost and e is the corresponding electricity price/cost annual escalation rate. Generally speaking, in order to predict the exact value of the effective cost coefficient, it is important to examine if the hybrid power station will be used autonomously or parallel to the national grid. For this purpose the self-utilization factor s is introduced (Kaldellis et al., 1989), defined as the ratio of the amount of the electricity used directly by the producer to the total energy produced by the hybrid power plant. In case that the energy produced is sold to the national grid the corresponding price ca is determined according to the existing legislation frame. Generally speaking, the effective cost coefficient is given as:

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Feasibility assessment for stand-alone and HEW systems co = s · cs + (1 − s) · ca

111 4.20

where cs is the market price for direct self-use of the energy produced. For a stand-alone HEW system s = 1.0. The corresponding gains Gn of the investment investigated (in constantpresent values) are determined using equations (4.15) and (4.19), i.e.: Gn = Rn − Cn

4.21

Besides, in the case that: Gn = Rn − Cn = 0

4.22

the break-even equation of the HEW station is obtained (Elton and Gruber, 1984). Accordingly, the pay-back period n* of the investment is estimated by solving equation (4.22). For this purpose, an iterative solution of the nonlinear break-even equation is required. Subsequently, in order to examine not only the economic viability but also the economic attractiveness of a similar investment, an additional parameter is defined (Kaldellis, 1999) called ‘economic efficiency (η*)’ of the hybrid power station. By definition, η* compares the net gains of the investment over an n (e.g. n = 10 or 20) year period in constant terms with the initial capital invested. Thus one may write:

η*n =

Gn [ICo ⋅ (1 − γ )] − Yn

4.23

The economic efficiency of a wind park may be either negative (investment not viable) or positive, while in the case that η* = 0 one may calculate the pay-back period; see also equation (4.22). For the exact solution of equations (4.22) and (4.23), the accurate value of all parameters appearing in these expressions is required. However, owing to the continuous changes in the value of the governing parameters, a detailed sensitivity analysis of the main variables of the problem (e.g. n*, η10, η20) is suggested, so as to investigate the impact of techno-economic factors on the economic behaviour of similar hybrid power applications and to assure the viability and attractiveness of similar investments (Kaldellis and Gavras, 2000; Kaldellis et al., 2005b). Finally, one of the most common methods to investigate the financial behaviour of an investment is by estimating the corresponding NPV of the investment after a predefined operational period, usually equal (or less) to the service period of the investment (Rothwell, 1997; Liu and Ye, 2003). More specifically, comparing the present value of the total investment cost and the corresponding total revenues, one has the ability to estimate the NPV of the investment after n years of operation, i.e.:

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Stand-alone and hybrid wind energy systems NPVn = Rn − Cn = Gn or NPVn = E ⋅ co ⋅ q ⋅

qn − 1 + γ ⋅ IC 0 + Yn − IC 0 − q−1

m ⋅ IC 0 ⋅ x ⋅

yn − 1 xn − 1 − cf ⋅ Mf ⋅ y ⋅ − Ψ ⋅ IC 0 y−1 x−1

4.24

where: q=

1+ e 1+ i

4.25

Note that one may also use the non-dimensional form ‘npv’: npv n =

NPVn IC 0

or npv n =

E ⋅ co qn − 1 Y ⋅q⋅ + γ + n − 1− IC 0 IC 0 q−1 m⋅ x ⋅

x n − 1 cf ⋅ Mf yn − 1 − ⋅ y⋅ −Ψ x−1 y−1 IC 0

4.24a

In this context, the IRR of an investment operating during an n year period is predicted (Talavera et al., 2010) by setting the NPVn value equal to zero. Thus we get: IRR = i*, when NPVn (i*) = 0 or npvn (i*) = 0

4.26

For the estimation of the IRR an ‘expert type’ numerical code should be developed, in order to solve the non-linear break-even equation (4.26). Note that equation (4.26) is similar to equation (4.22).

4.5

Reliability impact-loss of load cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system

The reliability of a stand-alone HEW system is usually expressed either using the number of hourly load rejections during a given time period (e.g. 1 year period) or in terms of loss of load probability LLP (Kaldellis et al., 2004; Celik, 2007). Therefore the no-load rejection case – or equivalently the LLP = 0 value – corresponds to total energy autonomy of the system during the complete time period examined. However, in several cases there is a possibility of not exactly fulfilling the load demand for specific time

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intervals, since the desired system reliability is directly dependent on the applications supported. In these cases the system size (especially the energy storage capacity and the diesel-oil consumption) is significantly reduced, making the stand-alone HEW systems more financially attractive. The key-element of selecting the system dimensions on the basis of minimum permitted reliability (or LLP) is the non-energy (electricity) fulfilment cost parameter A. More precisely, A describes (in C/h) the cost of not covering the electricity demand of the system per hour. In order to obtain the no-load rejection case one simply assigns to A an arbitrary high value (i.e. A → ∞). Of course one should take into consideration that the exact value of A is not easily determined and may also depend on the reliability level (hours with no electricity) selected. In an attempt to include the required system reliability in the proposed analysis, the non-energy (electricity) fulfilment cost function Nn is introduced in the generalized total operational cost function of the system; equations (4.15) and (4.16). Actually Nn describes the corresponding noenergy fulfilment (or alternative energy coverage) cost function for an n year time period, also in constant values. Thus the no-energy fulfilment cost function can be approximated as: ⎛ 1+ a⎞ − 1 1+ a ⎝ 1+ i ⎠ Nn = h ⋅ A ⋅ 1+ i 1+ a − 1 1+ i n

4.27

where α is the time-mean annual change of A (Muselli et al., 1999; Kaldellis, 2003b) and h represents the hours per annum that the consumption is not covered by the existing HEW system. More precisely, one may write: h = 8760 · (1 − R)

4.28

with R being the corresponding reliability level. At this point it is important to mention that in equation (4.27) it is indirectly assumed that A remains constant and independent of h during a year. Substituting equations (4.27) and (4.28) into equation (4.16), one finally gets: x n − 1 cf ⋅ Mf yn − 1 ⎡ ⎤ Cn = IC 0 ⋅ ⎢(1 − γ ) + m ⋅ x ⋅ + ⋅ y⋅ + Ψ ⎥ − Yn + N n x−1 y−1 IC 0 ⎣ ⎦

4.29

Thus, for any reliability level R selected by the consumer, the minimum cost value Cn is predicted and therefore the minimum total cost can be computed based on the A value decided. Recapitulating, for every case analysed, Cn is a function of R, since all the governing parameters of the

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hybrid system (Qmax, Mf, No, z) are in fact functions of R, hence a minimum Cn value can be estimated according to the desired R level and the assumed numerical value of A (Kaldellis, 2003b).

4.6

Electricity generation cost of a typical standalone hybrid electricity generation wind-based (HEW) system

Using the above analysis and considering that the proposed HEW system produces approximately E kW h per year, one may estimate the corresponding energy production cost ce by dividing the present value of the installation total cost with the corresponding annual electricity production. In order to obtain more realistic results and have the opportunity to compare the present cost of the proposed solution with the current electricity generation values, the corresponding annual escalation rate of the HEW station electricity generation cost p is also included (Kaldellis, 1991). Thus, using equation (4.16) one may write: xn − 1 m ⋅ IC 0 ⎛ x ⎞ x − 1 cf ⋅ Mf ⎛ y ⎞ IC 1−γ + ⋅ ⋅ ce = 0 ⋅ + ⋅ ⋅ n ⎝ z⎠ ⎝ z ⎠ zn − 1 z 1 − E E E z⋅ z−1 z−1 Ψ Υn − zn − 1 zn − 1 E ⋅ z⋅ E ⋅ z⋅ z−1 z−1

yn − 1 y−1 + zn − 1 z−1 4.30

where: z=

1+ p 1+ i

4.31

Bear in mind that the proposed model also includes the diesel-only solution (i.e. IC0 = φ.Nd, No + NRES = 0, rb = 0, Mf = Mmax) as well as the zero-diesel configuration (i.e. ICd = 0, rd = 0, Mf = 0). Analysing the terms of equation (4.30) one may distinguish the impact of the major terms on the electricity generation cost of the HEW station. More precisely, one should take into consideration the critical role of the annual energy consumption (production) of the installation, underlining the significance of the optimum sizing. Accordingly, the first term of the RHS of equation (4.30) describes the impact of the initial cost of the installation (equations (4.1) and (4.7)), taking also into consideration the possibility of state subsidization, the capital cost impact and the corresponding service period of the hybrid power station.

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At this point it is important to clarify that in case that z → 1.0 (i.e. p ≈ i) the term [(zn − 1)/(z − 1)] of equation (4.30) tends to n, i.e. the service period of the installation, thus equation (4.30) reads: ce =

x n − 1 co ⋅ Mf yn − 1 m ⋅ IC 0 Υ IC 0 Ψ ⋅x⋅ + ⋅ y⋅ + − n ⋅ (1 − γ ) + n⋅E n⋅E x−1 n⋅E y−1 E⋅n E⋅n

4.32

Subsequently, the second term of the RHS of equation (4.30) describes the maintenance cost impact, while the third term presents the impact of the annual diesel-oil consumption. The last two terms of equation (4.30) express the influence of the variable (replacement) cost and the impact of the residual value of the installation on the electricity generation cost.

4.7

Socio-environmental impacts of stand-alone hybrid electricity generation wind-based (HEW) systems

HEW systems are characterized as one of the most environmentally friendly and socially advanced solutions for the electrification of remote consumers worldwide. In most cases they replace heavy polluting diesel-electric generators, thus avoiding the corresponding air pollutants’ emission, limiting oil transportation and minimizing oil imports. On top of these, remote consumers, being far from decision centres and having limited political influence, usually feel abandoned, facing a dramatically insufficient infrastructure. In several cases, their importance is not only based on socio-economic criteria but also on the preservation of national interests. In this context, the proposed HEW solution, although suggesting small systems, may be used to cover the pressing energy needs of these remote consumers and thus improve their quality of life by satisfying vital daily needs through electrification. On the other hand, one should also investigate any potential environmental impacts related to the operation of a similar HEW system. At this point it is important to remember the relatively limited size of the proposed installation, usually based on a small wind turbine, a fair number of PV panels, a rational energy storage (lead–acid batteries) device along with the corresponding electronic components and a diesel-electric generator operating mainly as back-up engine.

4.7.1 Wind turbine Objectively speaking and depending on the landscape characteristics, modern wind turbines – with a hub height of 60–100 m and a blade length

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of 30–50 m – are a noticeable addition to the scenery (Kaldellis and Kavadias, 2004). However, the size of the specific engines (rated power less than 100 kW) utilized and the remoteness of the area minimize the visual impact. Besides, it is a matter of aesthetics – to a large extent – that configures how people perceive wind turbines fitting into the landscape. Reaction to the sight of a wind turbine is highly subjective, with most remote consumers, however, appreciating them as a welcome symbol of clean and abundant energy to support their activities. Another objective visual impact is the effects of periodic reflections (glinting) and sunlight interruption (shadow flicker) from the rotor blades (European Commission, 1999). Wind turbines, like other tall structures, will cast a shadow (or a reflection) on the neighbouring area when the sun is visible. This becomes a serious problem when turbines are sited very close to workplaces or dwellings, occurring during periods of direct sunlight. These effects may be easily predicted and avoided by carefully considering the machine-site and the surface finish of the blades. A common guideline used is a minimum distance of 6–8 rotor diameters between the wind turbine and the closest neighbour. Another important aspect of wind turbines’ operation is noise emission. Sound emissions from wind turbines may have two different origins, i.e. mechanical noise and aerodynamic noise. Additional analysis reveals (Kaldellis et al., 2006c) that for most turbines with rotor diameters up to 20 m the mechanical component is the dominant one, whereas for larger rotors the aerodynamic component is the significant one. More precisely, mechanical noise may originate in the gearbox, in the drive train (the shafts) and in the electrical generator of the wind turbine. On the other hand, three main categories of aerodynamic noise sources (Berglund et al., 1996; Persson and Ohrstrom, 2002) may be distinguished: •

Discrete low-frequency noise at the blade passing frequency and its harmonics. • Self-induced noise due to direct radiation by the attached boundary layer on the rotor blade, due to flow field separation at the blade trailing edge and finally due to trailing edge instabilities involving quasi-discrete frequencies. • Broadband noise due to interaction between the inflow turbulence and the rotor. For almost all-existing commercial wind turbines operating under normal conditions, the most significant noise source is the self-induced noise of the blades. Generally speaking, no landscape is constantly completely quiet, since birds, animals and human activities create sound. Thus, when the wind meets different objects at a certain speed, it causes a sound. From a technical point of view, as wind speed approaches 6–7 m/s, the noise from the

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wind in leaves, shrubs, trees, masts, etc. (background noise), will gradually mask any potential sound from wind turbines. Of course, sound reflection or absorption from terrain and building surfaces may change the sound picture in different locations. It is, therefore, important to chart the potential dispersion of sound in different directions (Kaldellis et al., 2003). Other parameters being equal, sound pressure will increase with the fifth (4th to 6th) power of the speed of the blade relative to the surrounding area, which explains why modern wind turbines operate at low rotational speed. On top of that, the energy in sound waves (and thus the sound intensity) drops with the square of the distance from the sound source. Summarizing, the sound pressure level at a distance of 40 m from a typical machine is 50–60 dB(A), about the same level as a conventional speech. Ten years ago, wind turbines were ‘louder’ than they are today. Serious effort has been devoted to create the present generation ‘quiet’ machines, paying much attention to both the design of the blades (Kaldellis et al., 1991; Guidati et al., 1999; Tachos et al., 2009) to avoid boundary layer separation (Kaldellis, 1993a) and to the mechanical parts of the machine. As a result, noise is a minor problem for modern carefully sited wind turbines. Subsequently, taking into consideration the relative limited size of wind turbines used in a HEW and the special land characteristics of a stand-alone installation, it is fairly obvious that the impact on birds is almost negligible, while there is also no serious land occupation by the proposed installation (Kaldellis et al., 2003). The last issue briefly analysed concerns the energy and the materials used during the construction and installation of a wind turbine. Though wind turbines do use energy-intensive materials, such as steel, glass reinforced polyester (fibreglass), and concrete (for foundations), according to three separate European studies (European Commission, 1999) they quickly repay the energy consumed during their construction. As expected, most of the energy used to manufacture the turbine is contained in the rotor and nacelle. But more than one-third of the total energy consumed by the wind turbine is contained in the foundation and the tower of the machine. According to the results obtained, at good windy sites, wind turbines pay for the energy embedded in their materials within the first 3–4 months, while even at poor sites, energy payback occurs in less than a year. Finally, from the data gathered, it is clear that the material inputs required for a wind farm are dominated by the concrete (reinforced) foundations for the turbine and by the steel from which the turbine towers are fabricated. It is conceivable that a wind farm could, on reaching the end of its operating life, be refurbished by installing new nacelles and rotors on top of the existing towers and foundations. This would reduce the material inputs required for the ‘second generation’ wind farm by even more than

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80%. Lastly, if there is sufficient demand for the secondary raw materials, wind turbines can be regarded as being mainly composed of recyclable materials. The principal unresolved issue from an environmental perspective is the recycling of rotor blades (Holttinen et al., 1999). At this point it is important to mention that the operation of HEW systems contributes (Kaldellis, 2002d; Kaldellis et al., 2008) to CO2 emissions reduction. More specifically, for every kW h produced by the wind turbine almost 700 gr of carbon dioxide (otherwise emitted by the diesel generator) are avoided. Finally, the operation of wind-based power stations strongly contributes to the reduction of SO2 and NOx emissions assumed responsible for acidification agents. The most important quantified effects of acid deposition are upon human health, building materials, historical monuments and commercial forestry. Furthermore, there are major impacts upon ecosystems, both terrestrial and aquatic. According to damage costs derived using previous estimates of acidification (Hohmeyer, 1988), an optimistic value is approximately 6000C per tonne of either SO2 or NOx.

4.7.2 PV generator The PV technology has distinct environmental advantages over conventional technologies for electricity generation. The operation of PV systems does not produce any noise, toxic gas emissions or greenhouse gases. As in the wind turbine case, PV generators contribute to reducing the emission of CO2 along with the additional air pollutants produced from fossil fuels (Tsoutsos et al., 2005; Kaldellis et al., 2008). Although many researchers characterize PV stations as especially land-intensive installations, the actual PV’s land-use requirements are similar to those for coal production and combustion. Besides, PV’s material requirements are extremely low (e.g. 1 MW h/g semiconductor material), while the semiconductor materials used in solar cells do not pose the environmental problems related to other conventional technologies, e.g. uranium and fission by-products (Miles et al., 2005). As with any energy source or product, there are some minor hazards associated with the manufacture, use and disposal of solar cells. Although the PV industry uses far smaller amounts of toxic and flammable substances than many other industries, its use of hazardous chemicals may present occupational and environmental hazards (Moskowitz and Fthenakis, 1991; Patterson et al., 1994). On the other hand, the recyclability of PV systems (at the end of their useful life) adds to the environmental benefits, and can further enhance market support. Also, recycling addresses the public’s concerns regarding certain materials used in PV modules (e.g. Cd, Pb), which can create barriers to market penetration (Fthenakis, 2000).

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Another important issue related with the application of PV generators is the corresponding energy pay-back (amortization) period (Kaldellis et al., 2009c). More specifically, the life-cycle energy analysis should involve the stages of construction, installation, maintenance and final decommissioning of the plant. Details concerning the energy sustainability of PV stand-alone systems may be found in several references (see for example Alsema and Nieuwlaar, 2000; Raugei et al., 2007; Kaldellis et al., 2009d). According to the available information the energy pay-back period of a PV array varies between 4 and 7 years, depending on the technology applied and the solar potential of the installation area. Besides, the system batteries add another 3.5 to 5 years in comparison with the energy pay-back regarding only the PV modules and the balance of the system (BOS) components.

4.7.3 Battery bank The environmental impacts of the HEW batteries result either from the manufacturing process or due to the operation and final disposal of them (Rydh and Sandén, 2005; Rudnik and Nikiel, 2007). The solid waste generated during the manufacturing process consists of the following: • • • • • •

dross; scrap plates; scrap batteries; scrap plastic material; scrap envelope material; sludge.

Accordingly, wastewater generated during the process is due to the following: • • • •

washing and cleaning of equipment; battery washing; discharge from scrubber; process cooling water being discharged.

Lead and sulphates are the major impurities in wastewater from this facility. The water used for cooling batteries in the wet room (charging section) and the water used to wash batteries consists of sulphate impurity. Air emissions during the process occur as a result of handling of plates, parting and collection of plates and other similar activities, which generate lead dust. The air in the plant is thus contaminated with lead particles. The ventilation system on site takes air from the site and discharges it into the atmosphere. Sulphur dioxide is emitted from the air emissions from the wet room and is discharged through a scrubber into the atmosphere. Although

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quite rare, during the HEW operation, improper handling of batteries may result in explosion, burn or heat generation. Finally, one of the main disadvantages of batteries as an energy storage option is their end-life disposal issue (Morrow, 2001). The more environmentally friendly solution is based on recycling the batteries, keeping in mind that under no circumstances can batteries be incinerated as this can cause them to explode (Kaldellis and Kondili, 2006).

4.7.4 Electric and electronic equipment The materials used for construction of the electric and electronic equipment in a HEW system are steel, aluminium, copper and regular electronic equipment, which are associated with the standard industrial hazards (Konstantinidis et al., 2001). Thus, one may find the corresponding (generally low or medium) environmental impacts in the international literature.

4.7.5 Diesel-electric generator The utilization of an internal combustion engine is directly related with all the environmental impacts of a typical thermal power station (Spyropoulos et al., 2005). For the specific small diesel-electric generators employed in several applications one may note the leaked or spilled diesel fuels, the corresponding diesel smoke along with the SO2 and NOx emissions, etc. All these contribute to global warming, water and soil pollution, remarkably affecting the life quality and the health status of the nearby communities. Summarizing the analysis of socio-environmental impacts of HEW systems one may state that the environmental attractiveness, the reduction of dependence on energy imports, the existing natural resource preservation as well as the implicit costs of conventional energy systems (e.g. accumulation of dangerous pollutants) have not been yet sufficiently considered in the existing cost–benefit analysis of wind-based systems (Kaldellis, 1993b). Although it is very difficult to quantify all components of social cost, one should not disregard this important parameter during the evaluation of HEW systems. In this context and based on the free market economic theory, the social cost of fossil fuel-based electricity generation has to be introduced in the energy market price, according to the rule ‘the polluter pays for the damage’. This additional amount must be used in order to cover the social cost of the energy production as well as to prevent the environmental deterioration and the local energy resources’ overexploitation. At the same time, the wind energy applications have to be encouraged, due to their net positive

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social effects. It is therefore possible to subsidize similar investments, paying in advance the expected socio-environmental gains from their operation replacing fossil fuel-based power stations. This is currently the case for all the RES-based applications all around the world. For example, in Greece remarkable initial cost subsidies (up to 60%) are considered by the existing legislation (Kaldellis and Kodossakis, 1999).

4.8

Analysis of case studies of stand-alone hybrid electricity generation wind-based (HEW) systems

In the current section one may find some representative results concerning the financial performance of selected HEW systems. For this purpose three representative wind potential areas have been selected, namely the islands of Kea (medium wind potential), Kithnos (high wind potential) and Andros (very high wind potential). More precisely, Andros is a small to medium-sized island (the second biggest one) of the Cyclades complex (population 12 000, area 384 km2), located in the middle of the Aegean Sea (Fig. 4.3). The local terrain includes several rocky mountains with relatively sharp slopes. The island has one of the best wind potentials in Greece (average wind speed approximately 10 m/s), while on the island

V = 3.2

V = 5.3 Andros

Athens V = 3.2

V = 7.5

Kea

Naxos

Kithnos

Aegean Sea V = 5.3

h = 30 m

4.3 Wind potential map for Aegean Sea area at 30 m height.

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Probability density function f(V)

there is quite an old wind park of 9 × 225 kW V-27 wind turbines, which has been operating with outstanding results since 1993 (Kaldellis and Kodossakis, 1999). Kithnos is also a small island (1700 inhabitants, area 94 km²) in the Aegean Sea, located approximately 60 km southeast of Athens. The topography of the island is typically Aegean, i.e. gentle slopes, absence of flat fields, low mountains and sparse vegetation. Owing to the insufficient infrastructure (e.g. road network) there are many isolated consumers, who have no access to the local electrical grid. The island has an outstanding wind potential, since in several locations the annual mean wind speed approaches 7 m/s, at 10 m height. Finally, Kea is a small island (2300 inhabitants, area 103 km2) close to Athens. The local topography is similar to Kithnos, while the corresponding wind potential is good enough (annual mean wind speed ≈ 6.0 m/s) to feed contemporary wind turbines for electricity production. Using the available wind speed data (Kaldellis and Tsesmelis, 2002) for a 3-year period, the experimental 3 year mean wind speed probability density function distribution f(V) is created, for all three regions investigated; see also Figs 4.4 to 4.6. It is important to mention that the 3 year period selected includes the maximum calm spells during a 15 year period, where extensive wind speed measurements exist. As expected, the Andros wind potential is quite higher than those for Kithnos and Kea, while the last two regions maintain similar quality wind potential. Of course the zero-wind speed possibility is much higher in Kea (≈20% of the year) than in Kithnos. The zero-wind speed possibility is almost zero (≈2%) for Andros.

0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0 0.5

2.5

4.5

6.5

8.5 10.5 12.5 14.5 16.5 18.5 20.5 22.5 24.5 26.5 28.5 Wind speed (m/s)

4.4 Wind potential data for Andros Island.

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Probability density function f(V)

0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5

Wind speed (m/s)

4.5 Wind potential data for Kithnos Island.

Probability density function f(V)

0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5

Wind speed (m/s)

4.6 Wind potential data for Kea Island.

Finally, in Table 4.1 the annual average wind speed values for all three years and regions examined are presented, along with the corresponding Weibull parameters (i.e. C is the wind speed normalizing factor and k is the corresponding shape factor) and the maximum duration of calm spells. According to the available information, based on detailed measurements of 3 years, one may conclude that Andros possesses an excellent wind potential with very high wind speeds and limited calm spell periods. Subsequently, although no considerable difference is noted, the wind potential of Kithnos is slightly better than that of Kea. Bear in mind that both islands present high wind speeds all year round, although the existence of remarkable calm spell periods cannot be disregarded.

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8.892

9.478

1.842

36

C (m/s)

k

Max calm spell duration (h)

34

1.834

10.234

9.565

37

1.83

9.777

9.011

3rd year

178

1.364

6.563

6.4535

1st year

2nd year

1st year

_ V (m/s)

Kithnos

Andros

185

1.337

6.717

6.7855

2nd year

Table 4.1 Annual wind potential characteristics of the remote areas analysed

188

1.264

6.445

6.392

3rd year

165

1.302

5.273

5.482

1st year

Kea

158

1.299

5.165

5.403

2nd year

161

1.313

5.438

5.596

3rd year

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4.8.1 Comparison of available solutions for a stand-alone system Applying an optimum sizing methodology (Kaldellis and Tsesmelis, 2002) for a stand-alone wind-based system for the island of Andros (high wind potential) the corresponding first installation cost of the system is quite high, approaching 18 000C. In order to check the viability of the proposed wind-based solution to meet the electricity demand of a remote consumer (Etot ≈ 4700 kW h/year) at rational cost, a preliminary comparative study is undertaken including also the possibility to realize an electrical grid connection or to use a small autonomous diesel-electrical generator system. Grid extension solution According to the existing market data the typical grid extension cost for a remote consumer to be connected to the local grid using overhead-medium voltage lines is approximately 10 000C/km. At the same time the current final electricity market price for all Greek consumers is 0.12C/kW h. Neglecting for simplicity reasons the time-variations of the above parameters, the total electricity cost CGC of the consumer after n years of utilizing the extended electrical grid, being at a distance z (km) from the existing electrical network, can be described by the following relation: CGC = 1000 · z + 0.12 · Etot · n

4.33

Use of a diesel engine The most widely applied solution for the remote consumers to fulfil their electrification needs is to install a small internal combustion engine in combination with a small electrical generator. Although the overall efficiency of such a system is quite small (ηd ≈ 20%) the corresponding buy cost is very low (≈150–250C/kW), increasing the short-term economic attractiveness of this solution. On the other hand, the service period life of a wholeyear operating system is taken equal to 6 years and the corresponding M&O cost (mainly due to fuel cost) is between 3100 and 5000C per year. Consequently, selecting a 5 kW autonomous system and accepting a 0.8 to 1.7C/l cost of the diesel oil used (the maximum value takes into account the increased transportation cost), the total electricity cost Cd of the installation after n years of operation is given as: Cdmin = 700 + 3100 · n + Vn

4.34

Cdmax = 700 + 5000 · n + Vn

4.35

or

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where Vn describes the replacement cost of the diesel engine every 6 years. Wind energy-based stand-alone solution Using the optimum configuration dimensions (under the no-load rejection restriction) of the stand-alone system for every island analysed (e.g. Andros, IC0 = 18 000C) and a 3% (m = 0.03) annual M&O cost coefficient (Kaldellis, 2002b), the total electricity production cost by applying the wind energy solution can be approximated as: CWE = IC0 + m · IC0 · n + V′n

4.36

while V′n term is used to describe the battery replacement cost (e.g. for Andros, 11 700C) every 7 years. For comparison purposes, the calculation results are summarized in Fig. 4.7, for various distances from the existing grid (1 ≤ z ≤ 10 km) and for the three regions investigated. As is clearly stated by the results of Fig. 4.7, for z < 1 km the grid connection is the best choice, for medium to long-term operation of the system (n > 4 years). Accordingly, for Andros Island, the proposed stand-alone system is an economically interesting solution for (2 ≤ z ≤ 4 km), especially when the maximum diesel cost values are taken into consideration. For z ≥ 4 km the proposed stand-alone wind power solution is by far the best alternative, excluding the minimum diesel cost production scenario and short-term operation cases (i.e. n ≤ 6 years). Electrical grid (1 km) Electrical grid (3 km) Electrical grid (5 km) Electrical grid (7 km) Electrical grid (10 km) Diesel (min) Diesel (max) Andros (stand-alone) Kithnos (stand-alone) Kea (stand-alone)

180 000 160 000 Total cost (a)

140 000 120 000 100 000 80 000 60 000 40 000 20 000 0 0

2

4

6

8

10

12

14

16

18

Years

4.7 Total cost comparison of available electrification solutions.

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Subsequently, the proposed stand-alone configuration for Kithnos presents a financially competitive advantage for installation regions being more that 7 km (z ≥ 7 km) away from the local grid and for medium to long-term operation cases (n ≥ 9 years). Finally, for Kea, the financial advantage of the proposed stand-alone wind power system, in comparison with the other options, is validated for relatively long distances from the local electrical network (z ≥ 12 km) and for long-term operation of the system (n ≥ 12 years). In all other cases, the diesel or the grid extension solutions should be preferred on a pure energy production cost basis and under the current techno-economic situation.

4.8.2 Operational years’ impact on the total cost of a wind-based stand-alone system The next case study includes the total cost analysis of a wind-based standalone system (without diesel-oil contribution) for the island of Kithnos. Emphasis is given in order to investigate the impact of operational years of the system on selecting the minimum cost (optimum) configuration. Actually, using the available wind potential data (Fig. 4.5) and applying the optimum sizing algorithm by Kaldellis (2002c), one may find in Fig. 4.8 the no-load (energy autonomy) curve (No–Qmax), combining the minimum wind turbine rated power No and battery capacity Qmax values that guarantee the energy autonomy of the system for the entire 3 year period examined. Accordingly, in Fig. 4.8 the constant initial cost curves (i.e. IC0 = ct) are drawn, using equation (4.1), with Np = 5 kW. The minimum first installation cost solution is based on a 9.5–10 kW wind turbine and on 18 000 A h (24 V, 60 000 30 000 a 36 000 a 42 000 a Zero load points

50 000

Qmax (Ah)

40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.8 No-load rejection configuration on the basis of minimum initial cost (included 40% state subsidization) for Kithnos.

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75% DODL) battery capacity, while the corresponding initial investment cost (without taking into account the 40% State subsidization) is approximately 70 000C. Subsequently, considering that a similar stand-alone wind power system is being developed to operate during the next 10–20 years, it seems appropriate to investigate primarily the 10 year total energy production cost distribution, if a medium-term evaluation of the available energy autonomy (No–Qmax) combinations is expected. Thus, in Fig. 4.9 the constant C10 curves are also given, pushing the minimum energy production cost configuration to lower Qmax values (= 15 000 A h) and to higher wind turbine sizes (No = 14–15 kW). During the calculations it is assumed that the local economy inflation rate g is 4%, the corresponding market capital cost i is 9% and no remarkable technological improvements concerning batteries occurs (ρb = 0), i.e. battery replacement takes place every 7 years of operation. The minimum 10 year energy cost solution future value is almost 200 000C or 135 100C in constant values. On the other hand, the minimum first installation cost solution is by 8% more expensive than the respective minimum 10 year total cost. Finally, extending our calculation to 20 years (i.e. long-term operation), being the usually acceptable wind turbine replacement period, the corresponding minimum energy production cost C20 value is obtained (Fig. 4.10), for 16 kW wind turbine rated power and 13 500 A h battery capacity. The corresponding minimum 20 year energy cost is 280 700C in constant (present) values.

60 000 100 000 a 150 000 a 200 000 a Energy-autonomy points

50 000

Qmax (A h)

40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.9 No-load rejection configuration on the basis of minimum 10 year cost, Kithnos.

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400 000 E 500 000 E 600 000 E 700 000 E Energy autonomy points

50 000 40 000 Qmax (A h)

129

30 000 20 000 10 000 0 3

5

7

11

9

13

15

17

No (kW)

4.10 No-load rejection configuration on the basis of minimum 20 year cost, Kithnos.

Table 4.2 Optimum stand-alone wind power system dimensions, using variable financial evaluation criteria for Kithnos island Minimum cost solution

Initial cost

10 years cost

20 years cost

Wind turbine size Battery capacity

9.5 kW 18 000 A h

14.5 kW 15 000 A h

16.0 kW 13 500 A h

Recapitulating, the minimum energy production cost scenario is quite different if the evaluation is based on the system initial cost value only, instead of using the system 10 year or 20 year total cost configuration (see Table 4.2). More precisely, for medium or long-term operation, a remarkable increase of the wind turbine rated power occurs, along with a significant decrease of the necessary battery capacity.

4.8.3 Reliability impact on the total cost of a wind-based stand-alone system In the following, the above-described total cost analysis (Section 4.4) applies to two separate cases, for which detailed wind potential data are given (Figs 4.4 and 4.5). More precisely, the first case concerns Andros (excellent wind potential), while the second case examined corresponds to a stand-alone system installed on the island of Kithnos (medium–high quality wind potential). Note that in both cases the analysis is carried out by using one year’s wind speed measurements (Kaldellis, 2003b).

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Stand-alone and hybrid wind energy systems R = 100% R = 99.9% R = 99.5% R = 99% R = 98% R = 95% 30 000 E 40 000 6 50 000 E 60 000 E 70 000 E Best points

Battery capacity Qmax (A h)

12 000 10 000 8000 6000 4000 2000 0 2000

4000

6000

8000

10 000

12 000

14 000

16 000

Wind turbine rated power No (W)

4.11 The relation between the configuration of a stand-alone wind power system and the 10 years total cost, for variable reliability values; Andros.

Thus, in Fig. 4.11, six distinct numerical curves are drawn representing the zero load rejection solution (R = 100%, h = 0) and the R = 99.9%, 99.5%, 99%, 98%, 95% cases for Andros. More specifically, each point belonging to these curves represents a stand-alone wind power system minimum configuration (i.e. minimum wind turbine rated power and minimum battery capacity) that guarantees a given reliability value for Andros and for a year-long period. In the same figure the corresponding 10 year total cost constant-value curves are also drawn, in order to estimate the minimum total cost solution for every reliability level. All the minimum cost points are represented by the ‘best points’ curve also given in the figure. In fact, there is a remarkable total cost diminution as the required theoretical reliability is decreased from 100% to 95%. This 10 year total cost reduction may be in the order of 55% as the desired system reliability drops from the theoretical value of 100% to 95%. Even for high reliability values (e.g. 99%) the 10 year total cost diminution is significant (almost 20 000C), while the corresponding hours without electricity are less than 100 per year. It is, however, worth mentioning that for moderate reliability decrease – between 100% and 99% – a considerable battery capacity diminution appears (approximately 50%), while the corresponding wind turbine size remains unaffected. For lower reliability values, though, the battery capacity remains almost constant as the wind turbine rated power decreases from 6 to 2.5 kW.

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Feasibility assessment for stand-alone and HEW systems

R = 100% R = 99.9% R = 99.5% R = 99% R = 98% R = 95% 70 000 E 90 000 E 110 000 E 130 000 E 150 000 E Best points

40 000 Battery capacity Qmax (A h)

131

35 000 30 000 25 000 20 000 15 000 10 000 5000 0 2000

4000

6000

8000

10 000

12 000

14 000

16 000

Wind turbine rated power No (W)

4.12 The relation between the configuration of a stand-alone wind power system and the 10 years total cost, for variable reliability values; Kithnos.

Similar conclusions apply to Kithnos (Fig. 4.12). According to the calculation results obtained, there is considerable variation of the system optimum size as the desired reliability varies between 95% and 100%. More specifically, the optimum battery capacity declines from 11 900 A h (R = 100%) to only 4300 A h (R = 95%), while at the same time the wind turbine rated power required is also decreased from 12 to 7 kW. Finally, the corresponding initial cost reduction is more than 50%. As in the Andros island case, the reliability decrease – between 100% and 99% – is mainly realized by reducing the optimum battery capacity (from 11 900 to 7500 A h), while the corresponding wind turbine nominal power decreases by less than 1 kW. For inferior reliability, on the other hand, the battery capacity is fairly decreased, while the wind turbine rated power detracts from 11 to 7 kW. Summarizing, one may clearly state that the dimensions and the initial investment cost of a stand-alone wind power system are substantially limited as the required – by the consumer – system reliability decreases from the theoretical value of 100% to a fair value, like 99% or in less crucial applications to 95%. To be more precise, the parameter that usually determines the reliability lower boundary of a stand-alone system is the noenergy fulfilment cost per hour (or the alternative energy coverage cost) of the remote installation.

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150 000 140 000

Total cost (E)

130 000 120 000 110 000

A=0 A = 10 A = 15 A = 20 A = 30 A = 50

100 000 90 000 80 000 70 000 60 000 50 000 94

95

96

97

98

99

100

Annual reliability (%)

4.13 The impact of system reliability on a stand-alone wind power installation 10 years operational cost for Kithnos.

In this context, the proposed methodology is applied to an autonomous wind-power system operating in Kithnos. The present analysis considers the no-energy fulfilment cost parameter A, while the results obtained are expressed as a function of the system reliability. Thus, in Fig. 4.13 the 10 years system total cost (see equations (4.27) to (4.29)) is given as a function of the desired system reliability, using the hourly no-energy fulfilment cost A as the fundamental parameter of the problem. According to the results of Fig. 4.13, for 0 < A ≤ 25C/h there is an optimum reliability value that minimizes the 10 years system total cost. For higher A values, the analysis ‘dictates’ the maximum technically realized system reliability. On the other hand, for low A values (A → 0) the system reliability cannot be estimated by a similar model, thus other factors may determine the installation characteristics, e.g. in this approach Rmin is set equal to 95%. As a general conclusion from the several representative cases analysed (Kaldellis, 2003b), one may state the following: • •

•

When the A value is approximately identified, there is an optimum system reliability value (R ≈ 98–99.5%) minimizing the system total cost. When the desired (or minimum acceptable) reliability limit is determined by the requirements of the applications supported by the standalone installation, system configuration and total cost is a function of the numerical value of parameter A. In cases that both R and A are given, the optimum system size and total operational cost may be computed, depending mainly on the available wind potential of the installation site.

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4.8.4 The impact of PV panels on the total cost of a HEW system One of the most expensive components of a HEW system is the battery bank – necessary to guarantee system reliability. Thus, in cases of increased system autonomy, the battery contribution to the initial or the total operational cost is found to be dominant (Kaldellis, 2002c). In addition, batteries should be replaced every 4–7 years (Cherif et al., 2002), thus increasing the operational cost of the system. Hence, in cases with remarkable solar potential, the introduction of a small PV generator (Kaldellis et al., 2000; Muselli et al., 1999), in a stand-alone HEW station is expected to smooth out the system energy production, significantly decreasing the energy storage requirements, without modifying the first installation cost of the system (Kaldellis, 2003c; Rever, 2001). Additionally, the subsidy percentage for small PV systems is normally considerably higher (50–55%) than the corresponding one for small wind power stations (30–40%). On top of that, the operational lifetime of a contemporary PV system is close to 30 years. In this context, in the current section the possibility is considered of reducing the battery size of stand-alone wind power installations – mainly installed in medium – low wind potential areas – by incorporating a small PV generator (Kaldellis et al., 2006a). More precisely, in order to check the viability of the proposed wind– solar-based solution, to meet the electricity demand of a remote consumer at a rational cost, a preliminary comparative study is undertaken, including also the possibility of using a small autonomous diesel-electrical generator system. The most widely applied (to now) solution for the remote consumers is to install a small internal combustion engine in combination with an appropriate electrical generator. Although the efficiency of such a system is quite small (≈20%) the corresponding buy cost is very low (≈150–250C/kW), increasing the short-term economic attractiveness of this solution. Consequently, selecting a 5 kW autonomous system the total electricity cost Cd of the installation after n years of operation is given from equation (4.35). Accordingly, using a wind–PV–battery configuration (Kaldellis et al., 2006a) based (Kithnos) on a wind turbine of rated power 7.5 kW and taking into consideration a 3% (m = 0.03) annual M&O cost coefficient, the total electricity production cost by applying the wind–solar energy solution can be approximated by equation (4.36), where the V′n term is used to describe the battery replacement cost (depending on the PV panels selected) every 7 years. The initial installation cost IC0 of the proposed solution includes the ex-works price of the wind turbine (7.5 kW), the cost of buying the battery and the PV panels (Table 4.3) and the corresponding balance of

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Table 4.3 Main parameters of the Kithnos stand-alone system analysed Photovoltaic module number

Battery capacity (A h)

Initial cost (C)

Initial cost (C), including subsidy

Battery replacement cost (C)

0 20 50 100

16 700 13 100 8 800 4 600

58 666 57 898 58 930 66 949

29 333 28 949 29 465 33 475

39 425 31 518 21 840 12 009

160 000 z=0 z = 20 z = 50 z = 100 Diesel

140 000

Cost (E)

120 000 100 000 80 000 60 000 40 000 20 000 0 0

2

4

6

8

10

12

14

16

18

20

Years

4.14 Life-cycle cost analysis: comparison between the proposed wind-solar based and a typical dispatchable diesel generator solution.

the plant cost, including any additional electronic equipment required (equation (4.1)). Bear in mind that similar applications, based on the exploitation of renewable energy sources, are strongly subsidized by the Greek state: up to 50% for combined wind-solar based systems. For comparison purposes, the calculation results are summarized in Fig. 4.14, for various combinations of PV modules and battery capacity (Table 4.3). As is clearly shown by Fig. 4.14, the dispatchable diesel generator scenario presents a financial advantage during the first 5–8 years of operation of the installation, excluding any excessive oil price augmentation. Accordingly, the proposed stand-alone HEW system is by far the best option, especially if the number of PV modules exceeds 20 (i.e. z > 20). On top of these, a significant battery size reduction is encountered for several representative HEW stand-alone systems located in islands of

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medium–low wind potential, when a small PV generator is introduced to the original wind power stand-alone system, designed to meet the electricity needs of a typical remote consumer (Kaldellis et al., 2006a). On the other hand, the incorporation of PV panels in similar systems may be characterized as a rather expensive improvement, which does not have practical results. Contrary to this general belief, one may prove that this proposal is in fact financially beneficial, if the money saved by the replaced battery capacity counterbalances or overwhelms the introduced PV panels’ ex-work price, under a constant wind turbine rated power value (see also Fig. 4.14). In that case the entire system energy production cost is respectively reduced. One should not disregard the fact that a typical lead–acid battery must be replaced every 4–7 years, while the PV generator has a lifespan of more than 20 years (Masini and Frankl, 2002). This fact is obvious if one compares the z = 0 and the z = 100 solutions of Fig. 4.14. More precisely, the z = 100 solution is definitely less expensive than the z = 0 one on a life-cycle base analysis, although the z = 0 solution (zero PV panel utilization) presents a lower initial cost; see also Table 4.3. This may be attributed to the lower battery replacement cost when using PV panels as well as to the 50% initial cost subsidy by the Greek state. In this context, one may consider a greater ratio value of the battery reduction per PV panel (51Wp) (in absolute terms) than the value calculated by equation (4.37). More specifically, in equation (4.37) one may estimate the marginally considered ΔQ/Δz value according to the following relation:

( ΔQ Δz) =

Γ B

4.37

where Γ is the ex-works price of one (51Wp) PV polycrystalline panel and B is the present value of the purchase cost plus the replacement cost (every 4–7 years) corresponding to the battery capacity (24 V) reduction by 1 A h. More specifically, if the slope of the curve of the decrease in battery capacity versus the PV panel number (ΔQ/Δz) for each wind turbine size tested is steeper than the value of equation (4.37), then the battery replacement by PV panels is a less expensive option. On the other hand, if the corresponding slope is less than the one of equation (4.37), lead–acid batteries should be used instead of additional PV modules (Kaldellis et al., 2006a). Using the available current market prices (e.g. Hawkes, 1997; Haas, 2002), the numerical value of equation (4.37) varies between −250 (battery decrease versus the PV panel increase) and −100. Thus, when the battery reduction rate per PV panel introduced exceeds (in absolute terms) a specific value (e.g. |ΔQ/Δz| = 150), the replacement of lead–acid batteries by PV panels is financially beneficial. For instance, the expense of installing one additional PV panel of 51Wp is below the purchase and replacement

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25 000 Nw = 5.0 kW Nw = 7.5 kW Nw = 10 kW Nw = 15 kW ΔQ/Δz = −150

Battery capacity (A h)

20 000

15 000

10 000

5 000

0 0

25

50

75

100

125

150

175

200

225

250

PV panels

4.15 Maximum battery size reduction due to PV panel introduction in a wind-solar stand-alone system of Kea (solar potential type ‘C’).

cost of the battery (greater or equal to 150 A h) module – to guarantee the system energy autonomy – in constant (present) values. In order to get a clear-cut picture of the proposed modification, Fig. 4.15 demonstrates the battery capacity versus PV panels’ variation, resulting for the Kea island HEW system analysed. More specifically, in Fig. 4.15 one may find the (Qmax–z) distributions for selected constant wind turbine rated power values (ranging from 5 to 15 kW). In the same figures the |ΔQ/Δz| = 150 constant slope lines are also drawn. Bear in mind that the precise ΔQ/Δz value depends on the local market prices and the technological time evolution expected. The financial attractiveness of the proposed battery substitution by PV panels is more obvious for stand-alone systems based on relatively small wind turbines (i.e. z ≤ 115 for No = 5 kW and z ≤ 60 for No = 10 kW). Besides, the lower the available wind potential of the stand-alone HEW system location the bigger the PV panel number adopted. On the other hand, in case of better solar potential available more PV panels can be incorporated to replace battery modules. Recapitulating, one may state that the introduction of an appropriate number of PV panels leads to a significant battery size reduction of a winddriven stand-alone system. This reduction is in proportion to the corresponding solar potential and in reverse proportion to the available wind potential. The exact size of the PV power penetration in the wind power stand-alone system should be the result of a detailed cost analysis based on the batteries’ and the PV panels’ market prices, with additional consideration of the expected forthcoming technological improvements of the sector.

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4.8.5 Operational years’ impact on the total cost of a HEW stand-alone system The next case analysed concerns a wind–diesel system used to cover the energy demand of a remote consumer living in an area of high wind potential; the island of Andros. Applying the proposed solution (Kaldellis et al., 2006d) for the Andros case, one may obtain the (Qmax–No) distribution that guarantees one year energy autonomy for various typical annual oil quantities (i.e. Mf = 0 kg/year up to Mf = 1000 kg/year); see Fig. 4.16. Bear in mind that approximately 2000 kg of oil are necessary in order for the dieselelectric generator to meet the electricity requirements of the specific consumer under investigation without any other additional energy source. Rationally, the dimensions of the hybrid system are remarkably reduced as the contribution of diesel oil is increased. In fact, this reduction is greater when small quantities of diesel oil are used, while for larger oil quantities the battery bank size is slightly decreased for a given wind turbine rated power. Accordingly, the constant initial cost (IC0 = ct) curves are drawn in the same Fig. 4.16, which however does not take into account the annual oil quantity consumed. In this context, one should certainly select the maximum diesel oil consumption solution, since this choice minimizes the initial cost of the hybrid station. In order to obtain a clearer idea concerning the feasibility of a similar HEW system, Fig. 4.17 shows the 10 year cost variation for selected representative cases. More specifically, Fig. 4.17 presents:

Battery capacity (A h)

25 000

Mf = 0 Mf = 25 Mf = 50 Mf = 100 Mf = 500 Mf = 1000 ICo = 20 000 E ICo = 30 000 E ICo = 40 000 E

20 000

15 000

10 000

5000

0 0

2000

4000

6000 8000 10 000 Wind power (W)

12 000

14 000

4.16 Energy autonomous configuration for a wind-diesel hybrid system, including first installation cost, Andros.

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16 000

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Stand-alone and hybrid wind energy systems

100 000

Mf = 0 (kg/year) Mf = 100 (kg/year) Mf = 250 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year) Diesel only system

90 000 Ten years cost (E)

80 000 70 000 60 000 50 000 40 000 30 000 20 000 10 000 0 0

2000

4000

6000

8000

10 000

12 000

14 000

Wind turbine rated power (W)

4.17 Ten year cost analysis of a wind–diesel hybrid system, Andros.

Table 4.4 Optimum stand-alone wind power system dimensions, using various approximations, for Kithnos

Wind turbine size Battery capacity 10 year cost in constant values

• • • • • •

10 year cost

10 years (γ = 0)

10 years (rb = 0.1)

10 years (i = 18%)

14.5 kW 15 000 A h 135 000C

15 kW 14 000 A h 180 000C

12.5 kW 16 000 A h 115 000C

17 kW 13 000 A h 154 000C

the autonomous wind–battery solution (Mf = 0 kg/year); the diesel-only solution (Mf = Mfmax = 2000 kg/year); the 5% annual diesel-oil penetration (Mf = 100 kg/year); the 12.5% annual diesel-oil penetration (Mf = 250 kg/year); the 25% annual diesel-oil penetration (Mf = 500 kg/year); the 50% annual diesel oil penetration (Mf = 1000 kg/year).

After a closer inspection of the calculation results and considering the numerical values of Table 4.4 regarding the parameters of equations (4.14) and (4.16), we may state the following comments for the 10 year cost solution: •

The optimum zero-oil solution should be based on a 4 kW wind turbine and 7100 A h battery capacity, while the corresponding 10 year cost is fairly higher than 35 300C.

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•

In any case the zero-oil solution is slightly more expensive than the diesel-only solution, i.e. by almost 5000C (≈15%), on the 10 year basis. However, external cost is excluded from the data presented. • By increasing the diesel oil contribution the 10 year cost is remarkably reduced, being considerably lower than the diesel-only solution. • For each Mf = ct configuration there is a minimum cost area, which leads to lower battery capacity and wind turbine rated power as the diesel-oil penetration increases. This situation is quite different for a 20 year time horizon. More precisely, even the autonomous wind power solution is less expensive than the dieselonly system operation (Fig. 4.18). Additionally, the 20 year system cost diminishes as the oil penetration increases. This situation is inverted after a minimum cost point is achieved (Fig. 4.19). Thus the optimum configuration system is based on a 2.5 kW wind turbine, 1700 A h battery capacity, 300 kg/year diesel-oil consumption, while the corresponding 20 year cost in present values is 26 300C, less than 50% of the one corresponding to the diesel-only solution. Using Fig. 4.19, the optimum diesel oil contribution that minimizes the 10 year system cost can also be estimated. Hence, the corresponding optimum configuration for the specific hybrid system under investigation is based on a 2 kW wind turbine and a battery bank of 1000 A h, while the annual fuel consumption is 500 kg/year and the minimum 10 year cost approximates 17 300C in present values, being less than 60% of the dieselonly solution (≈29 300C).

140 000

Mf = 0 (kg/year) Mf = 100 (kg/year) Mf = 250 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year) Diesel only system

Twenty years cost (?)

120 000 100 000 80 000 60 000 40 000 20 000 0 0

2000

4000

6000

8000

10 000

12 000

14 000

Wind turbine rated power (W)

4.18 Twenty year cost analysis of a wind–diesel hybrid system, Andros.

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10 000 Andros–10 year cost Andros–20 year cost Kea–10 year cost Kea–20 year cost

Mean annual cost (E)

9000 8000 7000 6000 5000 4000 3000 2000 1000 0

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Annual diesel-oil consumption (kg/year)

4.19 Comparison of mean annual cost of typical wind–diesel hybrid configurations on 10 year and 20 year basis.

Finally, for comparison purposes Fig. 4.19 shows the corresponding 10 and 20 year financial results for Kea. Actually, considering the calculation results regarding two extreme wind potential cases, a HEW stand-alone system presents significant cost advantages in comparison with diesel-only or wind power stand-alone systems. These advantages are more obvious for high wind potential areas and for long-term operation. More specifically the mean annual cost for Andros is almost 40% of the corresponding cost of Kea; see Fig. 4.19. Additionally, the minimum 10 year annual cost for Andros is 1700C for Mf ≈ 500 kg/year, while the corresponding value for Kea is 2800C, while the annual diesel-oil consumption is approximately 1250 kg/year. The electricity production cost difference is more obvious in the case of low diesel-oil penetration, due to the wind potential variation between the two islands examined. The same behaviour is also valid for the 20 year operation of the wind– diesel installation. It is interesting to note that the optimum system configuration for both islands is realized for lower diesel-oil penetration than the 10 year optimum solution. On top of this, as time passes the mean annual cost becomes lower for both islands, hence the corresponding 20 year mean annual value is 1300C and 2200C respectively. Note that the optimum solution is moving towards lower diesel-oil contribution values, underlining the competitive advantage of power stations based on renewable energy sources, provided that a life-cycle cost analysis is considered. Interesting conclusions may also be drawn by analysing the 10 year minimum cost distribution (see Fig. 4.20). This figure shows that for low diesel-oil penetration the main cost contribution is due to the high battery cost (including the variable M&O cost battery replacement) and the fixed

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Feasibility assessment for stand-alone and HEW systems Wind turbine cost Battery cost M&O cost Variable M&O cost

40 000

Total cost analysis (E)

35 000

141

Electronic equipment cost Installation cost Fuel cost

30 000 25 000 20 000 15 000 10 000 5000 0

0

25

75 100 150 200 250 500 1000 2000 50 Annual diesel-oil consumption (kg/year)

4.20 Total 10 year cost analysis of a typical wind–diesel hybrid system, high wind potential case.

M&O cost. The wind turbine purchase cost contribution cannot be disregarded, as it represents approximately 15% of the total system cost. However, for high diesel-oil penetration, the diesel-oil purchase cost represents over 50% of the entire system cost. On top of this, for optimum system configurations the diesel-oil and the battery bank correspond to 40% and 35% of the total system cost in Andros. Recapitulating, one may state that in this area of high wind potential, a wind–diesel hybrid system presents a competitive advantage in comparison with a diesel-only or a wind-based stand-alone system. The optimum system configuration is based on 2–2.5 kW wind turbines and 1000–1700 A h battery capacity. These solutions save over 75% of the fuel required by a diesel-only system annually in order to obtain full energy autonomy of the installation, while the corresponding 10 or 20 year total operational cost ranges between 60% and 50% of the diesel-only solution respectively.

4.8.6 Electricity generation cost of a HEW stand-alone system The last case study examined concerns the electricity generation cost of a typical stand-alone HEW system. The analysis above (Section 4.6) is applied (Kaldellis and Kavadias, 2007) to typical remote consumers located in Andros. In this context Fig. 4.21 demonstrates the energy production cost of the examined stand-alone system (rated power up to 15 kW) for a 10 year service period of the installation. According to the results obtained, for each Mf value there is a minimum production cost point, which corresponds to a specific system configuration (No, Qmax) that guarantees the

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3.0

Mf = 0 (kg/year) Mf = 50 (kg/year) Mf = 100 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year) Diesel only system

Electricity cost (E/kW h)

2.5 2.0 1.5 1.0 0.5 0 0

2000

4000

6000

8000

10 000

12 000

14 000

Wind turbine rated power (W)

4.21 Ten year electricity production cost of a wind–diesel hybrid system, Andros.

remote consumer energy autonomy at a minimum electricity production cost. Also, one may observe that by increasing the contribution of diesel-oil, a remarkable cost decrease is initially encountered. However, after the 500 kg/year value, the corresponding electricity production cost starts to increase, designating the existence of an optimum configuration. Figure 4.21 also shows the diesel-only and the wind-power (Mf = 0) stand-alone systems, both presenting a higher operational cost. Finally, the optimum stand-alone system electricity production cost is below 0.5C/kW h, a value directly comparable with the operation of bigger diesel-only autonomous power stations in several Greek islands (Kaldellis and Zafirakis, 2007). The situation is slightly improved for a 20 year operation (Fig. 4.22), since even the wind-power stand-alone solution (Mf = 0) is more financially attractive than the diesel-only installation. In Figs 4.23 and 4.24 one may find the calculated (Kaldellis and Kavadias, 2007) electricity production cost variation as a function of the wind turbine rated power, for 5, 10, 15 and 20 years of the hybrid system operation and for a low (Mf = 100 kg/year) and a high (Mf = 500 kg/year) annual diesel oil contribution. In both cases one may observe that there is a remarkable electricity cost decrease with the increase of the installation service period, especially in cases of high fossil fuel participation. Accordingly, in Fig. 4.25 the minimum electricity production cost distribution versus the annual oil quantity consumed for various operational periods of the installation is demonstrated. After a thorough investigation of Fig. 4.25 one may state the following:

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Feasibility assessment for stand-alone and HEW systems

Electricity cost (?/kW h)

2.5

143

Mf = 0 (kg/year) Mf = 50 (kg/year) Mf = 100 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year) Diesel only system

2.0

1.5

1.0

0.5

0 0

2000

4000

6000

8000

10 000

12 000

14 000

Wind turbine rated power (W)

4.22 Twenty year electricity production cost of a wind-diesel hybrid system, Andros. Hybrid station electricity production cost variation vs system service period (low oil contribution, Mf = 100 kg/year) 5.0 5 year operation 10 year operation 15 year operation 20 year operation

4.5 Energy cost (?/kW h)

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0 0

2000

4000

6000

8000

10 000

12 000

14 000

16 000

Wind turbine rated power (W)

4.23 Electricity production cost values for variable hybrid system service periods.

•

•

For zero (wind only) or low diesel-oil contribution cases there is a considerable cost decrease between 5 and 10 years and between 15 and 20 years of system operation. The cost decrease between 10 and 15 years is quite small, owing to the increase of the variable M&O cost contribution, e.g. replacement of the necessary major components of the installation.

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Stand-alone and hybrid wind energy systems Hybrid station electricity production cost variation vs system service period (high oil contribution, Mf = 500 kg/year)

1.8 1.6 Energy cost (?/kW h)

1.4 1.2 1.0 0.8 0.6 5 year operation 10 year operation 15 year operation 20 year operation

0.4 0.2 0 0

2000

4000

6000

8000

10 000

12 000

14 000

16 000

Wind turbine rated power (W)

4.24 Electricity production cost values for variable hybrid system service periods. Electricity production cost vs annual diesel-oil consumption for 5, 10, 15 & 20 years operation (Skiros island)

Electricity cost (?/kW h)

1.8

10 year operation 15 year operation 5 year operation 20 year operation

1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0 0

200

400

600

800

1000

1200

1400

1600

1800

2000

Annual fuel consumption (kg/year)

4.25 Life-cycle hybrid system minimum electricity production cost vs annual diesel-oil consumption.

•

•

The minimum electricity production cost is remarkably decreased between the fifth and the tenth year of operation of the system, being accordingly almost constant up to the twentieth year of operation. There is a significant optimum annual oil consumption decrease (approx. 300 kg/year) when the desired service period of the hybrid station

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145

increases from 5 to 20 years, leading also to remarkable environmental benefits. In all cases examined, the optimum life-cycle electricity production cost of the wind–diesel system investigated is quite lower than the corresponding value of the small thermal power stations already operating in several tiny Greek islands.

Recapitulating, the proposed wind–diesel–battery (HEW) stand-alone system is a financially viable electricity generation solution that can meet the electricity needs of remote consumers located throughout the Aegean Sea.

4.9

Sensitivity analysis of the financial behaviour of stand-alone hybrid electricity generation windbased (HEW) systems

In the following some representative results concerning the financial behaviour of wind-based stand-alone hybrid systems are demonstrated. For this purpose detailed sensitivity analysis results are taken into consideration.

4.9.1 Sensitivity analysis of the financial behaviour of a wind-only stand-alone system Impact of wind potential In an attempt to investigate the impact of the wind potential on the optimum (minimum 10 year total cost) solutions obtained, two additional cases are analysed on top of the Kithnos island one (Fig. 4.9), using 3 years’ detailed wind speed and meteorological data. More specifically, for the Andros island case (high wind potential, annual average wind speed approximately 9.5 m/s), the corresponding no-load rejection curve is given in Fig. 4.26 along with the corresponding 10 year total cost curves. The minimum 10 year cost energy autonomy wind power-based solution consists of a 5.5 kW wind converter and a 6700 A h battery size. Accordingly, for the island of Kea (medium quality wind potential, annual mean wind speed equal to 5.5 m/s), the minimum 10 year operational cost solution demands (Fig. 4.27), higher wind turbine rated power (19 kW) than Andros and 20 000 A h battery capacity. Recapitulating, according to the results obtained, one may state that both the wind turbine rated power (and cost) and the battery purchase and replacement costs become much greater as the mean average wind speed value of the HEW system location decreases.

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Stand-alone and hybrid wind energy systems Andros 10 year energy production cost (i = 9%, g = 4%, rb = 0)

16 000

80 000 E 90 000 E 100 000 E Energy autonomy points

14 000

Qmax (A h)

12 000 10 000 8000 6000 4000 2000 0 3

5

7

9 No (kW)

11

13

15

4.26 No-load rejection configuration on the basis of minimum 10 year cost, Andros.

Kea 10 year energy production cost (i = 9%, g = 4%, rb = 0) 90 000 200 000 E 250 000 E 300 000 E Energy autonomy points

80 000 70 000 Qmax (A h)

60 000 50 000 40 000 30 000 20 000 10 000 0 4

6

8

10

12

14

16

18

20

No (kW)

4.27 No-load rejection configuration on the basis of minimum 10 year cost, Kea.

Impact of subsidy The Greek state and the European Commission support clean energy production applications with 20–60% grants, based on the exploitation of available renewable energy resources. This subsidy is given as a percentage of the first installation cost, since all the renewable energy applications are

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Kithnos 10 year energy production cost (i = 9%, g = 4%, rb = 0, y = 0) 60 000 200 000 E 250 000 E 265 000 E 300 000 E Energy autonomy points

Qmax (A h)

50 000 40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.28 No-load rejection configuration on the basis of minimum 10 year cost, zero initial cost subsidization.

characterized as capital intense ones. Any financial measures taken in favour of renewables only partly quantify (Kaldellis, 1993b, 1997) the remarkable socio-environmental benefits resulting from the substitution of fossil fuels by renewable energy-based power stations. In an attempt to estimate the impact of state subsidization on the minimum 10 year cost solution obtained for Kithnos (Fig. 4.9), the no-subsidization calculation results concerning the 10 year operation of the proposed stand-alone wind power configuration are given in Fig. 4.28. From this figure, it is apparent that cancellation of the subsidization schemes leads to slightly larger wind turbines (∼15 kW) and smaller batteries (14 000 A h), while the corresponding minimum C10 solution approaches 179 000C in present values (Table 4.4). Impact of the improvement of battery technology The battery is one of the most important parts (Drouilhet et al., 1995; Kavadias and Kaldellis, 2000) of an energy autonomous wind power system, (a) storing the energy surplus during windy days for use during high consumption and low wind speed periods, and (b) maintaining the voltage in the system. Besides, the batteries used represent a remarkable percentage rb (up to 80%) of the complete system’s initial cost, hence any technological improvement concerning this sector will significantly ameliorate the economic behaviour of the entire system. In this context, by introducing a 10% annual improvement (ρb = 0.1) of commercial lead–acid batteries

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Stand-alone and hybrid wind energy systems Kithnos 10 year energy production cost ( i = 9%, g = 4%, rb = 0) 60 000 100 000 E 150 000 E 170 000 E 200 000 E Energy autonomy points

Qmax (A h)

50 000 40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.29 No-load rejection configuration on the basis of minimum 10 year cost, battery technology improvement incorporated.

operational characteristics, the medium-term optimum solution is realized (Fig. 4.29), using 12–13 kW wind turbines and approximately 16 000 A h batteries capacity. The corresponding 10 year operational cost is now 114 850C at present value (Table 4.4). Impact of the local economy One of the most important parameters describing the local market situation is the corresponding annual inflation rate. More precisely, the inflation rate expresses the tendency of everyday life cost to increase and it is quantitatively approximated by the average rise in price levels. Also, the value of the inflation rate greatly influences the corresponding capital cost index, since usually the capital cost is the sum of the inflation premium, the pure time-preference and the risk premium (Myddelton, 1995; Kaldellis, 2000). Thus, by using the values experienced within the local economy during the previous decade (1990–99), i.e. i = 18%, g = 10%, the calculation results are summarized in Fig. 4.30. Here, it is obvious that the optimum (10 year minimum cost) solution tends to higher wind turbine rated power (N *o → 17 kW) and lower battery capacity (Q* max → 13 000 A h) values in comparison with the results of Fig. 4.9. However, although the 10 year total cost of the system seems much higher (400 000C) than the present economy situation (200 000C), there is no substantial differentiation in constant values (154 000C versus 135 000C; Table 4.4).

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Kithnos 10 year energy production cost (i = 18%, g = 10%, rb = 0) 60 000 300 000 E 350 000 E 400 000 E 450 000 E Energy autonomy points

Qmax (A h)

50 000 40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

17

No (kW)

4.30 Zero-load configuration on the basis of minimum 10 year cost, local market capital cost impact. Table 4.5 Central values of the main parameters used in the presented sensitivity analysis Parameter Annual mean wind speed (Andros) Return on investment Diesel-oil current price Diesel-oil price annual escalation rate Initial investment cost Fixed M&O cost coefficient Local market annual inflation rate Electricity price annual escalation rate

Symbol _ V i co e IC0 m g p

Numerical value

Units

9.5 8 1.5 6 Equation (4.7) 2 2 3

m/s % C/kg % C % % %

4.9.2 Sensitivity analysis of the financial behaviour of a HEW stand-alone system In the last case study the impact of the key parameters on the electricity production cost of a HEW stand-alone system is examined as a function of the annual diesel-oil consumption. For this purpose, the central values of the problem governing parameters are included in Table 4.5. Impact of wind potential As stated above, several representative types of wind potential have been investigated by Kaldellis and Kavadias (2007); see also Fig. 4.31. According to the results obtained, the wind potential impact is dominant (Fig. 4.32),

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16 Andros Island

Naxos Island

Skiros Island

Kea Island

Wind speed (m/s)

14 12 10 8 6 4 2

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4.32 Wind potential impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

since the corresponding electricity production cost remarkably decreases as the wind potential improves. This difference is increasing for low dieseloil penetration, while above the value of Mf = 1000 kg/year all distributions are convergent towards Kea (the lowest wind potential case) curve. It is important to note the significant difference between Andros and Kea, since

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– in anticipation of Mf = 600 kg/year annual diesel oil consumption – the electricity production cost in Kea is more than double the corresponding value of Andros. Finally, one may easily conclude that as the available wind potential becomes more intense, the minimum electricity production costpoint is moving towards a lower diesel-oil contribution. Return on investment index Generally speaking, the return on investment depends on the local market economic wealth and more precisely on the existing investment opportunities, timing of repayment, risk of the investment and any government subsidies (Kaldellis, 2000). In addition, its numerical value varies with the inflation rate of the economy, in order to obtain a positive inflation-free return on investment index. According to the data of Fig. 4.33 – concerning the electricity production cost of a wind-diesel system situated in Andros; see also Table 4.5 – the return on investment index is directly proportional to the electricity generation cost value. Besides, the return on investment index has a greater influence on low diesel-oil penetration cases, owing to the bigger initial capital invested. On the other hand, for diesel-only installations the corresponding impact is minimized. Finally, for every 1% increase of the return on investment index, the corresponding electricity production cost increase is approximately 0.006C/kW h, and for diesel-oil annual consumptions below 600 kg/year.

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1.2

Diesel-oil cost: co = 1.2 (€/kg) Diesel-oil cost: co = 1.5 (€/kg)

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4.34 Diesel-oil current price impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

Current diesel-oil price The exact value of the current diesel-oil price takes into account not only its market price, but also its transportation and storage cost, which is quite high for stand-alone consumers located in remote islands. In this context, high prices lead to relatively higher electricity production cost values, especially in cases of significant diesel-oil contribution (Fig. 4.34). As a result, the impact is dominant on diesel-oil penetrations exceeding 1000 kg/year, while it is almost negligible for annual diesel-oil consumptions below 200 kg/year, underlining thus the fossil-fuel independence of similar standalone systems based mainly on renewable energy sources. Annual escalation rate of the diesel-oil price The term ‘diesel-oil price annual escalation rate’ is used here to describe the gradual changes of the diesel-oil price annually. As it is obvious from Fig. 4.35, regarding Andros, the electricity production cost of the standalone system investigated is strongly influenced by the corresponding annual escalation rate, in cases of considerable diesel-oil annual consumption. More precisely, the electricity production cost is increased as the diesel-oil escalation rate is amplified. Thus, for each 3% increase of e, the corresponding ce increase is almost 0.1C/kW h. Zero impact is encountered on annual diesel-oil consumption beneath 200 kg/year.

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10 year cost analysis 1.2 Oil escalation rate: e = 3%

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4.35 Diesel-oil price annual escalation rate impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

Battery ex-works price The battery bank is one of the most important components of stand-alone systems, storing the wind energy surplus of high wind speed periods, in order to cover the energy deficit in low wind speed and high-load demand cases. As already stated, a typical lead–acid battery should be replaced every 4–7 years; an extra expense. The battery price impact becomes more important when the diesel-oil contribution to electricity generation is low (less than 500 kg/year) (Fig. 4.36). As expected, the electricity production cost gets lower as the battery price reduces. On the other hand, the battery cost impact is approaches zero as the annual diesel-oil consumption tends to the diesel-only solution. Installation turnkey cost The initial investment cost (turnkey cost) includes the ex-works price of the equipment needed (wind turbines, battery bank, electronic equipment, etc.) and the corresponding installation cost. The application of new technological achievements and the economies of scale decrease most system component prices in the international market. However, several parameters have to be taken into account, in order to foresee the future evolution of the ex-works prices in the local market. According to the results by Kaldellis and Kavadias (2007) (Fig. 4.37), the electricity production cost grows as the turnkey cost of the installation increases. This impact is higher

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1.2 Battery price base scenario (–20%)

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10 year cost analysis 1.2 Turnkey cost base scenario (+10% )

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4.37 Investment turnkey price impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

for medium–low diesel-oil penetration (up to 600 kg/year) while for higher diesel-oil contribution the corresponding influence is quite restrained. In this context, the electricity production cost decreases by almost 0.07C/kW h for the Andros stand-alone system for each 10% decrease in the installation turnkey cost.

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Conclusions

An integrated cost–benefit model is developed that is able to evaluate the financial behaviour of hybrid electricity generation wind-based systems on a long-term operational schedule. The proposed cost–benefit analysis is an integrated tool for the financial evaluation of similar projects, since it suggests calculation methods for each of the system parameters. For this purpose the existing information concerning the initial investment and the maintenance and operation cost of similar small power stations are taken into account. The developed method leads to the estimation of • the pay-back period, • the financial efficiency, • the net present value (NPV) and • the internal rate of return (IRR) of any HEW installation. The main parameters to be predicted are the wind turbine rated power, the corresponding battery capacity and the annual oil consumption required in order to guarantee energy autonomy of the entire stand-alone installation. Subsequently, the reliability impact on the total cost function is investigated, while emphasis is also placed on the socioenvironmental benefits of stand-alone HEW systems. Furthermore, in order to obtain a realistic comparison between the existing fossil fuel-based solutions and the proposed HEW installations, the electricity generation cost is estimated, taking the desired service period of the complete installation into consideration. The last part of the chapter is devoted in an attempt to demonstrate the clear competitive advantages of the proposed HEW solution for selected representative case studies. More precisely, the proposed theoretical model is applied to several typical wind potential cases, possessing annual mean wind speed values between 6.0 and 10 m/s. For all cases investigated, the predicted electricity production cost compares favourably with today’s real electricity production cost data, resulting from the operation of existing autonomous diesel-only power stations. Finally, a quite extensive sensitivity analysis has been carried out, in order to demonstrate the impact of the main techno-economic parameters on the energy production cost of optimum sized HEW power stations. According to the results obtained, one should point out that there is a remarkable decrease in diesel-oil consumption as the desired service period of the hybrid station increases, in order to minimize the corresponding lifecycle electricity production cost. The possibility of remarkably reducing the energy storage requirements of wind-based stand-alone systems by adding a rational number of PV panels has been investigated. On the basis of the calculation results, utilizing long-term real field measurements, one may

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definitely state that the introduction of a rational number of PV panels in a wind-based stand-alone system remarkably decreases the system energy storage requirements, improves the entire installation reliability, simplifies the corresponding maintenance procedure and strengthens the financial competitiveness of similar renewable energy applications. In view of the uncertain future concerning the oil prices worldwide and associated fossil fuel consumption environmental concerns, an increasing interest in hybrid power stations is being shown in many regions worldwide. Taking into account the detailed and extensive analysis carried out, HEW systems may be the most cost-effective electrification solution for numerous isolated consumers, located in regions of fairly good wind potential. On top of this, subsidy possibilities – granted for example by local authorities or via European Union funds – should greatly increase the economic attractiveness of similar environmentally friendly electricity production applications. Recapitulating, the outcomes of the present chapter are the development of a complete cost–benefit analysis of the proposed system that can be a valuable tool for the evaluation of any similar project and the result that a properly sized HEW system is a motivating prospect for the energy demand problems of numerous existing isolated consumers all around the world.

4.11

References

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Kaldellis, J.K., 2002d. Renewable energy sources and the reduction of air pollution: risk assessment in Greece. In: International Conference on the Protection and Restoration of the Environment VI, July 1–5, Skiathos Island, Greece. Kaldellis, J.K., 2003a. Feasibility evaluation of Greek State 1990–2001 wind energy program. Energy Journal, 28, 1375–1394. Kaldellis, J.K., 2003b. An integrated feasibility analysis of a stand-alone wind power system, including no-energy fulfillment cost. Wind Energy Journal, 6, 355–364. Kaldellis, J.K., 2003c. Optimum techno-economic energy-autonomous photovoltaic solution for remote consumers throughout Greece. Journal of Energy Conversion and Management, 45, 2745–2760. Kaldellis, J.K., 2004. Parametric investigation concerning dimensions of a standalone wind power system. Journal of Applied Energy, 77, 35–50. Kaldellis, J.K., 2007. The contribution of small hydro power stations to the electricity generation in Greece: technical and economic considerations. Energy Policy Journal, 35, 2187–2196. Kaldellis, J.K., Gavras, T.J., 2000. The economic viability of commercial wind plants in Greece. A complete sensitivity analysis. Energy Policy Journal, 28, 509–517. Kaldellis, J.K., Kavadias, K.A., 2001. Optimal wind-hydro solution for Aegean Sea islands electricity demand fulfillment. Journal of Applied Energy, 70, 333–354. Kaldellis, J.K., Kavadias, K.A., 2004. Evaluation of Greek wind parks visual impact: ‘The Public Attitude’. Fresenius Environmental Bulletin, 13, 413–423. Kaldellis, J.K., Kavadias, K.A., 2007. Cost–benefit analysis of remote consumers’ electrification on the basis of hybrid wind–diesel power stations. Energy Policy Journal, 35, 1525–1538. Kaldellis, J.K., Kodossakis, D., 1999. The present and the future of the Greek wind energy market. In: European Wind Energy Conference and Exhibition, March 1–5, Nice, France. Kaldellis, J.K., Kondili, E., 2006. Environment and Industrial Development, Volume II, 1st Edition. Stamoulis, Athens. Kaldellis, J.K., Tsesmelis, M., 2002. Integrated energy balance analysis of a standalone wind power system, for various typical Aegean sea regions. Wind Energy Journal, 5, 1–17. Kaldellis, J.K., Vlachos, G., 2005. Optimum sizing of an autonomous wind-diesel hybrid system for various representative wind-potential cases. Applied Energy Journal, 83, 113–132. Kaldellis, J.K., Zafirakis, D., 2007. Present situation and future prospects of electricity generation in Aegean archipelago islands. Energy Policy Journal, 35, 4623–4639. Kaldellis, J.K., Ktenidis, P., Kodossakis, D., 1989. Small size wind energy systemsfeasibility study for the Greek socio-economic environment. In: 2nd European Symposium on Soft Energy Sources and Systems at the Local Level, October 16–21, Crete, Greece. Kaldellis, J.K., Ktenidis, P., Papadopoulos, E., 1991. Future possibilities and aerodynamic limits for the design of advanced wind turbine blades. In: 3rd European Symposium on Soft Energy Sources and Systems at the Local Level, September 11–14, Chios, Greece.

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Kaldellis, J.K., Doumouliakas, J., Michalis, K., 2000. Optimum stand-alone PV solution, including financial aspects. In: World Renewable Energy Congress VI, July 1–7, Brighton, UK. Kaldellis, J.K., Kavadias, K., Christinakis, E., 2001. Evaluation of the wind-hydro energy solution for remote islands. Journal of Energy Conversion and Management, 42, 1105–1120. Kaldellis, J.K., Kavadias, K.A., Neonakis, J.K., 2002. A probabilistic computational method for the economic evaluation of soft energy applications in course of time. In: 4th GRACM Congress on Computational Mechanics, June 27–29, Patras, Greece. Kaldellis, J.K., Kavadias, K.A., Paliatsos, A.G., 2003. Environmental impacts of wind energy applications: myth or reality? Fresenius Environmental Bulletin, 12, 326–337. Kaldellis, J.K., Koronakis, P., Kavadias, K., 2004. Energy balance analysis of a stand-alone photovoltaic system, including variable system reliability impact. Renewable Energy Journal, 29, 1161–1180. Kaldellis, J.K., Kondili, E., Kavadias, K.A., 2005a. Energy and clean water coproduction in remote islands to face the intermittent character of wind energy. International Journal of Global Energy Issues, 25, 298–312. Kaldellis, J.K., Vlachou, D.S., Korbakis, G., 2005b. Techno-economic evaluation of small hydro power plants in Greece: a complete sensitivity analysis. Energy Policy Journal, 33, 1969–1985. Kaldellis, J.K., Kostas, P., Filios, A., 2006a. Minimization of the energy storage requirements of a stand-alone wind power installation by means of photovoltaic panels. Wind Energy International Journal, 9, 383–397. Kaldellis, J.K., Kavadias, K.A., Papantonis, D.E., Stavrakakis, G.S., 2006b. Maximizing the contribution of wind energy in the electricity demand problem of Crete island. Wind Engineering Journal, 30, 73–92. Kaldellis, J.K., Kavadias, K.A., Kaldelli, El., Kondili, E., 2006c. Analyzing the relation between noise-visual impact and the public attitude towards wind energy applications in Greece. In: International Conference of Protection and Restoration of the Environment, July 3–7, Chania, Crete, Greece. Kaldellis, J.K., Kondili, E., Filios, A., 2006d. Sizing a hybrid wind-diesel stand-alone system on the basis of minimum long-term electricity production cost. Applied Energy Journal, 83, 1384–1403. Kaldellis, J.K., Kavadias, K.A., Koronakis, P.S., 2007. Comparing wind and photovoltaic stand-alone power systems used for the electrification of remote consumers. Journal of Renewable and Sustainable Energy Reviews, 11, 57– 77. Kaldellis, J.K., Kondili, E.M., Paliatsos, A.G., 2008. The contribution of renewable energy sources on reducing the air pollution of Greek electricity generation sector. Fresenius Environmental Bulletin, 17, 1584–1593. Kaldellis, J.K., Zafirakis, D., Kavadias, K., 2009a. Techno-economic comparison of energy storage systems for island autonomous electrical networks. Journal of Renewable and Sustainable Energy Reviews, 13, 378–392. Kaldellis, J.K., Zafirakis, D., Kaldelli, El., Kavadias, K., 2009b. Cost benefit analysis of a photovoltaic-energy storage electrification solution for remote islands. Renewable Energy Journal, 34, 1299–1311.

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Kaldellis, J.K., Simotas, M., Zafirakis, D., Kondili, E., 2009c. Optimum autonomous photovoltaic solution for the Greek islands on the basis of energy pay-back analysis. Journal of Cleaner Production, 17, 1311–1323. Kaldellis, J.K., Zafirakis, D., Kondili, E., 2009d. Optimum autonomous stand-alone photovoltaic system design on the basis of energy pay-back analysis. Energy Journal, 34, 1187–1198. Kavadias, K.A., Kaldellis, J.K., 2000. Storage system evaluation for wind power installations. In: Wind Power for the 21st Century International Conference, September 25–27, Kassel, Germany. Kavadias, K.A., Neonakis, J., Kaldellis, J.K., 2000. Economic viability of wind farm investments in Greece, using probabilistic analysis. In: Wind Power for the 21st Century International Conference, September 25–27, Kassel, Germany. Kodossakis, D., Kaldellis, J.K., 1997. A complete time-depending economical viability analysis of wind turbine installations. In: FLOWERS’97 World Energy Conference, July 30–August 1, Firenze, Italy. Konstantinidis, P., Skordilis, A., Kaldellis, J.K., 2001. Recycling of electric and electronic waste in Greece: possibilities and prospects. In: 7th International Conference on Environmental Science and Technology, September 3–6, Syros, Greece. Liu, Y., Ye, L., 2003. Economic performance evaluation method for hydroelectric generating units. Energy Conversion & Management, 44, 797–808. Masakazu, I., Masayuki, S., Shoichi, Y., 2003. Present status and future prospects of small-scale hydropower plants. Fuji Electric Journal, 76, 581–585. Masini, A., Frankl, P., 2002. Forecasting the diffusion of photovoltaic systems in southern Europe. A learning curve approach. Technological Forecasting and Social Change Journal, 70, 39–65. Miles, R.W., Hynes, K.M., Forbes, I., 2005. Photovoltaic solar cells: an overview of state-of-the-art cell development and environmental issues. Progress in Crystal Growth and Characterization of Materials, 51, 1–42. Mondol, J.D., Yohanis, Y.G., Norton, B., 2009. Optimising the economic viability of grid-connected photovoltaic systems. Applied Energy, 86, 985–999. Morrow, H., 2001. Environmental and human health impact assessments of battery systems. Industrial Chemistry Library, 10, 1–34. Moskowitz, P.D., Fthenakis, V.M., 1991. Environmental, health and safety issues associated with the manufacture and use of II–VI photovoltaic devices. Solar Cells, 30, 89–99. Muselli, M., Notton, G., Louche, A., 1999. Design of hybrid-photovoltaic power generator, with optimization of energy management. Solar Energy, 65, 143–157. Myddelton, D., 1995. The Essence of Financial Management. 1st Edition, PrenticeHall, Englewood Cliffs, NJ. Patterson, M.H., Turner, A.K., Sadeghi, M., Marshall, R.J., 1994. Health, safety and environmental aspects of the use of cadmium compounds in thin film PV modules. Solar Energy Materials and Solar Cells, 35, 305–310. Persson, W.K., Ohrstrom, E., 2002. Psycho-acoustic characters of relevance for annoyance of wind turbine noise. Journal of Sound and Vibration, 250, 65–73. Raugei, M., Bargigli, S., Ulgiati, S., 2007. Life cycle assessment and energy pay-back time of advanced photovoltaic modules: CdTe and CIS compared to poly-Si. Energy, 32, 1310–1318.

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5 Stand-alone wind energy systems D. WOOD, University of Newcastle, Australia and P. FREERE, Monash University, Australia

Abstract: This chapter describes small wind turbines and their use in stand-alone power systems with conventional battery storage. Turbine components, such as the generators, blades and towers are reviewed to highlight their influence on turbine performance and safety in standalone operation. A major issue in designing these systems is the proper characterisation of the wind resource, which is complicated by the need to consider the time dependence of the electrical loads to accurately determine the required battery size. Small turbines, less than about 50 kW in rated power, are used for a range of stand-alone applications from small systems for village electrification in developing countries, to larger systems for remote power in western countries. The type and power requirement of the load can have a significant impact on the design of the system and the choice of inverter. Key words: stand-alone power, remote power, wind turbines, battery, system sizing.

5.1

Introduction

The subject of this chapter is stand-alone power systems containing wind generators without photovoltaic (PV) arrays. It is necessary, however, when designing a remote power system to know enough about the relative merits and disadvantages of each renewable technology to make an intelligent choice of which to use. We give some typical comparisons between the two. Most wind-only systems are small and so we describe in considerable detail the technology of small wind turbines. This is done mainly by comparison with large turbines, on the grounds that the reader is likely to be more familiar with that technology, and, if not, it is easier to find information on large turbines than small ones. The International Electrotechnical Commission safety standard for small wind turbines, IEC (2006), defines a small wind turbine as having a rotor area of less than 200 m2, which corresponds to a rated power of 50 kW or less. This is approximately the range we will consider. The turbine’s generator and control system have a large influence on the overall safety and functionality of the complete system. With current microprocessor technology it is possible to integrate turbine control with system 165 © Woodhead Publishing Limited, 2010

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governance, a combination that was not done even in the 1990s. After discussing the control and electronics we consider the design of remote power systems, focusing on the assessment of the power produced and the estimation of the loads to be powered.

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Wind speed and direction changes are usually too great for a wind turbine to generate power and supply it directly to a load. The only exceptions are turbines supplying a load requiring only an average amount of power over, say, several days, such as some water pumping applications. In all other circumstances, energy storage or another energy source is required in combination with the wind turbine. In a stand-alone system, the most common energy storage is batteries, but flywheels (Fig. 5.1) and thermal storage are possible. Energy storage using batteries is expensive but can be reduced by adding in other energy sources such as PV systems or diesel generators. Systems with diesel generators do not need to use energy storage at all and the wind turbine reduces the diesel fuel use. Many parts of the world have neither grid power nor secure access to conventional fuels. Renewable energy systems of wind turbines and/or PV

5.1 PowerStore Flywheel System in a wind diesel system at Ross Island, the Antarctic [http://www.pcorp.com.au/index.php?option=com_ content&task=view&id=161&Itemid=198, viewed 21 April 2009].

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5.2 A small wind–PV system. MG4520 200 W wind turbine with 40 W of PV panels. The vibration of the tower caused a resonance in the PV panel frame, and so they were moved from the wind turbine tower.

systems may then be the preferred choice for remote power. We now briefly compare the two technologies. For stand-alone systems, the choice of wind turbines, PV, or a combination (Fig. 5.2) relates directly to the cost of producing the energy and storing it when there is no wind or sunlight. This simple rule can have different outcomes depending on the climate. For example, where the wind and solar resource are reasonably uniform over the year, such as much of the Australian east coast, then the choice of energy source may well be based only on cost. On the other hand, areas such as West Bengal and parts of Bangladesh have calm, sunny days in ‘winter’ and windy, cloudy days during the monsoon and therefore require hybrid systems. A detailed cost comparison of wind-only and PV-only power systems for the Greek islands, where both resources are abundant, has been given in Kaldellis et al. (2007). The other major consideration is the environment where the energy is to be produced. It may not be practicable to have rotating blades in some built-up areas due to noise or proximity to buildings or people, or rare birds.

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Reliability may be another consideration – anything that rotates will always need more maintenance than stationary generators such as PV. PV, however, does need maintenance – cleaning is an obvious one. Keeping plants and trees from growing too high and shading the panels is another. Water ingress can also be a problem. Wind turbines will suffer wear of the moving parts and fatigue of cyclically stressed parts (e.g. blades), but PV panels will suffer erosion of the glass from dust and sand, and may suffer from excessive wind loading due to their large surface area. PV panels and turbine towers with guy-wires are susceptible to vandalism. If incremental expansion of the system is a future possibility, then PV must be considered seriously, as it can be incrementally enlarged by adding a few more panels and batteries and possibly another inverter to the grid. To increase the output of a wind turbine, usually requires adding another wind turbine giving a large increase in power production at a high price. The total generating capacity of small wind turbines sold in the USA in 2007 was 9.7 MW for US$42M (AWEA, 2008). This represents an average cost of US$4.33 per watt. The cost varies between US$3 and US$5 per watt and the cost per kW h of production is between US$0.10 and US$0.15. Routine maintenance is quoted as US$0.01 to US$0.05 per kW h. Table 5.1 compares typical costs of small wind and PV electricity. However, we warn the reader that comparative costing is site specific. It is very sensitive to the energy source quality and the ease of installation. If the site is sufficiently windy, then it is cheaper to install a wind turbine than PV panels, but if needed, PV panels can usually be added quite simply.

5.2.1 Pre-feasibility analysis To determine the basic suitability of a stand-alone wind power system, it is necessary to have estimates of the electrical load and the power supply. The existing or future load in kW h per day can be estimated by summing the Table 5.1 Comparative costs of small wind turbine and PV electricity, taken from AWEA (2008) Small wind

Solar PV

Residential (on- or off-grid 2 kW system) US$ per W of capacity US$ per kW h of production (cost of energy)

$3–5 $0.10–0.15

$9 $0.40

Commercial scale (on-grid 50 kW system) US$ per W of capacity US$ per kW h of production (cost of energy)

$3–5 $0.10–10.15

$6.80 $0.27

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power requirements in kW multiplied by the number of hours per day that each appliance is to be used. It is then necessary to estimate the kW h per day that will be produced by the turbine. The site for the wind turbine of a stand-alone power system is largely determined by the location of the users but there may be flexibility in choosing the specific site. Wind sites that are many kilometres from the user are not useful owing to the extra losses on the long electrical power transmission wires or costs incurred in using step-up (at the turbine) and step-down transformers (at the load). The transformers would allow transmission at a higher voltage, thereby reducing the electrical current and hence the transmission losses. Particularly good sites are often characterised by vegetation leaning away from the prevailing wind direction. Local pastimes such as kite flying or sailing, also give a good indication of windiness – as do walled gardens to protect the garden from the wind. However, anecdotal evidence of windiness can often be skewed by the memory of major wind storms, rather than indicating a general windiness. It is advised that wind measurement be undertaken if no wind speed data are available, or a small wind turbine be installed to ascertain whether it is windy enough to be worth considering further. For many small systems and in developing countries in particular, there may be very limited information on available wind speeds.

5.2.2 Modelling and simulation of stand-alone wind energy systems Manufacturers of larger wind turbines often supply a power versus wind speed curve for the turbine. Some small wind turbine manufacturers supply only the wind speed at which the turbine delivers rated power, in which case it is necessary to use an approximate curve from a similar turbine. Ten minute averages of the wind speed are combined with the wind turbine power curve to determine the energy output of the system over the period covering the averages. A spreadsheet program can be written to perform this. If a Weibull curve for the probability distribution of the 10 min wind speed in the area is applicable, or can be assumed, then it can be used along with the average wind speed to determine the energy output in kW h per day for the period. The Canadian National Research Council RETscreen website (http://www.retscreen.net/) has downloadable, free software to determine the average energy production as described above, along with a large database of wind speed data from around the world, and the power curves for a number of small wind turbines. These issues are considered in more detail in Section 5.6.

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5.3

Small wind turbine technology

In this section we describe the basic components of small wind turbines mainly by contrast to those of the better known large turbines. However, there are aspects of turbine operation that are common to turbines of all sizes. The main operating parameter is the tip speed ratio, TSR, defined as the circumferential velocity of the blade tips divided by the wind speed. The TSR controls the blade aerodynamics, in particular the angle of attack of the airflow over the blades. This, in turn, sets the lift : drag ratio and therefore the power output (Burton et al., 2001). Most turbines operate with a TSR between 5 and 10, with the lower values typical of three or more blades and the higher values of two blades. Generally it is preferable to operate at constant TSR as wind speed varies, which is the approximate behaviour of the turbine data in Fig. 5.3, and, ideally, that TSR gives optimum power extraction efficiency. It can be a challenging control problem to maintain the optimum TSR as the wind speed varies in the absence of an anemometer – these are usually too expensive for small turbines. Part of a typical power curve (power output versus wind speed) is shown in Fig. 5.3. The major differences between large and small turbines usually occur near the ‘cut-in’ wind speed, the lowest at which power is produced and at the top end where small turbines tend to have a lower ‘rated’ wind speed and the differences in safety mechanisms become important.

