Fuel Cell Micro-grids (Power Systems)

Power Systems

Shin’ya Obara

Fuel Cell Micro-grids

123

Shin’ya Obara, PhD Department of Mechanical Engineering Tomakomai National College of Technology Nishikioka 443, Tomakomai Hokkaido, 0591275 Japan

ISBN 978-1-84800-337-8

e-ISBN 978-1-84800-340-8

DOI 10.1007/978-1-84800-340-8 Power Systems Series ISSN 1612-1287 British Library Cataloguing in Publication Data Obara, Shin'ya Fuel cell micro-grids. - (Power systems) 1. Fuel cells 2. Electric power transmission I. Title 621.3'12429 ISBN-13: 9781848003378 Library of Congress Control Number: 2008936991 © 2009 Springer-Verlag London Limited Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: deblik, Berlin, Germany Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com

Contents

1

A Small-scale Fuel Cell Cogeneration System Considering Partial Load and Load Fluctuation....................................................... 1 1.1 Introduction ................................................................................... 1 1.2 System Configuration .................................................................... 2 1.2.1 System Outline................................................................. 2 1.2.2 Energy Loss and Mass Loss............................................. 4 1.2.3 System Model and Equations........................................... 5 1.2.4 Partial Load Operation ..................................................... 7 1.3 Energy Balance and Objective Function ....................................... 8 1.3.1 Energy Balance ................................................................ 8 1.3.2 Electric Heater Operation................................................. 9 1.3.3 Thermal Storage Operation .............................................. 11 1.4 Energy Output Characteristics....................................................... 11 1.4.1 System Operation Map..................................................... 11 1.4.2 Load Fluctuation and Fuel Consumption ......................... 12 1.5 Case Study..................................................................................... 14 1.5.1 System Outline................................................................. 14 1.5.2 Results of the Operation Plan........................................... 14 1.5.3 Annual Operation Cost of the System.............................. 15 1.6 Conclusions ................................................................................... 16

2

Equipment Arrangement Planning of a Fuel Cell Energy Network Optimized for Cost Minimization.......................................................... 2.1 Introduction ................................................................................... 2.2 System Scheme.............................................................................. 2.2.1 The Energy Network........................................................ 2.2.2 Fuel Cell System .............................................................. 2.2.3 Heat Source Use Order .................................................... 2.2.4 City Gas Reformer ........................................................... 2.2.5 Operation Model of the System .......................................

17 17 18 18 19 20 20 21 v

vi

Contents

2.3 2.4 2.5 2.6

2.7

2.8 3

Amount of Heat Release of the Hot Water Piping Network (HWN)................................................................. Energy Balance.............................................................................. 2.4.1 The Balance of Power ...................................................... 2.4.2 The Balance of Heat......................................................... Cost Calculation and Objective Function ...................................... 2.5.1 Cost Calculation............................................................... 2.5.2 Objective Function........................................................... Analysis Method and Case Study .................................................. 2.6.1 Optimization Using a Genetic Algorithm ........................ 2.6.2 Equipment Characteristics Model .................................... 2.6.3 Operation of the Heat Storage Tank and Boiler ............... 2.6.4 Specification of Hot Water Piping and a Hot Water Circulating Pump ............................................................. 2.6.5 Analysis Flow .................................................................. 2.6.6 Analysis Conditions ......................................................... Analysis Result.............................................................................. 2.7.1 Operation Plan of the Fuel Cell and the Reformer ........... 2.7.2 Amount of Hot Water Heat Release and the Hot Water Piping Route..................................................................... 2.7.3 Quantity of Flow of the Hot Water Circulating Pump ..... 2.7.4 Operation of the Heat Storage Tank and the Boiler ......... 2.7.5 Cost Analysis Results....................................................... 2.7.6 Consideration of Analysis Accuracy................................ Conclusions ...................................................................................

Effective Improvement in Generation Efficiency due to Partition Cooperation Management...................................................................... 3.1 Introduction ................................................................................... 3.2 System Configuration .................................................................... 3.2.1 Scheme of the FC Micro-grid .......................................... 3.2.2 System Configuration ...................................................... 3.2.3 Operating Method ............................................................ 3.3 Installation Planning of the FC Micro-grid.................................... 3.3.1 Generation Efficiency of the Micro-grid.......................... 3.3.2 The Power Demand Model .............................................. 3.3.3 The Analysis Method ....................................................... 3.4 Case Study..................................................................................... 3.5 Analysis Results and Discussion ................................................... 3.5.1 Generation Efficiency of the Stand-alone System ........... 3.5.2 Generation Efficiency of the Central System................... 3.5.3 Generation Efficiency of the Partition Cooperation System ......................................................... 3.6 Conclusions ...................................................................................

22 24 24 24 25 25 26 26 26 27 29 29 30 32 34 34 36 38 39 40 41 42 43 43 44 44 45 46 46 46 49 50 51 52 52 54 54 59

Contents

4

5

Fuel Cell Network System Considering Reduction in Fuel Cell Capacity Using Load Leveling and Heat Release Loss........................ 4.1 Introduction ................................................................................... 4.2 Load Leveling and the Arrangement Plan of the Fuel Cell ........... 4.2.1 The Fuel Cell Network System ........................................ 4.2.2 Power Generation Characteristics of the Fuel Cell .......... 4.2.3 Load Leveling Using Water Electrolysis ......................... 4.2.4 Distribution of the Fuel Cell ............................................ 4.2.5 Energy Balance Equation................................................. 4.2.6 Operating Method of the System ..................................... 4.3 Analysis Method............................................................................ 4.3.1 Procedure of Analysis ...................................................... 4.3.2 Solution Parameters ......................................................... 4.4 Case Study..................................................................................... 4.4.1 Energy Demand Pattern and Network System ................. 4.4.2 Reduction Effect of the Fuel Cell Facility Capacity ........ 4.4.3 Route Planning Result of the Hot Water Piping............... 4.4.4 Result of the Fuel Cell Arrangement Plan ....................... 4.5 Conclusions ...................................................................................

vii

61 61 62 62 63 65 65 66 68 68 68 70 71 71 72 73 75 76

Equipment Plan of a Compound Interconnection Micro-grid Composed of Diesel Power Plants and a Fuel Cell............................... 77 5.1 Introduction ................................................................................... 77 5.2 Compound Interconnection Micro-grid ......................................... 78 5.2.1 The Micro-grid Model ..................................................... 78 5.2.2 The CIM Model ............................................................... 80 5.2.3 Facility Scheme................................................................ 80 5.2.4 The CIM Operating Method............................................. 82 5.3 Equipment Characteristics............................................................. 82 5.3.1 Diesel Engine Power Generator ....................................... 82 5.3.2 The Proton Exchange Membrane-type Fuel Cell ............. 84 5.4 Analysis Method............................................................................ 84 5.4.1 Route Plan of the Compound Interconnection Grid ......... 84 5.4.2 Analysis Flow .................................................................. 85 5.4.3 The Power Demand Model .............................................. 86 5.5 Case Study..................................................................................... 88 5.5.1 The Urban Area Model .................................................... 88 5.5.2 Complex Community....................................................... 89 5.5.3 The Residential Area Model ............................................ 91 5.6 Conclusions ................................................................................... 93

viii

6

7

Contents

The Effective-use Method of Exhaust Heat for Distributed Fuel Cells....................................................................... 6.1 Introduction ................................................................................... 6.2 Outline of the Fuel Cell Energy Network System ......................... 6.2.1 System Outline................................................................. 6.2.2 The Path and the Amount of Heat Release from the Hot Water Piping............................................... 6.2.3 Heat Energy Balance........................................................ 6.2.4 The Heat Release Amount for a Hot Water Network....... 6.3 Model of the Fuel Cell................................................................... 6.3.1 Characteristics of Electric Power and Heat Output.......... 6.3.2 Energy Demand Pattern and Capacity of the Fuel Cell.... 6.4 Case Analysis ................................................................................ 6.4.1 Weather Conditions in Sapporo ....................................... 6.4.2 Analysis Method .............................................................. 6.5 Analysis Results ............................................................................ 6.5.1 The Optimal Path and the Amount of Heat Release from a Hot Water Piping Network ................................... 6.5.2 Optimal Path of the Energy Demand Pattern and the Hot Water Piping................................................. 6.5.3 The Influence of Load Fluctuations ................................. 6.6 Conclusions ................................................................................... Load Response Characteristics of the Fuel Cell for Individual Cold-region Houses ........................................................ 7.1 Introduction ................................................................................... 7.2 System Configuration .................................................................... 7.2.1 System Outline................................................................. 7.2.2 Method of Power Generation ........................................... 7.2.3 Method of Heat Supply .................................................... 7.2.4 Controller and Auxiliary Machinery ................................ 7.2.5 Model of Operation Control............................................. 7.3 The Time Constant of Each Piece of Equipment........................... 7.3.1 The Time Constant of the Fuel Cell Stack ....................... 7.3.2 Town Gas Reformer......................................................... 7.3.3 Inverter and System Interconnection Device ................... 7.3.4 The Time Constant of the Heat Pump.............................. 7.4 Analysis Method............................................................................ 7.5 Results and Discussion .................................................................. 7.5.1 Control Variables of the Controller.................................. 7.5.2 Step Response Characteristics of the System................... 7.5.3 Step Response Characteristics with Power Load Fluctuation ........................................... 7.5.4 Application to an Individual Cold-region House ............. 7.6 Conclusions ...................................................................................