5.3.1 The generator The ideal wind turbine generator would start producing power as soon as the wind blew, and its output power would rise with the increase in power as the wind increases. If the output of the generator were shorted, the 6

400 TSR

5

300 4

250 200 150

3 Output power

TSR

Output power (W)

350

2

100 1

50 0

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Wind speed (m/s)

5.3 Measured wind turbine power curve of a MG4520 200 W wind turbine in a wind tunnel.

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generator would always slow the turbine to a halt without harming the generator. Finally, the ideal generator must be controllable so that it provides ‘maximum power point tracking’, MPPT, allowing the maximum energy to be extracted. Wind and generator speed In practice, a generator will always have a minimum speed before it produces enough voltage to feed power into a load, such as a battery or an electrical grid. Trying to reduce the speed at which the generator can operate rapidly becomes worthless, as at low wind speeds, there is so little energy anyway (Fig. 5.3). As the generator increases speed, its output voltage will increase and it will feed extra power into the load. However, rarely will it automatically match the optimum power/speed characteristics of the rotor so as to extract the maximum amount of energy from the blades and wind. Generally, for stability reasons, a wind turbine and its generator will be designed so that its speed is higher than the optimum speed, thereby reducing the chance of stalling. Types of generators Most small stand-alone wind turbines (typically under 5 kW) use permanent magnet generators, PMGs Figs 5.4 and 5.5. They have the advantage of not requiring external excitation. PMGs are the simplest to use as they need only a rectifier to produce a direct current (DC) voltage for a battery, but magnets are brittle and many are temperature sensitive. Synchronous generators (like car alternators) are similar to PMGs, but need a field current to produce the magnetic field, so are intrinsically less efficient. Furthermore, at low wind speeds, the energy from the wind may not be enough to provide the field winding current. Nevertheless, it is

5.4 Simple permanent magnet generator (PMG). Left: rotor. Right: one of three stator windings.

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5.5 Ironless stator between permanent magnet rotors from a Rutland 910 turbine.

possible to get satisfactory performance from these generators if they are connected through a rectifier to batteries. However, if they are connected to an AC load with a constant frequency, the speed of the turbine must be held constant and the efficiency is compromised unless a rectifier and inverter are added. Both squirrel cage and doubly excited induction generators are AC generators. They can, with a suitable controller, charge batteries. Self-excitation reduces their efficiency, but the squirrel cage induction generator in particular is very robust. However, the output power of both generators is usually very sensitive to both the generator speed and the load level. Hence they require a sophisticated controller to keep the voltage and power output within useful bounds. Squirrel cage induction machines have neither brushes nor cogging torque, but often require a gearbox as they often rotate too rapidly to match the optimum TSR of the rotor. Three phase generators are usually preferred to single phase generators, as the former are smaller and usually cheaper for the same power output. All the generators mentioned above produce alternating current, AC. It is possible to get DC generators, but they use a commutator and brushes and require regular maintenance of the brushes and commutator, hence they are generally not suitable for small wind turbines. Other generator types are usually too specialised and expensive for small turbines, although this may change as different technologies mature and other generator manufacturers enter the market. Friction and cogging torque Some generators require a positive torque to get them turning and there may be additional frictional torque in the drive train, particularly if a gearbox is used. For whatever reason, a turbine not turning when the wind is blowing suggests a faulty or bad design, and damages the reputation of the manufacturer. Even if the turbine is turning, it may not be generating

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sufficient voltage to feed a battery or AC supply. However, this situation is usually less obvious to spectators. The resistive torque may be caused by: • • • •

high friction bearings (especially sealed bearings before being run in); brush friction; cogging torque, especially in permanent magnet generators; oil seals between generator and gearbox.

As noted above, PMGs are preferred for small turbines as they do not need gearboxes and have no brushes. Depending on the design, they may have significant cogging torque. Cogging torque is not inevitable, but a sophisticated magnetic design is required to minimise it. One technique to remove cogging torque is to use an iron-free rotor or stator. Often this entails generator windings embedded in resin, with no iron path for the magnetic flux through the rotor. This certainly removes the cogging torque, but reduces the magnetic flux and can lead to overheating of the windings as much of their heat dissipation is now through resin.

5.3.2 Other features of small turbines In comparison to large turbines, smaller turbines usually: • • • •

operate at higher rotational speeds for the same wind speed; have a tail fin to orient them into the wind which can lead to large gyroscopic (precessional) loads on the blades and main shaft; have smaller tower heights and experience lower average wind speeds; employ simpler and cheaper safety systems which can compromise their ability to withstand high winds. In this section we concentrate on the aerodynamic aspects of safety.

The higher rotational speed means a higher maximum frequency of blade rotation and a greater opportunity for exciting tower resonances and a larger number of blade fatigue cycles per unit time. Small blades also experience higher centrifugal loads as these depend on the product of the tip radius and the square of the rotational speed. Tail fins and yaw behaviour Small turbines typically either have a tail fin or the rotor is placed downwind of the tower and the coning of the blades provides yaw stability. It is often said that downwind rotors do not perform well in yaw but we are unaware of any detailed study of their behaviour. However, there can be noise and other problems associated with tower interactions on downwind machines. The behaviour of a ‘free yaw’ arrangement for upwind rotors is

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approximately that of a second order linear system, e.g. Bechly et al. (2002), and this can lead to high yaw rates and large gyroscopic moments on the blade root and main shaft. These loads are proportional to the product of blade inertia, angular velocity and yaw rate, and can be the largest loads on a small turbine according to the ‘simple load model’ of IEC (2006), see Wood (2009). Particular care is needed in designing the main shaft and it is usually necessary to use a high-grade steel with a high ultimate strength. Towers Towers for small turbines vary from the self-supporting tubular to lattice towers used for large turbines to guy-wired towers. Many towers are lowered and raised using a gin pole, cable and tractor or hand winch. In order to obtain development approval for installation of a turbine, the purchaser may have to demonstrate that the turbine and tower are designed to withstand the maximum wind speed for the site. This speed is usually given in the appropriate national standard for wind loads and is influenced by the ‘importance’ of the structure, usually defined in terms of the structure’s threat to human life if it fails. The importance level for a turbine may differ between a remote and an urban site. In Australia, for example, the maximum wind speed does not vary much apart from being significantly higher in areas susceptible to cyclones (the southern hemisphere equivalent of hurricanes and typhoons). However, cyclones and such like are predictable, and an acceptable protection against them is to lower the turbine before they are due, provided the turbine and tower will survive the non-cyclonic maximum wind speed. Guyed towers are the most common, probably because of the low purchase price and the space taken by the guy-wires is often not a problem for remote installations. On the other hand, the guy-wires often must resist the overturning moment on the base of the tower and so may require large foundations, which can be costly and a transport problem for remote sites. Lattice towers are easier to transport (when unassembled) but tend to have a lower service life than pole towers. Tubular, stand-alone towers require a smaller foundation but are usually heavier than the other types and therefore more expensive to purchase and transport. Hot-dipped galvanised tubular towers usually have the longest service life, and this can be a major consideration if the turbine is located near the ocean. The tower natural frequency is important for two main reasons. The first is that many national standards require a ‘dynamic analysis’ of tower safety if the natural frequency is less than, say, 1 Hz. Secondly, if this frequency falls within the range of blade frequencies, then tower resonance is possible. Because of the large frequency range of small blade operation, it

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is very difficult to avoid this situation. On the other hand, blade frequencies vary with wind speed so the opportunities for setting up resonance are limited. The optimum tower height for a small turbine, defined as that giving the maximum output power per unit capital cost, is typically 18–33 m depending on turbine size and wind regime (Wood, 2001). Part of the reason for these low heights is that small turbines are usually rated (for maximum power) at lower wind speeds, between 10 and 15 m/s, than large turbines and so the increase in wind speed as height increases causes the output power to be limited and then the turbine to be shut down for a larger proportion of the time. Wood’s (2001) analysis required many simplifications and ignored transportation and installation costs, and so probably over-estimates the optimum height, but it is clear that the best height is considerably lower for a small turbine than a large one. This, of course, lowers the average wind speed which may already be compromised by the need to site the turbine close to its load. In our experience a hub-height mean wind speed of 5 m/s is a good one for a small turbine. Low average wind speed requires good starting performance to extract the maximum possible energy. Starting and low wind speed behaviour Low wind performance is usually gauged by the cut-in wind speed, but Wright and Wood (2004) documented the well-known fact that the ‘starting’ wind speed for blades initially at rest can be much higher than the ‘stopping’ wind speed at which the blades come to rest. For their 500 W turbine with a PMG which had a cogging torque of 0.36 N m, the former was around 4.5 m/s and the later 2.5 m/s, giving a cut-in wind speed of 3.5 m/s, which appears respectable but means the turbine may not be producing power at the average wind speed of many sites! The high starting wind speed is unfortunate but understandable because small turbines rarely use blade pitch adjustment – another major difference from large turbines – which means typical angles of attack on stationary blades are very high, and the resulting aerodynamic torque is low. Two further aspects of starting are important. First, it is a slow process; the starting time scales as the inverse square of the wind speed (Wright and Wood, 2004), and secondly, the starting aerodynamic torque is determined largely by the root section of the blade, rather than the tip region where most power is produced. In combination with the poor performance of thick aerofoils at the low Reynolds numbers typical of small blades at low wind speed, this means that the appearance of a well-designed small blade near the root is very different from that of a large blade, where thick profiles are used to blend with the circular attachment section. Large blades are designed

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primarily to maximise power extraction efficiency, but this leads to poor starting performance (Wood, 2004). Fortunately, the different origins for starting and power-producing torque allow sacrificing a small decrease in efficiency against a major improvement in starting (Wood, 2004). Starting time also depends on blade inertia, as does the gyroscopic load during yaw. Thus there are two reasons, particular to small turbines, to minimise blade inertia, but this must be balanced against the increased responsiveness to gusts which may make the turbine harder to control. Overspeed protection Small wind turbines do not usually have automatic brakes for emergency stopping. The most common safety mechanisms are turbine pitch-up or tilt-up and furling; the latter is shown in Fig. 5.6. Pitch-up requires hinging the turbine in the horizontal plane behind the turbine’s centre of mass so that the moment about the hinge of the rotor thrust at high wind speed will exceed the restoring moment due to gravity. Some damping is usually required. Pitching is compromised by the fact that the gyroscopic moment on the main shaft may either assist or resist pitching depending on the sign of the yaw rate, so it is possible to have a situation where the turbine needs to pitch for safety but is prevented from doing so by rapid yaw in the wrong direction. Furthermore, it appears that the gyroscopic moment increases more rapidly than the moment due to thrust as turbine size increases.

5.6 Wind turbine fully furled but still rotating due to a changing wind direction.

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Pitching is, therefore, much more common on very small turbines. Furling requires an offset between the rotor axis and the yaw axis (the tower) and a collapsible tail fin. At sufficiently high wind speed, the yaw moment on the rotor exceeds that due to the fin and the rotor collapses towards the tail and yaws out of the wind. Furling often causes the rotor to have a mean yaw error during normal operation with a significant loss of power. Since the yaw moment due to the tail fin depends largely on the wind speed, it is difficult to ensure that furling protects against both high thrust at high wind speeds when the turbine is operating normally and runaway from a failure such as loss of load at moderate wind speeds. Furling can also be associated with very high gyroscopic loads if the direction of the collapse is also the direction of the turbine yaw (Wright and Wood, 2007). Stopping the turbine Either for protection of the turbine, or for maintenance of the turbine, it is necessary to be able to stop the blades rotating or to reduce their speed to an acceptably low value. The methods are a manually operated brake or furling, or lowering a hinged tower such that the blades are parallel to the ground. Most useful is a (possibly manual) brake as it allows work to be done up the tower, but in those circumstances, it is necessary that the turbine is locked in yaw, or else any wind direction change will possibly dislodge the tower-top maintenance worker.

5.4

Control and electronics

Wind turbine controllers are designed to provide appropriate electrical power to the load and to protect the turbine, and possibly also to protect the load. The main control issues are: • • • • • • • •

adequate voltage output; generator current limit; generator speed limit; power maximisation (MPPT); high wind speed protection (if this is not done aerodynamically); parking when power is not needed; temperature limits (generator, controller); lightning strikes.

It is possible that all of the above control functions are regulated by a single microprocessor. However, some secondary control is required in case the microprocessor fails. The electrical output from the generator is usually three phase AC with a variable voltage and variable frequency. This is usually rectified

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(converted to DC) and the energy stored in a battery. The DC loads (often lamps) can be fed directly from the battery, perhaps using a charge controller. An inverter is used to convert the battery DC voltage to fixed frequency and voltage AC, as found in ordinary mains electricity connected houses (e.g. 230 V/50 Hz, 110 V/60 Hz).

5.4.1 Controller A typical wind turbine controller incorporates the rectifier which is often associated with MPPT. A controlled rectifier also ensures that there is a current limit to protect the generator. The controller may also monitor the battery condition to ensure that the battery is not overcharged and is charged appropriately at other times (including boost charging, cell equalisation charging, etc.). The controller is often used as the primary form of overspeed protection, say by limiting or reducing blade speed when the rated (maximum) power has been produced. In this case, the ‘aerodynamic’ controls of pitch-up or furling are used only when there is an electrical fault. Some small turbines rely entirely on electronic overspeed protection.

5.4.2 Inverter The inverter will endeavour to produce the correct output voltage and frequency, and (usually) also monitors the battery. If the battery charge is too low, the inverter will turn off to protect the battery. However, DC loads may not be directly monitored and the battery may suffer damage from excessive discharge. Inverters generally use pulse width modulation (PWM). The result of PWM is a sinusoidally chopped square wave which must be filtered to produce a good approximation to a pure sine wave. When powering magnetic loads that are designed to work from sinusoidal voltages, such as an electric motor, the quality of the voltage is critical. Cheap inverters produce nearly square wave output, which can cause excessive heating in magnetic loads. However, many modern items have electronic power conditioners. For example, modern washing machines use variable speed drives, and computers have power electronic power supplies. These are insensitive to the voltage waveshape and can manage with a near square voltage. However, a near square voltage is likely to cause more electromagnetic interference and hence it is to be avoided in general.

5.4.3 Generator heat issues Excessive current in the generator leads to overheating and possibly a meltdown of the insulation, followed by melting of the wires. If a short

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circuit occurs in the windings, there will be a large current flowing, usually leading to rapid braking of the generator (and risk of damaging the blades). This may be followed by the melting of the copper wires and the end of power generation. Under these circumstances, the turbine will be unloaded and then speed up, possibly to the point of self destruction by the centrifugal forces on the blades. Since it is usually air cooled, the generator current capability is dependent on the air temperature, wind speed, thermal resistance from the wires to the air and also the heat loss in the generator. It is usual to rate the current carrying capacity of the generator at 25 °C ambient in free air and thermal characteristics can be measured as in Fig. 5.7. Typically the generator will be enclosed to protect it from the weather. Under a hot sun, the ambient temperature may well be far above 25 °C. A combination of direct sun, high ambient air temperatures and high wind speeds may require substantial derating of the generator. For example, recent weather in Melbourne, Australia combined these aspects: 46 °C ambient temperature, with direct sun (train rails exposed to the sun reached 75 °C) and wind speeds reputedly up to 180 km h. Inexpensive wind turbines with simple controllers and limited overspeed protection will be damaged unless the operator has the good sense (and is present) to turn them out of the wind or park the blades. Most wind turbine controllers will have a current limit, to protect the generator and power electronics by limiting the power output from the turbine, causing the rotor to accelerate. Usually in the design stage, the operating temperature is calculated for the design environment. An improvement is to use temperature sensors in the generator to either provide a warning or protect the generator. In low-power generators, it is possible to use polyswitches (or similar) which react to the temperature by going open circuit above a certain temperature. The difficulty is, again, that in protecting the generator, the load is removed from the turbine, which will now overspeed. Even simple protection is usually ignored for small turbines on the grounds that high power is usually produced only when there are high winds to provide sufficient cooling. Against this argument is the fact that small generators tend to be less efficient than large ones and therefore produce more heat per unit power.

5.4.4 Current limiting Most power electronic controllers limit the current by either briefly turning off the rectified DC output (as in a DC–DC converter) or turning off part of the AC waveform (i.e. phase control). For synchronous generators, the excitation can be adjusted. Then the controller can have a much smaller

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Stand-alone and hybrid wind energy systems Generator temperature curve

Temperature (°C)

45 40

Gen. shell

35

Gen. face plate

30

Gen. shaft

25 20 0

(a)

20

40

60

80

100

120

Time (min)

(b)

5.7 (a) Measurements of the temperature rise of a 200 W, 12 V PMG with no moving air with phase current of 6.5 ± 0.2 Arms. Ambient temperature = 20.9 °C. (b) Positions of temperature measurement indicated by arrows. Note that the generator has a 3 mm thick plastic cowling and the faceplate is covered by the blade attachment and a nose cone.

power rating than if it controls the full output of the generator. However, for small turbines, the associated cost reduction may not be significant. A simple method of current control used in some micro-wind turbines, has been to sense the temperature of the generator winding, and if it is too high, to insert an extra impedance thereby reducing the current.

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5.4.5 Generator overspeeding In the generator, the centrifugal forces from overspeeding can cause the rotor windings or magnets to fly off (depending on how the magnets have been attached to the rotor). Then these windings or magnets may jam the rotor, forcing it to stop rapidly, which may further damage the blades or gearbox. Some high-performance magnets are inflammable, and may catch fire once their protective coating is damaged. The first and foremost way of controlling the turbine speed is to keep the generator electrically loaded. Under normal circumstances, this works well until the current limit is reached. Then the electrical load must be reduced and the turbine will speed up. When aerodynamic overspeed protection does not work, another control mechanism is needed. If the controller has a sufficiently powerful microprocessor, it could detect overspeeding and operate a motor to turn the turbine out of the wind, or a brake to stop the turbine, or drastically increase the load on the generator temporarily to stall the turbine. Only the last of these can be implemented at no extra hardware cost.

5.4.6 Maximum power point tracking For nearly all turbines, the curve of turbine power coefficient (which can be interpreted as the efficiency of power generation) when plotted against the TSR has a single maximum at the ideal TSR. Thus for a given power output from the turbine there are two possible operating speeds. Usually, the turbine operates at the higher speed. However, by slowing the turbine, the generator voltage may not be enough to supply the load. Hence, it may be necessary to use a different load that is designed to operate at lower than the load voltage. To reach this new load-speed operating point, the turbine must go over the maximum power point and hence the current rating will surely be exceeded. For this brief time, the over-current protection must be turned off. MPPT algorithms have a large literature, and a wide range of different strategies has been investigated, e.g. Hong et al. (2009). MPPT generally aims to keep the TSR close to the ideal value as the wind speed changes without measuring the speed. However, the requirement is to maximise the output of the whole wind turbine, which includes the generator, whose efficiency may depend on the power output. A simple concept is to measure the output power and adjust the output power of the generator in whichever direction is required to increase the output (hill climb method). For a small turbine, in practice, the wind speed can be so variable that the output of the turbine is changing very quickly and trying to follow the changes may result in a time lag which removes any benefit. Consequently,

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some average turbine speed that is expected to maximise the output must be aimed at.

5.4.7 Controller protection Turbine controllers for most remote power systems are located in the same building as the batteries and have similar requirements for protection. For example, they must be kept dry; we have heard significant anecdotal evidence that moisture in controller enclosures can also be a major problem. In hot conditions, the electronic components must be well shaded and ventilated. The sensitivity to the heat depends on the grade of electronic component used and their lifetime usually reduces as the temperature increases.

5.4.8 Lightning strikes The energy in lightning strike (see Fig. 5.8) is such that a direct strike cannot be totally protected against. Furthermore, IEC (2006) exempts small wind turbine blades from having lightning protection, whereas the corresponding standard for large turbines mandates the use of conducting strips in the blades, see section 10.3 of Burton et al. (2001) for more details. However, nearby strikes which induce electric currents can be mitigated by providing a path for the lightning. It has been claimed that using magnetic devices as

5.8 Lightning damage at the trailing edge near the tip of a Westwind 20 kW wind turbine blade at the CSIRO Energy Centre, Newcastle. The blade was repaired at the University of Newcastle and is back in service. Photo courtesy of Phil May, Solartec Renewables.

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the main control element, together with spark gap arrestors, would assist to protect any control electronics, but as yet there appears to be little direct evidence, and only a little anecdotal evidence. Large turbines also have electrical connections from the conductors to an earthed tower conductor, the idea being to conduct the lightning safely to ground, with no room for a spark gap to appear, and produce heat. Similar protection can be added to small turbines but is often avoided, probably due to cost reasons, and often justified by the lower tower height; the frequency of lightning strikes is proportional to the square of the hub height plus blade radius, see for example, Rachidi et al. (2009).

5.4.9 Unforeseen conditions Control of a wind turbine is principally to maximise power output during normal operation and to protect the turbine, nearby people and property, in extreme conditions or after component failure. The desire to extract the maximum amount of energy from the turbine will be compromised by the need to maintain the turbine in working order. Hence at times, the turbine will have a reduced output in order that it may survive better and have a longer lifetime. Despite the best intentions and design, there will be circumstances where a manual decision and intervention is required to turn the turbine out of the wind, to lock the blades or to lower the tower. Even then, there may be circumstances that cannot be predicted or designed for; examples include an earthquake of an unheralded magnitude, wind speeds causing flying debris to bombard the turbine to destruction, or when an operator breaks a wrist from backlash in a faulty manual brake when trying to stop a turbine in a storm and the turbine is destroyed.

5.5

Stand-alone power systems

Stand-alone power systems are needed where there is no grid electricity (Fig. 5.9), or the grid electricity is unreliable. The basic electrical system is shown in Fig. 5.10. However, the uses of the electricity and hence the required power, may be different in the different situations. In an area that has never had grid electricity the first requirement is usually for lighting. A clear, bright light that does not produce heat is very welcome. This allows for reading, discussion, some hand work and trade at night. With the introduction of low-power LED lights it is possible for a small wind turbine to provide enough light for a moderately sized village. An example is the Practical Action wind turbine installations in Nepal (Shrestha, 2009), using turbines rated at less than 1 kW. Next to be powered are usually communication and entertainment systems – radios, televisions, etc. Further thoughts often turn to powered equipment such as a circular

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5.9 Stand-alone wind turbine with PV in Nepal.

AC loads DC loads

+

Three phase Rectifier/ generator controller output

Battery

Inverter

5.10 Basic electrical system for a stand-alone, battery wind power system.

saw or electric rice cooker or similar. In tropical areas or for remote medical clinics, a refrigerator is highly desirable. In western countries, turbines smaller than 1 kW are usually seen on yachts. Larger stand-alone power systems often provide power for houses that are so far from the grid that the cost of grid connection is prohibitive; in Australia, 1 km of power lines costs around A$15 000, but this applies only to level ground and does not include the cost of transformers if necessary. A well-designed energy-efficient house that does not use electricity for cooking or heating will typically require 10 kW h per day or less. This could be supplied by a 2 kW wind turbine at a good site. A fascinating study

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of the attitudes of remote power system users, with some good lessons for equipment suppliers, and system designers is given by McHenry (2009). Bigger stand-alone systems for hotels and tourist resorts are described by Dalton et al. (2009). In areas that have an unreliable electricity grid, maintenance of lifestyle tends to be the priority, leading to the ‘need’ to operate a refrigerator, washing machine or plasma TV. Then the addition of electric lighting is a small increment in load and would usually be added as a matter of course. For private households and other low-level electricity users, battery energy storage may be adequate – it is quiet and the maintenance level is low (but necessary). A wind or PV system to charge the battery may last for many years before it needs replacing. The only likely issue is that as people become accustomed to electrical energy, they would like more. Hence the availability of an upgrade path should be considered from the beginning. Industry requires larger amounts of power especially to start machines. Induction motors can take up to seven times their rated power to start, and hence for an industry, a diesel generator may be the cheapest way of providing the power, especially if the waste heat can also be used. With rising fuel prices and the uncertain availability of fuels in some countries, fuel saving may be critical. All forms of renewably powered stand-alone power systems are likely to increase rapidly in popularity.

5.6

Further aspects of system sizing

The stand-alone system that will be dealt with here is that of a wind turbine and lead–acid battery system. While lead–acid batteries will continue to be used for some time yet, other energy storage technologies are being actively explored (Sullivan et al., 2008), either as new types of batteries (Joerissen et al., 2004), improved charging methods, or flywheels, and fuel cells (Nelson et al., 2006). Specific examples of system sizing based on knowledge of the wind resource have been considered by Kaldellis (2004) and Roy et al. (2009) amongst others. Here we will give general guidance, because many systems, especially small ones, are installed at sites for which the wind resource has not been thoroughly characterised. The sizing of the system depends firstly on the electrical load and the demand management possibilities. The electrical load determines the amount of energy required and the demand management possibility determines whether part of the load can be moved to windier periods or be eliminated altogether. The next stage is to balance the wind generator output and the battery energy storage. Storing energy in a battery is roughly 75% efficient – Roy

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et al. (2009) assumed charging and discharging efficiencies are both 90%, and there are additional losses in the inverter, etc. – so better use is made of the wind energy if it can be used as it is generated. By measuring the wind speed over a suitable interval, or by using a small and inexpensive wind turbine, an estimate can be made of the wind energy resource, as an average over 1, 3 or 5 days, and then per month and per year. A good starting point is to assume five days of energy storage in the batteries at 75% efficiency, and then to determine the wind turbine size that would be required in the least windy five days of the year. It is then possible to vary the capacity of the wind turbine and batteries and compare the cost. If significant demand management is possible, then a reduction in capacity of the system can be adopted and re-optimised for size. The determination of average power output as explained in Section 5.2, gives no information as to the energy storage required. This can only be determined accurately by knowing the time dependence on the wind speed and the loads. For example, Roy et al. (2009) used hourly average wind speed data and an assumed hourly load. The RETscreen software, mentioned in Section 5.2.2, can be used for battery sizing.

5.6.1 Power output Determination of the power output is a major issue for small wind turbines on small towers. Monitoring as done for large wind turbines may cost a significant fraction of the turbine cost, and much of the available wind data, from meteorological bureaux, nearby airports, etc., is usually measured at the standard height of 10 m. Extrapolating from 10 m to, say, 20 m can introduce errors, largely because the two common ‘laws’ for the height dependence of the wind speed, the log law and the power law, may not hold so close to the ground, especially if there are ‘roughness’ elements, such as trees and buildings of similar heights on either the monitored site or the proposed site of the turbine. Furthermore, the wind resource can vary significantly over horizontal distances of the order of tens of metres. It is also likely that climate change over the life of a stand-alone system will have a significant influence on its performance. Extreme winds are expected to increase in probability, but this may be associated with a decrease in the wind resource for most of the time.

5.6.2 Wind maps and software A number of countries have wind maps, for example the UK Department of Trade and Industry map1, which gives wind speeds down to 10 m, 1

A good introduction to the DTI database is through: http://www.bwea.com/noabl/.

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but does not account for the local roughness. Another extremely useful resource is the previously mentioned Canadian National Research Council RETscreen website. There are some very useful rules-of-thumb for determining the best location for a small turbine, such as DoE (2005), and cheap anemometers and data loggers are available in many electrical stores. (Note that one recently purchased cheap anemometer and datalogger measured average wind speeds, but only recorded hourly maximum and minimum wind speeds.) Computational modelling of airflows for small turbine siting is in its infancy and the current high cost may well decrease substantially with time. Note that none of these resources are likely to give the time-ofday dependence of the wind speed.

5.6.3 Practical wind energy measurement As an alternative to the methods described above, it may be better and cognitively inspirational to install a small and inexpensive turbine, and measure the power produced. Inexpensive turbines usually charge batteries, and hence a simple method of measuring the energy output from the turbine is to apply a lamp to the battery. If over a few days the battery voltage sits under 12 V, then the wind turbine cannot produce the required amount of energy. Then a smaller wattage bulb should be tried. Conversely, if the battery voltage sits above 13 V, a higher wattage bulb can be tried. Over a year of testing in this manner, the power production can be determined, perhaps on a month by month basis, and data from nearby weather stations can be used to correct for seasonal variations.

5.6.4 Wind speed probability distribution and capacity factor Most turbine manufacturers supply power curves on their websites, and knowing the average wind speed allows a probability distribution of the wind to be approximated. The default probability distribution used in wind energy is the Weibull distribution, see section 2.4 of Burton et al. (2001), and Celik (2003). The default value of the ‘shape parameter’ is two. Summing the product of the wind speed probability and the power output at that wind speed gives the average power output of the turbine. The ratio of average to rated power is the ‘capacity factor’ and is the most important piece of information for any installation. For large wind turbine farms, this factor can reach 0.4–0.5, but values closer to 0.20 are typical for small wind turbines. Software packages such as RETscreen also determine the average power output and may save time by having the power curve and assumed probability distribution.

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5.6.5 Battery sizing In designing a complete remote power system, sizing the battery bank to ensure that there is always sufficient backup power may sometimes cost significantly more than a system designed for a more realistic, 95–100%, reliability (Kaldellis et al., 2007). In considering the reliability of the supply, an analysis should be performed as to what are the essential loads, desired loads and luxury loads. The essential loads must operate nearly every day for the stand-alone system to be worthwhile. The desired loads can wait a few days for the wind to blow. The luxury loads can wait for the days of plenty of wind and stored energy. For household use, the essential loads may be considered to be lighting and perhaps a radio, refrigerator and water pump. The desired loads may be a television and electric power tools, whereas the luxury loads may be a video projector or an electric popcorn maker. These loads have three electrical issues associated with them: • • •

the energy consumed per day; instantaneous power used; the current required to start.

The energy consumed over a day determines the size of the wind turbine and the battery storage. The power the load uses determines the rating of the inverter and the wiring size. The electrical current required to start a load determines the peak power rating of the inverter, size of battery and may affect the wiring size. The size of the battery is affected in this last situation by its ability to supply the starting currents. The maximum load, where all loads come on simultaneously, can usually be avoided with dramatic reductions in the cost of a system. For a small stand-alone PV system known to the authors, the choice was between using the refrigerator or the washing machine. Certainly the refrigerator did not cool properly while the washing machine was being used, but controlling the load in this manner halved the required power rating of the inverter and the distribution wiring. From the other perspective, windy weather is the time to use high-power appliances. The advantages are that the energy does not have to be stored before it is used (reducing energy storage losses), and if the batteries were fully charged, then this power would otherwise be wasted. It is a life style modification, but not usually particularly onerous – no more than waiting for a sunny day to wash and dry the clothes.

5.7

Conclusions

This chapter describes the basic technology of small wind turbines and their use in stand-alone power systems with battery backup. In particular we

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surveyed blade design especially for low wind speed performance, the choices for generator, controller and inverter. Turbine safety was a main consideration. The size of the power system is critical to its conception and design. These issues were addressed in terms of typical applications and the loads associated with them. It was emphasised that it is often difficult and expensive to determine the wind resource, but there is good worldwide information for many locations and useful general rules for best siting of a turbine. The design of a stand-alone wind turbine power system is based on the expected energy required and the expected wind speeds. Despite the available information, such expectations may be wrong for unforeseen reasons, and in our experience, often in the most difficult direction. One of the uncertainties is that of the climate change – what effect it will have on average wind speeds is uncertain, but in some areas, it appears that cyclonic and tornado activity is expected to increase significantly. In the worst case this may mean that the average wind speeds may decrease, but the peak wind speeds may increase, making it difficult to decide how to choose the turbine. In our experience, the best approach for relatively small systems is to start with a smaller and inexpensive turbine, battery and inverter system to power a limited number of loads to determine if this performs as expected. It will test if there is enough wind, if the battery storage is sufficient, if the loads are manageable and if the system can start the load, such as the particular refrigerator in use.

5.8

References

AWEA (2008). American Wind Energy Association Small Wind Turbine Global Market Study 2008. Bechly, M.E., Gutierrez, H., Streiner, S., Wood, D.H. (2002). Modelling the yaw behaviour of small wind turbines, Wind Engineering, 26, pp 223–239. Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E. (2001). Wind Energy Handbook, John Wiley & Sons. Celik, A. (2003). Energy output estimation for small scale wind power generators using Weibull representative wind data, Journal of Wind Engineering and Industrial Aerodynamics, 91, pp 693–707. Dalton, G.J., Lockington, D.A., Baldock, T.E. (2009). Case study feasibility analysis of renewable energy supply options for small to medium-sized tourist accommodations, Renewable Energy, 34, pp 1134–1144. DoE (2005). Small Wind Electric Systems, US Department of Energy. Available online at http://www.windpoweringamerica.gov/pdfs/small_wind/small_wind_ guide.pdf. Accessed: March 2009. Hong, Y.Y., Shiue-Der Lu, S.-D., Ching-Sheng Chiou, C.-S. (2009). MPPT for PM wind generator using gradient approximation, Energy Conversion and Management, 50, pp 82–89.

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IEC (International Electrotechnical Commission) (2006). 61400–2 revision 2, 2006, Wind turbines – Part 2: Design requirements for small wind turbines, obtainable from www.iec.ch. Joerissen, L., Garche, J., Fabjan, Ch., Tomzic, G. (2004). Possible use of vanadium redox-flow batteries for energy storage in small grids and stand-alone photovoltaic systems, Journal of Power Sources, 127, pp 98–104. Kaldellis, J.K. (2004). Parametric investigation concerning dimensions of a standalone wind-power system, Applied Energy, 77, pp 35–50. Kaldellis, J.K., Kavadiasa, K.A, Koronakisb, P.S. (2007). Comparing wind and photovoltaic stand-alone power systems used for the electrification of remote consumers, Renewable and Sustainable Energy Reviews 11, pp 57–77. McHenry, M.P. (2009). Why are remote Western Australians installing renewable energy technologies in stand-alone power supply systems?, Renewable Energy 34, pp 1252–1256. Nelson, D., Nehrir, M., Wang, C. (2006). Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems, Renewable Energy 31, pp 1641–1656. Rachidi, F., Rubinstein, M., Montanyà, J., Bermúdez, J.-L., Rodríguez Sola, R., Solà, G., Korovkin, N. (2009). A review of current issues in lightning protection of newgeneration wind-turbine blades, IEEE Transactions on Industrial Electronics, 55, pp 2489 – 2496. Roy, A., Kadere, S.B., Bandyopadhyay, S. (2009). Application of design space methodology for optimum sizing of wind–battery systems. Applied Energy doi:10.1016/j. apenergy.2009.04.032. 86, pp 2690–2703. Shrestha, R.Y. (2009). Small scale wind energy market and institutional model for Nepal, Wind Engineering, 33, pp 109–122. Sullivan, P., Short, W., Blair, N. (2008). Modeling the benefits of storage technologies with wind power, Wind Engineering, 32, pp 603–615. Wood, D.H. (2001). An improved determination of the optimum tower height for a small wind turbine, Wind Engineering, 25, pp 191–196. Wood, D.H. (2004). Dual purpose design of small wind turbine blades, Wind Engineering, 28, pp 511–527. Wood, D.H. (2009). Using the IEC simple load model for small wind turbines, Wind Engineering, 33, pp 139–154. Wright, A.D., Wood, D.H. (2004). The starting and low wind speed behaviour of a small horizontal-axis wind turbine, Journal of Wind Engineering and Industrial Aerodynamics, 92, pp 1265–1279. Wright, A.D., Wood, D.H. (2007). Yaw rate, rotor speed and gyroscopic loads on a small horizontal axis wind turbine, Wind Engineering, 31, pp 197–209.

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6 Hybrid wind–diesel energy systems G. BHUVANESWARI and R. BALASUBRAMANIAN, Indian Institute of Technology (Delhi), India

Abstract: Hybrid wind–diesel systems are gaining importance from both technical and economic points of view as they are capable of supplying electricity to remote locations, islands and mountainous areas, located far from the high-voltage grid. This chapter deals with certain salient points concerning the wind–diesel systems such as the circumstances under which such a hybrid wind–diesel system may be installed, system overview, design considerations, selection of generator ratings and control schemes especially under varying wind velocities and fluctuating load conditions, functioning of a battery energy storage system (BESS) and modelling as well as simulation aspects of the complete hybrid wind–diesel system. Key words: hybrid wind–diesel system, wind energy conversion system (WECS), battery energy storage system (BESS), control strategies, permanent magnet synchronous generator (PMSG), induction generators.

6.1

Introduction

The emerging trend of utilizing renewable energy resources for electricity production to the maximum extent has become necessary to limit greenhouse gas emissions and to combat the climate change and global warming menace. Globally, many countries are already intensely engaged in installing new power stations using renewable energy resources. Wind energy is one of the most promising options adopted globally because this technology is mature and has already been installed in large quantities (Jenkins et al., 2000; Masters, 2004; Richardson and McNerney, 1993). Many of these installations are operated under grid-connected mode. However, there is also a strong case for setting up many new stand-alone and hybrid wind–diesel/ wind–photovoltaic power systems and operate them in a decentralized mode to meet the loads at remote locations far from the main grid and in islands (Kaldellis and Kavadias, 2007). Such situations are widely prevalent in developing countries. This can also relieve the main grid from getting over-stressed and also reduce the usage of fossil fuels in the thermal power plants. In those sites where a good wind regime exists, it is extremely beneficial to install wind energy conversion systems (WECS) that can meet the base load demand. 191 © Woodhead Publishing Limited, 2010

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One of the limitations of WECS, however, is the fact that the wind velocity continuously fluctuates both in the daily cycle as well as seasonally. In view of this, it is desirable to have a hybrid system which will supplement the output of the WECS so that most of the energy needs are met. Wind– diesel systems are appropriate for those locations where diesel oil is available easily (Freris, 1990). The diesel-based generation system has to be used sparingly due to cost and greenhouse gas emission considerations. Also, diesel engines have to be run at a minimum of about 20–40% of their full capacity because of technical limitations of running them on lower loads and because of economic considerations. Further, while operating in conjunction with WECS, diesel engines cannot be turned on and off very frequently because such switchings will lead to fatigue stress developing on the engine mechanical parts. There is also a finite minimum start-up time requirement for these diesel engines. This necessitates the use of an energy storage mechanism that would be able to meet the shortfall when the wind velocity is on the low side even to meet the base load demand. When these energy storage systems are employed, they would be very useful for storing some of the generated power when the wind velocity is high and the load on the system is low. One of the mature and wellestablished technologies for energy storage is the battery energy storage systems (BESS) (Barton and Infield, 2004; Borowy and Salameh, 1997). The coordinated functioning of these three components, viz. the WECS, diesel generation system and BESS, could be a profitable option from both technical and economic points of view. BESS can also aid in real and reactive power control of the entire distributed generated system to achieve optimum power factor at each of the buses (Chiang et al., 1995; Lin et al., 1992) in the system. This chapter presents the salient features of a modern wind–diesel hybrid generation system, its modelling aspects and some simulation studies on this system. The main emphasis of the chapter is on different electrical generators provided with appropriate power electronic converters in these hybrid systems and the corresponding control configurations.

6.2

Overview of wind–diesel generation system

Figure 6.1 shows a schematic diagram of a typical hybrid wind–diesel generation system. The modern trend in wind turbine design is to capture the maximum energy available in the wind through maximum power point tracking (MPPT) even in fluctuating wind velocity conditions (Mutoh and Nagasawa, 2006; Wai et al., 2007). There are also techniques available now to make the mechanical coupling between the wind turbine and the generator gearless, thereby avoiding mechanical transmission loss and increasing the efficiency and reliability of the system.

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6.1 Block schematic of a hybrid wind–diesel energy system with battery energy storage.

The generator used in these systems can be a synchronous generator or an induction generator. Many of the modern WECS use either a permanent magnet synchronous generator (PMSG) or a doubly-fed induction generator (DFIG). The PMSG has permanent magnets in its rotor to provide the necessary magnetic field in the air gap of the machine. The stator will carry the three phase armature winding where the voltage is generated. Induction machines can be used in the generating mode provided the excitation is available from the stator terminals either by means of excitation capacitors (if it is a stand-alone generator) or by means of grid connection. In a squirrel cage induction machine, the stator terminals are only accessible and the rotor is short-circuited. So, the excitation has to be supplied only through the stator terminals. On the other hand, in a wound rotor induction machine which can function as a DFIG, both stator and rotor terminals are accessible and hence the excitation can be provided from either or both of them. Power electronic controllers are used to optimize the output of these generators under varying wind velocity conditions to implement MPPT. These power electronic controllers also play a vital role in the independent control of the real power and the reactive power flow in the system so that the best possible efficiency and power quality are obtained. One desirable feature of these schemes is that the diesel generator will be put in service only when the prevailing wind velocity is insufficient to meet even the essential loads on the system. The preferred choice of generator that is used with the diesel engine is a synchronous generator. A

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restriction imposed on the diesel engine is that it has to necessarily operate at a minimum loading of about 20% for smooth running of the engine and also for keeping the engine efficiency at a reasonable level. Also, frequent switching on and off of the diesel engine is avoided in these systems to prevent undue fatigue stress developing in the engine. It is often not possible to match the chronological variations in the load demand with that of the variations in the wind velocity. This calls for an energy storage system which may be implemented with the help of a flywheel (mechanical energy storage) or a battery bank (electrical energy storage). In this chapter, BESS, which is a well-established technology, is considered. The power electronic controller used in the BESS should be able to support bidirectional power flow to enable charging and discharging of the batteries as required. Wind–diesel systems are normally implemented in remote locations and hence the reliability requirements of all the loads to be supplied by these systems may not be stringent. So, in the studies that have been presented here, the loads have been divided into three components, viz. an essential or high-priority load (e.g. a medical support system); a medium-priority load (e.g. industrial, agricultural and commercial loads); and a low-priority load (e.g. domestic loads). This kind of a load division will facilitate achieving the best economic operation with the required reliability for the different types of loads.

6.3

Wind turbine sizing in a hybrid wind–diesel scheme

When electrical power is to be supplied to an island or an isolated location far from the electrical grid, distributed generation (DG) is the only option available. Diesel-based power generation is very simple to install in these cases; but to reduce greenhouse gas emissions and also to cut down the operational cost, wind power generation can be a very appropriate option to supplement the electrical power generation from the DG set, especially when the wind regime in that particular geographical location is good (McKenna and Olsen, 1999). Before installing a hybrid wind–diesel system, it is essential to investigate the variation of wind speed in that geographical location with respect to various seasons and also the daily variation on an hourly basis in each season. The average wind speed should be such that with a feasible size of the wind turbine, the electrical power generated should be able to meet at least the essential load requirements. The equation that governs the relationship between the wind velocity and the generated power (Masters, 2004) is: P = 0.5(ρAυ3)ηCp

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where ρ is the air density, which is about 1.225 kg/m3 at a pressure of 1 atm and 15 °C, A is the area swept by the turbine blades in a horizontal axis wind turbine and is given by 0.25*π D2 where D is the diameter of the turbine blade, υ is the average wind speed, η is the efficiency of the gear box and the generator put together, which may be about 0.7 (considering the efficiency of the gear box to be 0.75 and the efficiency of the generator to be 0.93) and Cp is the measure of the rotor efficiency, which is about 0.5 at best. With the help of the above equation and depending upon the power requirement for the essential load, the wind turbine rotor diameter has to be calculated. This must then checked to see if it would be feasible to install such a wind turbine in the available space at an appropriate height.

6.4

Wind–diesel systems: design considerations

The starting point for the design of a wind–diesel system could be to assess the amount of load to be supplied by the distributed generation system. An attempt has to be made to meet the high-priority loads with WECS and BESS despite the variations in the wind velocity; this would be one of the preferred designs as this will minimize diesel oil consumption. As mediumpriority load includes agricultural, commercial and industrial loads, they will occur mainly during the day. Low-priority domestic load requirements will be considerable during the evenings and early morning hours. The diesel engine should be able to support any additional requirement that arises in the form of lower priority loads and also any shortfall caused by wind velocity falling to low values. There are two basic operational strategies possible to be adopted for the hybrid wind–diesel–BESS system. One strategy is to run the diesel engine on a continuous basis. However, in this case, the saving in the diesel fuel consumption will be minimal, despite the fact that the main purpose of installing a hybrid wind–diesel system is to reduce the operational cost. Also, a minimum amount of load should be there on the diesel engine all the time for optimizing its fuel consumption. The other operating strategy is to run the diesel engine on an intermittent basis as and when the requirement arises. In this case, the major problem will be due to the limitations on the frequency of on/off cycling of the diesel engine and also the wear and tear of the DG set caused by frequent switching. To avoid these problems, it is better to have an energy storage system installed. When the wind velocity is below cut-in speed, the energy storage system should be in a position to meet the high priority load demand. Although there are several

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types of energy storage systems (Kaldellis et al., 2009), BESS is one of the preferred methods of energy storage because of its ability to provide quick response, smaller size as compared to mechanical energy storage systems, and the space required is less.

6.4.1 Load assessment As mentioned in the previous section, the loads can be categorized into three groups, namely, high-priority, medium-priority and low-priority. To start with, it is essential to estimate the quantity of each of these loads and their respective variations on a daily and seasonal basis. The wind velocity profile also has to be studied with respect to its daily and seasonal variations. By comparing the chronological load variation and wind velocity variation profiles, one would be able to assess the mismatches that are likely to occur between these two. This will be the main consideration for fixing the capacity of the energy storage system that is to be installed.

6.4.2 Resource assessment The wind velocity data of the proposed site for the hybrid wind–diesel system have to be collected, analysed and studied thoroughly. The quantum of the high-priority load to be met will be the main criterion for arriving at the rating of the wind turbine to be installed. The design parameters of the wind turbine, such as the rotor diameter and the gear ratio, will be determined based on the wind velocity profile and the rating of the wind turbine (Kaldellis et al., 2006). Diesel oil is available at almost all sites, of course, at a high cost. This is why it was advocated earlier that the diesel oil consumption should be minimized as much as possible by making use of BESS.

6.4.3 Storage requirements As wind velocity fluctuations in the time range of seconds are also common, it is possible that the wind velocity may become too low even to meet the high-priority load requirements. Under such situations, BESS can play an important role. Similarly, when wind velocity reaches high values, BESS can store the excess power generated. Further, the diesel engine has restrictions in terms of minimum allowable loading on it (about 20–40% of its rating) and the frequency of switching it on and off. Also, sudden reductions in the load demand may occur in the system during the time period when the diesel engine is in operation. During such occasions, the presence of BESS will prove to be beneficial.

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Components of a hybrid wind–diesel system

The main components of a hybrid wind–diesel power generation system are: • • • • •

wind turbine with associated controls; diesel engine; electrical generator with power electronic controllers; loads; BESS.

6.5.1 Wind turbine The wind turbine should be controlled to capture as much energy as possible from the wind, within safe power limits of the generator and turbine, during low wind speed as well as high wind speed conditions. During low wind velocity conditions, for a given wind turbine design, the tip speed ratio has to be brought to a particular value to achieve MPPT. This is done by adjusting the electromagnetic torque of the generator by employing suitable power electronic controllers so that the quadrature axis component (the torque-producing component) of current in the three phase AC generator is appropriately manipulated. During higher wind speed conditions (greater than the rated wind velocity for which the turbine is designed), pitch control is activated so that generator capacity is not exceeded and the currents and voltages are within safe limits. During this condition, the generator will operate under constant power control.

6.5.2 Diesel engine The diesel engine will have a drooping speed vs. power characteristics which will be controlled by the governor action. The model used for the diesel engine can be a first order model with a single time constant representing the relationship between the fuel consumption rate and the mechanical torque production. The speed governor action is represented by a speed regulation percentage and an integral controller gain. The integral controller helps in eliminating the frequency error during steady state conditions.

6.5.3 Generators Normally the generators used along with the diesel engines are wound field three phase synchronous generators. The terminal voltage of the

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synchronous generator is maintained at the rated value by means of excitation control. The frequency control is achieved by means of the governor control mechanism of the diesel engine. The generator that goes with the wind energy conversion system could be an induction generator (IG) or a PMSG. If a nigh-rated WECS is being considered (which is very often operated in grid connected mode), then DFIG could be a suitable option. The advantages of using a DFIG are (i) the electrical power generation is possible at sub-synchronous as well as at super-synchronous rotor speeds depending upon the wind velocity; and (ii) the total output of a DFIG could be as high as the sum of the power carrying capacity of the stator winding and that of the rotor winding, when the rotor is driven at super-synchronous speeds. Since the rotor-induced voltage is at slip frequency it has to be converted to grid frequency with the help of a converter–inverter combination before being fed to the grid. The power electronic converters have to be controlled appropriately to achieve real power and reactive power balance under both sub-synchronous and supersynchronous rotor speeds. The power electronic converters in the DFIG scheme are connected to the rotor terminals and they handle only slip power and hence their rating can be much less than that of the overall rating of the induction machine. This results in large amount of savings in the cost of the power electronic converters. For relatively smaller ratings, squirrel cage induction generators (SCIG) with self-excitation capacitors could be an option. However, the machine can work in the generating mode, but only at super-synchronous rotor speeds. If the system is to be capable of generating over a large range of varying wind velocities, then it is essential to insert a converter–inverter circuit, between the stator terminals and the grid. In this case, suitable control has to be provided for these power electronic converters which should be designed for the full rating of the induction machine. The modern trend is to use PMSG as a direct driven (gear-less) generator along with the wind turbine especially in the distributed generating environment (Spooner and Williamson, 1996). The major advantage of using PMSG is that there is no need for supplying DC excitation to the rotor and it can be constructed with large number of poles so that even at lower wind velocity conditions, 50 Hz can be generated without gears. However, if the generator has to produce electricity for varying wind speeds, a converter–inverter circuit has to be inserted between the stator terminals and the load. The rating of these power electronic converters should be the same as that of the full rating of the PMSG. Appropriate controls have to be provided for the power electronic circuits to regulate the frequency and voltage and also to maintain the power factor of the generator at unity. The power electronic controllers also aid in achieving MPPT for varying wind velocity conditions.

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6.5.4 Loads As mentioned earlier, the loads have been categorized into three types in accordance with their priorities. The loads can be passive loads such as lighting and heating loads; they can be active loads consisting of industrial drives; they can be non-linear loads drawing harmonic-rich currents such as rectifier-fed DC motors, fluorescent lamps with electronic ballasts or power supply systems feeding computers or other medical electronic systems. Depending upon the studies that are undertaken for the system, the loads can be modelled suitably. If simple domestic loads have to be modelled, they are represented by a constant power lumped load. If power quality at the distribution level has to be studied for industrial drive kind of loads, a detailed model of the drive unit has to be adopted. The generator should be in a position to supply all these loads and still maintain a power factor of unity. This will be made possible by making use of power quality conditioners.

6.5.5 BESS The BESS consists of a bidirectional power electronic converter, a current limiting impedance and a battery bank. The voltage rating and the amp–hour capacity of the battery are to be decided based on the chronological variations of the wind velocity and the load. Further, the charging– discharging rates of the battery technology chosen should be able to cope with the actual chronological variations in the wind velocity and the connected essential load. The battery technology is constantly being researched and improved to increase the energy density and reduce the charging time. The technologies that are commonly used are lead–acid and nickel– cadmium batteries. But these two have energy densities of the order of 0.05 kW h/litre and 0.1 kW h/litre respectively. Although the nickel– cadmium battery is three times as expensive as the lead–acid battery, it has a better storage capacity than the former. Silver–zinc batteries are also available in the market which are prohibitively expensive but their energy density is comparable to that of nickel–cadmium batteries. However, their charging rate seems to be better than the other two battery technologies (Freris, 1990).

6.6

Control strategies for wind–diesel generation systems

In wind–diesel generation systems, the major goal is to use diesel oil sparingly, despite the varying wind velocity conditions, and yet meet the essential load demand. For this purpose, a suitably rated BESS has to be installed

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so that any shortfall in the demand can be met by this storage unit. So, the BESS has to be connected to the generator by a bidirectional converter and appropriate control strategy has to be adopted for controlling this power electronic converter. Different generators used with WECS call for different kinds of control strategies. For example, if a DFIG is used with a control requirement only on real power, then a current-linked configuration with thyristorized rectifier and inverter can be used. If it is necessary to have control on both real and reactive power flow from the generator, then voltage source converters have to be employed. The following sections discuss the control schemes for different types of generators and also for the BESS.

6.6.1 PMSG control with diode rectifier and chopper The block diagram of a PMSG with a diode rectifier, a gate turn-off (GTO) thyristor based chopper and an inverter is shown in Fig. 6.2. The PMSG is coupled to the wind turbine and its variable frequency three phase output is rectified with the help of a diode rectifier. Owing to the variations in the wind velocity, the output voltage magnitude and frequency of the PMSG will change. This will result in variations in the diode rectifier output which has to be boosted or stepped down for maintaining the DC link voltage at the inverter input terminals at a preset value. This DC voltage is converted into a fixed frequency (50 Hz) three phase sinusoidal voltage by means of a sinusoidal pulse width modulated (SPWM) inverter for feeding the load which requires a fairly regulated sinusoidal three phase voltage. The duty ratio control of the DC–DC converter is directly linked to the wind velocity, so it can be adjusted to achieve MPPT. The control schematics for the chopper and the inverter are shown in Fig. 6.3. The chopper duty ratio is adjusted such that the machine adjusts its electromagnetic torque for maximum power deliverability for a given wind velocity so that the tip Tm

w_Wind Tm

Gen_speed

A

w_Turb

N

B

Gen speed

m

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Wind turbine

C Permanent magnet synchronous machine

Load A B C

Wind_velocity

duty_ratio A

+

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g A

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–

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g A B – C Inverter +

6.2 Control scheme for PMSG with chopper and inverter.

© Woodhead Publishing Limited, 2010

Pulses

Hybrid wind–diesel energy systems Diode_rect_output

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[Duty_ratio]

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Discrete PI controller Id_actual Iq_actual

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vdvq

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-K-

P1 Uref P2

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DQ to ABC conversion Discrete three-phase PWM generator1

6.3 Control scheme for the chopper and inverter.

speed ratio of the wind turbine is modified to achieve MPPT (Ohyama et al., 2007). The inverter firing is controlled such that the DC input voltage to the inverter is maintained at a specified value and hence the output voltage and frequency of the inverter are appropriately regulated. The actual three phase currents of the inverter are measured and converted into two phase equivalent in the stationary reference frame itself by means of Clarke’s transformation. Subsequently, these two phase currents in the stationary reference frame are converted into direct and quadrature axes components of currents (Id and Iq) in a synchronously rotating reference frame by means of Park’s transformation. To generate the reference values of direct and quadrature axes components of inverter current, the following procedure is adopted: the DC link capacitor voltage is sensed at regular intervals and compared with a specified reference value; the output of this comparator is passed through a PI controller to determine the reference value of the direct axis component current (Id ref) through the inverter. The reference value of the quadrarture axis component of the inverter current (Iq ref) is set in accordance with the desired power factor. These two reference currents (Id ref and Iq ref) are compared with the actual currents drawn by the inverter (Id and Iq) in a comparator and appropriate devices in the inverter are fired such that the errors in the two components of current are nullified. Thus, the voltage and frequency output of the inverter are regulated.

6.6.2 PMSG control with voltage source inverters While using a diode rectifier and a chopper as mentioned in the previous section, the rectifier will draw harmonic-rich currents because of the presence of the chopper and a filter capacitor at its output DC terminals. This problem could be eliminated if two voltage source converters are used in cascade, one on the machine side and another on the load side. Such a scheme is shown in Fig. 6.4. This scheme is termed as a voltage-linked

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Stand-alone and hybrid wind energy systems Tm

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m

N

w_Turb

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Wind turbine

Gen_speed

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Load

g

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g +

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_

C

B C

Machine-side converter

Load-side converter

6.4 Control scheme for PMSG with voltage linked converters.

+

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PI

_

MPPT Desired speed Speed generator comp arator

Discrete PI controller

–K–

Torque Ref

Iq_ref Id_ref

IdIq_Ref [To_machine_side_conv] P1 –K– Uref DQ to ABC P2 conversion1 Discrete three-phase Current PWM Generator1 Regulator

vdvq

IdIq

Id_pmsg Iq_pmsg Id_actual Iq_actual Voltage_of_input_capacitor Ref_capacitor_voltage

+

IdIq PI

_ Capacitor voltage comparator1

PI controller

–K–

Vdvq

Id_Ref

IdIq_Ref

Iq_ref

Current regulator

–K–

P1 Uref P2

[To_load_side_conv]

DQ to ABC conversion Discrete three phase PWM generator1

6.5 Control scheme for the machine-side converter and the load-side converter.

scheme since the input and output converters are connected through a capacitor whose voltage is maintained constant by controlling the firing pulses to the load-side converter (Chinchilla et al., 2006). The control schemes for both the machine-side and load-side converters for this case are shown in Fig. 6.5. The machine-side converter is controlled such that MPPT is achieved for varying wind velocities in the complete range from the cut-in velocity to the rated wind velocity after which the pitch control gets activated. The quadrature axis component of current for the machine-side converter is adjusted such that appropriate value of electromagnetic torque is developed by the generator; the machine and the turbine speeds are adjusted to achieve the tip speed ratio corresponding to the maximum possible power extractable for a given wind velocity. The direct axis component of the current of the machine-side converter is adjusted to a specified value to achieve the desired power factor. When the wind speed becomes high, the pitch controller takes over to ensure that the

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machine still operates within its rated power and also within its safe speed limits. The load-side converter is triggered in order to maintain the voltage across the DC link capacitor at a constant value. This control is similar to the one discussed in the previous section where the PMSG is controlled with the help of a chopper and inverter.

6.6.3 Control schemes for induction generators The DFIG as well as SCIG are commonly used for wind energy conversion systems because of their ability to operate with varying turbine speeds. The major advantage of DFIG is that it can feed power to the grid (in the case of grid-connected WECS) or to the load (in the case of stand-alone WECS), both from the stator and from the rotor sides. The stator side can be connected to the load directly but the rotor voltage-which is at slip frequency has to be converted to grid frequency by a voltage-linked or current-linked converter cascade. The ratings of these converters could be lower and will correspond to slip power; so it results in a major saving in the cost of the converters. But, in SCIGs, the converters have to be rated for the full-load rating of the machine. If the machine has to be operated only over a small speed range, i.e. from synchronous speed (ωs) to (1 + smax) ωs where smax = slip at maximum torque, then there is no need for using a converter–inverter combination at the stator terminals of the SCIG before connecting it to the grid or to the load. If the SCIG has to operate over a wider range of wind velocity, it becomes absolutely necessary to connect it to the grid/load through a converter–inverter cascade. DFIG control using current linked converters The current linked converter cascade for the DFIG control is shown in Fig. 6.6. As this uses thyristorized converters on the rotor side as well as on the load side, there is no possibility of controlling the reactive power. But real power control can be achieved successfully. This current-linked controller can be operated successfully only for sub-synchronous motoring operation and super-synchronous generating operation. This is because the voltage can be reversed in both the converters depending upon the firing angle range at which each of them is operating, whereas the current cannot be reversed. For example, if the machine is operating in sub-synchronous motoring mode, the rotor side converter will be operating at a delay angle less than 90°, that is it will function as a rectifier. The load-side converter will be operating as an inverter at a delay angle which would be greater than 90°. In the super-synchronous generating mode, both these converters will reverse their roles. The firing scheme for the rotor-side converter should

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Stand-alone and hybrid wind energy systems

Gen_speed

m

Tm

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A

b

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c

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Load A B C

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g

g +

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+ A

_

_

C

B C

Rotor-side converter

Load-side converter

6.6 Current-linked converter-based controller for DFIG.

be capable of handling variable frequency and variable phase sequence as the voltage at the rotor terminals will be at slip frequency; the phase sequence of this voltage would be ACB, when it is operating in supersynchronous mode if originally it was having a phase sequence of ABC in the sub-synchronous mode. The grid-side converter has to be triggered with the help of a phase-locked loop (PLL) that would synchronize the triggering signals with a 50 Hz three phase sine-wave generated by an independent oscillator. DFIG control using voltage-linked converters If the DFIG has to be operated in all four quadrants of operation namely, sub-synchronous motoring and generation as well as super-synchronous motoring and generation, it is essential to use voltage-linked converters in its rotor terminals as shown in Fig. 6.7. When the machine is operating in sub-synchronous generation mode, the wind velocity will be low, so some slip power will be fed to the rotor through the converter–inverter combination. Both the mechanical power fed by the wind turbine and the electrical power fed by the converters to the rotor in the form of slip power will be converted into electrical power which will be fed to the grid through the stator terminals. On the other hand, when the wind velocity is high, the machine will be operating in super-synchronous generating mode, wherein the slip power will be flowing from the rotor terminals with the help of the power electronic converters to the load/grid. At the same time, the stator will also be feeding the generated electrical power to the load/grid.

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Hybrid wind–diesel energy systems

Pulses

m

Tm

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A

b

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c

C Doubly-fed induction machine

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205

A

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_

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Load-side converter

6.7 Voltage-linked converter for the control of DFIG.

The control scheme for the voltage-linked converter for the DFIG is similar to that discussed earlier for the PMSG case and hence is not repeated here. Pena et al. (1996) have proposed a vector control scheme for the gridside converter for the control of active power and reactive power drawn from the supply. MPPT can be achieved in the voltage-linked converterbased control scheme for a DFIG by controlling the rotor-side converter suitably (Datta and Ranganathan, 2002, 2003). Control of SCIG The SCIG can be tied to the grid/load directly, if it is operating within a small range of wind velocity values. It will generate a variable frequency voltage and current if it is being operated at wide speed ranges by a wind turbine. This has to be converted into 50 Hz voltage and frequency before being fed to the load. This calls for a converter–inverter combination to be connected at its stator terminals. The scheme is shown in Fig. 6.8. The control of this is similar to that of the scheme discussed earlier for the voltage-linked converters for PMSG.

6.6.4 Control of energy storage units The energy storage unit considered here is a battery energy storage system (BESS). The BESS is connected to the common bus which couples the terminals of both DG and the generator in WECS. The BESS is connected to this bus by means of a bidirectional power electronic converter as

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Stand-alone and hybrid wind energy systems

Wind_velocity

Tm

w_Wind

A

Tm w_Turb

Gen_speed

m

Gen_speed

B Wind turbine C Squirrel cage induction machine A B C

Load

Pulses

g

g

Pulses

+

+

A

A

B

B

–

–

C

C

Stator-side converter

Load-side converter

6.8 Control scheme for the squirrel cage induction machine.

From power electronic converter Id_actual Id_actual Total_power_generated

+

IdIq PI

Load_power_demand

– Power comparator

PI controller

-K-

P1 vdvq

Id Ref

IdIq_Ref

Iq_ref

Current regulator

-K-

[To_Battery_charger]

Uref

P2 DQ to ABC conversion Discrete three phase PWM generator

6.9 Control scheme for a BESS.

depicted in Fig. 6.1. The control for the BESS unit is shown in Fig. 6.9. The real power control is achieved by means of controlling the direct axis component of current of the power electronic converter and the reactive power control is by means of the quadrature axis component of current. The actual three phase currents of this power converter are converted into direct and quadrature axes components of currents by means of Clarke’s and Park’s transformations as explained in Section 6.6.1. The difference between the total power generated by the wind–diesel system and the power demand from the load side is computed and this is the input signal to the control unit of the BESS. This is passed through a proportional-integral (PI) controller to compute the direct axis component of current for the power electronic converter. The quadrature axis component of current is specified based on the desired power factor. The triggering signals for the devices in the power electronic converter are generated by the current controller based on the difference between the reference values of Id and Iq and the actual values of Id and Iq of the converter respectively. It is worth noting that this converter is capable of acting as a rectifier or an inverter.

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Hybrid wind–diesel energy systems

6.7

207

Modelling and simulation of wind–diesel systems

A model developed in the MATLAB/SIMULINK environment for a combined wind–diesel–BESS scheme is shown in Fig. 6.10. For the system illustrated here in the simulation studies, the sizing of various components has been done as follows: The average wind speed considered is 9.5 m/sec. The highest priority load to be met is considered to be 30 kW. The wind turbine should be able to generate this required power of 30 kW at the average wind speed. For this purpose, the wind turbine rotor diameter can be calculated by making use of equation (6.1) as: 30 × 10 3 0.5 × η × 1.225 × 0.5 × 9.53 2 = π ( D 2)

A=

‘D’ being the diameter of the swept area. Then, the value of D can be deduced to be 14.4 m. There should be enough space with a reasonable vertical clearance to install such a wind turbine. To calculate the ratings of the battery, the following calculations can be done Let the load be supplied with a 3-phase AC voltage of 240 V (line to line). When it is converted into DC using a PWM rectifier, the output DC will be about 250 V. For a 30 kW load to be met by a 250 V battery for a duration of one hour, the A h capacity of the 250 V battery should be 120. Initially the battery will go through constant current charging and subsequently, to maintain the battery voltage at the rated value, it will go through constant voltage charging. While discharging through the PWM inverter to meet the high priority load during low wind velocity conditions, it can give a back-up time up to a maximum of 1 h while feeding a 30 kW load. When the medium priority load of 25 kW is also turned on, the wind power generation unit alone will not be capable of meeting the complete load demand of 55 kW in which case the diesel engine has to be turned on. If the load happens to be less than the total generation, then battery will get charged. The rating of the DG set is also considered to be 30 kW, so that even at 40% load it would be able to generate about 12 kW. When the lowest priority load of about 20 kW is also on, then the wind generator, diesel generator and BESS together will have to meet the total load. In the model created for the wind–diesel system, the highest priority load itself has been taken in two parts consisting of 11 kW and 16 kW respectively. The wind energy conversion system uses a PMSG rated for about 27 kW with its terminal voltage being close to 230 V (line to line) when the wind velocity is at the rated value of 10 m/s. At this wind velocity the generator runs at the rated speed of 3000 rpm. If the wind velocity changes, the MPPT algorithm adjusts the speed of the generator by adjusting the

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Loads

B C

B C

B

B C C Three-Ph1

A

A

[I_pmsg]

[V_pmsg]

B2

–

+ C

IBGT Inverter

C

B

A

g

Vdc

[V dc]

lab

Pwm controller

Three-Ph

A

A

Vabc pulse

B5

B3

Vf_

pm

–

+

[cont]

D1

[Vdc]

-K-

[V d1]

Vdc1

+ v–

L1

O

Rad to RPM1

c

i + –

w

Vt

Vt

Pm

m

vtref

wref

+

[Wind]

[V d1]

1 Vtref(pu)

1.0

wref(pu)

–

C

B

A

Bridge

C1

+

[Wind]

S

N

-K-

m

Pu to Nm

MPPT

I_Bat

V_Bat

Q_Wind

[cont]

Bus



[Gen_speed]

Chopper duty controller

pulses Vdc_ref

I_Bat Q_Bat P Q measurement

V_Bat

I_wind Q_Load

P_Bat

[I_pmsg]

I_Load

V_wind Q_DG

Vdc

P_Load

P_DG

V_Load P_Wind

I_DG

V_DG

[V_pmsg]

[I_Load]

[V_Load]

[I_DG]

[V_DG]

selector Permanent magnet synchronous machine

C

B

A

Tm

Wind Turbine

Wind speed (m/s)

Pitch angle (deg) Tm (pu)

Generator speed (pu)

R

Diesel Engine Speed&Voltage Control

Vf (pu)

Pmec(pu)

Wind speed

+ –v Vdc

Clock

[Gen speed]

C IBGT Inverter1

B

A

g

pulse

Synchronous machine pu standard

C

B

A

m

[V_DG]

[I_pmsg]

I_Bat

V_Bat

[I_DG]

[V_DG]

[DG_speed]

6.10 Complete SIMULINK model for the wind–diesel system with BESS.

[I_Load]

[V_Load]

B4

A B C



g a k

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developed electromagnetic torque or the quadrature axis component of current of the generator. This results in a variation in the magnitude and frequency of voltage generated by the machine and hence, it is converted into DC and then back to AC of a specified frequency with the help of an inverter before being fed to the load. As the output of the inverter is in the form of a pulse width modulated (PWM) voltage waveform, it has to be converted into a sinusoidal voltage by connecting an inter-phase reactor of a few millihenries (mH) of inductance. The rating of the diesel generator employed is also chosen to be 30 kW to match the sum of priority 2 and priority 3 loads put together. The diesel engine is controlled by a governor mechanism. It is coupled to a normal (wound field) synchronous generator whose output is perfectly sinusoidal. The two generators from the DG and the WECS are paralleled together to feed the loads from a common bus. The battery charger is connected to the same bus where the load is connected. The battery charger has the ability to allow bidirectional power flow. So, if the wind velocity is low and the generated power is less than the load demand, then the battery would discharge to supply the additional power required by the load. On the other hand, if the wind velocity is high enough and the load is lower, then the battery would get charged. A set of simulations have been run on this system and the results are presented in the following sections.

6.7.1 A combination of WECS–BESS feeding a load The first simulation has been done only with a WECS–BESS combination feeding a load. The total load connected to the system has been considered as a combination of two loads, one of 11 kW and another of 16 kW capacities respectively. The circuit breaker that connects the 16 kW load is first closed at 0.6 s, once the system reaches steady state after the initial starting transients; the same circuit breaker is subsequently opened at 1 s. The wind velocity varies from the original value of 10 to 8 m/s at 0.8 s. It remains constant at the value of 8 m/s until 1.4 s, after which it changes back to 10 m/s. The power variations for this simulation case are shown in Fig. 6.11, which clearly show that the battery feeds power to the load when the load demand is higher and wind velocity is at 8 m/s. This happens from 0.8 to 1 s. For rest of the duration, the battery power is negative which means it is getting charged. Figure 6.12 gives the response of the PMSG that is coupled to the turbine for the variations in the wind velocity. It may be observed from this figure that as the velocity decreases, the voltage and current outputs of the generator diminish. The torque and the speed also are suitably varied by the controllers according to the wind velocity variations so that the maximum power output for that wind velocity is achieved. The

© Woodhead Publishing Limited, 2010

Load power

PMSG output

Battery output

210 2

Stand-alone and hybrid wind energy systems × 104

0 –2 –4 4

0.2

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1 Time (s)

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1.6

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2

× 104

2 0 –2 2

× 104

1 0

6.11 Power variations: battery output, WECS output and load consumption in Watts.

voltages at the PMSG terminals are converted in to DC and then back to AC at 50 Hz with the help of a PWM inverter and hence the voltages are in the form of a PWM waveform.

6.7.2 A combination of WECS–DG–BESS feeding a load The next set of simulations has been done with the DG set also in service. The disturbances given to this system are wind velocity changes and the load changes. The response of the system for these disturbances is shown in Fig. 6.13. The total load included in the system for this case amounts to 40 kW. The DG is capable of generating a real power of 30 kW. The generator that is connected to the diesel engine is a wound field synchronous machine and the engine speed is regulated with the help of a governor. The total load on the system is 15 kW to start with. From 0 to 0.4 s, the load remains constant at 15 kW and the WECS generates about 25 kW of power at a wind velocity of 10 m/s. The DG generates a power of about 6 kW (20% of its rated power). So, the excess power generated, which is about 16 kW, is utilized in charging the battery. At 0.4 s, the wind velocity reduces to 8 m/s. The generated power from the WECS decreases to 12 kW due to which the battery is able to tap a power of only 3 kW. At 0.6 s, the load increases to 22 kW; at this point, the battery releases its energy and is able to meet the shortfall in the generated power that amounts to 4 kW. The load increases further to 26 kW at 1 s and

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Wind velocity (m/s)

Hybrid wind–diesel energy systems (a) 12 10 8 6 0

Vs (V)

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6.12 Response of PMSG for wind speed variations: (a) wind velocity, (b) PMSG line to line voltage, (c) PMSG phase current, (d) electromagnetic torque, (e) speed of the PMSG.

DG steps up its generation by another 4 kW. Now the BESS, DG and WECS together are able to meet the load demand. At 1.1 s, the wind velocity changes to 10.5 m/s and hence the WECS is able to deliver about 30 kW of power. At this point, the BESS starts storing energy again. At 1.2 s, there is a reduction in the load demand and the DG power is also brought down to 20% of its rating. The excess power generated goes towards battery charging.

6.8

Conclusions

Hybrid wind–diesel power generation systems are gaining a lot of importance because of their suitability from both economic and technical points

© Woodhead Publishing Limited, 2010

Battery power

212

Stand-alone and hybrid wind energy systems × 104 1 1 –1 –2 0.2

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× 104 3.0 2.5 2.0 1.5 1.0 0.2 14 000 12 000 10 000 8000 6000 0.2 × 104 2.5 2.0 1.5 0.2

Time (s)

6.13 Power flow in watts from WECS, DG and BESS during load changes.

of view especially for meeting loads at remote locations such as the rural areas far away from the main high-voltage grid, mountainous areas and islands. In this chapter, a description of some modern hybrid wind–diesel systems including salient considerations in the design of such systems, different state-of-the-art types of generator such as PMSG and DFIG used for WECS and the control strategies adopted in them have been presented. The salient aspects of modelling and some typical simulation results have been presented for the system for varying wind velocities and varying load conditions. The key factor in the control of a hybrid wind diesel system is to minimize the diesel fuel consumption, simultaneously maintaining the power output of the diesel engine at least at a specified minimum permissible value whenever it is running and at the same time limiting its on–off switching durations within the permissible range under both varying wind and load demand conditions. It would be advantageous if the wind power generator could be controlled in such a manner as to achieve the maximum power capture by the wind turbine rotor under varying wind velocity condi-

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tions. For this purpose, various control schemes have been suggested in the literature and some of them have been discussed in this chapter. Considering that wind power generation itself is a growing and fertile area of research, it is difficult to do justice to all the aspects of this topic in the limited space available. The idea was to cover most of the salient aspects so that the reader can build on this further.

6.9

Future trends

The future electrical energy systems can be visualized as a combination of entities having two distinct structures, one set consisting of highly efficient thermal power plants of both coal as well as gas-fired types operating with almost zero greenhouse gas emissions, nuclear power plants and major storage type hydro-power plants interconnected by high voltage transmission lines and the other set consisting of DG systems utilizing renewable/ non-conventional energy resources such as wind, solar, small hydro and agro/small industrial wastes, interconnected through medium/low-voltage distribution lines and feeding local loads. The wind–diesel system, which is the subject of this chapter, falls under the second category. Though these systems are normally called wind–diesel systems, they may also use biofuels, produced from the locally available wastes/energy plantations grown locally. More and more of this type of system are expected to be installed in the future because they hardly pollute the atmosphere, keep the transmission losses at a low level because of proximity of the loads to these generators. Incidentally this also becomes an effective method for the disposal of wastes generated. When many of these distributed resources are operated together, it becomes necessary to address a host of technical issues, such as optimal operation of these systems, coordination of protection systems of these DGs, stability of these systems and the quality of power generated by these systems. Coordinated control of wind and diesel power generation systems along with the energy storage unit is a challenging task. The controllers for these systems have to be designed not only to operate properly and stably in steady state for various wind and load conditions that arise in the system, but should also be able to yield good dynamic response while going from one steady state to another steady state. For ensuring this, as these systems evolve, one can think of a more sophisticated control system, which will be able to tune their control parameters on-line using self-tuning regulators, fuzzy logic controllers or neuro-fuzzy controllers. Some of these have been attempted in the literature (Ko et al., 2003; Mufti et al., 1996), although actual implementation of these intelligent algorithms in WECS are still rare. Looking into the development of new types of generating scheme, switched reluctance generator and permanent magnet brushless generators are

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seriously being explored in WECS especially for lower power generation capacity. This has opened up a lot of new avenues for research in design and development of variable speed generation systems using the abovementioned generating schemes. BESS is also undergoing a lot of changes due to new materials that are being used in the battery technology that reduce the charging time and increase the storage capacity. Some of these will change the face of wind–diesel system completely.

6.10

References

Barton J P and Infield D G, ‘Energy storage and its use with intermittent renewable energy’, IEEE Transaction on EC, Vol. 19, No. 2, June 2004, Pages 441–448. Borowy B S and Salameh Z M, ‘Dynamic response of a stand-alone wind energy conversion system with battery energy storage to a wind gust’, IEEE Transactions on EC, Vol. 12, No. 1, March 1997, Pages 73–78. Chiang S, Huang S C and Liaw C M, ‘Three-phase multi functional battery energy storage system’, IEE Proceedings on EPA, Vol. 142, No. 4 July 1995, Pages 275–289. Chinchilla M, Arnaltes S and Burgos J C, ‘Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the grid’, IEEE Transactions on EC, Vol. 21, Issue 1, March 2006, Pages 130–135. Datta R and Ranganathan V T, ‘Variable speed wind power generation using doubly fed wound rotor induction machine – A comparison with alternative schemes’, IEEE Transactions on EC, Vol. 17, No. 3, Sept 2002, Pages 414–421. Datta R and Ranganathan V T, ‘A method of tracking peak power points for a variable speed wind energy conversion system’, IEEE Transactions on EC, Vol. 18, No. 1, March 2003, Pages 163–168. Freris L L, Wind Energy Conversion Systems, Prentice Hall International (UK) Ltd, 1990. Jenkins N, Allan R, Crossley P, Kirschen D and Strbac G, Embedded Generation, IEE Press, 2000. Kaldellis J K and Kavadias K A, ‘Cost–benefit analysis of remote hybrid wind–diesel power stations: Case study Aegean Sea islands’, Energy Policy, Volume 35, Issue 3, March 2007, Pages 1525–1538. Kaldellis J K, Kondili E and Filios A, ‘Sizing a hybrid wind–diesel stand-alone system on the basis of minimum long-term electricity production cost’, Applied Energy, Volume 83, Issue 12, December 2006, Pages 1384–1403. Kaldellis J K, Zafirakis D and Kavadias K, ‘Techno-economic comparison of energy storage systems for island autonomous electrical networks’, Renewable and Sustainable Energy Reviews, Volume 13, Issue 2, February 2009, Pages 378–392. Ko H S, Niimura T and Lee K Y, ‘An intelligent controller for a remote wind-diesel power system – Design and dynamic performance analysis’, Proceedings of the PES General Meeting, Vol. 4, July 2003. Lin C E, Shiao Y S, Huang C L and Sung P S, ‘A real and reactive power control approach for battery energy storage system’, IEEE Transactions on Power Systems, Vol. 7, No. 3, August 1992, Pages 1132–1140.

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Masters G M, Renewable and Efficient Electric Power Systems, Wiley-Interscience, 2004. McKenna E and Olsen T L, Performance and Economics of a Wind–Diesel Hybrid Energy System, National Renewable Energy Laboratory, 1999 www.nrel.gov/ docs/fy99osti/24663.pdf Mufti M, Balasubramanian R and Tripathy S C, ‘Self-tuning control of wind–diesel power systems’, Proceedings of the IEEE Conference PEDES, 1996, Vol. 1, Pages 258–264. Mutoh N and Nagasawa A, ‘A maximum power point tracking control method suitable for compact wind power generators’, Proceedings of the IEEE Power Electronics Specialists Conference (PESC), 18–22 June, 2006, Pages 1–7. Ohyama K, Arinaga S and Yamashita Y, ‘Modeling and simulation of variable speed wind generator system using boost converter of permanent magnet synchronous generator’, European Conference on Power Electronics and Applications, 2–5 Sept 2007 Pages 1–9. Pena R, Clare J C and Asher G M, ‘Doubly fed induction generator using back to back PWM converters and its application to variable-speed wind-energy generation’, IEE Proceedings on EPA, Vol. 143, No. 3, May 1996, Pages 231–241. Richardson R D and McNerney G M, ‘wind energy systems’, Proceedings of the IEEE, Vol. 81, Issue 3, March 1993, Pages 378–389. Spooner E and Williamson A C, ‘Direct coupled permanent magnet generators for wind turbine applications’, IEE Proceedings on EPA, Vol. 143, No. 1, January 1996, Pages 1–8. Wai R J, Lin C Y and Chang Y R, ‘Novel maximum power extraction algorithm for PMSG wind generation system’, IET EPA, Vol. 1 (2), 2007, Pages 275–283.

© Woodhead Publishing Limited, 2010

7 Hybrid wind–photovoltaic energy systems G. NOTTON, University of Corsica, France

Abstract: Photovoltaics (PVs) offer consumers the ability to generate electricity in a clean, quiet and reliable way by a direct conversion of solar light energy into electricity. This chapter begins with a brief presentation of solar and wind resources while special attention is given to their complementarity. After discussing their design, each subsystem is presented and the calculation of kW h cost is discussed. Optimal methods for hybrid system sizing are shown while, finally, two cases studies which implement and illustrate the various points discussed in the chapter are presented. Key words: wind/photovoltaic system, solar and wind energy resources, optimal sizing method.

7.1

Introduction

Solar and wind energy resources vary greatly over time and do not usually match with the time distribution of the load; thus photovoltaic (PV) or wind energy systems alone must be oversized if each system is used separately, leading to high electrical energy costs. Integrating solar and wind energy into the same system attenuates fluctuations in the power produced, improving total system performance and reliability, and significantly reducing the size of storage required. Wind–PV systems combine both wind and PV technologies, often coupling them to an engine generator and an energy storage system. Of course, the sizing of such a system is much more complicated than that of a single source system due to the higher number of variables and parameters to be considered in the optimal design. The design of such a system involves the determination of optimum values for the wind turbine’s rated power, PV array peak power and storage capacity (and sometimes also the engine generator characteristics) that meet the required reliability conditions for the system. In the following sections, a brief presentation of both solar and wind renewable energy sources, and of each subsystem, is given before some consideration of the optimal sizing of such a hybrid system is presented.

7.2

Renewable energy resources and their potential

Solar and wind power are accepted as dependable and widely available renewable energy sources. No renewable system can be implemented 216 © Woodhead Publishing Limited, 2010

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without a precise study of the available resources due to the high variability of the two sources. In a wind–PV energy system, the problem is doubly important because of the presence of two energy sources. A good summary of the energy model is given by Jebaraj & Iniyan (2006).

7.2.1 The solar energy resource In any solar energy conversion system, knowledge of global solar radiation is extremely important for the optimal design and forecasting of the system’s performance. Solar radiation arrives at the surface of the Earth in a spectrum of wavelengths, ranging from X-rays to radiowaves, the sun radiating as a black body at 5777 K; however, 99.9% of the emitted energy is between 0.2 and 8 μm. Each wavelength possesses a distinct ability to work and effect system transformation. In PV conversion, the range of useful wavelengths is between 0.35 and 1.1 μm for a silicon PV cell but, generally, global solar irradiation is measured in its entire spectrum for PV system studies (using a pyranometer or silicon irradiance sensor). The difference noted in the levels of spectral irradiance between the top of the atmosphere and the surface of the Earth is due to the absorption taking place by various chemical compounds, such as O2, O3, H2O and CO2, by aerosols and by Rayleigh scattering (Fig. 7.1). Owing to atmospheric effects, solar radiation at the Earth’s surface consists of two components: beam radiation from the sun (without change of direction), and diffuse radiation received from the

Direct normal spectral irradiance (W m–2 mm–1)

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Black body at 5777 K

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H2 O

H2O,CO2 H2O,CO2 H2O 0 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 O3 Visible

Wavelength (mm)

7.1 Direct normal spectral irradiance and identification of various molecular absorbers.

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Solar radiation reflected back to space Thin clouds Beam

Cloud

Beam Anisotropic diffuse radiation on horizontal surface

Aerosols

Beam

Sky diffuse

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b Air molecules

Ground-reflected diffuse

Inclination

7.2 Attenuation of the extraterrestrial radiation and components of the solar radiation onto a tilted surface.

sky; when a solar collector is inclined, it receives a third component: ground reflected radiation (Fig. 7.2). The total radiation received on a tilted surface Gβ is expressed by (Iqbal, 1983): Gβ = Gb,β + Gr,β + Gd,β

7.1

where Gb,β is the beam radiation on the tilted surface; Gr,β is the diffuse reflected radiation on the tilted surface; Gd,β is the sky diffuse radiation on the tilted surface. The components of solar irradiance for clear and cloudy skies are shown in Fig. 7.3. Horizontal solar global radiation is the form most commonly measured. Depending on the objective (whether for sizing, behaviour simulation, etc.) the time-step of useful meteorological data varies from hourly to monthly average daily data. Solar irradiation on non-horizontal surfaces is much less available, and is difficult to model owing to the effect of diffuse radiation anisotropy over the sky’s dome. Converting solar irradiation from the horizontal to the inclined plane is realized by using accurate models for monthly average values (INES, 2009) while less reliable methods are used for data measured on an hourly basis (Notton et al., 2006). Additionally, solar data can also be found on the web (NASA, 2009; NREL, 2009; UMass Lowell, 2009). Knowledge of the sun’s position allows two useful pieces of information to be obtained: the solar panels’ optimal inclination and a solar diagram for the estimation of shading on the PV modules. Solar position is computed from various angles, such as declination, zenith angle and hour angle (Iqbal, 1983). Optimal inclination depends on latitude, φ, on the seasonal

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1000

Solar irradiance on tilted plane (W m–2)

03 April b = 30°

21 August b = 45°

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400 Beam 300 Ground-reflected diffuse

200 100 0 5

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7.3 Two examples of tilted solar irradiances.

distribution of the load and on the meteorological conditions of the site. For long periods without marked seasonal weather conditions, the estimation of the inclination effects are based on calculations from extraterrestrial or beam irradiation, in order to avoid the use of a diffuse radiation model; in these conditions, the inclination depends on φ and is, for a yearly, winter and summer optimal solar irradiation, equal to φ, φ + 10 ° and φ − 10 °, respectively (Duffie & Beckman, 2006). Figure 7.4 shows the impact of the inclination angle (for Ajaccio, France) on extraterrestrial and ground solar irradiation. It is useful to estimate shading with a solar diagram: Fig. 7.5 traces the apparent path of the sun in the sky for a given latitude, point by point, indicating hours, in the actual local solar time. To put the masks of the sun path on the diagram, the height and azimuth of a few important points were measured, including a house and a tree, as shown in Fig. 7.5. In this example, the house will shade the PV system form the sun at solar noon on 21 December. There are numerous PV sizing softwares that integrate the calculation of a solar mask.

7.2.2 Wind energy resource The wind energy per unit of cross-sectional area during a time period Δt is: Ewind =

1 ρa v3 Δt 2

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12.5 Inclination of PV modules 11.5 0°

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Daily extraterrestrial solar irradiation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

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7.4 Influence of panel inclination on monthly mean values of solar irradiations from extraterrestrial and ground estimated data (for Ajaccio).

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7.5 Position of the sun and solar diagram for shading estimation.

where air density ρa = 1.23 kg m−3 at 15 °C and at sea level. Ambient temperature, pressure and air humidity influence the air density. This energy cannot be entirely used by a wind turbine, because only the energy available between the cut-in and cut-out speeds is useful. The energy output of a wind turbine is obtained by coupling the wind speed probability distribution and the turbine’s power curve, as seen in Fig. 7.6. The Weibull probability density functions are commonly used and widely adopted (Celik, 2003; Chang & Tu, 2007; Ngala et al., 2007; Bagiorgas et al., 2007; Elamouri & Ben Amar, 2008; Kaldellis, 2008); this function is a special case

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900

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Wind turbine 2 kW Production: 2435 kW h Number of running hours at nominal power: 1218 h Average power: 0.278 kW Production coefficient Cp: 0.139

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7.6 Coupling between wind distribution and wind turbine power curve for production estimation.

of the gamma distribution and is characterized by its probability density function f(v) and cumulative distribution function F(v) in the following formulae: k v f ( v) = ⎛ ⎞ ⎛ ⎞ ⎝ A⎠ ⎝ A⎠

(k −1)

⎡ v k⎤ exp ⎢ − ⎛ ⎞ ⎥ ⎣ ⎝ A⎠ ⎦

⎡ v k⎤ F ( v) = 1 − exp ⎢ − ⎛ ⎞ ⎥ ⎣ ⎝ A⎠ ⎦

7.3 7.4

where A is the scale parameter (in m s−1), k is the unit-less shape parameter and v the wind speed. The most common method to calculate Weibull distribution parameters is based on the use of Equation 7.4, performing the logarithm calculation twice for the two terms of the equation and then employing a least-squares fit method to calculate the two coefficients, A and k (Fig. 7.7), characterizing the wind potential of a site. Since most available wind speed measurements are made near the ground (generally at 10 m) it is necessary to extrapolate the wind speed profile within the surface atmospheric boundary layer. The most common extrapolation is based on a power-law velocity equation preferred by engineers for its mathematical simplicity (Justus et al., 1976; Zoumakis, 1993): v ⎛ z⎞ =⎜ ⎟ v0 ⎝ z0 ⎠

α

7.5

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Experimental cumulative distribution

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0.5

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y = 1.4201x – 1.936 R 2 = 0.9998

Least-squares fit method

3 2 1 0 –1 –0.5 0 –1 –2 –3 20 –4 Ln {–Ln [1–F(v)]}

Frequency

7.7 Method to calculate the Weibull distribution.

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 3

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k = 1.42

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Wind speed m s–1 Experimental verification

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Frequency

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where α depends on the topography and climatic conditions, v and v0 are the wind speeds at heights z and z0 (z0 being the reference height).

7.2.3 Complementarity of renewable resources

Wind energy Wind speed (W h m–2) (m s–1)

Solar and wind energy are non-depletable, site-dependent, non-polluting, and potential sources of alternative energy. As specified by Ozdamar et al. (2005), the biggest problem for a separate use of wind energy and solar energy is their discontinuity: a solar energy system by itself cannot provide a continuous source of energy due to its low availability during each no-sun period and during winter, while a wind energy system cannot itself satisfy constant load demand due to the different magnitudes of wind speed from one hour to another. In general, the variations/fluctuations of solar and/or wind energy generation do not match the time distribution of load demand on a continuous basis. The complementary nature of wind and solar resources in the US was examined as early as 1981 by Aspliden (1981), and a more recent study was realized by Reichling & Kulacki (2008). Special attention was paid to the energy sources’ complementarity in studies of wind/solar hybrid systems (Aspliden, 1981; Katti & Khedkar, 2007; Gilau & Small, 2008; Mahmoudi et al., 2008; Reichling & Kulacki, 2008). The yearly variability of these two sources is illustrated in Fig. 7.8 (for Ersa, France). Combining wind and solar energy together in a hybrid power plant helps to smooth this variation. The decision whether or not to use both resources also depends on the load to be supplied, because the objective is to make production equal consumption. Actually, in most cases, it is desirable that the two sources are complementary. In this study, we quantify this 30 20 10 0

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7.8 Illustration of the variability of the renewable energy resources (wind speed, wind energy, solar irradiation) in Ersa (France).

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complementarity firstly on a monthly basis, and secondly on an hourly basis in a monthly and yearly analysis using hourly data for solar radiation per unit of horizontal plane, and with wind velocity measured at 10 m above ground. We cannot quantitatively compare wind energy per unit of cross-sectional area (Eq. [7.2]) and solar energy per unit area of horizontal plane because the two unit areas do not refer to the same type of surface. Thus, at first, we observed the variation of the two renewable resources on a monthly basis: what we wanted to see was that, during a no-sunny month (in winter), wind energy was higher than during sunny periods. Two parameters were used: •

the correlation coefficient (CC) which quantifies the temporal simultaneity of solar and wind power: CC =

{⎡⎣∑

∑ ( yi i =1 N

N i =1

( yi − y ) ( xi − x )

}

N 2 2 − y ) ⎤ ⎡ ∑ i =1 ( xi − x ) ⎤ ⎦⎣ ⎦

12

7.6

•

where yi and xi are the ith monthly mean _ values _ of daily wind energy and daily solar irradiation values, and y and x are the respective wind energy and solar irradiation mean values; • the ratio between the two annual renewable energies: R=

Ewind Esolar

7.7

_ _ where E solar and E wind are the annual average daily solar irradiation per m2 and daily wind energy per m2. When CC is near 1, the two renewable sources vary in the same way. For a good complementarity, CC must be near −1. The spatial variability of the wind resource is much greater than the solar resource’s, thus: the higher R is, the more appreciable the renewable potential becomes. For example, the results for two Corsican sites are shown in Fig. 7.9: Ersa has a good renewable potential and the two resources are complementary; in Ajaccio, the solar resource is about the same but the wind potential is low and its monthly distribution is close to that of solar energy. It is important to see the behaviour of these two resources on a daily scale because it plays an important role in sizing storage. For each month, we computed the two previous parameters: CC where yi and xi are the ith monthly mean values of hourly wind energy and solar irradiation at hour i, and R is the ratio of the daily wind and solar energies. If R is used to demonstrate how wind energy can complete solar energy in terms of quantity, a new parameter is necessary to quantify the available renewable

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225 Monthly mean value of daily wind energy per m2 (kW h m–2)

Monthly mean value of daily solar irradiation (kW h m–2)

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25 Ajaccio CC = 0.693 R = 0.433 Ersa CC = –0.903 R = 2.992

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WE Sliven (March) SE Sliven (March) WE Ersa (November) SE Ersa (November) Wind Speed Sliven (March) Wind Speed Ersa (November)

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Sliven March CC = –0.612 R = 0.307 P = 289 984 W h2 m–4 Ersa November CC = +0.934 R = 9.127 P = 33 245 750 W h2 m–4

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7.10 Two examples of wind and solar energy daily repartition (Ersa, France and Sliven, Bulgaria).

energy: the product of the two renewable energies (P). Figure 7.10 shows the monthly distribution of the two renewable resources for Sliven, Bulgaria (with good complementarity but low renewable potential), and Ersa, France (with bad complementarity but good potential). More specifically, in Fig.