95 95 96 96 98 100 101 102 102 103 104 104 105 107 107 108 110 111 113 113 114 114 116 117 117 118 119 120 123 124 124 125 126 126 128 130 133 135

Contents

8

9

10

Load Response Characteristics of a Fuel Cell Micro-grid with Control of the Number of Units .................................................... 8.1 Introduction ................................................................................... 8.2 The Micro-grid Model................................................................... 8.2.1 Power Quality of the Micro-grid...................................... 8.3 Response Characteristics of System Configuration Equipment .... 8.3.1 Generation Characteristics of the Engine Generator ........ 8.3.2 Generation Characteristics of the Fuel Cell ..................... 8.3.3 Output Characteristics of the City Gas Reformer ............ 8.3.4 Inverter and Interconnection Device ................................ 8.3.5 Generation Efficiency of the Fuel Cell System................ 8.4 Control Variables and Analysis Method........................................ 8.5 Load Response Characteristics of the Micro-grid ......................... 8.5.1 Step Response Characteristics.......................................... 8.5.2 Application of the Electric Power Demand Pattern of a House ........................................................................ 8.5.3 Generation Efficiency of the Fuel Cell ............................ 8.6 Conclusions ................................................................................... Dynamic Characteristics of a PEM-FC/ Woody Biomass Engine Hybrid Micro-grid ......................................... 9.1 Introduction ................................................................................... 9.2 System Scheme.............................................................................. 9.2.1 The Hybrid Micro-grid..................................................... 9.2.2 The Micro-grid System Operating Method...................... 9.3 Control Response Characteristics of PEM-FC and SEG ............... 9.3.1 The Control Block Diagram............................................. 9.3.2 Response Characteristics of PEM-FC .............................. 9.3.3 Response Characteristics of SEG..................................... 9.4 Results of Dynamic Characteristics Analysis of the PWHC Micro-grid ..................................................................................... 9.4.1 Power Response Characteristics of PWHC...................... 9.4.2 Response Characteristics of SEG and the PEM-FC Micro-grid Using the Power Load Pattern for Houses..... 9.4.3 Response Characteristics of the PWHC Micro-grid Using the Power Load Pattern for Houses ....................... 9.5 Conclusions ................................................................................... A Fuel Cell and Hydrogenation Engine Hybrid System Considering Efficiency Improvement for Partial-load Operation...... 10.1 Introduction ................................................................................... 10.2 System Scheme.............................................................................. 10.2.1 The HCGS Model ............................................................ 10.2.2 Operation Method of the System .....................................

ix

137 137 138 138 142 142 143 144 144 144 145 147 147 148 150 152 153 153 154 154 157 157 157 159 160 161 161 163 165 167 169 169 170 170 171

x

Contents

10.3 10.4

10.5 10.6

10.7 11

Equipment Characteristics............................................................. 10.3.1 Output Characteristics of NEG ........................................ 10.3.2 Output Characteristics of PEM-FC .................................. Power and Heat Output Characteristics of HCGS ......................... 10.4.1 Output Characteristics of NEG and PEM-FC .................. 10.4.2 Method Operating FC or NEG with the Threshold Value of the Load (OM-C)............................................... 10.4.3 Operation Method Using PEM-FC Corresponding to a Base Load (OM-D) ................................................... Case Study..................................................................................... 10.5.1 Power and Heat Demand Model ...................................... 10.5.2 Analysis Method .............................................................. Results and Discussion .................................................................. 10.6.1 City Gas Consumption ..................................................... 10.6.2 Generation Efficiency and Total Efficiency..................... 10.6.3 Carbon Dioxide Emissions............................................... 10.6.4 Capacity of the Heat Storage Tank .................................. Conclusions ...................................................................................

CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine and a Fuel Cell.......................... 11.1 Introduction ................................................................................... 11.2 System Scheme.............................................................................. 11.2.1 The IMPE Model ............................................................. 11.2.2 Operation Method of the Micro-grid................................ 11.2.3 Equipment Scheme .......................................................... 11.3 Equipment Characteristics............................................................. 11.3.1 Output Characteristics of the Gas Engine Power Generator .............................................................. 11.3.2 Carbon Dioxide Emissions of NEG ................................. 11.3.3 The PEM-FC System ....................................................... 11.4 Case Study..................................................................................... 11.4.1 The Urban Area Model .................................................... 11.4.2 The Power Demand Model .............................................. 11.4.3 Analysis Flow .................................................................. 11.5 Results and Discussion .................................................................. 11.5.1 Power Load of the Micro-grid.......................................... 11.5.2 Capacity of the Power Plant............................................. 11.5.3 Power Generation Efficiency ........................................... 11.5.4 Carbon Dioxide Emissions............................................... 11.5.5 Heat Demand and Exhaust Heat Output .......................... 11.6 Conclusions ...................................................................................

173 173 177 179 179 180 181 182 182 183 184 184 185 186 187 188 189 189 190 190 191 191 193 193 195 196 197 197 198 198 200 200 202 202 202 204 204

Contents

12

13

Development of a Fast Operation Algorithm of a Fuel Cell System with Solar Reforming ................................................................ 12.1 Introduction ................................................................................... 12.2 System Configuration .................................................................... 12.2.1 The Fuel Cell System with Bioethanol Solar Reforming (FBSR) ................................................. 12.2.2 Installation Method of FBSR ........................................... 12.2.3 Control of Reformed Fuel ................................................ 12.3 Energy and Mass Balance.............................................................. 12.3.1 Energy Balance ................................................................ 12.3.2 Mass Balance ................................................................... 12.4 Dynamic Operation Prediction of SRF.......................................... 12.4.1 Analysis Procedure of the Operation Prediction Algorithm........................................................ 12.4.2 Structure of the Neural Network ...................................... 12.4.3 Learning Calculation........................................................ 12.4.4 The Operation Prediction Process .................................... 12.5 Preparation of the Training Signal Using a GA............................. 12.5.1 Dynamic Operation Plan of a Representative Day........... 12.5.2 Chromosome Model and Analysis Flow.......................... 12.5.3 System Operation............................................................. 12.5.4 Objective Function and Adaptive Value .......................... 12.6 Case Study..................................................................................... 12.6.1 Analysis System............................................................... 12.6.2 Characteristics of the System ........................................... 12.6.3 Analysis Condition........................................................... 12.7 Results and Discussion .................................................................. 12.7.1 Energy Supply by FBSR .................................................. 12.7.2 Analytic Accuracy of the Operation Prediction ............... 12.7.3 Analysis Error of the Heat Storage Prediction ................. 12.7.4 Relationship Between the Difference in Weather Characteristics and the Operation Prediction Error.......... 12.8 Conclusions ................................................................................... Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation ................................................................ 13.1 Introduction ................................................................................... 13.2 The Micro-grid Model................................................................... 13.3 Response Characteristics of System Configuration Equipment .... 13.3.1 Power Generation Characteristics of the Fuel Cell .......... 13.3.2 Output Characteristics of the City Gas Reformer ............ 13.3.3 Power Generation Characteristics of Wind Power Generation............................................... 13.3.4 Generation Efficiency of the Fuel Cell System................ 13.3.5 Inverter and System Interconnection Device ...................

xi

207 207 208 208 209 209 210 210 210 210 210 211 212 214 215 215 216 217 217 218 218 218 219 221 221 222 225 227 230 231 231 232 233 233 233 234 235 236

xii

Contents

13.4 13.5

13.6

Control Parameters and Analysis Method ..................................... Load Response Characteristics of the Micro-grid ......................... 13.5.1 Step Response .................................................................. 13.5.2 Load Response Characteristics of Cold-region Houses ... 13.5.3 Power Generation Efficiency ........................................... Conclusions ...................................................................................

237 238 238 239 241 242

References......................................................................................................... 245 Index ................................................................................................................. 251

Notation

Act: Act_FC: a: b: C: C: C' : C Boiler : C Me : C Utility,E : C Utility,H : D: Dc : Di : Do : Dp : E , E : EC : EF : Eg : Errday : ErrN : E sys , E SYS :

“If” action Each fuel cell operation Time constant in a primary delay system transfer function [s] Constant part in a primary delay system transfer function Cost [US $] Capacity [kW] Maximum load [kW] Boiler fuel unit cost [US $/mol] Methanol unit cost [US $/mol] Commercial power unit cost of electricity [US $/kJ] Purchase unit cost of thermal energy [US $/kJ] Control variable of derivative action Outside diameter of the heat insulating material [m] Inside diameter of the hot water piping [m] Outside diameter of the hot water piping [m] Heat-insulating-mold outside diameter of hot water piping [m] Power [kW] Capacity of power generation [kW] Power of an inverter outlet [kW] Production of electricity [kW] Difference of the power generation of the system and demand on a representative day [W] Error of output data and training signal of NN Electric power of an inverter outlet [W] xiii

xiv

ΔE , E N : ΔE AUX : ELF : F: FO , f o : g: G,g: G ′ , g′ : gc : gd : gy : H: Hn : Hr , Hw : h: h: h , h ′ : hw : h∞ :

I: I: i , j,k : Jc : J cl : Jf : Js : K: kc : kp : L: Ln : l: l xy : M:

Notation

Power consumption [kW] Auxiliary consumption power of the machinery [W] Load factor Flow rate [mol/s] Objective function Acceleration of gravity [m/s2] CO2 emission [g/s] CO2 emission [g/(s･kW)] Weight of facility cost Weight of operation cost Weight of facility installation cost Thermal energy [kW] Heat power demand [W] Heat release of hot water piping [W] Capacity of generation [W] Overall heat transfer coefficient in the surface of heat insulating mold [W/m2K] Data of the amount of exhaust heat storage Heat transfer coefficient inside hot water piping [W/m2K] Heat transfer coefficient between heat insulation material and open air [W/m2K] Control variable of integral action Current [A] The number of a neuron Unit cost of equipment capacity [US $/W] Unit cost of hot water piping [US $/m] Installation unit cost of equipment [US $/set] Unit cost of fuel [US $/J] Coefficient of overall heat transmission [W/m2K] Heat conductivity of piping material [W/mK] Heat conductivity of heat insulation material [W/mK] Length of hot water piping [m] Number of neurons of the n layer Distance [m] Length of hot water piping between S x and S y [m] Fuel cell installation number