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7.10, R, P and CC values are demonstrated for two representative months for each of the two sites. It is impossible to make a general conclusion about the complementarity of the two renewable resources, because any conclusion depends on the site and on the period considered. Moreover, for a more complete assessment, the load curve (i.e. load distribution versus time) needs to be taken into account, and a positive correlation between electricity demand and availability of electricity generated from solar and wind power needs to be looked for. We need to know which energy system among a PV system alone, a wind energy system, or a combined PV–wind system, is the most suitable to supply the demand (from the national grid and/or from a remote load). The influence of the complementarity of these sources on the sizing of a wind–PV hybrid system is important and, sometimes, installing a hybrid PV–wind system will entail no benefit (see Section 7.6 below).

7.3

Design and configuration of a wind–photovoltaic (PV) hybrid energy system

The concept of a wind–PV hybrid energy system is shown in Fig. 7.11. In this general configuration, an engine generator has been added. The wind energy conversion system (WECS) and the PV system are operated in parallel in order to supply electrical power to the load, and the excess energy generated is supplied to batteries. For emergency cases, where wind/ solar generation and stored energy are not sufficient to supply the load, the

DC Load Control system PV array

Inverter

AC Load

Electrical interface

AC/DC battery charger Wind turbine

Engine generator

Battery bank

7.11 Diagram of PV–wind–engine generator hybrid system.

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back-up engine generator operates and is used to charge the battery and/ or to supply directly to the load.

7.4

Modelling and simulation of a wind–photovoltaic (PV) hybrid energy system

7.4.1 PV system The following description is restricted to crystalline silicon solar modules which account for about 90% of the world’s PV production. Other technologies, such as amorphous silicon, CdTe, copper-indium-diselenide (CIS) and tandem cells have been developed, but their use in power systems is still limited. PV module performance is highly influenced by weather conditions, especially solar radiation and PV module temperature. The shortcircuit current Isc and the open-circuit voltage Voc are the two main parameters of the I–V curve (Fig. 7.12). Isc is almost proportional to solar irradiance and Voc increases slowly when solar irradiance increases. When the PV temperature increases, Voc leads to a decrease of the available maximum electrical power, in spite of a small increase of the short-circuit current Isc. The working point of a PV panel depends on the load characteristic; when the battery is connected, its voltage imposes PV voltage. Sometimes, a maximum power point tracker (MPPT) is used and is connected to the PV array to extract the maximum available power, whatever the solar irradiance is.

4.0

PMPP

Isc 3.5

IMPP

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25 °C, G = 1000 W m Power

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7.12 I–V and P–V curves of a PV module.

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A PV array is constituted by Ns modules in series and Np modules in parallel; the total PV power is: PPV = Np × NS × Pmodule × ηMPPT × ηoth

7.8

where ηMPPT is the MPPT efficiency (generally around 95%) and ηoth represents other existing losses (cable resistance, imbalance, dust, etc.). Note that if no MPPT is used, i.e.ηMPPT = 1, another coefficient must be introduced in the equation to take into account that the PV module does not operate in MPP conditions. The PV power is estimated using both energy and electrical models. The simplest model expresses a direct relationship between the output power Pmodule and in-plane solar irradiance, using the PV module efficiency, and also being dependent on irradiance Gβ and cell temperature θcell: ηPV = ηPV,ref[1 − β1(θcell − θcell,ref) + γ1logGβ]

7.9

where ηPV,ref is the reference module efficiency at θcell,ref = 25 °C and at Gβ = 1000 W m−2, γ1 and β1 are the solar irradiance and temperature coefficients of the PV module, respectively, both depending on the material used. The parameters (θcell,ref; ηPV,ref; β1; γ1) are given by the manufacturer for the silicon (β1 = 0.0048 °C−1; γ1 = 0.12) (Evans, 1981) but often γ is taken as equal to 0 because the solar irradiance does not have a high influence on the PV efficiency (Habib et al., 1999; Deshmukh & Deshmukh, 2008). In Fig. 7.13, the influence of the cell temperature on the experimental efficiency of a 700 Wp BP585F monocrystalline silicon PV array is shown. 0.16 0.14

PV array efficiency

0.12 Cell temperatures

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under 11 °C 13–15 °C 17–19 °C 21–23 °C 25–27 °C 29–31 °C 33–35 °C 37–39 °C 41–43 °C 45–47 °C over 49 °C

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7.13 Influence of the cell temperature on m-Si PV array efficiency.

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Id1

Iph

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Rsh

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I

U

7.14 Equivalent circuit of a PV module (with one or two diodes).

The PV module can also be modelled using an electrical equivalent circuit (Fig. 7.14) consisting of a current source in parallel with one diode (Hecktheuer et al., 2002; Rosell & Ibanez, 2006; Karatepe et al., 2007), or with two diodes (Eicker, 2003; Priyanka et al., 2008). Although the second model is more accurate, the first is more often used in wind–PV systems modelling (Underwood et al., 2007; Soltani & Debbache, 2008) and is briefly presented here. The voltage–current equation is: q ⎡ (V + RS I )⎤⎥ ⎫⎪ V + RS I ⎪⎧ ⎢ ⎦ I = I L − I 0 ⎨exp ⎣ A0 KTcell − 1⎬ − Rsh ⎩⎪ ⎭⎪

7.10

where IL is the photocurrent, I0 the diode-saturation current, q the standard electron charge, A0 the diode ideality factor and K the Boltzmann constant. Rs and Rsh are the series and shunt resistances, respectively. Then, Pmodule = V × I and the maximum power point is found from (∂P/∂V) = 0. The characteristic equation of the PV module is a transcend equation which has no analytical solution. Assuming that an MPPT is used, the formulas for calculating the optimal operating point current and voltage under arbitrary conditions, and using easily available parameters, have the following forms (Lasnier et al., 1988; Borowy & Salameh, 1996; Ai et al., 2003): ⎧ ⎡ ⎛V ⎞ ⎤⎫ I MPP = I sc,ref ⎨1 − C1 ⎢exp ⎜ MPP,ref ⎟ − 1⎥ ⎬ + ΔII ⎝ C2Voc,ref ⎠ ⎦ ⎭ ⎩ ⎣

7.11

⎡ ⎛ Gβ ⎞ ⎤ VMPP = VMPP,ref ⎢1 + 0.0539 log 10 ⎜ + β0 ΔT ⎝ Gβ ,ref ⎟⎠ ⎥⎦ ⎣

7.12

VMPP,ref −1 I Voc,ref ⎛ ⎞ ⎡ V ⎤ C1 = ⎜ 1 − MPP,ref ⎟ exp ⎢ − MPP,ref ⎥ and C2 = ⎝ I I sc,ref ⎠ ⎛ ⎞ ⎣ C2Voc,ref ⎦ ln ⎜ 1 − MPP,ref ⎟ ⎝ I sc,ref ⎠

7.13

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Stand-alone and hybrid wind energy systems ⎛ Gβ ⎞ ⎛ Gβ ⎞ ΔI = α 0 ⎜ ΔT + ⎜ − 1⎟ I sc,ref ⎟ ⎝ Gβ ,ref ⎠ ⎝ Gβ ,ref ⎠

7.14

ΔT = Tcell − Tcell,ref

7.15

with α0 and β0 being the module current and voltage temperature coefficients, respectively. The PV module power is calculated by multiplying the voltage by the current in MPP conditions. In all these models, the cell temperature θcell appears, influencing the I–V characteristics and the electrical efficiency of the PV module. The most common method to determine θcell consists of using the normal operating cell temperature (NOCT) (calculated for a wind speed v = 1 m s−1, an ambient temperature θa = 20 °C and a hemispherical irradiance Gβ = 800 W m−2) given by PV module manufacturers:

θ cell = θ a + ( NOCT − 20 °C )

Gβ 800

7.16

Other methods to determine the cell temperature are given by Jones & Underwood (2001) and Mattei et al. (2006). If a PV module is shaded, very dramatic effects occur on its power– voltage curve and, even if only a very small fraction of the module or cell is shaded, a very significant power reduction takes place. This partial shading can occur as a result of chimneys, trees, parts of other buildings, etc., and special attention should be given to this problem so as to avoid a high reduction of the PV system’s performance (Hecktheuer et al., 2002; Eicker, 2003; Karatepe et al., 2007). In Fig. 7.15, the influence of the partial shading of one cell on the I–V and P–V curves is shown. With only 50% of one cell shaded (out of the 36 serial cells), power is reduced by 25%, and with 100% of one cell shaded, the power falls by 57%. Special attention should be paid to this problem when designing a system.

7.4.2 Small and medium wind turbines Three items of data are essential to calculate the output power of a WECS: • • •

the power curve (joining aerodynamic, mechanical transmission and converting efficiencies) given by the manufacturer; the hourly data of wind speed for the installation site; the hub height.

Different WECS with the same rated power can generate, at the same site, very different amount of electrical energy because of the difference of the power curve (Notton et al., 2008). This influence is even more important when storage is present because it introduces a lag between production and

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Hybrid wind–photovoltaic energy systems 5.0 Open symbols: power (right y-axis) Solid symbols: current (left y-axis)

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7.15 Some experimental I–V curves for BP585F PV module with various percentages of one shaded cell.

consumption. Thus, the sizing of a wind system is strongly influenced by the wind turbine’s power profile (Notton et al., 2001). An inventory of WECS from 0.2 to 20 kW available on the European market was undertaken. The form of the power curves vary greatly and the WECS output power can reach a value of 40% greater than PWECS,rated. Among the 59 power curves studied, eight types of WECS power profiles were selected (see Fig. 7.16; the electrical power P has been divided by the rated power PWECS,rated for a better comparison). Three models usually used in hybrid system sizing are also shown: the linear model, Pallabazzer model (Pallabazzer, 1995; Underwood et al., 2007) and Chang model (Chang & Tu, 2007). These models are not representative of small and medium WECS and, considering the importance of the power curves on system sizing, it is preferable to use real WECS power curves. To obtain the WECS output power, wind speed measured at 10 m must be first calculated at hub height, using Eq. [7.5] and then coupled to the WECS power curves.

7.4.3 Battery storage Lead–acid batteries are usually used for energy storage in hybrid systems to store surplus energy, to regulate system voltage and to supply load in case of insufficient solar radiation and/or wind. Only 2 or 3 days of autonomy is required for batteries in wind–PV hybrid systems, while 5 to 6 days

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1.0

p = (P/Pnom)

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7.16 Eight types of WECS power curve (0.2–20 kW) and three wellknown models.

of autonomy are necessary in separate PV or wind systems (Muselli et al., 1999; Deshmukh & Deshmukh, 2008). Other storage means can be used but lead–acid batteries are a low-cost, maintenance-free and highly efficient technology. Battery behaviour modelling is very complex and various models are available (Zhou et al., 2008). Battery capacity depends on maximum depth of discharge (DOD), temperature and age. A battery’s state of charge (SOC) is generally expressed as a percentage, according to the following: during the charging process: SOC(t + 1) = SOC(t) · [1 − σ(t)] + [Ibat(t) · Δt · ηc(t)/Cbat]

7.17

during the discharge process: SOC(t + 1) = SOC(t) · [1 − σ(t)] − [Ibat(t) · Δt/ηdis(t)]/Cbat

7.18

with (1 − DOD) ≤ SOC(t) ≤ 1 where σ(t) is the hourly self-discharge rate depending on the battery state but often taken constant at about 0.02% (Yang et al., 2007). Cbat is the nominal capacity of the battery (A h). The charge efficiency ηc depends on the SOC and the charging current and has a value between 0.65 and 0.85 (Yang et al., 2007; Diaf et al., 2008a) and the discharge efficiency ηdis is generally taken equal to 1 (Ai et al., 2003; Diaf et al., 2008a). For high DOD, phenomena such as sulfatation, freezing or stratification occur in the battery and reduce the battery lifetime, thus generally DOD is taken between 50

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and 80%. Ibat = P/Vbat(t) is calculated from an energy balance between the input power (wind + PV) and the output power (load) and depends on the hybrid system configuration. Vbat(t) can be calculated by simple or complex models (Ai et al., 2003; Yang et al., 2007; Zhou et al., 2008) and using parameters depending on SOC. In a first approximation, Vbat(t) can be taken as constant. The influence of the temperature on the rated capacity of the battery and the floating voltage is sometimes taken into account (Ai et al., 2003; Zhou et al., 2008; Diaf et al., 2008a).

7.4.4 DC/AC and AC/DC converters In a PV–wind hybrid system, several electrical converters can be used: • •

DC/AC converters or inverters to supply AC load (between charge regulator and load); AC/DC converters or rectifiers, after the wind turbine or engine generator.

Using inverters has three major disadvantages: a high cost depending on the quality of the output signal (square, pseudo-sinus or sinus); a decrease in the overall system performance (inverter efficiency depending on the load ratio and self-consumption); and a risk of failure. Modern electronic inverters are efficient over a wide range of outputs. If a stand-alone inverter performance reaches 87–95% at two-thirds of its rated capacity, its efficiency decreases sharply when the power supply falls below this value and can reach values under 50% at a very small load. An inverter requires some power just to run itself, so inverter efficiency will be low when running very low loads. In a typical home, there are many hours of the day when electrical load is very low. One solution consists of using as many inverters as AC loads to supply; thus, each converter has a higher performance, increasing the overall system reliability but with a significant increase of the system cost. The best solution in the opinion of many authors is the mixed system, in which the hybrid system is divided into two subsystems, a DC one for lighting, radio and television, for instance, and an AC one for other equipment; in this way any inverter will be turned on only when an AC load requires it. Consequently, it can be seen that sizing an inverter correctly/well for its required purpose is important (Tsagas, 2002): if it is undersized, there will not be enough power; demanding more than its limit will shut it off; if it is oversized, it will be much less efficient (due to standing losses) and more costly to buy and run. Moreover, some inverters operate without interruption even if no electrical charge is supplied, and thus have a significant self-consumption.

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The choice of electrical signal (square, pseudo-sinus or sinus) produced by an inverter depends on the type of connected appliances, but the inverter price increases with the quality of the signal and its performance, and so can be increased up to four times for the same nominal power. In most publications, inverter efficiency is taken as a constant and equal to 90–95%, which is sometimes high compared with commercial data but, actually, varies with the load. When the load is fluctuating, it is desirable to use the inverter efficiency curve versus load power in any modelling. If the wind turbine has AC output, the use of a rectifier is necessary to charge the battery. If an auxiliary engine generator is used, a rectifier must be connected. The rectifier efficiency depends on the type of AC power, type of rectifying elements, type of rectifier and the percentage of load of the unit, as for the inverter. The AC/DC converter efficiency is generally taken to be some percentage points lower than the equivalent inverter efficiency. Wind turbine rectifier peak power is calculated from the WECS nominal power. For an auxiliary generator rectifier, its peak power is computed according to the maximum battery charge current rate, at around 20% of the battery’s nominal capacity (Sandia National Laboratories, 1995; Yang et al., 2003). Sometimes, when an engine generator is used, the inverter is replaced by an inverter-charger capable of converting DC from batteries to AC for the load, as well as converting AC from the generator to DC to charge batteries. Switching from one mode to another can be done manually or with an automatic transfer switch.

7.4.5 Auxiliary engine generator An auxiliary generator is used in case of a long lack of wind or sun. It can just charge the batteries, which is the usual case, or simultaneously charge the batteries and the AC load directly. The choice of a generator depends on the nature of the load. Generally, diesel generators are used because they are more economical but, on the other hand, it is difficult to find diesel generators with very low power and, in that case, gasoline generators can be used. A diesel generator’s running speed is a function of the expected running time; if it is used occasionally, to charge a battery for example (in the case of a hybrid system), a 3000 rpm unit may be sufficient; for more frequent use, a 1500 rpm is recommended. To determine the rated capacity of the engine generator, two cases are considered:

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if the generator is directly connected to the load, then the rated capacity must be at least equal to the maximum power demand; if it is used only as a battery charger, then (as stated in Section 7.4.4) the maximum battery charge current rate is around 20% of the battery nominal capacity, and the rated capacity of the generator is Cbat · Vbat/5ηrect.

In the first configuration, the generator has a rated capacity greater than in the second case and often runs at partial load even if it charges the battery simultaneously; moreover, it consumes more fuel. In the second configuration, the generator is chosen in such a way that it always runs at full load, i.e. with a higher efficiency. A linear relationship links the fuel consumption Qv to the produced power PEG (Fig. 7.17). The no-load fuel consumption for a small engine generator may be a high fraction of the full-load fuel consumption Q0v (25–40%) (Notton et al., 1996; Kaldellis, 2007). Thus, generator efficiency is higher at high load than at partial and low loads. ⎛ Qv ⎞ ⎛ PEG ⎞ ⎜⎝ 0 ⎟⎠ = a ⎜⎝ 0 ⎟⎠ + (1 − a) Qv PEG

7.19

1.3 1.2 1.1 1.0

Gasoline Diesel 3000 rpm Diesel 1500 rpm Gasoline engine generator Qv° (l/kW h) = 0.7368PGE−0.2954

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Specific consumption at nominal power (l/kW h)

The specific fuel consumption Q0v depends on several parameters defining the quality of the engine. It varies greatly for low rated power (<5 kV A) but is relatively constant for high powers (Calloway, 1986; De L.Musgrove, 1988) (Fig. 7.17). The fuel generator is started automatically according to the battery state of charge, or manually by the operator. Generators require periodic maintenance: oil change, air, oil and fuel filter changes. The annual maintenance

Nominal power P° (kW)

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7.17 Specific consumption of engine generator and fuel consumption variation.

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cost is often taken as proportional to the investment cost (between 5 and 23%, according to the literature). This hypothesis is not applicable to a hybrid system because the generator is not continuously used and the annual engine running time changes. Maintenance is, then, calculated as proportional to the running time or as the sum of fixed and variable costs linked with the operating time (Notton et al., 1998). Calculation of the annual running hours allows operation and maintenance (O&M) costs and the lifetime of the generator to be computed.

7.4.6 Energy management and control unit All the energy fluxes cross by this essential subsystem which optimally manages the system in such a way that the load demand is continuously satisfied. There are several common operating strategies (Katti & Khedkar, 2007; Nema et al., 2009) which can be employed in a wind–PV–engine generator system: •

•

• •

the load is directly supplied by PV and wind generators; the wind turbine provides AC current directly to the load or (more usually) is converted to DC current via a rectifier; if the total power generated by renewable generators is higher than the demand, additional power is charged into the batteries; when the storage is full, this additional power is lost or used for another utilization; if the total power generated by renewable generators is less than the demand, additional power is discharged from the batteries; if batteries cannot supply this additional power, the electrical load shuts down and/or a back-up generator is started; the back-up generator, then, can only charge batteries (more usual) or simultaneously charge the battery and supply the load.

The battery control strategy determines the effectiveness of battery charging and energy source utilization (Zhou et al., 2008; Nema et al., 2009). It prevents both overcharge and over-discharge, both of which can be detrimental to a battery’s life. If a battery’s voltage rises to the overcharge protection voltage, first the PV array is totally or partially disconnected, and then the wind turbine is unplugged. The load is disconnected (totally or partially) or the power generated is dissipated by a connected resistance if the voltage goes below the over-discharge voltage. The load is fed again when the voltage reaches the connection voltage. If a back-up generator is used, it is started when the voltage reaches the starting voltage (more than the over-discharge voltage) and stopped at a voltage threshold depending on the strategy used. In some systems, the charge controller is more ‘intelligent’ and more efficient: in addition to the voltage, it also takes the SOC, temperature and other parameters into consideration.

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7.4.7 Cost calculation The kW h cost depends on many factors, both physical (configuration, strategy, subsystem size, load power and energy, site implantation, renewable energy potential, etc.) and economic (inflation rate and discount rate). The life-cycle cost (LCC) is the sum of the initial installation cost (material and installation), O&M costs, replacement costs and fuel costs if an auxiliary generator is present (see Fig. 7.18). In adding all of these costs together, any of them that occur after installation day must be updated, i.e. every cost must be expressed at its present value. Only some information is given here and, for further explanation, readers are referred to the literature (see Notton et al., 1998; Kaldellis & Kavadias, 2007; Kaldellis et al., 2007; Diaf et al., 2008a). The cost of material is often given by unit of power or energy, and the literature offers a wide range of assumptions for estimating hardware costs (Notton et al., 1998; Diaf et al., 2008a). The specific price of each subsystem (PV array, wind turbine, battery, electrical converters, etc.) depends on the size of the subsystem and is generally expressed by a power law (Kaldellis & Kavadias, 2007; Kaldellis et al., 2007, Diaf et al., 2008b). Figure 7.19 shows

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Hardware cost

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Regulator

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O&M cost Annual fuel consumption

Rectifier inverter

System lifetime Sub-system lifetime

Fuel cost Inflation rate

Fuel cost per litre

Replacement cost

System cost Load consumption

7.18 Methodology for cost calculation.

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Stand-alone and hybrid wind energy systems (b) Battery specific price (€/kW h)

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7.19 Specific cost for (a) small wind turbine, (b) battery, (c) engine generator (d) inverter.

the influence of size on the specific costs for small wind turbines, batteries, engine generators and inverters from data collected on the French market. General formulations for the estimation of specific prices are given in the references (see Notton et al., 1998; Kaldellis & Kavadias, 2007; Kaldellis et al., 2007; Diaf et al., 2008a). Installation is often estimated as a percentage of material costs (Notton et al., 1998; Diaf et al., 2008b). The literature gives a very wide range of percentages for PV and wind subsystem hardware costs, ranging from 15% to 50%, with the average value around 25%. Hardware and installation costs constitute the investment costs. The annual O&M cost, CO&M,0, is generally calculated as a percentage of the investment cost, or as a cost with mixed fixed and variable parts (the variable part being about 1% per year for PV and 3% per year for wind). For a fuel engine, O&M costs depend partially on the annual running time (Notton et al., 1998; Kaldellis & Kavadias, 2007). The lifetime of a PV–wind hybrid system is often considered to be 25 years but inverters, batteries and regulators have a shorter lifetime and must be changed several times; thus, a replacement cost must also be taken into account. To compute this cost, the lifetime of each device must be

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known, something which is not always easy to estimate, particularly for the battery because its lifetime depends on the charge–discharge regime. Each predicted future cost must be converted into a cost at the year of installation, year 0. A mean annual increase of the cost gi must be assumed to calculate the present value; gi can be given different values for O&M, for each subsystem, and for produced electricity. With interest rate i, the present value Xi,0 (base year 0) of an expenditure Xi,j realized in the year j is: ⎡ ( g + 1) ⎤ X i ,0 = X i , j ⎢ i ⎥ ⎣ ( i + 1) ⎦

−j

7.20

where gi depends on the subsystem (inverter, battery, regulator, etc.) or O&M costs being considered. gi and i depend on various economic parameters and are difficult to estimate. The influence of these parameters on a hybrid PV–wind system is important (Notton et al., 1998). For calculation of the replacement cost, the years of replacement are determined and then each replacement cost calculated (Notton et al., 1998; Kaldellis & Kavadias, 2007). System maintenance is required each year and the cost of this maintenance increases over time, as the system ages. Thus, the total O&M cost CO&M is calculated by: L ⎡ ( g + 1) ⎤ CO&M = CO&M,0 × ∑ ⎢ i ⎥ j =1 ⎣ ( i + 1) ⎦

j

7.21

with L being the hybrid system lifetime (in years). The total cost of a hybrid system is, then, the sum of investment costs, replacement costs, O&M costs and fuel costs. The electricity generation cost CkW h (C/kW h) is the total cost of the installation during the L year service period, Ctot,syst, divided by the total energy generation during the same period, Eprod, taking into account the mean annual escalation rate, gelec, of the produced electricity price: CkW h =

7.5

Ctot,syst ⎡ ( g + 1) ⎤ Eprod ∑ ⎢ elec ⎥ j =1 ⎣ ( i + 1) ⎦ L

j

7.22

Sizing and optimization of a wind–photovoltaic (PV) hybrid energy system

7.5.1 Methodology During the operation of the PV–wind system described in Section 7.3 above, two situations can occur. First, if hourly energy demand EL(t)/ηinv is

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less than the sum of the output hourly energies of the wind generator ηrect × EWT(t) and PV array EPV(t). In this case, the energy surplus [EL(t)/ηinv] − ηrectEWT(t) − EPV(t) is stored via the charge controller in the battery. The new SOC is then calculated using Eq. [7.17]. If the SOC reaches 100%, the residual energy is generally lost or forwarded to low priority loads. Second, if the hourly energy demand EL(t)/ηinv is greater than the renewable energy system output power [ηrectEWT(t) + EPV(t)]. The energy deficit [EL(t)/ηinv] − ηrectEWT(t) − EPV(t) is covered by the batteries if SOC(t) > (1 − DOD), and the SOC is calculated using Eq. [7.18]. If the energy stored in batteries is insufficient to satisfy the energy load requirements for hour t, there are two possibilities: 1

If no auxiliary fuel generator is connected, an energy deficit LPS(t) (load energy not provided during the time interval [t − 1,t]) is created: LPS(t) = EL(t) − [EPV(t) + ηrectEWT(t) + SOC(t − 1)Cbat − (1 − DOD)Cbat] · ηinv

2

7.23

If an auxiliary generator is connected, it is started and is stopped when the battery reaches a given SOC threshold (often 100%).

Two reliability concepts are used in sizing a hybrid system (Ai et al., 2003; Yang et al., 2007): •

The loss of load probability (LLP or LOLP): a temporal concept defined as the power failure time period Tf divided by the total working time of the hybrid system T; LLP = Tf/T

•

7.24

the loss of power supply probability (LPSP): an energy concept defined as the ratio of power deficit LPS(t) to the sum of the load demand EL(t) during the same period: T

LPSP = ∑ LPS ( t ) t =1

T

∑E

L

(t )

7.25

i =1

An LLPS or LLP equal to zero means that the load is always satisfied; an LLPS or LLP equal to 1 means that the load is never supplied. In case 2 above, LLPS is always zero because, in case of system failure, the auxiliary generator is started; only the running time and fuel consumption increase. A flowchart synthesising the methodology and various input and output parameters is shown in Fig. 7.20.

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Hybrid wind–photovoltaic energy systems Load power profile AC and/or DC

PV array efficiency

Hourly meteorological data solar irradiation, wind speed (10 m) temperature

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Start/stop number

7.20 Synthesis of the methodology and input and output data.

7.5.2 Excess energy, solar fraction and gross production Wasted or excess energy EEXC is energy not produced by a renewable energy converter (PV modules and wind turbine) because battery capacity is at its highest level (SOC = 1) and the load does not require all power produced. This excess energy can also have been produced and dissipated in a resistance or sent towards another load but not used for the main load. Often, a dimensionless parameter is used by dividing the excess energy by the total produced energy, and thus allowing that part of the energy produced but not used by the system to be known (as a fraction or percentage). The performance of renewable energy systems can be characterized by a renewable energy sources fraction (RESF), often called solar fraction because wind energy comes from the sun, which is the fraction of energy that is produced orderived from a renewable source (RESF = 1 if no fuel engine is used). Generally, excess energy is not taken into account when calculating RESF, defined by: RESF =

EPV + EWT − EEXC EPV + EWT − EEXC + EAUX

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with EAUX being auxiliary generator energy (this parameter only being used when an auxiliary generator exists). Another parameter characterizing system performance is gross production, PRG, originating from a solar source (PV + wind) in units of load energy (EL), and defined by: PRG =

EPV + EWT EL

7.27

7.5.3 Sizing optimization Optimum sizing is generally calculated on an hourly basis, to take the temporal distribution of the load and the energy sources into account. To meet an energy load with a given LLPS value, several PV–wind hybrid system configurations are possible. Some configurations are not technically feasible (storage too large, incompatibility between generator power and storage size, etc.). The optimized wind–PV system under investigation must meet the energy demand with minimum costs, with minimum fuel use or green gas emissions, or with the minimum energy pay-back period (a comparison of produced energy with the energy content of the entire system). Nema et al. (2009) write that various optimization techniques are used in the literature to compute the optimal design of hybrid systems, such as linear programming, a probabilistic approach, iterative techniques, dynamic programming and multi-objective genetic algorithms. The assessment of wind/PV system costs using annualized life-cycle costing methods is an important step in system sizing; indeed, it is often the main criterion of optimization used in the literature. Using well-costed optimized systems increases the economic attractiveness of such systems and their acceptance by users.

7.6

Wind–photovoltaic (PV) hybrid energy system: case studies

Figure 7.21 presents a household with an electrical AC load. The PV–wind hybrid systems (without an auxiliary generator) are located at distant sites about 130 km away: in Ajaccio (a non-windy site with an annual average wind speed of 3.5 m s−1), and in Ersa (a windy site, with an annual average wind speed of 7.11 m s−1). The renewable energy potential of these two sites is shown in Fig. 7.9; the solar potential is almost equal but the wind potential is very different. The PV–wind hybrid system was sized to satisfy the load with an LLPS equal to zero, i.e. all the load was satisfied without interruption. For each configuration (PV array peak power, wind turbine rated power, battery

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600 500 400 300 200 100 0

0

4

8

12 Time (hour)

16

20

24

7.21 House electrical load.

capacity) the electricity generation cost CkW h (C/kW h) was computed using the assumptions presented in Section 7.4.6. Figure 7.22 shows the PV subsystem and wind turbine subsystem sizes (on the left axis) and corresponding levelized kW h costs (on the right axis), for various batteries with different storage sizes (from 2 to 5 days of storage). For Ajaccio, a non-windy site, the ‘best’ configuration found is for 3 days’ storage, while for Ersa, the ‘best’ configuration is for only 2 days’ storage. The optimal configurations for a PV–wind hybrid system, for PV alone and for wind alone, are given in Table 7.1. Battery size decreases when a hybrid system is used, wherever the site is, and it is one of the main advantages of using hybrid systems because the battery bank represents a high percentage of total levelized costs. Kaldellis et al. (2006) obtained the same conclusions showing that the introduction of PV panels into a wind energy system considerably reduces the complete installation dimensions, and decreases the corresponding operational costs owing to the significant battery capacity reduction imposed. A PV–wind hybrid system is very suitable for Ersa compared with the two other systems, and the kW h cost is reduced by 35%. For Ajaccio, a PV system alone is more suitable because the wind potential at that site is not sufficient for the addition of a wind turbine, which would not provide any benefit to the profitability of the production system but would result in an increase in the system’s complexity.

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4000

2 days’ storage

3 days’ storage

4 days’ storage

5 days’ storage

2.3

3500 Ajaccio

2.1

1.7

2000 1500

1.5

1000

1.3

500 0

4500

500

1000

2 days’ storage

4000

1500 2000 2500 Wind turbine power (W) 3 days’ storage

3000

4 days’ storage

3500

5 days’ storage

Open symbols: kW h cost (right y-axis) Solid symbols: PV peak power (left y-axis) Ersa

3500 PV peak power (Wp)

1.1

Open symbols : kW h cost (right y-axis) Solid symbols : PV peak power (left y-axis) 0

kW h cost (€/kW h)

1.9

2500

0.9 4000

2.3 2.1

3000

1.9

2500

1.7

2000

1.5

1500

kW h cost (€/kW h)

PV peak power (Wp)

3000

1.3 1000 1.1

500 0

0

50

1000

1500 2000 2500 3000 Wind turbine power (W)

3500

0.9 4000

7.22 System configurations and energy levelized cost for LLPS = 0.

For the optimal configurations of the hybrid system, the SOC evolution over a year is plotted in Fig. 7.23. Even though the minimum SOC reached by the two systems is the same, the SOC variation differs from one site to another. As Ai et al. (2003) have said, using a PV–wind hybrid system increases the batteries’ lifetime remarkably compared with the utilization of either a wind system or a PV system alone, because the charge regime is more appropriate; prolonging the battery lifetime reduces the electricity production costs.

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Table 7.1 Optimal sizing for PV–wind hybrid system, PV alone and wind alone systems in Ajaccio and Ersa Site

Battery storage size (days)

PV–wind hybrid system Ajaccio 3 Ersa 2

PV peak power (W)

Wind turbine rated power (W)

Levelized kW h cost (C kW h−1)

2350 850

200 600

1.54 0.92

Wind alone system Ajaccio Ersa

6 5

0 0

4600 1200

2.54 1.40

PV alone system Ajaccio Ersa

3 3

2450 2050

0 0

1.52 1.38

The monthly values of PV energy, wind turbine energy, excess energy and load energy are plotted in Fig. 7.24. If the annual energy produced at each site with the corresponding optimized PV–wind hybrid system are quite similar, the monthly distribution of these energies differs greatly. The monthly variation is more pronounced for Ajaccio than for Ersa due to a better complementarity of the two renewable resources in Ersa, as seen in Section 7.2.3. Thus, the profitability of such a hybrid system is linked to a great extent to the characteristics of the solar and wind resources. The annual excess energy represents about 55% of total electrical renewable energy for the two cases studies but its monthly distribution changes: for Ajaccio, between 23% in December to 70% in July; for Ersa, between 44% in December to 61% in June. Gross production (PRG) varies between 1.48 to 3.98 for Ajaccio and between 2.15 to 3.02 for Ersa depending on the month, with a yearly average around 2.5–2.7. Thus, on a yearly basis, an optimized PV–wind hybrid system produces 2.5 times more energy than the load energy and about 55% of the energy produced by both the wind turbine and the PV modules is lost, owing to the necessity to reach an LLPS equal to zero (giving total autonomy). This important element of excess energy is linked to the utilization of two random power sources and the necessity to satisfy the load at all times. It has been shown that increasing the LLPS greatly reduces the excess energy (Notton et al., 1996, 2001), improving the system performances. Adding an auxiliary source as an engine generator allows total autonomy to be reached without increasing the energy excess and with a reduction in size of the other components, which in turn induces a reduction of the kW h cost.

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1.0

Hourly state of charge

0.9 0.8 Ajaccio

0.7 0.6 0.5 0.4 0.3

DOD 0

876

1752

2628

3504

4380 Hours

5256

6132

7008

7884

8760

5256

6132

7008

7884

8760

1.0

Hourly state of charge

0.9 0.8 Ersa

0.7 0.6 0.5 0.4 0.3

0

876

1752

2628

3504

4380 Hours

7.23 Hourly variation of battery SOC for optimal hybrid system configuration.

These results demonstrate the importance of a thorough preliminary study of the potential for solar and wind and their complementarity; the characteristics of renewable resources (both their energies and temporal distribution) strongly influence the sizing, the SOC and the distribution of energy.

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450 Ajaccio

400

Ersa

Monthly energy (kW h)

350

Epv Ewt Etot Eexc Eload

300 250 200 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month

7.24 Monthly energy balance for the optimal PV–wind hybrid system in Ajaccio and Ersa.

7.7

Future trends

Various research projects and improvements concerning PV–wind hybrid systems are already under way or must be realized soon, including the following: •

New controlling methods using artificial intelligence and other expert systems to manage energy flux control. These advanced methods will improve the performances of such systems and the quality of the electricity provided. • New battery technologies, which are more reliable and have reduced maintenance needs, have been developed for other applications and are being implemented in hybrid energy systems. Other storage means are also in development, such as the promising fuel cells, flywheels or hydropumps for large energy systems. • The reduction of the kW h cost, which can be achieved by decreasing the manufacturing cost of various components (particularly of PV cells), or by the development of improved optimization procedures in the design of wind–PV systems. • The design and development of software tools for pre-feasibility, sizing, simulation and open architecture research, specifically for application to hybrid wind/PV systems.

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7.8

Conclusions

Using a PV–wind energy system substantially reduces the size of the production subsystem (wind turbine or PV modules) and of the storage required, compared with a single source only system, provided that the wind and solar potential are suitable. Consequently, a hybrid energy system can significantly reduce the total life-cycle costs and make the utilization of renewable sources more profitable compared with conventional electrical systems. The wind resource is more site-dependent than the solar resource, while solar potential can be considered identical over a larger area. Moreover, wind is more unpredictable than direct sunshine and its variation more important. If demand increases at a particular site, it will be more practical to add new PV modules than another wind turbine because installing a new wind generator needs a new mast and often a new converter while adding new PV modules can be realized without important modifications to the system design.

7.9

References

Ai, B, Yang, H, Shen, H, Liao, X (2003), ‘Computer-aided design of PV/wind hybrid system’, Renew Energy, 28, 1491–1512. Aspliden, C (1981), ‘Hybrid solar-wind energy conversion systems meteorological aspects’, Report no. PNL-SA-10063. Richland: Pacific Northwest Laboratory. Bagiorgas, HS, Assimakopoulos, MN, Theoharopoulos, D, Matthopoulos, D, Mihalakakou, GK (2007), ‘Electricity generation using wind energy conversion systems in the area of Western Greece’, Energy Convers Mngt, 48, 1640– 1655. Borowy, BS, Salameh, ZM (1996), ‘Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system’, IEEE Transactions on Energy Conversion, 11–2, 367–375. Calloway, TM (1986), ‘Design of an intermediate-sized autonomous photovoltaicdiesel power plant’, Sandia Report, SAND85-2136. Celik, AN (2003), ‘Energy output estimation for small-scale wind power generators using Weibull-representative wind data’, J Wind Eng Ind Aerodynamics, 91, 693–707. Chang, TJ, Tu, YL (2007), ‘Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: a case study of Taiwan’, Renew Energy, 32, 1999–2010. De L.Musgrove, AR (1988), ‘The optimization of hybrid energy conversion systems using the dynamic programming model RAPSODY’, Int J Energy Res, 12, 447–457. Deshmukh, MK, Deshmukh, SS (2008), ‘Modeling of hybrid renewable energy systems’, Renew Sustain Energy Rev, 12, 235–249.

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Diaf, S, Notton, G, Belhamel, M, Haddadi, M, Louche, A (2008a), ‘Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions’, Appl Energy, 85, 968–987. Diaf, S, Belhamel, M, Haddadi, M, Louche, A (2008b), ‘Technical and economic assessment of hybrid photovoltaic/wind system with battery storage in Corsica island’, Energy Policy, 36, 743–754. Duffie, JA, Beckman, WA (2006), Solar Engineering of Thermal Processes, Wiley, 3th edition. Eicker, U (2003), Solar Technologies for Buildings, Wiley. Elamouri, M, Ben Amar, F (2008), ‘Wind energy potential in Tunisia’, Renew Energy, 33, 758–768. Evans, DL (1981), ‘Simplified method for predicting photovoltaic array output’, Solar Energy, 27(6), 555–560. Gilau, AM, Small, MJ (2008), ‘Designing cost-effective seawater reverse osmosis system under optimal energy options’, Renew Energy, 33, 617–630. Habib, MA, Said, SAM, El-Hadidy, MA, Al-Zaharna, I (1999), ‘Optimization procedure of a hybrid photovoltaic wind energy system’, Energy, 24, 919– 929. Hecktheuer, LA, Krenzinger, A, Prieb, CWM (2002), ‘Methodology for photovoltaic modules characterization and shading effects analysis’, J Braz Soc Mech Sci, 24–1, 126–132. Iqbal, M (1983), An Introduction to Solar Radiation, Academic Press Inc. INES (Institut National de l’Energie Solaire), 2009, http://www.ines-solaire.com [accessed January 2009]. Jebaraj, S, Iniyan, S (2006), ‘A review of energy models’, Renew Sustain Energy Rev, 10, 281–311. Jones, AD, Underwood, CP (2001), ‘A thermal model for photovoltaic systems’, Solar Energy, 70, 349–359. Justus, CG, Hargraves, WR, Yalcin, A (1976), ‘Nationwide assessments of potential output from wind powered generators’, J Appl Meteorol, 15, 673–678. Kaldellis, JK (2007), ‘An integrated model for performance simulation of hybrid wind–diesel systems’, Renew Energy, 32, 1544–1564. Kaldellis, JK (2008), ‘The wind potential on the maximum wind energy penetration in autonomous electrical grids’, Renew Energy, 33–7, 1665–1677. Kaldellis, JK, Kavadias, KA (2007), ‘Cost–benefit analysis of remote hybrid winddiesel power stations: case study Aegean Sea islands’, Energy Policy, 35, 1525–1538. Kaldellis, JK, Kostas, P, Filios, A (2006), ‘Minimization of the energy storage requirements of a stand-alone wind power installation by means of photovoltaic panels’, Wind Energy, 9, 383–397. Kaldellis, JK, Spyropoulos, G, Kavadias, K (2007), Computational Applications of Soft Energy Resources: Solar potential-photovoltaic applications–Solar heat systems, 1st ed. a Stamoulis. Karatepe, E, Boztepe, M, Colak, M (2007), ‘Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells’, Solar Energy, 81, 977–992. Katti, PK, Khedkar, MK (2007), ‘Alternative energy facilities based on site matching and generation unit sizing for remote area power supply’, Renew Energy, 32, 1346–1362.

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Lasnier, F, Ang, TG, Lwin, KS (1988), Solar Photovoltaic Handbook, Energy Technology Division, Asian Institute of Technology. Mahmoudi, H, Abdul-Wahab, SA, Goosen, MFA, Sablani, SS, Perret, J, Ouagued, A, Spahis, N (2008), ‘Weather data and analysis of hybrid photovoltaic–wind power generation systems adapted to a seawater greenhouse desalination unit designed for arid coastal countries’, Desalination, 222, 119–127. Mattei, M, Notton, G, Cristofari, C, Muselli, M, Poggi, P (2006), ‘Calculation of the polycrystalline PV module temperature using a simple method of energy balance’, Renew Energy, 31, 553–567. Muselli, M, Notton, G, Louche, A (1999), Design of hybrid-photovoltaic power generator with optimization of energy management. Solar Energy, 65–3, 143–157. NASA, 2009, Atmospheric Science Data Center, http://eosweb.larc.nasa.gov/ [accessed January 2009]. Nema, P, Nema, RK, Rangnekar, S (2009), ‘A current and future state of art development of hybrid energy system using wind and PV solar: a review’, Renew Sustain Energy Rev, 13–8, 2096–2103. Ngala, GM, Alkali, B, Aji, MA (2007), ‘Viability of wind energy as a power generation source in Maiduguri, Borno state, Nigeria’, Renew Energy, 32, 2242– 2246. Notton, G, Muselli, M, Louche, A (1996), ‘Autonomous hybrid photovoltaic power plant using a back-up generator: a case study in a Mediterranean island’, Renew Energy, 7–4, 371–391. Notton, G, Muselli, M, Poggi, P (1998), ‘Costing of a stand-alone photovoltaic system’, Energy, 23–4, 289–308. Notton, G, Muselli, M, Poggi, P, Louche, A (2001), ‘Decentralized wind energy systems providing small electrical loads in remote area’, Int J Energy Res, 25, 141–164. Notton, G, Poggi, P, Cristofari, C (2006), ‘Predicting hourly solar irradiations on inclined surfaces based on the horizontal measurements: performances of the association of well-known mathematical models’, Energy Convers Mgmt, 47, 13–14, 1816–1829. Notton, G, Lazarov, V, Stoyanov, L (2008), ‘Study of small scale wind turbine productivity according to wind speed distributions and power curves’, Proceeding of World Renewable Energy Congress X, 19–25 July 2008, Glasgow, Scotland, 2259–2264. NREL (National Renewable Energy Laboratory) (2009), Renewable resource data center, http://www.nrel.gov/rredc/ [accessed January 2009]. Ozdamar, A, Ozbalta, N, Akin, A, Yildirim, ED (2005), ‘An application of a combined wind and solar energy system in Izmir’, Renew Sustain Energy Rev, 9, 624–637. Pallabazzer, R (1995), ‘Evaluation of wind-generator potentiality’, Solar Energy, 55, 49–59. Priyanka, S, Singh, SN, Lal, M, Husain, M (2008), ‘Temperature dependence of I–V characteristics and performance parameters of silicon solar cell’, Sol Energy Mat Sol Cells, 92, 1611–1616. Reichling, JP, Kulacki, FA (2008), ‘Utility scale hybrid wind–solar thermal electrical generation: a case study for Minnesota’, Energy, 33, 626–638.

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Rosell, JI, Ibanez, M (2006), ‘Modelling power output in photovoltaic modules for outdoor operating conditions’, Energy Conv Mngt, 47, 2424–2430. Sandia National Laboratories (1995), Stand-alone Photovoltaic Systems: a Handbook of Recommended Design Practices, Sandia National Laboratories Report SAND87–7023, Albuquerque, New Mexico. Soltani, F, Debbache, N (2008), ‘Integration of converter losses in the modelling of hybrid photovoltaic–wind generating system’, Europ J Sci Res, 21–4, 707–718. Tsagas, I (2002), ‘Laboratory evaluation of DC/AC inverters for stand-alone and grid-connected photovoltaic systems’. MSc’s Dissertation, University of Strathclyde, Energy Systems Research Unit. UMass Lowell (2009), Solar Energy Engineering, Solar Irradiation Database, http:// energy.caeds.eng.uml.edu/fpdb/Irrdata.asp [accessed January 2009]. Underwood, CP, Ramachandran, J, Giddings, RD, Alwan, Z (2007), ‘Renewableenergy clusters for remote communities’, Appl Energy, 84, 579–598. Yang, HX, Lu, L, Burnett, J (2003), ‘Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong’, Renew Energy, 28, 1813–1824. Yang, H, Liu, L, Zhou, W (2007), ‘A novel optimization model for hybrid solarwind power generation system’, Solar Energy, 81, 76–84. Zhou, W, Yang, H, Fang, Z (2008), ‘Battery behavior prediction and battery working states analysis of a hybrid solar-wind power generation system’, Renew Energy, 33, 1413–1423. Zoumakis, NM (1993), ‘The dependence of the power-law exponent on surface roughness and stability in a neutrally and stably stratified surface boundary layer’, Atmosphera, 6, 79–83.

7.10 A A0 CC Cbat C1, C2 CkWh CO&M Ctot,syst EAUX EEXC Eprod E_wind E_wind Esolar EPV EWT

Nomenclature scale parameter in the Weibull distribution diode ideality factor correlation coefficient battery capacity coefficient used in the MPP PV power model electricity generation cost operation and maintenance cost total cost of the hybrid system hourly auxiliary generator energy hourly energy surplus total electrical production wind energy per unit of cross-sectional area annual wind energy annual solar energy hourly photovoltaic energy hourly wind turbine energy

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m s−1

Ah

C kW h−1 C C Wh Wh kW h W h m−2 W h m−2 W h m−2 Wh Wh

252

Stand-alone and hybrid wind energy systems

EL Gβ Gb,β Gr,β

hourly load energy total solar radiation on a β tilted plane beam solar radiation on a β tilted plane ground reflected solar radiation on a β tilted plane sky diffuse solar radiation on a β tilted plane current battery current photocurrent diode-saturation current current at maximum power point short-circuit current Boltzmann constant hybrid system lifetime loss of load probability (also called LOLP) (no dimension) power deficit loss of power supply probability number of analysed data number of PV modules in series number of PV modules in parallel power of the engine generator rated power of the engine generator PV module power total PV power engine generator fuel consumption engine generator fuel consumption at rated power serial resistance shunt resistance battery state of charge PV cell temperature power failure time period total working time of the hybrid system voltage battery voltage open-circuit voltage present value of i value of an expenditure i the year j annual increase of the cost electricity price escalation rate interest rate

Gd,β I Ibat IL I0 IMPP Isc K L LLP LPS LPSP N Ns Np PEG P0EG Pmodule PPV Qv Q0v Rs Rsh SOC Tcell Tf T V Vbat Voc Xi,0 Xi,j gi gelec i

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Wh W m−2 W m−2 W m−2 W m−2 A A A A A A 1.38 × 10−23 J K−1 year

Wh

W W W W l kW h−1 l kW h−1 Ω Ω % Κ h h V V V C C

Hybrid wind–photovoltaic energy systems k q v v0 xi, yi _ _ x, y z z0 α α0 β β0 β1 Δt ηc ηdis ηinv ηPV ηMPPT ηoth ηrect φ γ1 ρa σ θa θcell ref

shape parameter in the Weibull distribution standard electron charge wind speed reference wind speed at the height z ith value mean values of x and y hub height reference height exponent depending on topography PV module current coefficient inclination angle PV module voltage coefficient PV module temperature coefficient time interval battery charge efficiency battery discharge efficiency inverter efficiency PV module efficiency maximum power point tracker efficiency other losses efficiency rectifier efficiency latitude PV module solar irradiance coefficient air density hourly battery self-discharge rate ambient temperature PV cell temperature in reference conditions

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1.6 × 10−19 C m s−1 m s−1

m m A K−1 ° V K−1 °C−1 s

° kg m−3 °C °C

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8 Hybrid wind–hydrogen energy systems T. TSOUTSOS, Technical University of Crete, Greece

Abstract: Hydrogen can be produced from wind-generated electricity by various methods including both grid-independent and grid-assisted wind–hydrogen generation, wind power for grid-electricity and hydrogen generation, an integrated wind–hydrogen utility energy system, and a grid-independent integrated wind–hydrogen energy system. The future environmental implications of a potential large-scale wind–hydrogen economy will depend on how much hydrogen we use, how fast our use increases, the amount of fossil fuel emissions that can be saved, and the steps we take to control hydrogen emissions. Key words: wind–hydrogen, hybrid systems, fuel cells, environmental impact assessment.

8.1

Introduction

There have been several studies on the cost of using renewable energy for electrolysis. However, there is a potential to generate relatively inexpensive hydrogen from wind energy. Wind power may be produced at a very low cost in the regions with enough wind resources. It can be used to generate hydrogen on both small and large scales. This chapter presents the most common electrolysis technologies (alkaline, proton exchange membrane, solid oxide electrolysis cells) and the process of electrochemical electricity generation (using fuel cells). Critical issues concerning hybrid wind–hydrogen systems and their typical applications are presented, and different designs for hydrogen storage systems are discussed (including liquid hydrogen systems, metal hydride storage, novel hydrogen storage methods). The main steps in the design of isolated wind–hydrogen systems are outlined before moving on to discuss a case study on the Greek island of Karpathos. A general environmental impact assessment follows (including discussion of future emissions, European levels and potential overall environmental impacts), together with a discussion of hydrogen safety. An assessment of market potential and general barriers for wind–hydrogen systems is presented before, finally, a discussion of technology developments and future trends. 254 © Woodhead Publishing Limited, 2010

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255

Design of wind electrolysis production systems

8.2.1 Introduction Water splitting in its simplest form uses an electrical current passing through two electrodes to break water into H2 and O2. Commercial low temperature electrolysers have system efficiencies of 56–73% (70.1–53.4 kW h/kg H2 at 1 atm and 25 °C) (Holladay et al., 2009). The most common electrolysis technology is alkaline based, but proton exchange membrane (PEM) electrolysis and solid oxide electrolysis cells (SOEC) units are now under development. Although the least developed, SOEC electrolysers are the most electrically efficient but still face challenges with corrosion, seals, thermal cycling and chrome migration. PEM electrolysers have none of the corrosion and seal issues that SOEC face, and are more efficient (though more costly) than alkaline systems. Alkaline systems are the most developed, lowest in procurement cost, but also the least efficient. Wind power may be produced at a very low cost in those regions with enough wind resources. It can be used to generate hydrogen on a small or a large scale (Sherif et al., 2005). Alkaline electrolysers Alkaline electrolysers are typically composed of electrodes, a microporous separator and an aqueous alkaline electrolyte of approximately 30 wt% KOH or NaOH. In alkaline electrolysers, the most common cathode material is Ni, with a catalytic coating such as Pt. For the anode, Ni or Cu, coated with metal oxides such as Mn, W or Ru, are used. In an alkaline cell, water is introduced to the cathode where it decomposes into hydrogen and OH− (NAS, 2004). PEM electrolyser PEM-based electrolysers typically use Pt black, Ir, Ru and Rh for electrode catalysts and a Nafion membrane which not only separates the electrodes, but also acts as a gas separator (NAS, 2004). SOEC SOECs partially replace the electrical energy required to split water with thermal energy (Holladay et al., 2009). Higher temperatures increase the electrolyser efficiency by decreasing the anode and cathode overpotentials, which cause power loss during electrolysis.

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8.2.2 Electrochemical electricity generation (fuel cells) Fuel cells (FCs) are amongst the most promising hydrogen technologies. In an FC, H2 combines with O2 without combustion in an electrochemical reaction (the reverse of electrolysis) and produces direct current (DC) electricity. There are several types of FCs, depending on the type of electrolyte used (Larminie and Dicks, 2003); •

Alkaline fuel cells (AFC) use 85 wt% KOH as the electrolyte for high temperature operation (250 °C) and 35–50 wt% for lower temperature operation (<120 °C). • Proton exchange membrane fuel cells (PEMFC) use a thin (∼30 μm) proton conductive polymer membrane as the electrolyte. The catalyst is typically Pt with loadings of about 0.3 mg/cm2. The operating temperature is typically between 60 and 80 °C. • Phosphoric acid fuel cells (PAFC) use ∼100% concentrated H3PO4 as the electrolyte. The matrix used to retain the acid is usually SiC, and the electrocatalyst in both the anode and cathode is Pt. The operating temperature is typically between 150 and 220 °C. • Molten carbonate fuel cells (MCFC) have the electrolyte composed of a combination of alkali (Li, Na, K) carbonates, retained in a ceramic matrix of LiAlO2. Operating temperatures are between 600 and 700 °C where the carbonates form a highly conductive molten salt, with carbonate ions providing ionic conduction; consequently noble metal catalysts are not required at such high operating temperatures. • Solid oxide fuel cells (SOFC) use a solid, nonporous metal oxide, usually Y2O3-stabilized ZrO2 as the electrolyte. These cells operate at 900– 1000 °C where ionic conduction by oxygen ions takes place. Low temperature (600 °C) solid oxide FCs are now being developed. The main reactions taking place at the anode and cathode of these FCs are presented in Table 8.1. A typical FC consists of an electrolyte, in contact with two porous electrodes. Electrochemical reactions occur at the triple interface between the porous electrode, electrolyte and reactants. Low temperature FCs (AFC, PEMFC, PAFC) require noble electrocatalysts to Table 8.1 Fuel cell reactions Fuel cell type

Anode reaction

Alkaline Proton exchange Phosphoric acid Molten carbonate Solid oxide

H2 H2 H2 H2 H2

+ 2OH− → 2H2O + 2e− → 2H+ + 2e− → 2H+ + 2e− + CO3− → H2O + CO2 + 2e− + O− → H2O + 2e−

Cathode reaction ½O2 ½O2 ½O2 ½O2 ½O2

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+ + + + +

H2O + 2e− → 2OH− 2H+ + 2e− → H2O 2H+ + 2e− → H2O CO2 + 2e− → CO3− 2e− → O−

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achieve practical reaction rates at the anode and cathode. High temperature FCs (MCFC and SOFC) can also utilize CO and CH4 as fuels. The reversible potential of the above electrochemical reactions is 1.229 V (under standard conditions, i.e. 25 °C and at atmospheric pressure), which corresponds to the Gibbs free energy equation (Sherif et al., 2005): ΔGo = n FEo where: ΔGo = Gibbs free energy at 25 °C and atmospheric pressure, n = number of electrons involved in the reaction, F = Faraday’s constant and Eo = reversible potential at 25 °C and atmospheric pressure (V). In general, the reversible potential is lower at higher temperatures (reaching ∼1.0 V at 1000 K), and higher at higher pressures or higher concentrations of reactants. FCs are typically operated between 0.6 and 0.8 V. The space shuttle FC (alkaline) is designed to operate at 0.86 V and 410 mA/cm2. PEM fuel cells have the highest achievable current densities, between 1 and 2 mA/cm2 at 0.6 V with pressurized H2 and air. Figure 8.1 shows the operating principles of various types of fuel cell (FC). The theoretical FC efficiency is: Load e− Depleted fuel and product gases out

Depleted oxidant and product gases out H2

OH

H2 O H2

O2

AFC

H2O H+

PEMFC PAFC

O2 H2O

H2 CO2 H2O H2

CO3

O2

MCFC

CO2 O

O2

SOFC

H2O

Fuel in

Oxidant in

Anode

Electrolyte

Cathode

8.1 Operating principles of various fuel cells.

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where ΔH = hydrogen’s enthalpy or heating value (higher or lower). The theoretical FC efficiency, defined as a ratio between electricity produced and the higher heating value of hydrogen consumed is therefore 83%. The lower heating value of hydrogen results in an efficiency of 98%. Since the actual voltage of an operational FC is lower than the reversible potential, the FC efficiency is always lower than the theoretical one. For a hydrogen/oxygen or hydrogen/air FC operating with 100% fuel utilization, the efficiency is a function of cell voltage only. For such an FC, the efficiency in an operating range between 0.6 and 0.8 V is between 48% and 64%.

8.2.3 Critical issues for wind–hydrogen systems Although water electrolysis is a mature technology, its use in conjunction with wind power raises particular issues (Sherif et al., 2005): •

Direct coupling of an electrolyser with a wind turbine implies intermittent operation with a highly variable power output. The problem, particularly with alkaline electrolysers, is that at very low loads the rate at which H2 and O2 are produced may be lower than the rate at which these gases permeate through the electrolyte and mix with each other. This may create hazardous conditions inside the electrolyser. Hydrogen flammability limits in oxygen are between 4.6% and 93.9%, but the alarms and automatic shutdown of electrolysers are set at much safer concentrations. • Another problem related to operation with a highly variable power source is thermal management. An electrolyser takes time to reach its normal operating temperature, but due to intermittent operation it may operate most of the time at a temperature below nominal, which results in a lower efficiency. • The efficiency of an electrolyser is inversely proportional to the cell potential, which in turn is determined by the current density, and that in turn directly corresponds to the rate of hydrogen production per unit of electrode active area (the part of the electrode with electrochemical activity). A higher voltage would result in more hydrogen production, but at a lower efficiency. Typically, cell voltage is selected at about 2 V, but a lower nominal voltage (as low as 1.6 V) may be selected if efficiency is more important than the size (and capital cost) of the electrolyser. • There are power losses in voltage regulation and some power is needed for auxiliary equipment (such as pumps, fans, solenoid valves, instrumentation, and controls). Typical industrial electrolysers have electric-

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ity consumption of between 4.5 and 6.0 kW h/Nm3, corresponding to an efficiency of 65–80%, and advanced electrolysers have been reported with an efficiency of 90%. Coupling of a source such as a wind turbine with an electrolyser can result in a somewhat lower efficiency, owing to losses related to power/voltage matching. Voltage regulators, either AC/DC or DC/DC, consume some power. These devices may be designed to operate with an efficiency as high as 93–95%, but this high efficiency may be achieved only in a very narrow power range. In a highly variable mode of operation, such as with the input from a wind turbine, this efficiency may be considerably lower.

8.2.4 Applications Hydrogen may be produced from wind-generated electricity in a variety of ways (Sherif et al., 2005). Grid-independent wind–hydrogen generation A wind turbine can be connected to an electrolyser to produce hydrogen, which can then be used in a variety of applications. This system circumvents the problems related to grid connection and generation imbalance charges. The electrolyser will be exposed to a variable power supply, and a DC/DC or AC/DC power regulator should be part of the power control box in order to match the electrolyser’s voltage-current requirements at any power with minimum conversion losses. Grid-assisted wind–hydrogen generation If a grid is available, one way to eliminate problems with intermittent electrolyser operation is to combine the wind turbine with an input from the grid. The power conditioning/controls unit supports this, and the electrolyser gets constant DC power input by combining the output from the wind turbine with any input required from the grid. In this way, the electrolyser can operate at its optimal design point at all time. Since the only down-time would be for maintenance, the capacity factor could reach over 90%, which would significantly improve its operational efficiency and economic performance. Wind power for grid-electricity and hydrogen generation An electrolyser can be used to ‘peak shave’ output from a wind turbine if required by the grid operator. In such a case, any excess power from the

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wind turbine is used to generate hydrogen. However, under this mode of working, the problems with intermittent electrolyser operation and its low capacity factor remain. A variation of this application is to operate the electrolyser at constant power and ‘dump’ excess power from the wind turbine onto the grid (as long as there is enough absorbing capacity in the grid, of course). Integrated wind–hydrogen utility energy system One way to deliver a constant (or any required load) profile to the grid is to equip a wind turbine with an energy storage device, such as a combination of electrolyser and a FC with hydrogen storage (a regenerative FC). The electrolyser and FC functions can be included in a single stack (unitized version) or in two separate stacks (discrete version). Grid-independent integrated wind–hydrogen energy system A similar system can be used for an autonomous, grid-independent power supply. In this case, energy storage could be critical, and regenerative hydrogen FCs might become cost competitive with other energy storage options (batteries, flywheel, compressed air, pumped hydro, etc.) in the future. The only difference between this and the previously described system is in the power conditioning and control unit, which does not need to synchronize with the grid, but has to be able to provide the desired AC or DC voltage outputs. This system can also incorporate a photovoltaic (PV) array for added security and power supply diversification.

8.3

Design of hydrogen storage systems

8.3.1 Introduction As an energy carrier, hydrogen has to be stored to overcome daily and seasonal differences between energy source availability and demand. It can be stored as gas or liquid, in metal hydrides, chemical hydrides, glass microspheres or cryo-adsorbers (Sherif et al., 2005). Large underground hydrogen storage in various natural deposits, such as aquifers or depleted petroleum and natural gas fields, is likely to be technologically and economically feasible (Taylor et al., 1986). Hydrogen storage systems are much more expensive than natural gas storage systems of the same type and same energy content, owing to hydrogen’s lower volumetric heating value. Technical problems relating to the underground

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storage of hydrogen are not anticipated, other than an expected loss of 1–3% of working gas per year. Today, above-ground pressurized gas storage systems are used in the natural gas business in various sizes and pressure ranges, from standard pressure cylinders (50 l, 200 bars) to stationary high-pressure containers (over 200 bars) or low-pressure spherical containers (>30 000 m3, 12–16 bars). This range of applications and storage systems could similarly be used for hydrogen storage. Pressurized hydrogen tanks, made of ultra-light but strong new composite materials that allow pressures in excess of 200 bars, have been developed and used in prototype automobiles and buses. A storage density of more than 0.05 kg of H2 per 1 kg of total weight is easily achievable (Mitlitsky, 1996).

8.3.2 Liquid hydrogen storage Hydrogen liquefaction is an energy-intensive process. It requires amounts of energy equal to about one-third of the energy in the liquefied hydrogen. Hydrogen liquefaction and use of liquid hydrogen are usually practised only when achieving high storage density is absolutely essential, such as in aerospace applications. Some prototype hydrogen-powered automobiles, as well as commercially available automobiles, also use specially developed liquid hydrogen tanks (Braess and Strobl, 1996).

8.3.3 Metal hydride storage Hydrogen can form metal hydrides with some metals and alloys. During the formation of the metal hydride, hydrogen molecules are split and hydrogen atoms are inserted in spaces inside the lattice of suitable metals and/or alloys. In this way, effective storage comparable to the density of liquid hydrogen is created. However, when the mass of the metal or alloy is taken into account, the metal hydride gravimetric storage density is comparable to the storage of pressurized hydrogen. The best achievable gravimetric storage density is about 0.07 kg of H2/kg of metal for a high temperature hydride such as MgH2. During the storage process, heat is released, which must be removed in order to allow the continuity of the reaction. During the hydrogen release process, heat must be supplied to the storage tank. One advantage of storing hydrogen in hydriding substances is the safety aspect: serious damage to a hydride tank (e.g. a collision) would not pose a fire hazard, since hydrogen would remain in the metal structure. Table 8.2 lists some hydriding substances suitable for use as hydrogen storage media, while Table 8.3 provides a list of hydrogen storage types and densities.

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Table 8.2 Hydriding substances as hydrogen storage media Medium

Hydrogen content (kg/kg)

Hydrogen storage capacity (kg/l)

Energy density (kJ/kg)

Energy density (kJ/l)

MgH2 Mg2NiH4 VH2 FeTiH1.95 TiFe0.7Mn0.2H1.9 LaNi5H7.0 RENi5H6.5 Liquid H2 Gaseous H2 (100 bar) Gasoline

0.070 0.0316 0.0207 0.0175 0.0172 0.0137 0.0135 1.00 1.00

0.101 0.081

9 933 4 484 3 831 2 483 2 440 1 944 1 915 141 900 141 900 47 300

14 330 11 494

0.096 0.090 0.089 0.090 0.071 0.0083

13 620 12 770 12 630 12 770 10 075 1 170 35 500

Table 8.3 Hydrogen storage types and densities

Large volume storage (102–104 m3) Underground storage Pressure gas storage (above ground) Metal hydride Liquid H2 Stationary small storage (<100 m3) Pressurized gas cylinder Metal hydride Liquid H2 tank Vehicle tanks (0.1–0.5 m3) Pressurized gas cylinder Metal hydride Liquid hydrogen tank

kg H2/kg

kg H2/m3

0.01–0.014 0.013–0.015 ∼1

5–10 2–16 50–55 65–69

0.012 0.012–0.014 0.15–0.50

∼15 50–53 ∼65

0.05 0.02 0.09–0.13

15 55 50–60

8.3.4 Novel hydrogen storage methods Hydrogen can be physically adsorbed on activated carbon, and ‘packed’ on the surface and inside the carbon structure more densely than if it had just been compressed. Amounts of up to 48 g H2 per kg of carbon have been reported at 6.0 MPa and −186 °C (Sherif et al., 2005). The adsorption capacity is a function of pressure and temperature, therefore at higher pressures and/or lower temperatures even larger amounts of hydrogen can be adsorbed. For any practical use, relatively low temperatures are needed (<100 K). Since the adsorption is a surface process, the adsorption capacity of hydrogen on activated carbon is due largely to the high surface area of

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the activated carbon, although there are other carbon properties which affect the capability of activated carbon to adsorb hydrogen. Hydrogen can also be stored in glass microspheres of approximately 50 μm diameter. The microspheres are filled with hydrogen by heating them to increase the glass permeability to hydrogen. At room temperature, a pressure of approximately 25 MPa is achieved, resulting in storage density of 14% mass fraction and 10 kg H2/m3. At 62 MPa, a bed of glass microspheres can store 20 kg H2/m3. The release of hydrogen occurs by reheating the spheres to again increase the permeability.

8.4

Optimization of wind–hydrogen power systems

8.4.1 Introduction The optimization of this sustainable energy planning scheme requires various scenarios which vary in their level of intervention in the current energy system, and therefore in the subsequent increase in renewables penetration. Optimization of renewable energy hybrid systems looks at the process of selecting the best components and the size of the system, with appropriate operation strategies to provide a cheap, efficient, reliable and cost-effective power supply. A techno-economic analysis usually looks at cheapest costs while neglecting to consider the optimization of wind–hybrid systems by minimizing excess capacity. A number of well-known models, but also several heuristic ones, have been developed to cover these needs (Various, 2000; de Zoysa et al., 2007); this study focuses on the most popular model, HOMER, which is also well-tested freeware. HOMER, NREL (2008)’s ‘micropower optimization model’, is a very capable piece of software for planning small energy systems that are either off-grid or grid-connected. The model can deal with various types of energy mixtures, from simple conventional or RES power systems, to complicated hybrid systems using hydrogen (Zoulias and Lymberopoulos, 2007; Giatrakos et al., 2009). One drawback of HOMER is that the system’s optimization-based modelling methodology selects technologies based on their relative costs, resulting in allocation of the whole market share to the cheapest technology. Fortunately, it is possible to implement this method of analysis even on an island where, often, energy production is monopolistic. In HOMER, energy mixtures for each scenario are prioritized by means of an economic evaluation, promoting the most viable and sustainable solution. Therefore, specifying exact costs in HOMER is mandatory; this may prove unreliable, since hydrogen-related costs are still unknown (since they are not yet being commercially available), and literature-based values projected for the future have been used in this study.

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8.4.2 A step-by-step design of isolated wind–hydrogen systems Load profile By analysing hourly load data provided by a power supply, HOMER creates load curves in daily and monthly graphs. A representative graph (Fig. 8.2) depicts the variations in demand on a monthly basis, marking maximum and minimum recorded loads. Integration of sustainable energy technologies Optimization models, such as HOMER, use mathematical programming to identify alternative configurations of available power units to minimize the cost of energy services, accounting for various user-defined constraints (e.g. a CO2 emissions target). As a result, such models chose technologies based predominantly on their specific costs, resulting in allocation of total market share (within the models) to the cheapest technology. Wind energy calculations There are various means of calculating actual wind potential at a specific location. Various models take a site’s coordinates and calculate theoretical wind potential. Although these models usually provide results that are close to those measured, raw data from meteorological services could be used.

10,000

Max Daily hight Mean Daily low Min

Average value (kW)

8,000

6,000

4,000

2,000

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ann Month

8.2 Karpathos’s monthly average grid energy demands during the year 2004.

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Hydrogen electricity in stand-alone power systems (SAPS) Hydrogen production for stand-alone power systems (H-SAPS) is an environmentally-friendly means of storing excess electricity produced by renewable energy sources (RES), serving grid power needs when RES production declines due to natural causes (Zoulias et al., 2006). Hydrogen-fuelled vehicles Isolated systems have a promising potential for sustainable mobility applications. Hydrogen-fuelled cars are considered the cleanest feasible technology of the near future, with zero-emissions and zero-engine noise characteristics. Calculation of emissions estimates The impact of pollution depends on the scale of the power production system, as well as the quality of the fossil fuel used. Small-scale generators usually consume more fuel, as they are less effective. Large-scale thermoelectric factories additionally produce thermal pollution by warming the sea nearby. The type of fuel consumed depends on how close the unit is to populated areas.

8.4.3 Case study: Karpathos Results of a case study on the Greek island of Karpathos (Giatrakos et al., 2009) are presented here. The island’s energy requirements are today served mainly by existing diesel generators, which provide a total capacity of 12 000 kWe. Simulating the island’s power demands, while limiting the wind generators’ total to match existing capacity (275 kWe), HOMER calculated the size of diesel generator required to achieve a zero annual capacity shortage. This resulted in an optimal capacity of 8000 kWe, proving that the existing 12 000 kWe ensured a 50% capacity reserve margin. A financial analysis exported a levelized1 coefficient of performance (COE) at 0.231c/kW h, taking into account a CO2 emission fee of 30c/t, and calculated the amount of annually consumed diesel to be 8 922 170 l. RES penetration reached 6.6% of annually consumed electricity (Table 8.4). Total greenhouse gas (GHG) emissions were 23 958.9 t of CO2. 1

Levelized is an economic assessment of the cost the energy-generating system including all the costs over its lifetime: initial investment, operations and maintenance, cost of fuel, cost of capital; this is the minimum price at which energy must be sold for an energy project to break even.

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Table 8.4 Annual electrical energy production: current situation based on year 2004 load Component

Production

Fraction

(kW h/yr)

Windmatic 15 S

Wind turbines Diesel

1 966 311 27 631 318

7% 93%

Total

29 597 628

100%

Diesel

'04 Load 81 MW h/day 8.1 MW peak

AC Present system architecture

The most important issue of this proposed technology scheme is the island’s complete independence from fuel oil, through a 100% stand-alone energy system based only on indigenous renewable resources (Kaldellis, 2007). Under those circumstances, Karpathos may even become an applicant to be one of Europe’s ‘sustainable communities’, as defined by the European White Paper on Renewable Energy. This scenario envisages integration of hydrogen production and storage facilities to tackle the intermittent nature of RES by hydrogen re-electrification. At the same time, the existing diesel generators will be kept as a reserve. Implementing a hydrogen storage cycle, to reassure demand coverage when RES production declines, is an energy demanding and expensive course of action; the system’s required RES capacity must reach 22 MWe, three times the annual peak, while 70% of the total annual electricity production by RES has to be used for hydrogen production (Table 8.5). In detail, the proposed energy system will include power units consisting of 34 E-33 wind turbines, with a total capacity of 11.39 MWe, along with the predefined 10 MWp of PV generators. The hydrogen-producing system should consist of a 14 MWe rated electrolyser, a 250 t low-pressure storage tank and, finally, an 8000 kWe alkaline FC generator supplying power directly to the AC grid. A total of 73.8 GW h of renewable derived electricity should be produced to drive annual H2 production; with the 3.7 GW h of discarded production, the system can be described as oversized, dealing with just 29.6 GW h annual demands. This, though, is mainly caused by the absence of highefficiency hydrogen conversion and storage equipment. It is clear that, during high wind and PV production months, hydrogen production decreases, justified by the low re-electrifier output, and vice versa. In the beginning of its annual cycle, the hydrogen tank contains 50% of its maximum storage capacity. Corresponding to the reduction in use of

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Table 8.5 Electric energy production and consumption 100% RES scenario Load

Consumption

Component

(kW h/yr)

Production

Fraction

Total fraction

(kW h/yr)

AC primary load

29 595 272

H2 Generator PV Wind AC total

25 452 000 1 201 000 2 942 272 29 595 272

86% 4% 10% 100%

28%

H2 electrolyser load

73 871 688

PV Wind H2 total Excess wind & PV

16 399 745 57 471 942 73 871 687 3 678 536

22% 78% 100%

69%

Total

103 466 960

Total

107 145 495

3% 100%

hydrogen during the spring and summer months, the tank level starts to increase in mid-July. At the end of the year, the tank’s storage level decreases back to its initial level (about 50%).

8.5

Environmental impact assessment of wind–hydrogen systems

8.5.1 Estimates of future emissions The future environmental implications of a potential large-scale wind– hydrogen economy will depend on how much hydrogen we use, how fast our use increases, the amount of fossil fuel emissions that can be saved, and the steps we take to control hydrogen emissions. There has been considerable recent controversy in the scientific literature about how much hydrogen a global-scale hydrogen economy would release into the atmosphere (Larsen et al., 2004). Our view is that these hydrogen emissions are probably much less important than the overall atmospheric emissions of CO2, CO and NOx from reformers and other hydrogen plants, and emissions of these gases will be reduced as conventional technologies are replaced by their hydrogen equivalents (Schultz et al., 2003). Of particular interest here are emissions of CO2 and NOx: CO2 is important because it is the biggest contributor to climate change; NOx levels drive the oxidising capacity of the atmosphere (essentially the OH concentration), and so regulate the lifetime of the greenhouse gas methane, and they control the amount of photochemical ozone formed in the troposphere.

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8.5.2 Environmental impacts at European level The size limitations (up to 300 kWe generation) and energy system type (SAPS) chosen strongly influence the impact which can be made on the environment at a European level. It was assumed, for this study, that 50% of the largest market segment, ‘rural villages, settlements and houses’ (Zoulias et al., 2006) could be covered by RES systems; it was also assumed that this segment has diesel-based power generation, and the total (maximum) energy demand supplied by diesel was around 1 TW h (∼900 GW h). This is less than 0.0001% of the total annual electricity generation from stationary applications in Europe. The total annual CO2 emissions saved by the introduction of hydrogen autonomous power systems (H-APS) into these market segments were then estimated to be about 1 Mt CO2. The potential emissions savings for CO2, CO, NOx and particles are summarized in Table 8.6. The environmental impact of integrating 100% RES is, obviously, greater at a local level. In pristine areas with a topography that does not allow for a high rate of air circulation, NOx and particle emissions to the air may be of great negative impact to the environment. For rural tourism, and especially so-called eco-tourism, NOx, CO and particles may be of special concern. These local emissions may be avoided altogether by using distributed hydrogen or hydrogen generated from RES. Noise pollution, which is often overlooked, is another important issue for rural applications that are important for user categories such as tourism and rural residences, but perhaps less important for communications, water treatment and other technical/commercial installations. Electrolysis from RES would result in a very clean hydrogen cycle, and also represents a potentially enormous source of hydrogen (Dunn, 2002).

8.5.3 Potential environmental impacts The potential environmental impacts of a global hydrogen economy are as follows (Tsoutsos, 2008): Table 8.6 Estimates for annual emissions savings on a European scale Emissions

With gas cleaning technology

Without gas cleaning technology

CO2 (t/yr) CO (t/yr) NOx (t/yr) Particles (t/yr)

∼1 000 000 ∼2 100 ∼2 300 ∼130

∼1 000 000 14 000–28 000 4600–14 000 300–1400

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•

Increased hydrogen release would lower the oxidizing capacity of the atmosphere, and so increase the lifetime of air pollutants and greenhouse gases such as CH4, hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs). • Increased hydrogen release would lead to increased water vapour concentrations in the atmosphere, with potential consequences for cloud formation, stratospheric temperatures and stratospheric ozone loss. • Increased hydrogen release could exceed the uptake capacity of hydrogen by micro-organisms in the soil, currently the main way in which hydrogen is removed from the atmosphere. The result would be that hydrogen concentrations in the atmosphere would increase quicker, which would reinforce the consequences described above. • Conversely, generating hydrogen from sustainable sources would reduce emissions of carbon monoxide and NOx, with a consequent fall in tropospheric ozone levels. This would improve air quality in many regions of the world. Furthermore, CO2 emissions would be reduced, thereby slowing the global warming trend. The following sections address each of these points and attempt to judge the likelihood that they will become topics of concern in the future (Tsoutsos, 2008). Changes in oxidizing capacity Hydrogen acts as a significant sink for hydroxyl radicals, and increased atmospheric concentrations of hydrogen could lead to a decrease in OH concentration. This in turn could increase the atmospheric lifetime of greenhouse gases and other pollutants, with undesirable consequences for climate change and air quality (Hauglastine and Ehhalt, 2002). Although this argument is qualitatively correct, the anticipated changes in OH levels due to changes in the atmospheric hydrogen concentration are marginal. At present, hydrogen accounts for the destruction of less than 10% of all OH globally, so if hydrogen concentrations were to double (which seems unlikely, given the emissions estimates above) this would produce a change in OH concentrations of only a few percent. However, significant changes in the oxidizing capacity of the atmosphere could well arise from other emission changes associated with the shift towards hydrogen, most notably emissions of NOx (Schultz et al., 2003). More research is clearly needed to produce reliable estimates based on probable emission scenarios.

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Changes in atmospheric water vapour Water vapour is produced from the oxidation of hydrogen, and this could have different consequences depending on where in the atmosphere it is released. Tromp et al. (2003) suggest that increasing atmospheric hydrogen concentrations by a factor of four would increase the amount of water vapour in the stratosphere by up to 30%. According to these researchers, this could decrease the lower stratospheric temperature at the polar vortex by about 0.2 °C, which in turn could trigger additional polar ozone losses of up to 8% (polar ozone depletion is very sensitive to small temperature changes). Another model, however, showed a much weaker effect on stratospheric temperatures and ozone loss; as discussed above, hydrogen levels are more likely to increase by 20% than by 400% in the coming decades. Even if we use the more pessimistic model (Tromp et al., 2003), the consequences for stratospheric temperatures and ozone concentrations are therefore expected to be negligible. Soil uptake Large uncertainties are attached to the uptake of atmospheric hydrogen by soil microorganisms or organic remnants. Studies using isotopically labelled hydrogen suggest that soil uptake provides about 75% of the total hydrogen sink, but there is a large margin of error. Little is known about the detailed processes by which hydrogen is absorbed in soil. At the moment, there is no sign that the process of hydrogen uptake in soil is becoming saturated. Increased fossil fuel combustion has presumably increased atmospheric hydrogen concentrations significantly in the last century, but there has been no detectable increase since 1990. If hydrogen uptake in the soil were becoming saturated, the concentration of hydrogen in the atmosphere could be expected to have increased, even if hydroxyl radical concentration were increasing as well. Since the amount of hydrogen released is not expected to change very significantly in the next few decades, there is currently no reason to expect serious consequences from changes in the soil uptake rate. There are many unknowns, however, and further research is urgently required. Carbon dioxide emissions As long as hydrogen is reformed from fossil fuels, the CO2 emissions generated can easily rival today’s emissions from power plants and traffic. However, from the standpoint of avoiding CO2 emissions in the short to

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medium term, centralized facilities appear preferable, because this might allow efficient capturing and storing of the CO2 produced. In the long term, it is obvious that hydrogen generation has to be based on renewable sources to avoid the environmentally adverse effects of carbon dioxide. Conclusions FCs, by nature of their lack of a combustion process, have extremely low emissions of NOx and CO. As emissions standards become increasingly stringent, FCs will offer a clear advantage, especially in areas struggling to meet these standards. FC CO2 emissions are also generally lower than from other technologies due to their higher efficiencies. While there are still large uncertainties about the current budget of atmospheric hydrogen and the consequences of a large-scale shift towards a hydrogen economy, present knowledge indicates that there are no major environmental risks associated with this energy carrier, and that it bears great potential for reducing air pollution world wide, provided that the following rules are followed: •

•

Hydrogen should not be produced using electricity generated by burning fossil fuels. Instead, natural gas or coal reformers should be used at first, and replaced by renewable energy sources, such as wind, as soon as possible. CO2 capture from reformers should be seriously considered. Leakage in the hydrogen energy chain should be limited to 1% wherever feasible, and global average leakage should not exceed 3%. Atmospheric hydrogen concentrations should be carefully monitored. Sufficient research should be carried out to obtain a better understanding of hydrogen sources and sinks, and to provide an early warning system in case anything has been overlooked.

Using electricity from coal-fired power plants, for example, could increase CO2 emissions by a factor of two to four. But, as long as efficient technology is employed, significant change in CO2 emissions is not expected in the coming decades.

8.5.4 Hydrogen safety Hydrogen poses risks if not properly handled or controlled. This risk must be considered relative to common fuels such as gasoline, propane or natural gas. Some of the specific physical characteristics of H2 could theoretically make it more dangerous in certain situations (Sherif et al., 2005). Since hydrogen has the smallest molecule, it has a greater tendency to escape through small openings than other liquid or gaseous fuels. Based on the properties of hydrogen such as its density, viscosity and diffusion

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coefficient in air, its propensity to leak through holes or joints of low pressure fuel lines may be only 1.26–2.8 times faster than a natural gas leak through the same hole (and not 3.8 times faster as frequently assumed based solely on diffusion coefficients). Experiments have indicated that most leaks from residential natural gas lines are laminar2 (Thomas, 1996). Since natural gas has over three times the energy density per unit volume, a natural gas leak would result in more energy release than a hydrogen leak. For very large leaks from high-pressure storage tanks, the leak rate is limited by sonic speed. Owing to its higher sonic velocity (1308 m/s), hydrogen would initially escape much faster than natural gas (the sonic velocity of natural gas being only 449 m/s). Hydrogen has a flame velocity seven times faster than that of natural gas or gasoline. A hydrogen flame would therefore be more likely to progress to a deflagration, or even to a detonation, than would other fuels. In general, hydrogen appears to pose risks of the same order of magnitude as other fuels. In spite of public perception, in many aspects hydrogen is actually a safer fuel than gasoline and natural gas. As a matter of fact, hydrogen has a very good safety record, as a constituent of the ‘town gas’ widely used in Europe and the USA in the nineteenth and early twentieth centuries, as a commercially used industrial gas, and as a fuel in space programmes. There have been accidents, but nothing that would characterize hydrogen as more dangerous than other fuels.

8.6

Market potential and barriers for wind–hydrogen systems

8.6.1 Introduction Wind–hydrogen-based power systems offer a potential combination of performance and flexibility that is creating interest among end users. Current trends in developed counties clearly favour sustainable micropower, particularly from the standpoint of increased energy efficiency and reduced emissions. Meeting the increasingly competitive challenges of the market will require harnessing the full resource capability of available and emerging technologies with characteristics of high efficiency, reasonable cost, extreme reliability and minimized environmental impact.

2

Laminar flow is a flow regime characterized by high momentum diffusion and low momentum convection; it is the opposite of turbulent flow.

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However, there are a number of barriers preventing the exploitation of H-APS on a wide scale, and to setting up applications in the industrial sector, such as (Tsoutsos et al., 2004): • • • • • • •

high capital cost; lack of standard systems; safety issues; onerous planning regulations; limited coordinated research; immaturity of technology; lack of adequate awareness with regard to the benefits of the technology.

These barriers can often make an H-APS project uneconomic and unfavourable, and can frequently present such a confused and uncertain option to potential end users that more traditionally purchased power approaches are favoured.

8.6.2 The results of the H-SAPS project Within the framework of the H-SAPS project (Various, 2000; Tsoutsos et al., 2004) a consultation was developed with various groups of key stakeholders: • • •

demand side (owners/operators); supply side (system installers, hydrogen technology providers, renewable energy providers, consultants); institutions (trade associations, research institutes, utility companies).

The methods adopted for the consultation took the form of questionnaires (brief and focussed on-line, more detailed hand-out at workshops), telephone interviews and feedback from networking, workshops and dissemination. The main drivers and barriers, which were identified from the methods adopted, are listed in Table 8.7. As a general conclusion, the following points were highlighted: • • • • • •

SAPS installers felt that there was no standard system and a lack of partnerships between suppliers of equipment. 85% were concerned with cost and immaturity of the technology. Costs and potential benefits need to be demonstrated. Further demonstration/pilot projects need to be undertaken in order to prove the technology and benefits. Energy storage was mentioned as an issue to be tackled in a number of responses. Onerous planning regulations were also an important point.

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Table 8.7 Main drivers and barriers for the introduction of hydrogen in APS (Various, 2000) Barriers

Drivers

Lack of standard systems

Establish partnerships and standardization of systems Establish pilot/demonstration projects Legislation required to make it less onerous Government incentives required Efficient coordination of research Further research and development needed Further research and development needed

Safety issues Onerous planning regulations Low funding Limited coordinated research Immaturity of technology Lack of decision makers’ awareness of benefits of technology High capital cost

Need for lower cost of components and systems

8.6.3 Strengths, weakness, opportunities and threats (SWOT) analysis A SWOT (strengths, weaknesses, opportunities and threats) analysis was realized by Zoulias et al. (2006) in order to map the most obvious success factors. Strengths and weaknesses refer to the hydrogen in autonomous power systems (H-APS) and constitute so-called internal factors, which can be influenced. Opportunities and threats refer to the external environment affecting the market development of the H-APS. Strengths, weaknesses, opportunities and threats were identified, then new elements were added and the SWOT analysis was quality assured by means of workshops and questionnaires that were sent to interested parties (e.g. renewable energy and hydrogen technology providers, system operators and users) (Tsoutsos et al., 2004). The complete SWOT analysis, including critical success factors for technology, market and environment, is shown in Tables 8.7 and 8.8–8.10. These tables helped to focus on the presumed main success factors that affect the H-APS potential all the way through the data collection, the analyses and, finally, recommendations. Furthermore, it served as a structure and methodology where new success factors could be introduced as they were identified (Glöcker et al., 2003).

8.7

Future trends

Hydrogen has some unique characteristics that make it an ideal energy carrier (Veziroglu and Barbir, 1993):

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Technology

Weaknesses • Procurement cost • Technology immaturity of FCs and PEM electrolysers • Low availability of small electrolysers • Lack of component and system lifetime experience

Strengths

• Able to handle power fluctuations and therefore ideal for integration with intermittent RES • Already existing experience in handling of compressed gases • Noise level of the main competing systems (e.g. DEGS) • Seasonal energy storage without energy loss over time • Potential for highdensity energy storage • Self-sufficient energy supply

Table 8.8–8.10 Main strengths, weaknesses, opportunities and threats

• Emergence of large-scale markets for hydrogen

Opportunities

• Limited practical experience due to few true H-APS installed • Competing technologies prove to be perfectly adequate

Threats

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Market

Weaknesses • Low availability and high cost of small electrolysers • Missing codes and standards (safety issues, technical specifications, etc.) • Lack of after-sales support • Few dedicated complete system deliverers • Lack of awareness of capabilities and potential benefits of hydrogen

Strengths

• Already existing experience in handling of compressed gases • Self-sufficient energy supply • No need for fuel transport infrastructure

Table 8.8–8.10 Continued

• Already existing SAPS in which hydrogen technologies can be incorporated • Current EU and national financing schemes • New job opportunities • Diversification of companies involved in the energy sector • Energy costs in SAPS relatively high

Opportunities

• Potential end users have no experience • Limited practical experience due to few true H-APS installed • Inadequate legislative framework (standards, regulations, permissions of installation) • Competing technologies prove to be perfectly adequate • SAPS owners/end-users refuse to accept the new technology • Negative common perception of the large scale impact of hydrogen on climate change

Threats

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Environment

• Noise level of the main competing systems (e.g. DEGS) • Potential for highdensity energy storage • Able to handle power fluctuations and therefore ideal for integration with intermittent RES • Reduced environmental impact compared to conventional energy sources • Guaranteed power from a RES system • Lack of recycling and re-use schemes for hydrogen technology • Reduction of environmental impact • Replace/reduce batteries, diesels

• Negative common perception of the large scale impact of hydrogen on climate change

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•

It can be produced from and converted into electricity with relatively high efficiency. • The raw material for hydrogen production is water, which is available in abundance. Hydrogen is a completely renewable fuel, since the product of hydrogen utilization (either through combustion or through electrochemical conversion) is pure water or water vapour. • It can be stored as liquid, gas, or solid (as metal hydrides). • It can be transported over large distances using pipelines, tankers, or rail trucks. • It can be converted into other forms of energy in more ways and more efficiently than any other fuel, i.e. in addition to flame combustion (like any other fuel), hydrogen may be converted through catalytic combustion, electro-chemical conversion, and hydriding. • Hydrogen as an energy carrier is environmentally compatible. It produces small amounts of NOx if it is burned in air at high temperatures. Additionally, current European energy policy promotes the integration of modern sustainable energy technologies. Within the framework of this policy, autonomous areas, such as certain islands, are destined to become ‘Renewable Islands’, maximizing the share of RES. This policy improves the island’s energy autonomy and financial independence from fossil fuels, while at the same time encouraging new investment in RES and creating an environmentally friendly profile (Papadaki et al., 2003; Kaldellis et al., 2006). Remote areas, particularly those that boast RES, could easily adopt such energy systems, with the addition of the necessary energy-storing infrastructures that will ensure permanent energy sufficiency. The essential energy storing means of the future could be hydrogen, which is produced by the excess of energy produced by RES, and then stored and reused for power production or for hydrogen-fuelled combustion vehicles. Hybrid wind systems can provide an instant power output that varies between zero and maximum installed capacity. In an autonomous power system (APS), where the installed RES capacity may eventually surpass the maximum annual load of the system, their output may instantly be even higher than the total load. The unexploited wind potential of an autonomous area’s electricity network should drive its power utilities and other independent producers to invest in RES-to-electricity schemes (mainly wind parks). Their high productivity, affordability and life expectancy outperforms any other competing technology, renewable or not, and therefore it should become the highest priority addition to an island’s energy system. Simultaneously, H-APS should drive a step-by-step transformation timeline of the grid into a 100% RES power supply system. A start should be

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made in small applications in remote villages and residences, for as long hydrogen applications remain small scale. Over a 20-year horizon, during which time large-scale hydrogen applications should become available, more populated areas will finally consume CO2-free electricity.

8.8

Sources of further information and advice

Nowadays there is a global interest in hydrogen as an energy carrier, especially in the case of hybrid wind–hydrogen systems. Amongst the most important electronic information sources for developments in science and technology are: Centre for Renewable Energy Sources, www.cres.gr European Environment Agency, www.eea.europa.eu H2NET: The UK Hydrogen Energy Network, http://www.h2net.org.uk/ PDFs/Prod2001/llandrindod_high.pdf HSAPS project, www.hsaps.ife.no National Renewable Energy Laboratory (USA), www.nrel.gov US Department of Energy, www.eere.energy.gov/hydrogenandfuelcells

8.9

References

Braess H H, Strobl W (1996), ‘Hydrogen as a fuel for road transport of the future: Possibilities and prerequisites’, in: Veziroglu, T N, et al. (Eds.), Hydrogen Energy Progress XI, vol. 2, Int. Association of Hydrogen Energy, Coral Gables, FL, 1363–1404. de Zoysa A, Wijesooriya P, Martin J (2007), ‘Enhancing energy sustainability a software tool on optimization of hybrid energy systems’, Energy Engineering, 104(2), 15–24. Dunn S (2002), ‘Hydrogen futures: toward a sustainable energy system’, International Journal of Hydrogen Energy, 7, 235–264. Giatrakos G, Mouchtaropoulos P, Naxakis G, Tsoutsos T, Stavrakakis G (2009), ‘Sustainable energy planning on a renewable energy – hydrogen stand alone power system. Application in Karpathos island, Greece’, Renewable Energy, 34(12), 2562–2570. Glöckner R, Ulleberg Ø, Zoulias M, Taylor P, Vosseler I (2003), ‘Market potential for the introduction of hydrogen in stand-alone power systems’, in: European Hydrogen Energy Conference, Grenoble (CP5/212). Hauglastine D A, Ehhalt D H (2002), ‘A three-dimensional model of molecular hydrogen in the troposphere’, Journal of Geophysical Research, 107, D17, 4330. Holladay J D, Hu J, King D L, Wang Y (2009), ‘An overview of hydrogen production technologies’, Catalysis Today 139, 244–260. Kaldellis J K (2007), ‘An integrated model for performance simulation of hybrid wind–diesel systems’, Renewable Energy, 32(9), 1544–1564.