Notation

M: N: N, n: N bd : N cr : NE : N ge : NR : out zy : P: P: Pc : Pg : PI: Q: Q: Q B, t : Qf : : Q RM, t , Q : ΔQ q: q: q : qw : R: R: R: R cr : R dh : rcs : rmu : S: Sm, j,i : So,k, j : Sx : s , s ′ :

xv

The number of buildings with a fuel cell Number of sets Layer number Number of buildings linked to a network Nnumber of individuals of the chromosome model Installed number of NEG Generation number Number of grid routes The output of the neuron y of layer z Power [W] Control variable of proportional action Generation capacity [kW] Pressure gauge Proportion integration control Quantity of heat [W] Amount of fuel supply [kg/s] Town gas quantity of flow for burner [kg/s] Quantity of flow [m3/s] Town gas quantity of flow for steam reforming [kg/s] Hydrogen quantity lost [g/s] Heat flux sensor Calorific valve [kW] Amount of global solar radiation [W/m2] Volume flow rate [m3/s] Tnternal resistance [ Ω ] Load factor [%] Operating period of the fuel cell s Probability of survival [%] Period of daylight [s] Probability of mutation [%] Probability of crossover [%] Quantity of thermal storage [kW] The intermediate layer's neuron connected to j from neuron i The output layer's neuron connected to k from neuron j Number of buildings (x = 1, 2, …, bd ) Data of the amount of hydrogen storage

xvi

T: t: t ge : t gs : Δt : U: u: u: u: V: V , VS : WB : WH : Wp : WR : w n,j, m i : w: Δw : wo : x zy : Yc : Yd : Yf : yj:

Notation

Temperature Sampling time [Hour] Operation finish time of a fuel cell [o'clock] Startup time of a fuel cell [o'clock] Sampling interval [s] Capacity of facility Input [kW] Power demand [W] Power load of a micro-grid [W] The number of auxiliary machinery with electricity consumption Thermal storage volume [m3] Town gas calorific valve supplied to a burner [kW] Calorific valve of the hydrogen included in reformed gas [kW] Water head of hot water circulation pump [m] Town gas calorific valve supplied to a steam reforming [kW] Weight of the neuron i of layer m, and the network of neuron j of layer n Weight before modification The amount of modifications of weight Weight of the component of the objective function Input to the neuron y of layer z Equipment cost [US $] Operation cost [US $] Equipment installation cost [US $] Training signal

Greek Symbols

α: β: φ: φ CT : φ IT : η: η , η RM : v:

Calorific value of fuel [kJ/mol] Heat transfer accompanying reaction [kJ/mol] Efficiency [%] Inverter efficiency [%] DC-DC converter efficiency [%] Learning rate in the NN Reformer efficiency [%] Electric power output [W]

Notation

ρ: ρw :

xvii

Density of thermal storage medium [kg/m3] Density of water [kg/m3]

Subscripts

a: atm : B , cb : BE: BL: bh : bo : bd : bw : CC: CL: CS: Day : DEG : E: Er : Ex : Ey : Ez : EX: el : F, f : fca : fco : hp : hw : in : l: m: m: n: Me:

Cylinder Open air Burner Bioethanol Boiler Back-up heat source Boiler Number of houses Blower Commercial power Reformed gas cooler Cell stack Representative day Diesel engine generator City gas engine generator (NEG) Reforming gas Exhaust gas of SEG Cooling water of SEG Heat radiation of SEG Off gas of the PEM-FC Electrolyzer Fuel cell Fuel cell generated with air Fuel cell generated with oxygen Heat pump Heat release of hot water piping Input Building model with PEM-FC system and NEG Building model with NEG Number of each building Building model without PEM-FC system and NEG Methanol

xviii

N: need : out : p: pih : pump : R , r , rm : ra : SEG : S: sep : st : sub : sys : w: ∞:

Notation

Gas engine generator Energy demand Output City gas engine Hot water piping Pump Reformer Radiator Stirling engine Reformer burner Threshold value of low loading and high load Heat storage tank Auxiliary machinery System Heat loss elements (e.g., case, duct, and pipe) Outside air

Equipment Symbols

B/L: CB: CGS: C/O, CO: DA: D C/A C: F/C: FS: G /T: HC: H CGS: Heater: I/T: NEG : OU: P/I: Rad: RE, R/M:

Boiler Catalyst burner unit Cogeneration system Carbon monoxide oxidization unit DA/AC converter DC-AC converter Fuel cell Fuel cell stack unit Generator Hydrogen cylinder Hybrid cogeneration Electric heater Inverter Gas engine generator CO oxidation unit Interconnection device Radiator Reforming unit

Notation

SH: SR: ST: S/T, St: SU: VP: WP:

xix

Carbon monoxide shift unit Solar reforming unit Heat storage tank Heat storage tank Shifter Vaporizer unit Purifier water producing equipment

The Names of Buildings

AP1: AP2: CV: DF: DH: FF: SF: SO:

Apartment house (single person × 10) Apartment house (single person × 3, two persons × 2, three persons × 2, four persons × 1) Store, day-long business Family household (2 persons) Two-household house (5 or more persons) Family household (3–4 persons) Single-person household Small office

Chapter 1

A Small-scale Fuel Cell Cogeneration System Considering Partial Load and Load Fluctuation

1.1 Introduction The solid high-polymer-film-type fuel cell (PEM-FC) system is used as the power supply equipment for transportation and replaces an internal combustion engine. A reduction of the environmental load is expected through the cogeneration system’s (CGS) use of the PEM-FC system as a distributed power supply to individual houses, apartments, and so forth [1–3]. The growing use of distributed power systems, such as fuel cells, the reduction of power-transmission losses, and an increase of waste heat recovery are expected. Therefore, the reduction of carbondioxide emission is also expected as compared to conventional energy supply methods using commercial electric power. At present, a decisive method of hydrogen supply and storage in houses is not proposed. Fuel cell CGS of the reforming type which obtains hydrogen from natural gas and methanol is promising. In order to provide high efficiency and to perform a catalyst reaction by methanol steam reforming, it is necessary to manage the heat quantity [4–7]. Furthermore, if approximately 3% carbon monoxide is contained in the gas after the steam reforming reaction and is supplied to a fuel cell, poisoning of the electrode catalyst will occur, and power generation will be difficult. Thus, after preparing the shift unit and CO oxidation unit in the process after the reforming reaction, the CO concentration is approximately 10 ppm. Since the shift reaction, CO oxidation reactions, and the reforming reaction are performed using a catalyst, each reaction system is controlled in a suitable temperature range. Moreover, the evaporation and reforming reactions of methanol fuel (methanol solution) are endothermic, while other reactions are exothermic, such as cell reactions. Under operation with unsuitable temperature management of each reaction system, the by-product generating rates of reacted and unreacted substances increase in the process of catalyst reaction. The fuel cell system with a reformer is a complicated heat system; considering that the electric energy or heat demand of individual houses change, one should prepare energy buffers, such as battery and thermal storage, which should make the chemical reaction stable. S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

1

2

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

This study considers the optimal operation when minimizing fuel consumption (making it equal to the operation cost) into an objective function assuming the introduction of the PEM-FC cogeneration system installation of a methanol steam reformer for individual houses with energy load fluctuation. Furthermore, a simulation that introduces this system into the energy consumption pattern of individual houses in Sapporo in Japan is performed, and the operation of the system and the operation cost are compared with those of conventional energy systems.

1.2 System Configuration 1.2.1 System Outline Figure 1.1 shows a schematic diagram of the fuel cell CGS for the individual houses investigated in this study. In this chapter, the evaporation unit 2, reformer unit 3, shifter unit 4, and CO oxidization unit 5 of Fig. 1.1 are collectively called a reformer. In the evaporator 2, methanol fuel is evaporated by the combustion gas supplied from the catalyst burner 12. The methanol fuel reformed by reformer unit 3 and a reforming gas with much hydrogen are generated. The shifter unit 4 and CO oxidation unit 5 are formed on removing carbon monoxide of the reforming gas. The electric power generated by the fuel cell stack 1 is changed into an alternate current by the DC/AC converter 11. The electric power supplied to a demand side is chosen to be a commercial electric power system or the fuel cell system

Fig. 1.1 System configuration

1.2 System Configuration

3

with a change over switch 6. Fuel cell heat exhaust is stored in the thermal storage tank 10. The electric heater 9 is installed in the thermal storage tank, and the heat medium in the thermal storage tank can be heated by the heater 9 and the back-up boiler 8. The energy generation cost by CGS is compared with commercial power (utility) cost, the cheaper one is chosen, and power supply is changed with the high-speed-changeover switch 6. The electric power supplied from the system is outputted in CGS or the utility. In addition, the power generation capacity in CGS is determined to be greater than the quantity of the maximum electricity demand assumed. The pressure of the reforming gas is made slightly higher than the atmospheric pressure. The off-gas of the catalyst burner 12 is supplied to the reformer unit 3, and the temperature of reforming reaction is controlled. The heat supplied to a user is based on values other than an endothermic reaction by the evaporation unit 2 and reforming unit 3 among the combustion gas of the catalyst burner 12, waste thermal energy of the fuel cell stack 1, and the back-up boiler 8. All the waste thermal energy is brought together in the thermal storage tank 10, and heat exchange is carried out with tap water. If the waste thermal energy exceeds the thermal storage capacity, a part will be released to the atmosphere from the radiator 7. The CGS operation cost in a sampling time of electric energy and thermal energy is calculated from the quantity of methanol fuel consumed by the reformer operation and the back-up boiler operation. The total cost of the energy supply is added and estimated to be higher than the fuel costs and the utility energy supply costs. Reaction equations in the reformer and fuel cell stack are shown in Fig. 1.2. With respect to evaporation and reforming, they are endothermic reactions, and

Fig. 1.2 Fuel-cell system with methanol steam reforming

4

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

Fig. 1.3 Temperature target

CO shift, CO oxidization, and cell reaction are exothermic reactions. Figure 1.3 shows the target temperature region in each reaction system, and inputs and outputs of heat. The exothermic heat of the CO oxidation reaction device is 283 kJ/mol by combustion of carbon monoxide and hydrogen, and the exothermic heat of the fuel cell stack is 265 kJ/mol by the fuel cell reaction. Reforming is performed after having increased the temperature of methanol steam to about 570 K. After that, the target temperature in each reaction system falls in step. The off-gas temperature of the fuel cell exit is set to 340–360 K.

1.2.2 Energy Loss and Mass Loss Figure 1.4 shows the energy loss and mass loss with each unit. These loss values, however, do not include heat release from the case, duct, and pipe. The off-gas of the fuel cell stack is given as the heat source of the evaporator and reforming units. When requiring an additional quantity of heat, methanol burns with a catalyst burner. Heat is radiated after its generation in the shifter and CO oxidization units. The rate of conversion from the methanol in the reforming unit to hydrogen is expressed as φ RE , and the back-up boiler efficiency is expressed as φ Boiler . A blower for oxygen supply is installed in the catalyst burner, the CO oxidization unit, and the fuel cell. The above-mentioned blower, methanol fuel pump, and methanol pump for catalyst combustion, system controller, and back-up boiler consume electric energy. An estimation as regards the loss of electric energy by hydrogen combustion in the CO oxidization unit ( φ O 2 ), joule loss ( R FS ) by PEM-FC, the concentration fault voltage in the electric pole ( φ Air , φ Me ), and the DC/AC converter ( 1 − φ DA ) must be made.