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Kaldellis J K, Kondili E, Filios A (2006), ‘Sizing a hybrid wind-diesel stand-alone system on the basis of minimum long-term electricity production cost’, Applied Energy 83(12), 1384–1403. Larminie J, Dicks A (2003), Fuel Cell Systems Explained, 2nd ed., John Wiley & Sons Ltd, Chichester, 14–15. Larsen H, Feidenhans’l R, Sønderberg Petersen L (ed) (2004), Hydrogen and its Competitors, Risø National Laboratory, November. Mitlitsky F (1996), ‘Development of an advanced, lightweight, high pressure storage tank for on-board storage of compressed hydrogen’, in: Proc. Fuel Cells for Transportation TOPTEC, Alexandria, VA, SAE, Warrendale, PA. NAS (National Academy of Science) (2004), The Hydrogen Economy: Opportunities, Costs, Barriers, and R&D Needs, National Academies Press, Washington, DC. NREL, National Renewable Energy Laboratory, analysis.nrel.gov/homer/; 06/2008. Papadaki M, Andonidakis E, Tsoutsos T, Maria E (2003), ‘A multicriteria decision making methodology for sustainable energy development’, Fresenius Environmental Bulletin, 12(5), 426–430. Schultz MG, Diehl T, Brasseur GP, et al. (2003), ‘Air pollution and climateforcing impacts of a global hydrogen economy’, Science, 302(5645), 624–627. Sherif SA, Barbir F, Veziroglu TN (2005), ‘Wind energy and the hydrogen economy – review of the technology, Solar Energy, 78, 647–660. Taylor J B, Anderson J E A, Kalyanam K M, Lyle A B, Philips L A (1986), ‘Technical and economic assessment of methods for the storage of large quantities of Hydrogen’, International Journal of Hydrogen Energy, 11(1), 5–22. Thomas C E (1996), ‘Preliminary hydrogen vehicle safety report’, The Ford Motor Company, Contract No. DE-AC02-94CE50389, US Department of Energy. Tromp T K, Shia R L, Allen M, Eiler J M, Yung YL (2003), ‘Potential environmental impact of a hydrogen economy on the stratosphere’, Science, 300(5626), 1740–1742. Tsoutsos T (2008), ‘Barriers and benefits of hydrogen-based autonomous power systems’, in: Hydrogen-based Autonomous Power Systems, Zoulias E I and Lymberopoulos N, Springer-Verlag, London. Tsoutsos T D, Zoulias E I, Lymberopoulos N, Glöckner R (2004), ‘H-SAPS market potential analysis for the introduction of hydrogen energy technology in stand alone power systems’, Wind Engineering, 28(5), 615–619. Various (2000), ‘Market potential analysis for introduction of hydrogen energy technology in stand-alone power systems (H-SAPS)’, European Commission, DG for Energy and Transport, ALTENER Programme, Contract No. 4.1030/Z/01-101/2000. Veziroglu T N, Barbir F (1993), ‘Hydrogen: its comparison with fossil fuels and its potential as a universal fuel’, in: The Future of Energy Gases, Howell D G (ed), United States Geological, ISBN-10: 999367396X, 715–724. Zoulias E I, Lymberopoulos N (2007), ‘Techno-economic analysis of the integration of hydrogen energy technologies in renewable energy-based stand-alone power systems’, Renewable Energy, 32(4), 680–696. Zoulias E I, Glockner R, Lymberopoulos N, Tsoutsos T, Vosseler I, Gavalda O, Mydske H J, Taylor P (2006), ‘Integration of hydrogen energy technologies in stand-alone power systems. Analysis of the current potential for applications’, Renewable and Sustainable Energy Reviews, 10/5, 432–462.

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Abbreviations

AC AFC APS COE DC DEGS FC GHG H-APS HCFC HFC H-SAPS MCFC PAFC PEM PEMFC PV RES SAPS SOEC SOFC SWOT

alternating current alkaline fuel cell autonomous power systems coefficient of performance direct current diesel engine generator sets fuel cell greenhouse gas hydrogen autonomous power systems (APS with hydrogen as the longer term energy storage option) hydrochlorofluorocarbon hydrofluorocarbon hydrogen stand-alone power systems (SAPS with hydrogen as the longer term energy storage option) molten carbonate fuel cell phosphorus acid fuel cell proton exchange membrane proton exchange membrane fuel cell photovoltaic renewable energy sources stand-alone power systems solid oxide electrolysis cells solid oxide fuel cell strengths, weaknesses, opportunities and threats

© Woodhead Publishing Limited, 2010

9 Hybrid wind–hydropower energy systems O. A. JARAMILLO, O. RODRÍGUEZ-HERNÁNDEZ and A. FUENTES-TOLEDO, Universidad Nacional Autónoma de México, Mexico

Abstract: Useful concepts are presented to help understand the different factors involved in coupling water and wind as complementary energy systems. Different wind–hydro energy systems are described with the objective of maintaining a balance between generation and load at all times and supply energy when the customer needs it. Most of the documented or proposed systems are intended to function at high power levels, and are operated as on-grid connection, or as stand-alone systems. Key words: wind–hydropower systems, WHPS, water pumping system, pumped storage systems, hybrid system.

9.1

Introduction

Wind energy or hydro energy can be utilized in a number of applications requiring shaft power. Examples include water pumping, fodder cutting, oil-seed pressing, grain grinding, paper pulp production and the generation of electricity in capacities ranging from a few watts or kilowatts (for micro, mini and small hybrid systems) to several megawatts (in medium and large wind–hydropower systems). The main advantage of a wind–hydropower system (WHPS) is the possibility of complementing wind and hydraulic power in order to optimize available local resources, ensuring high levels of quality, reliability and performance. With cost reductions in installation and operation, a WHPS currently represents a viable solution to satisfy electricity demands in isolated or non-electrified locations. Diesel generators, when operating in a low load mode, show reduced efficiency in operation, with high maintenance costs and a short lifetime for the installation. WHPS can reduce these problems and take advantage of locally available renewable resources, providing a viable, environmentally friendly, and socially acceptable, technical option. To limit the impact of the intermittence of wind power, a pumped storage system is used, combining the water storage ability of a hydraulic storage plant with the wind power plant. The main objective is to limit the active power output variations of the wind energy resource, taking grid needs and 282 © Woodhead Publishing Limited, 2010

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the available stored energy into account. The problem is formulated as an optimization problem, with the constraints being techno-economical parameters and social requirements. Based on wind power forecast information, the power demand of the grid, and a penalty cost, an optimal operation strategy is proposed for the WHPS, offering an efficient combination of wind energy and storage management. The purpose of this chapter is to achieve an overview of the state of the art of wind–hydro energy systems. The chapter is organized as follows. In Section 9.2, the need to couple wind and hydro energy is described, together with some of the advantages of such systems. In Section 9.3, different types of wind–hydropower systems are reported, from the oldest wind–hydro system (the water pumping system), to micro, mini and small hydropower systems, medium and large wind–hydropower systems, and pumped storage systems (the most important system for this technology). In Section 9.4, a list of the most important wind and hydropower technology analysis models and tools are reported, and a literature review of wind–hydro energy systems is included. The benefits and limitations of wind–hydro energy systems are described in Section 9.5, first with an analysis of individual systems and then, at the end of the section, with a presentation of the benefits and limitations of WHPS as a whole. In Section 9.6, some different aspects that could be included in policies to operate wind–hydro power systems are described. Environmental impacts and the economics of wind– hydro systems are included in Sections 9.7 and 9.8, respectively. Finally, in Section 9.9, the main conclusions are presented. It is hoped that this work will provide a useful overview of wind–hydro energy systems.

9.2

The need to couple wind–hydropower systems (WHPS)

Often the size of a hydraulic or wind facility installation is not, by itself, enough to satisfy electricity demands throughout the year, either for economic reasons or because another source of electricity generation is present; in the case of the latter, that source may become an energy complement to the hybrid facility. Even in cases where the facility size is correct, it is still possible for consumption to peak above estimated demand, or for there to be an abnormally long period of drought or no wind to power the turbines. Of course, energy supply must be ensured and it is therefore advisable to have a generating system that allows additional security to cope in any of these situations. In order to ensure an acceptable state of charge of batteries, and an extension of their life, a conventional energy source is often considered as an auxiliary system. Wind energy, like hydro, is an indirect product of solar

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energy; therefore, they both vary widely through the year, generally, in the northern hemisphere, having high values during the winter months and low values during the summer months; in tropical climates these variations are likely to relate to monsoon conditions (Freris and Infield, 2008). This is why seasonal constraints are important; available water can be stored during winter, which is perfectly complementary to wind power that has its minimum availability in summer. Micro and mini hydropower systems do not necessarily need to be located near a river: a small flow of water is sufficient, provided that there is a suitable water flow or a suitable head height. Different kinds of turbine are designed for different combinations of slope and flow: the smaller hydraulic turbines work with little water and steep slopes. However, wind turbines need to be located in particular locations where the wind resource is available and terrain conditions allow installation of the technology. Currently, modern hydropower turbines offer the conversion of 80% of hydraulic energy into electricity, while wind turbines transform 40% of the kinetic energy of wind into electricity.

9.3

Different types of wind–hydropower systems (WHPS)

Hydropower comes from converting energy in flowing water into useful mechanical power by means of a water wheel or a turbine. Wind power is the conversion of kinetic wind energy, using wind turbines, into a useful form of power, such as electricity. In both cases, mechanical energy is converted into electricity using an electric generator, in which electrical power is measured in watts (W), kilowatts (kW) or megawatts (MW). In this chapter, WHPS are classified as large, medium, small, mini and micro according to their installed power generation capacity. A micro-power system is generally classified as having a generating capacity of less than 100 kW, while systems that have an installation capacity of between 100 kW and 1000 kW (1.0 MW) are referred to as mini power systems; both systems are commonly off-grid connected or stand-alone systems. A small WHPS is defined by a capacity of more than 1.0 MW and less than 10 MW. Medium-power systems have a capacity of between 10 and 30 MW, while a large WHPS refers to a system with a capacity greater than 30 MW. These systems are commonly on-grid connected. Renewable energy generators are either stand-alone or grid-connected. In stand-alone systems, WHPS (with or without other back-up generators or storage) supply the main part of the demand. In a grid-connected WHPS, the renewable energy feeds power to a large interconnected grid, also fed by a variety of other generators. The crucial distinction here is that the

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power injected by the hybrid system is only a small fraction of that generated by the totality of generators on the grid. The distinction between stand-alone and grid-connected generators is useful but is not always clearcut. Sometimes confusion arises when the word grid is used to refer to a relatively small stand-alone electrical network; the term grid is often used loosely to describe the totality of the network. Grid-connected specifically means connected to any part of the network. Integration specifically means the physical connection of the generator to the network with due regard to the secure and safe operation of the system and the control of the generator so that the energy resource is exploited optimally. The proper integration of any electrical generator into an electrical power system requires knowledge of the well-established principles of electrical engineering. The integration of generators powered from renewable energy sources is fundamentally similar to fossil fuelled powered generators and is based on the same principles, but renewable energy sources are often variable and geographically dispersed (Freris and Infield, 2008). The development of a WHPS in order to supply electricity depends on the scale of the utility. Large and medium WHPSs can be placed in different regions of a country and they are operated in combination in order to complement each other and provide stability to the electrical grid. Wind power is non-dispatchable, meaning that, for economic operation, all the available output must be taken when it is available and other resources, such as hydropower, and standard load management techniques must be used to match supply with demand. Small, mini and micro WHPS are commonly established in regions where wind and hydraulic resource are available. They are designed to supply energy to the local region and in many cases are stand-alone systems. The intermittency of wind seldom creates problems when using wind power to supply a low proportion of total demand. Where wind is to be used for a moderate fraction of demand, additional costs for compensation of intermittency are considered to be modest.

9.3.1 Water pumping systems Water pumping systems are a special type of WHPS. These systems use wind energy to supply shaft power that is used in a direct manner to pump water or to generate electricity to drive an electrical pump. Wind-powered water pumps have been used over many centuries in countries such as the Netherlands; even today, a large number of these devices are used in remote localities. Water pumping systems can be applied over a vast area better than surface water applications based on large irrigation dams. Owing to the large amount of water needed for irrigation, wind power is rarely used to

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irrigate crops. However, larger and more efficient wind turbines are capable of generating enough electricity for use in irrigation projects (Gipe, 1993; Gasch and Twele, 2002). In developing countries where many regions are not connected to the electrical power supply, wind power can be applied to generate mechanical power or electrical power to pump water from shallow depths. Solar energy and conventional diesel motors can also be considered for improving water supply, though in the case of diesel motors (used to drive electrical motors) it is essential to consider the fuel supply (Gipe, 1993; Gasch and Twele, 2002). A high number of blades were used in old, low tip-speed ratio rotors for water pumps, and for applications which needed high starting torque. Modern, high tip-speed ratio rotors for generating electrical power have only two or three blades. The number of rotor blades is indirectly linked to the tip speed ratio, λ, which is the radio of the blade tip speed and the wind speed (Manwell et al., 2002):

λ=

ωR v

9.1

where ω (s−1) is the frequency of rotation, R (m) is the radius of the aerodynamic rotor and v (m s−1) is the wind speed. The conversion of wind energy into hydraulic energy by a wind pumping system can be made if the wind speed is greater than 2.5–3.0 m s−1, with a capacity factor greater than 45% (i.e. the ratio of the actual output of the wind turbine over a period of time and its output if it had operated at fully rated capacity the entire time). It is possible to operate a wind pumping system if the water level of the reservoir does not change significantly, and if it is also possible to store three days or one week of water in order to account for no wind days. In order to operate a mechanical drive to pump water, the wind turbine must be placed close to the water reservoir, and the main parts of the system must be sheltered from the weather. In the case of wind turbines that supply electrical power to pump water, the wind turbine can be placed far away from the water reservoir, in order to maximize the conversion of wind energy. Four types of wind pumping can be distinguished: village water supplies, irrigation, livestock water supplies and drainage. Direct mechanically coupled wind turbines are the most common method for pumping water to croplands and livestock. Many more recent wind turbines are electrically coupled, with the water pump connected to the wind turbine via a motorgenerator connection. A typical wind pump is depicted in Fig. 9.1. There are currently three types of wind power systems to pump water: two use mechanical power to pump water, while the third converts wind power to electrical energy (Gasch and Twele, 2002):

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Complete windmill

Tower Sucker rod

Well seal

Clay

Casing Sand or gravel

Drop pipe

Water flows in Perforations

Cylinder plunger Cylinder

Storage tank

Unsaturated zone Water and air in pore spaces

Water table

Water fills all pore spaces

Strainer

Saturated zone

Bedrock

9.1 Windpumping system and storage tank. Storage is necessary to provide water during no wind period (Aermotor Windmill Company, Inc.).

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Mechanical (piston pump). This system converts rotary wind power to vertical motion, using a snake rod and a piston pump to lift water. Mechanical (air lift pump). This system uses wind power to charge a compressor that pumps air to lift water. Electrical pump. The electrical pumping system channels the energy generated directly to the water pump, and/or to a battery storage system.

System design depends on specific energy needs, whether a battery storage system is required, and on the amount of wind available at the site. Hybrid wind/solar/conventional systems are considered when a wind resource is not available during some months of the year (i.e. during the summer when water demand increases). New helical pumping systems can be powered by either solar–photovoltaic (PV) power or wind power, and backed up by a diesel or battery system. A helical pump (a positive displacement pump) should provide higher flow rates at deeper pumping depths with lower power requirements than a centrifugal pump (a high volume pump). Another aspect to take into account when selecting a system is that batteries can account for more than 20% of the total capital investment. In order to estimate the size of a wind turbine required to pump water it is necessary to consider three main parameters: the pump head (H m), the required water flow rate (Q m3 s−1) and the mean wind speed (v¯ m s−1) of the locality. The actual delivered power of the rotor must equal the required hydraulic power, namely: 1 Cpηm ⎛ ρair Av 3 ⎞ = ρw gHQ ⎝2 ⎠

9.2

where Cp is the coefficient of performance, or efficiency of wind conversion, of the rotor, ηm is the mechanical efficiency of the wind pump, ρair is the density of the air (taken to be 1.15 kg m−3), A (m2) is the area of the rotor, ρw is the density of the water (taken to be 1000 kg m−3) and g (m s−2) is the acceleration due to gravity. The rotor area can be expressed as: A=

(1000 kg m −3 )(10 HQ) (0.58 kg m −3 )Cpηm v 3

9.3

and the rotor diameter D is obtained from D=

4A π

9.4

It is important to note that the required water flow rate, the pump head and the wind speed will vary throughout the year; therefore it is convenient to estimate the mean value of each variable for every month (Omer, 2008). The electrical power of different water pumps is depicted in Fig. 9.2.

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700 600 Power consumption turbo pump

Electrical power (W)

500

Optimun operating line wind turbine Power consumption piston pump

400 300 Cut-in turbo pump 200 100 3 m s–1

4 m s–1

5 m s–1

6 m s–1

0 0

10

20

30

40

50

60

Rotor speed (rpm)

9.2 The optimal power characteristics of a small wind turbine compared with the power consumption of a piston pump and a centrifugal pump.

A comparison of the operating characteristics of helical and centrifugal water pumps against the power characteristic of a high-speed wind turbine shows that the operating characteristics of the water pump can be matched more easily to the power characteristics of a wind rotor if the water pump is centrifugal (Fig. 9.2). The simple reason is that the characteristics of the two ‘fluid flow machines’, wind rotor and centrifugal pump, are a better match (Hau, 2006). Although the electrical transmission of power from wind turbine to water pump involves a twofold energy conversion, with corresponding losses of about 30% in total, in most cases this loss is more than compensated for by optimal siting of the wind turbine (Hau, 2006). Electric water pumps that are plugged into an outlet using alternating current (AC) are generally not built to operate very efficiently because there is no control on the amount of electrical power supplied and the AC motors must run at fully rated capacity in order to operate efficiently. Wind pumping systems are designed to use direct current (DC) provided by a wind turbine, although some newer versions use a variable frequency AC motor and a three phase AC pump controller that enables them to be powered directly by wind turbines. Because wind turbines are expensive and their power production can be variable, wind pumping systems need

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to be as efficient as possible; that is, they need to maximize the total number of litres of water pumped per watt of electricity used. They must also be able to pump during low wind speed conditions. In order to meet these demands, pump manufacturers needed to change their water pump designs. Most conventional AC pumps use a centrifugal impeller that ‘throws’ the water into motion. A multi-stage centrifugal pump has a series of stacked impellers and chambers. When operating at low power, the amount of water pumped by centrifugal pumps drops dramatically. This makes centrifugal pumps somewhat limited in solar applications (though efficient centrifugal pumps are available). Many designers of water pumps have taken the approach of using positive displacement pumps, which bring water into a chamber and then force it out using a piston or helical screw. These generally pump slowly than other types of pumps, but have a good performance under low-power conditions, and can achieve high lift. Both submersible (with the pump remaining underwater) and surface pumps are available. Surface pumps are less expensive than submersible pumps but they are not well suited for suction and can draw water only from about 6 vertical metres. Surface pumps are excellent for pushing water long distances. In some case, both kinds of pump are employed in the same system, when the pump head is higher than 6 m and the water is pumped long distances. Around the world, many countries, such as India, China, Australia, Greece and Egypt, are conducting programmes for water pumping by using wind energy. The United States carried out one of the most important programmes in this area. In September 2004, R. Nolan Clark and Brian D. Vick from USDA Agricultural Research Service (Bushland, Texas), started a research project named ‘Remote Water Pumping and Electric Power Generation with Renewable Energy’. One of the main objectives of the project was to develop and evaluate autonomous wind-powered water pumping systems for irrigation, livestock, and farmstead water by developing farm-size inverter and rectifier-based controllers to increase the amount of usable energy available to the pump, and by developing control strategies that prioritize and distribute electrical power to multiple loads (for example, to pumps, water heaters, batteries and balancer loads). The project was closed at the end of August 2009 (Clark and Vick, 2009).

9.3.2 Integration of micro, mini and small hydropower systems (stand-alone systems) A micro, mini or small hydropower system can produce enough electricity for a home, farm, ranch or village. Micro and mini hydropower systems are relatively small power sources that are appropriate in most cases for

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individual users, or for groups of users who are independent of the electricity supply grid. Most micro and mini hydropower sites are categorized as low or high head. Low head refers to a change in elevation of less than 3 m; a vertical drop of less than 0.6 m will probably make a mini hydroelectric system unfeasible. A higher head is a better option than a lower head because less water is needed to produce a given amount of power and it is possible to use smaller and less expensive equipment. Micro, mini and small hydro systems have the following components: •

• •

A water turbine that converts the energy of flowing or falling water into mechanical energy that drives a generator, which generates electrical power – this is the heart of a micro-hydropower system. A control mechanism to provide stable electrical power. Electrical transmission lines to deliver the power to its destination.

Depending on the site, the following may be needed to develop a micro and mini hydropower system: • • • • • •

An intake or weir to divert stream flow from the water course. A canal/pipeline to carry the water flow to the forebay from the intake. A forebay tank and trash rack to filter debris and prevent it from being drawn into the turbine at the penstock pipe intake. A penstock pipe to convey the water to the powerhouse. A powerhouse, in which the turbine and generator convert the power of the water into electricity. A tailrace through which the water is released back to the river or stream.

It is important to note that the simplest micro and mini hydro plants are run-of-the-river systems, which means that they do not include a dam or reservoir. There are two main types of hydro turbines: impulse and reaction. The capacity of these can vary from some kilowatts to megawatts. The type of hydropower turbine selected for a project is based on the head and the flow, or volume of water, at the site. Other deciding factors include how deep the turbine can be set, efficiency and cost. An impulse turbine generally uses the velocity of the water to move the runner and discharges to atmospheric pressure. The water stream hits each bucket on the runner. There is no suction on the down side of the turbine, and the water flows out the bottom of the turbine housing after hitting the runner. An impulse turbine is generally suitable for high-head, low-flow applications. Examples of impulse turbines include the following: •

A Pelton wheel, which has one or more free jets discharging water into an aerated space and impinging on the buckets of a runner. Draft tubes are not needed for an impulse turbine since the runner must be located

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above the maximum tailwater to permit operation at atmospheric pressure. • A Turgo wheel, which is a variation on the Pelton and is made exclusively by Gilkes in England. The Turgo runner is a cast wheel whose shape generally resembles a fan blade that is closed on the outer edges. The water stream is applied on one side, goes across the blades and exits on the other side. • A cross-flow turbine, which is drum-shaped and uses an elongated, rectangular-section nozzle directed against curved vanes on a cylindrically shaped runner. It resembles a ‘squirrel cage’ blower. The crossflow turbine allows the water to flow through the blades twice. The first pass is when the water flows from the outside of the blades to the inside; the second pass is from the inside back out. A guide vane at the entrance to the turbine directs the flow to a limited portion of the runner. The cross-flow was developed to accommodate larger water flows and lower heads than the Pelton. A reaction turbine develops power from the combined action of pressure and moving water. The runner is placed directly in the water stream flowing over the blades rather than striking each blade individually. Reaction turbines are generally used for sites with lower head and higher flows compared with those sites which suit impulse turbines. Examples of reaction turbines include the following: •

A propeller turbine, which generally has a runner with three to six blades in which the water contacts all of the blades constantly, like a boat propeller running in a pipe. The pressure is constant through the pipe; otherwise, the runner would be out of balance. The pitch of the blades can be fixed or adjustable. Besides the runner, the major components are a scroll case, wicket gates and a draft tube. There are several different types of propeller turbines: 䊊 A bulb turbine, in which the turbine and generator are a sealed unit placed directly in the water stream. 䊊 A straflo turbine, in which the generator is attached directly to the perimeter of the turbine. 䊊 A tube turbine, in which the penstock bends just before or after the runner, allowing a straight line connection to the generator. 䊊 A Kaplan turbine, in which both the blades and the wicket gates are adjustable, allowing for a wider range of operation. • A Francis turbine, which has a runner with fixed buckets (vanes), usually nine or more of them. Water is introduced just above the runner and all around it and then falls through, causing it to spin. Besides the runner, the other major components are the scroll case, wicket gates and draft tube.

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A kinetic energy turbine, also called a free-flow turbine, which generates electricity from the kinetic energy present in flowing water rather than the potential energy from the head. The system can operate in rivers, artificial channels, tidal waters or ocean currents. Kinetic systems utilize the water stream’s natural pathway. They do not require the diversion of water through channels, riverbeds or pipes, although they might have applications in such conduits. Kinetic systems do not require large civil works; however, they can use existing structures such as bridges, tailraces and channels. A typical mini hydropower plant is depicted in Fig. 9.3.

For a hydroelectric plant, energy output is calculated through (Liu et al., 2003): EH = ∫

t =T

0

gρwηhQ ( H − ΔH ) dt

9.5

where ηh is the overall efficiency of the hydroelectric generating units, ρw (kg m−3) is the density of water, H (m) is the head of the hydroelectric generating unit at time t, and ΔH (m) is the total hydraulic loss of the hydraulic system. Figure 9.4 depicts the efficiency of Kaplan, Francis and Pelton turbines. Hydroelectric generating units operating at their full capacity over a period T (h) can deliver a hydro energy rate EHR as follows: EHR = (T)(nUIG)

9.6

A – Intake/weir B – Canal/pipeline C – Forebay tank D – Penstock pipe E – Powerhouse F – Tailrace G – Transmission line

A B C

D

G

E F

9.3 Schematic representation of mini-hydro power station.

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96

Efficiency (%)

94 92 90 88 86 84 82 80 0

Francis turbines

Kaplan turbines

1-jet Pelton turbines 76

152

228

NS 304 380 456 Specified speed

532

608

684

9.4 Efficiency of Francis, Pelton and Kaplan turbines.

where IG (W) is the rated power of each hydroelectric generating unit used, and nU is the number of generators. The capacity factor of the hydropower plant is established by: E CFH = H = EHR

∫

t =T

0

gρwηhQ ( H H − ΔH ) dt

(T )( nU IG )

9.7

As we all know, a wind turbine is a rotating machine which converts kinetic wind energy into mechanical energy. If the mechanical energy is used directly by machinery, such as by a pump or grinding stones, the machine is usually called a windmill. If the mechanical energy is then converted to electricity, the machine is called a wind generator or wind turbine. Wind turbines can be separated into two types based on the axis in which the turbine rotates. Turbines that rotate around a horizontal axis (horizontal axis wind turbines; HAWT) are more common; vertical-axis wind turbines (VAWT) are found less frequently. Small wind turbines may be as small as a 50 W generator for boat or caravan use. Small units often have direct drive generators, direct current output, aeroelastic blades and lifetime bearings, and use a vane to point into the wind. Turbines used in wind farms for commercial production of electric power are usually three-bladed and pointed into the wind by computer-controlled motors, with capacities from hundreds of kilowatts to 2 or 3 MW. These turbines have high tip speeds of up to six times wind speed, high efficiency, and low torque ripple, all of which contribute to good reliability. The length of the blades is in the range of 20–40 m or more, mounted on tubular steel towers ranging from 60 to 90 m tall. The blades rotate at 10–22 revolutions per minute. A gear box is commonly used to step up the speed of the generator, although some designs use direct drive of an annular generator. Some models operate at a constant speed, but more energy can

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Ideal cp (momentum theory)

0.6

Theoretical power coefficient (infinite number of blades, L/D = ∞)

Rotor power coefficient

0.5 Three-bladed rotor

0.4

Two-bladed rotor One-bladed rotor

0.3

Darrieus rotor

0.2 Dutch windmill American wind turbine

0.1

Savonius rotor 0

0

2

4

6

8

10

12

14

16

18

Tip-speed ratio l

9.5 Power coefficients of wind rotors of different designs.

be collected by variable-speed turbines that use a solid-state power converter to interface to the transmission system. All turbines are equipped with shutdown features to avoid damage at high wind speeds. Figure 9.5 depicts the rotor power coefficient of different kind of wind turbines. The wind power output from a wind turbine PWT(v) is determined by the power curve of the wind turbine, which can be described by: 0, v < vI ⎧ ⎫ ⎪ ⎪ PWT ( v) ⎨(WT ) ⋅ [) ( v)], vI ≤ v < vO ⎬ ⎪ ⎪ 0, vO ≤ v ⎩ ⎭

9.8

where vI (m s−1) and vO (m s−1) are the cut-in and cut-out speed, respectively, WT(W) is the rated power of the wind turbine and F(v) is a desirable polynomial function to fit the turbine performance curve. In this case, F(v) is normalized with the rated power WT of the wind turbine. For a pitch control wind turbine, a continuous function F(v) can be established by ) ( v) =

a1 ⎛ v − a2 ⎞ 1 + exp ⎜ − ⎟ ⎝ a3 ⎠

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9.9

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where a1 (m s−1), a2 (m s−1) and a3 (m s−1) are the regression constants of the turbine power curve. It should be noted that use of an exponential expression in equation 9.9 is for regression purposes only. The wind energy output of one wind turbine can be established by: EWT = ∫

vO

vI

∫

t =T

0

= TWT

(∫

vO

t =T

vI

0

PWT ( v) P ( v) dtdv = WT ∫ F ( v) P ( v) dv∫ vO

vI

)

dt

9.10

F ( v ) P ( v ) dv K a K p

In this expression, the product F(v)P(v) does not depend on time, P(v) is a probability density function (a continuous mathematical function) that is used to model the wind speed frequency curve by fitting measured data from a time-series over a period of time, and Ka and Kp are the availability factor and performance factor of the wind turbine, respectively. In many cases the integrals in this equation cannot be solved by an analytical formulation. They can, however, be calculated by using typical numerical integration techniques. A wind turbine operating at its full capacity over a period T can deliver a rated wind energy, EWR, as follows: EWR = TWT

9.11

and the capacity factor of a wind turbine is estimated through CFWT =

vO EWT = ∫ F ( v) P ( v) dv vI EWR

9.12

In order to facilitate the analysis of a hybrid wind–hydro system, it is possible to assume that the energy output of a wind farm, EW, could be scaled linearly according to the number of wind turbines. In other words, the wake and array losses are not taken into account and it is assumed that the wind turbines operate under the same environmental conditions; thus: EW = lEWT

9.13

where EWT is established by equation (9.10) and l is the number of wind turbines. It is possible to assume that each wind turbine presents the same capacity factor, CFWT, which also corresponds to the capacity factor of the entire wind farm: CFW = CFWT

9.14

In a more realistic calculation, it is necessary to consider a wind farm availability and performance factor (Jaramillo et al., 2004). Therefore, the energy obtained from a WHPS can be estimated by using:

© Woodhead Publishing Limited, 2010

Hybrid wind–hydropower energy systems EWH = EWT + EH = ∫

t =T

0

vO

gρwηhQ ( H − ΔH ) dt + TWT ∫ F ( v) P ( v) dv vI

297 9.15

A WHPS cannot operate at maximum capacity all the time, since allowances have to be made for maintenance, equipment failure and energy source availability. To determine a facility’s real energy output, its overall capacity factor must to considered. This ratio is generally expressed as a percentage. In many cases, much more wind capacity would be required to achieve the same annual energy output as hydro because of the 30% capacity factor for wind vs the 60% for hydropower plants. Numerous communities in isolated locations, on islands and in developing countries, are connected to small independent electric grids powered by diesel generators. They may range in size from relatively large island grids of many megawatts down to systems with a capacity of a few kilowatts. Isolated and island grids vary significantly. Some isolated grids powered by diesel generators provide power for only part of the day to conserve fuel. Others have large voltage swings due to the effects of one or two significant loads on the system. Large isolated grids provide power at stable voltages and constant frequency. In general, isolated grids are weak grids in which voltage and frequency are susceptible to disruption by interconnected loads and by generation. The basic challenges with regard to the integration of a WHPS consist, therefore, of the following two aspects: •

•

How to keep an acceptable voltage level for all power system consumers: customers should be able to continue to use the same type of appliances that they are used to. How to keep the power balance of the system: that is, how wind power production and other generation units can continuously meet consumers’ needs.

With a wind farm, there is no guarantee that the wind will blow when energy is needed. However, the capacity of a hydropower plant can easily be guaranteed, because the quantity of water sent through the turbines is controlled at all times. It is necessary to identify the best operation strategy of a combined WHPS with little water storage ability, determining the amounts of wind and hydropower to be generated for each hourly period. It is important to take into account that the amount of wind power also varies between different years. Available hydropower and available wind power correlate, since years with plenty of rain are often also years with higher winds. The correlation depends on where the wind power generation is located. WHPS schemes are strongly site specific. As a consequence, many important economic and environmental aspects of hybrid plant schemes vary widely, i.e. technical variables, specific investment costs, total

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production costs, peak load production possibilities and external effects. The strong heterogeneity of WHPS schemes complicates any adequate definition and characterization of the hybrid power plant. Fortunately, new analysis tools allow a multidimensional characterization and typology of WHPS schemes. In particular, geographic information systems provide a powerful set of tools for quantitatively analysing interactions between WHPS schemes and their physical, social and economic environment. Sustainability must become the benchmark for all future WHPS schemes.

9.3.3 Medium and large wind/hydropower generation systems Both medium and large WHPS have been developed as options in energy strategic planning. Wind power is connected to the grid and hydropower is used to compensate for wind power fluctuations, in the context of a global energy balance in the overall system. This kind of power system is presented as a way to balance energy and shows that hydro energy can be displaced by wind energy: in this case, a pond absorbs fluctuations in wind energy after a wind-driven pump converts any energy surplus into an available energy reserve by pumping water up to the pond, from where it can later be used to produce hydropower. This system takes advantage of the pond’s capacity to absorb the random supply of wind energy to turn it into energy that can be used later. In many cases, wind power and hydropower are seasonally complementary, so that combining the two can produce constant power. Clearly, wind and hydro resources vary from country to country and from place to place, and any analysis and evaluation of WHPS must, therefore, be conducted on a case by case basis (Jaramillo et al., 2004). A number of medium and large WHPS have been proposed to supply constant power. The hybrid system consistently combines wind power and hydropower in real time in order to guarantee constant power. As has already been said, wind power is subject to short-term fluctuations and hydropower is used to compensate for these variations. This indicates that the hydropower plant in any WHPS must be able to increase or decrease production very quickly in order to obtain constant power. The constant power from a hybrid system can be expressed as the sum of wind power PW (W) and hydro power PH (W): PF = PW + PH

9.16

Taking into account the fact that PF does not depend on time (since it is a constant power) and considering a period of time T, the energy delivered by the hybrid system can be estimated by:

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Hybrid wind–hydropower energy systems PFT = ∫

T

t =0

PW ( t ) dt + ∫

T

t =0

PH ( t ) dt

299 9.17

Considering the capacity factor of both facilities we can write: PF = CFWNW + CFHNH

9.18

and hydropower output is established by PH = CFHNH

9.19

where CFH and CFW are the capacity factors for the hydroelectric plant and the wind farm, respectively; and where NH (W) and NW (W) are the rated powers for the hydroelectric plant and the wind farm, respectively. When there is little or no wind, the hydroelectric plant must supply 100% of total power to guarantee constant power output. In other words, the constant power, PF, that could be delivered should be equal to the rated power for hydro, NH: NH = nUIG

9.20

When the wind farm operates at rated power CFW + 1, constant power should be equal to the rated power for the wind farm NW: NW = lWT

9.21

where l is the number of wind turbines. It is important to note that the rated power for the wind farm NW and the rated power for hydro NH must be the same to guarantee firm power PF PF = NW = NH = N

9.22

In summary, when the wind farm is operating at full capacity there is no need to generate hydropower. Therefore the hydropower capacity factor is CFH = 0. In periods of low wind, the hydropower capacity factor is CFH = 1 and the wind farm capacity factor is CFW = 0. In fact, the range for both capacity factors is 0 ≤ CF ≤ 1. Therefore: CFW + CFH = 1

9.23

where 0 ≤ CFH ≤ 1 is the complement of 1 ≥ CFW ≥ 0. The rated power for the hybrid wind–hydro system NWH is established by the sum of the rated power of wind NW (W) and the rated power of hydro NH (W): NWH = NW + NH = 2N

9.24

Therefore the capacity factor of the hybrid wind–hydro system is CFWH = 1/2, this is PF = 0.5(NWH) = N

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9.25

300

Stand-alone and hybrid wind energy systems

Among the most important parameters that affect the energy performance of the hybrid system are the following (Jaramillo et al., 2004): • • • • •

the wind resource; the number of wind turbines used; the hydro resource (available hydropower); the behaviour of the selected water reservoir (depending on hydrological conditions); the capacity of the hydroelectric plant.

The conditions that determine the maximum firm power supplied by the WHPS are: • • •

the hydroelectric capacity (which depends on the volume of water in the reservoir); the performance of the capacity factor of the wind farm (which depends on the availability of the wind resource); the performance of the wind turbines.

Thus, the main problem is to determine the rated powers for both wind and hydro facilities in such a way that the maximum firm power output is attained.

9.3.4 Pumped storage systems A pumped storage hydroelectric power plant has two or more reservoirs at different elevations. At low electricity demand from the consumers in the region served by the facility, the excess available power is used to pump water from the lower reservoir into the upper one. When demand increases, the potential energy stored in the upper reservoir is released. Water is let out of the upper reservoir in a controlled manner, passing through penstocks and turbines to generate electricity. Pumped storage systems require dams to hold the water in the reservoirs. These dams are generally smaller than those used in large impoundment facilities. Pumped storage power plants can be found in regions where the terrain is hilly or only gently rolling, but there must be a significant difference in average elevation between the reservoirs (Douglas, 1990; Jog, 1989). Pumped hydroelectric energy storage is the oldest kind of large-scale energy storage and was first used in the 1890s in Italy and Switzerland. In the 1930s, reversible hydroelectric turbines became available and, in fact, until 1970 it was the only option commercially available for large energy storage. Pumped hydroelectric stations are in active operation, and new ones are still being built (Farret and Godoy-Simões, 2006). Their turbines can operate as both turbine generators and in reverse as electric motordriven pumps. Variable speed machines, offering greater efficiency, are the

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latest in large-scale engineering technology, generating in synchronization with the network frequency but operating asynchronously (independent of the network frequency) as motor pumps. Pumped storage projects differ from conventional hydroelectric projects in that they pump water from a lower reservoir to an upper reservoir when there is low electricity demand. In the case of a WHPS, wind turbines supply electric power to drive motors that pump water into an upper reservoir. The initial potential energy associated with the head is transformed into kinetic energy. One part of this energy is associated with the velocity of a mass m′. The other part is the pressure, with the enthalpy given by the pressure P over the density of water multiplied by the remaining mass (m-m′) (Farret and Godoy-Simões, 2006): 1 P potential Einitial = mgh = m′ ⋅ u2 + ( m − m′ ) = E kinetic + enthalpy 2 ρ

9.26

There are frictional losses, turbulence and viscous drag, and the turbine also has an intrinsic efficiency. For the final conversion of hydropower to electricity, generator efficiency must also be considered. Therefore the efficiency of pumped hydro systems must consider the ratio of the energy supplied to the consumer and the energy consumed while pumping. The energy used for pumping a volume V (m3) of water up to a height h (m) with pumping efficiency ηP is given by Epumping =

ρW ghV ηp

9.27

and the energy supplied to the grid while generating with efficiency ηg is given by Egenerator = ρghVηg

9.28

In a WHPS, when wind energy decreases, water is released from the upper reservoir. It drops downward through high-pressure pipelines, passes through turbines and finally pools in the lower reservoir. The turbines drive power generators that create electricity. When the wind energy production exceeds demand, water is pumped up and stored in the upper reservoir, usually with an early morning surplus. A representative schema of the wind–hydro pumped storage system is presented in Fig. 9.6. The economics of the wind–hydro pumped storage system depends on two technological aspects. The first is that a suitably designed generator can be run backwards as an electric motor: the machine which converts mechanical energy into electrical energy can equally well carry out the reverse process. A suitably designed turbine can also be run in both

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Stand-alone and hybrid wind energy systems Upper reservoir

Hybrid power station (HPS)

Penstock

Diesel power station (DPS) D

D

Wind farms (W/F)

Lower reservoir

HPS wind farm

D Turbines

Pumps

9.6 Scheme of a pumped storage system.

directions: extracting energy from the water as a turbine and delivering energy to the water as a pump; the complete reversal is thus turbogenerator to electric pump. The machines must of course be designed for this dual role, but the cost savings are obviously significant. The second technological aspect is the availability of wind energy to generate electricity in order to drive the pumping system. The size of the wind power plant depends on the wind energy resource, the capacity factor, and in an indirect manner depends on the capacity of the hydro power plant, since both systems are combined. Pumped storage hydropower is now used by utilities to respond to large and rapid load changes.

9.4

Research and development of wind–hydropower systems (WHPS) (modelling/simulation and evaluation experience)

Many simulation models have been developed for hybrid power system design. European researchers, for example, have developed numerous analytical models of varying sophistication and general use for wind–diesel systems (Infield et al., 1990). Similarly, work at the University of Massachusetts (Manwell et al., 1997) has produced a number of system models for wind–diesel–hybrid power systems. It is possible to classify these models into two broad categories: logistical and dynamic models.

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9.4.1 Classification of models Logistical models (Infield et al., 1990) are used primarily for long-term performance predictions, component sizing, and providing input to economic analyses, and can be divided into the following categories (Manwell et al., 2002): •

•

•

Time-series (or quasi-steady state): this type of model requires the longterm time-series input of variables such as wind speed, solar insolation and load. Probabilistic: models of this type generally require a characterization of long-term load and resource data (e.g. monthly or seasonal) as inputs. These analytical models are based on the use of statistical modelling techniques. Time series or probabilistic: as the name implies, models in this category are based on the use of a combined time series and statistical approach.

Dynamic models are used primarily for component design, assessment of system stability and determination of power quality. They are generally used for hybrid power systems with no storage capability, or systems with minimal storage, such as a flywheel. Depending on the time step size and the number of modelled components, dynamic models can be divided into the following categories (Manwell et al., 2002): •

Dynamic mechanical model: this type of model is based on the mechanical equations of motion and power balances. It can be used to calculate a first approximation of the dynamic behaviour of a system and to find such long-term effects. • Dynamic mechanical, steady state electrical model: this class of model is based on the mechanical equations of motion and the steady state equations of the electrical components of the system. It offers a first approximation of the electrical behaviour of the system. • Dynamic mechanical and electrical model: models of this type are based on the dynamic equations of motion of the mechanical and electrical components of the system. They are intended to investigate the electrical stability of the system (millisecond scale) and mechanical vibrations.

9.4.2 Computational tools and software The following is a list of wind and hydropower technology analysis models and tools. Most of these tools can be applied on a global, regional, local or project basis (NREL, 2009).

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•

Stand-alone and hybrid wind energy systems

Job and Economic Development Impact (JEDI) model. JEDI models are easy to use, spreadsheet-based tools that analyse the economic impacts of constructing and operating power generation and biofuel plants at local and state level. Regional Energy Deployment System (ReEDS). This model is a multiregional, multi-time period, Geographic Information System (GIS), and linear programming model of capacity expansion in the electric sector of the United States. The model, developed by NREL’s Strategic Energy Analysis Center (SEAC), is designed to conduct analysis of the critical energy issues in today’s electric sector with detailed treatment of the full potential of conventional and renewable electricity-generating technologies as well as electricity storage. The principal issues addressed include access to and cost of transmission, access to and quality of renewable resources, the variability of wind and solar power, and the influence of variability on the reliability of the grid.

Table 9.1 shows some of the models and tools that can help understand the renewable energy technologies and their uses. Most of these tools can be applied on a global, regional or local basis.

9.4.3 Literature review This section presents a chronological review of the available literature, including the principal ideas and concepts used by the authors to develop their own ideas. In 1993, Soder presented a methodology to analyse operation planning with an emphasis on keeping reserves; results from the calculations included instantaneous, fast and slow reserve margins at each hour of the planning period. In the same year, Sinha (1993) developed a model that stimulated the performance and economics of a combined wind–hydro–diesel plant with pumped storage. This was a theoretical study to demonstrate how the cheapest subset configuration of plant sizes can be identified, illustrating in particular the trade-off between hydro storage capacity and wind turbine penetration, and showed that a water reservoir can be used as a store for excess wind output. In 2001, Kaldellis et al. investigated the possibility of creating a combined wind–hydro energy station in a medium size island of the Aegean Archipelago. Their study is based on a techno-economic analysis. They showed that the electricity demand of the remote system was covered in any case, the imported fuel was minimized, the renewable energy sources penetration exceeded 90%, and the negative environmental effects were remarkably reduced. In the same year, another work by Kaldellis and Kavadias developed and subsequently applied the methodology of an

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Table 9.1 Models and tools used to evaluate different renewable energy technologies Tool

Description

ENERGY-10

Identifes the best combination of energy-efficient strategies, including daylighting, passive solar heating, and high-efficiency mechanical systems Provides dynamically generated maps of renewable energy resources that determine which energy technologies are viable solutions in the United States Provides news and information on green power markets and related activities. The site provides up-to-date information on green power providers, product offerings, consumer protection issues, and policies affecting green power markets HOMER’s optimization and sensitivity analysis algorithms allow the evaluation of the economic and technical feasibility of a large number of technology options and to account for variation in technology costs and energy resource availability A tool to conduct detailed long-term performance and economic analysis on a wide variety of hybrid power systems Analyses the transition to a hydrogen economy. It costs out numerous pathways – from production to distribution – finding the economic mode for hydrogen to be delivered in a user-defined region A levelized cost-of-energy model, which simulates a detailed 20-year nominal dollar cash flow for renewable energy projects Designed to conduct analysis of the critical energy issues in today’s electric sector with detailed treatment of the full potential of conventional and renewable electricity generating technologies as well as electricity storage A computational tool capable of designing an autonomous village electrification system using the lowest cost combination of centralized and isolated generation

Geographic Information System (GIS) Green Power Network (GPN)

HOMER

Hybrid2

Hydrogen Deployment System (HyDS) RETFinance

Regional Energy Deployment System (ReEDS) Village Power Optimization Model for Renewables (ViPOR)

optimal wind–hydro solution in order to define the most beneficial configuration of a proposed renewable station for several typical Aegean Sea islands. They reported that, in all cases analysed, the renewable energy sources penetration exceeded 85%, while a significant part of the system’s wind energy surplus was forwarded to a desalination plant for clean water production. In 2002, Bakos studied the operation of a hybrid WHPS aimed at producing low cost electricity. A specific application on the island of Ikaria in Greece was studied, and typical results were presented and compared with

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the results produced from a simulation program based on the stochastic behaviour of the weather conditions. Useful conclusions were drawn regarding the feasibility of these applications on Greek islands and the expected electric energy saving. In that year, Bélanger and Gagnon studied wind performance within a mainly hydroelectric system. Wind–hydro combinations were simulated using hourly real data (1990–1996) for patterns of electricity demand, wind speeds and hydraulic flows in Quebec, in order to investigate the additional back-up capacity requirements (imposed as a hidden cost somewhere in the electricity system) and determine the environmental impacts of the hydro-facilities providing the back-up when wind was down. They showed that wind power requires back-up capacity to compensate for wind fluctuations and concluded that, in large networks, it may not be necessary to build additional capacity, because reliability could be maintained by other tools, such as by buying ancillary services from other generators. They reported that it is necessary to take into account when hydropower is the option used to compensate for wind fluctuations, because a relatively large development of wind power could thus have significant effects on river flows: it could reduce the minimum flow during the dry season and increase the intensity of short-term flow fluctuations. These conclusions do not mean that wind power development is not justified, but simply that its assessment should include the impact of back-up generation. Also in 2002, Kaldellis proposed the development of a combined wind– hydro electricity production system as a method of handling energy shortage and excessive electricity production cost problems for the remote Aegean Sea islands, also taking advantage of the existing high-quality local wind potential. Kaldellis presented a parametrical investigation that included configuration of the number of wind turbines used, the selected water reservoirs’ size, the corresponding water pumps’ rated power and the local wind potential quality, along with the consumption characteristics of the electricity system. Throughout this parametrical analysis, the impact of all these parameters on system energy autonomy and electrical efficiency was examined. In 2004, Kaldellis developed an integrated, time-dependent computational framework concerning the economic behaviour of wind energy applications in Greece. Kaldellis analysed the local wind energy market situation over 15 years, and the proposed model predicted no additional wind parks in the Aegean Sea islands and Crete. Kaldellis established that the solution could be the development of additional energy storage systems, e.g. wind– hydro stations. In the same year, Castronuovo and Lopes published two works (Castronuovo and Lopes, 2004a,b) proposing the utilization of water storage to improve wind park operational economic gains and to attenuate the active power output variations due to the intermittence of the wind

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energy resource. They exploited the concept of the combined use of wind power production and hydro storage/production, through the development of an operational optimization approach applied to a wind generator park with little water storage ability. The optimization model defined the operational strategy to be followed, for the hours ahead, by a pump station and a hydraulic generator embedded in a wind/hydro pumping facility, using Portuguese energy remuneration rules as a techno-economical framework. The proposed methodology led to considerable yearly profits for wind generator production. In 2004, Jaramillo et al. published a theoretical study of how wind power can be complemented by hydropower. A conceptual framework was provided for a hybrid power station that produces constant power output without the intermittent fluctuations inherent when using wind power. Two hypothetical facilities were considered as case studies, and showed that the hybrid plant could provide firm power to the electrical distribution system. Jaramillo et al. analysed the levelized production cost of the hybrid system on a techno-economic basis. In 2005, Qin et al. reported the simulation of a mathematical model for a wind generator, as well as a method of wind power stability analysis and simulation in wind–hydro hybrid power systems by means of MATLAB. They reported two case studies a wind-hydro hybrid power system in Xinjiang Province in China. Kaldellis et al. presented two pieces of research in 2006. The first work (Kaldellis et al, 2006a) addressed the intermittent and stochastic behaviour of wind, proposing a combined wind–hydro configuration in collaboration with an appropriate desalination plant. The proposed solution leads to high wind energy penetration rates and restricts the operational hours of the existing internal combustion engines, additionally contributing to a reduction in air pollution. Additionally, significant volumes of desalinated water can be produced, remarkably reinforcing local community reserves of fresh water of the desired quality. The authors suggested that the configuration investigated could efficiently fulfil the electrical energy and clean water requirements of numerous remote communities on the basis of clean and low-cost wind energy, overcoming the intermittent and stochastic behaviour of the wind. In their second work of the same year (Kaldellis et al., 2006b), the authors presented a study to obtain an integrated methodology, with pumped-hydro storage, to maximize the contribution of wind energy to the Crete electricity supply and to improve grid stability. They reported an analysis of wind–hydro electricity production costs and compared them with the corresponding operational costs for existing thermal power plants. In 2007, Márquez-Angarita and Garcia-Usaola reported a combined strategy for wind–hydro generation in order to reduce wind fluctuations. A

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mathematical formulation for optimal bids and for optimal operation was presented, as well as results from real case studies. In the same year, Anagnostopoulos and Papantonis reported a numerical study of the optimum sizing and design of a pumping station unit in a hybrid wind– hydro plant. Their work addressed the issue of reducing the amount of wind energy generated that cannot be transformed to hydraulic energy, owing to the power operation limits of pumps and the resulting step-wise operation of the pumping station. By using a comprehensive evaluation algorithm, which also performs a detailed economic analysis of the plant using dynamic evaluation methods, the plant’s operation was simulated for a period of one year. A preliminary study of the entire plant sizing was first carried out, using an optimization tool based on evolutionary algorithms. The authors conclude that the use of a variable-speed pump constitutes the most effective and profitable solution, and its superiority is more pronounced for less dispersed wind power potential. In 2008, Pan et al. suggested a way to improve the power output characteristics of a wind farm; by the model proposed, a wind–hydro hybrid system could be adopted where water resources exist. A pumped storage system was divided into a pumping system and a generating system, and the model proposed was based on a genetic algorithm for the optimal operation of a wind–hydro hybrid system in order to get maximum profit. Pan et al. showed that it is possible to improve the all-round profitability of a wind farm with a pumped storage system, as well as proposing a way to optimize the size of a pumped storage system and calculate its capacity with given parameters. Also in 2008, Benitez et al. developed a nonlinear mathematical optimization program to investigate the economic and environmental implications of wind penetration in electrical grids, and to evaluate how hydropower storage could be used to offset wind power intermittence. They reported that, when wind power was added to an electrical grid consisting of thermal and hydropower plants, it increased system variability and resulted in a need for additional peak-load, gas-fired generators. When pumped hydro storage was introduced to the system, or if the capacity of the water reservoirs was enhanced, the hydropower facility could provide most of the peak load requirements, obviating the need to build large peakload gas generators. Again in 2008, Anagnostopoulos and Papantonis suggested that hybrid wind–hydro power generation could be a solution for isolated, autonomous electric grids in order to increase wind energy penetration and cost-effectiveness. They reported a numerical methodology for optimum sizing of various components of a reversible hydraulic system designed to recover the electric energy that is rejected from wind farms due to imposed grid limitations. The numerical procedure combines an evaluation algorithm that simulates plant operation in detail during a 12-month period,

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and automated optimization software based on evolutionary algorithms. The algorithm was applied to study a practical case using time variation data of rejected power from a number of wind farms installed on the island of Crete, Greece. The authors showed that a well-optimized design may be crucial for the technical and economic viability of an examined system. Two more projects were reported at the end of 2008. Vieira and Ramos reported an optimization model that determines the best hourly operation for 1 day, according to electricity tariff, for a pumped storage system with water consumption and inlet discharge. Wind turbines were introduced to the system. The rules obtained as output from the optimization process were subsequently introduced in a hydraulic simulator, in order to verify system behaviour. A comparison with a normal water supply operating mode was made and the energy cost savings with this hybrid solution were calculated. In December 2008, Kaldellis reported the possibility of creating a combined electricity generation facility in the Aegean Archipelago based on the exploitation of the wind or/and solar potential of an area, as well as on the utilization of an appropriate energy storage configuration, in order to replace existing thermal power stations with rational investment requirements. Kaldellis found that the proposed solution is not only financially attractive but also improves the quality of the electricity offered to local communities, substituting for the expensive and heavily polluting existing thermal power stations. In 2009, Vieira and Ramos developed an optimization model for energy efficiency in a water supply system. An optimization method to define pump operation planning, as well as to analyse the economic benefits resulting from using wind energy to pump water, while satisfying system constraints and population demands, was implemented. The authors set their model to minimize global operational costs. The model itself was developed in MATLAB, using linear programming and providing a planning strategy to take in each time step, which then influences the following hourly time steps. They showed that the insertion of a water turbine generates significant economic benefits for the water supply system. During 2009, Ab-Razak et al. discussed the optimization of a hybrid system in the context of minimizing excess energy and the cost of energy. A hybrid of peak hydro, solar, wind and generator, with battery as back-up, was the basis of the assessment. The system configuration of the hybrid was derived based on a theoretical domestic load at a remote location and with local solar radiation, wind and water flow rate data. Simulations were developed using HOMER, oriented to find the optimum combination and sizing of components. Another set of demand loads was used to investigate the effect of reducing the demand load against the dominant power provider of the system. Results showed that the cost of energy could be reduced

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by about 50% if demand load was increased to maximum capacity. The authors concluded that reducing the load to the capacity of the dominant power provider would reduce the cost of energy by 90%. In 2009, Daoutis and Dialynas studied the impact of wind parks and hydroelectric power plants on the reliability and operational performance of isolated power systems. They developed a probabilistic methodology to simulate the reliability and operational performance of these power systems more efficiently and realistically. They claim an effective computational methodology, based on the Monte-Carlo sequential simulation approach, which evaluates the reliability and operational indices of the conventional thermal and hydroelectric power plants, the wind parks and the overall indices of the system. Also in 2009, Angarita et al. reported a stochastic optimization technique that maximizes the joint profit of hydro and wind generators in a pool-based electricity market, taking into account the uncertainty of wind power prediction. They showed that a combined hydro–wind bid strategy is a very useful tool, generating agents to avoid penalty costs or income reduction. They conclude that final benefits will depend on penalty values, how long in advance wind forecasts are made, and the size of the ensemble and hydro model features. In the following section, the advantages and disadvantages of coupling wind and hydro energy are reported. First, these systems are separately analysed. Then, at the end of the section, the benefits and demerits of WHPSs are described.

9.5

Benefits and limitations of wind–hydropower systems (WHPS)

There are a range of advantages and disadvantages for coupling wind and hydro energy. Sections 9.5.1 and 9.5.2 describe the advantages and disadvantages of wind and hydro systems, respectively, as stand-alone systems. Section 9.5.3 discusses the advantages and disadvantages of joining the two energy systems.

9.5.1 Wind energy applications The main advantages of wind energy are as follows: •

•

It is a clean fuel source. It does not pollute the earth like power plants which rely on combustion of coal or natural gas. Furthermore, wind turbines do not produce atmospheric emissions that contribute to the greenhouse effect and acid rain. It is widely used around the world because of the mature and proven technology available.

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It is a renewable energy since wind is an indirect product of solar energy. It is a cheap renewable energy source. Costs can be competitive compared with conventional energy systems, depending on project financing and the wind source. Wind turbines are a great resource to generate energy in remote locations, such as in mountain communities and in remote countryside. Wind turbines can be a range of different sizes, supporting varying population levels. Wind turbines benefit the rural areas on which they are built. Wind turbines are always built on farms or ranches, where most of the best wind sites are found. This causes little trouble to the farmers and ranches since the wind turbines use only a small fraction of the land. Farmers can continue to work the land and get rent payment from the power plant owners. A wind farm can be modular, since any number of wind turbines can be integrated.

The main disadvantages of wind energy are as follows: •

•

•

•

•

• •

Wind power must compete with conventional generation sources on a cost basis. Depending on how windy a site is, the wind farm may or may not be cost competitive. Although the set-up costs of wind power plants have decreased considerably during the last 10 years, it still requires a higher investment than fossil-fuelled generators. The major challenge to using wind as a source of power is that wind is intermittent and it does not always blow when electricity is needed. Not all winds can be harnessed to meet the timing of electricity demands. Wind turbines generally produce less electricity than the average fossil fuel power station, requiring multiple wind turbines to be built in order to make an impact. Good wind sites are always located in remote areas that are far from cities where the electricity is needed. This increases the cost of electricity transfer. Wind resource development may compete with other uses for land and those alternative uses may be more highly valued than electricity generation. Transmission peaks and wind power production peaks do not always coincide. Although wind power plants cause less negative environmental impact than other conventional power plants, there are some concerns over the noise produced by rotor blades, over aesthetic (visual) impacts, and over birds which have occasionally been killed by flying into the rotors.

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9.5.2 Hydro energy applications Hydropower offers advantages over other energy sources but faces unique environmental challenges. The main advantages of hydropower are as follows: • • • • • •

• •

It is fuelled by water and is therefore a clean fuel source. Hydropower does not pollute the air like fossil fuel-burning power plants. It is a domestic source of energy that avoids energy dependence. The technology is mature and proven. It relies on the water cycle, which is driven by the sun and hence is a renewable power source. It is generally available as needed; engineers can control the flow of water through turbines to produce electricity on demand. Hydropower plants provide benefits in addition to clean electricity. Impoundment hydropower creates reservoirs that offer a variety of recreational opportunities, notably fishing, swimming and boating. Most hydropower installations are required to provide some public access to the reservoir to allow the public to take advantage of these opportunities. Other benefits may include water supply and flood control, or water for irrigation purposes. Hydropower can produce electricity at a constant rate (firm power) and the system can be dispatchable if water is stored in a reservoir. If electricity is not needed, the sluice gates can be shut stopping electricity generation. Water can be saved for use at another time when electricity demand is high.

The main disadvantages of hydropower systems as follows: •

•

•

Fish populations can be impacted if fish cannot migrate upstream past impoundment dams to spawning grounds, or if they cannot migrate downstream to the ocean. Upstream fish passage can be aided using fish ladders or elevators, or by trapping and hauling the fish upstream by truck. Downstream fish passage is aided by diverting fish from turbine intakes using screens or racks or even underwater lights and sounds, and by maintaining a minimum spill flow past the turbine. Hydropower can impact water quality and flow. Hydropower plants can cause low dissolved oxygen levels in the water, a problem that is harmful to riparian (riverbank) habitats and which is addressed using various aeration techniques to oxygenate the water. Maintaining minimum flows of water downstream of a hydropower installation is also critical for the survival of riparian habitats. Hydropower plants can be impacted by drought. When water is not available, hydropower plants cannot produce electricity.

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New hydropower facilities impact the local environment and may compete with other uses for land. These alternative uses may be more highly valued than electricity generation. Humans, flora and fauna may lose their natural habitat. Local cultures and historical sites may be impinged upon. Some older hydropower facilities may have historic value, so renovations of these facilities must also be sensitive to such preservation concerns, and to impacts on plant and animal life.

9.5.3 Combined wind and hydropower Electric utilities must maintain a balance between generation and load at all times. This means energy must be generated just when the customer needs it. Hydropower is very suitable as a complementary energy source for wind power. Hydro dams can work as energy storage for wind energy and, in this way, work as a buffer for the stochastic nature of wind power. For example, through interaction with Norwegian hydropower, western Denmark has been able to establish a functioning electricity supply with a wind power share of just 30%. Sections 9.5.1 and 9.5.2 analysed the benefits of distinct wind and hydro systems. The main advantages of a WHPS working as a joined system are as follows: • • • •

• •

• •

Wind and hydro generation complement each other perfectly: when the wind blows, hydro-dam water can be stored for use when it is not. Wind–hydro energy is a great option for developing countries to provide a steady, reliable supply of electricity. Wind–hydro energy works towards sustainable development. Wind–hydro energy operation has zero emissions of harmful substances. It does not contribute to global warming, the ‘fuel’ is free, and is quite evenly distributed around the world. Wind–hydro energy guarantees a reliable, high-quality supply of electricity at a competitive price. Wind–hydro energy can maintain a balance between generation and load at all times, and decrease the fluctuation of wind farm output, consequently improving daily operational profit. A wind–hydro system can benefit energy supplies if short-term forecasts for wind and hydro conditions are integrated into daily planning. The economic costs of conventional energy sources far exceed those of WHPS, including the full life cycle of wind power from production, processing, transformation, construction and operation.

The main disadvantages of WHPS are as follows: •

The energy from wind turbines must be stored in order to secure supply at times of insufficient wind.

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The potential of wind and hydropower is largest at remote sites with low population density and large distances to electricity consumers and the existing grid. Expensive grid connection is often one of the main reasons why wind/ hydro systems are not developed. The investment cost of WHPS far exceeds those of conventional energy sources. The operation cost of WHPS during the grid operation must be included, since the intermittency and the reactive power must be controlled.

The operation polices of a WHPS can help reduce some of the disadvantages of coupling wind and hydropower. The next section gives a brief description of operating procedures.

9.6

Different operational policies and techniques for isolated grids

A WHPS requires clear operating policies and techniques to be outlined, in order to increase renewable energy penetration while achieving satisfactory operation of the electrical power systems, and ensuring the technical viability and competitive costs of the WHPS itself. One of most important objectives is to increase wind penetration levels in electric power systems, particularly in cases of isolated grids, by using suitable storage methods. When a power system increases in size to the level of megawatts and beyond, battery storage, flywheels and other similar means become technically and economically unappealing; the only viable techno-economic solution is the development of pumped storage systems. Such systems require in a pump/turbine station and two water reservoirs, separated by sufficient altitude (typically a few hundred metres). In recent years, a number of WHPSs in isolated systems have been analysed, and have adopted simple and conservative operating policies. The policy adopted for stored energy is crucial for the viability of a WHPS, for the overall economics of the system, and also for the smooth operation of the autonomous electrical system. In some cases, traditional operating policies are proposed to substitute conventional peak units. In other cases, the operating policy followed is to supply dispatchable energy. Today, a modern operating policy for a WHPS offers satisfactory techno-economic development and ensures the stability of the grid, without disturbing the operation of the conventional generation system. Amongs the most important aspects of WHPS operation policies of the WHPS are the following: •

Costs associated with the regulation of a WHPS should be considered for optimization and detailed study; a trade-off can be made between

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•

• •

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additional regulation and increased revenue through coordination of the operating policies and power systems planning. Coordination of the operating policies and planning could lead to the operation off the optimal efficiency points. The extent of this effect, however, depends on the hydro turbine type. Optimization should be applied for the coordinated production planning. Hence a trade-off will be made between negative impact on the efficiency and overall revenue improvement. Through coordination, water is stored in hydro reservoirs, which leads to an increased water head and hence to increased WHPS efficiency. Any increase in regulations might lead to reductions in efficiency and to increased maintenance costs for WHPS.

9.7

Environmental impacts of wind–hydropower systems (WHPS)

This section addresses the net energy balance over the lifetime cycle of a WHPS. It is important to emphasize wind–hydro environmental impacts. These kinds of systems can help to reduce both carbon dioxide emissions and acid rain pollutants (sulphur dioxide and nitrogen oxide). It is also important to mention that these systems can avoid energy dependence. They decrease a country’s reliance on imported fuel and add to diversity of energy supply. Table 9.2 summarizes one estimate of waste products involved in the main forms of energy generation: fossil, nuclear and renewable. The lifetime cycle for using a WHPS must be analysed in order to establish the benefits and impact of using this technology. Although WHPS are clean technology in operation, the manufacture, transport, installation and dismantling of the plant will normally necessitate the use of some fossil fuels, and hence the emission of some greenhouse gases and pollutants. It is important to note that the total of pollutants per unit of energy produced by a WHPS is typically two orders of magnitude smaller than those produced by fossil fuels. A very important consideration when evaluating the potential of this kind of system (or any other renewable or conventional source) is the net energy balance. In other words, it is necessary to take into account the portion of useful energy that remains after the energy consumed in construction, fuelling, operating and dismantling. The lifetime cycle should consider the effects on humans, fauna, soil, water, air, climate and the landscape, the interaction between any of the foregoing and cultural heritage and material assets. Consideration also needs to be given to the use of natural resources, emission and pollutants, creation of nuisances and the elimination of waste or mitigation of its disposal.

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Table 9.2 A comparison of environmental impact of electric power generation technologies Emission of pollutants from electric power generation: the total fuel cyclea (tons/kWh) Energy Source

CO2

NO2

SO2

TSP

Conventional coal Natural gas IGCC Nuclear Photovoltaic Biomassb Geothermal Wind Solar thermal Hydropower

1058.2

2.986

2.971

1.626

824.0

0.251

0.336

1.176

8.6 5.9 0* 56.8 7.4 3.6 6.6

0.034 0.008 6.14 TR TR TR TR

0.029 0.023 0.154 TR TR TR TR

0.003 0.017 0.512 TR TR TR TR

CO

HC

Nuclear waste

Total

0.102

NA

1066.1

TR

TR

NA

825.8

0.018 0.003 11.361 TR TR TR TR

0.001 0.002 0.768 TR TR TR TR

3.641 NA NA NA NA NA NA

0.267

12.3 5.9 13.4 56.8 7.4 3.6 6.6

* With biomass fuel re-growth programme. TSP: Total suspended particulates. NA: not applicable. TR: trace elements. HC: hydrocarbons. IGCC: integrated gas turbine combined cycle. Note: The total fuel cycle includes resource fuel extraction, facility construction and plant operation. a Meridian Corporation, Energy System Emissions and Material Requirements. Prepared for the Deputy Assistant Secretary for Renewable Energy, US Department of Energy, Washington DC, February 1989, pp. 25–29. b Carbon dioxide data adapted by Council for Renewable Energy Education from Dr Robert L. San Martin, Deputy Assistant Secretary for Renewable Energy, Environment Emission from Energy Technology Systems: The Total Fuel Cycle, US Department of Energy, Washington DC, spring 1989, p. 5. Other emission data from Assistant Secretary for Environment, Safety, and Health, Energy Technologies and the Environment, Environmental Information Handbook, Office of Environmental Analysis, US Department of Energy, October 1998, pp. 333–334.

9.8

The economics of wind–hydropower systems (WHPS)

Although small wind systems involve a significant initial investment, they can be competitive with conventional energy sources on a life-cycle basis. The economics of remote wind systems are dependent on the choice of system, including storage technology, the wind resources at the site, electricity tariff, and available financing and incentives. These issues are very user- and site-specific, thus making any discussion of remote system eco-

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Table 9.3 Generation, investment and external costs for various power generation technologies in the USA Technology

Generation cost (cents/KW h)

Investment cost ($/W)

All external costsa (cents/kW h)

Coal, thermal Nuclear Gas combined cycle Small hydro Biomass, thermal Wind Solar, PV Solar, thermal

3–5 3–8 3–5 5–10 4–10 3–5 20–35 15–30

1.0–1.5 1.2–2.0 0.5–0.7 0.8–1.2 1.5–2.5 0.8–1.5 6.0–8.0 4.0–6.0

2.0–15 0.2–0.6b 1.0–1.4 – – 0.05–0.25 0.05–0.25 –

a b

Estimated cost to society and environment. Not including nuclear waste and decommissioning cost.

nomics non-generic. The cost-effectiveness of a WHPS relative to a PV system or conventional system cannot be determined solely by comparing the initial and annual operating costs. This is because these systems rely on different fuels that are available at different times. For example, a solar system without a battery cannot work at night. Therefore, a careful analysis of energy needs is essential when designing an optimal remote energy system. With reasonable assumptions concerning discount rates, capacity factors, and fuel costs, hydro and wind turbines can have the lowest lifecycle costs in locations where resources are sufficient. Table 9.3 reports the generation, investment and external costs for various power generation technologies in the United States. The techno-economic analysis carried out in this section is based on the levelized production cost (LPC, [$/kW h]) established by the Nuclear Energy Agency in 1983 (NEA, 1983). The LPC is the cost of one production unit (kW h) averaged over a power station’s entire expected lifetime. The total utilized energy output and the total costs over the lifetime of the power station are both discounted at the start of operation by a predetermined discount rate, and the LPC is derived as the ratio of the discounted total cost and utilized energy output (IEA, 1994): LPC =

TC n

∑ AUE y ⋅ (1 + r )

9.29 −y

y=1

where n is the number of years of economic lifetime, AUE (W) is the annual generated energy during year, r (%) is the discount rate, and TC

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(US$) is the discounted present value of the total cost of energy production, which is given as (IEA, 1994): n

TC = I + ∑ (OM y + SC y + RC y ) ⋅ (1 + r ) − SV ⋅ (1 + r ) −y

−n

9.30

y=1

where I (US$) is the total investment cost, OM (US$) represents the operating and maintenance costs during year y, SC (US$) is the social cost during year y, RC (US$) is the retrofit cost during a year, and SV [US $] is the salvage value after n years. The total investment cost includes the cost of the plant, land acquisition (unless a rent is paid, in which case this is a running cost), grid connection (though, in some European countries, the utility has to bear this cost), and initial financing costs (as opposed to repayment costs). Operating and maintenance costs include insurance, rent and rates set by the local administrative authority, as well as the costs of labour and materials used for operations and maintenance. The discount rate, r, given in real terms may be defined as the rate at which the nominal rate, a (%), exceeds the inflation rate, i (%), expressed as (IEA, 1994): 1+ r =

1+ a 1+ i

9.31

It is important to note that, in many cases, the LPC is obtained under the following assumptions: the investment cost does not include interest during construction; the annual utilized energy (AUE) is constant; the salvage value (SV) is taken immediately after the last year of production; the social cost (SC) and retrofit cost (RC) are considered negligible in some economies. In order to estimate the LPC for a WHPS, the total investment cost (IWH) can be estimated by ⎛ N ⎞ ⎛ N ⎞ I WH = ⎜ W ⎟ I W + ⎜ H ⎟ I H ⎝ N WH ⎠ ⎝ N WH ⎠

9.32

where IW (US$) and IH (US$) are the total investment costs for a wind farm and hydropower plant, respectively, and NWH (W) is the rated power of the hybrid system.

9.9

Conclusions

WHPS has a great future. With the exploitation of fossil fuels needing to be reduced due to their effect on the climate, WHPS technology:

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can be further developed in many parts of the world; can now be developed in places where fossil fuels are typically used, largely as a result of new technological developments and economic conditions; can now be perfected, following the development of new analytical tools; can be used as a storage technology, and thus become an ideal complement to the rapidly increasing harnessing of intermittent sun and wind power.

Today, a pumped hydroelectric storage is one of the most economically viable energy storage methods. At times of low electrical demand, excess electrical capacity is used to pump water into an upper reservoir. When there is higher demand, water is released back into the lower reservoir through a turbine, thereby generating hydroelectricity. Reversible turbine/ generator assemblies can act as both pump and turbine. However, the following techno-economic and ecological barriers hold back the development of electricity production based on WHPS: • •

•

• •

Production is dependent on the availability and variability of hydraulic and wind resources throughout the year. The integration and development of WHPS presents technological challenges in the areas of electrical engineering, electronics and control, and in civil engineering. In the case of adverse weather conditions (e.g. low temperatures) it is necessary to implement sophisticated control measures that increase operation costs. It is necessary to develop procedures in order to protect the environment during construction, operation and dismantling of a WHPS. It is necessary to develop standard procedures that ensure the correct operation of WHPSs.

It is clear that, in the coming years, WHPS will contribute to the diversification of the electricity market by ensuring the use of renewable energy with a low environmental impact.

9.10

Acknowledgements

We would like to thank Ing. José de Jesús Quiñones Aguilar, Dr Carlos Alberto Pérez Rábago and M. Eng. Fernando Sosa Montemayor for their technical assistance and for their management and synthesis of the information contained within.

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9.11

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Sinha A (1993), Modelling the economics of combined wind/hydro/diesel power systems, Energy Conversion and Management, 34, 7, 577–585. Soder L (1993), Reserve margin planning in a wind-hydro-thermal power system, IEEE Transactions on Power Systems, 8, 564–570. Vieira F, Ramos H M (2008), Hybrid solution and pump-storage optimization in water supply system efficiency: a case study, Energy Policy, 36, 11, 4142–4148. Vieira F, Ramos H M (2009), Optimization of operational planning for wind/hydro hybrid water supply systems, Renewable Energy, 34, 3, 928–936.

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10 Electro-chemical energy storage technologies for wind energy systems M. SKYLLAS-KAZACOS, University of New South Wales, Australia

Abstract: Electrochemical energy storage systems offer significant benefits compared with other types of energy storage when used in conjunction with wind turbines or photovoltaic arrays. Lead–acid batteries have a long history of application in remote area power systems and back-up power applications, but have serious life-cycle limitations when used in daily deep discharge applications. Several alternative secondary battery and flow cell technologies are already being developed and have begun to be implemented in these emerging applications. This chapter reviews the main electrochemical energy storage technologies currently under development and field testing and assesses their advantages and drawbacks for different applications. Key words: electrochemical energy storage, lead–acid, flow batteries, lithium batteries, sodium sulphur.

10.1

Introduction

Wind turbines have already seen widespread application in both grid-connected and remote electricity generation. Over the last decade, numerous large grid-connected wind farms have been installed in Europe, the United States and elsewhere, while wind–diesel grids currently power hundreds of small communities and remote townships in many regions of the world. The major economic and practical drawback of wind energy is its intermittent nature. Wind turbines not only demand that the wind is blowing, but also depend on cut-in and cut-out wind speeds – the wind speeds at which generation begins and is stopped to avoid damage respectively. In spite of careful consideration given to site selection so as to ensure a relatively stable wind source and speed, the wind and its strength cannot be guaranteed. The electricity production is inherently highly variable and difficult to predict. On shorter timescales the higher frequency ‘noise’ in electricity output from wind turbines causes problems for network stability and managing the short-term dispatch of generators to meet demand. Over longer timescales it means that it is difficult to match electricity generation to the daily and seasonal patterns of demand. As a consequence, wind turbines have an average production of only about 33% [1] of their rated power, meaning wind farms need to be oversized in order to accommodate 323 © Woodhead Publishing Limited, 2010

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reduced wind speeds, greatly increasing capital costs. Furthermore, wind power can provide only limited grid penetration before grid instabilities can occur. At or below the limit of penetration, a cut out in wind can be compensated for elsewhere on the grid, but above the limit of penetration the grid is too reliant on wind, so without some means of storage, the grid can be very vulnerable to power shortages. For non-grid-connected systems, an additional power source such as a diesel generator or a means of storage is required. Power generation systems in remote areas typically rely on diesel generators and as such are susceptible to the price of diesel that continues to show an upward trend. In order to overcome this, many remote communities are installing wind turbines to reduce their dependence on diesel.

10.2

Off-grid or remote power systems

A large proportion of the world does not have grid coverage and in the absence of a grid, Remote area power supply (RAPS) systems can meet electricity requirements. A RAPS system that has a combination of energy sources is termed a hybrid RAPS system. RAPS systems are generally used by houses, farms, and small communities and mine sites that are geographically isolated from the main grid and are ideal for areas of low population density or difficult terrain. Figure 10.1 shows a typical RAPS configuration. Electricity is generated by wind turbines, solar panels or micro hydro turbines, and is fed via a regulator or other form of power controller, into a bank of specially designed batteries, to be stored for use when needed. The power can then be passed via an inverter for use with normal mains appliances, or can be used directly from the batteries with DC appliances. A typical RAPS consists of a 1–5 kW wind turbine, a series of three or four solar panels, 3–4 days battery storage, and small diesel back-up generator. The energy sources usually produce unregulated, highly variable power flow. Regulator devices are therefore used to stop the battery bank from being overcharged when it is full, often diverting excess power somewhere else, such as to a water heater. This reduces any damage to the battery. Most household appliances use alternating current (AC) electricity, which is what comes out of the power point of a mains grid-connected house. However, the batteries used in RAPS supply DC electricity. The inverter converts DC electrical energy into AC form. The inverter needs to be sized to suit the electrical requirements, a common size being around 2–5 kW for domestic applications. The amount of storage and diesel bank needed in RAPS systems, however, will depend on the local solar irradiation, wind speeds and load

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Wind turbine Solar panel

Battery regulator controller DC to AC inverter

AC to DC converter Battery bank Diesel generator

AC load/user

10.1 Hybrid RAPS system using more than one form of renewable energy.

profiles. A typical residential load is illustrated in Fig. 10.2, along with an example of average daily wind speeds over a one-month period in an unidentified location in Australia. This figure illustrates the irregular nature of the wind power generation that is unable to meet the load’s power needs throughout the whole month. Not shown, are the hourly wind fluctuations that would add an additional supply continuity problem to the load (note: hourly wind speed data is difficult to obtain). Typical hourly load demands for a residential load are presented in Fig. 10.2 along with an example of a solar irradiation profile for an unidentified location in Australia. As expected, the solar power generation is unable to meet the load’s power demand during periods of cloud cover and at night. A hybrid power system that includes a solar array, wind turbine and battery would be able to meet most of the load power demand if the battery could be sized such that sufficient energy could be stored to cover several days of possible cloud cover and low wind speeds. The cost of such a large battery would be prohibitive, however, so that the best solution is to include a diesel generator to provide back-up power during the occasional periods when the battery state of charge has been unable to be maintained due to extended periods of continuous cloud cover and low wind speeds.

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Wind power (kW)

Wind speed (m s–1)

326

Wind speed 25 20 15 10 5 0 1

Days of month Wind power generated

1

Days of month

Demand load

Power load profile

1

Days of month

Power (kW)

Load minus wind power generated

1

Days of month

10.2 Typical daily wind power and load profiles (courtesy V-Fuel Pty Ltd).

10.3

Wind–diesel grids

Diesel grids are very common in both developed and developing countries that do not have extensive national grids to distribute electricity generated at the main fossil fuel, hydroelectric or nuclear power stations. They are particularly common in the outback regions of Australia, Asia, Africa and in island territories that are geographically isolated from the main grid. With the increasing cost of diesel fuel in recent years, combined with growing concerns over pollution problems associated with diesel generators, there has been a growing trend to integrate wind turbines into these grids so as to achieve both diesel fuel savings and greenhouse gas abatement. A wind–diesel grid typically comprises one or more AC wind turbines and one or more diesel generators connected to the AC load via a mini-grid.

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Australia

King Island Tasmania

10.3 Location of King Island, Australia.

An example of a wind-diesel grid operating in Australia is the system on King Island off the coast of Tasmania (Fig. 10.3). King Island is located in the ‘Roaring 40s’ where the average wind speed is greater than 8 m/s. It has a population of approximately 1800 and a total system load of around 14 000 MW h. The original wind farm that was installed in 1998 comprised three Nordex 250 kW wind turbines and approximately 2200 MW h of the island’s power was generated by wind; this representing a wind penetration of close to 16%. As seen in Fig. 10.4, a significant variation in the wind turbine output at different times of the day, is met by an increased output from the diesel generator. In 2001, Hydro Tasmania, operators of the King Island wind– diesel grid, decided upon a wind farm expansion with the objective of achieving 80% instantaneous wind penetration, providing 45–50% of the island consumption from wind energy. The wind farm expansion brought the total installed rated wind energy to 2.45 MW, allowing a reduction in diesel consumption by around 1 million litres and therefore further CO2 reduction of 3000 tonnes/year [2]. With such a high wind penetration, however, the significant wind speed variability would still require reliance on the diesel generator for back-up power during periods of low wind speed. With the slow response times of diesel generators (in the order of minutes) and the possibility of serious power disruptions for the island, the integration of energy storage in this wind–diesel grid was an obvious solution to the problem. Hydro Tasmania therefore decided to install a vanadium redox flow battery as part of the wind farm expansion in order to store a portion of the wind energy from the turbines during periods of low demand and to feed this back to the grid during periods of low wind speed.

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3000 2500 System kW

System kW Wind farm kW Diesel kW Diesel start/stop

2000 1500 Diesel kW 1000 500

Time

0:50 1:50 2:50 3:50 4:50 5:50 6:50 7:50 8:50 9:50 10:50 11:50 12:50 13:50 14:50 15:50 16:50 17:50 18:50 19:50 20:50 21:50 22:50 23:50 0:50 1:50 2:50 3:50 4:50

Wind farm kW 0

20/April/1999 Time of day

10.4 King Island power system load profile (Courtesy Hydro Tasmania).

This effectively levels the output from the wind turbine and reduces the output power fluctuations from the wind farm. Further details of the King Island project are provided in Section 10.18.4.

10.4

Large grid-connected wind farms

Large grid-connected wind farms are currently in operation all over the world. An obvious benefit of large grid-connected wind farms is the displacement of significant fossil fuel-based power generation with relatively low-cost renewable energy. Not so obvious is the benefit that comes from reducing reliance on an imported commodity, making the price of generated electricity very predictable and stable. With the price of coal, oil and natural gas rising steadily in recent years, the use of large-scale wind generation in ensuring energy security is becoming increasingly important. A further limitation in achieving significant wind power penetration in many countries has been the lack of adequate grid infrastructure to enable the generated wind power to be distributed to the main load centres. Wind farms tend to be located in rural areas where the capacity of local transmission lines is often inadequate. Wind power growth in the past several years in many parts of the world has surpassed the grid’s ability to carry the energy, causing congestion and forcing wind farms to cut power production. By integrating storage into a grid-connected wind farm, therefore, the following benefits could be achieved:

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• •

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Excess power generated by the wind farm can be stored and transmitted at a steady rate that can be accommodated by the local transmission lines, allowing more wind energy to be captured and used. Wind speed fluctuations could be levelled out, leading to greater grid stability. Time-of-use tariffs and arbitrage become possible, allowing greater prices for wind energy that is dispatched during peak periods.

The use of energy storage as an integration technology can therefore potentially further increase the renewable penetration of both grid connected and off-grid wind–diesel systems and, in so doing, offer long-term tangible monetary and environmental benefits.

10.5

Energy storage

Advantages and disadvantages of different energy storage technologies are summarized in Table 10.1 along with a comparison of their capabilities for high power and high energy applications. Each technology has some inherent limitations or disadvantages that make it practical or economical for only a limited range of applications. Wind energy storage applications require high power capabilities, fast response and long storage times. When combining these requirements with performance and cost (Table 10.2), electrochemical systems are seen to be superior to the other forms of energy storage which are mainly mechanical in nature and therefore have relatively long response times compared with batteries and electrochemical capacitors. Although there are many types of energy storage technologies available, electrochemical energy storage systems provide direct conversion between chemical energy and electrical energy and are therefore particularly suited to the storage of electrical energy from all sources.

10.6

Fundamentals of electrochemical cells

10.6.1 Theoretical cell potential The cell potential of an electrochemical cell is the potential difference occurring between the two electrodes of the cell and arises due to the transfer of electrons through the external circuit of a cell that has not reached equilibrium. Cell potential is electrical work and the maximum electrical work that can be derived from an electrochemical cell is given by the Gibbs free energy change [5]: we,max = ΔG

10.1

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High power

Long cycle life, high efficiency

Electrochemical capacitors

High power and energy densities, high efficiency High power and energy densities, high efficiency High power and energy densities, high efficiency Low capital cost

Very high energy density High power and energy densities, high efficiency

Flywheels

Lead–acid

Other advanced batteries

Ni-Cd

Li ion

NaS

High capacity, low cost

Compresses air energy storage (CAES) Flow batteries: PSB Vanadium redox ZnBr Metal–air

Low energy density

Fully capable and reasonable Fully capable and reasonable

Fully capable and reasonable

Limited cycle life when deeply discharged Low energy density

Feasible but not quite practical or economical Reasonable for this application

Feasible but not quite practical or economical

Feasible but not quite practical or economical

Reasonable for this application

Fully capable and reasonable Fully capable and reasonable

Feasible but not quite practical or economical

Fully capable and reasonable

Fully capable and reasonable

Fully capable and reasonable

Fully capable and reasonable

Fully capable and reasonable

Energy application

Fully capable and reasonable

Not feasible or economical Fully capable and reasonable

Reasonable for this application

Not feasible or economical Not feasible or economical

Power application

High production cost

Electric charging is difficult Production costs, safety concerns (addressed in design) High production cost, requires special charging circuit

Special site requirement Special site requirement, need gas fuel Low energy density

High capacity, low cost

Pumped storage

High capacity, independent power and energy ratings

Disadvantages

Advantages

Technology

Table 10.1 Application comparisons [3]

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Minutes to few hours Minutes to few hours Seconds

Minutes to few hours Minutes

20–30

1–10 kW

10–20 10–30 N/A

1–5 kW

5–100 kW

10 kW–5 MW

10–20

25–80

1 kW–5 MW

10–100 kW

250–450

1100 kW

20–70

200–300

100 kW–10 MW

1 kW–10 MW

20–30

10 kW–100 MW

96–99%

90–97%

72–76%

40–50%

60–67%

95–98%

85–90%

72–85%

70–79%

70–85%

Efficiency (w/o power electronics)

* Note: Possible reduction due to life extension by partial refurbishment.

High power flywheels Long duration flywheels High-energy SuperCaps High-power SuperCaps

Zinc–air (rechargeable) Lead–acid

Ni-Cd

Li ion

NaS

Flow batteries

N/A

N/A

Volume energy density (kW h/m3)

100 MW–1 GW

100 MW–1 GW

Several hours Minutes to hours Several hours Minutes to few hours Minutes to few hours Minutes to few hours Hours

Pumped hydro

CAES

System power ratings

Discharge time at rated power

Technology

Table 10.2 Technology comparisons – performance and cost [3, 4]

10 000–100 000

20 000–60 000

200–1 500

100–300

1 000–4 000

5 000–7 000

2 100–4 500

2 000–14 000

9 000–30 000

20 000–50 000

Lifetime at 80% DoD (cycles)

8 000–10 000

100–400

1 500–6 000

5 000–7 000

350–1 500

500–950

800–3 000

850–5 000

300–950

110–2 000

50–110

80–200

Capital cost per unit energy ($/kW h – output)

100–800

200–700

4 000–10 000

250–800

400–900

2 800–5 000

800–1 500

1 500–4 000

1 000–2 800

400–2900

750–1000

800–2000

Capital cost per unit power ($/kW)

3–40

5–40

40–100

90–100

40–100

30–100

9–50

3–6 (with gas) 6–90*

0.1–2

Capital cost per cycle (cent/kW h – output)

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where: we, max = maximum electrical work (J) ΔG = Gibbs free energy change (J) The Gibbs energy can be determined using the potential difference or electromotive force (emf) of the cell [5]: ΔG = −nEF

10.2

where: ΔG = Gibbs free energy change (J) n = number of electrons transferred per unit overall reaction (mol) E = cell emf (V) F = Faraday constant (96 485 C mol−1) Furthermore, the cell emf is related to the concentration of reactants and products via the Nernst equation [5]: E = Eo −

RT ln Q nF

10.3

where: E = cell emf (V) E° = standard emf of the cell (V) r = gas constant (8.314 J K−1 mol−1) T = temperature (K) [Ox ] Q = reaction quotient = [Red ] Thus by combining the above equations, the electrical work produced by an electrochemical cell can be determined from the electrolyte concentration.

10.6.2 Actual cell potential Owing to irreversible losses within a cell, the actual cell potential is lower than the theoretical cell potential described above. The losses are due to three sources, activation polarization, ohmic polarization and concentration polarization, and result in an overall loss of potential: Vcell = Eo − losses

10.4

This is further expanded as: Vcell = EC − EA − ηC − ηA − iRcell where: EC = potential at the cathode versus a reference electrode (V) EA = potential at the anode versus a reference electrode (V) ηA = activation overpotential (V) ηC = concentration overpotential (V)

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i = current density (A cm−2) Rcell = cell resistance (Ω cm2) At low current density, the activation polarization is dominant as the barriers preventing current and ion flow need to be overcome. As the current increases, activation losses begin to increase accordingly and because cell resistance remains constant, ohmic polarization losses also increase with current. Concentration polarization or gas transport losses take effect at high current as this is where reactant flow to cell reaction sites becomes increasingly difficult. If the cell is operated in the current density region dominated by ohmic losses, the cell voltage can be more simply expressed as: Vcell = EC − EA − iRcell

10.6

In this case, Rcell includes both the ohmic and polarisation losses (Ω cm2).

10.6.3 Cell capacity The theoretical cell capacity of a cell is given by: QT =  ∫ Idt = m ( nF )

10.7

where: QT = capacity of the cell (A s) I = current passed through the cell (A) t = discharge time (s) m = moles of reactant required for complete cell discharge (mol) n = number of electrons transferred in the reaction (mol) F = Faraday constant (96 485 C mol−1) Because of self-discharge and incomplete reaction at the electrodes, the actual cell capacity is less than the theoretical value which assumes that all of the active material is utilized in the cell reactions. Cell capacity is usually described in terms of ampere hours (Ah) or as watt hours (W h).

10.6.4 Cell efficiencies Coulombic efficiency The coulombic efficiency is a measure of the amount of charge delivered by the discharge reactions of the cell relative to the number of coulombs used to charge the cell. It is calculated using the following expression:

ηc =

∫ I ∫ I

dis ch

dt

dt

× 100%

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10.8

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where: ηc = coulombic efficiency (%) Idis = discharge current (A) Ich = charge current (A) Coulombic efficiency is maximized by ensuring inefficient cell behaviour is minimized. Examples include voltage control to prevent side reactions such as solvent breakdown during charging.

Voltage efficiency Voltage efficiency measures the effects of cell polarization or cell losses. It is calculated via the following equation:

ηv =

∫ V ∫ V

dis ch

dt

dt

× 100%

10.9

where: ηv = voltage efficiency (%) Vdis = discharge voltage (A) Vch = charge voltage (A) Voltage losses due to internal resistance, activation overpotential and concentration over-potential can reduce the voltage efficiency.