1.2 System Configuration

5

Fig. 1.4 Energy loss and mass loss with each unit

1.2.3 System Model and Equations Figure 1.5 shows profiles of the hydrogen generation quantity, the volume rate of carbon monoxide in the quantity of reforming gas in the shift unit exit, and the volume rate of hydrogen conversion. The methanol inversion rate shown in the figure is the rate at which the methanol fuel supplied to the system finishes the steam reforming reaction. This model is applied for the actual measurement using an experimentally manufactured cell stack. S/C expresses the molar ratio of water to methanol and is 1.4 in this model. The average data in the trial production PEM-FC system for 3 kW power generation is shown in Fig. 1.5. In this chapter, the following assumptions are made regarding system operation. (a) The temperature of the evaporation unit, reforming unit, and the shift unit can be controlled in a suitable range as shown in Fig. 1.3. (b) Although a catalyst reaction is dependent on the space velocity (SV) value, CO concentration in the shifter unit exit is always less than 1.0 volume %. (c) The temperature range and SV value in the CO oxidization unit is appropriately managed so that CO concentration in the fuel cell stack entrance may be set to 10 ppm or less. By flow rate of reforming gas, the number of CO oxidization catalyst paths where reforming gas flows is changed. Moreover, the amount of cooling of the catalyst paths is controlled by a blower to react in the regal temperature range.

6

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

Fig. 1.5 Characteristics of shifter product gas output

Fig. 1.6 Characteristics of the fuel cell

The output characteristic model of a single cell is shown in Fig. 1.6. For the single cell output characteristic the model depends on the CO concentration in the reforming gas. The sampling time t k (Eqs. (1.1) and (1.2)) divides one day into N intervals (here N = 24 ), and is defined by the following equations by setting the starting time to t r . Here k = 0, " N − 1 . For the quantity of methanol consumption at the system from t k to Δt , the amount of supply to the evaporation unit is FVP,t k , the amount of supply to the catalyst burner unit is FCB, t k , and the amount * of supply to the back-up boiler unit is FBoiler, t k . Equation (1.3) shows that the CGS operation is cheaper than the utility energy supply. E Utility, t k expresses utilities power and FBoiler,t k is a flow rate of a boiler. In terms of energy supply cost it is cheaper to supply the same amount of electric power and thermal energy by the system of Fig. 1.1 rather than supplying from the utility when Eq. (1.3) is needed. t k = t r + k⋅ Δt

(1.1)

1.2 System Configuration

7

Δt = 24 /N * C Me ⋅ FCB,t k + C Me ⋅ FVP, t k + C Boiler ⋅ FBoiler, tk

< C Utility,E ⋅ E Utility,t k + C Utility,H ⋅ FBoiler,t k

(1.2) (1.3)

The following equation defines the energy supply cost of CGS in this chapter.

C CGS, t k = (C Utility,E ⋅ E Utility,t k + C Me ⋅ FVP, t k + C Me ⋅ FCB, t k + C Boiler ⋅ FBoiler,t k ) ⋅ Δt

(1.4)

The C Boiler term of Eqs. (1.3) and (1.4) shows the boiler fuel unit price of CGS, and C Utility,H shows the boiler fuel unit price of a utility. In the analysis example described later, the kerosene fuel unit price is used for C Utility,H for methanol or the kerosene fuel unit price for C Boiler . The boiler used for utility and CGS backed heating has the same unit, and * therefore, FBoiler, is replaced by FBoiler,t k and C Utility,H is replaced with by tk C Boiler of Eq. (1.4).

1.2.4 Partial Load Operation The boiler efficiency differs by the input and output temperatures of medium, hot water supply quantity, and so forth. When the boiler efficiency falls below the total efficiency of the fuel cell CGS, a part of the generated electric energy is converted into heat in an electric heater. Figure 1.7 shows the model of the relationship between the methanol fuel supply quantity to the reformer and the energy output. The dashed line denotes electric energy output and the solid line expresses exhaust heat output produced by the cell reaction and the internal resistance of the fuel cell stack. In this model, the maximum output is approximately 3 kW with the methanol quantity of supply of 0.032 mol/s. The electric energy load E needs is in a smaller range than the greatest outputting point of the fuel cell stack and sets this operating point to Pa . The heating of H Boiler is required by the boiler in order to satisfy the energy demand quantity ( H needs ) at the point Pa . When changing the electric energy ΔE into thermal energy in an electric heater, the driving point Pa moves the maximum output point Pb . H *Boiler shows the heating quantity of the back-up boiler, and Δh shows the thermal output differential between points Pa and Pb . Therefore, (Δ h + ΔE) is the decrease in heating quantity of the back-up boiler. ΔH Fuel is the increase in fuel supply of the fuel cell system with the previous driving point shift. In the case of electrical thermal exchange, a favorable condition is defined by following equation. ΔH Fuel + H *Boiler /φ * < H Boiler /φ

(1.5)

Here, φ* denotes the back-up boiler efficiency with boiler heating H *Boiler .

8

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

Fig. 1.7 Characteristics of the fuel cell stack output

1.3 Energy Balance and Objective Function 1.3.1 Energy Balance The following equations denote the energy balance of the system shown in Fig. 1.1. (a) Electric energy:

E FS, t k + E Utility,t k = E DA,t k + ΔE Heater, t k + ΔE DA, t k + ΔE Boiler,t k + ΔE Controller, t k +

Pump

∑ ΔE Pump,n,t k + n =1

Blower

∑ ΔE Blower,m,t

m =1

(1.6) k

(b) Thermal energy:

α Me ⋅ FCB, t k ⋅ φ CB + α Me ⋅ FBoiler, t k ⋅ φ Boiler + H REFORMER, t k + H St, t k (1.7) = H System, t k + H Rad, t k + H w, loss, t k Here, H REFORMER,

H SH,

tk

= H VP, t k + H RE, t k + H SH, t k + H CO, t k + H FS, t k

(1.8)

H VP, t k = β VP ⋅ FVP, t k

(1.9)

H RE, t k = β RE ⋅ FVP, t k ⋅ (1 − φ RE )

(1.10)

tk

= β SH ⋅ F VP,

tk

⋅ (1 − φ RE )(1 − φ SH )

(1.11)

1.3 Energy Balance and Objective Function

9

Fig. 1.8 Thermal and electric energy output of the system

H CO, t k = β CO ⋅ FVP, t k ⋅ (1 − φ RE )(1 − φ SH )(1 − φ CO ) + β O 2 ⋅ FVP, t k ⋅ φ O 2

(1.12)

H FS, t k = β FS ⋅ FVP, t k ⋅ (1 − φ RE )(1 − φ SH )(1 − φ CO ) ⋅ φ Me ⋅ φ Air + I 2 ⋅ R FS

(1.13)

H DA,t k = E FS ⋅ (1 − ϕDA )

(1.14)

The left-hand sides of Eqs. (1.6) and (1.7) denote the input energy of the system, and the right-hand sides of both equations denote the output energy. The electric energy output of the system is given as E DA,t k in Eq. (1.6), and thermal energy output is given as H System,t k in Eq. (1.7). Figure 1.8 shows the CGS energy output diagram; this illustration is made up of E DA,t k and H System,t k . The running fuel cost is estimated as shown in Fig. 1.8. F1 to F3 are operation areas of the system stated in the section “Load Fluctuation and Fuel Consumption”, and (a) to (d) express the routes of load fluctuation. The objective function J C is given by Eq. (1.15) as the minimization of the energy cost for one day. A random method is used in the search analytical algorithm of the optimum operation plan of the system which made Eq. (1.15) an objective function. JC =

N −1

∑ C CGS, t

k =0

k

(1.15)

1.3.2 Electric Heater Operation The maximum capacity of the electric heater H Heater,max is divided into L pieces, and the quantity of the loads of the heater is determined based on

10

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

ΔEHeater,t k = E Heater,max /L . The following equation is the quantity of electric energy generation required of a fuel cell stack.

E ′FS, t k , l = E Needs, t k + ΔE Heater, t k ⋅ l

(l = 0,1,", L−1)

(1.16)

Equations (1.17) and (1.18) are made to fit the curve of Fig. 1.7. Electric energy output and thermal energy output are divided into approximately two or more domains of the quantity of fuel supply, and the approximation equation in each domain is created. Electric energy output: EFS,t k = a FS,1⋅ (FVP,t k )2 + a FS,2⋅ (FVP,t k ) + a FS,3

(1.17)

Thermal energy output: HFS,t k = bFS,1⋅ (FVP,t k ) 2 + bFS,2⋅ (FVP,t k ) + bFS,3

(1.18)

“a” and “b” in approximation Eqs. (1.17) and (1.18) express coefficients of the respective approximation equations, and Eq.(1.19) shows restricted conditions. FVP,min ≤ FVP,t k ≤ FVP,max

(1.19)

The fuel quantity of supply FVP, t k is calculated by giving E′FS,t k ,l of Eq. (1.16) to E FS, t k of Eq. (1.17), and the amount of heat outputs H FS, t k is further calculated from Eq. (1.18). The quantity of electric energy E′FS, t k ,l and the backed heating quantity H Boiler,t k of the boiler can be calculated by substituting the electric energy output E FS, t k of a fuel cell stack, the heat output H FS, t k , and other factors into Eqs. (1.6), (1.7), and (1.8), and solving for H Boiler,t k . The calculation method of the quantity of thermal storage H St, t k is described later. The fuel quantity of supply FBoiler,t k is calculated by substituting H Boiler,t k in the expression of the thermal energy output the relationship shown in Fig. 1.9. FBoiler,t k and FVP, t k are substituted in Eq. (1.4) and the operation cost of CGS is estimated. The above calculation is performed under the restricted conditions of Eq. (1.16), and it determines the minimum operation cost for the sampling time t k .

Fig. 1.9 Relation of fuel supply and electric power consumption of the boiler

1.4 Energy Output Characteristics

11

1.3.3 Thermal Storage Operation Thermal storage will be needed if the heat output of a system exceeds the thermal energy demand. Equations (1.20) and (1.21) are restricted conditions, and use the quantity of the maximum thermal energy storage capacity, and the maximum temperature of the thermal medium. When thermal energy input exceeds the quantity of the maximum thermal storage capacity, heat is radiated from a radiator in the thermal energy exceeding maximum. Equation (1.22) defines the quantity of thermal energy storage, using specific heat Cp and thermal storage loss φSt including heat release loss. 0 ≤ SSt, t k ≤ SSt, max

(1.20)

T∞, t k ≤ TSt, t k ≤ TSt, max

(1.21)

SSt, t k − SSt, t k −1 = {H St,in, t k − H St,out, t k − ϕSt ⋅ ρ⋅ C p ⋅ V⋅ ( TSt, t k − T∞, t k )} ⋅ Δ t

(1.22)

The thermal storage temperature for sampling time t k is calculated by TSt,t k = SSt,t k ( ρ ⋅ C p ⋅ V ) . Through the heat transfer medium into the thermal storage tank (0.09 m3, the coefficient of heat storage loss is 10%), tap water and waste heat are exchanged. The quantity of thermal energy of the tap water outputted from the thermal storage tank performs heating by the back-up boiler, when it is less than the heat demand. Electric energy is converted into thermal energy by the electric heater (maximum heat output is 3.0 kW) installed in the thermal storage tank.

1.4 Energy Output Characteristics 1.4.1 System Operation Map Figure 1.8 shows the result of calculating the relationship of electric energy and thermal energy output when making the calorific value of fuel supply to CGS into a parameter. Table 1.1 lists the specifications of the fuel cell system assumed in this chapter, and Table 1.2 lists the efficiency of each reaction unit. Each value of Table 1.2 was decided from measurement results of each device. Figure 1.8 was obtained by the introduction of the value of characteristic of fuel cell CGS of Fig. 1.7 and Table 1.2 for the optimization analysis. Area A of the lower part of Fig. 1.8 (hatching part) shows an independent energy supply operation by the fuel cell system. Area B (solid line) shows the operation when incorporating back-up heating by the boiler to the fuel cell system. Area C (dashed line) shows the operation when incorporating an electric heater in area B. The fuel consumption in area A is determined only by the quantity of electric energy output; in the large region of thermal load, the fuel consumption of the boiler is added to the fuel consumption of the fuel cell, because the operation of the boiler is applied to the operation

12

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

Table 1.1 Fuel cell CGS specifications

Table 1.2 Fuel cell CGS setting value

of the fuel cell system. In area A, the fuel supply for the same quantity of electric energy generation is minimum compared with other cases of operation. When the driving point at the electric heater is moved (area C), there is area in which thermal energy output increases also by the same fuel quantity supply compared with area B (this area has a dashed line above a solid line). This domain is in the small portion of electric energy output. Figure 1.9 shows the model of the fuel quantity of supply and electric energy consumption of the back-up boiler (methanol fuel is assumed). The back-up boiler output is 55 kW at the maximum, it is assumed it uses space heating and a hotwater supply, and that the hot-water temperature is 340 K or more and the thermal efficiency is 85%. Heat release loss of the case or duct is set to 10%, and the utility electric energy supply E utility,t k and thermal storage H St, t k are taken as zero. CO concentration at the entrance of the fuel cell is assumed to be 10 ppm or less. In the analysis, output characteristics of the fuel cell are improved, when the CO concentration falls below this value, and these characteristics were calculated as being constant.

1.4.2 Load Fluctuation and Fuel Consumption The energy demand in individual houses is fluctuated. In particular, the electric energy load is significant [8]. Then, the fuel consumption of the system with changing electricity demand and heat demand is analyzed. The fuel consumption in the virtual load fluctuation areas F1–F3 are shown in Fig. 1.8. The load fluctuation area F1 is located in the area in which backed heating by the back-up boiler is possible, and F3 mainly occurs in the fuel cell stack independent operation area. Area F2 includes independent operation of the fuel cell, and the area by the boiler

1.4 Energy Output Characteristics

13

in which backed heating is possible. Load fluctuations of areas F1, F2, and F3 were set up, respectively, as ±30%, ±50%, and ±70% for thermal energy demand, and ±60%, ±60%, and ±70% for electric energy demand, respectively. These load fluctuation ranges were determined supposing power consumption of home electric appliances, and thermal energy consumption of hot-water supply and space heating. As shown in Fig. 1.7, if the driving point passes over the maximum power point Pb , the efficiency of the fuel cell stack will fall. Then, each of the load fluctuation areas of F1–F3 shall be in the range of electric energy lower than Pb . Figure 1.10 in the system which combined PEM-FC-CGS and the methanol boiler shows the results of measuring the quantity of fuel consumed at each point of the F1–F3 areas. (a) to (d) is the route of load fluctuations described in Fig. 1.8. The pattern Q1 shown in the figure is the result of calculating for the operation system which performs neither heat conversion of electric energy, nor change in utility (commercial power) energy supply. Although Q2 performs heat conversion of electric energy, a change in a utility is not performed. Q3 is result of the operation system which performs heat conversion of electric energy, and change in the utility. In this Chapter, the electric energy unit price was set to 6.03 c$/MJ for the energy supply by the commercial power, and 0.0000251 $/kJ and the price was set to 380 $/m3 for the energy unit price when using methanol as the boiler fuel, and the boiler fuel kerosene is set at 373 $/kJ. These unit prices are the charges in Japan. On the other hand, the unit price of the methanol in US is approximately 250 $/m3, and commercial power unit price is approximately 2.28 c$/MJ. In the following analyses, a methanol unit price and commercial power unit price of Japan are used. When the results of the Q2 system and Q3 system are compared, there is no difference in fuel consumption observed in all the areas of F1–F3. Therefore, the change to the utility energy supply is not performed. However, the Q2 system becomes advantageous over the Q1 system in many cases. In the case study of the following Section, the Q1 and Q2 systems use methanol fuel, and

Fig. 1.10 Comparison of fuel consumption

14

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

kerosene oil is assumed as the fuel for the boiler for utilities by Q3. Therefore, there are different operation areas from the fuel consumption in load average value in the width of load fluctuation, the path of load fluctuation and the area of system operation.

1.5 Case Study 1.5.1 System Outline The case of installing the methanol-steam-reforming-type PEM-FC shown in Fig. 1.1 as CGS in individual houses is considered. On the assumption of the utility of space heating and hot-water supply, the temperature of the hot-water output from CGS is set at 340K, and the coefficient of heat radiation loss of cases and ducts is set at 10%. Reaction efficiencies and electricity consumptions of each element use the values in Table 1.2. The measured values for individual houses in the Sapporo district in Japan are used for the standard load pattern [8]. In the standard electric energy load pattern, the annual difference between minimum and maximum energy demand quantities on a representative day is 7%, and the patterns of other months are almost similar. Heat demand quantities on representative days differ greatly in the season. The thermoelectricity ratios (the value which was obtained by dividing the thermal energy demand quantity by the electric energy demand quantity on the representative days) are, 0.8 for July, 11.0 for January and 6.4 as an annual mean value. The optimization problem of the operation plans are analyzed by the setting of an objective function for the minimization of the energy supply cost.

1.5.2 Results of the Operation Plan The analysis results of operation plan of the system of representation day in January and September are Figs. 1.11(a) and (b), respectively. In the system with heater, operation cost of CGS may be less from energy supply cost by the commercial power and kerosene boiler. For example, 5:00, 6:00 in January of a result, etc. The energy cost of combination of the fuel cell and the back-up boiler is expensive compared with the energy supply by the commercial power and kerosene boiler.

1.5 Case Study

15

Fig. 1.11 Operating energy cost

1.5.3 Annual Operation Cost of the System Figure 1.12 shows a comparison result of the energy supply cost on a representative day for each month. This figure is the case where the boiler (methanol fuel), or the boiler and the electric heater are combined with methanol steam reforming type fuel cell, and the results of comparing the energy cost by the utility (commercial power and kerosene boiler). The annual cost ratio of CGS with a boiler to utility cost is 2.16. Moreover, the annual cost ratio of CGS with an electric heater to utility cost is 1.42. If methanol unit price is 267 (=380/1.42) $/m3, the operation cost for one year of the system with a boiler and a heater is almost the same as the energy cost for one year of the conventional utility. If a methanol unit price is 190 (unit price of the half of 380 $/m3) $/m3, the operation cost of this system will be less than the energy cost by the utility to all the month. On the other hand, if methanol unit price is 176 (=380/2.16) $/m3, the operation cost for one year of a system with a boiler is almost the same as the energy cost for one year of the conventional utility.

16

1 A Small-scale Fuel Cell Cogeneration System Considering Partial Load

Fig. 1.12 Monthly energy cost of co-generation system

1.6 Conclusions As a CGS for the individual houses, the operation cost of solid polymer membrane fuel cell system with the methanol steam reforming was examined. The thermal conversion of electric energy by an electric heater was introduced into the system in order to avoid the lowering of generation efficiency of a fuel cell in low-load operation. By the introduction of the energy demand pattern in the Sapporo district in Japan, and performing optimization analysis with the objective of determining the operation cost of the system, the following results were obtained. (1) The system driven by carrying out thermal conversion of electric energy by the electric heater may reduce the operation cost further than when the system is not introduced, in the case of marked fluctuations in thermal load in lowelectric-power demand. (2) The energy supply cost to individual house of methanol-steam-reforming-type fuel cell CGS with a back-up boiler and an electric heater will be less than the energy supply cost by the present utility, if the unit price of methanol decreases from the present 380 $/m3 to 267 $/m3. The methanol unit price in US is approximately 250 $/m3, and commercial power unit price is approximately 2.28 c$/MJ. In this case, in order for the energy supply cost of propose system to be less than the energy supply cost of the utility, the unit price of methanol needs to be 107 $/m3.