Energy efficiency The overall energy efficiency of the cell is a measure of the amount of actual energy (W h) released on discharge relative to the amount of energy required to charge the cell. It can be calculated via the following expression:

ηe =

∫ I ∫ I

dis

Vdis dt

ch

Vch dt

× 100%

10.10

where: ηe = overall energy efficiency (%) Idis = cell current during discharge (A) Vdis = cell voltage during discharge (V) Ich = cell current during charge (A) Vch = cell voltage during charge (V). A simpler method of calculating the energy efficiency is to multiply the coulombic and voltage efficiencies as follows: ηe = ηcηv

10.11

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335

Diesel generator Chemical energy

Some heat

Heat

Chemical energy

Mechanical motion

Electricity

Electricity

10.5 Comparison of diesel vs electrochemical power conversion [6].

10.7

Types of electrochemical energy storage technologies

Several types of electrochemical energy storage technologies are currently in existence ranging from conventional lead–acid batteries to more advanced lithium ion batteries and redox flow cells. Electrochemical power sources involve direct conversion of chemical energy into electrical energy. By comparison, the conversion of chemical to electrical energy by a diesel generator involves several steps as illustrated in Fig. 10.5. Since electrochemical systems eliminate mechanical and thermal steps associated with other methods of generation and storage, very high conversion efficiencies, up to 80–90%, are possible. A number of electrochemical energy storage technologies that are suitable for wind energy storage are described in more detail in the following sections.

10.8

Electrochemical capacitors (EC)

Electrochemical capacitors (EC) store electrical energy in the capacitor of the electric double layer (EDL), which is formed at the interface between an electrode and an aqueous or non-aqueous electrolyte. The capacitance and energy density of these devices are thousands of times larger than electrolytic capacitors. The electrodes are often made with porous carbon material. Compared with lead–acid batteries, EC capacitors have lower energy density but they can be cycled tens of thousands of times and are much more powerful than batteries (fast charge and discharge capability). While the small electrochemical capacitors are well developed, the larger units with energy densities over 20 kW h/m3 are still under development.

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Electrochemical capacitors are therefore likely to find practical application in hybrid electric vehicles and for stabilization of short-term fluctuations of wind turbines. In an example of an island wind system, S&C Electric Company has used the electronic shock absorber (ESA) on the island of Hawaii [7]. The ESA uses supercapacitors housed in a 10 m trailer to supplement forty 100 kW turbines that are used to power local water pumps. The ESA provides stored energy to control the rate of power flow to and from the wind farm. By controlling the ‘ramp rate’ of the wind power, the ESA keeps the island’s diesel generator power grid stable and optimizes the use of power from the wind turbines resulting in lower cost of operation for the island grid. The S&C ESA system went into service in May 2006.

10.9

Fuel cells

A fuel cell is an electrochemical device that combines a fuel such as hydrogen, with an oxidant such as air or oxygen to produce electricity, heat and water (Fig. 10.6). Where a fossil-derived fuel is used in the fuel cell, carbon dioxide will also be produced as a by-product of the conversion reaction. Like a battery, fuel cells convert the energy produced by a chemical reaction into usable DC electric power. However, the fuel cell will produce electricity as long as fuel is supplied. A fuel cell system is made up of a fuel cell stack, power conditioning equipment, fuel and oxidizer tanks, fuel

Load

Anode

Cathode

H2

Air H2

O2 Electrolyte

H2

O2

H2

Air

H2O vapour

H2O vapour Porous electrodes

10.6 Schematic of hydrogen fuel cell [6].

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reformer, heat exchangers and control system. Fuel cells can be connected in series in a stack to produce voltages suitable for conversion to standard AC supply. Parallel connection may also be required to achieve appropriate power rating. A range of fuel cell technologies has been developed over the last 50 years and these are classified according to the type of fuel and electrolyte used. Table 10.3 summarizes the features of each type of fuel cell along with their advantages and disadvantages. Fuel cells operate best on pure hydrogen. However, because of safety issues associated with its storage and transportation, considerable effort has gone into developing in situ methods of producing hydrogen by reforming methane, methanol or other hydrogen containing fuel. Typically, a gasoline internal combustion engine (ICE) is 18–20% efficient; hydrogen ICEs are about 25% efficient; methanol fuel cells are about 38% efficient; and hydrogen fuel cell vehicles like the Toyota FCHV-4 are up to 60% efficient – three times better than today’s gasoline-fueled engines [8]. The amount of energy produced by hydrogen per unit weight of fuel is about three times the amount of energy contained in an equal weight of gasoline, and almost seven times that of coal [4]. Hydrogen energy density per volume is quite low at standard temperature and pressure however. Volumetric energy density can be increased by storing the hydrogen under increased pressure or storing it at extremely low temperatures as a liquid. Hydrogen is thus regarded as a chemical medium for storing energy. In order to operate hydrogen fuel cells for large-scale energy generation, however, an economical and efficient method for producing hydrogen is needed. Most methods of producing hydrogen involve splitting water (H2O) into its component parts of hydrogen (H2) and oxygen (O2). The most common method involves steam reforming of methane (from natural gas), although there are several other methods [9] such as: •

• •

• •

steam reforming converts methane (and other hydrocarbons in natural gas) into hydrogen and carbon monoxide by reaction with steam over a nickel catalyst; electrolysis uses electrical current to split water into hydrogen at the cathode (+) and oxygen at the anode (−); steam electrolysis (a variation on conventional electrolysis) uses heat, instead of electricity, to provide some of the energy needed to split water, making the process more energy efficient; thermochemical water splitting uses chemicals and heat in multiple steps to split water into its component parts; photoelectrochemical systems use semi-conducting materials (such as photovoltaics) to split water using only sunlight;

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Liquid phosphoric acid soaked in a matrix

Liquid lithium, sodium and/or potassium carbonate molten salts soaked in matrix Solid zirconium oxide with small addition of yttria

Phosphoric acid (PAFC)

Molten carbonate (MCFC)

Solid oxide (SOFC)

Aqueous KOH solution soaked in a matrix

Solid organic polymer perfluorosulphonic acid

Polymer electrolyte membrane (PEM)

Alkaline (AFC)

Electrolyte

Fuel cell type

600–1000 °C (1112–1832 °F)

600–1000 °C (1112–1832 °F)

175–200 °C (347–392 °F)

90–100 °C (194–212 °F)

60–100 °C (140–212 °F)

Operating temperature

Table 10.3 Comparison of different fuel cell technologies [8]

Electric utility

Electric utility

Electric utility Transportation

Military Space

Electric utility Portable power Transportation

Applications

High efficiency Fuel flexibility Can use variety of catalysts Solid electrolyte reduces corrosion and management problems Low temperature Quick start-up

Up to 85% efficiency in cogeneration of electricity and heat Can use impure H2 as fuel High efficiency Fuel flexibility Can use variety of catalysts

Cathode reaction faster in alkaline electrolyte so high performance

Solid electrolyte reduces corrosion and management problems Low temperature Quick start-up

Advantages

High temperature enhances corrosion and breakdown of cell components

Low temperature requires expensive catalysts High sensitivity to fuel impurities Expensive removal of CO2 from fuel and air streams required Requires platinum catalyst Low current and power Large size/weight High temperature enhances corrosion and breakdown of cell components

Disadvantages

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photobiological systems use micro-organisms to split water using sunlight; biological systems use microbes to break down a variety of biomass feedstocks into hydrogen; thermal water splitting uses a very high temperature (approximately 1000 °C) to split water; gasification uses heat to break down biomass or coal into a gas from which pure hydrogen can be generated.

If fuel cells are to be used to store energy from renewables such as wind, however, electrical energy should be employed to generate the hydrogen fuel, so a water electrolyser must be used in conjunction with the fuel cell. Although significant progress is being made with the development of efficient electrocatalysts for water splitting, even with an energy efficiency of 70% for the water electrolysis reaction, the round trip overall energy efficiency for hydrogen as an energy storage medium would be less than 50%. Other impediments to using hydrogen fuel cells for renewable energy storage include their high costs and problems with poisoning of the noble metal catalysts by trace levels of CO/CO2 thereby requiring high-purity hydrogen that further adds to their cost. Fuel cells therefore offer an alternative power generation technology, but should not be viewed as an efficient method for storing energy.

10.10 Lead–acid battery Although battery technologies can be classified as primary or secondary depending on the reversibility of their electrode reactions and their ability to undergo charge–discharge cycling, only secondary batteries will be considered in this and the following sections since only these can be used for energy storage applications, starting with lead–acid batteries. Lead–acid batteries are a low-cost and popular storage choice for power quality, uninterruptible power supply (UPS) and some spinning reserve applications. Its application for energy management, however, has been very limited due to its short life cycle and poor deep discharge capabilities. The amount of energy (kW h) that a lead–acid battery can deliver is not fixed and depends on its rate of discharge. Lead–acid batteries, nevertheless, have been used in a few commercial and large-scale energy management applications. The largest one is a 40 MW h system in Chino, California, built in 1988. Some are now sealed and use a gel electrolyte (rather than free acid) which make the battery more robust and safe for transport and use in remote areas. Lead–acid batteries are made up of a series of positive and negative grids onto which is pasted lead dioxide or spongy lead respectively. Polyethylene

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microporous separators are used to prevent electrical shorting between the positive and negative plates and the entire assembly is immersed in sulphuric acid that acts as the electrolyte and supplies the sulphate ions for the electrode reactions. Lead–acid batteries can be plate or tubular types, and can be either flooded or sealed. Flooded cells have the electrodes/plates immersed in the electrolyte. During charging, oxygen gas may be produced at the positive electrode and hydrogen at the negative due to the breakdown of water. In flooded cells, the gases produced during charging are vented to the atmosphere, so distilled water must be occasionally added to bring the electrolyte back to its required level. The most familiar example of a flooded lead–acid cell is the 12 V automobile battery. Sealed batteries confine the electrolyte and are often referred to as maintenance-free batteries since they do not require addition of water. Sealed lead–acid batteries are designed so that the oxygen generated during charging is captured and recombined in the battery. This is called an oxygen recombination cycle and works well as long as the charge rate is not too high. They have a vent or valve to allow gases to escape if internal pressure exceeds a certain threshold. Too high a rate of charge may result in internal mechanical damage, or cause rupture or thermal runaway. A schematic diagram of a typical flooded lead–acid battery made up of several cells to provide the desired voltage is given in Fig. 10.7.

10.10.1 Electrode reactions The electrode reactions occurring during charging and discharging of lead– acid batteries are as follows:

Sulphuric acid electrolyte Cell connector

Separator

Positive electrode (PbO2) Cell divider Negative electrode (Pb)

10.7 Schematic section view of typical flooded lead–acid battery.

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Positive electrode: discharge ⎯⎯⎯⎯ ⎯⎯⎯ → PbSO4 + 2H 2O E o = 1.685 V PbO2 + H 2SO4 + 2H + + 2e− ← ⎯ charge

Negative electrode: discharge ⎯⎯⎯⎯ ⎯⎯⎯ → PbSO4 + 2H + + 2e− Pb + H 2SO4 ← ⎯ charge

E o = −0.356 V

Overall reaction: discharge ⎯⎯⎯⎯ ⎯⎯⎯ → 2PbSO4 + 2H 2O Ecell = 2.041 V PbO2 + 2 H 2SO4 + Pb ← ⎯ charge

10.10.2 Limitations of flooded-type lead–acid batteries for large-scale energy storage Although lead–acid batteries work well in starter applications in vehicles where they regularly undergo shallow discharge, the performance of the flooded-type batteries under the deep discharge conditions required in most energy storage applications is limited by the following constraints: •

The battery cannot be left in discharged state for extended times due to problems of sulphation. This causes large lead sulphate crystals to grow on the electrode surfaces that are difficult to react, leading to capacity loss and failure. • If the battery is not gassing charged, there is danger of 䊊 cell out of step condition, 䊊 stratification of electrolyte, 䊊 reduction in battery life. • Gassing charge results in 䊊 explosive hydrogen gas generated, 䊊 corrosive vapour generated, 䊊 reduction in electrolyte level. • The battery needs to be oversized for maximum life. • The state-of-charge of battery is difficult to monitor. Despite these limitations, however, lead–acid batteries have for many years been the only commercially available batteries that are economically viable for practical use. Some battery types now use a gelled electrolyte (rather than free acid), making the battery more robust and safe for transport and use in remote areas.

10.10.3 Valve regulated lead–acid (VRLA) batteries Valve-regulated lead–acid (VRLA) batteries are also referred to as ‘recombinant’ batteries. Unlike flooded batteries, which lose water as a result of

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oxygen and hydrogen evolution at the positive and negative electrodes respectively during charging, in VRLAs, oxygen will recombine with the hydrogen to reform water [10]. A valve is used a safety feature in case the rate of hydrogen evolution becomes too high. Since the battery system is designed to eliminate the emission of gases on overcharge, room ventilation requirements are reduced and no acid fumes are emitted during normal operation. As there is no need to top up water lost due to electrolysis, this reduces inspection and maintenance, so that VRLAs are also referred to as ‘maintenance free’. The first VRLA batteries had the sulphuric acid electrolyte immobilized as a gel by the addition of 5–8 wt% of fumed silica. Unlike a traditional wet-cell lead–acid battery, these ‘gel-type’ batteries do not need to be kept upright and virtually eliminate the electrolyte evaporation and spillage common to the wet-cell battery. They also have greater resistance to extreme temperatures, shock and vibration. These batteries are sometimes referred to as sealed lead–acid batteries, but they are not completely sealed. The valve regulation system allows for excess gas to be vented. The second system employs a glass-microfibre separator or absorbent glass mat (AGM) which has a high porosity and can absord a high volume of electrolyte. AGM batteries are more widely used than the gelled type of cells because of lower cost and higher power ratings [10].

10.10.4 The UltraBattery A recent advance in lead–acid battery technology developed by the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) is the UltraBattery, a hybrid energy storage device that integrates a supercapacitor with a lead–acid battery in one unit cell [11]. The UltraBattery incorporates carbon plates at the negative electrode to act as supercapacitor hybrid electrodes, allowing fast charge acceptance compared with the conventional technology. This feature makes the lead–acid battery more suitable in hybrid vehicle applications, but could also be important for short-term wind turbine output power stabilisation. The cycle life of this hybrid system under deep discharge applications has yet to be demonstrated, however, so its most likely application will be in hybrid vehicles and other applications requiring short-term energy storage capabilities.

10.10.5 Lead–acid battery energy storage demonstrations Although lead–acid batteries have yet to be field tested in large-scale wind farms, they are commonly used in remote area and hybrid wind power systems. Several large-scale lead–acid based energy storage systems were

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Table 10.4 Examples of lead–acid battery storage systems [12] Plant name & location

Year of installation

Rated energy/ (MW h)

Rated power/ (MW)

$/kW h (1995$)

$/kW

CHINO California HELCO Hawaii PREPA Puerto Rico BEWAG Berlin

1988 1993 1994 1986

40 15 14 8.5

10 10 20 8.5

805 456 239 707

201 304 341 70

also commissioned in 1980s and 1990s, some of which are summarized in Table 10.4. The Chino lead–acid battery installation was one of the first large-scale battery storage systems to be connected to the grid for the purpose of loadlevelling. More recently, a 5-MVA, 3.5 MW h valve-regulated lead-acid battery system was installed at a lead recycling plant in the Los Angeles, California, area [13]. The system provides power-quality protection for the plant’s pollution-control equipment, preventing an environmental release in the event of a loss of power. The system carries the critical plant loads while an orderly shutdown occurs. The battery system also in discharged daily during the afternoon peak (and recharged nightly), reducing the plant’s energy costs. Although there have been a significant improvements in the performance and cycle life of lead–acid batteries in recent years, their deep discharge performance is still limited and this has restricted their application in largescale energy storage systems such as wind farms.

10.11 Nickel–metal hydride batteries The development of the present-day nickel–metal hydride battery (NiMH) appears to have evolved out of the efforts by scientists to develop suitable materials for the safe storage and transportation of hydrogen for use in fuel cells. Like the nickel–cadmium battery, the NiMH battery employs a nickel hydroxide positive electrode. The NiMH battery, however, uses a hydrogen-absorbing alloy for the negative electrode instead of cadmium. As such, it eliminates potential health problems associated with the use and recycling of a heavy metal. The operation of the NiMH cell is illustrated in Fig. 10.8.

10.11.1 Electrode reactions The main reactions that take place during charge and discharge are as follows [14]:

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Charge MHx

H

H+

H+

OH

OH

Ni

H

H

H+

H+

O

OH Ni

M

Discharge

Negative electrode Hydrogen absorbing alloy

Positive electrode Nickel hydroxide

10.8 Schematic of NiMH battery.

Positive electrode reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → NiOOH + e− Ni (OH )2 + OH − ← ⎯ discharge

Negative electrode reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → MH ad + OH − M + H 2O + e− ← ⎯ discharge

Overall: ⎯⎯⎯⎯ ⎯⎯⎯ → NiOOH + MH ad Ni (OH )2 + M ← ⎯ charge

discharge

NiMH batteries are similar to the earlier Ni-Cd, except they use a metal alloy negative electrode which adsorbs hydrogen. The hydride electrode has a higher energy density than Cd, thus a higher capacity for the same size. NiMH batteries are more environmentally safe than Ni-Cd since there is no cadmium, making disposal easier. Like Ni-Cd, however, they can suffer from memory effect and the price is still too high for large-scale storage applications.

10.12 Li ion battery The main advantages of Li ion batteries, compared with other advanced batteries, are their high energy density (300–400 kW h/m3, 130 kW h/t), high efficiency (near 100%) and long cycle life (3000 cycles @ 80% depth of discharge). The cathode in these batteries is a lithiated metal oxide (LiCoO2, LiMnO2, LiFePO4, etc.) and the anode is usually made of graphitic carbon with a layer structure. The electrolyte comprises lithium salts

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Charge e–

LiNiO2 positive electrode

Passive layer

345

Oxygen

Nickel

Lithium

Electrolyte separator

10.9 Schematic of Li ion battery [15].

(such as LiPF6) dissolved in organic carbonates. When the battery is charged, the lithium atoms in the positive electrode are oxidized to lithium ions and migrate through the electrolyte toward the negative carbon electrode where they combine with external electrons and are deposited between carbon layers as lithium atoms (Fig. 10.9). This process is reversed during discharge.

10.12.1 Cell reactions Typical cell reactions for a lithium ion cell depend on the type of anode and cathode material used. For a cell using a carbon anode and LiCoO2 cathode the cell reactions are as follows: Negative electrode reactions: charge ⎯⎯⎯⎯ ⎯⎯⎯ → Li x C 6 xLi + + xe− + 6C ← ⎯ discharge

Positive electrode reactions: ⎯⎯⎯⎯ ⎯⎯⎯ → Li(1− x )CoO2 + xLi + + xe− LiCoO2 ← ⎯ charge

discharge

Overall cell reaction: ⎯⎯⎯⎯ ⎯⎯⎯ → Li(1− x )CoO2 + Li x C 6 LiCoO2 + 6C ← ⎯ charge

discharge

10.12.2 Applications Although Li ion batteries work well in small portable appliances, challenges need to be overcome for large-scale Li ion batteries. The main hurdle is

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the high cost (above $600/kW h) due to special packaging and internal overcharge protection circuits to prevent the deposition of metallic lithium at the negative electrode during accidental overcharge that could lead to fires and explosions. These safety concerns have to date delayed the deployment of Li ion batteries in plug-in hybrid and all-electric vehicles. Although most of the development of the Li ion battery has been for mobile applications or portable devices, several companies are working to reduce the manufacturing cost of Li ion batteries to capture large energy markets. A megawatt size demonstration based on the Altairnano lithium– titanate material battery cells was installed and operated at a substation owned by Indianapolis Power & Light (IPL) in 2008 [16]. Two 1 MW battery storage devices consisting of a lithium ion battery stack, an AC-to-DC power conversion system, heating, ventilation, air-conditioning (HVAC) unit, and a control system were mounted in a portable tractor trailer-size container. The battery stacks were composed of a series arrangement of lithium ion cell packages mounted in racks within the trailer. The battery stack was designed to deliver 1 MW to the grid for a duration of 15 minutes. Potential applications for this new technology include market-based, regulation, ramp rate regulation for distributed resources (wind, solar) and critical peak price response. Such a system would be capable of providing short-term power output stabilization for wind turbines, however, for application in longer-term storage of wind energy, the cost of Li ion batteries would need to be further reduced to compete with other options.

10.13 Metal–air battery Metal–air batteries are the most compact and, potentially, the least expensive batteries available. Their high energy density and low cost may make them ideal for primary battery applications and they are also environmentally benign. The main disadvantage, however, is that electrical recharging of these batteries is very difficult and inefficient, so many developers offer an electrically rechargeable battery. Rechargeable metal–air batteries (Fig. 10.10) that are under development have a life of only a few hundred cycles and an efficiency of about 50%. The anodes in these batteries are commonly available metals with high energy density such as aluminium (Al–air) or zinc (Zn–air) that release electrons when oxidized. With both systems, the metal acts as the fuel and oxygen or air react at the positive gas porous electrode in a similar manner to fuel cells. The cathodes or air electrodes are often made of a porous carbon structure or a metal mesh covered with catalysts. The electrolytes are often a good OH− ion conductor such as KOH. The electrolyte may be in liquid form or a solid polymer membrane saturated with KOH.

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Discharge Load

e

e

e

e −

2OH Zn

O2

ZnO Electrolyte

Zinc ZnO

Cathode

Zn 2OH−

O2 e

e e

Air

Recharge

e

10.10 Charge and discharge operation of the Zn–air battery [17].

10.13.1 Cell reactions The discharge reactions of the Zn–air battery (illustrated in Fig. 10.10) are as follows: Positive electrode reaction: Zn + H2O → ZnO + 2H+ + 2e− Negative electrode reaction: O2 + 4H+ + 4e → 2H2O Overall reaction: 2Zn + O2 → 2ZnO While metal–air batteries offer the possibility of mechanical recharging by the replacement of the metal plates once they have been consumed, the reprocessing of the metal hydroxide product of the negative half-cell reaction is extremely energy intensive, giving round trip energy efficiencies of less than 40%. The electrical rechargeability of these batteries therefore needs to be developed further before they can compete with other rechargeable battery technologies for energy storage applications.

10.14 Sodium–sulphur (NaS) battery In the mid-1960s development work began on batteries using sodium for the negative electrodes. Sodium is attractive because of its high reduction

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potential of −2.71 V, its low weight, ready availability and low cost. These factors offer the prospect of batteries with very high power and energy densities. In order to construct practical batteries using sodium electrodes, however, the sodium must be used in liquid form and since the melting point of sodium is 98 °C, sodium-based batteries must operate at high temperatures, typically in excess of 270 °C. This in turn brings problems of thermal management and safety and places more stringent requirements on the rest of the battery components. The first sodium-based battery to be developed was the sodium sulphur (NaS) battery. As illustrated in Fig. 10.11, the NaS battery consists of liquid (molten) sulphur at the positive electrode and liquid (molten) sodium at the negative electrode as active materials separated by a solid beta alumina ceramic electrolyte. The electrolyte allows only the positive sodium ions to go through it and combine with the sulphur to form sodium polysulphides. The operating temperature of the NaS battery is greater than 350 °C, so sophisticated methods of construction of the battery pack are needed, together with auxiliary heating during charging and periods of non-use. It is essential that the battery temperature does not drop below 200 °C, otherwise freezing of the sodium electrolyte will cause damage due to mechanical stresses.

+

−

Current collector

Alumina seal Sodium negative electrode Sodium ions Beta alumina separator Sulphur positive electrode Cell case

10.11 Schematic of sodium–sulphur battery [3].

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10.14.1 Cell reactions The electrode reactions of the NaS battery are as follows: Positive electrode reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → xS + 2e− S x 2− ← ⎯ discharge

Negative electrode reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → Na ( liquid ) Na + + e− ← ⎯ discharge

Overall cell reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → 2Na + xS Na 2Sx ← ⎯ discharge

During discharge, the positive Na+ ions produced during oxidation of liquid Na metal at the negative electrode, flow through the electrolyte and electrons flow in the external circuit of the battery, producing about 2 V. Charging causes sodium polysulphides to release the positive sodium ions back through the electrolyte to recombine as elemental sodium. NaS battery cells have high efficiencies (about 89%) and have a pulse power capability over six times their continuous rating (for 30 seconds).

10.14.2 Development and commercialization The NaS battery was first developed in the Ford Research Laboratories in the 1960s as part of an early effort to develop a high-energy density electrochemical power source for electric vehicles [18]. In the early 1990s, pilot production was established by both ABB and RWE-Chloride, but as a result of several fires in 1994 in Ecostar vans using the batteries, production was stopped by ABB and, soon after, RWE-Chloride’s subsidiary, Silent Power, also terminated their programme. The only companies currently commercializing the NaS battery technology are Tokyo Electric Company (TEPCO) and NGK Insulators Ltd. Several large-scale demonstration systems have recently been completed. TEPCO and NGK Insulators Ltd developed the high-density sodium– sulphur (NaS) battery during the 1980s. As part of that development, more than 20 NAS battery demonstrations were installed in Japan, including two 6 MW plants at TEPCO stations [19]. Each plant provides up to 48 mW h of energy storage for daily load levelling applications, which reduce the amount of generation needed during hours of peak usage. NaS battery technology has now been demonstrated at over 190 sites in Japan totalling more than 270 MW with stored energy suitable for 6 hours daily peak shaving. The largest NaS installation to date is a 34 MW, 245 MW h unit for wind stabilization in northern Japan. US utilities have

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deployed 9 MW for peak shaving, back-up power, firming wind capacity and other applications [20]. Nagoya-based NGK’s sodium–sulphur units, costing a reported 294 million yen ($2.9 million) per megawatt, store electricity for sale when demand is greatest and have 4.3 times the capacity of lead–acid devices. According to Hironobu Matsunaga, a spokesman for NGK, it is extremely difficult to uniformly produce the high-grade ceramics used in the devices. At present, NGK is the only company able to make NaS batteries successfully [21].

10.14.3 Recent NaS battery installation plans In September 2007, American Electric Power (AEP) announced plans to install an additional 6 MW of NaS batteries to support their wind generation operations [22]. These batteries, to be supplied by NGK Insulators Ltd, will be used to enhance reliability of AEP’s electricity grid, provide support for weak sub-transmission systems, support load growth, mitigate equipment overload and to offset intermittent wind power. AEP’s goal is to instal at least 25 MW of NaS battery capacity by 2010, and up to 1000 MW of NaS advanced energy storage solutions by 2020 to support its electricity grid operations. Currently, AEP will be utilizing NaS battery technology at its West Virginia and Ohio service territories starting in 2008. Additionally, the company expects to work together with wind energy developers to identify a third deployment location for this type of energy storage solution. One of the key benefits of the NaS battery technology usage with wind energy generation sites is the technology’s ability to help offset the irregular nature of wind generation. Each NaS battery is capable of delivering 1 MW of power over 7 hours. Recharged nightly, these batteries provide back about 80% of the electricity used to recharge them. The system is very large; however, installation is not permanent which allows flexibility within the user’s grid sites depending on power needs at the time. As an emerging technology, the cost of such a system is relatively high and in the case of the AEP systems is expected to be about $27 million for 6 MW of capacity. This translates to approximately $4500 a kilowatt, including the price of the energy storage system and substation improvements.

10.15 The zero emissions battery research activity (ZEBRA) battery Like the sodium–sulphur battery, the so-called ‘ZEBRA’ (acronym for zero emissions battery research activity) battery is a high-temperature battery

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Current collector −

+ Alumina seal

Nickel chloride positive electrode

Beta alumina separator Sodium negative electrode Cell case

10.12 Schematic of ZEBRA battery.

that employs a molten salt electrolyte with a liquid sodium negative electrode [23]. It has a central positive electrode mainly consisting of nickel and sodium chloride plus some additives and a liquid tetrachloroaluminate electrolyte (NaAlCl4), contained within a beta alumina tube electrolyte (Fig. 10.12). The cell works in a range of temperature between 270 and 350 °C and during charge sodium ions formed in the central positive electrode move through the wall of the beta alumina tube to form the liquid sodium negative electrode which is contained by a square section mild steel case. Since the operating temperature of the ZEBRA battery is below that of molten sulphur, it is regarded as somewhat safer than the NaS battery.

10.15.1 Cell reactions The technical name for the battery is Na–NiCl2 battery, so the cell reactions reflect this chemistry. Positive electrode reactions: charge ⎯⎯⎯⎯ ⎯⎯⎯ → NiCl 2 + 2e− Ni + 2Cl − ← ⎯ discharge

Negative electrode reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → Na ( liquid ) + Cl − NaCl + e− ← ⎯ discharge

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Overall cell reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → NiCl 2 + 2 Na 2NaCl + Ni ← ⎯ discharge

The cell chemistry was later altered to improve the useful capacity. The cell is assembled in the over-discharged state and during the first charge the following reactions occur [24]: At 1.6 V:

Al + 4NaCl = NaAlCl4 + 3Na

At 2.35 V:

Fe + 2NaCl = FeCl2 + 2Na

At 2.58 V:

Ni + 2NaCl = NiCl2 + 2Na

The ability to assemble the ZEBRA battery in its overdischarged state provides a significant safety advantage compared with the NaS cell. Additional safety benefits include its ‘in-built’ overcharge and over discharge reactions. The charge capacity of the ZEBRA cell is determined by the quantity of salt (NaCl) available in the cathode. In the event of any accidental overcharge therefore, the liquid salt NaAlCl4 supplies a sodium reserve following the reversible reaction: 2NaAlCl4 + Ni ↔ 2Na + 2AlCl3 + NiCl2 This overcharge reaction requires a higher voltage than the normal charge reaction thereby acting as a ‘safety’ valve so that any further charge current is stopped automatically as soon as the increased open circuit voltage equalizes the charger voltage. On the other hand in over-discharge mode, the surplus sodium in the anode compartment maintains current flow at a lower voltage according to the following cell failure reaction that runs without a ceramic failure: 3Na + NaAlCl4 → 4NaCl + Al

10.15.2 Applications Most of the early development of the ZEBRA battery was undertaken by Anglo American and Daimler Benz. Although most of the demonstrations of the ZEBRA battery to date have been for mobile applications in cars, buses and boats, a number of groups are now developing and offering these systems for stationary applications such as telecom back-up power [25].

10.16 Flow batteries Flow batteries differ from conventional batteries in that their active material is in the form of two redox couple solutions that are stored in external tanks and pumped through a stack of electrochemical cells where the charge

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and discharge reactions take place at inert electrode surfaces. Flow cells are therefore similar to fuel cells in their configuration, but unlike fuel cells, they can be electrically charged and can therefore be used for the storage of energy. A typical cell stack is made up of a series of cells that are electrically connected to adjacent cells via bipolar electrodes that act as an anode on one side and a cathode on the opposite side (Fig. 10.13). Ion selective membranes are used to separate each half-cell so as to prevent mixing of the two redox cell solutions as they are pumped through the cell stack. Flow cells fall into two different categories. The first type is the metal/ halide flow cell that involves the deposition of a metal at the negative electrode during charging. Examples of metal/halide cells are the zinc–bromine (Zn/Br) and the zinc chloride (Zn/Cl) systems, the capacity of each being determined by the quantity of zinc metal deposited at the negative electrode. Although considerable work on the Zn/Cl cell was undertaken in the 1960s and 1970s, the programme was abandoned due to the complexity of the refrigeration system needed to keep the dissolved chlorine gas from escaping. The zinc bromine battery was originally developed by Exxon Research and Engineering Corporation in the 1970s and while it was successfully field tested in electric vehicles by the Austrian company SEA in the 1980s and 1990s, most recent development has focused on stationary applications.

Cell stack (two cells)

Catholyte tank

+

− Current collector End electrode Porous carbon felt Membrane Bipolar electrode

10.13 Bipolar electrode flow cell stack configuration.

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10.17 Zn/Br battery In each cell of a Zn/Br battery, two different electrolytes flow past carbon– plastic composite electrodes in two compartments separated by a microporous polyolefin membrane as illustrated in Fig. 10.14. In its discharged mode, the electrolytes in each half-cell reservoir comprise a solution of ZnCl2. During charge, metallic zinc is deposited (plated) as a thin film on the negative side of the carbon–plastic composite electrode, while bromine evolves as a dilute solution on the other side of the membrane, reacting with other agents (organic amines) to make thick bromine oil that sinks down to the bottom of the electrolytic tank. It is allowed to mix with the rest of the electrolyte during discharge when the Br− ions combine with Zn2+ ions produced by the oxidation of the zinc to give zinc bromide, generating 1.8 V across each cell. The net efficiency of this battery is about 75% [3].

10.17.1 Cell reactions Positive electrode: charge ⎯⎯⎯⎯ ⎯⎯⎯ → Br2 ( complexed ) + 2e− 2 Br − ← ⎯ discharge

The Br2 is thus complexed into an oily liquid that separates out from the aqueous layer and sinks to the bottom of the electrolyte reservoir, thereby preventing the emission of bromine vapours. During discharge, the complexed bromine in the organic layer is converted to Br− ions and re-enters the aqueous phase.

Catholyte tank

+

− Zn

2+

charge

Carbon electrode

Zn2+ discharge

Br− discharge

Br2 organic liquid phase

Zinc deposit at charged state

Br− charge

Separator

10.14 Schematic of Zn/Br flow cell [26].

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Negative electrode: charge ⎯⎯⎯⎯ ⎯⎯⎯ → Zn Zn 2+ + 2e− ← ⎯ discharge

Overall cell reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → Zn + Br2 (complex ) ZnBr2 ← ⎯ discharge

Uniform electrolyte flow distribution is a critical feature of cell design that is needed to ensure uniform plating of the zinc metal, while shunt current reduction techniques must be used to prevent the plating of zinc metal in the flow channels that would otherwise cause blockages to electrolyte flow. For the smooth operation of the Zn/Br cell, regular zinc stripping cycles are also needed to prevent the formation of zinc dendrites that could lead to short-circuits and potential fires.

10.17.2 Development and commercialization Although considerable effort was made by Johnson Controls in the United States and SEA in Austria to develop the Zn/Br battery for electric vehicles in the 1980s and 1990s, mobile applications of this technology have been phased out in recent years, and efforts are now focused on stationary applications. Most of the earliest Zn/Br stationary demonstration projects were undertaken in the 1990s in Japan. In 1991, Meidisha demonstrated a 1 MW/4 MW h Zn/Br battery at Kyushu Electric Power company. Some multi-kW h units are now available pre-assembled, complete with plumbing and power electronics. Although a few companies continue to develop the Zn/Br technology, the main Zn/Br battery developers are Premium Power Corporation in the United States and ZBB Energy Corp, an Australian company that recently moved its operations to the United States. In May 2004, ZBB was awarded a multi-year, cost-shared contract with the California Energy Commission (CEC) Public Interest Research Program (PIER) to demonstrate a 2 MW/2 MW h zinc-bromine energy storage system (Z-BESS) as a utility peak shaving resource [27]. Phase 1 of the programme involves manufacturing the first 500 kW/500 kW h Z-BESS and delivering the unit to the Distributed Utility Integration Test (DUIT) site in San Ramon, California. The Z-BESS unit will be tested for 6 months under controlled conditions to establish response characteristics, controls protocols and data acquisition requirements. The 500 kW h Z-BESS incorporates ten 50 kW h battery modules are aligned in two independent strings on each side of a standard 6 m military cargo container. A string of five modules aligned on one side of a shipping container is shown in Fig. 10.15. The shipping container, power conditioning system (PCS) and cooling equipment are mounted on a 14 m flatbed

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10.15 A 250 kW h string of five modules in a standard shipping container [26].

trailer for operation and transportation to site. The Z-BESS unit is rated at 500 kW h of energy storage with a peak power output of 500 kW (250 kW h/250 kW per string). A 500 kW/625 kV A PCS, supplied by Satcon Power Systems, is mounted to the back end of the trailer. The twostage unit, comprising choppers and inverter/converter, enables the bidirectional flow of power from the battery to the electrical grid. The inverter was designed for three phase output connection of 480 V a.c., 60 Hz operation. The output of the four trailer mounted 500 kW h systems will be connected to the utility at a substation location designated by Pacific Gus & Electric (PG&E). The system will either remain at one utility site for the entire demonstration period, or may be moved to a second demonstration site for the latter half of the demonstration period. The Z-BESS will be tested at the utility demonstration site(s) for duration of 18 months.

10.17.3 Redox flow batteries The redox flow batteries are flow batteries that employ two fully soluble redox couple solutions in each half-cell. Unlike the Zn/Br flow battery, the redox flow battery has all reactants and products in the solution phase and no metals are plated on the electrodes during charging. The redox flow cell thus stores energy in the solutions, so that the capacity of the system is determined by the size of the electrolyte tanks, while the system power is determined by the size of the cell stacks. The redox flow cell is therefore

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more like a rechargeable fuel cell than a battery. Initial work on redox batteries began in the 1970s with the development of the iron–chromium battery by NASA [28, 29]. Of the redox flow cell technologies that have undergone development in the last 30 years, however, most interest has been directed towards the vanadium redox battery (G1 VB) developed at the University of New South Wales (UNSW) in Sydney [30] and the sulphur/ bromine system developed by Innogy in the United Kingdom [31]. Of these, however, only the UNSW vanadium redox battery has reached commercial fruition.

10.18 All-vanadium redox battery (G1 VB) Of the redox flow cells developed to date, the all-vanadium redox battery, or G1 VB system, has shown the greatest potential with high energy efficiencies of over 80% in large installations and long cycle life. The allvanadium redox flow battery employs the V(III)/V(II) and V(V)/V(IV) redox couples in sulphuric acid as the negative and positive half-cell electrolytes respectively [32, 33]. The basic G1 VB concept is shown in Fig. 10.16 while an early UNSW G1 VB prototype is illustrated in Fig. 10.17.

10.18.1 Cell reactions The charge–discharge reaction occurring in the vanadium redox cell are: Positive electrode reaction: ⎯⎯⎯⎯ ⎯⎯⎯ → VO2 + + 2H + + e− VO2+ + H 2O ← ⎯ charge

discharge

V(IV)/V(V)

V(III)/V(II)

Positive half-cell electrolyte reservoir

Cell stack

Energy storage (kW h)

Energy conversion (kW)

Negative half-cell electrolyte reservoir

Energy storage (kW h)

10.16 Vanadium redox flow cell concept with separate energy conversion and energy storage components.

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10.17 Early 1 kW/4 kW h G1 VB prototype at UNSW (courtesy University of New South Wales).

Negative electrode reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → V 2+ V 3+ + e − ← ⎯ discharge

Overall cell reaction: charge ⎯⎯⎯⎯ ⎯⎯⎯ → VO2 + + V 2+ + 2H + V 3+ + VO2+ + H 2O ← ⎯ discharge

During the charge–discharge cycles, H+ ions are exchanged between the two half-cell electrolytes through the hydrogen-ion permeable polymer membrane. The cell voltage is 1.4–1.6 V and the net efficiency of this battery can be as high as 85%. Like other flow batteries, the power and energy ratings of G1 VB are independent of each other.

10.18.2 Unique features of the G1 VB A unique feature of the G1 VB that distinguishes it from other redox flow batteries is the use of the same element in both half-cells. This avoids problems of cross-contamination of the two half-cell electrolytes during longterm use and means that the electrolytes have an indefinite life so that waste disposal issues are minimized. Other advantages of the G1 VB include the following: •

Low cost for large storage capacities. Cost per kW h decreases as energy storage capacity increases, typical projected battery costs for 8 or more hours of storage being as low as US$150 per kW h.

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Existing systems can be readily upgraded and additional storage capacity can be easily installed by changing the tanks and volumes of electrolyte. High energy efficiencies between 80 and 90% in large installations. Capacity and state-of-charge of the system can be easily monitored by employing an open-circuit cell. Negligible hydrogen evolution during charging. Can be fully discharged without harm to the battery. All cells fed with same solutions and therefore are at the same state-of-charge. Long cycle life (more than 12 000 charge–discharge cycles have been demonstrated by SEI) [34]. Easy maintenance. Can be both electrically recharged and mechanically refuelled.

10.18.3 G1 VB demonstrations and commercialization G1 VB was pioneered in the UNSW in the early 1980s with considerable development taking place between 1984 and 2001. A number of licences to the UNSW G1 VB technology were granted to companies in Japan, Thailand and Canada between 1993 and 2001. One licensee, Sumitomo Electric Industries (SEI) in Japan, has completed a number of medium to large-scale installations, including a 500 kW/2 MW h G1 VB used for emergency back-up power in a semiconductor factory in Japan and a 4 MW/6 MW h battery installed at the Subaru wind farm on the Japanese island of Hokkaido. As of late 2008, G1 VB storage has been applied at over 20 commercial sites worldwide covering up to 8600 MW h [35].

10.18.4 Wind energy demonstrations of the G1 VB King Island, Tasmania, Australia In 2003, a G1 VB system was installed on King Island in Tasmania as part of Hydro Tasmania’s upgrade of the local wind farm. The main features of the system upgrade were as follows [2]: • • • • •

two Vestas V52, 850 kW wind turbines; total installed rated wind energy of 2.45 MW; 200 kW G1 VB installed by Pinnacle G1 VB; control system; demand side management study.

The G1 VB key specifications included:

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200 kW for 4 hours – energy storage capacity; 300 kW for 5 minutes – to comfortably allow time to start a diesel generator if required; 400 kW for 10 seconds – to support power system.

The island’s load profile was shown in Fig. 10.4 and the objective of the G1 VB installation was to load level the wind turbines and allow a greater renewable energy penetration for the island with significant savings in diesel fuel. The G1 VB stacks and electrolyte tanks for the system are presented in Fig. 10.18. Subaru project, Hokkaido, Japan The largest G1 VB installation to date is the 4 MW/6 MW h G1 VB system that was installed at the 30.6 MW rated Tomomae wind farm on the Japanese Island of Hokkaido by Sumitomo Electric Industries for J-Power [7]. This project was funded under Japan’s New Energy Development Organisation (NEDO) and the objective was to optimize the control strategy for the G1 VB to maximize the efficiency and optimize the operation of the storage system. The G1 VB stacks are enclosed in cabinets as shown in Fig. 10.19 along with the electrolyte tanks.

10.18 G1 VB stacks (left) and electrolyte tanks (right) at the King Island G1 VB installation (courtesy Hydro Tasmania).

Cell stack cabinets

Electrolyte tank

10.19 4 MW/6 MW h G1 VB installed at Tomomae wind farm on the Japanese island of Hokkaido [36].

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While the all-vanadium battery has been shown to offer excellent performance and capabilities for a range of energy storage applications, its use in mobile systems has been restricted due to the low energy density of the vanadium sulphate electrolytes. The redox battery’s unique properties that allow both electrical recharge and mechanical refuelling had attracted considerable interest since it was first proposed [37, 38]. Further work was therefore undertaken at UNSW in 2000–2001 to develop a system with a higher energy density that could potentially be applied to electric vehicles.

10.19 Vanadium bromide redox battery (G2 V/Br) In 2002, the original UNSW inventors of the G1 VB system patented a new Generation 2 vanadium bromide redox cell that employs a vanadium bromide electrolyte in both half-cells [39]. The negative half-cell reaction involves the V(II)/V(III) redox couple reactions, while the positive electrode employs the Br−/Br3− couple. A new start-up company, V-Fuel Pty Ltd, was established in 2005 by the inventors and the Victoria Governmentfunded Centre for Energy and Greenhouse Technologies to commercialize this system for renewable energy storage and other applications. While Generation 1 VRB employs a solution of vanadium sulphate in sulphuric acid in both half-cells, the vanadium bromide redox fuel cell employs the VBr2/VBr3 couple in the negative half-cell electrolyte and the Br−/ClBr2− or Cl−/BrCl2− couples in the positive half-cell. The higher solubility of vanadium bromide allows energy density to be almost doubled (to around 50 W h/kg). As in the Zn/Br cell, the bromine produced at the positive electrode during charging is complexed to produce an oily organic phase that settles to the bottom of the reservoir and prevents the formation of bromine gas emissions [40].

10.19.1 Cell reactions Positive half-cell reactions: charge ⎯⎯⎯⎯ ⎯⎯⎯ → Br3 − ( complexed ) + 2e− 3Br − ← ⎯ discharge

Negative half-cell reactions charge ⎯⎯⎯⎯ ⎯⎯⎯ → VBr2 + Br − VBr3 + e− ← ⎯ discharge

10.19.2 G2 V/Br development Since 2005, further development of the G2 V/Br has been undertaken by V-Fuel Pty Ltd in the following areas:

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Identification of stable, chemically resistant, low-cost membrane for the bromide system. Identification of suitable carbon felt electrode materials for negative and positive half-cell reactions. Development of stable bromine complexing agent compositions to eliminate bromine vapour emissions. Laboratory cell charge–discharge cycling at range of temperatures with selected membrane and electrode materials.

To date, overall energy efficiencies between 70 and 75% have been achieved in small laboratory test cells, but further work is underway to increase this with the use of lower resistance materials and optimized cell design and electrolyte flow distribution systems for the bromine complexed phase.

10.20 Summary Of the electrochemical energy storage systems currently under development, the ones that have reached an advanced stage of development and demonstration in large-scale grid connected applications are the lithium ion, sodium–sulphur, zinc–bromine and vanadium redox flow battery technologies. While lithium ion and sodium–sulphur systems show good performance, issues surrounding cost and safety still require attention. The ZEBRA battery overcomes many of the safety concerns of the NaS system, but has to date mainly been developed for electric vehicle applications and cost may be a limitation to its use in stationary systems requiring high storage capacities. Since flow batteries separate power output and storage time, they offer the greatest flexibility, particularly in applications requiring several hours of storage. Zinc bromine batteries have been successfully demonstrated in a number of grid-connected applications, but issues relating to the uniform deposition of zinc metal at the negative electrode during charging limit their flexibility during long-term operation and lead to possible fires and other safety issues. The vanadium redox flow battery uses the same electrolyte in both halfcells and does not involve the deposition of any solid material. For wind and solar energy storage applications requiring several hours of storage therefore, the vanadium redox flow battery offers low cost, long cycle life and high overall energy efficiency. Energy storage systems based on redox flow cells are therefore well suited for the stabilization of short-term and long-term fluctuations in output power from wind farms and turbines. To date more than 20 medium to large-scale G1 VBs have been installed in a wide range of demonstrations and field trials covering applications ranging from wind and solar energy storage, emergency back-up power and load

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levelling and several companies are now commercializing the G1 VB in Austria, China, Japan, Thailand and Australia. Although a large number of MW h size NaS and G1 VB systems have been installed in various grid-connected applications since the mid-1990s, however, the fragmented nature of the power industry around the world has made the more widespread uptake of this and other energy storage technologies by the renewable energy industry, slower than anticipated. With increasing global commitments to greenhouse gas abatement, however, this is expected to change dramatically in the near future, while the rapid economic expansion in India and China will lead to growth in off-grid and grid-connected back-up power applications for the telecom, industrial and commercial markets.

10.21 References 1. Roberts, P., The End of Oil – The Decline of the Petroleum Economy and the Rise of a New Energy Order, Bloomsbury, 2004. 2. ‘King Island – Towards a Sustainable, Renewable Energy Future’, Hydro Tasmania, http://www.hydro.com.au/documents/Energy/King_Island_Renewable_ Energy_PK_2008.pdf (accessed 27 March 2009). 3. Electricity Storage Association website: http://www.electricitystorage.org (accessed 11 July 2008) 4. Baldock, T. E., Tomkins, M., Dalton, G., Skyllas-Kazacos, M., Kazacos N., Renewable Energy Sources For The Australian Tourism Industry, Final Report for CRC for Sustainable Tourism, 2006. 5. Atkins, J., de Paula, J., Atkins’ Physical Chemistry, 7th ed, 2002, Oxford University Press. 6. McNicol, B. D., Rand, D. A. J. (Editors), Power Sources for Electric Vehicles, Elsevier Science Ltd, 1984. 7. Electricity Storage Association Newsletter, July 2006, http://electricitystorage. org/news_newsletter.htm (accessed 26 March 2009). 8. US Department of Energy website: http://www.eere.energy.gov/ hydrogenandfuelcells/ (accessed 26 March 2009). 9. The National Hydrogen Association website: http://www.hydrogenassociation. org/general/factSheet_production.pdf (accessed 27 March 2009). 10. Rand, D. A. J., Moseley, P. T., Garche, J., Park, C. D., Valve-regulated Lead– acid Batteries, Elsevier, 2004. 11. Commonwealth Scientific and Industrial Research Organisation website: http:// www.csiro.au/science/UltraBattery.html (accessed 10 July 2008). 12. Sandia Report No. SAND97-0443. UC-1350. 13. ‘U.S. Climate Change Technology Program – Technology Options for the Near and Long Term’. August 2005: www.climatetechnology.gov/library/2005/techoptions/tor2005-212.pdf (accessed 10 July 2008) 14. Linden, D., Reddy, T., Handbook of Batteries, McGraw-Hill, 2002. 15. Electricity Storage Association: http://www.energystorage.org/technology/li_ ion.htm (accessed 8 July 2008).

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16. ‘Summary of KEMA Validation Report: Two Megawatt Advanced Lithium-ion BESS Successfully Demonstrates Potential for Utility Applications’, 27, June 2008 (http://www.b2i.cc/Document/546/KEMA_Carina_validation_report_ public_final.pdf (accessed 10 September 2008). 17. Electricity Storage Association: http://www.energystorage.org/technology/ metal_air.htm) (accessed 10 September 2008). 18. Westbrook, M. H., The Electric Car: Development and Future of Battery, Hybrid and Fuel-cell Cars, Institution of Electrical Engineers, Society of Automotive Engineers, Contributor Institution of Electrical Engineers, IET, 2001. 19. Kamibayashi, M., Tanaka, K., ‘Recent sodium sulfur battery applications’, Transmission and Distribution Conference and Exposition, 2001, IEEE/PES, Volume 2, 2001, 1169–1173. 20. Electricity Storage Association website: Chttp://electricitystorage.org/tech/ technologies_technologies_nas.htm (accessed 18 March 2009). 21. Rial, P., ‘Sodium–sulfur Battery Powers NGK’s Unique Wind Energy’, http:// www.bloomberg.com/apps/news?pid=20601080&sid=abv9kUMdZueY&refer= asia (accessed 10 July 2008). 22. Frost & Sullivan, ‘Sodium sulfur battery chemistry making an impact on wind energy operations’, http://www.frost.com/prod/servlet/market-insight-top. pag?docid=107712739 (published: 26 Sep 2007, accessed 10 July 2008). 23. Larminie, J., Lowry, J., Electric Vehicle Technology Explained, John Wiley and Sons, 2003. 24. Galloway, R. C., Haslam, S., The ZEBRA electric vehicle battery: power and energy improvements, Journal of Power Sources, 80, 1999 164–170. 25. MES-DEA SA, Switzerland, www.mes-dea.ch (accessed 18 March 2009). 26. ZBB Energy website: http://www.zbbenergy.com/ (accessed 12 September 2008). 27. Lex, P., ‘Demonstration of a 2-MWh peak shaving Z-BESS’, ZBB Energy Corporation, Menomonee Falls, Wisconsin, www.zbbenergy.com. 28. Giner, J., Swette, L., Cahill, K., Lewis Research Centre, National Aeronautics and Space Administration, NASA CR-134705, US Department of Energy, 1976. 29. Thaller, L. H., NASA TM-79143, Lewis Research Centre, National Aeronautics and Space Administration, US Department of Energy, 1979. 30. Skyllas-Kazacos, M., Robins, R. G., ‘All-vanadium redox battery’, US Pat. No. 4,786,567, 1986. 31. Zitto, R., ‘Electrochemical apparatus for energy storage and/or power delivery comprising multi-compartment cells’ PCT/GB93/02110, October 1993. 32. Sum, E., Skyllas-Kazacos, M., A study of the V(II)/V(III) redox couple for redox flow cell applications, J. Power Sources, 15, 1985 179–190. 33. Sum, E., Rychcik, M., Skyllas-Kazacos, M., Investigation of V(V)/V(IV) system for use in positive half-call of a redox battery, J. Power Sources, 16, 1985 85–95. 34. ‘Development of redox flow battery system’, Sumitomo Electric Industries Technical Review No. 50, June 2000, 88–94. 35. Electricity Storage Association website: http://electricitystorage.org/tech/technologies (accessed 18 March 2009). 36. McDowell, J., International Renewable Energy Storage Conference, IRES2006, Gelsenkirchen, Germany, 30–31 October 2006.

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37. Rychcik, M., Skyllas-Kazacos, M., ‘Characteristics of new all-vanadium redox flow battery’, J. Power Sources, 22, 1988 59–67. 38. Kasherman, D., Skyllas-Kazacos, M., Wegner, P., ‘The vanadium battery – vehicle power source for the future’, Proceedings of Workshop on Vehicles: Energy and Environmental Impacts, ERDIC, UNSW, October 1990. 39. Skyllas-Kazacos, M., ‘Vanadium chloride/polyhalide redox flow battery’, Prov. Patent Application PR7221, 24 August 2001, PCT application, PCT/AU02/01157, August, 2002. 40. Skyllas-Kazacos, M., Kazacos, N., Kazacos, M., ‘Novel vanadium halide redox flow battery,’ PCT application No PCT/AU2004/000310, 15 March 2004.

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11 Flywheel energy storage technologies for wind energy systems A. J. RUDDELL, STFC Rutherford Appleton Laboratory, UK

Abstract: This chapter provides an overview of flywheel storage technology. The rotor design and construction, the power interface using flywheels, and the features and key advantages are discussed. The status of flywheel technology is described, including a description of commercial products, specifications, and capital and running costs. Research and development needs and actions are considered. Applications of flywheels requiring continuous cycling at high power are discussed, including the application to smoothing wind power fluctuations in autonomous power systems and weak grid networks. Finally, sources of further information and references to technical papers and reports are listed, for the reader wishing to investigate further. Key words: flywheel, flywheel energy storage (FESS), energy storage, wind power smoothing, wind–diesel.

11.1

Introduction

Flywheels store kinetic energy in a rotating mass, with the amount of stored energy (capacity) being dependent on the rotor inertia as determined by the mass and form, and rotational speed. An accelerating torque causes a flywheel to speed up and store energy, while a decelerating torque causes a flywheel to slow down and regenerate energy. The earliest applications of flywheels include potter’s wheels and grindstones used for sharpening tools. Since the industrial revolution, flywheels have been used in most rotating engines and machines for very short-term energy storage, for example to smooth the torque pulses in internal combustion engines. Flywheels are simple and effective in applications where the flywheel is directly mechanically coupled to smooth the shaft speed of rotating machinery. In such cases the kinetic energy storage provided by the rotor inertia requires no further interface to the mechanical system, although a mechanical gearbox may be used to increase the effective capacity. 366 © Woodhead Publishing Limited, 2010

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A new application of flywheels is in the storage of electrical energy, which is achieved by the addition of an electrical motor/generator and power converter. The electrical motor/generator may be integrated with the flywheel, and operates at variable speed, and the power converter is usually provided by a power-electronic variable speed drive. The main feature of flywheel energy storage systems (FESS) generally is that they can be charged and discharged at high power for many charge– discharge cycles. Typical state-of-the-art composite rotor designs have specific energy in excess of 100 W h/kg (360 kJ/kg), and high specific power. The state-of-charge is easily assessed as a function of angular velocity, which is readily measured. The main drawbacks of flywheels compared with other electrical storage technologies are the high cost, and the relatively high standing losses. Many flywheels have high self-discharge rates, and the lowest rates currently achieved for complete flywheel systems, with electrical interface powered, are around 20% of the stored capacity per hour. Flywheel energy storage technologies broadly fall into two classes, loosely defined by the maximum operating speed. Low-speed flywheels, with typical operating speeds up to 6000 rev/min, are constructed with steel rotors and conventional bearings. For example, a typical flywheel system with steel rotor developed in the 1980s for wind–diesel applications had energy storage capacity around 2 kW h @ 5000 rev/min, and rated power 45 kW. The rotor specific energy was 5 W h/kg, and the system specific power was 100 W/kg. High-speed flywheels, with operating speeds up to 50 000 rev/min, use advanced composite materials in the rotor construction, and have been intensively developed to increase the energy storage density and reduce unit cost. The high speed flywheel concept originated at Lawrence Livermore National Laboratory (LLNL), when Post and Post (1973) recommended that flywheels be made of composite materials instead of metal, thus presenting a new approach to rotor design. The LLNL developments reached commercial stage in 1994, with the technology being licensed to Trinity Flywheel for manufacturing. Composite materials are suitable for high-speed flywheel rotors due to their low density and high strength, enabling higher energy storage capacity on a specific mass basis. Low mass flywheels are attractive in mobile applications, and other components such as bearings can be smaller and lighter. A further important consideration is that composite rotors fail in a less destructive manner than metallic rotors and are thus intrinsically safer. The main stationary applications of flywheels are in uninterruptible power supplies (UPS), and trackside support in traction (rail) and mining systems. Emerging applications are power quality (PQ) systems, peak shaving in electrical power systems, and power smoothing in renewable energy systems.

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11.2

Flywheel design and construction

11.2.1 Theory of energy storage using flywheels The kinetic energy stored in a rotating mass, where J is the moment of inertia, and ω is the angular velocity, is: E=

1 2 Jω 2

11.1

The moment of inertia is a function of the mass and shape of the flywheel rotor, where x is the distance of the differential mass dmx from the axis of rotation: J = ∫ x 2 dmx

11.2

In the case of an imaginary flywheel where the mass m is concentrated in the rim at radius r, the moment of inertia is given by: J = mr2

11.3

Combining Equations 11.1 and 11.3 gives: E=

1 2 2 mr ω 2

11.4

Equation 11.4 shows that high angular velocity is more important than mass to achieve high stored energy. The specific energy per unit mass Em is: Em =

1 2 2 rω 2

11.5

The minimum operational speed of the flywheel is mainly limited by the drive system torque, since by definition, where P is the power, and T is the torque: P = Tω

11.6

Therefore if the power is maintained constant, the torque increases as speed is reduced. Torque limitations, and the fact that flywheels store most energy at high speeds, means that the ratio of minimum to maximum operational speeds s = ωmin/ωmax is usually chosen to be not less than 0.2. The useful stored energy is then E = Emax(1 − s2), where Emax is the energy stored at maximum speed ωmax. When s = 1/3, the useful stored energy is nearly 90% of Emax.

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11.2.2 Rotor materials The tensile strength of the material defines the upper limit of angular velocity. For example, in the case of an imaginary flywheel with mass concentrated at the rim at radius r, the tensile stress σ in the rim at angular speed ω is σ = ρr2ω2

11.7

Equation 11.7 thus defines the maximum angular velocity ωmax for a maximum tensile strength of the material σmax. The maximum stored energy is then E=

1 σ max m 2 ρ

11.8

Equation 11.8 shows that the maximum energy that may be stored for a given mass is achieved by a flywheel made from a material which combines high tensile strength with low density. Therefore to achieve high specific energy (at high speeds), composite materials are better than metal (see Table 11.1). Of course the achievable specific energy for a practical flywheel is much lower, taking into account the rotor shape, manufacturing defects, cyclic fatigue degradation and overall safety margin. The characteristics of composite materials are dependent on many factors including fibre content by volume and the construction technique, and density and strength parameters are often quoted for a composite with 60% v/v fibre. Therefore the parameters in Table 11.1 should be regarded as indicative.

Table 11.1 Specific strength of rotor materials (based on data from Genta, 1985, and Taylor et al., 1999)

Steel (AISI 4340) Aluminium alloy 2024 GFRP S2-glass CFRP carbon fibre T1000

Density (kg m−3)

Strength (MN m−2)

Specific strength (MN m−2)/ (kg m−3)

7800 2650

1800 450

0.231 0.170

32 24

1920 1520

1470 1950

0.766 1.283

106 178

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11.2.3 Rotor configurations and construction Many configurations of rotor are possible; see, for example, Genta (1985) and Bolund et al. (2007). Practical designs range from steel rims or disks to composite hollow cylinders. The details of construction and manufacturing methods are usually proprietary to each manufacturer, and therefore only a generalised description is possible here. The manufacturing process for steel flywheels includes casting, forging, machining and balancing. Some early designs used several steel disks welded to a shaft; however, this is suitable only for low-speed operation because of problems related to welding and of balancing the individual disks and the overall rotor. Unfortunately the performance and safety of rotors made from isotropic material are limited by the mechanical properties and fatigue and fracture behaviour, which can result in breakage into large fragments and catastrophic failure. Steel rotors are notably used in the Active Power and Piller flywheels. In the Active Power flywheel, shown in Fig. 11.1, the rotor is solid forged steel and operates at up to around 8000 rev/min. In this innovative design, the containment is provided by a cast iron enclosure with integrated field coil, magnetic bearing and armature. The bearings are a combination of ceramic ball bearings on a steel race, with magnetic lift to increase bearing life. Composite rotors can be operated at much higher speeds with specific energy up to around five times that of a steel flywheel. Composite flywheels are constructed using two basic methods, described by Taylor et al. (1999). In the filament-winding process, fibre filaments first pass through a resin bath to become impregnated, and are then wound on a rotating mandrel. This makes a cylindrical rotor which is anisotropic, and is very strong in the longitudinal direction of the fibre, capable of withstanding high hoop stress which is the dominant stress. However, the material strength is much lower in the transverse direction of the fibre and excessive radial stress could cause radial delamination prior to fibre breakage in the circumferential direction. This places a limitation on the thickness of the rotor in the radial direction. Multi-layer rotors using different fibres can be manufactured to address the variation of radial stress, and to optimise the construction of a cylindrical rotor with significant wall thickness. Tzeng et al. (2006) analysed the radial and hoop stress profiles, and developed models for use in the design of high performance rotors and to predict performance and longterm durability. Failure of composite flywheels is usually relatively benign, as the process of delamination and fibre breakage results in many thin and long fragments, reducing the risk of external damage or injury. Gabrys and Bakis (1997) described the use of elastomeric matrix composites to modify the stress

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11.1 Cutaway/exploded view of Active Power steel flywheel (source: Active Power).

profiles, such that the maximum hoop and radial stresses are near to the outside diameter of cylindrical rotors. This may result in a rotor which fails first on the outside of the cylinder, reducing the risk of catastrophic failure. The second method is the resin-transfer moulding process (RTM), where mats or weaves of fibres are first arranged in a mould, then low-viscosity resin is injected into the mould. RTM offers the possibility of cheaper mass production, but the properties of rotors are not as good as those made using the filament-winding process. A flywheel constructed by Urenco Power Technologies (UPT) (Tarrant, 1998) using the filament wind process had a cylindrical rotor of mass 110 kg, and energy storage capacity of 2 kW h when operated at up to 37 800 rev/

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Top bearing

Steel container

Stator

Bottom bearing

11.2 Cutaway view of UPT composite flywheel (source: Urenco Power Technologies).

min. The construction of this flywheel is shown in Fig. 11.2. Although this flywheel was successfully used in field trials in the traction application, and was used to demonstrate smoothing wind power fluctuations from a wind turbine in Japan, UPT ceased production in 2003 (EPRI-DOE, 2004). A much larger flywheel manufactured by Beacon Power Corporation has a maximum speed of 16 000 rev/min and 25 kW h storage capacity. The rotor is constructed as a composite rim (hollow cylinder), fabricated from a layered combination of high-strength, lightweight fibre composites, including graphite and fibreglass combined with resins (see Fig. 11.3). Friction and drag are reduced by the use of hybrid magnetic bearings to levitate the rotor, and the containment is a sealed vacuum to reduce losses.

11.2.4 Bearing types The rotor bearing design is important to achieve low losses and to minimise maintenance. The bearing losses achieved are usually small compared with other losses when driving the flywheel at operational power levels. However, bearing losses may be significant when idling for long storage periods, as for example, in the UPS application. Most flywheels operate at high speed and use high-specification bearings. For example, the Active Power fly-

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11.3 Cutaway view of Beacon Power composite flywheel (source: Beacon Power Corporation).

wheel uses a combination of ceramic ball bearings in a steel race, with magnetic lift to increase bearing life. The UPT flywheel used a low stiffness self-balancing concept, and the bearing system consisted of a passive magnetic bearing at the top, and a low loss pivot bearing at the bottom of the vertical axis. The bearings required no maintenance and had losses of less than 10% per hour. Magnetic bearings are often used in high-speed flywheels, using a combination of permanent magnets and controlled electromagnets to support the weight and stabilise the rotor. High-temperature superconductor (HTS) bearings with extremely low rotational loss have been developed (Strasik et al., 2007). These bearings have rotational drag orders of magnitude lower than that of conventional magnetic or mechanical bearings. An energyefficient flywheel with superconductor bearings could be constructed with a bearing loss of <2%/day, including parasitic power to cool the HTS bearing.

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11.2.5 Containment technology The container for high-speed flywheels is evacuated or helium filled, to reduce aerodynamic losses and resulting rotor stresses. The operating pressure is generally a medium vacuum in the range 100 to 0.1 Pa where the losses due to windage are negligible. Safety of containment in the event of catastrophic failure of the rotor is an important consideration, and developers have carried out extensive safety testing.

11.2.6 Power interface The electrical interface includes the motor/generator, a variablespeed power electronics converter and power controller. The motor/ generator is usually a high-speed permanent magnet machine, integrated with the rotor, known as an integrated synchronous generator (ISG). However, it is also possible to use a synchronous reluctance motor/ generator or induction machine. Modelling and simulation at the design stage have been described by several researchers; see, for example, Bolund et al. (2007). The interface to the variable speed flywheel motor/generator is variable frequency and voltage AC, and the power electronic converter may be single-stage (flywheel ISG AC ⇔ DC bus), or double stage (flywheel ISG AC ⇔ DC bus ⇔ AC network), according to the application requirements. Systems designed for UPS applications may have a DC interface at 36, 48, or 96 V DC to provide a retro-fit solution compatible with conventional battery banks. The power electronics interface is usually a pulse width modulated (PWM) bidirectional converter using insulated-gate bipolar transistor (IGBT) technology. Typically a power electronics interface can achieve a full-load efficiency of greater than 90%, but this falls off at low loads. The flywheel system controller continuously monitors critical parameters including at least flywheel rotational speed, containment vacuum, bearing temperatures and vibration; to detect out-of-limit and abnormal values. It is required to provide fail-safe shutdown in the event of critical problems, and may provide remote signalling of warnings and alarms. The power controller operation will depend on the application. For example, where the system is interfaced to an AC network, reactive power as well as active power will be controlled. Voltage fluctuation in AC systems can be limited to less than 2%. In UPS systems bidirectional power control can be achieved by monitoring the voltage level, such that as the DC interface voltage falls, the flywheel system regenerates power to support the load.

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375

Features and limitations of flywheel storage technology

11.3.1 Key features The key features of flywheels are their ability to operate continuously at high power, and to withstand continuous cycling, typically for up to 107 cycles. The state-of-charge is a direct function of rotor speed, and is therefore known with certainty. They typically have a relatively low storage time (ratio of capacity to power) of up to several minutes, with capacity in the kW h range. Therefore flywheels are well suited to applications requiring short-term storage at high power, and a high number of charge–discharge cycles.

11.3.2 System performance The data and performance characteristics of flywheel systems produced by the main manufacturers have been reviewed (EC Investire, 2003) where the data was compiled from information provided by manufacturers in data-sheets and in response to a questionnaire. The capacity of single rotors ranges from 0.25 to 25 kW h; however, multiple rotor modules can be paralleled in a common electrical interface. The capacity available in the rotor is directly proportional to the square of the rotational speed and is unaffected by discharge rate or temperature. A key advantage of flywheels is that capacity is always clearly indicated by rotational speed. Flywheels are operated between a design minimum and maximum speed, which defines the useful stored energy or available capacity. In principle the available capacity could be increased at low discharge rates by lowering the minimum speed, at reduced torque and power, but this may increase fatigue and reduce the cycling lifetime.

11.3.3 Energy and power density Steel rotors have specific energy up to around 5 W h/kg, while high-speed composite rotors have achieved specific energy up to 100 W h/kg. These specific energies are clearly much lower than the theoretical maxima for the materials used, as shown in Table 11.1. The specific power is mainly a function of the flywheel hub, the electrical motor/generator, and the power electronic interface, and specifications up to around 1600 W/kg have been quoted for the rotor. However the specific energy and power of the complete system may be reduced by at least a factor of ten when the weight of the complete system, including containment, vacuum system and electrical interface, is taken into account.

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11.3.4 Energy losses The energy usage during operation is relatively high mainly due to bearing losses and electrical losses in the motor/generator and power electronics converter when operating. Reduction of standing losses in the bearings is a key development objective, particularly for UPS applications. Many commercial flywheel products have a high power to capacity ratio, where the power electronics switching losses can be a high proportion of the total losses, and the overall efficiency is a function of power, usually greater than 80% for power in the range 10% to 100% of rated power. The maximum efficiency quoted by some manufacturers is 96%. This indicates a possible in–out efficiency of 92%, although this would be achievable only during rapid cycling and not when energy is stored for a period between charge and discharge. The self-discharge losses could be analysed in two modes: open-circuit, where the power interface is switched off and the losses are those associated with the rotor including the bearings; and standby, where the power interface is switched on (perhaps intermittently) to maintain a constant speed. Flywheels are never operated for long periods in ‘open-circuit’ mode, and manufacturers generally specify average losses in ‘standby’ mode only. The average power supplied in standby mode is assumed to be equivalent to the total losses in the rotor and power interface. An equivalent self-discharge rate in units of nominal capacity C per hour can be calculated from manufacturers’ data, as the ratio of losses to capacity C. Standby self-discharge per hour are found to be in the range 0.18 to 2.0 times the stored capacity. These high self-discharge rates confirm that flywheels are usually not a suitable choice for long-term energy storage, other than for standby power where reliability is paramount.

11.3.5 Cycling service and lifetime The high cycling capability of flywheels is one of their key features, and is not dependent on the charge or discharge rate. Full-cycle lifetimes typically quoted for flywheels range from in excess of 105, up to 107. To put this in perspective, the highest cycling lifetimes would only be exceeded after 20 years’ operation with continuous cycling at the rate of one full charge– discharge cycle every 100 minutes. In most applications the limiting factor is more likely to be the standby lifetime, typically quoted as 20 years.

11.3.6 Environmental and safety considerations The materials used in flywheel systems are generally non-hazardous, and mainly consist of steel, aluminium, copper, glass and carbon fibre, epoxy

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resin, silicon (power electronics), and NdFeB rare earth magnets. The rotor is manufactured from either steel or composite fibre. Resins used in the composite rotor have special handling requirements during the manufacturing process, but are non-hazardous when cured. The containment vessel is usually made from steel. The power electronics interface includes silicon semiconductors and heat-sinks, which may contain beryllium oxide (beryllia), which is hazardous if dust is released from filing or machining. Flywheel systems present no particular problems for disposal at end-oflife. Much of the system including the containment comprises steel and other metal parts including copper and aluminium, can be recycled using standard techniques. Composite rotors can be safely disposed of in a landfill site. Techniques for recycling composite rotor materials are under development, including methods to extract high value carbon fibre from end-of-life components and manufacturing scrap (BERR, 2005–2008). Several processes exist for recovery of rare earth magnetic materials, as reviewed by Ellis et al. (1994); however, further research is required to develop commercial processes. The main hazard during operation is the possible fatigue failure of the rotor and the sudden release of the stored energy in heat and flying debris which could be catastrophic. In the case of failure of a composite rotor, the debris can be fully contained within the containment, and any gas emissions are typically no more harmful than the exhaust from an internal combustion engine. All manufacturers have carefully considered the safety of rotors and their containment, and consortia have been formed in both Europe and US to investigate safe operation. In Europe, the EC funded the Flysafe project in 1999. The US Defense Advanced Research Projects Agency (DARPA) assembled a Flywheel Safety and Containment Consortium in 1995 to address the issue of flywheel safety. The DARPA Consortium included leading manufacturers as well as national laboratories and research centres. The levels of electrical radiated and conducted emissions are likely to be similar to any standard power electronic drive, and are limited by international standards. Acoustic noise levels are generally low, less than 72 dB(A) at 1 metre; being mainly due to the power electronics drive cooling fans.

11.4

Technology status of flywheel storage technology

11.4.1 Commercial products Flywheel systems using steel or composite rotors have been successfully developed and are being produced by several manufacturers. The technology is already highly developed, and standard products are on the market.

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The main markets for flywheel systems are UPS systems, power quality improvement and traction applications. Analysis by Taylor et al. (1999) indicated that flywheels can be cost competitive with batteries in some UPS applications. There are already some applications of high-power and lowenergy flywheel systems for smoothing wind power fluctuations in weak networks, and new requirements are emerging for stability improvement and protection of wind farms against network voltage dips. These applications are ideally suited to the high-power cycling capabilities of flywheels. The development of lower loss and reduced cost systems with longer storage times could make flywheel systems competitive with batteries in standalone renewable energy systems. The main manufacturers include Active Power and Piller using steel rotors, and Beacon and Pentadyne using high-speed composite rotors. The Active Power flywheel is incorporated into a range of products for various applications including UPS, and more than 2000 flywheels are in use, altogether amounting to over 55 million run hours. The system configuration is a flywheel motor-generator that interfaces via power electronics to the DC link of a three phase AC UPS, providing ride-through energy for up to 2 minutes. The rated power of a single rotor module is 250 kW, with capacity around 1 kW h, and up to eight modules can be operated in parallel to provide 2000 kW with capacity around 8 kW h. Beacon Power markets the Smart Energy 25 flywheel with storage capacity 25 kW h, capable of supplying 100 kW for 15 minutes. The high storage time is potentially applicable to a wide range of applications. Multiple individual flywheels can be integrated into a matrix to extend the power and storage capacity, for example a matrix of ten flywheels provides a system with 1 MW power rating and 250 kW h storage capacity. This could provide highly responsive frequency regulation capabilities for increased grid reliability. Piller has a well-established product called Powerbridge, primarily designed for application in UPS systems, and for load levelling in local grids, such as DC railway systems, and is now being successfully used by Powercorp in wind diesel and remote power systems. The system incorporates a unique electrical machine, and in the UPS application it provides support after loss of power until a diesel generating set can be started and brought on-line. The product is fully commercial in series production, and since introduction in 1996 over 700 installations are in operation worldwide in high reliability applications. The configuration is a steel flywheel operating at up to 3600 rev/min, a synchronous generator and power electronic converter. The rated power is 1.65 MW with capacity 4.6 kW h. The rotor is a steel disc, with sealed ball bearings. This is a high-power machine, with 10 kW losses at standby, and 60 kW losses when operating at rated power.

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Frost and Sullivan (2003) reported that a few manufacturers share most of the European market, and at the time of the report Piller was the market leader with 47% of the market. The European market for flywheel energy storage for all applications was expected to grow by around 8% annually.

11.4.2 Cost of the storage technology The cost of composite rotors is dominated by the cost of carbon fibre, rather than by the equipment or time used in manufacture. Composite rotors use carbon fibre (CFRP) for highly stressed parts of the rotor, as well as glass fibre (GFRP), which enables the mass to be reduced and the speed to be increased for a given energy stored. The cost of steel is around $1 per kg, while the cost of S-glass and high-strength carbon fibre ranges from $10 per kg up to $30 per kg or even more depending on specification. Although composite materials are much more expensive than steel, the additional cost can be offset by the reduced mass required. For example, although the cost of a composite rotor material per kg is up to 15 times more expensive than steel, the rotor can be operated at higher speeds, for example typically by a factor of up to 5, and the mass required for the same storage capacity can be reduced by a factor of up to 15. Therefore the material cost of the rotors for a given capacity is roughly similar at around $700–$800 per kW h; see Table 11.2 (Taylor et al., 1999). These costs do not include the cost of a hub for the composite rotor, nor do they include the cost of manufacturing. Taylor et al. (1999) reported that the bearings represent an additional cost of around 30–70% of the material cost of the rotor. The resulting cost of around $1000 per kW h seems expensive compared with a lead–acid battery; however, flywheels may be competitive in applications such as power quality improvement, which require low capacity and high power. A good example is the Active Power system, providing a storage time of 14 seconds. Most manufacturers offer flywheels

Table 11.2 Comparison of steel and composite rotors (from report by Taylor et al., 1999)

Diameter (m) Height (m) Weight (kg) Max. speed (rev/min) Useful energy (kW h) Material cost ($/kg) Capacity cost ($/kW h)

Steel rotor

Composite rotor

0.64 0.23 555 7000 0.8 1 690

0.46 0.20 32 29 000 0.6 15 800

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with storage times in the region of 5–30 seconds, where the capacity is low, the power requirement drives the cost, and the most significant component cost is the power electronics drive. A cost analysis model developed by Taylor et al. (1999) predicts the total cost of a flywheel system with 5 second storage time to be in the region of $200–500 per kW. Here the power requirement drives the cost, and although the equivalent cost per kW h capacity is very high, additional capacity can be added with low incremental cost. Power electronics technology and IGBT devices have developed rapidly in the last decade, and it is likely that there will be future cost reductions. The Sandia cost analysis model (Taylor et al., 1999) predicts the total cost of a system with a 1 hour storage time to be in the region of $1000–3000 per kW. Systems with this storage time are not manufactured at this time; however, reduced losses using HTS bearings and cost reductions of the rotor and bearing assembly may make systems with longer storage times competitive. The cost of energy throughput of a flywheel energy storage system operated for its full cycling lifetime is potentially low. Assuming a cycling lifetime of 106 cycles, a system with 5 second storage time has a potential cost of energy throughput of $0.14–0.36 per kW h. Systems with longer storage times could have very low energy costs, for example a system with 1 hour storage time could have a potential cost of energy throughput of $0.001–0.003. Flywheel systems can be economically competitive with battery-based UPS systems. The Sandia model (Taylor et al., 1999) predicts that a flywheel system (capital cost $800 per kW, parasitic load of 4% of rated power, and operating and maintenance (O&M) cost of 2% of capital per year), could compete with a battery-based UPS (initial capital cost $450 per kW, and battery replacement costs $525 per kW).

11.4.3 Research and development The main activity is in the United States, where most manufacturers are located. The main activities are aimed at reducing the overall cost of flywheel systems, and reducing the losses and extending the life of bearings. Lawrence Livermore Laboratory, where pioneering work was begun by Post and Post (1973), continues the development of passive magnetic bearings, which have a long lifetime, do not require maintenance or lubrication, and have reduced frictional losses. Passive bearings are favoured because they are self-contained, unlike active magnetic bearings that require external electronics and electric power. Current research in the Composite Manufacturing Technology Center at Pennsylvania State University is aimed at developing a cost-effective manu-

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facturing and fabrication process for advanced composite rotors (Gabrys and Bakis, 1997). This includes the development of a rapid filament winding process for GRP and CRP, the measurement of strain in high-speed rotors using opto-electronics devices, and the determination of fatigue behaviour of composite rotor material using coupon tests. The NASA Glenn Research Center has an Aerospace Flywheel Development Program that aims to achieve a five-fold increase in the specific energy of existing spacecraft batteries, and to achieve a two-fold increase in battery life in low-Earth orbit applications. A flywheel has been developed and has achieved full-speed operation at 60 000 rev/min, and although this is targeted at spacecraft, it is possible that there will be technology transfer for other applications. The US DOE HEV program is considering flywheels for hybrid electric vehicle (HEV) applications. Flywheels could be used in HEVs in ways that exploit the ability to deliver very high-power pulses. One concept combines a flywheel with a standard engine, providing assistance during acceleration, and absorbing braking energy. Another concept uses flywheels to replace chemical batteries, although the energy density of a flywheel system is generally considered to be too low. Fiske and Ricci (2006) propose development of a Power Ring flywheel which claims that specifications up to 50 MW and 5 MW h are technically feasible. The Center for Electromechanics (CEM), University of Texas is involved in Flywheel and Alternator Development for the Advanced Locomotive Propulsion System (ALPS), and leads the US Flywheel Safety and Containment Program, a consortium of several leading flywheel developers. Advances in high strength materials, rotor dynamics, containment, non-destructive evaluation and thermal management provide a technology base for much of the commercial development that is underway. The CEM jointly participated with EPRI to conduct a flywheel battery commercialisation study (EPRI, 1999) to explore the feasibility of producing high-energy density flywheel-battery for the UPS application, focusing on a specification of 250 kW, 1 kW h to provide up to 15 seconds ride-through in industrial power quality applications. The report examines various design options, and concludes that a marketable system could be produced. In addition, the CEM believe that compact flywheels are feasible with megawatt power and about 500 MJ (∼140 kW h) stored energy, levels which are of interest for electric utility line stabilisation. Several EC-funded projects have investigated the application of flywheels to renewable energy generation, particularly the smoothing of power fluctuations generated by wind turbines. One of these projects includes the development of magnetic bearings. Details are available from EC Cordis (see Section 11.6). The magnetic bearing development was

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concerned with large diameter (1.0–1.5 m diameter), 100–150 kg flywheels, at speeds of the order of 5000 rev/min in hybrid magnetic bearings, using a combination of permanent and active magnetic bearings. Most standard flywheel systems have storage times in the region of 5 to 30 seconds, where the high-power electronics interface can be a significant part of the total capital cost. Current development of the flywheel itself is aimed at rotor cost reduction by achieving higher specific energy and reduced rotor mass, using high-performance fibres. Advanced bearings are being actively developed including the use of HTS magnetic bearings, to provide reduced losses, higher efficiency, reduced running costs and longer bearing life. The potential advantages of superconducting bearings in highspeed rotating machinery are well-known. Viznichenko et al. (2008) studied the application of this technology to the UPT 2 kW h flywheel rotor, and proposed a new superconducting bearing design to replace the magnetic passive bearing. A US-DOE project consortium aimed to develop a 100 kW, 5 kW h flywheel system for UPS applications (Strasik et al., 2007). The development and use of high-temperature conducting (HTS) bearings was a key part of the project. A 10 kW h system was successfully tested in 2003 at Boeing’s test facility, achieving bearing losses of less than 0.1% per hour, and results lead to an improved design which has been successfully tested at up to 15 000 rev/min. The project confirmed the feasibility of using HTS bearings in flywheel systems, achieving very low loss of around 2% per day. Rotor and bearing developments are particularly significant in systems with storage times greater than 1 hour, where the rotor and bearing costs become the most significant system cost. Together with enhanced design tools, these developments could result in improved energy and power densities, reduced manufacturing costs, and enhanced reliability levels (Baker, 2008). To summarise, the key issues for research and development include: • • • • • • • •

improvement of rotor materials (tensile strength, stability) and manufacturing technology; improvement in magnetic and mechanical bearings (losses, lifetime); cost of materials and manufacturing processes; safety and certification; power electronics interface and control, including losses, pulsed power techniques, storage modules, and response time; hybrid storage topologies, for example coupled with a battery to extend the capability range; application-specific control strategies; instrumentation, condition monitoring, lifetime prediction.

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383

Application of flywheel storage technology

11.5.1 Introduction The key advantages of flywheels are the ability to cycle continuously at high power, with a relatively low capacity and storage times in the order of tens of seconds, and the high cycling lifetime. Flywheel energy storage has been used in UPS, traction and electricity distribution applications. Further advantages including high reliability, and the certain knowledge of stateof-charge, are attractive in UPS applications. Products are available from several manufacturers including, for example, Piller, Active Power and Pentadyne. The ability to cycle repeatedly at high power is required in rail track-side support applications. Field-trials were conducted using the UPT flywheel, and other manufacturers identify track-side support as a potential application of flywheel energy storage. The Electric Power Research Institute (EPRI) and the US Department of Energy conducted a study into the application of energy storage technologies (EPRI-DOE, 2003, 2004) to optimise and support electricity transmission and distribution networks. A range of storage technologies was considered for the various application categories, and it was concluded that flywheels, with short duration high-power characteristics, are suitable for grid stabilisation and short duration power quality applications. The inclusion of flywheel energy storage in a power system with significant penetration of wind power and other intermittent generation has been studied by Nyeng et al. (2008). A simulation model of a hydropower plant, Beacon flywheel system and control system was used to demonstrate the response to an external fluctuating regulation signal. In this case the control objective was to utilise flywheel storage to reduce hydro plant power fluctuations, and this was demonstrated by simulation. The techniques could be extended to absorb wind power fluctuations, in the case of a more complex power system. Flywheel energy storage is becoming increasingly used in remote and autonomous power systems with a high level of renewable energy.

11.5.2 Wind power in weak grid applications Good wind energy sites are often found in rural areas where connection to the electricity utility would be at the distribution level. Unfortunately the grid connection may be weak, and there may be problems maintaining power quality, especially where increased size and capacity of wind farms is proposed. Tuckey (2008) described the national electricity regulations for connecting generators in the Australian context, while the principles of

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maintaining standards of voltage flicker, fault ride-through, stability and protection, active and reactive power and voltage control are universally applicable. The need to maintain standards specified in the regulations could preclude connection of wind turbines in areas with weak grids. Tuckey (2008) considered the use of short-term flywheel energy storage to mitigate the problems, and concluded that storage operated with wind turbines could ensure that the regulations and power quality requirements are satisfied.

11.5.3 Autonomous wind power systems Autonomous power systems are generally powered by diesel generating sets (gensets). In wind–diesel systems wind turbines are included to reduce diesel loading when wind power is available, thus saving diesel fuel and reducing the cost of energy (Hunter and Elliot, 1994). Clearly the economics of such systems depends on many factors, not least of which are the capital cost of the equipment, the cost of diesel fuel (particularly in remote areas) and the available wind energy resource. Unfortunately there are significant difficulties achieving a high level of renewable energy penetration in isolated power systems (Hamsic et al., 2007). Diesel gensets have a minimum loading, typically specified as 40% of rated power. Operation below minimum loading results in cooler running of the diesel engine, resulting in build-up of combustion products in the cylinders, higher maintenance and reduced lifetime. Controlling the gensets to operate above minimum loading can mean that wind power has to be curtailed or dumped. There are various ways this problem can be addressed, for example in systems with several diesel gensets, the operation of the gensets can be scheduled according to wind power and demand. Some systems have put the dumped energy to good use, for example it can be used for water heating, but this requires the existence of a heating load. In any case spinning reserve is required to provide security of supply during lulls in wind power. Other related problems are the need to maintain power system stability and voltage control. Highly dynamic governor control resulting from wind power fluctuations can reduce genset performance in terms of supply quality (frequency and voltage regulation), and also fuel efficiency and emissions. The inclusion of energy storage can address these problems, and enable diesel gensets to be switched off when sufficient average wind energy is available. Early flywheel installations include a directly coupled synchronous flywheel in a wind–diesel system at Punta Jandia, Fuerteventura. The directly coupled transmission means that limited capacity of the flywheel can be utilised while maintaining system frequency within limits, and variable speed power electronics converters offer a better solution (Bleijs et al.,

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30 25 20 15 10 5 0 0

Flywheel power (kW)

Wind turbine power (kW)

1993). Other transmissions have been demonstrated, including a mechanical continuously variable transmission (CVT) described in EC project JOR3-CT95–00700, and a hydrostatic transmission (HT) system (Carillo et al., 2009). Results from an experimental wind–diesel system (Ruddell et al., 1994) illustrate the possible fuel-saving benefits (see Fig. 11.4). The system consists of a 30 kW wind turbine with induction generator, a 48 kW diesel genset and a 30 kW flywheel energy storage system. In this system the genset alternator can be declutched from the diesel engine, which can then be stopped, and continues to supply reactive power to the wind turbine and load. During the first half of the period of operation shown in Fig. 11.4, the FESS is discharging (average 6 kW) and smoothing the wind power (average 16 kW) to supply a consumer load of 20 kW (shown on the wind turbine power graph), and genset alternator losses (2 kW). The average wind power is not sufficient to supply the load, and when the flywheel energy store is fully discharged a logistic decision is made at the mid-point of the period shown, to start and connect the diesel engine. In the second half of the period, the genset operates at a low level (average 11 kW), and the flywheel energy storage system (average −4 kW) is charging while continuing to

Diesel genset power (kW)

15 10 5 0 –5 –10 –150

25 20 15 10 5 0 –5 0

100 Time (s)

200

100 Time (s)

200

100 Time (s)

200

11.4 Experimental wind–diesel–flywheel system.

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smooth the wind power (average 13 kW). The genset should thereafter be operated at greater than its minimum loading, and when the FESS is fully charged then some power may have to be dumped. Supply voltage and frequency are maintained within the usual statutory limits.

11.5.4 Commercial developments and tools Powercorp have developed PowerStore, an energy storage device which can increase renewable energy penetration and improve quality of supply, primarily in remote power systems where reliability in service is an important requirement. The PowerStore is based on the use of Powerbridge, a flywheel energy store manufactured by Piller for application in the highreliability (UPS) market, as described earlier. The main components are the flywheel, an AC–DC–AC converter system rated at 500 kV A or 1000 kV A and a control and control and supervisory system to allow remote operation (see Fig. 11.5). The system is shunt-connected to the power system and can operate independently to stabilise frequency, or it can operate in response to a control signal from a remote power transducer. The PowerStore is built into a shipping container, facilitating transportation and installation on remote sites (see Fig. 11.6). Bindner et al. (2009) compared the use of three simulation tools for wind–diesel systems (IPSYS, LI and HOMER) based on simulation of the Coral Bay wind–diesel system, which includes three 200 kW wind turbines, seven 320 kW low-load diesel gensets, and a 500 kVA Powerstore flywheel system. The use of these models provide good assessment of the performance of the real system. However, operation of the models with a 10 minute time-step did not permit the explicit modelling of a storage component with this capacity. The HOMER simulation tool was developed at the National Renewable Energy Laboratory (NREL) for assessment of renewable power systems,

400 VAC 50/60 Hz

Flywheel grid Flywheel drive inverter 700 VDC 400 VAC 60–120 Hz Motor generator

Fixed frequency

Variable frequency Real power Reactive power

Real power

2.9T 1800–3600 rpm

11.5 Block diagram of the Powerstore flywheel energy storage system (source: PowerCorp).

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11.6 Cutaway view of the Powerstore containerised building (source: Powercorp).

distributed power systems and hybrid power systems. It is easy to use and can be used for system sizing optimization and sensitivity analysis. It is expected that future versions of HOMER, including a Powerstore component, will facilitate economic analysis of a wind–diesel system including a flywheel energy store.

11.5.5 Installations There are now many operating wind–diesel systems worldwide, notably in Alaska, Canada, Australia, Chile and Spain (Baring-Gould, 2009). The systems range from relatively simple designs where the penetration of wind is relatively low, to complex systems where advanced controls and components, including energy storage, are required. Technical solutions available to achieve a high penetration of wind energy while maintaining power system stability include low-load diesel technology, advanced control systems, dynamic loads and load shedding. Powercorp and Verve Energy have worked together to develop wind– diesel system technology, and have completed installations in Australia, most of which benefit from the inclusion of low-load diesel gensets, a product developed by Powercorp and marketed by Diesel and Wind Systems (D&WS). However, not all systems require storage. For example, a large wind-diesel system at Denham in Australia currently has three wind turbines with total rated power of 690 kW, diesel gensets with total capacity 1720 kW total capacity and 40% minimum loading, and a 250 kW low-load

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diesel with 10% minimum loading. When first installed the system included a flywheel energy storage system, but the FESS was replaced with an alternative solution using a low-load diesel generator and a dynamic grid interface with 100 kW power sink capability. Storage is required to achieve high penetration and maximum utilisation of wind power, and high levels of supply reliability. Wind–diesel installations at Coral Bay in Australia, and at Flores Island and Graciosa Island in the Azores, have achieved instantaneous wind power penetration of up to 90%. The system at Flores is notable in that it is possible to operate using hydro and wind generation only, with the diesel gensets off, in the ultimate fuel saving mode. Such high penetration systems can achieve 50% diesel fuel savings per annum. Complex control algorithms ensure correct scheduling of the generators, while operating with variable wind power generation, to meet the variable consumer load demand. An installation at Ross Island in Antarctica is in the construction phase, where the Powerstore, shown in Fig. 11.7, is being used to integrate the McMurdo Station and Scott Base diesel power systems together with a wind farm (Langworthy, 2009). Flywheel energy storage has also been installed to compensate for wind power fluctuations and provide end-of-grid support, for example at Kalbarri, located on the northern fringe of the main Western Australia grid. Here a Powerstore operates together with a STATCOM to provide reactive and active power compensation, to improve supply quality and voltage regulation at the end-of-grid location.