Chapter 2

Equipment Arrangement Planning of a Fuel Cell Energy Network Optimized for Cost Minimization

2.1 Introduction In recent years, uses of the distribution of fuel cells have been studied [9, 10]. Furthermore, fuel cell systems are connected by a network and the micro-grid of the electrical power operated in cooperation under the objective function given beforehand is investigated [11–13]. Moreover, compound utilization of green energy equipment is considered to have little environmental impact although it is unstable, and stable power-generator equipment is investigated by the electrical power grid of small energy equipment [11]. Construction of an electrical power micro-grid may to develop competition with the existing energy infrastructure other than in short-term utilization, such as a backup power supply at the time of disaster. Installation of both a micro-grid of solid polymer membrane-type fuel cells and a hot water piping network used to supply fuel cell exhaust heats to each building is anticipated to cause a large reduction in environmental impact [9, 10]. In order to achieve the energy network of electrical power and heat described above, cooperative control of the distributed energy equipment according to the objective function given beforehand, operation planning, the arrangement design of equipment, and capacity design of equipment are required. The fuel cell energy network (FEN) investigated in this paper examines the micro-grid (FMG) used for an electric power supply, and the hot water piping network (HWN) that uses the exhaust heat of equipment for supply or collection from each house. In small-scale FEN, since the power transmission length is short, the energy loss of electric power transmission is small, and the loss of exhaust heat transport can also be maintained at low levels. When loss of electric power transmission is improved rather than heat transport due to the difference in energy unit price, the total energy cost is generally low. However, in cold regions, houses, apartment houses, office buildings, hospitals, etc., have a great annual heat demand. It is expected that the heat transport loss of FEN in buildings with high heat demand has a large influence on the efficiency of the system. Therefore, when transporting a lot of S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

17

18

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

heat using HWN, route planning of piping considering the heat release loss of HWN and an arrangement plan of equipment are required; these aspects depend on the route planning of HWN, and the optimum arrangement plan of heat output equipment depends on the energy demand pattern of each building linked to FEN, and the position of each house. The heat medium (hot water) temperature that flows through the inside of the HWN in a house outlet is decided by the heat supply and demand of each house, and the heat release of HWN is dependent on the hot water temperature and the piping length. Thus, in this paper, a plan is made to optimize the equipment arrangement of the fuel cell, reformer, and boiler, and an FEN with high energy efficiency and cheap facility cost is planned by optimizing the route of HWN used for supplying the exhaust heat of a fuel cell and a reformer to each house. If the scale of FEN is large, the total length of HWN will increase and heat release will also increase. From this, it is expected that the increase in the scale of FEN will lead to a decline in the energy efficiency of the system. However, compared with FEN that is not optimized, the cost and efficiency of the system may be improved by optimizing the equipment arrangement of each building linked to FEN, and the route of HWN. That is, compared with FEN that is not optimized, the same facility cost as FEN that has an optimized equipment, arrangement is expected and a HWN route whose scale (the number of houses linked to FEN) can be extended. In this paper, the arrangement plan of FEN that differs in the number of houses, and the route plan of HWN are conducted using a genetic algorithm [14–19]. From this analysis, the operation cost of the system, facility cost, and the installation cost of a facility are investigated, and the cost per house connected by FEN is investigated.

2.2 System Scheme 2.2.1 The Energy Network The model of the fuel cell energy network (FEN) assumed in this paper is shown in Fig. 2.1. Each building is connected with the micro-grid (FMG) of electrical power, and the hot water piping network (HWN) used for waste heat recovery and heat supply. A fuel cell assumes a solid polymer membrane model, and a reformer assumes the steam reforming of city gas. Each facility of the fuel cell, the reformer, and the boiler is capacity free in each building, and it is assumed that it can be installed freely. It is necessary to optimize and perform design arrangement and capacity planning of this equipment based on the objective function given beforehand for the system. Houses in which fuel cells are installed, and the houses in which the installed reformers are connected with a reformed gas piping network are shown in Fig. 2.1. Supposing a gas piping network reformed from an existing city gas piping network is used, FMG decides to use the existing commercial electrical power network more. Therefore, in the analysis of cost in this paper, equip-

2.2 System Scheme

19

Fig. 2.1 Fuel cell network model with eight houses

ment cost of a reformed gas piping network and FMG is not taken into consideration. Since the route of HWN is planned considering the heat release in HWN that connects each house, they differ depending on outside air temperature. The outside air temperature of a representative day differs in different seasons, so the heat release of HWN is calculated using an outside air temperature model in summer (August), winter (February), and mid-term (May), and the optimal route is explored.

2.2.2 Fuel Cell System Figure 2.2 shows a model in which a fuel cell is installed in house S x . The reformed gas produced by the reformer is supplied to the fuel cells through a reformed gas network (town gas piping network). The power generated by the fuel cells is supplied to FMG through a DC–AC converter, an inverter, and an interconnection device, and supplies the load in each house. As shown in Fig. 2.2, the exhaust heat of a fuel cell is supplied to the heat load of house S x , but when heat remains, heat is supplied to other houses through HWN. On the other hand, when heat is insufficient, heat is obtained and supplied by the HWN.

20

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

Fig. 2.2 Fuel cell introduced into a house

2.2.3 Heat Source Use Order The heat source that supplies the heat demand in each house gives priority to the exhaust heat of the fuel cell and the reformer installed in the same house. When such exhaust heat is insufficient for the heat amount demanded, heat is obtained from HWN. Although a heat storage tank is installed in HWN, it stores the heat when the heat that flows through HWN remains. The stored heat can be used by conducting a time shift. A boiler is operated when heat runs short, even if it supplies the heat obtained from HWN to a house.

2.2.4 City Gas Reformer Figure 2.3 shows the model of installing a reformer in house S y . Although city gas is supplied to the reformer, in order to remove the carbon monoxide and water in the reformed gas, carbon monoxide oxidation equipment and a dryer are installed. The exhaust heat of the reformer can be supplied to each house through HWN.

Fig. 2.3 Reformer and cylinder is installed in a house

2.2 System Scheme

21

2.2.5 Operation Model of the System Figure 2.4 shows a model of equipment arrangement planning at the time of connecting the distributed fuel cell with an energy network. As shown in Fig. 2.4(a), in order to fulfill the demand of the electric power and the heat of six houses (from S A to S F ), a reformer is installed in houses S B and S F , and a boiler is installed in houses S A , SC , and S D for a fuel cell at houses S A , SC , and S E . Each house is connected with HWN and can transport the exhaust heat of the fuel cells and reformers, and the heat output of the heat storage tank and boilers with the heat medium (hot water) that flows through the inside of the piping. Here, the demand model of the electric power and the heat of each house is made to have the

Fig. 2.4 Arrangement plan of the distributed fuel cells

22

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

characteristics shown in Fig. 2.4(b) and (c), respectively. As shown in Fig. 2.4(e), the exhaust heat output in each fuel cell in this case depends on the production of electricity (Fig. 2.4(d)) of the fuel cell. Moreover, Fig. 2.4(f) shows a model of the exhaust heat output of a reformer, and Fig. 2.4(g) shows a model of the heat output of a boiler. The hot water quantity of heat that flows through the inside of HWN is decided from the heat demand model shown in Fig. 2.4(c), and the model of the heat output in each piece of equipment is shown in Fig. 2.4(g) from Fig. 2.4(e). The heat release of HWN equipped with thermal insulation is dependent on the difference in temperature between the hot water and the atmospheric air. Figure 2.4(h) shows a model of the heat release of HWN that connects each house. Therefore, the amount of heat release in HWN differ depending on which equipment is installed in the house linked to a network, and the route of HWN.

2.3 Amount of Heat Release of the Hot Water Piping Network (HWN) Figure 2.5(a) shows the model of incomings and outgoings of the heat of HWN that connects house Si , Si +1 , and Si + 2 . As shown in Fig. 2.5(b), the hot water of temperature Tin,Si , t and the quantity of heat H in,Si , t inputs into Si through HWN. The exhaust heat output of a fuel cell when generating the fuel cell installed in S i is determined so that the amount E need,Si , t of the electricity demand of sampling time t that can be supplied is H fc,Si , t . Moreover, H need,Si , t is the heat amount demanded in Si . The hot water quantity of heat ( H out,Si , t ) outputted from Si is H in,Si , t + H fc,Si , t − H need,Si , t . The cost of fuel cell exhaust heat H fc,Si , t differs according to the production of electricity E fc,Si , t of a fuel cell; the following section describes the details of these relations. Although the temperature of the hot water outputted from Si is Tout,Si , t , there is heat release of H w,Si − (i +1) , t for the piping as it arrives in Si +1 from Si . Therefore, with the hot water inputted into Si +1 , the temperature falls to Tin,Si +1 , t , and the quantity of heat is H in,Si +1 , t = (H out,Si , t − H w,Si − (i +1) , t ) . Furthermore, in house Si +1 , the exhaust heat H fc,Si +1 , t is outputted by the generation operation of the fuel cell. The analysis of “the hot water quantity of heat that sets the heat amount demanded in Si +1 to H need,Si+1 ,t , and is outputted from Si +1 is H out,Si +1 , t = H in,Si +1 , t + H fc,Si +1 , t − H need,Si +1 , t ” is calculated for all the houses. Figure 2.5(c) shows a model of HWN that connects Si and Si +1 . The inside diameter of the hot water piping is set to D i,Si − (i +1) , the outside diameter is set to D o,Si − (i +1) , and the outside diameter of the heat insulating material with which the piping is equipped is expressed as D c,Si − (i +1) . When the heat conductivity of the piping material and the thermal insulation is set to k p and k c , respectively, the coefficient of the overall heat transmission ( K Si − (i +1) ) of the hot water and the surface of the heat insulating materials is expressed by the following equation.