11.7 Engineers inspect the Powerstore flywheel at Ross Island, Antarctica (source: PowerCorp).

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11.5.6 Conclusions Flywheel energy storage systems provide a solution to the problems encountered in high-penetration hybrid power systems, providing power smoothing in the range of seconds to minutes. Each power system has a unique specification and operational requirements, and the application of simulation tools and prior experience at the design stage is important to achieve an optimum system. Table 11.3 Sources of further information (accessed October 2009) Category

Organisation

Web-link

Flywheel and component manufacturers

Active Power Beacon Power Magnet-Motor Optimal Energy Systems Inc. Pentadyne Piller Tribology Systems Inc.

http://www.activepower.com/ http://www.beaconpower.com/ http://www.magnet-motor.de/ http://www. optimalenergysystems.com/ http://www.pentadyne.com/ http://www.piller.com/ http://www.tribologysystems. com/

Flywheel research and development centres

Pennsylvania State University The University of Texas, Center for Electromechanics Politecnico di Torino, Department of Mechanics European Commission Projects

http://www.psu.edu/

Powercorp Diesel and Wind Systems (D&WS) Danvest Energy Northern Power

http://www.pcorp.com.au/ http://www.daws.com.au/

Remote power systems

PitchWind Sustainable Automation Verve Energy

http://www.utexas.edu/ research/cem/ http://www.dimec.polito.it/en

http://cordis.europa.eu/search/ (see Table 11.4)

http://www.danvest.com/ http://www.northernpower. com/ http://www.pitchwind.se/ http://www. sustainableautomation.com/ http://www.verveenergy.com. au/

Design tools

HOMER NREL

http://www.homerenergy.com/ http://www.nrel.gov/homer

Energy storage industry association

Electricity Storage Association

http://www.electricitystorage. org/

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Table 11.4 EC projects including flywheel R&D (accessed October 2009) Project title

EC contract

Search term in Cordis: http://cordis.europa. eu/search/

Flywheel energy storage for wind power generation Research, development and technological testing of a high-energy flywheel of 20 kW h energy storage and 10 kW power Power converters for flywheel energy storage systems Hydrogen generation from stand-alone wind-powered electrolysis systems Wind-powered generators and high-energy, low-speed flywheels running in hybrid magnetic bearings

JOR3-CT97-0186

JOR3970186

JOR3-CT96-0035

JOR3960035

JOR3-CT95-0070

JOR3950070

JOU2-CT93-0413

JOU20413

JOR3-CT98-0238

JOR3980238

Wind–diesel system technology has now been well developed over many years. Particularly significant developments in wind–diesel include low-load diesel genset technology (as described by D&WS, Table 11.3), and energy storage systems such as Powerstore (see Powercorp, Table 11.3). Although commercial uptake and replication have taken time, there are now a significant number of operational systems, most notably in Alaska (BaringGould, 2009), and Australia and the Azores (Langworthy, 2009), and it is believed the technology is mature and ready for widespread deployment.

11.6

Sources of further information and advice

There are many sources of information provided by manufacturers and research institutions. The short lists in Tables 11.3 and 11.4 should assist further reading and investigation.

11.7

References

Baker J. (2008). New technology and possible advances in energy storage, Energy Policy, Vol 36, Iss 12, 4368–4373 Baring-Gould I. (2009). Technology, performance, and market report of wind– diesel applications for remote and island communities, European Wind Energy Conference EWEC 2009, Marseille, France, March 2009. Conference Paper NREL/CP-500-44679 available from http://www.nrel.gov/publications/ (Accessed October 2009)

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BERR (2005–2008). Collaborative Research and Development: Materials and structure. Recycling carbon fibre. Report number URN 06/2044. Available from: http://www.berr.gov.uk/files/file34992.pdf (Accessed October 2009) Bindner H., Cronin T., Rickert T., Ezawa B. (2009). Performance simulation of wind diesel systems – comparison of two simulation models using real life data, European Wind Energy Conference EWEC 2009, Marseille, France, March 2009 Bleijs J.A.M., Smith G.A., Freris L.L., Ruddell A.J., Infield D.G. (1993). A wind/ diesel system with flywheel energy buffer, Proc. IEEE/NTUA Joint International Power Conference, Athens Power Tech APT93, Athens, Greece, 5–8 September 1993 Bolund B., Bernoff H., Leijon M. (2007). Flywheel energy power storage systems, Renewable and Sustainable Energy Reviews, Vol 11, Iss 2, 235–258 Carillo C., Feijóo A., Cidrás J. (2009). Comparative study of flywheel systems in an isolated wind plant, Renewable Energy, Vol 34, Iss 3, 890–898 EC Investire (2003). Storage Technology Report ST6: Flywheel, Investire Network, EC contract ENK5-CT-2000-20336. Available from http://www.itpower.co.uk/ investire/ (Accessed October 2009) Ellis T.W., Schmidt F.A., Jones L.L. (1994). Methods and opportunities in the recycling of rare earth based materials, The Metallurgical Society (TMS) Conference on High Performance Composites, Rosemont, IL, 10–15 Oct 1994. Available from http://www.osti.gov/bridge/ (Accessed October 2009) EPRI (1999). Flywheel Battery Commercialization Study, EPRI Report TR-113541 August 1999. Available from http://www.epri.com (accessed October 2009) EPRI-DOE (2003). EPRI-DOE Handbook of Energy Storage for Transmission and Distribution Applications. EPRI Report 1001834. Available from http://www. epri.com (accessed October 2009) EPRI-DOE (2004). EPRI-DOE Handbook Supplement of Energy Storage for Grid Connected Wind Generation Applications, EPRI Technical Update Report 1008703. Available from http://www.epri.com (accessed October 2009) Fiske O.J., Ricci M.R. (2006). Third generation flywheels for high power electricity storage, MAGLEV’2006, The 19th International Conference on Magnetically Levitated Systems and Linear Drives, September 2006, Dresden, Germany Frost & Sullivan (2003). European Emerging Energy Storage Technology Markets Gabrys C.W., Bakis C.E. (1997). Design and manufacturing of filament wound elastomeric composite flywheels, Journal of Reinforced Plastics and Composites, Vol 16, 488–502 Genta G. (1985). Kinetic Energy Storage: Theory and practice of advanced flywheel systems, Butterworth Hamsic N., Schmelter A., Mohd A., Ortjohann E., Schultze E., Tuckey A., Zimmermann J. (2007). Increasing renewable energy penetration in isolated grids using a flywheel energy storage system, International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), April 2007, Portugal Hunter R., Elliot G. (editors) (1994). Wind–Diesel Systems: A guide to the technology and its implementation, Cambridge University Press Langworthy A. (2009). From Denham to Esperance and Ross Island: Powercorp’s experience in high penetration wind diesel systems, International Wind-Diesel Workshop. Ottawa, Canada, 1–2 June 2009. Available from: http://arctic.pembina. org/wind (Accessed October 2009)

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Nyeng P., Yang B., Ma J., Makarov Y., Pease J.H., Hawkins D., Loutan C. (2008). Coordinated multi-objective control of regulating resources in multi-area power systems with large penetration of wind power generation, 7th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms, May 2008, Madrid, Spain Post R.F., Post S.F. (1973). Flywheels, Scientific American, Vol 229, No. 6, 17–23 Ruddell A.J., Bleijs J.A.M., Freris L.L., Infield D.G. (1994). A flywheel energy store with a continuously variable gearbox for wind power conditioning, European Wind Energy Conference EWEC 94, Thessaloniki, Greece, October 1994 Strasik M., Johnson P.E., Day A.C., Mittleider J., Higgins M.D., Edwards J., Schindler J.R., McCrary K.E., McIver C.R., Carlson D., Gonder J.F., Hull J.R. (2007). Design, fabrication, and test of a 5-kWh/100-kW flywheel energy storage utilizing a high-temperature superconducting bearing, 20th International symposium on Superconductivity ISS 2007, Tsukuba Japan, 6 November 2007 Tarrant C.D. (1998). The Pirouette flywheel energy storage system, Electrical Energy Storage Systems Applications and Technologies EESAT’98, 16–18 June 1998, Chester, UK Taylor P. et al. (1999). A Summary of the State-of-the-art of SMES, Flywheel, and CAES, Sandia report SAND991854 Tuckey A.M. (2008). The integration of wind power into power systems: a case for flywheel energy storage, 7th International Workshop on Large-scale Integration of Wind Power and Transmission Networks for Offshore Wind Farms, May 2008, Madrid, Spain Tzeng J., Emerson R., Moy P. (2006). Composite flywheels for energy storage, Composites Science and Technology, Vol 66, Iss 14, 2520–2527 Viznichenko R., Velichko A.V., Hong Z, Coombs T.A. (2008). Advantage of superconducting bearing in a commercial flywheel system, 8th European Conference on Applied Superconductivity (EUCAS 2007), Journal of Physics: Conference Series 97, 2008

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12 Compressed air energy storage technologies for wind energy systems A. CAVALLO, Princeton, USA

Abstract: Wind-generated electricity can be transformed economically from an intermittent resource to a fully controllable power supply using existing compressed air energy storage (CAES) systems. Electricity from a wind–CAES system can be transmitted to distant cities using highvoltage transmission lines and is fully competitive technically and economically with electricity from nuclear or fossil fuel generators. This analysis indicates how a modern industrial society could be supplied with energy almost exclusively from intermittent wind (or solar) resources, nearly eliminating fossil fuel or nuclear generation of electricity. However, crafting policies that facilitate deployment of wind/ CAES/transmission systems remains a formidable challenge. Key words: wind storage transmission systems, wind compressed air energy storage (CAES), wind energy systems analysis, intermittent renewable energy storage, eliminating fossil fuel consumption.

12.1

Introduction

The current wind turbine deployment strategy, which emphasizes low-cost local power and ignores transmission and back-up costs, has served the purpose of introducing significant amounts of wind energy onto utility grids; unfortunately such a strategy also relegates intermittent energy to a much inferior position relative to existing power generators. If we are to supply energy to modern industrial economies mainly with intermittent renewable resources, a new approach is required, one that acknowledges transmission and storage costs, and regards intermittent wind (and solar) energy/transmission/storage systems as fully equivalent to existing power plants. Intermittent renewable resources are immense and theoretically could, if coupled with much more attention to efficiency and conservation1, provide all of the energy required by modern industrial economies such as those of Europe, North and South America, China, and Japan. In North America, offshore and onshore wind resources2,3 exceed current demand by at least a factor of four, and the solar resources of the US southwest and southern Europe are equally impressive. In Europe, onshore wind resources are already (2007) providing a substantial contribution4 to the electricity supply in Denmark (20%), Germany (7%) and Spain (12%), and the excellent offshore wind resources of the North Sea are just beginning to be exploited; 393 © Woodhead Publishing Limited, 2010

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in the United Kingdom, offshore development zones5 with over 30 GW of potential output have been or are about to be licensed. In the Far East, India and especially China have excellent wind resources and substantial wind turbine construction industries and are expanding their installed wind turbine capacity rapidly. India increased its installed capacity by 22% in 2008 to over 9500 MW; China increased installed capacity by 67% to over 9500 MW (2008) and in the immediate future is planning several 10 000 MW arrays in areas of good wind resources (Inner Mongolia, Gansu and Xinjiang)6; these areas are located at great distances from demand centers and will require the construction of long distance, high-voltage transmission lines and special efforts to integrate large amounts of intermittent power onto China’s relatively undeveloped grid. China appears to be ready to meet its stated 2020 installed wind turbine capacity goal of 30 GW by 2012, and may soon increase its 2020 goal to 100–150 GW, an astonishing development7. Yet the idea that we could power modern industrial economies with these diffuse, intermittent resources seems not worth considering. Admittedly, fossil fuels, the major alternative, have considerable advantages. Their high energy density, transportability, and versatility, as well as their enormous profitability for producers and affordability for consumers make them the power sources of choice for our societies. Their positive attributes are so overwhelming, and have become so familiar that their disadvantages are overlooked or ignored. The fundamental limitations on these energy sources are real and, increasingly, cannot be denied. While most pollution generated by burning fossil fuels, such as emission of oxides of sulfur and nitrogen, particulates and heavy metals, can be minimized at acceptable cost, carbon emissions and the associated anthropogenic climate change are an increasingly severe threat to the global ecosystem. In addition, and probably of greatest immediate importance, it must be acknowledged that fossil fuels are a finite resource. Petroleum, which supplies about 37% of the world’s primary energy8, is of greatest concern, with production now flat or declining in non-OPEC (Organization of Petroleum Exporting Countries) producers as a whole. OPEC producers are well aware of the global distribution of this resource9,10 and between 2003 and June 2008 raised prices (OPEC annual average basket price11) by about a factor of four. Natural gas prices are linked to petroleum prices, formally outside the United States and informally within, and have also increased substantially over the past five years. While oil and gas prices have declined since July 2008, the magnitude and location of petroleum resources have not changed and are insufficient to continue current energy consumption patterns based on low fuel prices for much longer.

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Thus, one of the primary advantages of fossil fuels, their low cost relative to renewable resources, is beginning to disappear due to resource constraints12. Moreover, demand for energy will increase in the near future. With the Earth’s current population of about 6.5 billion people, it is clear to many owners of fossil fuel resources, if not yet to consumers, that there is not enough petroleum and natural gas in the ground to provide personal mobility and comfortable living and working spaces for everyone. Recent oil and natural gas price signals, while inconsistent and confusing over the short term, are clear on an annual, long-term basis. Constraints on fossil fuel resources are already a factor that must be considered in evaluating the viability of modern industrial societies. In addition, the widespread disregard of intermittent renewable resources due to their unfavorable technical characteristics compared with fossil fuels is not justified. This ignores the viable engineering solutions to these challenges, which can be dealt with in a cost-effective manner with proven technologies. Currently, the best methods for dealing with the variability and uncertainty of the wind is by a combination of curtailment of wind turbine output during infrequent periods of high wind speed, transmission system upgrades, and back-up with fossil fuel power plants. However, if most of our energy demand is to be met by intermittent renewable energy, back-up using stored intermittent energy, transmission and generation will need to be considered together, with the aim being to minimize strongly both the dependence on fossil fuels and the cost of energy delivered to the consumer. If wind-generated electricity is to be a credible alternative to fossil or nuclear power, its technical characteristics must be equal to those of existing suppliers. It must be available as the need arises, independent of the fluctuating source, and in sufficient quantity to power major cities, industrial and commercial complexes, not just homes and farms. Since the best intermittent resources are remote from demand centers, both long distance transmission and large-scale energy storage will be essential. Storage systems will need to smooth power fluctuations not just over hours or days as the wind resource changes, but also seasonally. Furthermore, large ramp rates (increases or decreases of thousands of megawatts over several hours caused by stationary or fast moving weather systems) will need to be accommodated. These events have already been encountered in Texas13 and Germany14 as a weather system moves into and becomes stationary over an area with a large installed wind turbine capacity. Wind-generated electricity must also be affordable at demand centers far from good wind resources. It cannot be assumed that consumers should pay any price for renewable energy or that utilities should be obliged to build transmission regardless of cost, or risk damage to or reduced lifetime

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for their equipment to accommodate intermittent power under any circumstances. Wind turbines now provide the lowest cost renewable electrical energy. Integrating large numbers of wind turbines with compressed air energy storage (CAES) plants and transmission lines transforms intermittent wind energy into a fully controllable power source and can provide energy on the scale and of the quality required by a modern industrial society. Based on the known costs and the technical characteristics of current wind turbines, CAES, and transmission technologies, there is every indication that this electricity will be both affordable and technically acceptable. While electricity from renewable resources cannot compete with low-cost fossil fuels, it can supply clean, reasonably priced power for the industrial, commercial, and private sectors.

12.2

Current status and future progress of compressed air energy storage (CAES)

A conventional large-scale CAES system15 (Fig. 12.1) consists of a motordriven compressor, a combustor, a turboexpander, a generator, a heat exchanger (recuperator) and an underground storage volume such as a solution mined cavern in a salt dome, a porous rock formation such as a depleted gas reservoir, a stratigraphic or structural trap, or a purposely excavated cavern or an abandoned mine. CAES is derived from gas turbine technology. In a gas turbine, fuel is burned in a combustor with high-pressure air from a compressor; energy is

Power from turbine array

Air

M

C

Fuel

Power to grid

TE-G

Exhaust

Underground storage

HX

12.1 Schematic of first generation CAES plant, showing M (motor), C (compressor), TE-G (turboexpander-generator), and HX (heat exchanger).

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extracted from the hot gas flow in a turboexpander which in turn drives both a generator and the compressor, which itself consumes about 60% of the generated power. By using energy from another source rather than from the turboexpander to compress air, energy is effectively stored, and may be recovered at a later time as needed. To store wind-generated electricity, power from the wind turbine array is used to drive the motor-compressor to charge the underground reservoir at a pressure of up to 85 bar, depending on the depth of the storage volume. When power is needed, air is withdrawn from the cavern, heated by combustion with natural gas, fuel oil or biomass-derived fuel16 in the turboexpander which drives a generator to produce electrical energy that is then fed into the grid. Air from the cavern is preheated by the exhaust from the turboexpander in a heat exchanger to improve the CAES plant efficiency.

12.2.1 Current CAES systems Two CAES plants have been built and have operated reliably for many years. The first was completed17 in 1978 in Huntorf, Germany. It has an output of 290 MW and uses two underground salt caverns that operate between 48 and 66 bar to provide 2 hours of storage. Originally designed to provide peaking power and possibly starting power to nuclear power plants in north Germany in the event of complete loss of power on the grid (black start operation), it now also provides balancing for the large number of wind turbines installed in the region. The 110 MW CAES plant in Macintosh, AL, was put into service18,19 in 1991. It uses a solution mined cavern in a salt dome with a volume of 0.55 million cubic meters (19 million cubic feet) to provide up to 26 hours of storage (110 MW output for 26 hours), and is operated in conjunction with a large coal-fired power plant. Power is stored during periods of low demand, late at night and on weekends, and is generated during periods of peak demand to avoid unnecessary cycling of the coal plant. The compressor train, clutches (used to switch the motor/generator between the compressor and the turboexpander) and motor/generator of the Macintosh plant are shown in one end view (Fig. 12.2), and the combustor and motor generator are shown in Fig. 12.3; this equipment was built and installed by Dresser-Rand (Olean, NY). A proposed CAES plant of up to 2700 MW in Norton, OH, would use this same technology; an abandoned limestone mine 675 m below the surface would provide a storage volume of 9.7 million cubic meters for about 30 hours of storage at full output power (Figs 12.4 and 12.5). Twenty compressor–motor/generator–combustor/turboexpander Dresser-Rand CAES modules of 134 MW each would ultimately be utilized in the plant.

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12.2 Macintosh, Alabama, USA, CAES compressor train, clutch and motor/generator.

The Dresser-Rand CAES train uses axial and centrifugal compressors (60 MW minimum, 100 MW maximum power) to pressurize a storage volume up to 85 bar. To generate power20, air is withdrawn from the reservoir and heated in a recuperator (air to air heat exchanger) by the exhaust (350 °C) from the combustion chamber; the heated air is then fed into a high-pressure (HP) air turbine at a pressure of 51 bar and a flow rate of 182 kg s−1, and produces a maximum of 35 MW of power. The exhaust from the HP turbine is directed into a low-pressure (LP) turbine (a standard gas turbine with the compressor removed); the air is mixed with fuel and burned at a temperature of 880 °C in the eight LP combustors; a turboexpander extracts energy from the high-temperature, high-pressure (17 bar) gas which flows into the recuperator and then to the exhaust stack.

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12.3 Macintosh, Alabama, USA, CAES combustion turbine, expander and motor/generator.

Selective catalytic reduction units are built into the recuperator duct; these are catalytic beds that convert NOx in the combustor exhaust to N2 by a reaction with aqueous ammonia that is injected into the exhaust gas from the turboexpander. The existing plants (Huntorf, Germany, and Macintosh, USA) use a motor-generator that is connected by a clutch either to the turboexpander for electricity generation or to the compressor for energy storage. This reduces costs by having a single component used for two different applications, but also reduces flexibility and increases the amount of time needed to switch between compression and generation. A straightforward modification would use a separate motor to drive a compressor, which can then be optimized to local requirements (more or less rapid rates of compression, for example).

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Norton energy storage configuration

Off peak electricity in Peak-day electricity out

Low & high pressure expanders

Air

Air

Fuel

Compressors

Heat exhaust

Motor/ Turbines Recuperator generator

2200 feet

Air

338 million cubic feet limestone cavern Air in/out

12.4 Schematic of the Norton, Ohio, USA proposed 2700 MW CAES plant showing the underground cavern 2200 feet (675 m) below the surface, with a volume of 338 million cubic feet (9.7 million cubic meters), and the above ground components (figure courtesy of Haddington Ventures).

Several variations of the CAES concept have been examined. CAES with steam injection (CAESSI) and humidification (CASH) of the air injected into the combustor have been evaluated21. These plants have the following additional components: an air saturator, an economizer (water heated by the exhaust gas), and possibly a thermal energy storage system, which can store the heat of compression for use during power generation to preheat and humidify the incoming air. Hot water generated in the economizer is

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Norton Mine depth schematic Depth (feet) 0' 200'

Mine shall

Water well

400' 600' 800' 1000' 1200' 1400' 1600' 1800' 2000' 2200'

Norton mine

2400' 2600' 2800'

12.5 Schematic and views of the abandoned limestone mine to be used by the Norton, Ohio, USA, CAES plant.

used to feed the saturator, which adds heat and mass to the incoming air. These additional components increase complexity but improve plant efficiency. A CAES plant has two figures of merit: the fuel-related heat rate (kJ/ kW h (Btu/kW h)) and the energy ratio (kW hin/kW hout), which is a measure of how much energy is extracted from the compressed air. The energy ratio is less than one since fuel is burned in the CAES plant and more energy generated than is used to compress the air. Compared with conventional CAES, CAESSI and CASH (with and without thermal storage) have somewhat higher (19–38%) heat rates, significantly lower (30–38%) energy ratios and somewhat lower (3–10%) capital costs. For natural gas at $2.85/million Btu, the improvement in operating costs was too small (a maximum of about 6%) to justify further study. However, higher natural gas prices could change this judgment. Another figure of merit used for energy storage systems is the round trip efficiency, or the ratio of the energy output to the energy input. Since CAES burns fuel to generate power, a straightforward comparison is not possible. One possible computation22 converts the energy content of the natural gas CAES uses into an equivalent electrical power assuming the fuel was burned in a gas turbine at an efficiency of 47.6%, and subtracts this from the energy output of the CAES plant. The round trip efficiency of the CAES system is then 66%. This compares reasonably well with other energy storage systems such as pumped hydroelectric (75%) and batteries23.

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More recently a demonstration plant for an advanced CAES system24,25 has been proposed.

12.2.2 Advanced CAES concepts This advanced or second generation system (Fig. 12.6) would use a standard combustion turbine in place of the combustor/turboexpander in the current CAES design; this is expected to reduce costs significantly; another advantage is that all components are off-the-shelf and are used at their design parameters. To generate power, air from the storage reservoir flows through a recuperator heated by the exhaust from the combustion turbine. The high-temperature, high-pressure air then flows through an expander which is coupled to a generator. The air exhausted from the expander at lower temperature and pressure is finally injected into the combustion turbine to further boost efficiency. By utilizing the highest efficiency combustion turbines, such as the General Electric LMS100®26, which has 45% efficiency at full power and 40% efficiency at 50% power, it should be possible to build a CAES plant that is less costly and more efficient than current designs. Variations on this approach, such as the addition of inlet cooling and bottoming cycles, are also under consideration.

12.2.3 CAES advantages and research issues The CAES system has many advantages. Based on gas turbine technology, it is proven and reliable. It has a high ramp rate in either the charge or the generation mode, and can be started up and reach full output power in less than 10 minutes; it can also be started up without power from the grid Power from turbine array

Air

M

C

Power to grid

TE-G Fuel Exhaust HX

Underground storage Gas turbine

12.6 Schematic of one possible advanced CAES plant, M (motor), C (compressor), TE-G (turboexpander-generator), HX (heat exchanger), and gas turbine.

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(‘black start’). The plant can be operated as a single cycle gas turbine if necessary, although this would be exceptional. Since the storage volume is underground, even large reservoirs have a minor environmental impact. The above-ground portion of the CAES plant occupies a modest area, which is typical for gas turbine-based technology and would be located in a remote location, not too distant from the wind arrays which supply its compression power, factors which further minimize its environmental impact. A critical question for CAES plants is the availability of suitable underground formations that are in reasonable proximity to good wind areas. While some areas of the US Great Plains and Northern Europe have thick salt deposits or salt domes, many other areas do not. It is believed27 that other geological features (depleted gas fields, and other similar capped porous rock structures) are reasonably widely available, but this is not certain, and it would be most worthwhile to do a detailed survey to confirm this.

12.3

Texas: the Ridge Energy wind compressed air energy storage (CAES) study

A more detailed simulation by Ridge Energy28 examined the possibility of using a CAES plant in north Texas with local wind turbine arrays. The wind resource there has a strong diurnal cycle with the strongest winds occurring late at night, decreasing during the day when demand and prices are greatest. Wind turbine arrays with a nameplate capacity of about 500 MW were coupled to a 270 MW CAES plant on an existing grid; study parameters are listed in Table 12.1. Using 2 years of wind data for their simulation, Ridge Energy found that a storage capacity of 10 000 MW h (about 50 hours at full output power) was needed to provide reliable power during periods of peak use. The cost of the 10 000 MW h storage volume, assumed to be solution mined salt dome caverns, was estimated to be about 16% ($101/kW) of the total project cost ($633/kW). Natural gas provided about 5.5% of the total energy generated (1924 GW h); the CAES plant added $21–22 MW h−1 to the cost of wind energy at about $30 MW h−1. Comparing the life-cycle cost of a wind–CAES plant to other thermal generation technologies (Table 12.2, advanced and conventional combustion turbine and combined cycle plants, integrated gasifier combined cycle (coal) and pulverized coal) the wind/CAES system (with gas at $6/million Btu, coal at $1.2/million Btu, no carbon tax, 80% capacity factor and ignoring transmission costs for all plants) was less costly than all but the pulverized coal plant.

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Table 12.1 Ride energy storage wind/CAES plant simulation parameters Wind resource

Not fully defined, average wind speed 7.7–9.0 m s−1 for seven sites (Wind Class 6, 600–800 W m−2) at 60 m

Wind turbine

Various models (Vestas 1.8, GE 1.5, Mitsubishi 1.0, Micon 1.65)

Wind turbine arrays

440–500 MW total rated capacity

CAES plant

Charging: Generation: Heat rate: Energy ratio: Variable O&M cost: Storage capacity:

2 units, 60 MW minimum per unit, 200 MW total maximum 2 units, 67.5 MW minimum per unit, 270 MW total maximum 4500 Btu kW h−1 0.8 kW hin(kW hout)−1 $1.50 MW h−1 10 000 MW h (50 hours)

The study made two important assumptions. The first was that the CAES plant itself was not an independent, stand-alone unit but was understood to work in close coordination with the wind turbine arrays. If this were not the case, the cost of power for compression would not be assured and the investment risk would be much greater. The second was that the production tax credit29 (PTC, $0.018/kW h in 2005, now $0.021/kW h) was included in the cost of wind energy; the PTC is available only for 10 years, which is less than the assumed term of the debt or the plant lifetime. Thus, actual costs are somewhat higher. This study demonstrated that firm (dispatchable, or controllable) power from a wind–CAES plant could be directly compared to power from the thermal plants if the wind turbine arrays and the CAES plant operated on a fully integrated basis. The wind–CAES plant is technically completely equivalent to accepted, conventional thermal technologies and competitive economically. Moreover, as natural gas and coal prices increase in the near term, the wind–CAES plant will become the technology of choice.

12.4

Wind integration issues

The energy content of the wind resource depends on the wind velocity to the third power, so that small changes in wind velocity (e.g. 10%) translate to large changes (30%) in the available wind power. The wind velocity is both variable over all timescales and uncertain, although good weather predictions can minimize this. The short-term variability of wind is a great challenge to wind turbine designers. For utilities, however, large arrays of

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1200 36 1.5% 50% 25 15 5% 11.5% 30 N/A N/A N/A N/A 0.051

633 14.07 1.5% 50% 25 15 5% 11.5% N/A 0.0015 6 4500

CAES 430 10.74 1.5% 50% 25 15 5% 11.5% 75.4 0.00430 6 10,450 80 0.078

CT

563 12.28 1.5% 50% 25 15 5% 11.5% 96.30 0.0021 6 7000 80 0.058

CC

483 8.58 1.5% 50% 25 15 5% 11.5% 80.20 0.0032 6 8550 80 0.065

Adv CT

639 10.74 1.5% 50% 25 15 5% 11.5% 104.96 0.00214 6 6350 80 0.055

Adv CC

1800 35.43 1.5% 50% 35 20 5% 11.5% 300 0.0021 1.20 8500 80 0.055

IGCC

1400 25.76 1.5% 50% 35 20 5% 11.5% 233.50 0.0032 1.20 9500 80 0.048

PC

CT, combustion turbine; CC, combined cycle; IGCC, integrated gassifier combined cycle; PC, pulverized coal. Source: Ridge Energy, 2005, The Economic Impact of CAES on Wind in TX, OK and NM, Ridge Energy Storage and Grid Services; report prepared for the Texas State Energy Conservation Office, Austin TX (Ref. 20).

ICC ($/kW) Annual FO&M Inflation (%) Debt/equity Project life Debt term Interest rate Cost of equity Annual cost ($/kW year) Variable O&M ($/kW h) Fuel cost ($/MBtu) Heat Rate (Btu/MW h) CF Cost of Power ($/kW h)

Wind

Wind/CAES

Table 12.2 Energy storage wind CAES plant compared with other generation options

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hundreds of wind turbines average out fluctuations30 on timescales up to 1 or 2 hours; they must compensate for longer-term variations, including diurnal and seasonal variations in wind-generated power. As a general guideline, if intermittent power is less than 20% of the average utility demand, existing fossil fuel power plants can be used to back up the variable power. As the number of intermittent generators increases, however, this approach becomes more difficult. Large fossil fuel plants are much less efficient when run at part load, and some plants (e.g. nuclear power plants) cannot easily decrease their power output rapidly, if at all. Substantial and rapid changes in the power output of most fossil fuel generators such as coal plants and combined cycle natural gas plants increase the thermal stress on major components and increase the need for maintenance as well as the risk of failure. In addition, transmission grids, designed for generators located closer to demand centers, become overloaded much more easily. Finally, since major intermittent resources are found some distance from cities and industrial centers, long distance transmission lines are needed; these are costly and difficult to build due to local opposition, and so should be used as efficiently as possible. As we move from the stage where more local intermittent resources are used as fuel savers to where distant intermittent resources are used to satisfy a large fraction of demand over long transmission lines, new approaches to intermittent energy integration are needed to minimize fossil fuel consumption and cost to the consumer and to maximize system reliability.

12.4.1 Wind turbine capacity factor In order to understand how best to couple large-scale wind turbine arrays located far from demand centers to utility grids, it is necessary to understand the wind turbine capacity factor31 (CF), which is the turbine average output power divided by the maximum power output (Equation 12.1): CF =

Pavg Pmax

12.1

This is used as a measure of a generator’s reliability and ability to provide power to the grid; for nuclear power plants and other constantly available or baseload plants, the capacity factor is about 90%; for wind turbines it is typically around 30%. Using long distance transmission lines with such low capacity factor generators is not desirable, and the question is whether the transmission line capacity factor can be increased and at what cost. Currently, the most important design criterion for a wind turbine is its local cost of electricity. This is what local utilities and consumers will pay for; the capacity factor of the turbine is not relevant.

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The trade-off between the cost of electricity and wind turbine capacity factor can be understood by examining these parameters as the maximum wind turbine output, that is the size of the generator, is varied for a turbine with a fixed rotor diameter. For this case, the power output curve of the Vestas V-27/225, an efficient medium sized machine32, will be used for this example; the result applies to any wind turbine designed to minimize local cost of electricity. The turbine is assumed to operate in a wind regime with a wind power density, Pw, equal to 450 W m−2 (wind class 4) and a Weibull k factor of 2.5, which is typical of conditions that are found over large areas of the US Great Plains33 and many other locations around the world. We assume also that the only change in the cost of the wind turbine is due to the increase or decrease in the gearbox ($105/kW) and generator ($35/kW) as the maximum power output is raised above or below 225 kW34. If the power output of a wind turbine can be written as P(v) = Pmax · g(v), where Pmax is the maximum wind turbine power output, then the yearly average power output (Pavg) of a wind turbine is given by: Pavg = Pmax ∫ f ( v) ⋅ g ( v) dv = Pmax CF

12.2

Here v is the wind velocity and f(v) is the Weibull probability density function which generally is a good representation of wind speed distributions in many locations, and has been used to model the wind turbine– transmission–CAES systems discussed here. However, in designing power plants it is essential to understand the local wind resource thoroughly, and to model systems with real wind data taken as close to the hub height of the wind turbine as possible over a minimum of one year, and preferably over several years. The local cost of energy (COE) is calculated as: COE = WTLC + O & M

12.3

The cost of wind turbine operation and maintenance (O&M) is assumed to be $0.01/kW h; wind turbine levelized cost (WTLC) is calculated assuming an installed capital cost of $850/kW, adjusted above and below 225 kW as explained above, times a capital charge rate of 0.106, and divided by the average power computed from Equation 12.2 multiplied by 8766, the number of hours in a year. The results of these calculations are shown in Fig. 12.7. Note that there is a broad range of generator and gearbox size (Pmax = 125–350 kW) over which the wind turbine cost of energy is virtually constant, and that there is a very shallow cost minimum around 225 kW. The capacity factor, in contrast, varies much more rapidly over this range of Pmax. It is evident that the capacity factor has been strongly diminished to achieve a very small reduction in the wind turbine cost of energy. In this

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0.75

0.07

0.70 0.65

0.06

0.60

0.05

0.55 COE

0.04

0.50 0.45

0.03

Capacity factor

Cost of energy ($/kW h, 1992$)

408

0.40

0.02

CF

0.01

0.35 0.30

0 0

100

200

300

0.25 400

Maximum wind turbine output (kW)

12.7 Cost of electricity and wind turbine capacity factor (CF) as a function of maximum turbine output power.

example, the cost minimum is found at a Pmax of 225 kW, at a capacity factor of 0.384. At a Pmax of 150 kW the computed power cost is 10% higher while the capacity factor has increased by a factor of 1.33, to 0.51. Wind turbine design has favored capturing the small amount of power at higher wind velocities at the expense of a larger capacity factor on the perfectly reasonable assumption that the local cost of energy should be minimized. By better understanding the trade-off between capacity factor and the cost of electricity a more intelligent choice can be made between these two parameters given other system constraints, such as storage system size and cost, transmission system charges or other system limitations. A practical application of this analysis is given in the next section.

12.4.2 Wind turbine arrays and transmission systems The conventional approach35 to coupling a wind turbine array to a transmission line is to match the peak output of the array to that of the transmission line and to neglect the cost of the transmission system in the total cost of power delivered to consumers. There are good reasons for this strategy. Wind-generated electricity is generally more expensive than electricity generated from fossil fuels, and any added expense such as transmission line costs further widens the gap. In addition, for low levels of intermittent energy, transmission upgrades are a negligible expense, and would be paid for by the local utility. Finally, the prospect that large-scale wind energy

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development would occur in the near future was remote, since fossil fuel supplies were assumed to be adequate far into the future. Up to about 2005, most wind energy development occurred in areas with good wind resources close to transmission or demand centers, (e.g. Denmark, Germany, Spain), or was not large enough to stress transmission resources (e.g. California). As increasing amounts of intermittent wind energy have been added to the grid in places such as northern Germany, west Texas, and China, the limitations of the existing transmission system have become increasingly obvious. Moreover, the disadvantages of fossil fuels, such as resource constraints, climate change, security of supply, and geopolitical challenges, also can no longer be dismissed or ignored. Thus the need to understand how large-scale intermittent energy supplies might be used to supply a major fraction of energy demand is increasingly urgent. This requires a systems approach, which is an analysis of generation, transmission, and storage as a unified entity, including how best to use transmission assets. To minimize fossil fuel consumption, stored intermittent energy should be relied upon as much as possible for spinning and ready reserves rather than coal or natural gas generators. To illustrate this approach36,37 the cost of electricity delivered to southern California from a wind turbine array located 2000 km away in Kansas over a 2000 MW high-voltage direct current (HVDC) transmission line will be computed. This is a system for which transmission costs cannot be neglected; system parameters are summarized in Table 12.3. The wind regime is assumed to be wind class 4, with Pw = 450 W m−2, k = 2.5 at 50 m. The Vestas V27-225 wind turbine was used in this simulation. While larger turbines are now available, the wind turbine efficiency (measured by the power coefficient, the ratio of the power generated to the wind power input) has not

Table 12.3 Wind energy baseload plant parameters Parameter

Value

Wind plant output Wind resource Wind turbine

2000 MW (CAES, transmission line availability 100%) 450 W m−2, Weibull k = 2.5, 50 m elevation 225 kW, 50 m hub height (V-27-225 power output curve) Array and other losses: 15% 2000 MW HVDC line Charging: 2250 MW Generation: 1500 MW Hours of storage (at discharge rate): variable Energy input/energy output: 0.67 Charging efficiency: 0.9

Oversized wind array Transmission line CAES system

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changed significantly. Thus, the same results would be obtained (assuming the same per unit installed cost) with fewer turbines. Normally, the number of wind turbines in the array (N) times the maximum wind turbine output (Pmax) would be set equal to the transmission line capacity: for Pmax = 225 kW, N would equal about 8900 Vestas V-27-225 turbines. The capacity factor of this system is the capacity factor of the array, which is the wind turbine capacity factor for the assumed wind regime reduced by array losses. The number of wind turbines in the oversized array is given by: (1 − A) · N · Pmax = 2000 MW

12.4

Here, A is the array losses, assumed to be 15%, which accounts for the reduced output of wind turbines at the center of a large array relative to those at the array edge. The number of turbines in the array is first increased to 10 235 to compensate for array losses, and the capacity factor of the transmission lines increases from 0.31 to 0.41. As the number of wind turbines in the array is increased further, Pmax for the wind turbines begins to decrease: with 12 600 turbines, Pmax equals 160 kW; Pavg decreases by 4.3%, and the system capacity factor increases by 20%, to 0.49. As noted in the previous section, large gains in the capacity factor are possible for a small sacrifice in average turbine output. The resulting array has a maximum power output that exceeds the capacity of the transmission line, or is oversized relative to the maximum power of the line. From a qualitative perspective, at lower wind velocities, which occur most often, more wind turbines are available in the oversized array to fill the transmission line and reduce the per unit cost of transmission; the reduced cost of transmission partially or fully compensates for the cost of the additional turbines in the array. As the wind velocity increases, more power must be spilled, but a substantial increase in the utilization of the transmission line is possible at little or modest increase in the cost of delivered power. The cost of delivered power from the oversized array and the transmission line will now be examined. Transmission costs were based on the actual construction expenditures for a 2000 MW, ±450 kV, 2222 A, 1500 km HVDC line38 between James Bay, Québec, Canada, and the Boston area in Massachusetts, USA. The line consisted of two main conductors (12 Ω each), and a converter station and capacitor bank at each end. System losses, including 0.6% losses in each converter station, were 7% of transmitted power at full power. This line is used to bring large blocks of inexpensive hydroelectricity from the great dams on the rivers feeding into James Bay to the US Northeastern population centers. Accordingly, the system cost for the Kansas–California line was taken to be $1520 million.

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Cost ($/kW h, 1992$)

0.08

0.06 WTLC 0.04

O&M + royalty TMLC + TMl

0.02

0 0.36

0.41

8.9

10.1

0.49 0.55 0.62 0.70 System capacity factor 12.6

15.15

19.1

27.1

0.78 42.9

3

Number of wind turbines (× 10 )

12.8 Cost of electricity delivered to demand center versus system (transmission line) capacity factor and number of wind turbines for conventional baseline case (8900 turbines) and oversized wind turbine arrays.

Delivered COE from the system is the wind array levelized cost (WALC), O&M and royalty, plus the levelized transmission line cost including transmission line losses (TMLC + TML), and is shown in Fig. 12.8. For the system parameters chosen (Table 12.3), transmission costs nearly double the cost of delivered power. As the number of turbines in the array increases, the cost of delivered power decreases at first as the slow increase in local cost of electricity from the oversized array is more than compensated by the increase in capacity factor and the decrease in transmission costs. The delivered cost of energy for the oversized array is less than the baseline case up to a system capacity factor of about 0.62, and a system capacity factor of 0.70 is still economically justifiable (6% increase in the delivered cost of electricity above the baseline case), and is attainable without any storage. The delivered power is still intermittent, but the transmission line is used at a much higher capacity factor for a negligible increase in the delivered cost of electricity. As the number of wind turbines in the array increases above what is necessary to obtain a 62% capacity factor, the amount of spilled power increases rapidly, and at a capacity factor of 78% as much power is spilled as is transmitted. This spilled power is available to charge an energy storage system, and can be used to increase the capacity factor even further.

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Indeed, the addition of CAES transforms the wind-generated electricity from intermittent to controllable source of power, and allows this system to be fully competitive technically with any other type of power plant.

12.4.3 Wind turbine arrays, transmission, and CAES As shown above, from a systems perspective, initially the most cost-effective way to handle transmission overloads is by curtailment of wind turbine array output rather than by transmission upgrades; for expensive long distance transmission lines, it is economically justifiable to increase the number of wind turbines in the array and spill surprisingly large amounts of power. In other words, an uneconomically large investment in transmission capacity would be necessary to capture a small amount of additional energy. As the number of wind turbines increases, at some point curtailment becomes an increasingly expensive solution, and either the transmission grid must be upgraded to handle much larger amounts of intermittent power, or storage can be added to capture the power that would otherwise be lost and returned to the grid as needed. Just as the oversized array/ transmission line provides the lowest cost power to the demand center, adding storage also allows the per unit cost of transmission to be reduced, makes best use of existing transmission resources, and provides additional savings since the need for spinning and ready reserve is largely eliminated. Demands on the storage system used in conjunction with the large-scale wind turbine arrays of the type discussed in this chapter are formidable. Storage system output must be about the same as transmission line capacity (2000 MW) to cope with low wind velocity periods, and while the intervals of low output are distributed randomly, one might estimate39 that the plant should be able supply full power for about 20 hours. This implies a storage capacity of about 40 000 MW h, or 40 GW h. In addition, the plant must be able to increase or decrease its power output or power input from perhaps 20% capacity to full capacity within 60 minutes to accommodate changes in the turbine array output. Finally, the plant must operate at high efficiency over the input and output range. The parameters of current storage technologies are listed in Table 12.4. Based on their total plant cost and storage capacity, batteries ($1500–2600/ kW plant cost) and flywheels ($4300/kW plant cost) are suited for smallscale, short duration (<4 hours), distributed applications, such as regulation (compensation for small variations in the power frequency and in the nominal voltage) and brief increases or decreases in demand. Supercapacitors and superconducting magnetic storage systems are useful for power regulation on smaller, highly critical equipment such as computer systems over very short timescales (<10 s), not for bulk power storage.

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Table 12.4 Commercial energy storage costs Technology

Compressed air energy storage (CAES) Large systems (100–300 MW) underground storage Small systems (10–20 MW) above ground Pumped hydroelectric (1000 MW) Battery (10 MW) Lead–acid Sodium sulfur Flow battery Flywheel (10 MW) Superconducting magnetic storage Supercapacitors

Capital cost ($/kW)

Storage cost ($/kW h)

Storage time (hours)

Total plant capital costa ($/kW)

730–590

2–1b

10

750–600

800–700

250–200

4

1800–1000

1500–2000

100–200

10

2500–4000

420–660 450–550 425–1300 3360–3920 200–250

330–480 350–400 280–450 1340–1770 650 000–850 000

4 4 4 0.25 0.003 (1 s)

1740–2580 1850–2150 1545–1300 3695–4313 350–489

250–350

20 000–30 000

0.03 (10 s)

300–450

a

Including power conditioning systems and systems necessary to provide power Does not include battery replacement cost, site permitting, interest during construction, or substation cost. b Solution mined salt. Source: Rastler, D., Electric Power Research Institute (EPRI) Journal, ‘New demand for energy storage’, September/October 2008, p 30–47 (www.epri.com).

Given the technical and financial requirements of the wind energy storage system, only pumped hydro or CAES can be considered for this application. Since suitable pumped storage sites are difficult to find even in hilly areas due to environmental restrictions, and in the Great Plains are completely unavailable, CAES is the single viable storage technology for use with large-scale intermittent energy systems. Note that the storage times chosen (10 hours) to compute capital costs (Table 12.4) are less than what has been assumed necessary for wind energy storage; as will be shown, a more accurate system simulation indicates that a minimum storage capacity of 50 hours is needed for routine day-to-day operation to provide firm power from intermittent wind, and about 250 hours of storage is needed to provide constantly available power throughout the year. CAES plants with large underground storage reservoirs can meet these demands.

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A baseload plant, one that provides constant power to the grid is the simplest wind/CAES/transmission system to examine in detail; this can be done using synthetic wind speed data. While this approach illustrates the broad advantages of this system, it cannot take the place of a detailed examination of the local wind resource and its interaction with the proposed wind/CAES/transmission plant in actual practice. The seasonally invariant wind resource was specified by the appropriate Weibull wind velocity distribution: given the wind power density and Weibull k factor; a random number generator was used to generate 2000 independent random numbers from this distribution. This is equivalent to 3 months of hourly averaged wind speed data. For each wind speed, the wind turbine output is computed and multiplied by the number of wind turbines in the array, then multiplied by factor (1 − A), where A is the assumed array and other losses. If the array output is greater than the transmission line capacity, and capacity available in the storage reservoir, the excess power (multiplied by 0.9 to account for various losses) is added to the reservoir. When the array output falls below the transmission line capacity and stored power is available, the amount of energy needed multiplied by the energy factor (which accounts for the CAES system generating more energy by burning fuel than is put into the storage reservoir) is withdrawn from the reservoir. System capacity factor, spilled power, and fuel consumption may be computed as different parameters are varied (compressor power, reservoir size, CAES output power). COE is the sum of WALC, transmission line levelized costs (including losses, TM + TML) and the CAES levelized cost. Results of the simulation are shown in Fig. 12.9, including a charge for spinning reserve for those examples without storage. The cost of spinning and ready reserve40 is dependent on the type of plant available for backing up intermittent wind, but is generally – but not always – less that $0.005/kW h for most systems with wind capacity penetration less than 20%. Note that the cost of energy delivered to the consumer for the oversized arrays is less than or equal to that of the base (no storage) case (maximum array nameplate power equal to transmission line capacity), up to a system with a capacity factor of about 60%. With the addition of storage, the cost of delivered electricity is still less than that of the base case. However, with storage, the delivered power can be constantly available, and much more valuable to a utility than large blocks of intermittent power, especially when intermittent renewable energy supplies the majority of power consumed. Unfortunately, capturing the value indicated by the systems approach is difficult given the current deployment strategy, in which transmission and back-up costs are paid by the local utility. In addition, most wind turbine arrays are built not by utilities, which have significant experience construct-

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0.07 0.06

Spinning reserve

COE ($/kW h)

0.05 CAES 0.04 0.03

TM + TML

0.02

WALC

0.01 0 0.36

0.41

0.49 0.59 0.7 Capacity factor

0.9

12.9 Wind/transmission/CAES plant costs as a function of transmission line (system) capacity factor.

ing large power plants, but by small companies that lack the expertise and financial reserves to build a CAES plant. Seasonal storage of wind energy In many areas of the world the wind resource is seasonal; for example, on the US Great Plains winds in spring are stronger than in winter and autumn, and weakest in summer. However, because of demand for air conditioning, peak electricity consumption in the United States occurs during the summer months. Seasonal storage of energy would be a great advantage not only to meet summer demand, but also to capture the much higher prices that are charged for power during the summer cooling season. The technical and economic feasibility of seasonal storage can be examined by studying a baseload wind system located in a region a wind class 4 (450 W m−2) annual average wind resource, with the spring winds class 5 (550 W m−2), summer winds class 3 (350 W m−2), and winter and autumn winds class 4. The results of the simulation are illustrated in Fig. 12.10. The most important point is that the delivered cost of electricity is not affected by the increased size of the storage reservoir, since most of the capital cost of the CAES plant is accounted for by the compressor and turbomachinery ($700/ kW), with storage adding $10–20/kW for 10 hours of storage in solution mined caverns (storage in capped porous rock formations is significantly cheaper15). Thus, even 250 hours of CAES storage are economically feasible (given the necessary underground formations), so that seasonal shifting of supply to meet demand is possible.

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Summer capacity factor

0.95

0.06 COE

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0.90 0.04 0.85

0.03

Summer CF

0.02 0.80 0.01 0.75 0

50

100

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0 300

Cost of energy ($/kW h, 1992$)

416

Hours of storage

12.10 Seasonal storage: cost of energy and summer capacity factor as a function of hours of storage.

The advantages of seasonal storage are actually understated, since a significant premium is paid for power during peak demand periods, especially during the summer months. There is a large economic incentive to store power during times of low demand (and price) and sell it during peak periods, not just on a diurnal or weekly basis, but also on a seasonal basis, at least in the United States.

12.4.4 Wind speed autocorrelation time and storage capacity for baseload wind systems There is an important property of the wind resource that is not captured by this synthetic data, and that is the tendency of the wind velocity to change slowly rather than abruptly. That is, the wind is persistent, and the wind speed distribution is also characterized by an autocorrelation time that can be derived from actual wind data. Previous simulations have ignored this property of the wind resource, and it is important to understand if this is justified and to quantify the effect, if any, on the size of the storage reservoir needed by the CAES plant. A large autocorrelation time would imply a large storage volume, and this will affect the cost of the storage system and thus the baseload plant. This autocorrelation factor A(Δt) is written as: − Δt ⎞ A ( Δt ) = exp ⎛ ⎝ C ⎠

12.5

Here, C is the autocorrelation coefficient for a given set of wind speeds (in this case, hourly averages) with an autocorrelation time Δt. Wind speeds are assumed to be described by a Weibull distribution (450 W m−2, k = 2.5).

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The system and subsystem sizes of a representative wind energy baseload plant are listed in Table 12.3. While a careful simulation of a wind/CAES/ transmission system will need to make use of long-term site-specific wind data as well as take into account the actual operating characteristics of the compressors and turboexpander/generator, the assumed system parameters are adequate to demonstrate the general principles involved. To generate41 the synthetic wind data with a specified autocorrelation time, two Gaussian sequences of random numbers with unit variance and zero mean are generated. These are combined with the appropriate weighting factors that control the autocorrelation process. For a range of wind speed autocorrelation times between 0 and 20 hours, 2200 random numbers were generated. As the autocorrelation coefficient increases, the size of the storage reservoir needed to maintain a high capacity factor increases substantially, as indicated in Fig. 12.11. For example, with 20 hours of storage, a wind resource with a 12 hour autocorrelation time reaches only 84% of the capacity factor expected for completely uncorrelated wind speeds. This corresponds roughly to a diurnal cycle in the wind velocity, which is frequently encountered in practice. In this example, a minimum of 60 hours of storage is needed to reach a system capacity factor of 90% for a baseload wind–CAES–transmission plant. The impact of the autocorrelation time on storage capacity is much larger than might be imagined, and must be due to the subtle interaction of the

100

Capacity factor (%)

95 80 hours of storage 90 60 40 85 20 hours of storage 80

75 0

5

10

15

Autocorrelation time (hours)

12.11 Capacity factor as a function of autocorrelation time (hours) for storage capacities of 20 to 80 hours.

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persistence and stochasticity of the wind velocity. Succar42 also examined this question and obtained similar results: optimal storage system size was about 80 hours of storage with the hours of storage scaling as the autocorrelation time to the 0.53 power. Intuitively one might expect that the storage volume in hours would be approximately equal to the autocorrelation time, but it appears that the random nature of the wind speed strongly affects this relationship. The real issue with the substantial CAES storage volume requirements is not the cost but rather the availability of suitable geological structures.

12.5

Discussion and conclusions

The current wind energy integration strategy is to feed intermittent energy onto the grid and have other organizations cope with transmission and back-up (provide spinning reserve and ready reserve). This works very well as long as intermittent generators are a relatively small fraction of the total generation capacity. Initially, at least, transmission upgrade requirements are modest, and the supply variability is of the same magnitude as demand fluctuations on the grid. As the amount of intermittent power increases, more and more resources are needed, both physically in the form of transmission and back-up plants, and in the form of systems command and control, to insure that power quality and availability are maintained. From a systems perspective, the rationales for combining storage with wind turbine arrays are compelling. Large-scale transmission projects are not only costly but also challenging to build due to local opposition. Systems integration issues (spinning reserve and ready reserve) are greatly eased. As fossil fuel prices increase, wind–CAES systems will be increasingly competitive. However, several systemic factors work against the acceptance of intermittent resources combined with CAES plants as a viable replacement for fossil fuels. In a deregulated utility, different organizations are responsible for generation, transmission, and distribution. Most wind turbine arrays are built by small independent power producers, with transmission and back up the responsibility of two separate companies. While an integrated utility would easily be able to capture the system savings (base case versus oversized arrays and wind–CAES systems, Figs 12.8 and 12.9), given deregulated utilities, this is difficult or not possible. Currently, the independent power producers find the current deployment strategy profitable, and have no reason to pursue a more sophisticated, and more risky, approach. A possible solution to this problem is to ration transmission access43 and/ or provide access at strongly reduced rates to developers who operate CAES plants in conjunction with wind turbine arrays. This will both reduce

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the strain on transmission resources and provide a strong incentive for wind developers to consider storage as an integral part of their power plant. Another difficulty is that there is considerable reluctance to admit that we must move to large-scale renewable energy systems for reasons other than climate change44. For example, the UK Sustainable Development Commission did a thorough study45 of the issues surrounding large-scale UK wind energy development, including landscape and the environment, wildlife and ecology, telecommunications, noise, and network integration. Climate change was clearly the main motivation for expanding wind energy. Other compelling and more immediate rationales, such as eliminating dependence on finite (and increasingly expensive) fossil fuels, or minimizing imports from unstable or hostile nations, were never mentioned. This oversight is quite striking given the United Kingdom’s need to import oil, coal, and natural gas, including liquefied natural gas, in increasing quantities as the natural gas and petroleum deposits46 in the North Sea are depleted. Wind-generated electricity as well as other intermittent renewable resources, combined with CAES plants and the appropriate transmission upgrades, can supply most of the energy needed by modern industrial societies in Europe and North America, and probably many other areas. This energy will be reliable, affordable, and available in sufficient quantities to provide reasonable levels of comfort for the current and future population levels. Radically new technologies or immense expenditures on research and development are not needed. What is required is the acknowledgement of the urgent necessity of this new approach due to climate change, security of supply factors and to the imminent end of cheap fossil fuels. We should be optimistic that such a change in public perception is possible.

12.6

References and notes

1 von Weizsäcker, E., Lovins, A., and Lovins, L., 1997, Factor Four: Doubling Wealth, Halving Resource Consumption, A Report to the Club of Rome, Earthscan Publications Ltd, London. 2 Cavallo, A., 2007, Controllable and affordable utility scale electricity from intermittent wind resources and compressed air energy storage (CAES), Energy, 32, 120–127. 3 Elliott, D.L., Wendell, L., and Glover, G., 1991, An Assessment of the Available Windy Land Area and Wind Energy Potential in the US, Pacific Northwest Laboratory (PNL), 7789, UC-261, PNNL, Richland, Washington, WA. 4 Wiser, R., and Bolinger, M., 2008, Annual Report on US Wind Power Installations, Costs, and Performance Trends, Lawrence Berkeley Laboratory, www1. eere.energy.gov/windandhydro/. 5 Massey, J., 2009, Bidders emerge for round three licences, WindPower Monthly, 25, 4, 51.

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6 Qi, W., 2009, More complexes of 10 gigawatts each, Wind Power Monthly, 25, 3, 110. 7 Qi, W., 2009, China heads for first place ranking, Wind Power Monthly, 25, 6, 34. 8 International Energy Outlook, 2008, DOE/EIA-0484. 9 Cavallo, A., 2002, Predicting the peak in world oil production, National Resources Research, 11, 187–195. 10 Cavallo, A., 2008, OPEC, peak oil, and the end of cheap gas, Bulletin of the Atomic Scientists, http://www.thebulletin.org/web-edition/features/opec-peakoil-and-the-end-of-cheap-gas 11 See www.opec.org for OPEC basket price data. OPEC sets the price by gradually bringing supplies in line with demand. This is often not a smooth process. Prices are fixed both for economic and geopolitical factors, and are far above production costs for both OPEC and non-OPEC producers. 12 The International Energy Agency (IEA) in its recent World Energy Outlook (2008) noted that given the problems with fossil fuel supplies and environmental impact, ‘What is needed is nothing short of an energy revolution’ (p 37, Executive Summary, World Energy Outlook (2008), IEA, Paris, France, www.iea.org). Such forceful language is virtually never used by government organizations, and is a clear sign of the degree of concern regarding this challenge. 13 Holly, C., 2008, Loss of wind pushes Texas to brink of blackout, Power, 152, 5, 22. 14 Wind Report 2005, E.ON Netz GmbH, 95448 Bayreuth, Germany, www.eonnetz.com. 15 Schainker, R.B., Mehta, B., and Pollak, R., 1993, Overview of CAES technology, Proceedings of the American Power Conference, Chicago, Illinois Institute of Technology, Chicago, IL, pp 992–997. 16 Denholm, P., 2006, Improving the technical, environmental and social performance of wind energy systems using biomass based energy storage, Renew. Energy, 31, 9, 1355–1370. 17 Crotogino, F., Mohmeyer, K.U., Scharf, R., 2001, Huntorf CAES, More than twenty years of successful operation, Solution Mining Research Institute Meeting, Orlando, FL. 18 Nakhamkin, M., Andersson, L., Schaniker, R., Howard, J., and Meyer, R., 1993, 110 MW – 26 HR CAES plant performance test results and initial reliability indices, Proceedings of the American Power Conference, Chicago, Illinois Institute of Technology, Chicago, IL, pp 1016–1021. 19 Nakhamkin, M., Andersen, L., et al., 1992, AEC 110 MW CAES plant: status of project, J. Engineering for Gas Turbines, 114, 695–700. 20 Ridge Energy Storage, 2005, The Economic Impact of CAES on Wind in TX, OK and NM, report prepared for the Texas State Energy Conservation Office, Austin TX. 21 EPRI Report TR-103521, August 15, 1994, Comparative Analyses of Compressed-Air Cycles; CAES, CASH-ES, CRCAES/CRCASH-ES, and MRCAES/ MRCASH-ES, EPRI, PaloAlto CA, www.epri.com. 22 Denholm, P., and Kulcinski, G.L., 2004, Life cycle energy requirements and greenhouse gas emissions from large scale energy storage systems, Energy Conversion and Management, 45, 2153–2172.

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23 Cultu, N., 1988, Energy storage systems in operation, in B. Kilkis and S. Kakac, eds., Energy Storage, 551–574, Kluwer, Dordrecht. 24 EPRI Report 1016901, May 7, 2008, Advanced Compressed Air Energy Storage Demonstration, EPRI, Palo Alto, CA; www.epri.com. 25 Nakhamkin, M., 2008, Second generation of the CAES technology, presented at the CAES Scoping Workshop, Columbia University, October 21, 2008. 26 www.ge.com, GE Energy, LMS 100 Flexible Power. 27 Mehta, B., 1992, CAES geology, EPRI (Electric Power Research Institute) Journal, October–November, 38–41. 28 Ridge Energy Storage, 2005, op. cit. 29 The question of subsidies for renewable energy technologies is much more controversial than it should be, given that fossil fuels and nuclear power themselves benefit from immense tax breaks and favoritism. The most significant fossil fuel subsidy is the $1000 billion per year, including the cost of the wars in Iraq and Afghanistan that the US spends to provide secure petroleum and LNG supplies from the Middle East (Security of supply: a major neglected fossil fuel subsidy, Cavallo, A., Wind Engineering, 20, 2, 47–53, 1996). This fact is unmentioned and unmentionable in any discussion of energy policy. 30 In statistical terms, wind speed is a stochastic, or random, second order process; its mean is independent of time, and the correlation between values of the process at different points in time depends only on the time interval separating these points. This autocorrelation time can be extracted from long term wind velocity data. 31 Cavallo, A.J., 1997, Wind turbine cost of electricity and capacity factor, Journal of Solar Energy Engineering, 119, 312–314. 32 See www.windpower.org, the web site of the Danish wind industry association. Power output data are given for a wide variety of Danish wind turbines, including the V27-225. 33 Elliott, D.L., Holladay, C.G., Barchet, W.R., Foote, H.P., and Snadusky, W.F., 1987, Wind Energy Resource Atlas of the US, D)E/CH 10094-4. 34 Nordex, 1991, Maintenance Manual for Nordex 250/150 kW Wind Turbines, DK-7323, Denmark. The manual listed the replacement cost for the gearbox for these machines, giving a rough estimate of the cost per kW of a gearbox. This is a gross simplification of the design problem, but is sufficient to illustrate the principle. 35 Watkins, K., Blackfeet Area Wind Integration Study, PNUCC, 101 S.W. Main, Suite 1605, Portland, Oregon, 97204. 36 Cavallo, A.J., 1995, High Capacity Factor Wind Energy Systems, Journal of Solar Energy Engineering, 117, 137–143. 37 Succar, S., 2008, Baseload Power Production from Wind Turbine Arrays Coupled to Compressed Air Energy Storage, PhD Thesis, Princeton University. 38 Reason, J., 1990, HVDC line brings Canadian hydropower to New England, Electrical World, 3, 27–32. 39 The amount of storage required for wind/CASE applications depends on the autocorrelation time of the wind velocities, as well as other design objectives, such as seasonal storage, or daily power firming or peaking, and will be examined in a following section. Twenty hours of storage might be suitable for a peaking plant (4 hours at full output), while 50 hours might be needed for firming (12

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hours at full power). The more subtle details of the wind speed distribution are important in the design of large-scale wind turbine–CAES–transmission systems. Wiser, R., and Bollinger, M., 2008, op. cit. McFarlane, A., Veers, P., Schluter, L., 1994, Simulating high frequency winds for long duration, Proceedings of the Energy-Sources Technology Conference, American Society of Mechanical Engineers (ASME), Solar Energy Division (SED), vol. 15, Wind Energy, pp 175–180. Succar, S., 2008, op. cit., p. 153. Cavallo, A., 2008, Prorationing transmission resources with wind/transmission/ compressed air energy storage (CAES) systems: focus on Texas, Presented at ElectricPower 2008 (EP2008), May 6–8, Baltimore, MD. An exception to this oversight is the essay ‘Time to ask the right questions’, by Mary McCaffery, in the Special Report: UK Offshore, WindPower Monthly, June 2009. The issue of uncertain resource availability in the medium term is specifically mentioned as a justification for large scale off shore wind turbine arrays for the UK. Wind Power in the UK, May, 2005, Sustainable Development Commission, www.sd-commission.org.uk, Room 101, 55 Whitehall, c/o 3–8 Whitehall Place, London, SW1A 2HH. Oil production in the UK sector of the North Sea peaked in about 2000, and natural gas production is also in decline.

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13 Integration of renewable energy systems into remote micro-grids J. A. CARTA, University of Las Palmas de Gran Canaria, Spain

Abstract: This chapter presents micro-grids integrated by hybrid energy systems, loads and energy storage systems as a sustainable energy solution for remote areas in the world. The chapter begins by introducing the reader to micro-grid technology. This is followed by what needs to be taken into account before installation of micro-grids in remote areas, relevant technical design and installation questions are then addressed, and information is provided about simulation techniques, monitoring and control equipment and system optimisation models. Finally, the advantages, limitations and future trends of these systems are given, as well as sources of additional information for the reader. Key words: hybrid renewable energy systems, remote micro-grids, integration of hybrid energy systems.

13.1

Introduction

The generation systems that supply most of the world’s electricity consumption are centralised. Centralised generation (CG), which relies fundamentally on nuclear and fossil fuels, normally uses large power systems. These systems, generally built at strategically located sites, use extensive networks to transport and distribute the electrical energy to different areas of consumption, which can be some considerable distance from the power generation centre. With the growth of technology and the increasing need for energy systems that are sustainable from technical, social, economic and environmental points of view, we have seen the emergence of systems which have been given the name of embedded generation, on-site generation, dispersed generation, decentralised generation, or, more commonly, distributed generation (DG) (Borbely and Kreider, 2001; Turkson and Wohlgemuth, 2001; van der Vleuten, 2006; Chicco and Mancarella, 2009). Though as yet there is no precise and fully agreed upon definition of the term DG (Ackermann et al., 2001; El-Khatan and Salama, 2004; Pepermans et al., 2005; Jurado, 2007; Freris and Infield, 2008), there is agreement on what is one of its basic characteristics, namely that the generators are connected within the distribution network (DN) at medium or low voltages 425 © Woodhead Publishing Limited, 2010

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13.1 Configuration and structure of an electrical energy system.

rather than being connected to the transmission network (at high voltage), as normally occurs with CG (Fig. 13.1). One of the options proposed when setting up DGs is the use of subsystems known as micro-grids (MGs) (Jiayi et al., 2008). An MG comprises a low-voltage (LV) network fed by small-scale generators that can be driven using different energy sources (Poullikkas, 2007), and which supplies power to a variety of loads that are connected to it. Generators driven by renewable energy sources (Katiraei et al., 2007), cogeneration systems (CHP) which combine heat and power (Alanne and Saari, 2004; Breeze, 2005), and more traditional technologies such as diesel engines and gas turbines can be used for the generation system itself. MGs can also comprise various energy storage technologies such as fuel cells, batteries, flywheels, compressed air, pumped storage, superconducting magnetic energy storage (SMES), supercapacitors, etc. An MG can be designed to work safely and efficiently when connected to a DN at low (LV) or medium (MV) voltage, but it can also be designed for operation in isolation from the network (Kaundinya et al., 2009). The coordinated interconnection of the components of an MG, namely the hybrid energy systems (normally renewable and conventional) and the storage and load systems which work via the rapid actions of power electronic systems, can provide sustainable solutions for many remote areas of the world which cannot be reached by conventional electricity networks (Clark and Isherwood, 2004; Nguyen, 2007; Nouni et al., 2008).

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This chapter begins by introducing the reader to micro-grid technology. This is followed by the requirements that need to be taken into account before MGs can be installed in remote areas. Relevant technical design and installation questions are then addressed, and information is provided about simulation techniques, monitoring and control equipment and system optimisation models. The advantages, limitations and future trends of these systems are outlined and, lastly, sources of additional information for the reader are provided.

13.2

Hybrid micro-grid options

The micro-grid concept can be understood as a hybrid power system comprising small-scale sources of power generation (conventional and renewable) and storage devices which supply energy to nearby loads through intelligent coordination of the whole. Micro-grids can be applied to two different types of system: those designed for normal operation in connection with general electrical energy distribution networks, and those designed for operation in permanent isolation from such networks.

13.2.1 Normal interconnected hybrid micro-grids Interconnected micro-grids, suitable for use in countries with reliable distribution networks, are designed so that they can operate autonomously in steady state and be self-sufficient. However, in order to guarantee supply they are interconnected to the general distribution network (at a suitable point of common coupling, PCC), operating in parallel to it (Fig. 13.2). The connection of an MG to the general distribution network gives it a series of technical advantages in comparison to isolated MGs (Seare, 1999; Kaundinya et al., 2009). However, the MG also offers a series of benefits for the DN it is connected to (Pudjianto and Strbac, 2006). Disconnection of the MG from the DN can be undertaken for maintenance reasons or as a result of faults in either of the two grids. If a fault is detected the MG must be rapidly isolated from the DN using some sort of separating device (Barnes et al., 2007; Nikkhajoei and Lasseter, 2007). The connection interface can be made to allow the MG only to import energy from the general distribution network, or to allow bidirectional power flow, so that the MG can also inject power into the DN. This latter option modifies the one-way power flows, typical of power systems (Grainger and Stevenson, 1994), where the flow is from higher to lower voltage networks. Such bidirectional flow affords a high degree of flexibility to the MG operator as, in addition to guaranteeing supply, it also facilitates, economically speaking, optimum management of the purchase and sale of energy.

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Local control & protection (LC)

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LV MV PCC

CC Energy storage

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Load

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Renewable micro-sources Conventional Communication micro-sources

Static switch Substation

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13.2 Example of micro-grid architecture interconnected with centralised control.

MG structures proposed for normal interconnected operation with the general distribution networks range from basic structures (Jiayi et al., 2008) to those which employ local controls (LC) and protections, managed by a micro-grid central controller (MGCC) (Oyarzabal et al., 2006) (Fig. 13.2). A significant number of research projects have been undertaken in the field of interconnected MGs. Various references (Barnes et al., 2007; Hatziargyriou et al., 2007; Katiraei et al., 2007; Kroposki et al., 2008), which also indicate the research consortia, describe a variety of micro-grid topologies which have been implemented in America, Asia and Europe.

13.2.2 Stand-alone hybrid micro-grids There are many areas of the world, often in rural areas far from power plants, that have no electricity (Nfah and Ngundam, 2008; Shaahid and El-Amin, 2008; Kaundiya et al., 2009). As a result of the distances involved, constraints imposed by features of the terrain and the costs associated with the cabling of an electrical supply network, it is unlikely that many of these places will ever enjoy the benefits of installations connected to a main electrical grid (Singal et al., 2007). However, numerous proposals have been made to cover the demand for power in such remote areas through the use of power systems not connected to a main electrical network (Nguyen, 2007; Nouni et al., 2008; Shaahid and El-Amin, 2008; Nema et al., 2009). These systems are usually classified (Hunter and Elliot, 2005) as ‘decentralised’, ‘autonomous’, ‘stand-alone’ or ‘remote’.

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An important characteristic that distinguishes the design of remote MGs from interconnected networks is that the sources of power generation in remote systems have to be sized to completely cover the demands of the connected loads and to have sufficient reserve capacity to deal with any contingencies that might arise. However, energy produced from renewable microsources (wind, solar radiation, waves, etc.) is intermittent in nature. Thus, energy storage in this type of MG is essential, as it enables the adaptation of a random energy supply to the demand. In addition, as isolated hybrid systems do not have the voltage and frequency references that a distribution network provides in interconnected systems, they have a greater degree of complexity in terms of control engineering. Equilibrium has to be found between the output and load to ensure that the voltage and frequency of the MG are kept within acceptable limits. To guarantee such equilibrium at any given moment, the system management cannot dedicate itself exclusively to traditional power generation controls (feedback method), but instead also has to look at the loads, some of which it might on occasions be convenient to disconnect, connect or adjust (feedforward control). In this way, greater efficiency can be achieved in the use of renewable energies (Twidell and Weir, 2000).

13.3

General comments on the technological components of isolated micro-grids

The technologies that are most commonly proposed when setting up hybrid energy systems of remote MGs can be classified into technologies of power generation, of energy storage and of conversion and control (Fig. 13.3). Power generation technologies can be further subclassified into conventional and renewable types. Proposals for the conventional type generally include reciprocating internal combustion engines (RICE) (Shaahid and Elhadidy, 2007; Wichert, 1997; Shaahid and El-Amin, 2008), gas microturbines (Willis and Scott, 2000; Breeze, 2005; Amorin et al., 2006; Kariniotakis et al., 2006; Poullikkas, 2007) or Stirling engines (Corria et al., 2006; Lora and Andrade, 2009). The most commonly proposed renewable technologies are those of wind turbines (Diaf et al., 2008a), photovoltaic (PV) solar cells (Wichert, 1997; Kroposki and DeBlasio, 2000; Wichert et al., 2001; Dakkak et al., 2003; Shaahid and Elhadidy, 2007; Diaf et al., 2008a; Shaahid and El-Amin, 2008), fuel cells (Vosen and Keller, 1999; Laminie and Dicks, 2002; Barbir, 2005; Breeze, 2005; Khan and Iqbal, 2005b; Ramírez et al., 2008) and hydraulic turbines (Bueno and Carta, 2004b; Katsaprakakis et al., 2008). However, in coastal areas other renewable sources of energy can be employed, such as wave energy (Clément et al., 2002; Cruz, 2008), ocean current energy (Breeze, 2005), etc.

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Fuel cells Stirling engines

Wave energy technologies Ocean current technologies

Compressed air Supercapacitors Superconductors

Solar Biomass energy

13.3 Technologies for stand-alone hybrid micro-grids.

Proposed storage technologies, which can act as source and load, include batteries, flywheels, pumped storage and hydrogen storage (Bueno and Carta, 2004a,b; García and Weissen, 2006; Katsaprakakis et al., 2008; Smith et al., 2008; Strzelecki and Benysek, 2008). However, the possible use of other technologies should not be discarded, including superconducting magnetic energy storage (SMES), supercapacitors, compressed air (TerGazarian, 1994; Breeze, 2005), etc. Hybrid systems can comprise microsources that generate alternating current (AC) and microsources that generate direct current (DC). Hybrid systems can also have storage devices that supply and consume DC power. For example, PV solar cells, fuel cells and batteries are characteristic sources of DC. However, most loads usually consume AC power. Therefore, there is a need for power electronic converters which convert one type of current into the other to ensure that the various components can work together (Kroposki et al., 2006). Depending on the configuration of the MG, there may be a need for DC/AC converters, AC/DC converters, DC/DC converters, AC/AC converters, and bidirectional converters (AC/DC and DC/AC).

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13.3.1 Reciprocating internal combustion engines These engines can generate electricity if an electric generator is coupled to the output shaft and the waste heat can also be used for thermal applications (co-generation) (Mahon, 1992; King and Knight, 2002; Makartchouk, 2002). RICE are classified into Otto or diesel engines depending on how the fuel–air mixture is formed and ignited. They are suitable for operation both under continuous output and as a standby generator. Most small and medium-sized engines employed for power generation tend to use the fourstroke cycle rather than the two-stroke cycle. For capacities of between 10 kW and 4 MW, the rotation speeds are usually between 1000 rpm and 3600 rpm, depending on the grid frequency (50 or 60 Hz) with which the generators coupled to the engines have to synchronise. RICE can also operate under partial load conditions. Diesel engines, which can burn a wide range of fuels (including biofuels and oil-derived fuels), operate very well when dealing with decreases in load from 100% to 50%. Figure 13.4 shows one of two 60 kW diesel engines which were installed with an asynchronous wind turbine of 227 kW rated capacity to supply energy to a small, remote MG on the island of Fuerteventura (Spain) (Carta and González, 2001; Carta et al., 2003a).

13.3.2 Gas microturbines Figure 13.5 shows a schematic outline of a single cycle gas turbine (Poullikkas, 2007). Gas microturbines (25–300 kW) usually incorporate the

13.4 Diesel engine of a wind–diesel hybrid system installed in the Canary Islands (Spain).

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Shaft Compressor Combustion chamber Turbine Air (high pressure)

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Generator

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Fuel

13.5 Conceptual outline of a gas turbine.

strategies of the larger turbines and tend to be classified into two groups depending on the rotation speed (Poullikkas, 2007; Breeze, 2005). Those with hundreds of thousands of revolutions per minute (Willis and Scott, 2000; Kariniotakis et al., 2006) are normally directly coupled to a permanent magnet generator, with no gearbox inserted between it and the turbine. Power electronic converters are used to adapt the high-frequency output (1500–4000 Hz) to the grid frequency (50–60 Hz). The synchronous machine, fed from another source (batteries, for example) can function as a starter motor for the turbine, thereby simplifying the system design. The other type of microturbine normally rotates at speeds in the order of 3000 rpm and is connected to a conventional induction or synchronous machine through a gearbox (Kariniotakis et al., 2006). Gas microturbines can generally use a variety of fuels, including natural gas, gasoline, diesel, kerosene, naphtha, alcohol, propane and methane. However, most commercial devices nowadays use natural gas (Kroposki et al., 2006). One drawback to the use of gas microturbines is that their performance decreases sharply when working under partial load. Though the performance of gas microturbines, originally designed for commercial applications, is not comparable to that of large gas turbines, they can be used in MGs for electricity production and co-generation.

13.3.3 Stirling engines Stirling engines are alternative machines of external combustion (Senft, 2007). They are highly versatile engines in that they can use solar energy concentrated at a hot focal point (Willis and Scott, 2000), geothermal

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energy, or they can be run using biomass (Lora and Andrade, 2008; Corria et al., 2006) or other fuels. There are three basic configurations of Stirling engine, alpha, beta and gamma (Corria et al., 2006), but they all have the same operating principle, namely the difference in temperature between a hot focal point and a cold area of the engine. If this shaft is connected to an electric generator an electric current will be obtained (alternating or direct). With the technology currently available the performance of the Stirling engine is fairly constant (70–75% in comparison to the Carnot engine) under a wide range of operating conditions. In addition, as there are no intermittent explosions it is a notably quiet engine. However, though it can react to load changes as quickly as a diesel engine, the required regulation mechanism is more complex. The Stirling engine is presently at development stage and, though some have been built and marketed, their use is not widespread. The typical size of those models that are in use or under development ranges from 1 to 150 kW.

13.3.4 Power electronic converters Broadly speaking, the function of the technology associated with power electronics is to interrupt, control and convert the flow of electrical energy with the purpose of supplying voltages and currents that optimally satisfy load requirements. Power electronic equipment basically comprises a power circuit and a control circuit (Fig. 13.6). The power circuit connects the power input to the power output and consists of power semiconductors (diodes, transistors,

Power electronic system

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AC Information

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Information

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13.6 Block diagram of power electronic equipment.

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thyristors) and passive components (inductances, capacitors, resistances, etc.). The control circuit regulates the power circuit based on the signals it receives from it, from the load and from the power source. The device control system can communicate with a remote control and supervision system. Figure 13.7 shows the basic functions of power converters. The name given to a converter depends on its function. Autonomous DC to AC converters are known as inverters and are circuits which supply an alternating current of fixed or variable frequency from a DC input. The relationship between the DC and AC voltage can be variable. Rectifiers are converters that transform AC to DC. When they are exclusively made up of diodes the relationship between the input and output voltage is constant and, therefore, they are known as non-controlled rectifiers. When they have power transistors or thyristors the relation between the input and output voltage is variable and, therefore, they are known as controlled rectifiers. Direct converters of AC to AC can vary the Rectifier AC

DC

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13.7 Basic functions of power electronic converters.

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output voltage and frequency with respect to the input values. This conversion can be single-stage or two-stage via a DC link. The output voltage and frequency can be varied with respect to their input values via the cascade connection of a rectifier, controlled or not, and an autonomous inverter. With this configuration, since the power is transformed by two converters, the performance in cases of high power will be less. DC to DC converters are usually called choppers. Classification of these converters is varied, though three basic types can be distinguished: (a) DC/DC stepdown (or buck) converters, which give a lower output than input voltage; (b) DC/DC step-up (or boost) converters, which give higher output than input voltage, and (c) DC/DC buck–boost converters, which can generate an output voltage higher, lower or equal to the input voltage. It should be mentioned that this latter type of converter inverts the polarity of the output voltage. For a greater understanding of these types of devices there are general works of reference (Mohan et al., 2003; Rashid, 2006) and more specialised works of reference that can be consulted (Gyugyi and Pelly, 1976; Mitchell, 1988).

13.4

Architectures for stand-alone hybrid micro-grids

Figure 13.8 shows, with the exception of control and communication elements, some of the multiple possibilities for MG architectures that can be presented when combining types of energy sources, energy storage devices and loads. However, proposed architectures are usually classified into just a few main types. There are, for example, what are known as modular centralised DC bus architectures (Vandenbergh et al., 2001; Lingemann et al., 2008; Müller, 2008; Strzelecki and Benysek, 2008), modular centralised AC bus architectures (Carta and González, 2001; Bueno and Carta, 2004a,b; Lingemann et al., 2008; Mauch, 2008; Müller, 2008; Strzelecki and Benysek, 2008), hybrid architectures (Moutawakkil and Elster, 2006; Müller, 2008) and modular distributed AC bus architectures (Vandenbergh et al., 2001; Lingemann et al., 2008; Mauch, 2008). Some authors define modular centralised DC bus architectures (Fig. 13.9) as those in which each of the devices of the hybrid power system is connected to a main DC bus-bar, and from which they are connected to an AC bus-bar, where the energy is supplied to the loads that consume AC. However, other authors call this a hybrid DC/AC. For these authors, DC architectures are those which supply energy to DC loads, and there is therefore only DC/AC conversion when the MG is of the interconnected type (Strzelecki and Benysek, 2008). As can be seen in Fig. 13.9, in DC architectures which supply energy to loads that consume AC, the devices that generate AC have to use rectifiers to convert AC to DC and the devices

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DC storage: batteries

Fuel cell

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DC loads Photovoltaic array Hydrogen production: electrolyser

Other DC loads

13.8 Various configuration possibilities of an architecture for a remote MG.

that generate DC may need to use DC/DC regulators. Batteries need to use bidirectional converters to allow them to act as load and energy source. These converters have to provide a constant output voltage, regardless of the variations that may be present in the input voltage. To satisfy the users’ AC demands inverters have to be installed between the DC network and the AC network. In this architecture, the overall efficiency is reduced as a result of the different conversions that are required. In modular centralised AC bus structures (Fig. 13.10) each of the devices of the hybrid system is connected to a main AC bus-bar, from which energy is supplied to the loads that consume AC. If there are devices that generate DC then these architectures require inverters to carry out the DC/AC conversion. From an efficiency point of view, these systems present certain advantages over DC architectures. These structures can be used in remote areas and regions under development where electricity and potable water are basic necessities that need to be covered (Carta and González, 2001; Carta et al., 2003a). Architectures that fall under the concept of a modular distributed AC bus system (Fig. 13.11), differ from centralised systems in that they supply energy to the users from different points of the MG, which can have a radial

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or mesh-type structure. These structures cause the control to be more complicated than that of centralised architectures. An example of this type of distributed architecture is the architecture employed on the Greek island of Kythnos (Vandenbergh et al., 2001, 2008; Hatziargyriou et al., 2007).

13.5

Control and monitoring of hybrid micro-grids

Whatever its architecture, a remote MG requires some sort of control or coordination between the various components (generation, storage and loads) for all of them to contribute to ensuring the correct operation of the system.

13.5.1 Load control Traditionally, in conventional systems, equilibrium between output and consumption has been achieved by controlling output. However, in hybrid systems integrated in remote MGs there must also be some control of the

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13.10 Modular centralised AC bus architecture.

loads. Therefore, certain loads may receive connection/disconnection orders from a control system in order to automatically adjust consumption and ensure the equilibrium of the system. Dump loads are situated among controllable loads, and their most important function is to balance the system when the maximum instantaneous power output is higher than that demanded by the load (Carta and González, 2001; Carta et al., 2003a). When feasible, however, these normally resistive-type loads can be used for domestic water and general heating systems. In order to improve the operation of the MG, apart from the loads attempts also have to be made to control interruptible demand, minimising end-user problems. Such control requires ranking of the loads by criticality. In this context, critical loads which cannot be left without a power supply and may require different power quality levels have to be differentiated from controllable loads. The latter are loads that may be interrupted or accept a high degree of regulation of consumption without end users being affected at any moment. For this type of load there is a need for elements of communication with a local and/or central control system. Loads that

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PV

W tur ind bin e Control, management and monitoring

13.11 Modular distributed AC bus architecture.

can feasibly be temporarily interrupted are, for example, those with thermal inertia (air-conditioning, heating, etc.), certain applications of water pumping and desalination plants (Rozakis et al., 1997; Setiawan et al., 2009) for sea or brackish water, which need to be installed in areas with no potable water (Essam et al., 2008) for domestic or irrigation use. These loads which present flexibility that enable supply to be displaced in time are usually called deferrable loads (Kondoh et al., 2004). Desalination plants can be connected during periods of maximum energy production of the MG and the water stored in appropriately sized tanks ensuring user satisfaction without loss to the quality of the service (Carta and González, 2001; Carta et al., 2003a). There are loads that can adapt consumption to the energy supply with a certain margin, called adaptable loads. One example that can be mentioned is that of desalination plants. These plants can be divided into small capacity modules, which can be connected and

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disconnected from the MG in order to adapt to energy variations and achieve equilibrium (Carta et al., 2003b). In this case, central tanks also have to be used (and/or distributed among the places of residence). By continuously varying the operating parameters of these plants (pressure and flow in the case of reverse osmosis (RO), and voltage and flow in the case of electrodialysis reversal (EDR; Veza et al., 2004), to a certain degree it is also possible to have continuous variation of the power demand of the plants in order to adapt to the energy supply without having to order their disconnection. Studies undertaken of different desalination technologies (Carta et al., 2004; Subiela et al., 2004) have shown that RO systems (for both sea and brackish water) and EDR systems (for brackish water) (Veza et al., 2004) can be used to stabilise MGs without significantly affecting the quality of the water produced.

13.5.2 Control and monitoring strategies A remote MG requires a power management strategy and an energy management strategy. The response speed of these strategies is more critical for an MG than for conventional power systems (Katiraei et al., 2008). The MG control subsystem has to ensure the stability of the MG at any given moment, providing the correct quality of electrical supply. The main functions of this subsystem are voltage and frequency control and/or active/ reactive power control (Katiraei et al., 2008). Thus, for example, the power vs. frequency droop control (Lasseter, 2002), which is often used in large generators of conventional grids, can also help make MGs more robust (Piagi and Lasseter, 2006). Various control modes can be used to achieve stability. Barnes et al. (2007) broadly classify three types of control mode depending on how the transient stability is reached: physical prime mover, virtual prime mover and distributed control. In the first type, generation equipment, or a storage device of large capacity compared with the other components, acts as the master device responsible for ensuring transient stability between output and consumption. In the second type, a central controller coordinates a set of MG components (generation, storage, loads) using a fast telecommunications network, so that the devices behave as if they were one just one component. With distributed control, each generation and storage device, as well as each load, has a level of intelligence that enables it to take decisions compatible with the other components. In this context, it should be mentioned that power electronic converters can constitute elements of intelligence and of local control (Strzelecki and Benysek, 2008). The different types of control mentioned above are not incompatible. In fact, the quality of an MG would increase if a combination of these controls were implemented, as normally occurs in conventional grids.

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Using local controllers as support, the micro-grid central controller (MGCC) will be responsible for optimum regulation of energy storage and power generation, as well as controlling consumption (loads), performing monitoring tasks of the elements that make up the MG and guaranteeing the quality of the electrical supply. Bearing in mind the use of intermittent sources of generation (wind, solar radiation, etc.) in a hybrid energy system, it would be useful for the MGCC to have short-term prediction models (physical, statistical or physicalstatistical) to maximise its control planning (Fig. 13.12) (Reddy and Ranjan, 2003; Lange and Focken, 2005). It would likewise be ideal for the MGCC to be able to use algorithms which, based on demand forecasts (electrical and thermal), could optimise the performance of the MG from a technical and economic point of view in the estimations of renewable generation and availability of the different generation and storage components. If the MG is an interconnected type, the MGCC will also have to take into account parameters related to the electricity market (Tsikalakis et al., 2006; Tsikalakis and Hatziargyriou, 2008). Continuous monitoring and data recording are of vital importance for hybrid systems integrated into remote MGs when undertaking control and management tasks (including maintenance). These monitoring systems

Deferrable loads

Adaptable loads

Dump loads

Power

Electricity

Heat Time

MGCC

Signals

Real-time Intelligent control and management

Short-term prediction Management

Monitoring

Time

Power

Variable sources

PV

Wind turbine

Time

13.12 Diagram of control and monitoring system of a remote MG.

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have to be able to use supervisory control and data acquisition (SCADA) technologies with communication protocols that enable very fast data acquisition from the MG components and to send commands. It should be mentioned that there are commercially marketed protocols that enable communication in distributed environments. These include CAN-bus (controller area network), LON-Bus (local operating network), etc. In order for the monitoring system to be able to handle massive data flow, it is extremely important that the data can be processed efficiently and safely and that such data can be easily stored and recovered. Fibre optic cable (with appropriate fibres) is a suitable means for data transmission in an MG. The type and number of parameters that have to be measured and recorded, as well as the frequency of measurement, will depend on the MG architecture, on its different components and on the chosen control and management strategy. In any case, the parameters of the generation, storage and consumption devices must be monitored. Among the large number of parameters that can be monitored, the following should be mentioned: system frequency and voltage, load level of the storage devices, fuel consumption, active and reactive energies, start-ups and shut-downs of the various components, weather data, interruptions, failures, etc. Correct statistical treatment of the information gathered constitutes a valuable tool to ensure good maintenance and management.

13.6

Design and construction of hybrid micro-grids

Before designing and constructing an MG it is of vital importance to know the typology of the loads that will be connected to it and the quality of the required power, as well as to estimate the evolution of demand over time. An analysis of loads should take into account the performance of the loads in both steady and transient states. For example, in transient periods of connection some loads can generate current consumption peaks that must be taken into account. It will also be necessary to consider the imbalances that a high number of single phase loads can generate in a three phase system and the effects that these can have on the MG generators. In addition to the effects that certain loads can have on power generation, consideration must also be given to the disturbances that the generation system can cause to the loads. As for generation, an analysis must be made of the local characteristics of the renewable energy sources that can potentially be utilised, as well as the availability of personnel for system maintenance. The characteristics and design of the MG will also be related to the local conditions where the MG is to be set up (topography, climate, remoteness, etc.). In the following three sections a few comments are made regarding analysis of the demand, guidance is provided for the procedures and techniques that can be used for resource estimation, and some technical con-

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siderations in terms of design, installation and verification that are relevant for the integration of a hybrid energy system into a remote MG are described.

13.6.1 Analysis of demand In isolated hybrid systems it is of fundamental importance to analyse the type of loads that will be connected to the MG and to estimate their temporal evolution. A study performed on a long-term scale (of at least 1 year) will enable ranking of loads by criticality. This, when later drawing up a management strategy design, will help to ensure that critical loads can be prioritised when distributing the energy. The overlaying of the anticipated curves of the mean seasonal and daily evolution of demand with the estimated curves of renewable energy supply will make clear any existing mismatches between the two. Measuring and recording the daily and seasonal periods of energy mismatches, together with an analysis of the differences between the maximum and minimum load demanded, can be used as a basis for decisions when sizing the energy storage devices and setting the capacity and number of conventional energy sources that need to be integrated into the MG. The types of load that can potentially be connected to an MG are very varied: domestic, public, commercial, small industries, agricultural, cattle farming, etc. Electrical energy consumption in the domestic (residential) sector will depend on the geographical area, climate and standard of living of its inhabitants. Information should therefore be gathered about the type of appliances (cooker, TV, fridge, water heaters, freezers, washing machines, driers, dish-washers, other small appliances, etc.) and lighting used in the homes, as well as their specific consumption. Likewise, domestic electrical and thermal consumption habits need to be analysed in terms of level of consumption, daily distribution over time (for work days, weekends, etc.) and seasonal distribution (Willis and Scott, 2000; Ter-Gazarian, 1994). These studies should be carried out meticulously, taking into consideration the family structure (number of people, ages, etc.), type of dwelling, etc. (Eto and Moezzi, 1997). Depending on the results of this analysis, it may be possible to replace equipment of high consumption with other of lower consumption. Given that the balance between generation and load is extremely important in small isolated MGs, it is important to analyse the extent to which the domestic energy consumption habits can be modified without reducing the users’ quality of life. It may be possible to condition the operating periods of certain appliances to the power generation of the MG. In this case, the users could modify the shape of their daily consumption curves, changing the time of use of certain of their appliances with the aim of improving the system performance, or they could accept that some

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13.13 Estimated seasonal variation of power consumption in a small fishing village in the Canary Islands (Spain).

appliances would be connected/disconnected from the MG by a control system. Unlike the residential sector, the types of load in commercial and industrial activities tend to be very different from each other and need to be analysed on an individual basis. Figure 13.13 shows the seasonal variation of power consumption estimated when designing a wind–diesel system to supply electrical energy for domestic consumption, potable water and other services (sewage plant, freezer plant, public street lighting, winch to haul boats in from the sea, etc.) for a small fishing village on the island of Fuerteventura (Canary Islands, Spain) (Carta and González, 2001; Carta et al., 2003a). With regard to the management strategy, it was decided that all the water would be desalinated using wind energy only in the summer months (July and August), as this was the period of highest wind intensity. A tank was designed to store the treated water for the rest of the year. Figure 13.14 shows the mean daily power curves for various typical days.

13.6.2 Estimation of the resources The selection of the type of renewable energy that can be installed in an MG requires a previous analysis of that resource (Nema et al., 2009). The results of such an analysis will provide necessary information about the generation capacity that needs to be installed for a given demand, as well as the distribution over time of that resource.

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70

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Hours

13.14 Estimated mean daily power consumption curves for various typical days in a small fishing village in the Canary Islands (Spain).

Although nowadays there are various energy sources that can be integrated into an MG (Willis and Scott, 2000; Kroposki et al., 2006), the renewable technologies that are most commonly proposed are those of wind turbines and PV solar panels. Wind speed and direction data are required for the area where it is planned to install the wind turbines in order to provide estimations of the wind resource. Since, as a result of friction with the land surface, wind speed varies with height above ground level (Justus, 1978; Freris, 1990), it is advisable for the measuring devices to be installed on a tower of the same height as the hub of the proposed wind turbine. However, as an alternative, measurements can be taken at lower heights (generally 10 metres above ground level) and use can be made, with a degree of caution, of logarithmic (Freris, 1990; Gipe, 1995) or power (Mikkhail and Justus, 1981; Dixon and Swift, 1984; Freris, 1990; Spera, 1995) models. For accurate evaluation of the wind potential certain guidelines are usually accepted regarding the frequency and duration of measurements (Freris, 1990; Burton et al., 2001). An analysis of the recorded wind speed data has to be carried out to decide how best to exploit the wind energy in order to determine the power that can be obtained for a particular wind turbine. Using the temporal evolution of the recorded wind speed, time and frequency distributions are usually constructed. The frequency distribution can be used to calculate the mean energy extractable with a wind turbine with the help of a probabilistic calculation method (Ramírez and Carta, 2005; Carta et al., 2008, 2009). Temporal distributions of wind speed facilitate observation of the mean daily performance in a given period. Figure 13.15 shows the mean daily evolution for the months of highest and lowest wind intensity of an

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12 Wind – August 11

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Wind vane

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13.15 Mean daily evolution of wind speed at an anemometer station on the island of Gran Canaria (Spain).

anemometer station located 10 metres above ground level on the island of Gran Canaria (Spain). The mean wind speed fluctuations for these months can be seen, as well as the periods of the day when the wind blows at its highest intensity. The monthly mean wind speeds over a 1 year period (Fig. 13.16) show the seasonal behaviour of the wind and, therefore, the months in which the resource is most abundant and the months in which the wind will supply less energy. To estimate the solar resource it is necessary to have data for temperature and solar radiation on the surface of the PV solar panels in the area of their installation. The guidelines recommended for the duration of the measurement period are similar to those mentioned above for wind speed data. Most of the devices proposed for solar radiation measurement are variations of two basic types (Twidell and Weir, 2000), namely variations of the pyroheliometer, which measures beam (or direct) radiation, and the pyranometer, which measures global radiation (beam and diffuse). The instruments generally used to analyse solar radiation on a flat collector are thermometers and pyranometers which provide information about the air temperature and total irradiance on a horizontal surface. These devices generate a signal which, as with anemometers and weather vanes, is transmitted to another device where the information is processed to facilitate its interpretation and/or storage. An analysis similar to that performed for wind speed data has then to be carried out using the recorded temperature and radiation data. Figure 13.16 shows the seasonal variations in total irra-

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14 Wind

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13.16 Mean monthly wind speeds and solar irradiation over a 1 year period in an area on the island of Gran Canaria (Spain).

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13.17 Mean daily evolution of solar irradiation and air temperature at a weather station installed on the island of Gran Canaria (Spain).

diance on a horizontal surface recorded on the island of Gran Canaria (Spain), in the same place and over the same period of time as for the recorded wind speed data. Figure 13.17 shows the mean daily variations in irradiance and temperature.

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13.6.3 Designing and installation of a hybrid energy system A number of factors (Fig. 13.18) need to be taken into consideration when choosing the types of micro-sources of power generation and the storage devices that need to be combined, and when deciding upon the control and management strategies that are to be used. Of especial importance among these factors are the types of renewable energy resources available, the temporal evolution of the estimated amounts of energy that these sources can supply, the geographical and topographical characteristics of the remote area (related to climate, transportation of the MG components, the possibility of using pumped storage, etc.), the typology of the loads and the characteristics of the demand, the associated structural and operating costs of the MG and the level of quality of the energy supply demanded of it. The considerable number of factors involved in the design and construction of an MG and the relatively limited know-how currently available for such systems (with the exception of wind–diesel systems), means that the choice of technology, sizing and location for the system, so that it provides a suitable level of energy supply quality at minimum cost, is a challenging task for engineers. The consumption of conventional fuels should be minimised for MGs to be sustainable from technical, social, economic and environmental points

Deferrable loads

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Types of loads

Consumptions Access to transport

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Available techonologies DESIGN

Geographical and topographical characteristics of the zone Soil properties

Legislative issues

Demand = function (time) Environmental impact Renewable power = function (time)

13.18 Factors which condition the design of an MG.

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of view. Attempts should be made to introduce renewable energy sources with established and reliable technologies and storage devices that eliminate the time mismatches between generation and consumption. It should be mentioned that at the present time most research activity is aimed at developing MGs interconnected to reliable networks (Barnes et al., 2007). The design of such MGs differs from that of remote MGs, as there are usually fewer technical demands on its components. These MGs, when operating in stand-alone mode, do not normally cover the full demand of the loads. Interconnected MGs operate in stand-alone mode only for a small percentage of their operating time. There are also normally sufficient human and technical resources available at the site where interconnected MGs are located to ensure their correct installation, construction and maintenance. Indeed, one of the principal questions that engineers and constructors have to plan for and resolve when designing and building a hybrid energy system for integration into a remote MG is precisely the remoteness of such human and technical resources and the consequent lack of available know-how. Such circumstances mean that exceptional importance have to be given to the verification tests, which have to provide a specific level of confidence in terms of reliability of the components and their operation in the system. Such tests must figure in the list of technical conditions in the engineering project (Fig. 13.19). Ideally, and if possible, full and complete testing will be undertaken before transportation, using simulated loads, on individual and assembled components (or a simulation of them), as they are expected to operate in field conditions. Once the equipment has been selected it can be transported and installed following the specifications as indicated in the engineering project. The technical means should be specified in the project document as to how the transport and installation have to be carried out at the chosen site. Clearly detailed assembly and maintenance manuals should also be included. Once the MG components have been installed and calibration performed, the system has to be evaluated by following a programme of trials drawn up in the system design stage and which should also figure in the engineering project. The system should not be handed over to its operators until its reliable operation under the most extreme situations envisaged has been confirmed. It is also extremely important that the staff who will be in charge of the system’s operation have been fully trained before responsibility is handed over to them. The extent of the training will obviously depend on the complexity and capacity of the MG. When this training task is omitted the usual result is system failure. Collective systems of certain power, which require user management, need professionalisation of the maintenance aspects. The demands are less in the case of individual systems of small power.

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Stand-alone and hybrid wind energy systems Control, management and monitoring systems

Architecture

Components Verification tests Commissioning

Maintenance

Technical conditions

System assembly

Sources of finance analysis

Transportation

MG project document

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Request offers to the suppliers

Obtaining authorisations signing contracts

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Construction and operation start

Electrical infrastructure Commissioning

Telecommunication infrastructure

Installation of the system

Operation and maintenance training for personnel

13.19 Construction process diagram of an MG.

13.7

Modelling and simulation of hybrid micro-grids

Mathematical models are used for sizing the generation and storage devices of a remote MG and checking its performance in transient situation. Operating strategies and special features (resources and loads) of the area where it will be installed are taken into consideration. A review has been undertaken (Bernal-Agustín and Dufo-López, 2009) of the design and simulation models that have been used to date of standalone hybrid systems for the generation of electricity. As pointed out in this reference, the complexity of models of hybrid system components depends, principally, on the type of application (sizing, quasi-static state simulation, dynamic state simulation, etc.). The most commonly used models can be classified into two main groups: chronological methods (or time-series) and probabilistic methods. Timeseries models can in turn be classified in terms of the time step into long-

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term performance or quasi-dynamic models and short-term performance or dynamic models.

13.7.1 Quasi-dynamic models Quasi-dynamic models normally use time steps in the range of a few minutes to 1 hour (1 hour is the typical time step) and are generally utilised to simulate the overall system performance for at least one year. The input data (meteorological and of demand) are therefore usually data series with time steps of between 10 minutes and 1 hour. These models have two objectives, firstly to determine the energy balances in each time step, the periods of starts/stops of the system components (diesel, wind turbine, PV, etc.), the periods of storage device charging and discharging (batteries, pumped storage, etc.), fuel consumption, reduction of CO2 emissions into the atmosphere, dump load energy losses, etc., and secondly to provide information for an economic analysis of the system (net present value, payback period, levelised costs). A high percentage of the models used belongs to this group (Vosen and Keller, 1999; Manolakos et al., 2001; Wichert et al., 2001; Dakkak et al., 2003; Bueno and Carta 2004b; Khan and Iqbal, 2005b; Agrawal et al., 2007; Kaldellis, 2007; Shaahid and Elhadidy, 2007; Deshmukh and Deshmukh, 2008; Diaf et al., 2008a; Himri et al., 2008; Katsaprakakis et al., 2008; Shaahid and El-Amin, 2008). There are several software applications available including, amongst others, HOMER, HYBRID2, SOLSIM, SOMES, RAPSIM, HYBRIDS, INSEL and HOGA, that can be used for quasi-dynamic models (Hansen and Lundsager, 2000; Agrawal et al., 2007; Bopp and Lippkau, 2008; Bernal-Agustín and Dufo-López, 2009). Most of them enable simulations to be performed, fundamentally with 1 hour time steps, of PV–wind–diesel–battery hybrid energy systems. In addition, with some of these applications (HOMER, HOGA, HYBRID2) mini-hydro, hydrogen load, fuel cell, electrolyser and hydrogen tank simulation can be performed. HOMER, the most commonly used hybrid system simulation software, also allows thermal loads to be included. Wind turbine modelling Using the power curve supplied by the wind turbine manufacturer, the temporal evolution of the extractable electrical power can be estimated with a time series method (Carta et al., 2008). Figure 13.20 shows the power curves of various wind turbines of slow-rated capacity as a function of the wind speed at hub shaft height. Though the power curve of a wind turbine depends on the wind speed and air density (Carta and Mentado, 2007), in Fig. 13.20 the standard value of air density has been used (1.225 kg m−3).

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13.20 Characteristic power–speed curves of two wind turbines.

Some authors use analytic curves with linear, quadratic or cubic forms, or a combination of these, to represent the power curve PWT(v) of a wind turbine (Haslett and Kelledy, 1981; Koeppl, 1982; Jones 1986, 1988; Celik, 2003; Chang and Tu, 2007). However, the manufacturers do not usually supply information of the power curves of their wind turbines in continuous form, but rather in discrete form with N nodes (Pi, vi). Since the power curves are quite smooth it is possible to carry out an approximation (Carta et al., 2008) which consists of assuming that the variation between two nodes of the power–wind speed curve is linear. Then, given two points ‘i’ and ‘i + 1’ of the power curve, power as a function of speed in this section can be written as Equation 13.1. PWT(v) =

Pi +1 − Pi (v − vi ) + Pi vi +1 − vi

vi ≤ v ≤ vi +1

13.1

PV panel modelling Estimation of the output of a PV can be performed by using characteristic I–V curves of the chosen panels for different irradiances and temperatures, which are empirically obtained. Figure 13.21(a) shows, by way of example, the I–V curves of a panel for different radiation levels and constant temperature. Figure 13.21(b) shows a family of I–V curves for constant radiation and different temperature levels. For any point of the characteristic

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Current (A)

Current (A)

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Voltage (V)

13.21 I–V curves of a PV panel: (a) different radiation levels and constant temperature; (b) different temperatures and constant radiation.

I–V curves, the power output is given by the product of the current I by the voltage V. In practice, however, panel manufacturers do not provide curves for each operating condition. Normally, they establish certain reference conditions and nominal panel operational temperature conditions, from which the values can be deduced for other conditions with certain operational suppositions. In effect, the manufacturers tend to provide the cell/module electrical efficiency ηT,ref under standard conditions. These standard conditions are a solar radiation incident GT,ref of 1000 W/m2, (to the standard reference AM 1.5 G spectrum) and a cell temperature Tref of 25 °C. From these data, and through the use of models (Lorenzo, 1994; Castaner and Silvestre, 2002; Luque and Hegedus, 2003; Duffie and Beckmann, 2006) the temporal evolution can be estimated of the extractable electrical power with Ns modules in series and Np modules in parallel, when working under radiation incident GT on the panel surface and cell temperature Tc. Various models have been proposed (Skoplaki and Palyvos, 2009a,b) which correlate the efficiency under standard conditions ηT,ref with the efficiency ηc under other conditions of cell temperature Tc and solar radiation incident GT. Equation 13.2 shows a model that can be used to estimate the electrical power generated by a PV array as a function of the radiation incident GT, ambient temperature Ta and wind speed Vw. The most traditional linear expression has been used in this equation for electrical efficiency (function of ηT,ref, αp, Tc and Tref) (Evans and Florschuetz, 1977), while the Tc expression has been replaced by the expression, dependent on other variables, proposed by Duffie and Beckman (2006):

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Stand-alone and hybrid wind energy systems P (GT , Ta , Vw ) = ⎧ ⎡ GT ⎤ ⎫ 9.5 ⎛ ⎞ ηT, ref ⎨1 + α p ⎢Ta − Tref + ⎜ ⎬ ⎟⎠ (TNOCT − Ta ) ⎝ GT,NOCT ⎥⎦ ⎭ 5.7 + 3.8Vw ⎩ ⎣ 13.2 Am ηoth N s N p

In Equation 13.2, Am is the area of one module, ηoth is a factor (%) which represents other losses, such as those caused by wire resistance, dust accumulation, etc. and TNOCT is the normal operating cell temperature (°C). TNOCT is measured under open circuit conditions (in other words with no connected load) and is provided by the manufacturers. TNOCT is defined as the temperature that results from a radiation incident GT,NOCT of 800 W/m2, an ambient temperature Ta,NOCT of 20 °C and a mean wind speed of 1 m/s. αp is the temperature coefficient of power (%/°C), which depends principally on the material and indicates the effect of the temperature of the panel on the power output. Since the power output decreases as the temperature of the cell increases, αp takes negative values. As with ηT,ref , αp is normally provided by the manufacturer. αp, for crystalline silicon and real operating conditions, takes values close to −0.4%/°C (Lorenzo, 1994). The values of GT should be obtained by measuring on a surface with the same angle of inclination as that of the panels to be used. Often, however, only the global radiation is available measured on a horizontal surface. Since the surface orientation has a notable effect on the beam radiation, the beam and diffuse components of the horizontal radiation have to be determined using theoretical models. Relationships have been proposed between the global hourly horizontal radiation and its diffuse component, based on the clearness index, which is defined as the ratio of surface solar radiation to the extraterrestrial radiation. However, it should be borne in mind that these relationships are not very reliable (Lorenzo, 1994). As the diffuse component can come from different sources, several anisotropic models have been proposed. These models usually consider two or three components of the diffuse radiation (Lorenzo, 1994; Duffie and Beckmann, 2006). To estimate global radiation GT on an inclined surface of the PV various factors have to be taken into account including the slope of the surface, the azimuth of the surface, the latitude, the solar declination, the solar hour angle, the beam radiation on the horizontal surface and the various components of the diffuse radiation (Lorenzo, 1994; Duffie and Beckmann, 2006). Battery modelling Various battery models have been proposed in scientific publications (Salameh et al., 1992; Manwell and McGowan, 1993). Manwell and

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McGowan (1993) proposed a kinetic model to determine the amount of energy that can be charged or discharged from a battery bank at each time step. This model, which is based on the concept of electrochemical kinetics, supposes a battery consisting of two reservoirs. One reservoir stores the available energy, which is the energy that can be converted into direct current. The other reservoir contains the energy that cannot be immediately extracted. The sum total of stored energy in the battery at any moment is the sum of the energy stored in both reservoirs.

Hydraulic turbine modelling The power supplied by a hydraulic turbine (PHP) is commonly expressed in time-series models (Bueno and Carta, 2004a, 2006; Anagnostopoulos and Papantonis, 2007; Katsaprakakis et al., 2008) by Equation 13.3. In this equation, Qt(t) is the volume used by the turbine at the instant t, g is the acceleration due to gravity, ρ is the water density, Hn is the existing net height and ηHP is the efficiency of the system, which is equal to the product of the efficiencies of the various elements that participate in the energy production process: PHP(t) = Qt(t)gρHnηHP

13.3

Conventional generator modelling Conventional generator models are normally considered as a black box (Hunter and Elliot, 2005). In other words, they start from the hypothesis that these generators can supply, at each time step, from a minimum percentage of the load (around 40% of the load) up to their maximum power (Carta and González, 2001; Agrawal et al., 2007). Fuel consumption at each time instant is determined as a function of the power output at that instant. Normally, the fuel consumption percentage as a function of the power output is assumed to be linear for most constant speed internal combustion generators and for microturbines (Fig. 13.22). Quadratic relationships are normally proposed for variable speed diesel generators and fuel cells.

Electronic converter modelling In this type of simulation the power electronic converters constitute a black box. Converter modelling is undertaken using an efficiency which reflects the power loss between input and output.

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Load seen by the diesel generator (%)

13.22 Typical fuel consumption of a diesel generator set.

13.7.2 Dynamic models Dynamic models are described by time-dependent differential equations. These models can operate in timescales of the order of milliseconds or microseconds and use equations of motion of the mechanical and electrical devices to analyse the transient performance (both mechanical and electrical) of the system. They enable system stability to be studied on the basis of frequency and voltage variations and analyses to be performed of electrical and mechanical harmonics. Using the Laplace transform method (Dorf, 1989), which replaces differential equations with algebraic equations, the most complex equations can be relatively easily solved. Models have been developed, with differing degrees of success, to describe the dynamic performance of a wide variety of devices. These include fuel cells (Padullés et al., 2000; Dürr et al., 2007; Khan and Iqbal, 2005a; Kariniotakis et al., 2006; Ahmed et al., 2008; Onar et al., 2008), wind turbines (Das et al., 1999; Cidrás and Feijoo, 2002; Khan and Iqbal, 2005a; Kariniotakis et al., 2006; Ahmed et al., 2008; Leonardo and Reza, 2008; Onar et al., 2008; Sebastián, 2008), photovoltaic panels (Tan et al., 2004; Ahmed et al., 2008; Onar et al., 2008; Li et al., 2009), batteries (Takano et al., 2000; Dürr et al., 2006; Achaibou et al., 2008; Lee et al., 2008), supercapacitors (Onar et al., 2008; Mufti et al., 2009), hydraulic turbines (De Jaeger et al., 1994; Souza et al., 1999; Izena et al., 2006), diesel generator sets (Das et al., 1999; Leonardo and Reza, 2008; Sebastián, 2008), microturbines (Kariniotakis et al., 2006), etc.

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The software most commonly used in the above references for performing dynamic simulation of the hybrid energy system components is that of Matlab/Simulink (Beucher and Weeks, 2006).

13.7.3 Probabilistic models Probabilistic models do not usually require detailed physical understanding of all the system components and interactions (Hunter and Elliot, 2005). Probabilistic models use probability distributions to describe the loads and renewable sources (Carta et al., 2009). From these probability density functions, the PDFs of the components of renewable and conventional power generation which are required for system analysis can be determined (Haslett and Carlin, 1981; Gavanidou et al., 1993; Karaki et al., 1999; Hunter and Elliot, 2005; Kaldellis, 2008). Combined models have also been proposed (Manwell and McGowan, 1994).

13.8

Optimising integration of hybrid micro-grids

The typology of hybrid system architectures that can be proposed in the configuration of an MG for a particular remote area can be remarkable. This is because of the peculiarities of the various renewable energy sources, the plurality and characteristics of the connectable load systems, the variety of potentially usable energy storage systems, the different MG control and management modes that can be devised, etc. Given the different costs associated with each possible configuration and operating mode, the costs per energy unit generated will very probably differ, as will the percentages for fossil fuel consumption and (when applicable) atmospheric pollution, etc. An optimum system is one which at any given moment satisfies demand, with a certain reliability, at the lowest possible cost per energy unit generated. It is evidently important to know what the optimum system is. Simulation models are used for this purpose based on the hypothesis that the MGCC has to undertake optimum management (both technical and economic) of the various components. The associated costs (investment, operational and maintenance) will also have to be taken into consideration for each alternative that is proposed, and selection criteria of alternatives will have to be used. Reviews have been undertaken (Bernal-Agustín and Dufo-López, 2009; Nema et al., 2009) of the most relevant papers published to date on the optimum design, from an economic point of view, of PV and/or wind and/ or diesel systems with energy storage in batteries or hydrogen.

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13.8.1 Economic criteria for selection between alternatives As mentioned previously, costs and reliability are two extremely important questions when it comes to optimising hybrid energy systems into remote MGs. When more than one energy system is technically feasible, use is primarily made of criteria of an economic nature to decide between them. Generally speaking, the most commonly used evaluation criteria analyse life-cycle costing (LCC). Normally, when trying to optimise the system attempts are made to minimise the levelised cost of energy (total cost of the hybrid system divided by the energy supplied by it) (Diaf et al., 2008a,b; Yang et al., 2009) or the net present cost (investment costs plus the discounted present values of all the future costs that it is estimated the system will generate during its lifetime) (Shaahid and Elhadidy, 2007; Dalton et al., 2008). Clearly, the economic optimum will have restrictions set on it by the degree of reliability with which demand is to be covered. The total costs function which has to be minimised can include a customer damage function (CDF) due to power supply interruptions (Georgilakis and Katsigiannis, 2009; Kaviani et al., 2009). The CDF is an index (expressed in c/kW) that depends on the type of user and the length of any interruptions.

13.8.2 Evaluation of system reliability Reliability (Birolini, 2004) is a characteristic of an item (component, equipment, subsystem, system) expressed by the probability that the item in question satisfactorily performs its function during a specified time period and under given operating conditions. The reliability of a hybrid energy system integrated into a remote MG is related to the availability of energy supplied by it to satisfy load demand during a given time period. Demand may exceed supply for two main reasons. One is the random deviation of demand from its expected level when, for example, a demand peak exceeds the system’s installed capacity. System failure can also occur even if the load is not higher than the estimated values. High demand at a given instant, though not greater than the system’s installed capacity, might exceed the available capacity at that instant. Such non-availability of energy for a given period of time could be due to failure of one or more system component (in other words, non-availability related to component reliability) (Tavner et al., 2007). This non-availability of energy can also be due to inadequate atmospheric conditions (wind speed, solar radiation, etc.) which prevent the renewable generators from supplying sufficient energy to cover demand, despite their being sized to do so (in other words, non-availability due to the uncertainty of renewable energy sources). The reliability of such systems is usually evaluated with the use of indices, several of which have been proposed (Georgilakis and Katsigiannis, 2009;

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Kaviani et al., 2009). One of the most commonly used indices is the loss of power supply probability (LPSP), which is the probability that the hybrid system provides an insufficient energy supply and does not satisfy load demand (Abouzahr and Ramakumar, 1990, 1991; Xu et al., 2006). Reliability is also often measured using the loss of load probability index (LOLP). This is defined as the probability that the energy demanded by the load exceeds the energy that the system can supply (Deshmukh and Deshmukh, 2008). While a variety of methods exists for calculation of the reliability indices of a power system (Tina et al., 2006), there are two basic approaches for the use of such indices in the optimisation of stand-alone hybrid systems. One approach, based on chronological simulation, involves heavy computational work and requires demand and resource data for a specific period of time (normally 1 year) (Ofry and Braunstein, 1983). With this approach, the simulation time period increases considerably if modelling, with Monte Carlo simulations (MCS), of load randomness and renewable energy source availability has to be performed (Bakirtzis, 1992). The LPSP for a considered time period is usually calculated as the ratio between the sum of all the values of power supply loss during this period and the sum of the load power demand. An LPSP with a value of 0 indicates that the load will always be satisfied while an LPSP with a value of 1 means that the load will never be satisfied (Diaf et al., 2008a). Normally, for a considered time period T, the LOLP is determined as the ratio between the sum of all the times in which demand is not satisfied and T. The other approach (Bakirtzis, 1992; Ghali et al., 1997; Karaki et al., 1999) is based on probabilistic techniques, which use distribution functions to represent the random nature of the variables that intervene in the system (renewable energy resources, load demand, etc.), thereby eliminating the need for time-series data. In terms of the scope of the simulation, it should be mentioned that models have been proposed that consider the renewable resources (solar radiation and wind speed) and the load demand to be deterministic data. However, they assume that the system components (wind turbine generators, PV arrays, etc.) do not enjoy 100% availability, but are subject to failure (Kaviani et al., 2009). Other models assume the randomness of the renewable energy sources and of the load demand, but consider that the components do enjoy 100% availability (Ghali et al., 1997). There are also models that assume the randomness of the renewable energy sources and the loads and take into consideration failure probability of the system components (Gavanidou et al., 1993; Karaki et al., 1999).

13.8.3 Considerations on the emission of pollutants One parameter which is acquiring more and more importance in energy system designs is the amount of atmospheric contamination that these

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systems can generate (Strachan and Farrell, 2006; Tsikalakis and Hatziargyriou, 2007). In fact, externalities are beginning to play a role in the integration plans of energy resources. In other words, future costs that are the result of pollutant effects of power generation on the environment and human health (costs paid for by society, but not directly through electricity consumption or production) have begun to be added to life-cycle costing (Owen, 2006; Nguyen, 2008). External costs depend on the power generation technology used and vary from country to country. In life-cycle assessment different hybrid system configurations can be compared in terms of the amount of carbon dioxide generated over the complete lifetime of their components. In other words, not only is the contamination produced during the operational lifetime of the components taken into account, but also the contamination generated by the energy that the technology required for their manufacture will have consumed, as well as the energy that will be consumed during the disassembly and recycling of the hybrid energy system. It is clear that most renewable technologies (wind power, solar power, wave energy, hydropower, tidal power, etc.), unlike technologies that consume fossil fuels, do not emit pollutants (carbon dioxide, sulphur dioxide, carbon monoxide and nitrogen oxides) into the atmosphere while operating. Currently, however, if the complete lifetime of the components is taken into account, then contamination has in effect taken place. It should be borne in mind, for example, that silicon is a predominant material used nowadays in PV panels and requires high energy consumption for it to be obtained in pure form. Therefore, for the moment at least, PV panels cause a relatively high level of pollutants to be emitted into the atmosphere as a result of their manufacture with power technologies that burn fossil fuels. In this context, some authors have proposed the use of multi-objective optimisation for hybrid energy systems, aiming to simultaneously cover two or three objectives. Thus, proposals have been made for functions that aim to minimise the atmospheric emission of CO2 as well as the cost of the generated energy unit (Bernal-Agustín et al., 2006), and for functions that pursue the optimisation, in addition to the two parameters mentioned above, of the reliability of the hybrid system (Dufo-López and Bernal-Agustín, 2008).

13.8.4 System control strategies Since renewable microsources (wind power, solar power, wave energy, hydropower, tidal power, etc.) do not generate fuel costs or emit gaseous pollutants into the atmosphere, a feasible control strategy is for them to be the first choice of the optimisation software to supply energy to the system. If the MG has energy storage devices and the energy supplied by the renewable microsources is insufficient, then these storage devices will have to

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complete the energy supply to satisfy demand. If the renewable microsources supply more power than demanded, the storage devices will have to act as loads, absorbing the excess energy. Finally, if the sum of the power supplied by the renewable microsources and the storage devices is insufficient to cover demand, the MGCC will have to connect the conventional microsources. If, for some reason, the total energy supply of the MG is unable to cover the demand, the MGCC will have to perform load disconnection, on the basis of an established hierarchy. However, the control strategies proposed for hybrid systems can be wide-ranging and complex (Barley and Winn, 1996).

13.8.5 Software packages There are various software packages (HOMER, HYBRID2 and HOGA) (Hansen and Lundsager, 2000; Agrawal et al., 2007; Bopp and Lippkau, 2008; Bernal-Agustín and Dufo-López, 2009) that can simplify the task of evaluating such a wide variety of options and obtain an optimum hybrid system configuration. The three above-mentioned software packages consider control strategies, but only HOMER and HOGA enable economic optimization of the system to be undertaken. HOGA (Hybrid Optimization by Genetic Algorithms), developed by the Electrical Engineering Department of Zaragoza University (Spain), is the only one that allows multi-objective optimisation to be performed. However, to date, HOMER (Hybrid Optimization Model for Electric Renewables), developed by the NREL (National Renewable Energies Laboratory) has been the most commonly used.

13.9

Advantages and limitations of hybrid micro-grids

Since there are still many places in remote areas of the world that do not have access to an electricity distribution grid, the implementation of hybrid energy systems to cover the energy requirements of such places would be extremely useful for their inhabitants. Satisfying the energy demand of such areas using custom-made architectures which maximise the use of renewable technologies and respect the environment can be a sustainable alternative to a conventional electrical grid. In some areas, and given certain price scenarios of fossil fuels, such systems could entail, in addition to the economic and social development of the area, a decrease in CO2 emissions and an energy cost reduction. The use of established technologies using renewable energy sources also entails diversification, in energy terms, for developing countries with dependence on other countries for conventional fuels. In fact, most of the more developed countries (the United States, Europe, Japan, etc.), pursuing similar objectives (CO2

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reduction, decrease in energy costs and energy diversification), have begun to invest in MG research and development, particularly in interconnected systems. Bearing in mind the high degree of similarity between both types of grid, the advances that have been made in interconnected MGs could be transferred to isolated MGs. However, the implementation of hybrid energy systems in remote MGs has a number of limitations. One obvious limiting condition is the lack of any renewable and exploitable energy source in a particular area. It should also be mentioned that, for the moment, implementation of such MG systems is not widespread and, therefore, there is a lack of data with which to assess their reliability. The costs of the power electronic devices needed to control the microgenerators are high, and the know-how which is vital for operating and maintenance work is still limited. These MGs can require a high degree of technological sophistication which, in turn, needs the professionalisation of maintenance personnel, something often difficult in remote areas. Investment costs can be high for storage devices that increase the reliability of the energy supply and which compensate for the variability associated with renewable energy sources. Users have to accept that the implementation of energy-saving devices and modification of consumption habits to enable adaptation of demand to supply are fundamental requisites for the correct operation of MGs. Specific legislation will probably have to be introduced in countries where MGs are set up to regulate their use.

13.10 Future trends The most innovative hybrid technologies that have thus far been used to solve problems caused by energy shortage in remote areas have been based fundamentally on systems that use diesel engines and wind turbines, known as wind–diesel systems. In most cases, these systems operate using dump loads and batteries as power control elements (McGowan et al., 1988; Carta et al., 2003a; Hunter and Elliot, 2005). However, the increasing concerns of society with respect to the environment and pessimistic predictions of future oil production have renewed interest in recent years in technological research and development to better exploit the various renewable energy resources (Poullikkas, 2007; Willis and Scott, 2000). Some technologies, such as wind energy conversion systems (WECS), hydropower systems and PV systems have achieved a high degree of sophistication. The first two technologies have so far been most commonly used as systems connected to conventional energy grids. PV cells, which were initially utilised in the 1980s mainly in remote locations, saw their use extended in the 1990s to systems connected to conventional energy grids, and exploitation of this renewable energy continues to expand. Give the extent to which these three technologies have become established and the advances achieved with

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power electronic devices, most of the hybrid systems that have been proposed are based on them. The most commonly used storage system continues to be that of lead– acid batteries. However, in recent years there has been a noticeable increase in proposals for storage systems with devices that are still in demonstration stage or have only recently been marketed. Among others, these include sodium–sulphur batteries and flow batteries, for which there is limited operating experience. Likewise, magnetic superconductors and supercapacitors are relatively new technologies that have begun to be put forward for use, but about which there are presently no experimental operating data. Hydrogen-based fuel technology is still in an experimental phase, though its development and marketing have started, and will probably form part of future MGs given its acceptable operation under a wide load range. With regard to other power generation components, it should be mentioned that, in addition to traditional alternative internal combustion engines, gas microturbines have been fully developed and can now also be used. Stirling engines, however, as they are still in the research and development stage and their cost is high, will probably not be integrated into remote MGs in the near future. Other energy sources, principally sea-related, are still in the development and marketing stage, but will probably soon be able to form part of remote coastal MGs. Particular mention should be made here of those devices which exploit wave energy or extract ocean current energy. The development of the concept of the MG and the technologies that it entails will require a lot of work and effort to resolve questions on a wide range of subjects that remain unanswered. Research and development activities are currently being undertaken in many parts of the world to enable MGs, particularly of the interconnected type, to form part of the world’s future energy system (Coll-Mayor et al., 2007; Domijan et al., 2007; Hatziargyriou et al., 2007).

13.11 Sources of further information and advice In addition to the references quoted in this chapter, the following websites constitute sources of information which the reader can consult to find more information about MG research, development and demonstration (RD&D) projects which are presently being undertaken in various countries. • •

RD&D activities in Europe: http://www.microgrids.eu. RD&D activities in the US: http://www.certs.lbl.gov http://www.electricdistribution.ctc.com http://nrel.gov

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RD&D activities in Japan: http://www.nedo.go.jp RD&D activities in Canada: http://www.canmetenergy.nrcan.gc.ca

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14 Integration of stand-alone and hybrid wind energy systems into buildings K. A. KAVADIAS, TEI of Piraeus, Greece

Abstract: Stand-alone wind hybrid systems have turned into one of the most promising ways to handle the electrification requirements of numerous isolated consumers worldwide, including country houses, remote farms, shelters, telecommunication stations, small islands, light houses, etc. Given that in most countries the merit of the building sector in the national energy consumption reaches up to 40%, significantly contributing to the atmospheric pollution, wind hybrid energy systems are becoming a viable and reliable solution. According to experience and the international literature, a wind hybrid system, when sized properly, has the ability to cover the corresponding load demand even in cases of zero energy rejection requirements. Key words: building’s energy consumption, wind–photovoltaic hybrid systems, wind–diesel hybrid systems, stand-alone system sizing, autonomous energy system.

14.1

Introduction

People have always sought shelter to protect them from the environment in which they live. Natural shelters such as caves gradually gave way to what are now huge housing blocks, satisfying both the phenomenon of urbanism and the purpose of accommodating constantly increasing numbers of people. Along with the transition to different building types came an increase in energy consumption, keeping pace with the constant improvement of living standards and with the need to increase people’s productivity by maintaining comfortable living and working conditions throughout the year. Modern architecture, however, often neglects buildings’ energy needs, allowing energy consumption to exceed sensible limits. The resulting increased costs of operation and environmental aggravation in turn lead to even greater energy consumption for buildings (since even more extreme weather conditions have to be overcome to maintain the same levels of comfort). For societies to keep up with the modern way of living, a minimum energy consumption is both acceptable and required. However, there is a notable inequity between different regions of the world underlining the fact that, apart from weather conditions, standards of living may be the main 475 © Woodhead Publishing Limited, 2010

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60 000 Norway

50 000 GDP per capita

United States Ireland

40 000

Switzerland Austria Netherlands Denmark Finland Sweden Australia United Kingdom France Belgium GermanyJapan Greece Italy New Zealand Israel Cyprus Portugal

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14.1 GDP vs. primary energy consumption per capita for various countries, 2006.

driver behind the constant increase of energy consumption. In this context, Fig. 14.1 illustrates the relation between the gross domestic product (GDP) and the per capita energy consumption of different countries (Energy Information Agency, 2006; United Nations Development Programme, 2006). As can be seen, both weather conditions and standard of living affect the resulting energy consumption, i.e. northern countries use more energy than southern ones although standards of living are similar, while countries with identical climate conditions present appreciable difference in energy consumption. What is similar for most countries is the importance of the building sector in national energy consumption which, according to the latest official data, can reach up to a 40% share. On top of this, since building construction in certain countries such as China and India booms, demand for energy soars. Considering the energy consumption potential of the building sector, along with technological progress in many fields of construction and design, drastic energy reduction in the building sector is both realistic and of great importance in tackling climate change. There are three key points that ensure improvement of the energy sector in general (World Business Council for Sustainable Development, 2008): less energy use, local energy production, and better energy management through the operation of ‘intelligent’ grids. With energy saving, and by integrating hybrid systems into buildings, the building sector can contribute towards this aim.

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14.2

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Building sector characteristics

A close analysis of different building types and designs is necessary in order to understand the building sector’s energy saving potential. In this context, in addition to the key distinction of commercial and residential property, buildings can be divided into several sub-groups (World Business Council for Sustainable Development, 2008). The first distinction is that between new and existing buildings. At the moment, China is the dominant new building constructor among other developing countries. Two billion square meters of building stock is added each year in China, representing twice the existing office building stock of the United States, with the already existing Chinese stock exceeding 40 billion square meters. Note that this 5% rate of growth corresponds to only 1% for the industrial world, while in some developing countries even 5% is exceeded. Despite the high growth rate, new buildings can be advantageous from the point of view that they can be designed and built according to low energy use specifications. By contrast, achieving energy efficiency for existing property is a challenging undertaking, since retrofitting often means that serious technical difficulties and financial objectives have to be overcome. A second demarcation, between buildings located in rural and urban areas, derives from the diverse energy consumption patterns between the two, being more intense in cases of developing countries, where many rural homes still rely on biomass sources to cover a large portion of their energy needs. Largely depending on the balance between urban and rural properties, national building energy consumption averages can prove rather misleading in cases where there is a wide difference in the numbers of the two. However, economic development and technological progress allowing grid extension, supply of gas, and integration of stand-alone systems to areas far from urban centres, signal an increase of energy consumption in rural properties as well. Differences between developed and developing countries have already been addressed on the basis of both the construction rate of new buildings and the rural/urban property balance. Outside these factors, sophistication and effective implementation of building codes, relative costs in new buildings between materials and labor and local population living standards, are all critical parameters for the configuration of property demand and building energy use. Diversity between the developed and the developing world may also be illustrated by the floor space per capita factor, being directly related to the respective energy demand. Increasing floor space demand is related to large-scale economies, either established or galloping (e.g. China), while the opposite is valid for the developing economies of the

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world. In this context, increasing residential floor space per capita in developed markets is also a measure of wealth and thus energy consumption. However, energy efficiency should not only be an issue for higher incomes and larger properties; energy savings can be made by all sections of society. Further, since energy costs represent a considerable share of expenses for low-income households, achieving energy savings can bring significant financial gains. The influence of climate is clearly a critical factor for the design of buildings and resulting energy requirements. Obviously, demand for heating energy is highest in colder regions while more cooling power is necessary in hotter climates. Furthermore, climate influence on building design is evident, e.g. attention is given in colder climates to ensure better air tightness and effective insulation, while humidity and temperature comprise two of the most important parameters to keep in mind. In this context, the Koeppen climate classification (Köppen, 1918), adopted by organizations such as the American Society of Heating, Refrigeration and Air-conditioning Engineers (ASHRAE), define six major climate groups; ASHRAE themselves assess energy-related design conditions through a combination of factors, including heating and cooling days.

14.3

An overview of energy consumption in buildings

The sources of energy used to cover building energy demand vary greatly from country to country. For example, coal and biomass are common in China and India, though not in other countries where electricity is the main energy supplier. The result of this variation strongly influences primary energy consumption since electricity supply requires additional energy losses during power generation and distribution. Nevertheless, development and urbanization, and associated increases in electricity use, will alter the situation encountered in China and India; implying, of course, higher levels of energy consumption for a large share of the world’s population. At this point, it is important to note that on a life-cycle basis, the greatest value of energy consumption for any given building is attributed to its operation rather to its construction stage, thus energy sources used to cover energy demand during operation are of great importance to a building’s primary energy consumption. Attention must be given to the residential sector, which according to official data, accounted during 2006 for about 15% of delivered world energy consumption. Note that the term residential energy consumption considers a household’s energy demand excluding any transportation uses. Further, for residential buildings, it can be argued that the physical size of the structures is indicative of the energy consumed by their occupants. Indeed, larger homes require greater amounts of energy in order to cover

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heating, air-conditioning and lighting needs, while it is also common for more electrical appliances to be present. On the other hand, smaller structures normally use less energy, owing to less space being heated or cooled, moderate heat transfer owed to smaller surfaces being exposed to the outdoor environment, and due typically to fewer occupants. To give an example, residential energy consumption in China is lower by far than that corresponding to the United States, where the average residence space is estimated at 60 square meters per person, double that of China (www.allcountries.org, 2006). To recap, the type and amount of energy used by households present great variation among different countries, being largely dependent on local natural resources, the climate, the energy infrastructure available as well as the local inhabitants’ standards of living. In this context, households found in Organization for Economic Co-operation and Development (OECD) member countries, where higher income levels allow for the ownership of larger homes and more extravagant energy equipment, use considerably more energy than those belonging to non-OECD countries. For instance, in the United States, the GDP per capita in 2006 reached almost $43 000 (2005 US$), with residential energy consumption per person estimated at 10 500 kW h. The respective numbers for China during the same year were $4550 and 1200 kW h. Additionally, households in many countries still rely on traditional non-marketed energy sources, including wood and waste, used for heating and cooking needs, which are not captured by energy projections (e.g. International Energy Agency, 2009). Relative to this, the majority of Africans do not have the benefit of grid connection and, according to the International Energy Agency (IEA), most households in the sub-Saharan region are still reliant on wood and charcoal for cooking, including more than 95% of rural households in Angola, Benin, Cameroon, Chad, Congo (Kinshasa), Ethiopia, Ghana, Sudan, and Zambia (International Energy Agency, 2008). Other areas around the globe relying heavily on fuelwood, wood waste and charcoal for cooking include China and India, with 55% and 87%, respectively, of local populations being reliant on these resources. However, the current situation is about to alter considerably, once regional economic development allows incomes to rise and marketed fuels, such as propane and electricity, are also distributed to these areas. The commercial sector, also known as the services’ sector or the institutional sector, includes businesses, institutions, and organizations providing services. Given the magnitude and complexity of the sector, several different types of buildings can be encountered, along with a wide range of activities and energy-related services. The commercial sector encompass schools, stores, correctional institutions, restaurants, hotels, hospitals, museums, office buildings, banks, and sports arenas. Among these facilities,

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intensive energy use is typically recorded in buildings where space and water heating, lighting, cooling, and cooking are necessary. It should also be noted that energy consumption by traffic lights, city water, and sewer services, although not associated with commercial buildings, is also considered to be commercial energy use. Economic development and population growth should be considered first among the main drivers of commercial activity and thus of commercial energy consumption. It is obvious that, as population increases, the need for the provision of services (health, education, financial, and government) also increases. But for additional needs to be satisfied, economic resources, either domestic or foreign, must become available. Furthermore, economic growth itself largely affects commercial activity from the point of view that higher levels of economic growth and disposable income imply analogous levels of services offered by hotels and restaurants, etc., to meet business and leisure requirements, i.e. office and retail space is required to meet the expansion of businesses, cultural and leisure spaces are needed to house theaters, galleries, arenas, and others. As might be expected, as in the residential sector, commercial sector energy consumption per capita also appears to be higher in OECD countries. More specifically, commercial energy use per capita in non-OECD countries for the year 2006 averaged at only 380 kW h while the respective value for OECD countries reached 4800 kW h. On the other hand, lower rates of population growth in OECD countries suggest analogous lower rates of increase for commercial energy demand, while constant energy efficiency improvements such as replacement of old equipment with new, more energy-efficient stock will decelerate the growth of energy consumption. However, constant economic growth means an increase of business activity and consequently higher energy use in areas such as retail and wholesale trade, financial and leisure services. As a result, the United States presents the largest commercial energy consumption among OECD countries, although rapid increase expected in the economic and commercial fields of developing countries signals the rise of energy consumption for the service sector of these regions as well. Commercial energy consumption is expected to rise even more in developing countries, due to population growth, which is also growing more rapidly than in OECD countries. As a result, additional energy is required to cover the needs of education, health care, and social services for a greater number of people. More precisely, according to projections, total delivered energy use in the commercial sector of nonOECD countries will present a mean annual increase rate of 2.7% up to the year 2030 (2006 being the starting year) (International Energy Agency, 2009).

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481

European Union facts about hybrid energy systems in buildings

As far as the European Union is concerned, according to the European Commission Directorate-General for Energy and Transport, residential energy consumption per capita presents great variation amongst different countries, ranging from 1500 to 5000 kW h/cap in southern countries (e.g. Portugal, Spain and Greece), from 6000 to 8000 kW h/cap in northwest Europe, and exceeding 8000 kW h/cap in Scandinavian countries (European Union, 2004). Additionally, although most national averages remain fairly steady, presenting some fluctuations due mostly to changing weather conditions, southern European countries such as Spain and Greece have presented considerable increases in the energy consumption of the residential sector during the last decade. In terms of electricity consumption per capita for the residential sector, the strong diversity encountered is justified by the different levels of diffusion of electrical appliances, as well as by the degree to which electrical space heating is used. In fact, electrical space heating varies from 1000 kW h/ cap (e.g. Portugal and Italy) to around 2000 kW h/cap (e.g. the United Kingdom and France), even reaching 4500 kW h/cap for certain countries such as in Sweden (van der Linde, 2004). Similarly, figures for household energy use per capita, according to IEA, range from 3370 kW h/cap in the case of Portugal and 3600 kW h/cap in the case of Spain (southern countries), to 11 400 kW h/cap in the case of Finland and 17 700 kW h/cap in the case of Luxembourg. Note that these numbers can be compared directly to those figures for the United States (10 350 kW h/cap) and Canada (11 160 kW h/cap) (Balaras et al., 2007). To counter the existing situation, the European Parliament decided that, after 2018, all newly constructed buildings should be able to produce the energy they consume on-site (via solar panels, heat pumps, etc.); furthermore, Member States must set intermediate targets for existing buildings, meaning that minimum percentages of existing stock should become zeroenergy rated by 2020. Note that the European Parliament defines zeroenergy buildings (ZEB) as ‘buildings where, as a result of the very high level of energy efficiency of the building, the overall annual primary energy consumption is equal to or less than the energy production from renewable energy sources on site’ (EU Press Release, 2009). To meet the requirements of ZEBs, designers must experiment with several demand-side and energy supply strategies so as to obtain an optimum solution. In this context there are four well-documented definitions studied by the National Renewable Energy Laboratory (NREL) research team (Torcellini et al., 2006), including net-zero site energy, net-zero source energy, net-zero energy costs and

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net-zero energy emissions, which, according to the results of the study, can be interpreted differently.

14.5

Description of hybrid energy systems in buildings

Stand-alone hybrid energy systems have emerged as one of the most promising ways to handle the electrification requirements of numerous isolated consumers worldwide, including for houses in the country, remote farms, shelters, telecommunication stations, small islands, lighthouses, etc. Windbased stand-alone systems are typically coupled with an appropriate energy storage device (usually lead–acid batteries) to store surplus wind energy during high wind speed and low consumption periods. Advanced standalone wind hybrid systems could also include a complementary electricity production generator (e.g. photovoltaic power station, diesel-electric generator). These systems, if sized properly, combine: •

rational first installation cost, as the complementary electricity generator significantly decreases the required battery capacity; • low operational cost when combined with a photovoltaic power station, given that renewable energy sources are the main electricity production sources; • improved reliability, on account of the independent power sources that may cover the load demand of the installation. A typical wind hybrid system (see Figs 14.2 and 14.3), able to meet the electricity requirements of an isolated building, comprises: • • • •

a micro-wind converter; an additional electric generator unit (e.g. photovoltaic panels, diesel– electric generator); a storage system unit (e.g. batteries); auxiliary electronic equipment.

14.5.1 Wind turbine generator Most contemporary turbines considered suitable to satisfy the energy needs of buildings are horizontal axis upwind machines with two or three blades, usually made of a composite material such as fiberglass. The amount of power a turbine will produce depends primarily on the diameter of its rotor, since it is the rotor diameter that determines the quantity of wind intercepted by the turbine. The turbine’s frame is the structure onto which the rotor, generator, and tail are attached, with the tail keeping the turbine facing the wind. Because wind speeds increase with height, the turbine is mounted on a tower. In general, the higher the tower, the more power the wind system

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V (m s–1) t (h)

qα (°C)

rα (kg m–3)

t (h)

⊕

hin

Eπ (kW h)

N (kW)

t (h)

V (m s–1)

Ek (kW h) P/Po

00

24 t (h)

t (h)

Wind turbine UPS hcc = 0.95 Charge hk = 0.95 Inverter controller Po

AC/DC rectifier hrec = 0.95

Diesel

hB = 0.95 SFC

Control panel

AC load

Battery 1.0

Nd/Nd *

Q (A h)

U (V) 13 12 11 10

0 25 50

%

Depth of Ek = energy demand discharge Eπ = wind energy production N = wind turbine’s power output Nd/Nd *= diesel engine’s specific power P/Po = inverter’s specific power Pο = inverter’s nominal power Q = storage system’s capacity SFC = diesel engine’s specific fuel consumption t = time U = storage system’s voltage UPS = uninterruptible power supply V = wind speed hB = storage system’s efficiency hcc = charge controller’s efficiency hin = inverter’s efficiency hk = cable’s efficiency hrec = rectifier’s efficiency qα = ambient temperature rα = air density

t

14.2 A typical wind–diesel hybrid system for stand-alone electrical systems.

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V (m s–1) t (h)

qα (°C)

N (kW)

hin

Eπ (kW h)

Ek (kW h)

⊕

t (h)

V (m s–1)

t (h)

P/Po

00

24t (h)

rα (kg m–3) t (h)

Wind turbine UPS hcc = 0.95 Charge hk = 0.95 Inverter controller Po

AC/DC rectifier hrec = 0.95

hB = 0.95 PV array U (v)

G (W m–2) I (A)

t (h)

Q (A h) % Depth of discharge

t

0 25 50

W m–2 E (kW h) π

⊕ ⊕

qc (°C)

Control panel

Battery

13 12 11 10

qα (°C) ⊕ t (h)

AC load

hin

Ek (kW h)

U (v)

h/hp

t (h)

P/Po

00

24 t (h)

qc (°C) t (h) G = solar radiation I = photovoltaic array current PV = photovoltaic h/hp = photovoltaic’s specific efficiency qc = modules’ surface temperature

14.3 An integrated wind–photovoltaic hybrid system for stand-alone electrical systems.

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can produce. The tower also keeps the turbine above the air turbulence that may exist close to the ground because of obstructions such as hills, buildings, and trees. Note that relatively small investments in increasing the tower height can yield higher rates of return in power production. There are two basic types of towers: self-supporting (free standing) and guyed. Most home wind power systems use a guyed tower. Guyed towers, which are the least expensive, can consist of lattice sections, pipe, or tubing depending on the design, supported by guy wires. They are easier to install than self-supporting towers; however, because the guy radius must vary from one-half to three-quarters of the tower height, guyed towers require considerable space to install them. Tilt-down towers (which can be either self-supporting or guyed) are more expensive, but they offer the consumer an easy way to perform maintenance on smaller lightweight turbines, usually 5 kW or less.

14.5.2 Storage system unit Owing to the stochastic behavior of wind, wind generation cannot always provide firm capacity to an autonomous electrical power system (Lemstrom et al., 1999). Additionally, these fluctuations can – in some cases – cause problems related to stability, harmonics or flicker. An energy storage system, when sized appropriately, can match a highly variable wind power production to a generally variable and unpredictable system demand, limiting the energy production cost remarkably (e.g. by generating capacity savings). In this context, critical parameters concerning storage systems potentially used in a wind hybrid installation include the lifetime expectancy, the energy efficiency, the depth of discharge and the initial and operational cost. Short descriptions of the most common storage systems follow below. Battery storage systems Batteries are the most popular storage systems for wind energy installations’ support (Fig. 14.4). Their major advantage is their lack of kinetic parts, which considerably limits their maintenance and operational cost (Kaldellis et al., 1999). An important feature of a battery operation is ‘cycling’, which together with other operating parameters may strongly affect its lifetime and operational cost. The most common types of batteries are lead–acid (Pb2SO4), nickel–cadmium (NiCd) and silver–zinc (AgZn). Nickel–cadmium batteries exhibit both low efficiency and only a moderate discharge period; however, they also have a considerable energy storage capacity. Lead–acid batteries are cheaper but have a short lifetime period and small storage capacity. The most important factors affecting battery

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Anemometer

Thermometer

Solar panel PV array 610 Wp

Pyranometer

Electronic lamps Consumptions

Data logger Flowmeter PC

DC current 24 V Data signal Water circuit

Charge controller

Control panel Thermometer

Battery 24 V

Reservoir Water pump

14.4 Schematic presentation of the hybrid power station of the S.E.A. & ENVI.PRO. Lab with battery storage system installation.

design and operation are their depth of discharge (DOD) per cycle, their temperature of operation, life cycle, number of cells in series, their discharge–charge control, and periodic maintenance requirements. Flywheel storage systems Flywheel energy storage systems (see Fig. 14.5) are common in many transportation uses, including for busses, trains, cars, etc. A flywheel accelerates as energy is absorbed and decelerates when energy is delivered back to the system. The stored energy is the sum of the kinetic energy of the individual mass elements make up the flywheel. In order to optimize the energy-tomass ratio, a flywheel needs to spin at its maximum possible speed (Freris, 1990). The energy efficiency of such systems is about 80%. However, size and tolerance considerations at high angular velocities are a great disadvantage of the system. Flywheels are mainly used at wind–diesel systems for smoothing out the turbulence induced by power fluctuations. Pump–hydro systems In a pump–hydro storage system (Vlachou et al., 1999a), surplus energy is used to pump water into an elevated storage reservoir (Fig. 14.6). When a power deficit appears, a hydro-turbine, which drives an electric generator, is used to cover it. As well as having a high rate of energy extraction, pump–hydro storage systems can take up load in only a few seconds (4–10 s).

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Power input from wind turbine

Flywheel rotor

Generator

14.5 Typical flywheel storage system configuration.

Upper reservoir

Lower reservoir Water pump

Small hydro-turbine

14.6 Pump–hydro solution for isolated consumers.

Electrolysis–fuel cell storage systems Fuel cell energy storage systems (Argiropoulos and Kaldellis, 2000) are based on the principle of producing hydrogen in an electrolysis device, which is then stored in separate tanks or storage media, outside the cell itself. The quantity of the stored hydrogen (and therefore energy) in these tanks or storage media is independent of the fuel cell (production unit) size. In this way there are no restrictions (unlike with batteries) on storage

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Fuel produced by energy surplus of a wind turbine

Fuel processor

Energy converter

Power conditioner

AC power

14.7 Fuel cell energy storage system.

capacity, except for the size of the fuel tank (Fig. 14.7). Hydrogen, which is the basic catalyst for the fuel cell system, can be separated from oxygen in water via electrolysis. Although such systems may seem attractive for energy autonomous buildings, according to the current literature, hydrogen production in small-scale applications is not yet viable. Thus, electrolysis– fuel cell storage systems are simply mentioned, though not analyzed, in this chapter.

14.5.3 Complementary electric generator unit The quantity of energy that a wind power generator can produce strongly depends on the available wind at the installation area. Although total annual energy production might seem to cover the corresponding electrical energy needs of the building, satisfaction of the load demand by the energy produced should be examined on an hour by hour basis, at least. Duration of calm spells is an important parameter that influences the decision about choice of components and the size of a wind hybrid installation for a standalone connection to provide constant electricity for consumption. There could be situations where, although the calculated annual energy produced seems enough to cover a building’s power needs, long calm spells could cause a load failure. In order to confront such situations, larger storage systems, which significantly increase the operational cost of the plant, are usually considered. An interesting option is the installation of an additional, independent electric power generator which reinforces the electricity production system of the building. Several studies have shown that a wind turbine in combination with a secondary power generator, which could also be based on renewable energy sources (e.g. photovoltaics) or conventional fuel-based generators (e.g. diesel or gas), can limit the energy storage system’s size and in many cases reduce energy production costs. Another quite interesting option is the combination of the energy storage system with an alternative electric power generator unit. An example of such an installation is the combination of a wind turbine coupled with an appropriate hydrogen production system based on electrolysis, to be used

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as an energy storage system, and a fuel cell unit which uses the hydrogen produced and stored during low energy demand to produce electric power during low or very high wind speeds. An additional advantage of this specific installation is the opportunity to use the hydrogen produced as a fuel in appropriate devices (taking advantage of the heat produced by its combustion) or even as a fuel for hydrogen cars. Of course, the low energy efficiency of the cycle, i.e. from hydrogen production to the final power produced by the fuel cell, should also be borne in mind. Another option is the combination of a wind electric power generator coupled with a small pump-hydro unit in which water is pumped from a lower water tank to a higher water tank during low energy demand situations, and returned through the hydro-turbine to the lower tank during low or very high wind speeds. In such installations the water stored could also be used to cover any water supply needs.

14.5.4 Auxiliary electronic equipment The auxiliary electronic equipment needed to support a stand-alone wind hybrid system for a building depends on the application. Most manufacturers provide system packages that include all the necessary parts for the system. For example, the parts required for a wind turbine coupled with a pump–hydro storage system will be very different from those needed for a wind–diesel hybrid system. The balance of system required will also depend on whether the system is grid-connected or stand-alone. For a residential grid-connected application, the balance of system parts may include a controller, a power conditioning unit (inverter – to make turbine output electrically compatible with the utility grid), and wiring. Stand-alone systems, which in most cases are combined with batteries, also need a charge controller to keep the batteries from overcharging. Small wind turbines generate DC electricity. When using standard appliances that use conventional AC current, an inverter to convert DC electricity from the batteries to AC, is necessary.

14.6

Sizing procedure for hybrid energy systems in buildings

14.6.1 Wind turbine rated power A micro wind converter of rated power No is connected either to an AC load via an uninterruptible power supply (UPS) or to a storage system via a rectifier (if necessary), and an energy storage charge controller. In cases where a wind turbine generates direct current, the calculation procedure is similar to the one proposed here. The rated power of the selected wind

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turbine depends on the system’s electricity demand, the available wind potential, and the operational characteristics of the machine (Vlachou et al., 1999b). It should be remembered that wind-turbine output curves are given for standard day conditions, without air humidity. Thus, in real day conditions, ambient temperature and pressure, together with the relative humidity are used to obtain real air density and the corresponding wind turbine output (Kaldellis et al., 1996). More precisely, the nominal power No of a wind turbine is given as (Kaldellis and Kavadias, 2006): N min =

Etot Etot ≤ No ≤ = N max Δt ⋅ CF Δt ⋅ CF ⋅ η*

14.1

where Etot is the system electricity consumption (increased by 20% to take into account potential future energy consumption increases over the system’s lifetime) for the period Δt (e.g. 1 year), CF is the capacity factor of the installation for the same time period and η* the energy transformation coefficient, expressing the portion of the wind energy produced and stored via the storage system, which finally satisfies consumption (Kavadias and Kaldellis, 2000; Kaldellis et al., 2001). A realistic value for η* is 80%, including (in most cases) energy losses in the AC/DC inverter, the charge controller, the storage system and the DC/AC converter. Note also that the power output of the proposed wind turbine should be high enough to face the maximum (peak) load demand Np of the system, without using the inverter. The capacity factor is the product of the installation’s technical availability Δ with the mean power coefficient ω (Kaldellis, 2005), i.e.: CF = Δ · ω

14.2

More precisely, ω can be calculated as:

ω=∫

Vf

VC

N (V ) ⋅ f (V ) ⋅ d V No

14.3

with Vc and VF being the corresponding cut-in and cut-out wind speeds of the wind turbine analyzed, while N(V) is the corresponding power curve versus wind speed V (Fig. 14.8) and f(V) is the wind speed probability density function at the hub height, describing the local wind potential for the time period Δt. In cases where no detailed wind speed data exist for the area under investigation, the well-known Weibull distribution f(V) is used (Kaldellis and Kavadias, 2000; Eggleston and Stoddart, 1987).

14.6.2 Energy storage system’s main parameters An energy storage system is utilized in order to store energy during high electricity production periods and return it to consumption at low or very

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1.2 1.0

N/No

0.8 0.6 0.4 0.2 0.0

0

5

10 15 20 Wind speed (m s–1)

25

30

14.8 Typical non-dimensionalized wind turbine power curve.

high wind speed periods. This system is characterized by energy storage capacity Ess, nominal input Nin and output power Nss of the entire energy storage system. One should also take into account the desired hours of energy autonomy ho of the installation, the maximum permitted depth of discharge DODL and the energy transformation efficiency of the energy storage system ηss. The energy storage system includes an Ess capacity storage device, selected to be sufficient to store the energy produced during windy days, for use during calm spells. The storage system size is determined by the autonomy hours ho of the system, the total energy demand Etot for a period Δt, the efficiency of the storage system ηss and the maximum permitted depth of discharge DODL (Kaldellis et al., 1999). The maximum capacity (in W h) is given as: Ess =

Etot ⋅ ho Δt ⋅ ηss ⋅ DODL

14.4

The storage system capacity Ess varies between Ess(min) and Ess(max), where: Ess(min) = DODL · Ess(max)

14.5

while the DODL value is strongly related to the life duration (operational cycles) of the storage system. The nominal input power of the energy storage system Nin could be determined as: Nin = No · ηch

14.6

in order to be able to take advantage of the power output from the wind turbine No, also taking any energy losses taking place during the charging process (ηch) into consideration. The output power Nss of the storage system should be able to cover the peak power load of the building’s energy demand, and, therefore:

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Stand-alone and hybrid wind energy systems Np ⎞ ⎛ Etot N ss ≥ max ⎜ , ⎝ Δt ⋅ ηdch ηdch ⎟⎠

14.7

Finally, note that the ηdch parameter takes into account any energy losses during the discharging process.

14.6.3 Pump–hydro The rated power of the pump is determined by the maximum power of the wind turbine, since the water pump must have the capability to absorb the maximum power output of the wind turbine, i.e.: N in =

ρ ⋅ g ⋅ H ⋅ V ηp ⋅ ηel

14.8

where H is the pump’s head, ηp the efficiency, ηel the electrical efficiency, ρ the density of the water and g the acceleration of gravity. The static head of the pump ‘H’ must satisfy the expression: . 14.9 H ≥ (h1 − h2) + δHf = (h1 − h2) + Kp · V 2 where δHf is the total hydraulic losses, both lengthwise and local, when the water tank is used for energy storage. It should be noted that H and ηp depend on the operational characteristics of the selected pump. The nominal power of the pump–hydro installation results from the precondition that it covers the peak power demand of the building, with an optional future increase (of 20%). The exit power is given as (Kaldellis and Kavadias, 2006): . Nss = ρ · g · H′ · V ′ · ηH · η′el 14.10 . where V ′ is the flow rate of the turbine, H′ the hydro turbine’s head, ηH the efficiency and η′el the electrical efficiency of the system. Additionally, the following equation is also valid: . H′ ≤ (h1 − h2) − δH′f = (h1 − h2) − KH · V ′2 14.11 where h is the hydrostatic head and δH′f is the total hydraulic losses, both lengthwise and local, when the water circuit is used for energy production. Note that H′ and ηH depend on the operational characteristics of the hydro turbine selected. The dimensions of the upper water tank are defined by the available hydrostatic head which depends on the relative elevation between the upper and lower water tank, and by the required levels of energy autonomy for the system. For example, by selecting ho hours of energy autonomy, the useful volume Vo of the water tank is given as:

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Integration of stand-alone and hybrid WESs into buildings Vo =

Etot ⋅ ho = Vmax − Vmin Δt ⋅ ηΗ ⋅ ηel ⋅ ρ ⋅ g ⋅ H ′

493 14.12

where Vmax and Vmin are the maximum and minimum storage capacities of the upper water tank.

14.6.4 Diesel–electric generator In many stand-alone applications a diesel generator operating at constant speed and driving a suitable electric generator is used to cover the electricity requirements of the installation. Close attention should be paid to selecting the appropriate (Fig. 14.9) specific fuel consumption (SFC) of the diesel engine, especially under partial loading ND of the engine with nominal power N*D, in order to minimize the corresponding fuel consumption, i.e.: ⎛N ⎞ SFC = SFC ⎜ D ⎟ ⎝ N D* ⎠

14.13

It should be remembered that, even at zero load, diesel generator fuel consumption is almost 30% of the corresponding fuel consumption at rated power. On top of this, it is recommended that operation of the diesel engine below 30% of full load be avoided for long periods, to avoid serious maintenance problems, such as chemical corrosion and glazing. An estimate of the fuel mass rate mf of the diesel generator can be written as: mf = SFC · ND

14.14

Specific fuel consumption (gr/kW h)

380 360 Operation range

340 320 300 280 260 240 220 200 0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Relative power (N/Nopt)

14.9 Diesel–electric generator’s SFC distribution.

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1.2

1.3

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Thus, the total fuel consumption over a given period Δt is given as Mf = ∫

to + Δt

to

mf ⋅ dt

14.15

14.6.5 Photovoltaic power station In cases where the wind hybrid system includes photovoltaic panels, then the number z of the photovoltaic panels of N+ rated power each is determined by the following equation (Kaldellis et al., 2007): Etot Etot ≤ z⋅ N + ≤ Δt ⋅ CF ′ Δt ⋅ CF ′ ⋅ η**

14.16

z = z1 · z2

14.17

where:

where z1 is the number of strings of photovoltaic panels. The number of photovoltaic panels in series z2 can be calculated as: z2 =

U cc U PV

14.18

Note that CF′ is the photovoltaic installation capacity factor, UPV is the photovoltaic cell operation voltage, Ucc is the charge controller voltage and η** is the corresponding energy transformation coefficient, expressing the portion of the photovoltaic energy produced and stored via the storage system, which finally satisfies consumption. A realistic value for η** is 80–85%, including (in most cases) energy losses in the charge controller, the storage system and the DC/AC converter.

14.6.6 Auxiliary electronic equipment Determination of the AC/DC rectifier and the charge controller size is based on the operational characteristics of the wind turbine and the storage system (e.g. in case of battery storage system, U = 24 V or 48 V, charge rate Rch in A, where the charge current numerical value must not exceed 20% of the storage capacity value). Similarly, the UPS characteristics depend on the maximum load demand Np and the service time (e.g. 2 min), along with the operational voltage (e.g. 220–240 V). Finally, the size of the inverter is selected to fulfill the maximum load demand of the consumption Np, including a safety coefficient. In Fig. 14.10, a typical inverter efficiency profile is presented, as a function of the relative electrical load covered by the device.

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Efficiency (h)

1 0.98 0.96 0.94 0.92 0.90 0.88 0.86 0

1000

2000

3000

4000

5000

6000

Electricity load (W)

14.10 Typical 5 kW inverter efficiency evolution.

14.7

Operational modes of hybrid energy systems in buildings

During the long-term operation of a wind hybrid system able to satisfy the electrical needs of a building, the following situations may occur: •

•

The wind turbine production is higher than the load demand, and thus the energy surplus is stored to the energy storage system under a given estimated efficiency via the appropriate electronic devices. The corresponding storage capacity level is increased by the energy surplus, under the precondition that it has not reached its maximum capacity; if it has, this energy amount is forwarded to low-priority loads. In cases where the wind–hybrid system includes a photovoltaic power installation, then load demand is compared to the sum of the wind and photovoltaic power production. The wind turbine output is less than the load demand, thus the energy deficit is covered: 䊊 First, by the storage system, assuming it has not reached the depth of discharge limit. In this case, the storage capacity level is decreased by the energy deficit, also taking the storage discharge energy losses into consideration. 䊊 If the depth of discharge limit has been reached, the energy deficit (including line losses) is covered by the diesel–electric generator in expense of the fuel reserves of the installation, under the restriction that there is enough fuel available. 䊊 In the situation that the storage system’s depth of discharge limit has been reached and there is not enough fuel, the first degree storage protection limit is violated and the energy deficit is covered by the storage system branch, under the condition that the safety depth of discharge limit has not been reached.

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In the extreme case that the storage system and the diesel–electric generator cannot provide energy, then load management takes place and low-priority loads are rejected.

14.8

System performance and optimization of hybrid energy systems in buildings

The computational frame that was presented above estimates the dimensions of a stand-alone wind hybrid system for a building’s electricity needs, under the precondition that each electricity generator (wind turbine, photovoltaic power station, and diesel–electric generator) of the installation will be able to cover the electricity energy needs of the building. For the calculation of the appropriate dimensions of a stand-alone wind power system for an autonomous building, detailed wind speed (along with ambient pressure–temperature–humidity) data should exist. In cases where a photovoltaic power station is included, solar radiation data are needed, preferably at the inclination angle of the photovoltaic panels. Additionally, it is equally important for the system’s designer to obtain the electricity demand profile of the building. If the above-mentioned data exist, then algorithms could be used to simulate the operation of various-sized wind hybrid installations and estimate the best configuration which will satisfy any input restriction given. Such algorithms have been developed by the Soft Energy Applications and Environmental Protection Lab of TEI Piraeus (Figs 14.11 and 14.12). Specific algorithms are used in this section in order to provide an example of a stand-alone wind hybrid system’s optimization. The algorithms can be used to carry out the necessary parametrical analysis on an hourly energy production–demand basis. Examples of the capabilities of the algorithms are presented in Figs 14.14–14.19 below. In the examples presented, the wind hybrid system is used to meet the electrical requirements of a typical remote consumer (see Fig. 14.13), located at different wind and solar potential areas in Greece (Kaldellis et al., 2006). The corresponding load profile used is basically a rural household profile selected among several profiles provided by the Hellenic Statistical Agency. More precisely, the numerical load values vary between 30 W (refrigerator load) and 3300 W. According to the consumption profile, the annual peak load Np does not exceed 3.5 kW, while the annual energy consumption Etot is around 4750 kW h. In Fig. 14.14 the corresponding typical 7 day energy balance of a simple wind hybrid configuration for a low wind speed area is presented. In the same figure, the battery capacity variation is also given as a function of time. According to the diagram, the rated power of the wind turbine used is almost quadruple the isolated consumer peak load demand. Similarly, the

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Start Nin, Qin, Mfin, δN, δQ, δMf, Δt, δt, NFIN, QFIN, MfFIN Mf = Mf+δMf

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t > Δt

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14.11 Wind–diesel algorithm.

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t > Δt

Battery empty?

Battery empty?

Yes Q* = Q

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14.12 Wind-PV algorithm.

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14.14 Wind hybrid stand-alone system main parameters evolution, low wind potential area, winter time.

battery capacity diminution during the long-lasting calm spells, although large in absolute terms, does not lead to a significant depth-of-discharge increase of the storage system. Figure 14.15 presents the corresponding wind turbine rated power and battery bank capacity combinations that guarantee 1-year energy autonomy without any external energy input for various wind potential classes. The mean annual wind speeds for the specific areas are: for Andros 9.5 m/s; for

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100 000 Andros Island Naxos Island Skiros Island Kithnos Island Kea Island

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14.15 Comparison of wind stand-alone system configurations for selected areas.

Naxos 7 m/s; for Skiros 6.5 m/s; for Kithnos 6 m/s; and for Kea 5 m/s (Kaldellis, 2004). According to the results obtained, there is a significant battery capacity reduction as the wind turbine rated power increases. This increase is more abrupt for the high wind potential areas, while the medium wind potential areas present milder distribution. Additionally, for all regions examined, the battery size tends to an asymptotic value as the wind turbine size surpasses a specific value, which depends on the wind potential quality. Finally, it is important to note that for the relatively low wind potential areas the battery size is significantly larger than for the medium or high wind potential cases. Figure 14.16 presents the corresponding autonomous energy distributions (for a wind turbine–battery storage system) for various annual dieseloil consumption levels, for a high wind speed area. More precisely, each curve drawn corresponds to a given diesel–oil rate (e.g. Mf = 100 kg/y); the x-axis describes the wind-turbine’s rated power and the y-axis, the corresponding battery capacity. In the same figure, a zero-diesel solution is also included. According to the graph, there is a considerable battery capacity diminution by accepting a minimum (25 kg/y) diesel-oil consumption, representing approximately 1% of the annual diesel-only system fuel consumption. A significant battery capacity decrease is also encountered by accepting 100 kg/y diesel-oil consumption. For bigger diesel-oil quantities, the battery capacity is fairly reduced, excluding configurations based on very small wind-turbines, i.e. rated powers below 3 kW. Figure 14.17 presents similar calculations for a relatively low wind potential area. Using the information of Fig. 14.17, it can be easily concluded that, even by using significant diesel-oil quantities, the system’s dimensions

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14.16 Wind–diesel hybrid system optimization for a high wind speed area.

Mf = 0 (kg/year) Mf = 25 (kg/year) Mf = 50 (kg/year) Mf = 100(kg/year) Mf = 250 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year) Mf = 1250 (kg/year) Mf = 1500 (kg/year)

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14.17 Wind–diesel hybrid system optimization for a low wind speed area.

(mainly battery capacity) are much larger than the high wind speed area ones. On top of this, only by using significant annual diesel-oil quantities (e.g. Mf = 250 kg/y) is it possible to obtain a considerable battery capacity reduction. Finally, it can be seen that almost 1000 kg/y of diesel-oil should

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14.18 Stand-alone wind–photovoltaic configuration for a low wind and medium solar potential area.

be used to guarantee the system energy autonomy, exploiting a relatively small wind-turbine (i.e. below 3 kW). In Fig. 14.18 the combined wind–photovoltaic solution is applied to an area with a relatively low wind potential (slightly above 5.5 m/s at 10 m height). According to the results calculated, a wind turbine of rated power equal to 7 kW minimum should be used in order for the required battery capacity not to exceed 25 000 A h. Considering, however, the high solar potential of the area, a significant battery size reduction is encountered by introducing a small number of photovoltaic panels. At the same time, the wind turbine rated power requirement is also decreased. Specifically, using 20 photovoltaic panels of 51 Wp (or 1020 W), the required wind turbine rated power is less than 5 kW, while the corresponding battery capacity drops to 20 000 A h. In addition, a larger number of photovoltaic panels (e.g. z = 100 or 5.1 kW) can practically establish a viable energyautonomous solution by using only 8000 A h of battery capacity and a wind converter of 5 kW. Accordingly, Fig. 14.19 shows the resulting battery size reduction for a higher (i.e. higher than that corresponding to Fig. 14.18) solar potential area. In that case, however, owing to the higher solar radiation available, the ensuing battery size reduction is much greater since, by using 20 pho-

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z = 0 PV panels z = 10 PV panels z = 20 PV panels z = 50 PV panels z = 75 PV panels z = 100 PV panels z = 300 PV panels

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0 0

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14.19 Stand-alone wind–photovoltaic configuration for a low wind and high solar potential area.

tovoltaic panels, the corresponding battery reduction approaches 25% in comparison with the zero-PV panel (or wind-only) solution, while the corresponding wind turbine size decreases to 3 kW. Subsequently, installation energy autonomy can be achieved by using only 50 panels, instead of 100 (see also Fig. 14.18, where the corresponding battery capacity is less than 10 000 A h). Finally, by increasing the number of photovoltaic panels, not only is the battery capacity decreased but a considerable reduction is also encountered in the wind converter rated power requirement. The possibility of using a stand-alone wind hybrid system to meet the electricity demand of remote consumers is environmental friendly and can be a viable solution. By applying an analytical sizing procedure, such systems are able to guarantee the energy autonomy of the building for the entire period analyzed. For estimation of the optimum size, long-term wind speed and solar irradiance measurements, as well as other meteorological data are needed. According to the international literature and to experience, wind hybrid systems have the ability to cover the corresponding load demand, though in case of zero load rejection (i.e. where the systems must satisfy 100% load demand), the utilization of a back-up electricity generator system is proposed.

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14.9

References and further reading

Ameku K., Nagai B.M. and Roy J.N. (2008), ‘Design of a 3 kW wind turbine generator with thin airfoil blades’, Experimental Thermal and Fluid Science, 32/8, 1723–1730. Argiropoulos G. and Kaldellis J. (2000), ‘Are the fuel cells the solution of future energy demand problem?’, S-350, Lab. of Soft Energy Application & Envi. Pro., TEI of Piraeus. Bahaj A.S., Myers L. and James P.A.B. (2007), ‘Urban energy generation: Influence of micro-wind turbine output on electricity consumption in buildings’, Energy and Buildings, 39/2, 154–165. Balaras C., Gaglia A., Georgopoulou E., Mirasgedis S., Sarafidis Y. and Lalas D. (2007), ‘European residential buildings and empirical assessment of the Hellenic building stock, energy consumption, emissions and potential energy savings’, Building and Environment, 42, 1298–1314. Eggleston D. and Stoddard F. (1987), Wind turbine engineering design, Van Nostrand Reinhold. Energy Information Administration (2006), ‘International energy statistics’. EU Press Release (2009), ‘All new buildings to be zero energy from 2019 say MEPs’. European Union (2004), ‘European Union energy and transport in figures – 2004 edition, Part 2: Energy’. Brussels: DG for Energy and Transport. Freris L.L. (1990), Wind Energy Conversion Systems, Prentice Hall. International Energy Agency (2008), World Energy Outlook 2008. International Energy Agency (2009), World Energy Outlook 2009. Kaldellis J.K. (2004), ‘Parametric investigation concerning dimensions of a standalone wind-power system’, Applied Energy, 77, 35–50. Kaldellis J.K. (2005), Wind Energy Management 2nd ed., Stamoulis. Kaldellis J. and Kavadias K. (2000), Laboratory Applications of Renewable Energy Sources, Stamoulis. Kaldellis J.K. and Kavadias K. (2006), Computational Applications of Soft Energy Resources: Wind energy-hydro power, Stamoulis. Kaldellis J.K., Arrianas L., Konstantinou P. and Vlachou D. (1996), ‘Validation of aerodynamic behaviour of commercial wind-turbines’, presented at the 5th National Congress on Soft Energy Resources, B, 11–20, Democritos, Athens. Kaldellis J., Thiakoulis Tr. and Vlachou D. (1999), ‘Autonomous energy systems for remote islands based on renewable energy sources’, presented at 1999 European Wind Energy Conference and Exhibition, 968–971, Nice, France. Kaldellis J.K., Kavadias K. and Christinakis E. (2001), ‘Evaluation of the windhydro energy solution for remote islands’, Journal of Energy Conversion and Management, 42/9, 1105–1120. Kaldellis J.K., Kostas P. and Filios A. (2006), ‘Minimization of the energy storage requirements of a stand-alone wind pv’, Wind Energy, 9, 383–397. Kaldellis J. K., Spyropoulos G. and Kavadias K. (2007), Computational Applications of Soft Energy Resources: Solar potential–photovoltaic applications–solar heat systems, Stamoulis. Kavadias K.A. and Kaldellis J.K. (2000), ‘Storage system evaluation for wind-power installations’, presented at the International Conference Wind Power for the 21st Century, Kassel, Germany.

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Köppen W. (1918), ‘Klassifikation der Klimate nach Temperatur, Niederschlag und Jahreslauf’, Petermans Mitt., 64, 193–203. Lai C.-M. (2006), ‘Prototype development of the rooftop turbine ventilator powered by hybrid wind and photovoltaic energy’, Energy and Buildings, 38/3, 174–180 Lemstrom B., Rakkolainen J. and Peltola E. (1999), ‘A wind farm’s impact on the quality of electricity in weak network’, presented at 1999 European Wind Energy Conference and Exhibition, 747–749, Nice, France. Lu L. and Ip K.Y. (2009), ‘Investigation on the feasibility and enhancement methods of wind power utilization in high-rise buildings of Hong Kong’, Renewable and Sustainable Energy Reviews, 13/2, 450–461. Mithraratne N. (2009), ‘Roof-top wind turbines for microgeneration in urban houses in New Zealand’, Energy and Buildings, 41/10, 1013–1018. Muller G., Jentsch M.F. and Stoddart E. (2009), ‘Vertical axis resistance type wind turbines for use in buildings’, Renewable Energy, 34/5, 1407–1412. Nagai B.M., Ameku K. and Roy J.N. (2009), ‘Performance of a 3 kW wind turbine generator with variable pitch control system’, Applied Energy, 86/9, 1774–1782. Ozgener O. (2006), ‘A small wind turbine system (SWTS) application and its performance analysis’, Energy Conversion and Management, 47/11–12, 1326–1337 Peacock A.D., Jenkins D., Ahadzi M., Berry A. and Turan S. (2008), ‘Micro wind turbines in the UK domestic sector’, Energy and Buildings, 40/7, 1324–1333. Torcellini P., Pless S., Deru M. and Crawley D. (2006), ‘Zero energy buildings: a critical look at the definition’, NREL, ACEEE Summer Study, Pacific Grove California. United Nations Development Programme (2006), ‘Human development report’. van der Linde C. (2004), ‘Study on energy supply security and geopolitics’, ETAP program final report, Directorate General Energy & Transport, European Commission, TREN/C1–06–2002. The Hague: Reprovan de Kamp BV. Vlachou D., Christinakis E., Kavadias K. and Kaldellis J. (1999a), ‘Optimum windhydro energy station operation, using an advanced fluid flow analysis code’, presented at 3rd National Congress on Computational Mechanics, 811–820, Volos, Greece. Vlachou D., Messaritakis G. and Kaldellis J. (1999b), ‘Presentation and energy production analysis of commercial wind turbines’, presented at 1999 European Wind Energy Conference and Exhibition, 476–480, Nice, France. World Business Council for Sustainable Development (2008), ‘Energy efficiency in buildings’. www.allcountries.org (2006), ‘Floor space completed and housing conditions of urban and rural residents’, China Statistics 2006: Data from the National Bureau of Statistics of the Peoples Republic of China (accessed June 2009).

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15 Hybrid wind energy systems for desalination E. KONDILI, TEI of Piraeus, Greece

Abstract: In addition to power generation, the exploitation of renewable energy sources and hybrid energy systems can prove valuable in the production of fresh or even potable water. The objective of the present chapter is to describe the technology and implementation of water desalination systems with renewable energy sources (RESs) for freshwater supply. In this context, this chapter will: (a) review the current status, practices, advances, R&D activities and future prospects of the state of the art desalination technologies; (b) focus on the energy aspects of desalination and identify the critical parameters for the successful design and operation of hybrid energy desalination systems; and (c) give an insight into the future prospects of hybrid systems’ implementation in desalination processes. Key words: RES-based desalination, reverse osmosis, energy and water storage, desalination economics.

15.1

Introduction: the water scarcity problem

In addition to power generation, the exploitation of renewable energy sources and hybrid energy systems can prove valuable in the production of fresh or even potable water, especially in remote areas with difficult access to water and energy networks. In this context, the objective of the present chapter is to describe the technology and implementation of water desalination systems with renewable energy sources (RES) for fresh-water supply and highlight yet another valuable contribution that hybrid energy systems have made in the search for potential solutions to imperative social problems. Water is a valuable natural resource and access to fresh water is considered to be a basic human right. Water shortage is expected to be one of the most serious social and environmental problems to be faced in the coming years in many parts of the world. In fact, almost a quarter of the human population is suffering from an inadequate or poor quality fresh-water supply. Water scarcity does not only denote a lack of water in arid regions, but also a mismatch between water supply and demand; a problem with very strong spatial and temporal characteristics. Even in cases of positive total water balance, there may be periods of time in which water is not available. 506 © Woodhead Publishing Limited, 2010

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Since almost 97% of water on Earth is seawater, the desalination (i.e. the removal of salt from the virtually unlimited supply of seawater or brackish water) is considered to be a very promising method to meet water demand, and today it is widely applied in areas with limited water resources. As in any type of separation, the critical issue to address in water desalination is the process’s particularly high energy demand. The forms of energy that are used in desalination processes are mainly thermal and electrical, which will be analyzed later in this chapter. The use of thermal or electrical energy sources in desalination technologies can play a very significant role in reducing the operational costs of the units, improving their environmental impacts and extending their implementation in remote areas with difficult access to other energy sources. With this in mind, this chapter’s specific objective is to analyse and describe the use of renewable energy sources in desalination processes. This chapter will: •

•

• • •

review the current status, practices, advances, research and development (R&D) activities and future prospects of the state of the art desalination technologies; focus on the energy aspects of desalination and place emphasis on its use, technical issues and environmental and financial implications of various energy forms in desalination processes; demonstrate the basic desalination system design principles and the main difficulties in the implementation of these systems; identify the critical parameters and provide guidelines for the successful design and operation of hybrid energy desalination systems; give an insight into the future prospects of hybrid systems’ implementation in desalination processes.

15.2

Desalination processes and plants

15.2.1 General considerations Desalination is the process of removing salt from saline water and producing fresh, even potable water. Seawater desalination separates saline water into two streams: a fresh-water stream containing a low concentration of dissolved salts and a concentrated brine stream. A large number of desalination plants have been installed throughout the world, the majority of which can be found in the Middle East and the Caribbean islands, with very good prospects for development in China in the coming years. Desalination is still considered to be more expensive than other methods of water production, mainly due to its intensive use of energy. However, this picture is changing because new R&D efforts and technological advancements have begun to reduce the cost of production.

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In various cases, desalination has now proved a more reliable and economic means of water production when compared with methods such as dam construction or transportation of water by marine vessels. Even if desalination technology is used solely for the production of non-drinking quality water, it helps preserve existing water resources from over-exploitation and mismanagement. Desalination technology has evolved considerably over the past 50 years and has demonstrated its technical feasibility. Today, the average price of desalinated seawater is estimated to be only one-tenth of what it was 20 years ago, making it an attractive solution for the supply of water to islands. The total worldwide desalination capacity in 1971 was reported to be around 1.5 × 106 m3/day. In 1996 this had risen to 20.3 × 106 m3/day, with approximately 11 000 installations spread in 120 countries all over the world. Today, it is estimated that over 75 million people worldwide obtain fresh water by desalinating salt or brackish water. The International Desalination Association’s (IDA) Desalting Inventory 2004 Report shows that at the end of 2002 the installed and contracted brackish and seawater desalination plants worldwide totaled almost 13 600 desalination units in 10 350 plants, with a total capacity of 37.75 × 10 m3/day of fresh water [1]. The market volume increased from US$2.5 billion in 2002 to US$3.8 billion in 2005, with a growth rate in total desalination capacity of 12%, with over 15% per annum increase in plant and equipment investment. It is expected that the market will reach nearly US$30 billion by 2015. A dramatic increase is expected in new technologies and in small system applications in Asia, particularly in China. Currently, regions of the Middle East dominate demand with over 50% of the market share, followed by Asia-Pacific, America and Europe with almost 10% market share each [2]. The two main driving forces for this market development are the increasing number of water shortages and technology-driven cost reductions. Although desalination was previously considered a very expensive means of supplying water, the technological advancements (mainly focused in improved energy utilization) have allowed it to become a competitive method against other water supply approaches. However, with these production capacities worldwide, the large amounts of energy consumed in desalination plants must be considered, and as fossil fuels are used in certain desalination technologies, the negative environmental impacts of these processes must also be addressed. All desalination processes require energy to operate, depending on the separation technologies. The two most common desalination processes in use today are distillation processing and membrane processing (Fig. 15.1), together accounting for almost the total installed global desalination capacity.

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Seawater desalination processes

Thermal processes (phase change)

Membrane processes (single phase)

Multistage flash evaporation (MSF)

Reverse osmosis (RO)

Multi-effect distillation (MED)

Electrodialysis (ED)

Vapor compression (VC) mechanical (MVC)& thermal (TVC)

15.1 Main classification of desalination processes [3].

Thermal desalination (distillation) involves a phase change, while membrane processes are single phase. Today, most R&D efforts and technological innovations are oriented towards membrane processes and, more specifically, towards reverse osmosis (RO) processes, as will be discussed later in the chapter.

15.2.2 Distillation desalination processes In distillation desalination processes, saline water is heated to produce water vapor which in turn is condensed to form fresh water. These processes include multi-stage flash (MSF) distillation, multi-effect distillation (MED) and vapor compression distillation (VC). Each of these processes require thermal or mechanical energy to cause water evaporation. Almost 40% of the world’s desalination capacity is based on the MSF desalination principle. In the MSF desalination process, seawater feed is pressurized, heated and discharged to a chamber maintained slightly below the saturation vapor pressure of the water. Then a fraction of this water flashes into steam and condenses on the exterior surface of heat-transfer tubing [3]. Historically, distillation technologies have had the lion’s share in the seawater desalination market, partly because energy subsidies favored these more energy-intensive technologies, and partly because of the poor

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reliability of earlier membrane technologies. In particular, distillation is still the dominant process used in desalination plants in Middle Eastern countries where fossil fuels remain abundant and where areas suffer from serious fresh-water shortages.

15.2.3 Membrane/RO desalination processes It is worth noting that all of the major plants constructed or under construction by the private sector in certain non-oil-rich Mediterranean countries (Malta, Cyprus, Tunisia and Israel) have used membrane technologies which require electrical power as the only source of energy. Where fossil fuel prices are low, it is likely that distillation technology will continue to be used. However, even in the Middle East, where distillation technologies dominate, RO is gradually entering the market. Overall, given the current interest in global warming and sustainable development, the future development of desalination technologies may well shift towards low-energy desalination processes, i.e. favoring membrane technologies. Indeed, most new desalination plants now use membrane technologies. Membrane processes have considerable advantages in desalting water, and are now being widely applied in this market. The most widely applied membrane process, RO, represents more than 88% of membrane processes [4]. The RO process involves the forced passage of water through a membrane against the natural osmotic pressure to accomplish separation of water and ions. Under such high pressure the water molecules can pass through the membranes, and the salts are left behind as a briny concentrate. A typical RO system consists of four major subsystems (Fig. 15.2): • • • •

pre-treatment system; high pressure pump; membrane modules; post-treatment system.

Feed water pre-treatment is a critical factor in the operation of an RO system due to the membrane’s sensitivity to fouling. Pre-treatment commonly includes feed water sterilization, filtration and the addition of chemicals in order to prevent scaling and biofouling. The post-treatment system consists of sterilization, stabilization and mineral enrichment of the fresh water produced. The pre-treated feed water is forced by a high-pressure pump to flow across the membrane surface. RO operating pressure varies from 17 to 27 bar for brackish water and from 55 to 82 bar for seawater. Part of the feed water passes through the membranes, removing from it the majority of the dissolved solids, which is

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Pre-chlorination Chemicals for pH control Seawater pumping

Filtered water pumping

Sand filters

Recycled water Dechlorination

RO unit

High-pressure pumps

pH control

Pre-treatment

Chlorination Post-treatment

15.2 Typical reserve osmosis unit flow sheet.

termed the permeate water. The remaining water together with the rejected salts emerges from the membrane modules at high pressure as a concentrated-brine discharge stream. In large plants reject brine pressure is used by a turbine to partially recover energy, recovering between 20% and 40% of the overall consumed process energy. The critical magnitudes for RO processes are the energy saving (i.e. the percentage of the mechanical energy that can be recovered during the process) and the water recovery ratio (i.e. the ratio of the seawater input volume to fresh-water output volume). A large number of RO plants have been installed for both seawater (SWRO) and brackish water (BWRO) applications. The RO process is also widely used in manufacturing, agriculture, food processing and pharmaceutical industries. Almost 32% of the total RO unit installed capacity is found in the United States, 21% in Saudi Arabia, 8% in Japan and 8.9% in Europe. RO units are available in a wide range of capacities due to their modular design. Large plants are made up of hundreds or thousands of modules which are accommodated in racks. Very small units (down to 0.1 m3/day) are also available for marine purposes, houses or hotels. The emerging dominance of RO in recent years is mainly due to the improvements that have been made in the RO process, reflected in the reduction of both capital and operating costs, with significantly reduced energy consumption. Most of this progress has been achieved through improvements to the membranes themselves, with improved resistance to

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compression, longer life, higher possible water recovery, improved flux and improved salt passage. Apart from the need for an elaborate pre-treatment plant, RO processes have many advantages: • • • •

• •

The modular structure of the process makes it flexible enough to handle different plant capacities. The process is conducted at ambient temperature, which minimizes corrosion hazard. There is an embedded potential for water-power cogeneration when coupled with energy recovery systems. The rate of development in RO technology is high compared with other desalination processes, indicating that there may be further cost reductions in RO production of desalted water in the near future. Desalination by RO results in high salt rejection (up to 99%) and high water recovery ratios (up to 40%). Seawater reverse osmosis can produce potable water with salt content of about 500 ppm.