2.3 Amount of Heat Release of the Hot Water Piping Network (HWN)

23

Fig. 2.5 Heat model for the hot water piping network

K Si − (i +1) = 1

⎧⎪ D c,Si − (i +1) D o,Si − (i +1) 1 1 1 1 ln ln + + + ⎨ ⎪⎩ h w,Si − (i +1) ⋅ D i,Si − (i +1) 2 ⋅ k p D i,Si − (i +1) 2 ⋅ k c D o,Si − (i +1) h ∞,Si − (i +1) ⋅ D c,Si − (i +1)

⎫⎪ ⎬ ⎪⎭

(2.1) Moreover, the outside air temperature of time t is set to T∞, t for the outlet hot water temperature ( Tout,Si , t ) of a house by Si , and the heat release ( H w,Si − (i +1), t ) in piping of length lSi − (i +1) that connects Si and Si +1 is calculated by the following equation. However, the exhaust heat temperature of the fuel cell is set to 353 K (80°C) in the analysis example described later. H w,Si − (i +1), t = K Si − (i +1) ⋅ D o,Si − (i +1) ⋅ π⋅ lSi − (i +1) ⋅ ( Tout,Si , t − T∞ , t )

(2.2)

24

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

2.4 Energy Balance The number of the houses linked to a fuel cell network shown in Fig. 2.1 is set to N bd . Each number is set to N fc , N rm , and N bo , although a fuel cell, a reformer, and a boiler are installed in any of the houses. In the lower part of the figure, the energy balance of the power and the heat of the system in sampling time t is described.

2.4.1 The Balance of Power Equation (2.3) is a balance equation of power. The left-hand side of Eq. (2.3) expresses the power outputted to FMG from the fuel cell of the number N fc that is generated. On the other hand, the first term on the right-hand side of Eq. (2.3) expresses the power consumption in the number of the houses N bd linked to a network. The second term on the right-hand side of Eq. (2.3) expresses the power consumed with the circulating pump supplying hot water to HWN. In the analysis example of following section, the power of a hot water circulating pump is calculated as consumption according to a hot water quantity of flow. N fc

∑

E fc,i, t =

i =1

N bd

∑ E need, j,t + E pump,t

(2.3)

j=1

2.4.2 The Balance of Heat Equation (2.4) is a balance equation of heat. The left-hand side of Eq. (2.4) expresses the exhaust heat of a fuel cell, the exhaust heat of a reformer, the heat output of a boiler, and the heat output of a heat storage tank, respectively. On the other hand, the right-hand side expresses heat consumption with the number of houses N bd linked to a network, and the heat release in hot water piping that connects each house. Nfc

Nrm

Nbo

Nbd

Nbd

i =1

j=1

l=1

m=1

n =1

∑ Hfc,i,t + ∑ Hrm,j,t + ∑ Hbo,l,t + Hst,t = ∑ Hneed,m,t + ∑ Hw,n,t

(2.4)

2.5 Cost Calculation and Objective Function

25

2.5 Cost Calculation and Objective Function 2.5.1 Cost Calculation (1) Operation Cost

Equation (2.5) expresses the operation cost of a system required between Δt from sampling time t . The first term in the right-hand bracket of Eq. (2.5) expresses the operation cost of reformers. The operation cost of reformers is calculated by multiplying by the amount of city gas consumed by the reformers (the number is N rm ), and the unit price J s,rm of city gas. The second term in the right-hand bracket of Eq. (2.5) expresses the operation cost of the circulating pump used for a hot water network. The operation cost is calculated by multiplying by the power consumption and the power unit price. The third term in the right-hand bracket of Eq. (2.5) expresses the operation cost of boilers. Nst ⎞ ⎛ Nrm Yd,t = ⎜ ∑Qrm,i,t Js,rm + Epump,t ⋅ Js,pump + ∑Qbo,k,t ⋅ Js,bo ⎟ ⋅ Δt ⎜ ⎟ k=1 ⎝ i=1 ⎠

(2.5)

(2) Equipment Cost

Equation (2.6) calculates equipment cost from the installed capacity and the capacity unit price. The right-hand side of Eq. (2.6) expresses the equipment cost of fuel cells, reformers, boilers, heat storage tanks, HWN, and hot water circulating pumps, respectively.

Yc = +

Nfc

N rm

∑

∑

U pih,m ⋅ J cl,pih + U pump ⋅ J c,pump

i =1 N bd

∑

N bo

U fc,i ⋅ J c,fc +

U rm, j ⋅ J c,rm +

j=1

∑ U bo,k ⋅ J c,bo + Ust ⋅ J c,st k =1

(2.6)

m =1

(3) Equipment Installation Cost

Equation (2.7) is a formula for the installation cost of equipment. In this paper, cost is taken into consideration for the installation of the fuel cell, the reformer, the boiler, and the heat storage tank, which are shown on the right-hand side of Eq. (2.7). However, it assumes that the other equipment shown in Fig. 2.2 is included in the installation cost of a fuel cell, and all the equipment shown in Fig. 2.3 is included in the installation cost of a reformer.

Yf =

N fc

N rm

N bo

n =1

j=1

k =1

∑ J f,fc,n + ∑ J f,rm, j + ∑ J f,bo,k + J f,st

(2.7)

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2 Equipment Arrangement Planning of a Fuel Cell Energy Network

2.5.2 Objective Function The objective function of the system is calculated by Eq. (2.8) using the value of the cost of Eqs. (2.5), (2.6), and (2.7). The minimum arrangement planning of the equipment, capacity planning, and operation plan in the case objective function F is investigated. g c , g y , and g d in Eq. (2.8) express the weight of equipment cost, the weight of the installation cost of equipment, and the weight of operation cost, respectively. When any of the terms on the right-hand side of Eq. (30) is much larger than the other terms, it is necessary to determine a weight so that the objective function does not depend on the term too heavily. However, in the analysis of following section, all g c , g y , and g d are calculated as 1.0. Day

FO = g c ⋅ Yc + g y ⋅ Yf + g d ⋅ ∑ Yd, t

(2.8)

t =0

The third term on the right-hand side of Eq. (2.8) expresses the operation cost of the system on a representative day. In the analysis, an operation plan in the case of the value of Eq. (2.8) being the minimum is decided as the optimal solution. Since calculation of optimization of this paper is non-linear with many variables, it uses a genetic algorithm (GA) [14–19] for optimization calculation. The chromosome model showing the operating method of the system using GA decides that it is “a solution with high fitness value” as an individual with a small value of the objective function of Eq. (2.8).

2.6 Analysis Method and Case Study 2.6.1 Optimization Using a Genetic Algorithm (1) Chromosome Model

Figure 2.6 shows the chromosome model used in the optimization analysis of GA. The chromosome model composes gene models, and a gene model expresses the arrangement and the output of fuel cells and reformers, and the route of HWN. In the analysis example described later, the number of houses is set at 4–9, and the installation of a fuel cell and a reformer is determined at random using a gene model for each house. Furthermore, the output of a fuel cell and a reformer is also decided at random, and is expressed by the gene model.

2.6 Analysis Method and Case Study

27

Fig. 2.6 The chromosome model used for the genetic algorithm

(2) Expression of Hot Water Piping Route

As the section “Operation Model of the System” describes, according to the route of HWN, the amount of heat release of the whole system differs, and the efficiency of the system is affected. Therefore, the arrangement planning of equipment and the route plan of HWN are predicted to affect cost. In this paper, the path-planning program of HWN is developed using the idea of the traveling salesman problem (TSP) [19]. Herein, the view of the order expression of route by Dewdney is installed into TSP. According to this view, it is managed by a symmetrical number that is different from the housing number in the gene indicating the route of HWN. By this method, when gene manipulation such as cross-over and mutation is added to a chromosome model, models that show unachievable routes do not appear. From this, the analysis efficiency improves sharply.

2.6.2 Equipment Characteristics Model (1) Operation of a Fuel Cell

The production of electricity E fc,i, t ( i = 1,2,..., N fc ) of the fuel cell installed in any house at sampling time t determines the amount of power demanded

N bd

∑ E need, j,t j=1

in time t by random distribution. The gene model group of (a) of the chromosome models shown in Fig. 2.6 shows the number of installations of fuel cells N fc , and the houses in which they are installed. Moreover, although the group of the gene model of (d) expresses the generation rate in each fuel cell, the production of elec-

28

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

Fig. 2.7 Fuel cell performance

Fig. 2.8 Output characteristics of a hydrogen-air fuel cell

Fig. 2.9 Characteristics of the ratio of reformation

tricity E fc,i, t (however, i = 1,2,..., N fc ) of each fuel cell is decided from these values. The exhaust heat output of each fuel cell H fc,i, t (however, i = 1,2,..., N fc ) is decided by the characteristic model of the electric power and the exhaust heat output of the fuel cell shown in Fig. 2.7. The characteristics of the electric power and the heat output of a fuel cell shown in Fig. 2.7 were obtained by the experimental results of the fuel cell manufactured as an experiment.

2.6 Analysis Method and Case Study

29

(2) Operation of a Reformer

When the production of electricity of each fuel cell is determined, the quantity of reformed gas required for generation will be determined from the characteristic model of the production of electricity and the consumption of reformed gas of the fuel cell shown in Fig. 2.8. A characteristic of the reformer of Fig. 2.8 is the model created from the output characteristics of a fuel cell and a reformer manufactured as an experiment. By the reformer installed in each house, the quantity of reformed gas produced is decoded and determines the value of the gene model of (b) and (e) of the chromosome model shown in Fig. 2.6. If the amount of reformed gas production of each reformer is decided; the exhaust heat output H rm,Sx , t (however, x = 1, 2,..., N rm ) will be decided from the relation of the ratio of load and the ratio of reformation of Fig. 2.9.

2.6.3 Operation of the Heat Storage Tank and Boiler The heat of (H fc,total,t + H rm,total,t − H need,total,t ) is stored when the value to which N fc

is added exhaust heat H fc,total,t = ∑ H fc,i, t of the fuel cell and exhaust heat i =1

H rm,total, t =

N rm

∑ H rm, j,t

of the reformer compared with the heat demand

j=1

H need, total,t =

N bd

∑ H need,k,t

of all the houses linked to FEN at sampling time t is

k =1

exceeded. On the other hand, when H fc,total,t + H rm,total,t + H st, t is less than H need,total,t , the heat of (H need, total, t − H fc, total,t − H rm,total, t − H st, t ) =

N bo

∑ H bo,l,t

is

l =1

outputted to HWN from the boiler. On installing a boiler in all the houses, the heat output of each boiler is determined so that the heat balance in each house does not become negative. The city gas consumption of a boiler is calculated to a boiler efficiency of 85%.