The energy issues of desalination processes and plants are discussed in the following sections of the chapter.

15.3

Energy requirements of desalination processes

15.3.1 General issues All desalination processes use energy, which is the largest cost component in the operation of a desalination plant. The greatest potential for further efficiency improvement and cost reduction in the desalination process lies in improvements to energy consumption. In fact, energy consumption is considered to be the main reason that desalination has not yet been as widely applied as expected, and only 1–2% of the fresh water consumed worldwide comes from desalination. The share of energy in overall cost varies according to the plant, its operational parameters and location, as is shown in Figs 15.3 and 15.4 for thermal and membrane processes respectively. The energy consumption of a desalination process depends on a variety of factors, including: • • • • •

seawater salinity; technology being used; ability of the system for energy recovery; temperature of operation for membrane processes; performance ratio;

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Capital, 32%

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Thermal energy, 50%

Chemicals, 3% Personnel, 6%

15.3 Typical cost structure of thermal seawater desalination [4]. Electrical energy, 44%

Capital, 37%

Consumables, 3%

Maintenance and parts, 7%

Membrane Labor, 4% replacement, 5%

15.4 Typical cost structure of RO seawater desalination [4].

Table 15.1 Power requirements of various desalination processes [4,5] Processa

Gain output ratio

Electrical energy consumption (kW h/m3)

Thermal energy consumption (kW h/m3)

MSF MED MED-TVC MVC BWRO SWRO

8–12 8–12 8–14 N/A N/A N/A

3.25–3.75 2.5–2.9 2.0–2.5 9.5–17 1.0–2.5 4.5–8.5

6.75–9.75 4.5–6.5 6.5–12 – – –

a

MSF multi-stage flash, MED multi-effect distillation, MED-TVC multi-effect distillation – thermal vapor compression, MVC mechanical vapor compression, BWRO brackish water reverse osmosis, SWRO seawater reverse osmosis.

• •

heat losses; temperature differences, etc., for thermal processes.

Table 15.1 shows the major power requirements of desalination processes [4,5]. Theoretically the absolute minimum amount of energy required for desalination is about 0.8 kW h/m3 of water produced, depending on the salt

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content and regardless of the process used. In reality, the energy use is much higher than the theoretical minimum, and at its very highest it can be in the range of 3–15 kW h/m3 for seawater desalination, along with the older distillation plants. The development of reverse osmosis and improvements in energy recovery devices have changed this situation. With energy consumption in Mediterranean seawater RO plants lowered to 3 kW h/m3, seawater desalination is now within the reach of many communities. In RO desalination operations, the majority of the energy required is used for pressurizing the feed water, and on average a small RO plant’s energy consumption is approximately 6–8 kW h/m3 without energy recovery. Installing an energy recovery device reduces the energy consumption quite dramatically to 3–4 kW h/m3. Further unit energy consumption reductions have been noted, down to as low as 2 kW h/m3 [6,7]. For medium and large RO systems an energy recovery system can be used, recovering about 40% of the input energy. In countries making significant desalination investments, energy policies and energy investment planning should be revised to provide incentives for appropriate desalination processes, and to decide whether cogeneration of water and power is a suitable option according to the particular circumstances. This has become more significant for reasons ranging from integration of policies, water demand and power demand growing at a different rates and seasonal variations between water and power demands [8,9]. Thermal processes (MSF, MED) that operate with steam supplied by exhaust or bleeding steam (from back pressure or extraction steam turbines) are, however, economically attractive and comparable with RO energy cost [10].

15.3.2 RES-based desalination The use of RES in the operation of desalination plants is a feasible and environmentally compatible solution in areas with significant RES potential. The main driving forces for applying RES in desalination plants are: • • • • • •

continuing technological advancements in RES systems and their cost reduction; seasonal variability in water (and energy) demand, usually occurring in areas with high renewable energy availability; limited availability of conventional energy supply in remote areas; technological advancements in desalination systems; limitations presented by the environmental impacts of conventional desalination systems; and improved plant operation and maintenance of RES as compared with conventional power plants.

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To that end, a lot of research and development work has been carried out, and the problem of the optimal configuration/combination of a RES energy source with a desalination plant has attracted the attention of many researchers and construction and engineering companies. The problem of how best to couple RES with desalination systems is a complicated and interesting one, and its solution is not always obvious. In fact, this is a major decision-making issue, and forms part of the wider problem of infrastructure planning. Various criteria should be taken into account, including among others: • • • • • •

the renewable energy availability; the investment and operational cost, and the availability of financial resources; the system’s efficiency; the availability of operational personnel; the suitability of the system to the characteristics of the location; the possibility for future increase of the system capacity [11].

RES suitable for use in desalination processes include wind, solar thermal, solar photovoltaic (PV) and geothermal. As mentioned above, RES driven desalination systems fall into two categories. The first one includes distillation processes driven by heat produced by RES systems, and the second includes membrane and distillation processes driven by electricity or mechanical energy produced by RES. Various potential combinations between RES and desalination systems are shown in Fig. 15.5. Matching renewable energies with desalination units, however, requires a number of important factors to be considered, as the various combinations of RES-driven desalination systems are not always practicable or viable in certain circumstances. The optimum combination of technology Renewable energy sources Geothermal

Electricity

Heat

Solar

Solar thermal

PV

RO ED MVC TVC MED MSF Electricity

Wind

Heat

Shaft

Shaft

Electricity

Electricity MVC RO RO ED MVC

RO ED MVC TVC MED MSF MVC RO ED RO MVC

15.5 Combinations of RES and desalination methods (PV photovoltaic, RO reverse osmosis, ED electrodialysis, MVC mechanical vapor compression, MED multi-effect distillation, MSF multi-stage flash, TVC thermal vapor compression) [4].

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must be studied in connection to various local parameters such as geographical conditions, topography of the site, capacity and type of low-cost energy available, availability of local infrastructure (including electricity grid), plant size and feed water salinity. More specifically, the factors to be considered for selecting desalination processes suitable for a particular site include: •

• •

• • • • • • •

the amount of fresh water required in a particular application (i.e. the plant’s capacity) combined with the applicability of the various desalination processes; the seawater treatment requirements, i.e. the feed’s water salinity; the technical infrastructure of the area (e.g. road access, network), the local regulations concerning the land use, and the land area required or available, for the installation of the integrated energy and desalination unit; the remoteness of the area and the availability of grid electricity; the suitability and effectiveness of the process with respect to energy consumption; the capital cost of the equipment; robustness/low-maintenance criteria and simplicity of operation; compact size and ease of transportation to site; acceptance and support by the local community; operational organization at local level with relatively simple operator training.

Table 15.2 evaluates the combinations of desalination and RES according to certain energy-related criteria.

15.4

Integrated systems of renewable energy sources (RES) with desalination plants

15.4.1 General description Desalination using renewable energy is still at an early stage of development and implementation. One of the most likely markets for coupling RES with desalination is in small communities in remote locations, where there is no power grid connection or where energy is expensive. In the context of the utilization of more widely established RES (e.g. solar thermal–PV– wind), stand-alone desalination systems have been widely discussed. However, where only one source of renewable energy is available, the final system may still be configured in many ways. Much research continues to be done in this area, with many research teams focusing on specific technical issues, ways of integrating RES and desalination processes and how to optimize those processes (see also: the

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Well suited for desalination plants requiring thermal power (3) Typically good match with need for desalination (3) Output is intermittent (energy storage required) (1) Output is relatively unpredictable (2)

Suitability for powering desalination plants Well suited for desalination plants requiring electrical power (3) Typically good match with need for desalination (3) Output is intermittent (energy storage required) (1) Output is relatively unpredictable (2)

Photovoltaic

Output is intermittent (energy storage required) (1) Output is very stochastic/ fluctuates (1)

Well suited for desalination plants requiring electrical power (3) Resources is locationdependent (2)

Wind energy

Output is predictable (3)

Well suited for desalination plants requiring thermal power (3) Resources are limited to certain location (1) Continuous power output (3)

Geothermal energy

Note: 3 excellent compliance with criterion, 2 good compliance with criterion, 1 poor compliance with criterion.

Predictability of power output

Continuity of power output

Site requirements and resources availability

Solar thermal energy

Criterion

Table 15.2 Evaluation of various RES in desalination applications [12]

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reference list at the end of this chapter). Many small-scale and experimental projects have been installed but as yet there have been no serious industrial-scale projects. The Red–Dead project, aiming at linking the Red Sea with the Dead Sea, might be the first very large renewable energy-driven desalination scheme. It would have the potential to produce up to 850 millions of m3/year of potable water.

15.4.2 RO: wind desalination Wind power can now be regarded as a reliable and cost-effective power source for many areas of the world. Desalination systems driven by wind power are the most frequent renewable energy desalination plants (Fig. 15.6). More specifically, wind energy can be used efficiently providing that the average wind velocity is above 5 m/s. This makes wind-powered desalination a particularly interesting option for windy locations (such as islands), both for the solution of their energy supply problem and for the operation of seawater desalination plants. This option is becoming even more interesting given the new generation of small- and medium-sized wind turbines that have been and are being developed that offer reliabile service and low investment costs. Wind turbines may be classified depending on their nominal power: No as very small (No < 10 kW), small (No < 100 kW), medium sized (No < 0.5 MW) and large (No > 0.5 MW). All are based on mature technologies

Wind energy unit Grid

Desalination plant

Pre-treatment Wind

Generator Energy management

Pumps

Battery bank

Seawater

RO unit

Energy recovery

Posttreatment

Freshwater storage

Water Energy

15.6 Structures of a wind-based RO desalination plant [13].

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and are commercially available, except for very large power systems, which still require advancement. Wind energy systems and desalination couplings may either be connected indirectly via a small autonomous electricity grid, or via the direct coupling of the wind generator and desalination plant for the sole purpose of producing water. Wind power is considered to be the most suitable for application to small (1–50 m3/day) and medium (50–250 m3/day) scale RO desalination plants [8]. The main design variables that affect the design of a wind–RO system are: • • • • • • • • • • •

water demand and RO plant’s capacity; location of wind turbine and desalination plant (required site development, altitude, etc.); feed water salinity; wind speed distribution; configuration of energy system; water storage capacity; distribution of available power; desalination unit energy consumption; salt rejection; operating pressure; permeate flux, both in terms of overall product rate and specific rate (per unit membrane area).

Design issues The basic assumptions for calculating the energy efficiency of the wind turbines, with or without an energy storage system, may be considered as follows. For a wind turbine with a nominal power of No kW, we expect an energy production E in the order of magnitude of E = CF * No*8760 kW h/year. Note that the installation capacity factor CF usually varies between 20% and 30%. Depending on the type of desalination plant, the required amount of energy per m3 of potable water will also be given. Therefore, we may have a series of alternatives concerning the installed power of the wind turbine and the combined capacity of the desalination plant [14,15]. To smooth operational performance, surplus energy can be stored using batteries [9] or water pumping systems, and so storage sizing should be considered in the design stage. Many other parameters should also be taken into account, including the possible losses incurred through use of energy storage systems (i.e. for low-wind/no-wind operation) and the availability of a water storage systems. In addition, capital and maintenance costs should be carefully assessed.

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The variable nature of wind power does not affect water availability as water can be stored inexpensively for long periods of time without deterioration. If a plant is constructed with dimensions according to local wind conditions, then with appropriate energy storage facilities, fresh water could be made available at any time. The major problem with this type of installation, however, is that variable wind power may cause operational problems in the system. This is one of the most critical issues to be resolved in the design and implementation of a RES wind-based desalination project. Operational issues: technical difficulties RES are characterized by intermittent and variable intensity, whereas desalination processes are designed for continuous steady state operation. The variable nature of wind power means it can be problematic in process applications such as in a desalination plant. Although relatively predictable, wind is seldom constant, sometimes stopping altogether. The storage of wind energy in the form of electrical power is practical only when small amounts are involved. Storage batteries increase the total investment cost, so running a process of any magnitude on stored electrical energy is economically impracticable at present. If the product of the process can be stored inexpensively, however, then it may be practical to use large water storage equipment, which is relatively cheap, thereby allowing for downtime. To avoid the fluctuations inherent in RES, different energy storage systems may be used. The relative sizes of the wind turbine and the RO plant and the cut-in and cut-out criteria (for the RO plant to avoid excessive start-up and shut-down cycles) require careful design, but these are normally the only major obstacles at the design stage. If an intermediate energy storage system is necessary, however, it will mean a decrease in available energy, as well as increasing the cost of the plant, and would therefore impact on the wind turbine–RO plant configuration. For the operation of a wind-powered desalination plant, it is most important to have a plant that is insensitive to repeated start-up and shut-down cycles caused by changing wind conditions. At present, reverse osmosis is sensitive to stopping and starting due to time-consuming pre- and posttreatment processes, membrane fouling, and sensitive high-pressure pumps, which cannot be subject to erratic usage. Other drawbacks facing RO in remote areas include the requirement of skilled workers, the complex pre-treatment process, process chemicals requirements, and membrane maintenance and replacement.

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15.4.3 Wind–RO configuration possibilities A classification of the different wind-powered RO systems found in the literature has been made [16]. This was based on some of the points previously discussed: • • •

the existence of an alternative electrical supply (weak grid or diesel generator); the matching of the available wind energy to the load; and the operational characteristic of RO membranes.

Systems with back-up (diesel/grid) In these systems, an additional energy source is provided (a diesel-powered generator or even the local grid) so that the power supplied to the RO is constant. The back-up generation complements the power generated from the wind turbine to match the RO unit power consumption. The main benefit of these systems as in any hybrid wind–diesel configuration is the achievement of fuel savings, which may increase the generator availability and reduce overall energy costs. On the other hand, problems such as fuel shortages, diesel generator maintenance, interruptions or power cuts in the supply may lead to unavailability of the RO system since it may not be possible to power it using the wind turbine alone. Systems without back-up RO systems without an external energy source can be divided into two categories, those which run under approximately constant operating conditions, and those that experience variable operational conditions. Near constant operating conditions Near constant operation can be implemented by three different means: usage of storage devices, on/off switching of the RO units, and de-rating the wind turbine. In all three cases, an attempt is made to supply the individual RO modules with approximately constant power. Storage devices In this strategy, storage devices are employed to accumulate energy surplus during periods when the power generated by the wind turbine is greater than the load demand from the desalination unit. This surplus would then

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be used later when the generated power is insufficient to meet the load demand. One common way of storing the surplus energy is by using batteries. In this case, the relation between operational pressure, storage sizing and average wind speed should be considered at the design stage. Capital and maintenance costs must also be assessed carefully. A disadvantage of this approach to the system design is the rating of the energy storage system, which can make it economically unattractive at higher power levels due to the sizing of the battery bank. RO unit switching This strategy is based on the use of a higher-power wind turbine connected to multiple smaller RO units. Power control is achieved by switching the units on and off so as to match the power generated by the turbine to the demand for process energy. There is no limitation concerning system power rating, and this approach is feasible up to power levels of hundreds of kilowatts. Although frequent cycling of RO units is not usually recommended, problems can potentially be overcome by implementing different types of configuration. Higher power wind turbines operating at near constant speed could be connected to many smaller RO units switching on/off (load management), with power fluctuations smoothed out using short-term energy storage (for instance a flywheel). Wind turbine de-rating This approach consists of making use of the flat end of a pitch-controlled wind turbine power curve to operate the RO unit at approximately constant power. An implication of this configuration is that, since the turbine rated power is only achieved at high wind speeds, it would have to be de-rated by changing the settings of the pitching mechanism. This will cause the generated power to be flattened at lower wind speeds and consequently to have lower values. Therefore, the original rating of the turbine rotor would need to be considerably higher than the RO unit rated power, making the system more expensive. Variable operating conditions In contrast to systems that operate under constant conditions, another operational strategy is based on the establishment and imposition of certain operational limits. This means that, based on the input power to the RO unit (flow multiplied by pressure), a control strategy is determined which

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imposes a fixed operating point on the system that lies within the allowed region (i.e. the operational window of the RO unit). By doing this, an attempt is made to operate the system autonomously over a wider power range, without the need to use a back-up unit or storage devices. The overall effect is to reduce capital and operating costs. It must be emphasized however, that very little is known about the consequences of variable operation of RO membranes. It is recognized that mechanical fatigue can occur and that the lifetime of the RO elements may be shortened and performance impaired by operating in these conditions. Extensive laboratory testing was carried out so as to develop suitable mathematical models for individual components over a wide operating range.

15.4.4 Implementation projects A number of units have been designed and tested; however, most of them are in demonstration and experimental scale [17,18,19]. To date, the only practical experiments that have taken place have used small capacity wind-powered RO systems, though few conclusions have been drawn from such small-scale experimentation, as it is still difficult to control the usage of wind in a cost-effective way. Coupling of a variable energy supply system to a desalination unit requires either power or demand management. The prospects of this combination remain high, however, owing in a large part to the low cost of wind energy. The operational experience from early demonstration units is expected to contribute to improved designs and a large number of commercial systems are expected to be implemented in the near future. •

•

•

As previously stated, experimentation with RO–RES coupling has been going on for some time, albeit on a small scale. As early as 1982, a small system was set up at Ile du Planier, France; a 4 kW turbine coupled to a 0.5 m3/h RO desalination unit. The system was designed to operate via either a direct coupling or in combination with energy storage using batteries. Another case of RO–RES coupling has been developed on the island of Drenec in France, in 1990. The wind turbine in this case was rated at 10 kW and used to drive a seawater RO unit. More recently some R&D projects have been carried out, such as the wind desalination system built on a cement plant at Drepanon, near Patras, Greece. The project was initiated in 1992 and completed in 1995. The project called for the full design and construction of a 35 kW wind turbine (blades, generator, etc.), plus the installation of two RO units with a production capacity of 5 m3/day and 22 m3/day respectively.

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Unfortunately, since 1995 operational results have been poor due to the low wind regime. A very interesting experiment has been carried out at a test facility in Lastours, France, where a 5 kW wind turbine provides energy to a number of batteries (1500 A h, 24 V) and via an inverter to an RO unit with a nominal power of 1.8 kW. Furthermore a great amount of work on wind RO systems has been done by the Instituto Tecnologico de Canarias, ITC within several projects such as AERODESA, SDAWES and AEROGEDESA.

15.4.5 Implementation projects with hybrid energy systems Autonomous hybrid systems are independent and incorporate more than one power source. Diesel generators are mainly used as back-up systems. However, fuel transportation to remote areas poses the same difficulties as water transportation. RES penetration depends only on the economic feasibility and the proper sizing of the components to ensure quality and continuity of supply. One important application is the use of photovoltaics and wind generators to drive RO desalination units. Each desalination system has specific problems when it is connected to a variable power system. RO has to deal with the sensitivity of the membranes regarding fouling and scaling, as well as any unpredictable phenomena, such as start–stop cycles and partial load operation during periods of oscillating power supply. Several RO units with intermittent or infrequent operation have to replace their membranes regularly. On the other hand, units with storage back-up systems (e.g. batteries) increase the system’s initial costs and also increase the maintenance requirements. Most of the plants constructed to date have been as either research or demonstration projects forced to conclude by budget limitations or staff availability issues. GECOL and a consulting consortium of experts from ZSW, DEWI and LI are managing the implementation of an experimental research facility for seawater reverse osmosis desalination powered from renewable energy sources (SWRO-RES) on the Mediterranean coast of Libya. The nominal production of the plant will be 300 m3/day of drinking water to supply a local village with. Both wind energy conversion (WEC) and PV power generation will be integrated into a grid-connected power supply for a RO desalination plant, with power recovery by pressure exchange. The facility design is flexible, allowing for the integration of a diesel generator and electrochemical storage as power supply alternatives, as well as BWRO. The wide range of feasible plant configurations will mean that research can be extended to off-grid/stand-alone performance analysis of such hybrid systems.

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While the expected nominal power load for the operation of the RO desalination system is 70 kW (net power after recovery), the solar PV system is designed for 50 kW, and the WEC for 200 kW nominal output. The design aims at a reduction of the annual non-renewable energy consumption to about 40% of total annual energy consumption. The economic analysis of the integrated renewable energy systems predicts standardized water costs for the integration of Grid+WEC with RO at c1.8/m3, and for Grid+PV with RO at c1.9/m3 as compared with c1.3/m3 for operation using grid power (i.e. conventional plant) alone [20,21].

15.5

Environmental impacts of renewable energy sources (RES)-based desalination plants

15.5.1 Environmental aspects Desalination plants provide water to meet the supply needs of remote areas. They are usually implemented as the result of analyzing various solutions to the need for a water supply. For example, in several Greek islands, fresh-water requirements are normally met through the construction of large dams, ground reservoirs or desalination plants. On smaller islands, the only available solution is the transport of fresh water by ship, which can incur high costs and may not provide adequately hygienic conditions [10]. All these water supply methods have environmental ramifications, varying in severity according to the type of project, its location and its scale. The main environmental impacts of an RO desalination plant are: • • • • • •

noise disturbance; optical disturbance; land use; interference with public access to the coast; discharge of brine to coastal or marine eco-systems or, in the case of inland brackish water desalination, to rivers and aquifers; the emission of greenhouse gases from electricity and steam production using fossil fuel powered plant – this is eliminated/reduced where the energy source is renewable.

The area required for installation of RO desalination units is minimal, though it can be much larger for thermal desalination units, and in most cases these plants will be positioned close to the sea. However, as the areas requiring desalination plants are often small islands, land availability often merits strong opposition to the choice of desalination as a means of water production. On the other hand, installation of dams and ground reservoirs can have much worse environmental impacts and present much greater land

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use restrictions, which in turn provides a strong argument in favor of properly sized and located desalination plants. The floating desalination unit [22] is an innovative project that is hoped will resolve land use problems.

15.5.2 Floating desalination plant The first floating wind turbine/desalination plant in the world has been developed by a number of scientists and engineers, led by the University of the Aegean. Two of the most pressing environmental challenges of today – energy production and water supply – have been addressed by this innovative and practical solution to the water needs of Greek islands. The floating autonomous environmentally friendly and efficient desalination unit (FAEFEDU) is designed to produce potable water from seawater by generating power through its on-board wind turbines (Fig. 15.7). The unit sits on a special floating 20 × 20 m2 platform with an 8 m high water cylinder and a 22 m high wind turbine tower. The unit can adapt to any weather conditions. Water production is more than 70 m3/day – enough for the needs of about 300 people. In order to achieve the largest possible energy and desalination production, scientists focused on minimizing the scale and polluting effects of the central desalination unit, increasing the overall energy efficiency of the cycle. In addition, because the unit is autonomous, it is not required to be connected to the national electrical grid. Since the unit is portable, it can be

15.7 Floating desalination unit.

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stationed away from populated centers wherever needed, on a seasonal basis for instance, to service the needs of islands that have an enlarged population during summer months. In addition, the unit can be repositioned to take advantage of changing weather conditions. The unit has been co-financed by the European Fund for Regional Development and domestic national funds.

15.6

Economic considerations in renewable energy sources (RES)-based desalination

Various efforts have been made to develop tools for the design, the economic evaluation and the determination of the main parameters for basic RES-based desalination plants. Many articles and R&D projects have analyzed the design and financial aspects of these units [22–33], which reach various conclusions concerning the optimal decision under specific circumstances. A number of parameters affect the design of such a plant, and they also impact on the financial evaluation of the units. There is no specific and generally applicable tool for determining the cost of such a unit, as all the technical, environmental and social variables are site-specific. As a general rule, a seawater RO unit has low capital cost and significant maintenance cost due to the high cost of the membrane replacement. The cost of the energy used to drive the plant is also high. This chapter is intended to inform researchers and professionals working on RES-based desalination plants in two ways. Firstly, it attempts to exhaustively enumerate all the factors that should be taken into account with such an endeavor. Secondly, it provides real case studies, including design and cost specifications.

15.6.1 Parameters affecting the economics of desalination A detailed financial analysis leading to precise estimates should always be carried out before private investments are made. The investor may undertake the cost of the project expecting to benefit from the future sale of fresh water either on the free market or to the municipality it belongs to, like, for example, the Milos desalination plant [13]. Many such private investments are expected to be made in the coming years, particularly in areas with water shortages and a reliance on the tourist industry. Table 15.3 presents a synthesis of the most critical limitations and choices that affect the feasibility and financial attractiveness of a RES-based desalination project. More specifically, for the case of wind–RO desalination, the factors that are taken into account in water production costs are shown in Table 15.4.

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Table 15.3 Parameters affecting economics of RES-based desalination plants Parameters affecting economics of RES-based desalination plants

Comments

The desalination technology (thermal, RO)

In general, RO units have lower investment cost but high operation and maintenance costs Large capacity units are more expensive but the water unit cost is lower They define the size of the wind farm required for a given annual production of fresh water

Plant’s capacity The climatic conditions, the characteristic of wind turbines and the energy requirement of the RO plant The energy requirement of the desalination plant

The feed water salinity The location where the wind turbine and the desalination plant will be installed The configuration of the energy system The water storage capacity The available power distribution (e.g. the wind speed distribution, solar radiation)

This is determined by: (1) the water supply salt concentration and (2) the coupling of the energy and the desalination system BWRO is generally cheaper than SWRO Required siting, altitude, infrastructure preparation costs Main design decision determining the operation and the cost of the unit Design parameter determining the operation of the unit It affects the size, the configuration and, therefore, the investment cost

Table 15.4 Cost items of a wind-based desalination plant Investment cost Cost Cost Cost Cost

of of of of

land wind turbine energy storage systems the RO plant components

Annual operating cost Personnel cost Chemicals cost Electricity cost Maintenance and spares cost Membrane replacement

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15.6.2 Examples of RES-based desalination cost estimation In the following section, some case studies from real plants are presented. They are all different types of plants installed in different areas and each one has its own technical characteristics. Libya [27] A demonstration RES-desalination plant has been designed in Libya (Integrated Power and Water Point) that will supply up to 300 m3/day of water and 240 kW electricity to a village. For the 60 kW RO power demand a 275 kW wind turbine is integrated with a 300 kW diesel plant. The process simulation for the desalination of seawater with 4.3% salinity under nominal operation conditions yields 57% recovery rate at a specific energy consumption of 4.8 kW h/m3 (pumping included). The power demand at a nominal fresh-water output of 300 m3/day is 60 kW, based on the calculation of 3000 m3/day*4.8 kW h/m3/24 h/day = 60 kW. The resulting cost of water is c2.24/m3. In this specific plant, detailed measurements have been taken in order to make reliable calculations of the costs. Spain [11] As every wind farm with a particular type of turbine and a given wind regime has a nominal optimum production capacity for each desalination plant, this must be specified in every case under consideration. In this context, a wind farm proposed for installation in Gran Canaria with a nominal power of 460 kW and a wind regime (in the area of Pozo Izquierdo) with an average annual speed of 7.9 m/s and sited 10 m above ground level, was projected to employ an optimum of 11 RO plants each with a capacity of 100 m3/day. However, for technical and economical reasons the decision was made to use 8 RO plants, each with a capacity of 25 m3/day. The water cost of a large, wind-powered brackish water reverse osmosis unit with a capacity of 250 m3/day is of the order of c2/m3. A project in Tenerife, Spain, included a 200 kW wind turbine, operating at an average wind velocity of 7.5 m/s, with an expected yearly energy yield of around 600 MW h. This amount of energy was projected to be capable of producing over 200 m3/day water. Morocco [30] Morocco is characterized by a semi-arid climate, where the obligation to use other nonconventional water resources such as desalinated water or

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Stand-alone and hybrid wind energy systems Table 15.5 Seawater desalination Seawater desalination

RO

MVC

Number of desalination units Nominal unit water production (m3/day) Specific energy consumption (kW h/m3) Total nominal power (kW) Annual energy consumption (MW h/year) Lifetime (years)

5 1200 5 250 2190 20

2 1200 8 400 3500 20

waste water reuse is a necessity. In addition, Morocco has a large potential for wind and solar energy sources that could be used for seawater desalination. Here follows an estimation of the cost of desalinated water for three towns in the south of Morocco, using the method of levelized water cost (LWC). The cost was estimated for two seawater desalination processes: reverse osmosis and mechanical vapour compression (MVC) powered by wind turbines. Electric connection with the grid is available, so that the grid can be used to power the plant when RES are not available. This alternative is then compared with the baseline which consists of the grid-only configuration. The desalination processes studied were designed to produce 1200 m3/ day of fresh water, equivalent to the daily consumption of almost 10 000 inhabitants. Table 15.5 gives technical characteristics of the two desalination processes studied. The baseline water cost was evaluated at c0.91/m3 for RO. The cost breakdown structure for a wind-based RO-desalination unit, as shown for this plant in Morocco, is: • • • •

37.5% desalination investment cost; 31.6% wind turbine cost; 24.2% operation and maintenance of desalination unit cost; 6.7% operation and maintenance of wind turbine cost.

This adds up to an LWC of almost c0.85/m3. Milos island, Greece [13] A wind-based desalination unit on the Greek island of Milos, located in the Cyclades complex of islands, has been in operation since summer 2007. The unit has a capacity of 3000 m3/day. At the moment it operates at 2000 m3/day production of potable water. This is a private investment that has been subsidized by the state. The water is sold to the municipality of

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Milos, in a continuous effort to solve the urgent water shortage problem, especially during the summer months. The contract that has been signed between the private company and Milos Municipality refers to a selling price of water of almost c1.8/m3. The entire plant includes: • the desalination plant; • a wind turbine of 600 kW; • the storage tanks (capacity 3000 m3); • the remote monitoring and control system. Before the installation of the unit, water was transported from Athens at a very high cost and of very poor quality [33]. The implementation of this novel project has improved the quality of life of the island in many respects. The siting of the unit on such a tourist island as Milos could have been a major problem, mainly because of the optical and noise disturbance. Therefore, the unit was located on a hill that is not apparent from most of the island’s villages.

15.7

Future trends

Although present desalination technologies and various forms of RES are well developed, there is wide scope for improvements in efficiency, reliability, simplicity and investment costs in each one of these technologies. Therefore, further research efforts should be directed towards improving and enhancing the presently utilized technologies. It is also important that new technologies be investigated. There is a particular need for research and development in the coupling between desalination and RES. Serious progress in the field will take place if industrial-scale projects are implemented. Analysis of current trends in R&D activities shows that energy consumption in all desalination processes is much higher than the thermodynamic minimum requirement, and this energy consumption is the major component of the operating cost of a desalination plant. Research in this area is therefore focused on both reducing energy consumption, and the introduction of alternative, clean and sustainable energy sources. Development of high-flux membranes and the integration of energy recovery devices have been combined to greatly reduce overall energy consumption, resulting in a potential energy demand of below 2 kW h/m3. Coupling desalination processes with non-conventional energy sources would present further advantages in improving the environmental impacts of desalination and the long-term economic costs of RES-based desalination systems.

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15.8

Sources of further information and advice

In addition to all the references already cited, the contributions below will facilitate further reading and provide a rather more complete list of works in the field. Alghoul, M.A., Poovanaesvaran, P., Sopian, K., Sulaiman, M.Y., 2009. Review of brackish water reverse osmosis (BWRO) system designs. Renewable and Sustainable Energy Reviews, Vol. 13(9), pp. 2661–2667. Almulla, A., Hamad, A., Gadalla, M., 2005. Integrating hybrid systems with existing thermal desalination plants. Desalination, Vol. 174(2), pp. 171–192. Altener Programme, 2002. Renewable energy driven desalination systems – REDDES. Technical analysis of existing RES desalination schemes. Stylianos Loupasis. http://www.nad.gr/readsa/files/ TechnodatabaseREDDES.PDF Calì, G., Fois, E., Lallai, A., Mura, G., 2008. Optimal design of a hybrid RO/MSF desalination system in a non-OPEC country. Desalination, Vol. 228(1–3), pp. 114–127. Ekren, B.Y., Ekren, O., 2009. Simulation based size optimization of a PV/ wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions. Applied Energy, Vol. 86(9), pp. 1387–1394. Fadigas, E.A.F.A., Dias, J.R., 2009. Desalination of water by reverse osmosis using gravitational potential energy and wind energy. Desalination, Vol. 237(1–3), pp. 140–146. Fritzmann, C., Löwenberg, J., Wintgens, T., Melin, T., 2007. State-of-the-art of reverse osmosis desalination. Desalination, Vol. 216(1–3), pp. 1–76. Greenlee, L.F., Lawler, D.F., Freeman, B.D., Marrot, B., Moulin, P., 2009. Reverse osmosis desalination: water sources, technology, and today’s challenges. Water Research, Vol. 43(9), pp. 2317–2348. Hamed, O.A., 2005. Overview of hybrid desalination systems – current status and future prospects. Desalination, Vol. 186(1–3), pp. 207–214. Helal, A.M., El-Nashar, A.M., Al-Katheeri, E.S., Al-Malek, S.A., 2004. Optimal design of hybrid RO/MSF desalination plants Part II: Results and discussion. Desalination, Vol. 160(1), pp. 13–27. Helal, A.M., El-Nashar, A.M., Al-Katheeri, E.S., Al-Malek, S.A., 2004. Optimal design of hybrid RO/MSF desalination plants. Part I: Modelling and algorithms. Desalination, Vol. 154(1), pp. 43–66. Kamal, I., 2008. Myth and reality of the hybrid desalination process. Desalination, Vol. 230(1–3), pp. 269–280. Khawajia, A.D., Kutubkhanaha, I.K. Wieb, J-M, 2008. Advances in seawater desalination technologies. Desalination, Vol. 221(1–3), pp. 47–69.

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Kim, Y.M., Kim, S.J., Kim, Y.S., Lee, S., Kim, I.S. , Kim, J.H., 2009. Overview of systems engineering approaches for a large-scale seawater desalination plant with a reverse osmosis network. Desalination, Vol. 238(1–3), pp. 312–332. Kiranoudis, C.T., Voros, N.G., Maroulis, Z.B., 1997. Wind energy exploitation for reverse osmosis desalination plants. Desalination, Vol. 109(2), pp. 195–209. Moreno, F., Pinilla, A., 2005. Preliminary experimental study of a small reverse osmosis wind-powered desalination plant. Desalination, Vol. 171(3), pp. 257–265. Pestana, I., Latorre, F.J.G., Espinoza, C.A., Gotor, A.G., 2004. Optimization of RO desalination systems powered by renewable energies. Part I: Wind energy. Desalination, Vol. 160(3), pp. 293–299. Schiffier, M., 2004. Perspectives and challenges for desalination in the 21st century. Desalination, Vol. 165, 1–9. Tzen, E., Theofilloyianakos, D., Kologios, Z., 2008. Autonomous reverse osmosis units driven by RE sources, experiences and lessons learned. Desalination, Vol. 221(1–3), pp. 29–36. Voivontas, D., Yannopoulos, K.A., Rados, K., Zervos, A., Assimacopoulos, D., 1999. Market potential of renewable energy powered desalination systems in Greece, Desalination, Vol. 121, pp. 159–172. Warfel, C.G., Manwell, J.F., McGowan, J.G., 1988. Techno-economic study of autonomous wind driven reverse osmosis desalination systems. Solar & Wind Technology, Vol. 5(5), pp. 549–561.

15.9

References

1. Wangnick/GWI, 2005. 2004 Worldwide desalting plants inventory. Global Intelligence, Oxford, UK. (Data provided to the Pacific Institute and used with permission.) 2. http://ec.europa.eu/environment/etap/pdfs/waterdesalination.pdf 3. Al-Karaghouli, A., Renne, D., Kazmerski, L.L., 2009. Solar and wind opportunities for water desalination in the Arab regions. Renewable and Sustainable Energy Reviews, Vol. 13(9), pp. 2397–2407. 4. Eltawil, M.A., Yhengming, Y., Zuan, L.-Q., 2009. A review of renewable energy technologies integrated with desalination systems, Renewable and Sustainable Energy Reviews, Vol. 13(9), pp. 2245–2262. 5. Miller, J.E., 2004. Review of water resources and desalination technologies. Albuquerque, NM: Sandia National Laboratories; http://www. sandia.gov/ water/docs/MillerSAND2003_0800.pdf. 6. Forstmeier, M., Mannerheim, F., D’Amato, Shah, M., Liu, Y., Baldea, M., Stella, M., 2007. Feasibility study on wind-powered desalination. Desalination, Vol. 203(1–3), pp. 463–470.

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7. Habali, S.M., Saleh, I.A., 1994. Design of a stand – alone brackish water desalination wind energy system for Jordan. Solar Energy, Vol. 52(6), pp. 525–532. 8. Kaldellis, J.K., Kavadias, K.A., Kondili, E., 2004. Renewable energy desalination plants for the Greek islands – technical and economic considerations. Desalination, Vol. 170(2), pp. 187–203. 9. Kaldellis J.K., Kondili E., Kavadias K.A., 2005. Energy and clean water coproduction in remote islands to face the intermittent character of wind energy. Int. J. of Global Energy Issues, Vol. 25/3,4, pp. 298–312. 10. Vlachos G., Kaldellis J.K., 2004. Application of a gas-turbine exhausted gases to brackish water desalination. A techno-economic evaluation. Applied Thermal Engineering, Vol. 24(17–18), pp. 2487–2500. 11. Carta, J.A., Gonzhlezb, J., Subiela, V., 2004. The SDAWES project: an ambitious R&D prototype for wind powered desalination. Desalination, Vol. 161(1), pp. 33–48. 12. Delyannis, E., Belessiotis, V., 1996. A historical overview of renewable energies. In: Proc. Mediterranean Conference on Renewable Energy Sources for Water Production, EURORED network, CRES, EDS; p. 13–7. 13. Kondili E., Kaldellis J.K., 2008. Proceedings, World Renewable Energy Congress (WRECX) Editor A. Sayigh, July, Glasgow, Scotland, UK, pp. 2120–2128. 14. Koklas, P.A., Papathanassiou, S.A., 2007. Component sizing for an autonomous wind-driven desalination plant. Renewable Energy, Vol. 31(13), pp. 2122–2139. 15. Mathioulakis, E., Belessiotis, V., Delyannis, E., 2007. Desalination by using alternative energy: Review and state-of-the-art. Desalination, Vol. 203(1–3), pp. 346–365. 16. Miranda, M.S., Infield, D., 2003. A wind-powered seawater reverse-osmosis system without batteries. Desalination, Vol. 153(1–3), pp. 9–16. 17. Papapetrou, M., Epp, C., 2007. Autonomous desalination units based on Renewable energy systems – a review of representative installations worldwide. Solar Desalination for the 21st Century, Springer, Netherlands. 18. Tzen, E., Theofilloyianakos, D., Kologios, Z., 2008. Autonomous reverse osmosis units driven by RE sources, experiences and lessons learned. Desalination, Vol. 221(1–3), pp. 29–36. 19. Tzen, E., Christian Epp., Papapetrou M., 2006. Co-ordination Action for Autonomous Desalination Units Based on RE Systems, ADU-RES. http://www. evvec2006proceedings.info/allfiles2/628_Ewec2006fullpaper.pdf. 20. Kershman, S.A., Rheinlander, J., Neumann, T., Goebeld, O., 2005. Hybrid wind/PV and conventional power for desalination in Libya – GECOL’s facility for medium and small scale research at Ras Ejder. Desalination, Vol. 183(1–3), pp. 1–12. 21. Kershman, S.A., Rheinländer, J., Gabler, H., 2003. Seawater reverse osmosis powered from renewable energy sources – hybrid wind/photovoltaic/grid power supply for small-scale desalination in Libya. Desalination, Vol. 153(1–3), pp. 17–23. 22. Agashichev, S.P., 2004. Analysis of integrated co-generative schemes including MSF, RO and power generating systems (present value of expenses and ‘levelised’ cost of water). Desalination, Vol. 164(3), pp. 281–302.

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23. Atikol U., Hikmet S.A. 2005. Estimation of water production cost in the feasibility analysis of RO systems, Desalination, Vol. 184, pp. 253–258. 24. Ekren, O., Ekren, B.Y., Ozerdem, B., 2009. Break-even analysis and size optimization of a PV/wind hybrid energy conversion system with battery storage – a case study. Applied Energy, Vol. 86(7–8), pp. 1043–1054. 25. Fiorenza G, Sharma VK, Braccio G. 2003. Techno-economic evaluation of a solar powered water desalination plant. Energy Convers Manage, Vol. 44, pp. 2217–2240. 26. Garcia-Rodriguez, L., Romero-Ternero, V., Gomez-Camacho, C., 2001. Economic analysis of wind-powered desalination. Desalination, Vol. 137, pp. 259–265. 27. Rheinländer, J., 2007. De-central water and power supply integrating renewable energy – technical and economic performance prediction. In Solar Desalination for the 21st Century, pp. 111–126, Springer, Netherlands. Available online at: http://www.springerlink.com. 28. Saheb-Koussa, D., Haddadi, M., Belhamel, M., 2009. Economic and technical study of a hybrid system (wind–photovoltaic–diesel) for rural electrification in Algeria. Applied Energy, Vol. 86(7–8), pp. 1024–1030. 29. Warfel, C.G., Manwell, J. F., McGowan, J.G., 1988. Techno-economic study of autonomous wind driven reverse osmosis desalination systems. Solar & Wind Technology, Vol. 5(5), pp. 549–561. 30. Zejli, D., Elmidaoui, A., 2007. Moroccan potentialities of renewable energy sources for water desalination. In Solar Desalination for the 21st Century, pp. 127–138, Springer, Netherlands. Available online at: http://www.springerlink. com. 31. Zervos, A., Assimacopoulos, D., 2000. Estimating the cost of water produced by RES powered desalination systems. Mediterranean Conference on Renewable Energy Sources for Water Production, Santorini, Greece, June 2000. 32. Kaldellis J.K., Kavadias K., Vlachou D., 2000. Improving the economic viability of desalination plants. Mediterranean Conference on Policies and Strategies for Desalination and Renewable Energies, Santorini Island, Greece. 33. Kaldellis, J.K., Kondili, E., 2007. The water shortage problem in Aegean Archipelago islands. Cost-effective desalination prospects. Desalination Journal, Vol. 216, pp. 123–128.

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Index

ABB, 349 absorbent glass mat, 342 AC generators, 172 AC/DC converters, 233–4 Active Power flywheel, 370, 372–3, 378 cutaway/exploded view, 371 AERODESA, 524 AEROGEDESA, 524 Aerospace Flywheel Development Program, 381 ageing mechanism, 39 air lift pump, 288 air saturator, 400 alkaline fuel cells, 256 Altairno lithium–titanate material battery cells, 346 aluminium, 346 anemometer, 446 annual escalation rate, 114 APS see autonomous power systems arbitrage, 44 autonomous power systems, 274, 278 auxiliary electronic equipment, 489, 494 auxiliary generators, 234–6 balance of system, 33 battery, 485–6 modelling, 454–5 see also specific battery battery bank, 119–20, 153, 194 battery energy storage system, 53–6, 192, 199 control, 205–6 control scheme, 206 lead–acid batteries, 54–5 Li ion, 56 metal-air batteries, 56 Na–S, 55 Ni–Cd, 55

WECS–BESS feeding a load, 209–10 WECS–DG–BESS feeding a load, 210–11 battery sizing, 188 battery storage, 231–3 Beacon flywheel system, 383 beryllium oxide, 377 BESS see battery energy storage system black start, 403 brackish water reverse osmosis, 511, 524 bromine, 354 buildings characteristics, 477–8 energy consumption overview, 478–80 European Union facts about hybrid energy systems, 481–2 gross domestic product vs primary consumption per capita, 476 hybrid energy systems description, 482–9 auxiliary electronic equipment, 489 complementary electric generator unit, 488–9 storage system unit, 485–8 wind turbine generator, 482–5 operational modes of hybrid energy systems, 495–6 sizing procedure for hybrid energy systems, 489–94 5 kW inverter efficiency evolution, 495 auxiliary electronic equipment, 494 diesel–electric generator, 493–4 diesel–electric generator SFC distribution, 493 energy storage system’s main parameters, 490–2 non-dimensionalised wind turbine power curve, 491

536 © Woodhead Publishing Limited, 2010

Index photovoltaic power station, 494 pump–hydro, 492–3 wind turbine rated power, 489–90 stand-alone and hybrid wind energy systems integration, 475–503 stand-alone wind–PV configuration low wind and high solar potential area, 503 low wind and medium solar potential area, 502 system performance and optimisation of hybrid energy, 496–503 electricity profile of autonomous building analysed, 499 PV–diesel algorithm, 498 wind hybrid stand-alone system main parameters, 499 wind stand-alone system configurations, 500 wind–diesel algorithm, 497 wind–diesel hybrid system optimisation for high wind speed area, 501 wind–diesel hybrid system optimisation for low wind speed area, 501 bulb turbine, 292 BWRO see brackish water reverse osmosis CAES see compressed air energy storage; compressed air energy storage technologies CAES with humidification, 400 CAES with steam injection, 400 CAESSI see CAES with steam injection CAN-bus see controller area network-bus capacity factor, 490 capital cost, 41 carbon dioxide emissions, 270–1 Carnot engine, 433 CASH see CAES with humidification cell temperature, 230 centralised generation, 425 chemical energy storage, 53–9, 73 choppers, 435 climate change, 419 cogging torque, 172–3 commercial sector, 479–80 commodity storage, 44 Composite Manufacturing Technology Centre, 380 compressed air energy storage technologies, 51–2 abandoned limestone mine to be used by Norton, Ohio CAES plant, 401

537

capacity factor as function of autocorrelation time for storage capacities, 417 combustion turbine, expander and motor/generator, 399 compressor train, clutch and motor/ generator, 398 current status and future progress, 396–403 advanced concepts, 397–402 advantages and research issues, 402–3 current systems, 397–402 first generation CAES plant schematic, 396 second generation CAES plant schematic, 402 proposed CAES plant in Norton, Ohio, 400 Ridge Energy wind CAES study, 403–4 commercial energy storage costs, 404 Ridge Storage wind/CAES plant vs other generation options, 409 storage wind/CAES simulation parameters, 405 seasonal storage, 416 wind energy systems, 393–419 wind integration issues, 404–18 cost of electricity delivered to demand centre, 411 electricity cost and wind turbine capacity factor, 408 wind energy baseload plant parameters, 413 wind speed autocorrelation time and storage capacity for baseload wind systems, 416–18 wind turbine arrays and transmission systems, 408–12 wind turbine arrays, transmission, and CAES, 412–16 wind turbine capacity factor, 406–8 wind/transmission/CAES plant costs, 415 constant economic growth, 480 contingency reserve, 43 continuously variable transmission, 385 control circuit, 434 controlled rectifiers, 434 controller, 178, 182 controller area network-bus, 442 conventional generator modelling, 455 Coral Bay wind–diesel system, 386 cost calculation, 237–9 cost-benefit analysis, 155

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538

Index

coulombic efficiency, 333–4 cross-flow turbine, 292 customer damage function, 458 CVT see continuously variable transmission cycle efficiency, 37, 38 D&WS see Diesel and Wind Systems Daimler Benz, 352 DC/AC converters, 233–4 DC/DC buck–boost converters, 435 DC/DC step-down converters, 435 DC/DC step-up converters, 435 decentralised generation, 425 deferrable loads, 439 depth of discharge, 486, 491 desalination cost estimation examples, 529–31 Libya, 529 Milos island, Greece, 530–1 Morocco, 529–30 Spain, 529 economic considerations in RES-based desalination plants, 527–31 parameters affecting desalination economics, 527 wind-based desalination plant cost items, 528 energy requirements, 512–16 general issues, 512–14 power requirements of various seawater desalination, 513 RES-based desalination, 514–16 RO seawater desalination cost structure, 513 thermal seawater desalination cost structure, 513 evaluation of various RES applications, 517 future trends, 531–2 hybrid wind energy systems, 506–31 process main classification, 509 processes and plants, 507–12 distillation desalination processes, 509–10 general consideration, 507–9 membrane/RO desalination processes, 510–12 RES and desalination methods, 515 RES integrated systems, 516, 518–25 general description, 516, 518 implementation projects, 523–4 implementation projects with hybrid energy systems, 524–5 RO–wind desalination, 518–20

wind–RO configuration possibilities, 521–3 RES-based desalination plants environmental impacts, 525–7 environmental aspects, 525–6 floating desalination plant, 526–7 seawater desalination, 530 water scarcity problem, 506–7 desalination plants, 17–18 desalination systems, 15–18 DFIG see doubly-fed induction generator Diesel and Wind Systems, 387 diesel engines, 197 comparison of available solutions for a stand-alone system, 125–6 wind–diesel hybrid system installed in Canary Islands, 431 diesel-oil price, 152 diesel-oil price annual escalation rate, 152 diesel–electric generator, 120–1, 493–4 SFC distribution, 493 discharging time, 38 distillation, 509 distributed generation, 194, 425 Distributed Utility Integration Test, 355 distribution facility deferral, 45 doubly excited induction generators, 172 doubly-fed induction generator, 193, 198, 203–4, 212 current-linked converter-based controller, 204 voltage-linked converters, 204–5 Dresser-Rand CAES modules, 397, 398 DUIT see Distributed Utility Integration Test dynamic models, 303 EC project JOR3-CT95-00700, 385 economic efficiency, 111 economic optimisation, 93 economiser, 400 Ecostar van, 349 EDL see electric double layer EDR see electrodialysis reversal effective cost coefficient, 110–11 elastomeric matrix composite, 370 electric double layer, 335 Electric Power Research Institute, 383 electric water pumps, 289 electrical energy storage, 73–4 electrical energy system, 426 electrical pump, 288 electrochemical capacitors, 335–6 electrochemical cells, 329, 332–4 actual cell potential, 332–3 cell capacity, 333

© Woodhead Publishing Limited, 2010

Index cell efficiencies, 333–4 coulombic efficiency, 333–4 energy efficiency, 334 voltage efficiency, 334 theoretical cell potential, 329, 332 electrochemical energy storage technologies diesel vs electrochemical power conversion, 335 energy storage, 329 application comparisons, 330 technology comparisons, 331 types, 335–62 all-vanadium redox battery, 357–61 electrochemical capacitors, 335–6 flow batteries, 352–3 fuel cells, 336–9 lead–acid battery, 339–43 Li ion battery, 344–6 metal–air battery, 346–7 nickel–metal hydride batteries, 343–4 sodium–sulphur battery, 347–50 vanadium bromide redox battery, 361–2 ZEBRA battery, 350–2 Zn/Br battery, 354–7 wind energy systems, 323–63 fundamentals of electrochemical cells, 329, 332–4 large grid-connected wind farms, 328–9 off-grid or remote power systems, 324–5 wind–diesel grids, 326–8 electrodialysis reversal, 440 electrolysis, 255–60, 487–8 electronic converter, 455 electronic devices, 6 electronic shock absorber, 336 energy efficiency, 334 energy payback, 38–9 energy ratio, 38–9 energy saving, 511 energy storage, 3–4, 5–6 energy storage capacity, 38 energy storage system characteristics, 37–42 discharging time, reaction time, storage duration, 38 efficiency, energy ratio and energy payback, 38–9 energy and power density, 40 energy storage capacity, 38 influence on the environment, 40 lifetime, number of cycles, duty cycle requirements, 41

539

other features, 41–2 rated or available power, energy to power ratio, 37 self-discharge, parasitic losses, ageing mechanisms, maximum depth of discharge, 39–40 system costs, 41 chemical energy storage, 53–9, 60 battery energy storage, 53–6 battery energy storage configuration, 54 FC-HS configuration, 59 flow batteries, 56–8 flow battery energy storage configuration, 57 fuel cells and hydrogen storage, 58–9 fuel cells types and characteristics, 60 comparison, 63–72 cycle efficiency, 71 energy and power costs, 68–9 energy and power costs of each system, 69 energy and power densities, 66 energy storage capacity vs discharge time, 64–5 environmental and safety concerns, 70–1 mass and volume energy density, 67 mass and volume power density, 67 maturity, 71–2 maturity levels, novel concepts and cost distribution, 72 power and discharge time ratings for cumulative installed capacity, 64 power, discharge time and energy capacity ratings, 63 self-discharge plotted against recommended storage period, 65 self-discharge vs recommended storage duration, 65–6 service period and number of cycles, 66–8 service period vs number of cycles, 68 useful energy and power extraction response, 70 useful energy, power extraction response and cycle efficiency, 69–70 customer service, 46–7 energy management or peak shaving or demand charge reduction, 46 power quality and reliability, 46 renewable energy, 46–7

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540

Index

description, 32–42 energy flows, 36 energy generation management and frequency-voltage control, 35 main components, 32–5 operation principle and energy flows, 35–7 typical energy storage configuration, 33 electricity applications requirements, 47–9 alternatives, 49 duty cycle, portability and space requirements, 48 power output and discharge period requirements, 47 future trends, 72–4 chemical energy storage, 73 electrical energy storage, 73–4 mechanical energy storage, 73 generation, 42–4 application areas, 43 area control and frequency responsive reserve, 44 commodity storage or load levelling or arbitrage, 44 rapid, spinning or contingency reserve, 43 mechanical energy storage, 50–3 CAES configuration, 51 compressed air energy storage, 51–2 flywheel energy storage configuration, 53 flywheels, 52–3 PHS configuration, 50 pumped hydro storage, 50–1 renewable energy systems, 29–32 benefits from energy storage adoption, 31 contemporary ESSs, 49–50 critical role, 32 supercapacitors, 62–3 configuration, 62 superconducting magnetic energy storage, 61–2 configuration, 61 transmission and distribution, 44–6 distribution facility deferral, 45–6 transmission facility deferral, 45 transmission system stability, 44–5 transmission voltage regulation, 45 energy systems models, 82 EPRI see Electric Power Research Institute ESA see electronic shock absorber ESS see energy storage system

European Commission DirectorateGeneral for Energy and Transport, 481 European Fund for Regional Development, 527 ex-works price, 105, 106, 107 excess energy, 241–2 Exxon Research and Engineering Corporation, 353 FAEFEDU see floating autonomous environmentally friendly and efficient desalination unit FESS see flywheel energy storage systems fibre optic cable, 442 filament-winding process, 370 floating autonomous environmentally friendly and efficient desalination unit, 526 floating desalination plant, 526–7 flooded-type lead–acid battery, 341 schematic, 340 flow battery, 56–8, 352–3 see also specific flow battery flow cell stack configuration, 353 fluid flow machines, 289 Flysafe project, 377 flywheel, 166, 194 design and construction, 368–74 bearing types, 372–3 containment technology, 374 power interface, 374 rotor configurations and construction, 370–2 rotor materials specific strength, 369 theory of energy storage using flywheels, 368–9 flywheel energy storage systems, 486 application, 383–90 autonomous wind power systems, 384–6 commercial developments and tools, 386–7 installations, 387–8 wind power in weak grid applications, 383–4 configuration, 487 cutaway/exploded view Active Power steel flywheel, 371 Beacon Power composite flywheel, 373 PowerStore containerised building, 387 UPT composite flywheel, 372 EC projects including flywheel R&D, 390

© Woodhead Publishing Limited, 2010

Index engineers inspect PowerStore flywheel at Ross Island, 388 experimental wind–diesel–flywheel system, 385 features and limitations, 375–7 cycling service and lifetime, 376 energy and power density, 375 energy losses, 376 environmental and safety considerations, 376–7 key features, 375 system performance, 375 flywheel design and construction, 368–74 bearing types, 372–3 containment technology, 374 power interface, 374 rotor configurations and construction, 370–2 rotor materials specific strength, 369 theory of energy storage using flywheels, 368–9 key advantages, 383 sources of further information, 389 technology status, 377–82 commercial products, 377–9 research and development, 380–2 steel vs composite rotors, 379 storage technology cost, 379–80 wind energy systems, 366–90 Flywheel Safety and Containment Consortium, 377 flywheels, 52–3 Ford Research Laboratories, 349 Francis turbine, 292 fuel cells, 271, 336–9, 487–8 energy storage system, 488 hydrogen fuel cell schematic, 336 operating principles, 257 reactions, 256 technologies comparison, 338 theoretical efficiency, 257–8 types, 256 types and characteristics, 60 fuel cells and hydrogen storage, 58–9 fuel consumption, 235 fuel mass rate, 493 furling, 177 G2 V/Br see vanadium bromide redox battery gas microturbines, 429, 431–2 gear ratio, 196 GECOL, 524 gel electrolyte, 339 General Electric LMS100, 402

541

generation 2 vanadium bromide redox cell, 361 generators, 170–3, 197–8 friction and cogging torque, 172–3 heat issues, 178–9 overspeeding, 181 speed, 171 types, 171–2 Geographic Information System, 304 Gibbs free energy, 329 Gibbs free energy equation, 257 GIS see Geographic Information System glass microfibre separator, 342 global radiation, 454 greenhouse gas emissions, 40 grid extension, 125 grid power, 525 grid-connected WHPS, 283–4 gross production, 242 guyed towers, 174, 485 H-APS see hydrogen autonomous power systems H-SAPS see hydrogen stand-alone power systems Hellenic Statistical Agency, 496 HEV see hybrid electric vehicle HEW station income, 110 HEW systems see hybrid electricity generation wind-based systems high-pressure air turbine, 398 high-speed flywheels, 367 high-temperature superconductor, 373, 382 high-voltage direct current, 408 HOGA see Hybrid Optimisation by Genetic Algorithms HOMER see Hybrid Optimisation Model for Electric Renewables horizontal axis wind turbine, 294 HTS see high-temperature superconductor HTS bearing, 380 HVDC see high-voltage direct current hybrid electric vehicle, 381 hybrid electricity generation wind-based systems, 102 case studies analysis, 121–45 Aegean sea wind potential map, 121 annual wind potential characteristics of analysed remote areas, 124 electricity generation cost, 141–5 electricity production cost values, 143, 144 impact of PV panels on total cost, 133–6

© Woodhead Publishing Limited, 2010

542

Index

impact of system reliability, 132 Kithnos stand-alone system main parameters, 134 life cycle hybrid system minimum electricity production cost vs annual diesel-oil consumption, 144 life-cycle cost analysis, 134 maximum battery size reduction, 136 operational years’ impact on total cost, 127–9, 137–41 optimum stand-alone wind power system dimensions, 129, 138 reliability impact on total cost, 129–32 comparison of available solutions for a stand-alone system, 125–7 diesel engine use, 125–6 grid extension solution, 125 total cost comparison of electrification solutions, 126 wind-energy based stand-alone solution, 126–7 cost benefit analysis, 109–12 electricity generation cost, 114–15 first installation cost, 104–7 proposed autonomous HEW system, 104 specific price of existing PV installations, 106 impact on electricity production cost diesel-oil current price impact, 152 diesel-oil price annual escalation rate, 152, 153 investment turnkey price, 154 return on investment index, 151 wind potential, 150 maintenance and operation cost, 107–9 no-load rejection configuration basis of minimum 10 year cost, Andros, 146 basis of minimum 10 year cost, Kea, 146 basis of minimum 10 year cost, Kithnos, 128 basis of minimum 20 year cost, Kithnos, 129 basis of minimum initial cost, Kithnos, 127 battery technology improvement incorporated, 148 zero initial cost subsidisation, 147 reliability impact-loss of load cost, 112–14 sensitivity analysis of financial behaviour, 145–54

central values of main parameters, 149 impact of wind potential, 149–51 installation turnkey cost, 153–5 monthly average wind speed values, 150 return on investment index, 151 stand-alone system, 149–54 wind-only stand-alone system, 145–8 zero load configuration, 149 socio-environmental impacts, 115–21 battery bank, 119–20 diesel-electric generator, 120–1 electric and electronic equipment, 120 photovoltaic generator, 118–19 wind turbine, 115–18 stand-alone system configuration and 10 years total cost relation Andros, 130 Kithnos, 131 wind potential data Andros island, 122 Kea island, 123 Kithnos island, 123 wind-diesel hybrid system energy autonomous configuration, Andros, 137 mean annual cost comparison, 140 ten-year cost analysis, Andros, 138 ten-year electricity production cost, Andros, 142 total 10 year cost analysis, 141 twenty-year cost analysis, Andros, 139 twenty-year electricity production cost, Andros, 143 hybrid micro-grids advantages and limitations, 461–2 control and monitoring, 437–42 load control, 437–40 remote micro-grid control and monitoring system diagram, 441 strategies, 440–2 design and construction, 442–9 analysis of demand, 443–4 construction process diagram, 450 factors which condition micro-grid design, 448 hybrid energy system designing and installation, 448–9 mean daily power consumption curves, 445 mean monthly wind speeds and solar irradiation, 447

© Woodhead Publishing Limited, 2010

Index power consumption seasonal variation, 444 resources estimation, 444–7 solar irradiation and air temperature mean daily evolution, 447 wind speed mean daily evolution, 446 electrical energy system configuration and structure, 426 future trends, 462–3 isolated micro-grids technological components, 429–35 gas microturbines, 431–2 gas turbine conceptual outline, 432 power electronic converters, 433–5 reciprocating internal combustion engines, 431 Stirling engines, 432–3 modelling and simulation, 450–7 diesel generator set fuel consumption, 456 dynamic models, 456–7 probabilistic models, 457 PV panel I–V curves, 453 wind turbines characteristic power– speed curves, 452 optimising integration, 457–61 considerations on the emission of pollutants, 459–60 economic criteria for selection between alternatives, 458 software packages, 461 system control strategies, 460–1 system reliability evaluation, 458–9 options, 427–9 micro-grid architecture interconnected with centralised control, 428 normal interconnected hybrid microgrids, 427–8 stand-alone hybrid micro-grids, 428–9 quasi-dynamic models, 451–5 battery modelling, 454–5 conventional generator modelling, 455 electronic converter modelling, 455 hydraulic turbine modelling, 455 PV panel modelling, 452–4 wind turbine modelling, 451–2 renewable energy systems integration into remote micro-grids, 425–63 stand-alone hybrid micro-grids architectures, 435–7 modular centralised AC bus architecture, 438

543

modular centralised DC bus architecture, 437 modular distributed AC bus architecture, 439 remote micro-grid architecture various configurations, 436 technologies for stand-alone microgrids, 430 Hybrid Optimisation by Genetic Algorithms, 96, 451, 461 Hybrid Optimisation Model for Electric Renewables, 90, 94–5, 263, 264, 265, 309, 386–7, 451, 461 hybrid PV–wind–battery systems, 93 hybrid RAPS system, 325 hybrid wind energy systems applications, 13–24 covering electricity needs in remote villages in Chile, 22 domestic to community level electrification, 20–4 electrification of remote fishing community in Mexico, 23 electrification of remote fishing community in Mongolia, 23 experimental unit, 20 remote cell phone base station, 15 small desalination systems, 15–18 small wind turbine adjusted on relay mast, 14 telecommunications stations, 13–15 water pumping, 18–20 water pumping unit with PV panels, 19 wind turbine for water pumping, 19 wind-driven desalination plants, 17 wind-solar hybrid street lamps, 24 desalination, 506–31 economic considerations in RESbased desalination plants, 527–31 energy requirements, 512–16 future trends, 531 processes and plants, 507–12 RES integrated systems with desalination plants, 515, 518–25 RES-based desalination plants environmental impacts, 525–7 water scarcity problem, 506–7 description, 7–11 typical hybrid wind-based stand alone system, 9 design and performance optimisation, 81–98 future trends, 97–8 scope and objectives, 81–2

© Woodhead Publishing Limited, 2010

544

Index

energy storage opportunities, 11–13 energy balance for a high wind potential area, 12 energy systems modelling, 82–7 optimisation models, 86–7 scope and type of energy models, 82–4 specific problem types, 84 synthesis, design and operation energy models, 84–6 types and uses of energy models, 83 future trends, 24–6 small wind turbine for electrification in Kansas, 25 with PV panels for electrification in Netherlands, 25 hybrid electricity generation windbased system feasibility assessment, 102–56 case studies analysis, 121–45 cost benefit analysis, 109–12 electricity generation cost, 114–15 first installation cost, 104–7 maintenance and operation cost, 107–9 reliability impact-loss of load cost, 112–14 sensitivity analysis of financial behaviour, 145–54 socio-environmental impacts, 115–21 hybrid power station of S.E.A. and ENVI.PRO Lab with battery storage system installation, 486 integration into buildings, 475–503 building sector characteristics, 477–8 description of hybrid energy systems in buildings, 482–9 energy consumption in buildings, 478–80 European Union facts about hybrid energy systems in buildings, 481–2 optimisation techniques, 91–4 criteria, 91–2 economic and techno-economic optimisation, 92–4 optimisation methodologies relative merits and demerits, 97 overview, 3–26 powered T/C station wind-based system at Osmussaar, Estonia, 15 wind-PV based installation at Cesme-Izmir, Turkey, 16 wind-PV based installation in Turkey, 16

software tools for simulation and optimisation, 94–6 HOGA software system, 96 HOMER software system, 94–5 hybrid simulation and optimisation tools characteristics, 94 HYBRID2 software system, 95 synthesis, design and operation, 87–91 local conditions and system selection, 88–90 optimal design, 90–1 steps for energy system development, 89 with wind-PV-hydro energy storage system, 88 with wind–PV–diesel energy storage components, 89 wind-only stand-alone system sensitivity analysis of financial behaviour, 145–8 impact of subsidy, 146–7 impact of wind potential, 145 improvement of battery technology, 147–8 local economy impact, 148 wind–diesel hybrid system for standalone electrical systems, 483 wind–photovoltaic hybrid system for stand-alone electrical systems, 484 see also specific system hybrid wind-diesel energy systems, 191–214 components, 197–9 BESS, 199 diesel engine, 197 generators, 197–8 loads, 199 wind turbine, 197 control strategies, 199–206 BESS control scheme, 206 chopper and inverter control scheme, 201 control scheme for PMSG with chopper and inverter, 200 current-linked converter-based controller for DFIG, 204 energy storage units, 205–6 induction generators, 203–5 machine-side and load-side converter, 202 PMSG control with diode rectifier and chopper, 200–1 PMSG control with voltage source inverters, 201–3 PMSG with voltage linked converters, 202

© Woodhead Publishing Limited, 2010

Index SCIG control scheme, 206 voltage linked converter for DFIG control, 205 design considerations, 195–6 load assessment, 196 resource assessment, 196 storage requirements, 196 future trends, 213–14 modelling and simulation, 207–11 complete SIMULINK model, 208 PMSG response for wind speed variations, 211 power flow in watts from WECS, DG and BESS, 212 power variations, 210 WECS-BESS feeding a load, 209–10 WECS-DG-BESS feeding a load, 210–11 wind turbine sizing, 194–5 wind-diesel generation system, 192–4 block schematic, 193 hybrid wind-hydrogen energy systems, 254–79 abbreviations, 281 environmental assessment, 267–72 annual emissions savings estimates, 268 estimates of future emissions, 267 hydrogen safety, 271–2 environmental impacts, 268–9 carbon dioxide emissions, 270–1 changes in atmospheric water vapour, 270 changes in oxidising capacity, 269 European level, 268 soil uptake, 270 future trends, 274, 278–9 hydrogen storage systems, 260–3 hydriding substances as hydrogen storage media, 262 hydrogen storage types and densities, 262 liquid hydrogen storage, 261 metal hydride storage, 261 novel methods, 262–3 isolated systems step by step design, 264–5 emission estimates calculation, 265 hydrogen electricity in SAPS, 265 hydrogen-fuelled vehicles, 265 load profile, 264 sustainable energy technologies integration, 264 wind energy calculations, 264

545

market potential and barriers, 272–4 H-SAPS project results, 273 hydrogen in APS, 274 main strengths, weaknesses, opportunities and threats, 275–7 SWOT analysis, 274 optimisation, 263–7 annual electrical energy production, 266 case study: Karpathos, 265–7 electrical energy production and consumption, 267 Karpathos’s monthly average grid energy demands, 264 wind electrolysis production systems, 255–60 alkaline electrolytes, 255 applications, 259–60 critical issues, 258–9 electrochemical electricity generation, 256–8 fuel cell reactions, 256 fuel cells operating principles, 257 PEM electrolyser, 255 SOEC, 255 hybrid wind-photovoltaic energy systems, 216–48 case studies, 242–6 battery SOC hourly variation, 246 house electrical load, 243 monthly energy balance, 247 optimal sizing, 245 system configurations and energy levelised cost for LLPS, 244 design and configuration, 226–7 diagram of PV-wind-engine generator hybrid system, 226 PV-wind-engine generator hybrid system, 226–7 future trends, 247 modelling and simulation, 227–39 auxiliary engine generator, 234–6 battery storage, 231–3 cell temperature on m-Si PV array deficiency, 228 cost calculation, 237–9 cost calculation methodology, 237 DC/AC and AC/DC converters, 233–4 energy management and control unit, 236 I-V and P-V curves of PV module, 227 I-V curves for BP585F PV module, 231

© Woodhead Publishing Limited, 2010

546

Index

methodology and input and output data, 241 PV module equivalent circuit, 229 PV system, 227–30 small and medium wind turbines, 230–1 small wind turbine, battery, engine generator and inverter specific cost, 238 specific consumption of engine generator and fuel consumption variation, 235 WECS power curve, 232 nomenclature, 251–3 renewable energy resources and their potential, 216–26 direct normal spectral irradiance, 217 ground reflected radiation, 218 panel inclination on monthly mean values of solar irradiations, 220 position of the sun and solar diagram, 220 renewable resources complementarity, 223–6 solar and wind energy monthly variation, 225 solar energy resource, 217–19 tilted solar irradiances, 219 variability, 223 Weibull distribution, 222 wind and solar energy daily repartition, 225 wind distribution and wind turbine power curve, 221 wind energy resource, 219–23 sizing and optimisation, 239–42 excess energy, solar fraction and gross production, 241–2 methodology, 239–40 sizing optimisation, 242 hybrid wind–hydropower energy systems, 282–319 benefits and limitations, 310–14 combined wind and hydropower, 313–14 hydro energy applications, 312–13 wind energy applications, 310–11 different operational policies and techniques for isolated grids, 314–15 different types, 284–302 efficiency of Francis, Pelton and Kaplan turbines, 294 medium and large wind/hydropower generation systems, 298–300

micro, mini and small hydropower systems integration, 290–8 mini-hydro power station, 293 power coefficients of wind rotors, 295 pumped storage systems, 300–2 small wind turbine vs piston pump and centrifugal pump, 289 water pumping systems, 285–90 wind pumping system and storage tank, 287 wind–hydro pumped storage system, 302 economics, 316–18 models and tools used to evaluate different renewable energy technologies, 317 environmental impacts, 315–16 electric power generation technologies, 305 generation, investment and external costs for various power generation technologies, 315 models classification dynamic models, 303 logistical models, 303 research and development, 302–10 computational tools and software, 303–4 literature review, 304–10 the need to couple wind–hydropower systems, 283–4 HYBRID2, 90, 95, 451, 461 HYBRIDS, 451 hydraulic turbine modelling, 455 hydro energy advantages, 312 disadvantages, 312–13 Hydro Tasmania, 327, 359 hydro turbines, 291–3, 486 impulse turbine, 291–2 cross-flow turbine, 292 Pelton wheel, 291–2 Turgo wheel, 292 reaction turbine, 292–3 Francis turbine, 292 kinetic energy turbine, 293 propeller turbine, 292 hydrogen liquefaction, 261 production methods, 337, 339 safety, 271–2 hydrogen autonomous power systems, 268, 273, 274 hydrogen energy density, 337

© Woodhead Publishing Limited, 2010

Index hydrogen stand-alone power systems, 265, 273 hydrogen storage systems, 260–3 hydriding substances as storage media, 262 liquid hydrogen storage, 261 metal hydride storage, 261 novel methods, 262–3 storage types and densities, 262 hydrostatic transmission system, 385 ICE see internal combustion engine IGBT technology see insulated-gate bipolar transistor technology Indianapolis Power & Light, 346 induction generators control schemes, 203–5 DFIG control using current linked converters, 203–4 DFIG control using voltage-linked converters, 204–5 SCIG, 205 initial investment cost, 105 INSEL, 451 institutional sector, 479–80 Instituto Tecnologico de Canarias, 524 insulated-gate bipolar transistor devices, 380 insulated-gate bipolar transistor technology, 374 Integrated Power and Water Point 9, 529 integrated synchronous generator, 374 internal combustion engine, 337 internal rate of return, 112 inverter, 178, 434, 489 IPL see Indianapolis Power & Light IRR see internal rate of return ISG see integrated synchronous generator ITC see Instituto Tecnologico de Canarias J-Power, 360 Job and Economic Development Impact, 304 Kalbarri, 388 Kaplan turbine, 292 kinetic energy turbine, 293 Koeppen climate classification, 478 Kyushu Electric Power company, 355 Laplace transform method, 456 large wind–hydropower systems, 283 lattice towers, 174 Lawrence Livermore National Laboratory, 367, 380

547

LCC see life-cycle costing LCE see levelised cost of energy lead–acid battery, 5, 54–5, 135, 185, 231, 339–43 electrode reactions, 340–1 energy storage demonstrations, 342–3 flooded-type lead–acid batteries limitations, 341 storage systems, 343 UltraBattery, 342 valve regulated lead–acid batteries, 341–2 levelised cost of energy, 92, 458 levelised production cost, 316–18 levelised water cost, 530 Li ion battery, 56, 344–6 applications, 345–6 cell reactions, 345 schematic, 345 life-cycle costing, 458 lightning, 182–3 linear programming, 91 liquid hydrogen storage, 261 LLNL see Lawrence Livermore National Laboratory LLP see loss of load probability load levelling, 44 load operating network-bus, 442 loads, 199 logistical models, 303 LOLP see loss of load probability LON-bus see load operating network-bus loss of load probability, 91, 240, 459 loss of power supply probability, 91, 92, 240, 459 low-pressure air turbine, 398 low-speed flywheels, 367 low-voltage network, 426 LPSP see loss of power supply probability LWC see levelised water cost Macintosh plant, 397 combustion turbine, expander and motor/generator, 399 compressor train, clutch and motor/ generator, 398 MATLAB, 307, 309, 457 MATLAB/SIMULINK, 207 maximum depth of discharge, 39 maximum power point tracking, 171, 181–2, 192, 227 MCFC see molten carbonate fuel cells McMurdo Station, 388 mechanical energy storage, 50–3, 73 mechanical vapour compression, 530

© Woodhead Publishing Limited, 2010

548

Index

MED see multi-effect distillation medium-power systems, 283 metal hydride storage, 261 metal–air battery, 56, 346–7 cell reactions, 347 Zn-air battery charge and discharge operation, 347 MG4520 200 W wind turbine, 170 MGCC see micro-grid central controller micro-grid central controller, 428, 441 micro-grids, 426 construction process diagram, 450 factors which condition the micro-grid design, 448 micro-wind turbines, 180 micropower optimisation model, 263 micropower system, 284 Milos desalination plant, 527 MILP see mixed-integer linear programming mini power system, 284 MINLP see mixed-integer non-linear programming mixed-integer linear programming, 85 mixed-integer non-linear programming, 85 molten carbonate fuel cells, 256 Monte Carlo simulation, 310, 459 MPPT see maximum power point tracking multi-effect distillation, 509 multistage flash distillation, 509 MVC see mechanical vapour compression Na–NiCl2 battery, 351 National Renewable Energy Laboratory, 386, 481 NEDO see New Energy Development Organisation Nernst equation, 332 net present cost, 92, 458 New Energy Development Organisation, 360 NGK Insulators Ltd, 349 nickel, 351 nickel–cadmium battery, 55, 343 nickel–metal hydride battery, 343–4 electrode reactions, 343–4 schematic, 344 no-energy fulfilment cost, 113 NOCT see normal operating cell temperature noise emission, 116 non-controlled rectifiers, 434 normal operating cell temperature, 230, 454

NPC see net present cost NREL see National Renewable Energy Laboratory Nuclear Energy Agency, 316 open-circuit voltage, 227 Otto engines, 431 oxygen recombination cycle, 340 PAFC see phosphoric acid fuel cells parasitic losses, 39 PCC see point of common coupling PCS see power conditioning system Pelton wheel, 291–2 PEM see proton exchange membrane PEM electrolyser, 255 PEMFC see proton exchange membrane fuel cells permanent magnet generators, 171, 173 permanent magnet synchronous generator, 193, 198, 212 control scheme, 200 control with diode rectifier and chopper, 200–1 control with voltage source inverters, 201–3 with voltage linked converters, 202 permeate water, 511 petroleum, 394 phosphoric acid fuel cells, 256 photovoltaic generator, 135 hybrid electricity generation windbased systems socio-environmental impacts, 118–19 photovoltaic panel I–V curves, 453 modelling, 452–4 photovoltaic power station, 494 photovoltaic system, 217, 227–30 PHS see pumped hydro storage Piller flywheel, 370 piston pump, 288 pitching, 176, 177 PMSG see permanent magnet synchronous generator point of common coupling, 427 polyethylene microporous separators, 339–40 polysulphide bromide battery, 58 power circuit, 433–4 power conditioning system, 355 power curve, 5 power demand, 7, 10 power electronic converter, 432, 433–5 basic functions, 434

© Woodhead Publishing Limited, 2010

Index power electronic equipment block diagram, 433 power electronics technology, 380 power output, 186 power quality systems, 367 power reliability analysis, 91–2 Powerbridge, 378, 386 Powercorp, 386, 387 PowerStore Flywheel System, 166, 388 cutaway view of PowerStore containerised building, 387 energy storage system block diagram, 386 Premium Power Corporation, 355 probability density function, 457 propeller turbine, 292 proton exchange membrane, 255 proton exchange membrane fuel cells, 256 pulse width modulated bi-directional converter, 374 pulse width modulation, 178 pump-hydro systems, 486, 492–3 pump-hydro solution for isolated consumers, 487 pumped hydro storage, 50–1 PV system see photovoltaic system pyranometer, 446 pyroheliometer, 446 rapid reserve, 43 RAPS see remote area power supply RAPSIM, 451 reciprocating internal combustion engines, 429, 431 recombinant battery see valve regulated lead–acid battery rectifiers, 434 Red–Dead project, 518 redox flow battery, 356–7 Regional Energy Deployment System, 304 reliability, 458 remote area power supply, 324–5 Remote Water Pumping and Electric Power Generation with Renewable Energy, 290 renewable energy sources fraction, 241 renewable energy sources systems, 8, 29, 266 and desalination methods, 516 desalination economic considerations, 527–31 cost item of wind-based desalination plant, 528 parameters affecting economics, 528

549

desalination plants environmental impacts, 525–7 environmental aspects, 525–6 floating desalination plant, 526–7 energy storage technologies overview, 29–74 chemical energy storage, 53–9 comparison of energy storage systems, 63–72 contemporary energy storage systems, 49–50 customer service, 46–7 electricity applications requirements, 47–9 future trends, 72–4 generation, 42–4 mechanical energy storage, 50–3 supercapacitors, 62–3 superconducting magnetic energy storage, 61–2 transmission and distribution, 44–6 typical energy storage system, 32–42 evaluation in desalination applications, 517 general description, 516, 518 implementation projects, 523–4 implementation projects with hybrid energy systems, 524–5 integrated with desalination plants, 516, 518–25 integration into remote micro-grids, 425–63 architecture for stand-alone hybrid micro-grids, 435–7 control and monitoring of hybrid micro-grids, 437–42 design and construction of hybrid micro-grids, 442–9 future trends, 462–3 hybrid micro-grid options, 427–9 hybrid micro-grids advantages and limitations, 461–2 isolated micro-grids technological components, 429–35 modelling and simulation of hybrid micro-grids, 450–7 optimising integration of hybrid micro-grids, 457–61 RO–wind desalination, 518–20 design issues, 519–20 operational issues, 520 wind–RO configuration possibilities, 521–3 near constant operating conditions, 521 RO unit switching, 522

© Woodhead Publishing Limited, 2010

550

Index

storage devices, 521–2 systems with back-up, 521 systems without back-up, 521 variable operating conditions, 522–3 wind turbine de-rating, 522 Renewable Islands, 278 RES see renewable energy sources RESF see renewable energy sources fraction residential energy consumption, 478 residential sector, 478–9 resin-transfer moulding process, 371 RETscreen, 187 reverse osmosis system, 440, 509, 530 advantages, 512 subsystems, 510 unit flow sheet, 511 see also wind–RO desalination RICE see reciprocating internal combustion engines Ridge Energy, 403–4 RO see reverse osmosis system rotor diameter, 196, 207 round-trip efficiency, 38, 39 RTM see resin-transfer moulding process Rutland 910 turbine, 172 RWE-Chloride, 349 S & C Electric Company, 336 Sandia model, 380 SAPS see stand-alone power systems Satcon Power Systems, 356 SCADA see supervisory control and data acquisition SCIG see squirrel cage induction generators Scott Base diesel power system, 388 sealed lead–acid battery, 342 seawater reverse osmosis, 511 seawater reverse osmosis desalination powered from renewable energy sources, 524 SEI see Sumitomo Electric Industries self-discharge, 39 self-supporting tower, 485 self-utilisation factor, 110 services’ sector, 479–80 SFC see specific fuel cell consumption short-circuit current, 227 Silent Power, 349 silicon semiconductors, 377 Simulink, 457 sinusoidal pulsed width modulated inverter, 200 small wind turbine

comparative costs with PV electricity, 168 generator, 170–3 friction and cogging torque, 172–3 types, 171–2 wind and generator speed, 171 other features, 173 overspeed protection, 176–7 starting and low wind speed behaviour, 175 stopping the turbine, 177 tail fins and yaw behaviour, 173–4 towers, 174–5 technology, 170–7 vs large turbines, 173 small wind–hydropower systems, 283 Smart Energy 25 flywheel, 378 SMES see superconducting magnetic energy storage sodium, 347 sodium chloride, 351 sodium–sulphur battery, 55, 347–50 cell reactions, 349 development and commercialisation, 349–50 recent installation plans, 350 schematic, 348 SOEC see solid oxide electrolysis cells SOFC see solid oxide fuel cells Soft Energy Applications and Environmental Protection Lab, 496 solar energy, 223 solar fraction, 241–2 solar irradiation, 218 solar position, 218 solar radiation, 217 solid beta alumina ceramic electrolyte, 348 solid oxide electrolysis cells, 255 solid oxide fuel cells, 256 SOLSIM, 451 SOMES, 451 specific fuel cell consumption, 493 spinning reserve, 43 squirrel cage induction generators, 172, 198 control schemes, 203, 205, 206 stand-alone power systems, 183–5, 265 stand-alone systems, 283–4 stand-alone towers, 174 stand-alone wind energy systems, 165–89 applications, 13–24 domestic to community level electrification, 20–4

© Woodhead Publishing Limited, 2010

Index experimental unit, 20 remote cell phone base station, 15 small desalination systems, 15–18 small wind turbine adjusted on relay mast, 14 telecommunications stations, 13–15 water pumping, 18–20 wind turbine for water pumping, 19 wind-driven desalination plants, 17 wind-solar hybrid street lamps, 24 control and electronics, 177–83 controller, 178 controller protection, 182 current limiting, 179–80 generator heat issues, 178–9 generator overspeeding, 181 inverter, 178 lightning damage, 182 lightning strikes, 182–3 maximum power point tracking, 181–2 thermal characteristics, 180 unforeseen conditions, 183 description, 4–7 energy storage, 5–6 inverter efficiency curve, 7 operational modes, 7 system electronic devices, 6 typical wind-energy stand-alone system, 5 wind turbine, 4–5 design and performance optimisation, 81–98 future trends, 97–8 scope and objectives, 81–2 energy systems modelling, 82–7 optimisation models, 86–7 scope and type of energy models, 82–4 specific problem types, 84 synthesis, design and operation energy models, 84–6 types and uses of energy models, 83 future trends, 24–6 small wind turbine for electrification in Kansas, 25 with PV panels for electrification in Netherlands, 25 HEW system feasibility assessment, 102–56 case studies analysis, 121–45 cost benefit analysis, 109–12 electricity generation cost, 114–15 first installation cost, 104–7 maintenance and operation cost, 107–9

551

reliability impact-loss of load cost, 112–14 sensitivity analysis of financial behaviour, 145–54 socio-environmental impacts, 115–21 HEW system sensitivity analysis of financial behaviour battery ex-works price, 153 current diesel-oil price, 152 diesel-oil price annual escalation rate, 152 impact of wind potential, 149–51 installation turnkey cost, 153–5 return on investment index, 151 integration into buildings, 475–503 building sector characteristics, 477–8 description of hybrid energy systems in buildings, 482–9 energy consumption in buildings, 478–80 European Union facts about hybrid energy systems in buildings, 481–2 modelling and simulation, 169 overview, 3–26 power systems, 183–5 basic electrical system, 184 wind turbine with PV in Nepal, 184 powered T/C station wind-based system at Osmussaar, Estonia, 15 wind-PV based installation at Cesme-Izmir, Turkey, 16 wind-PV based installation in Turkey, 16 PowerStore Flywheel System, 166 pre-feasibility analysis, 168–9 small wind photovoltaic system, 167 small wind turbine and PV electricity comparative costs, 168 small wind turbine technology, 170–7 fully furled wind turbine, 176 generator, 170–3 ironless stator between permanent magnet rotors, 172 measured wind turbine power curve, 170 other features, 173 simple permanent magnet generator, 171 system sizing, 185–8 battery sizing, 188 power output, 186 practical wind energy measurement, 187 wind maps and software, 186–7

© Woodhead Publishing Limited, 2010

552

Index

wind speed probability distribution and capacity factor, 187 STATCOM, 388 state of charge, 232 Stirling engines, 429, 432–3 storage system unit, 485–8 battery storage systems, 485–6 electrolysis–fuel cell storage systems, 487–8 flywheel storage systems, 486 pump-hydro systems, 486 straflo turbine, 292 strength, weakness, opportunities and threats, 274, 275–7 Subaru wind farm, 359 sulphur, 348 sulphuric acid, 340 Sumitomo Electric Industries, 359, 360 supercapacitors, 62–3 superconducting magnetic energy storage, 426 supervisory control and data acquisition, 442 SWADES, 524 SWOT see strength, weakness, opportunities and threats SWOT analysis, 274 SWRO see seawater reverse osmosis SWRO-RES see seawater reverse osmosis desalination powered from renewable energy sources synchronous generators, 171, 193 system cost analysis, 92 tail fins, 173–4 techno-operational optimisation, 93 telecommunications, 13–15 TEPCO see Tokyo Electric Company tetrachloroaluminate electrolyte (NaAlCl4), 351 thermal desalination, 509 thermal energy storage system, 400 thermometer, 446 tilt-down tower, 485 tip speed ratio, 170, 197 TML see transmission line losses TMLC see transmission line cost Tokyo Electric Company, 349 Tomomae wind farm, 360 towers, 174–5 see also specific type of tower transmission facility deferral, 45 transmission line cost, 411 transmission line losses, 411 Trinity Flywheel, 367 TSR see tip speed ratio

tube turbine, 292 tubular towers, 174 Turgo wheel, 292 turnkey cost, 153–5 UltraBattery, 342 uninterruptible power supply, 339, 367, 378, 489 unmet load, 91 UPS see uninterruptible power supply Urenco Power Technologies (UPT), 371, 373 US Flywheel Safety and Containment Program, 381 V-Fuel Pty Ltd, 361 valve regulated lead–acid battery, 341–2 vanadium bromide redox battery, 361–2 cell reactions, 361 development, 361–2 vanadium redox battery, 57–8, 357–61 cell reactions, 357–8 demonstrations and commercialisation, 359 early prototype, 358 flow cell concept, 357 stacks and electrolyte tanks at King Island G1 VB installation, 360 Tomomae wind farm on Japanese Island of Hokkaido, 360 unique features, 358–9 wind energy demonstrations, 359–61 King Island, Tasmania, Australia, 359–60 Subaru project, Hokkaido, Japan, 360–1 vapour compression distillation, 509 vertical axis wind turbine, 294 Verve Energy, 387 Vestas V-27/225, 407, 409, 410 Vestas V52, 359 voltage efficiency, 334 voltage-current equation, 229 volumetric energy density, 337 G1 VB see vanadium redox battery WALC see wind array levelised cost water pumping systems, 18–20, 285–90 water recovery ratio, 511 WEC see wind energy conversion WECS see wind energy conversion systems Weibull distribution, 221, 416, 490 calculation method, 222 Weibull probability density function, 407

© Woodhead Publishing Limited, 2010

Index WHPS see wind–hydropower systems wind array levelised cost, 411 wind electrolysis applications, 259–60 grid-assisted wind-hydrogen generation, 259 grid-independent integrated windhydrogen energy systems, 260 grid-independent wind-hydrogen generation, 259 integrated wind-hydrogen utility energy system, 260 wind power for grid-electricity and hydrogen generation, 259–60 production systems, 255–60 critical issues for wind-hydrogen systems, 258–9 fuel cells, 256–8 wind energy, 187, 219–23, 264 advantages, 310–11 baseload plant parameters, 413 disadvantages, 311 seasonal storage, 415–16 wind energy conversion systems, 191–2, 230–1, 524 power curve, 232 power flow in watts, 212 WECS–BESS feeding a load, 209–10 WECS–DG–BESS feeding a load, 210–11 wind energy systems compressed air energy storage technologies, 393–419 current status and future progress, 396–403 Ridge Energy wind CAES study, 403–4 wind integration issues, 404–18 daily wind power and load profiles, 326 electrochemical energy storage technologies, 323–63 fundamentals of electrochemical cells, 329, 332–4 large grid-connected wind farms, 328–9 off-grid or remote power systems, 324–5 types, 335–62 wind–diesel grids, 326–8 flywheel energy storage technologies, 366–90 application, 383–90 design and construction, 368–74 features and limitations, 375–7 technology status, 377–82 hybrid RAPS system, 325

553

King Island, Australia location, 327 power system load profile, 328 G1 VB stacks and electrolyte tanks, 360 wind–diesel hybrid system installed in Canary Islands, 431 wind generator, 294 wind maps, 186–7 wind power, 259–60, 284 wind speed, 187, 194 wind turbine, 4–5, 197, 294 arrays and transmission systems, 408–12 arrays, transmission, and CAES, 412–16 characteristic power–speed curves, 452 HEW systems socio-environmental impacts, 115–18 modelling, 451–2 non-dimensionalised power curve, 491 wind turbine capacity factor, 406–8 wind turbine efficiency, 409 wind turbine levelised cost, 407 wind turbine rated power, 489–90 wind velocity, 194–5, 196 wind-diesel hybrid stand-alone system impact on electricity production cost battery bank purchase price impact, 154 diesel-oil price annual escalation rate, 153 investment turnkey price, 154 return on investment index, 151 wind potential, 150 wind-diesel-battery HEW stand-alone system, 145 wind-generated electricity, 419 wind-hydrogen systems see hybrid wind-hydrogen energy systems wind–diesel grids, 326–8 wind–diesel system, 385 wind–hydropower systems, 283–4 advantages, 282, 313 disadvantages, 313–14 see also hybrid wind–hydropower energy systems wind–RO desalination, 518–20 configuration possibilities, 521–3 near constant operating conditions, 521 RO unit switching, 522 storage devices, 521–2 systems with back-up, 521 systems without back-up, 521 variable operating conditions, 522–3 wind turbine de-rating, 522

© Woodhead Publishing Limited, 2010

554

Index

design issues, 519–20 operational issues, 520 windmill, 294 WTLC see wind turbine levelised cost yaw, 173–4 Z-BESS see zinc-bromine energy storage system ZBB Energy Corp, 355 zero emissions battery research activity battery, 350–2 applications, 352

cell reactions, 351–2 schematic, 351 zero-diesel solution, 500 zero-energy buildings, 481 zinc, 346, 347 zinc-bromide battery, 58, 354–7 250 kW h string of five modules in standard shipping container, 356 cell reactions, 354–6 development and commercialisation, 355–6 schematic, 354 zinc-bromine energy storage system, 355

© Woodhead Publishing Limited, 2010