2.6.4 Specification of Hot Water Piping and a Hot Water Circulating Pump As shown in Table 2.1, the hot water piping uses a coat of polyethylene tube with an inside diameter of 28 mm, and a wall thickness of 10 mm. The heat transfer coefficients between the piping inner wall and the hot water is 2500 W/m2K, the

30

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

Table 2.1 Specification of hot water piping

heat transfer coefficient between the outer surface of the piping and the atmospheric air is 20 W/m2K, and the heat conductivity of the piping is 0.043 W/mK. The overall heat transfer coefficient between the hot water and the atmospheric air is calculated using the value described above. Heat release H w,n, t (however, n = 1, 2,..., N bd ) of the hot water is calculated by multiplying the overall heat transfer coefficient by the piping length and the difference between the temperature of the hot water and the open air. The power of hot water circulating pump Ppump is calculated by Eq. (2.9). In the analyses of this paper, the water head Wp shall be 7 m considering pipeline friction.

Ppump = ρ w ⋅ g⋅ q w ⋅ Wp

(2.9)

2.6.5 Analysis Flow The flow of the analysis program of arrangement planning optimization of FEN developed in this paper is shown in Fig. 2.10. The initial data of the energy need pattern of each house, the position of the house, the outside air temperature, the overall heat transfer coefficient of the hot water piping, etc., the generation number, the selectivity probability, the number of individuals of a chromosome, mutation probability, and the cross-over probability that are the GA’s solution parameters are first inputted into the analysis program. The GA’s parameters are determined by applying a trial-and-error method to find the best accuracy and effectiveness of analysis. Next, the number that gave the chromosome model shown in Fig. 2.6 before is prepared at random. If the first generation’s chromosome model group is set, each term under balance equation (2.3) of electric power and heat balance equation (2.4) will be determined in the procedure of the sections “Equipment Characteristics Model” and ”Operation of the Heat Storage Tank and Boiler”. If a chromosome model is decoded and the operating conditions of each piece of equipment are set, cost can be calculated using Eqs. (2.5)–(2.7). From the result of the cost calculation, the fitness value of each chromosome model is calculated using Eq. (2.8). In a chromosome model group, the top 60% of individuals with a high fitness value is extracted, other models are discarded, and the new chromosome models determined at random are added. Intersection and mutation are added to the high chromosome model of the fitness value, and the newly created chromosome model by probability is first given to the program. Cross-over and mutation are added to the chromosome models of the high fitness value described above, and the newly prepared chromosome model by probability is first

2.6 Analysis Method and Case Study

31

Fig. 2.10 The chromosome model used in the genetic algorithm

given to the program. For the chromosome model to which gene manipulation is added, the fitness value of each model is re-calculated. Individuals with a low fitness value in a chromosome model are discarded, and a chromosome model that is newly determined is supplied at random. This calculation is iterated with the generation number first given to the program. Among the last generation’s chro-

32

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

mosome models, the highest model of fitness value is decided as a temporary optimal solution. In the temporary optimal solution, which is obtained from the analysis of Fig. 2.10 having been repeated at least 20 times, a solution with the highest fitness value is determined as the optimal solution. In the analysis of the following section, it is considered to be 5000 chromosome models, and the generation number is set at 10–20. Moreover, mutation probability and cross-over probability are set at 0.01.

2.6.6 Analysis Conditions The city area model assumed in the analysis of this paper is shown in Fig. 2.11. FEN to four buildings of houses S A , SC , SE , and SG of Fig. 2.11 is called the four-houses model, and FEN to five buildings S B , S D , S F , S H , and S I is called the five-houses model. Moreover, FEN to nine buildings S A to S I is called the nine-houses model. Although the energy demand of each house shown in Fig. 2.11 differs due to the number of residents, composition age, lifestyle, etc., it is analyzed by giving the simplest possible energy demand pattern to an analysis program in this analysis. Then, the average energy demand pattern of a 3–4 person household in Sapporo [8] that shows the energy demand pattern of each house in Fig. 2.12 is used. The power load pattern of Fig. 2.12(a) is consumption by household electric appliances and lighting, and electric air-conditioning equipment is not used throughout the year. For this reason, as shown in Fig. 2.12(a), there is not a large difference in the electricity demand pattern of each month. On the other hand, the items of heat load are hot water supply, baths, and space heating. Moreover, the outside air temperature model of Sapporo used for calculating the heat release in HWN is shown in Fig. 2.13 [20]. For Sapporo, a cold, snowy area, the annual average temperature is 288 K, and the mean temperature in February and August is 269 K and 294 K, respectively. The equipment efficiency, the energy cost, the cost of equipment, and the installation cost of equipment are used for cost

Fig. 2.11 The arrangement model of houses

2.6 Analysis Method and Case Study

33

analysis are shown in Table 2.2. The fuel cell and the reformer are calculated to be 2500 dollars/kW and 1500 dollars/kW, respectively. It is expected that these cost values will be attained within several years. The equipment costs of other boilers, heat storage tanks, and hot water circulating pumps are decided by a product catalog as reference. For equipment installation costs, such as for a fuel cell or a boiler, the installation cost of a common home boiler or a hot water supply system was assumed.

Fig. 2.12 The average energy demand pattern of family households of 3–4 persons in Sapporo

Fig. 2.13 Outside air temperature model in Sapporo

34

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

Table 2.2 Analysis conditions of the case study

2.7 Analysis Result 2.7.1 Operation Plan of the Fuel Cell and the Reformer Figure 2.14 shows the analysis result of the electric power of a fuel cell, and the exhaust heat output and the analysis result of the reformed gas output of a reformer when installing a fuel cell network into the four-houses model. Table 2.3 is the result of analysis using an energy demand pattern and the outside air temperature data of a representative day in February, May, and August, and is a result of the capacity of a fuel cell, a reformer, and a boiler and the installation location of the four-houses and the five-houses models. In the analysis result of the four-houses model shown in Fig. 2.14, although a fuel cell is installed in two houses, SC and SG , for all months, a reformer is scheduled to be installed in one house. The electric power and the heat output of fuel cells are different in their output characteristics of two sets to other months, although the two sets of outputs will be almost the same in February. The reason for this is explained in detail in the section “Quantity of Flow of a Hot Water Circulating Pump”, and it is because heat release by HWN is large, and a plan is made for representative days in February so that the heat transport using HWN can decrease. That is, although a fuel cell of the same capacity will be installed in a symmetrical position on a representative day in February, and exhaust heat is supplied to the house with the equipment installed and a nearby house, heat is not supplied to distant houses. Furthermore,

2.7 Analysis Result

35

Fig. 2.14 Analysis results of the operational schedule of the fuel cells and reformers installed in the four-houses model

36

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

Table 2.3 Results of the arrangement plan and equipment capacity for FEN

although a reformer is scheduled to be installed in house S E on a representative day in February, this result does not overlap with the houses in which fuel cells are installed in order to effectively use the exhaust heat output of the reformer.

2.7.2 Amount of Hot Water Heat Release and the Hot Water Piping Route Figure 2.15 shows the analysis results of the route plan of the hot water piping of the four-building model and the five-building model, and the flow direction of hot water. The route plan of hot water piping, the house used as the starting point of HWN, and the flow direction of hot water show the same results every month in the four-building model. On the other hand, although the route result of the hot water piping of a five-building model is the shortest route by which each house is connected in any month, each result of the house used as the starting point of HWN, and the flow direction of hot water differs every month. However, since the energy demand pattern of each house is the same, the house used as the starting point of HWN and the flow direction of the heat medium do not affect the objective function equation (2.8). Figure 2.16 shows the analysis result of the time change of the hot water piping heat release every month. Since it is dependent on the difference in temperature of a medium and the outside air temperature, there will be great heat release of HWN in February. Especially on representative days in February, there is great heat release in a time zone with little heat demanded. The heat release at each time in August is characterized by many upward slants to the right. This is because there is little heat demanded by each house, so the heat transported by HWN increases, and as a result, the hot water temperature rises and the heat release increases.

2.7 Analysis Result

37

Fig. 2.15 The results of hot water supply planning of the four-houses model and the five-houses model

38

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

Fig. 2.16 Heat release of HWN for the four-houses model

2.7.3 Quantity of Flow of the Hot Water Circulating Pump Figure 2.17 shows the analysis result of the heat-medium (physical properties using the value of water) quantity of flow that flows through the inside of HWN. The heat-medium quantity of flow (quantity of flow of a hot water circulating pump) determined that 363 K was not exceeded at any position of HWN. There is little heat-medium flow in the four-building model and the five-building model, and there will be little transportation of heat between each house in February. The difference in temperature of a heat medium and the open air is large, and the heat release will be large in February. Then, transportation of heat between each house through HWN is suppressed, and a plan is made so that the heat demand of each house may be supplied with the exhaust heat of the fuel cell installed in each house, the reformer, and the heat output of the boiler.

Fig. 2.17 Flow rate of the hot water circulating pump

2.7 Analysis Result

39

2.7.4 Operation of the Heat Storage Tank and the Boiler Figure 2.18 shows the analysis result of the amount of heat surplus that was set at the starting time of operation of the system as 0:00, and was calculated from the heat balance for every time. Since there is high heat demand at each time, there is little heat surplus in February compared with other months. From the characteristics of Fig. 2.18, the capacity and the operation plan of thermal storage can be designed considering the energy demand pattern of every month. Figure 2.19 shows the analysis result of the boiler output in each time of representative days in February. As Table 2.3 shows, on representative days in February, any model is planned so that a boiler can be installed in all the houses.

Fig. 2.18 The amount of heat surplus

Fig. 2.19 Heat quantity of a boiler in February

40

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

2.7.5 Cost Analysis Results Figure 2.20 shows the analysis results for cost operation (Fig. 2.20(a)) for every month, equipment cost (Fig. 2.20 (b)), and the installation cost (Fig. 2.20(c)) of equipment for the four-building model, the five-building model, and the ninebuilding model. Moreover, Fig. 2.20(d) shows the total cost as a result of Fig. 2.20(c) from Fig. 2.20(a). However, each result of Fig. 2.20 is arranged as cost per house. Furthermore, the cost of an independent system that installs a fuel cell, a reformer, a boiler, and a heat storage tank in an individual house, and that performs energy supply is shown in Figs. 2.20(b)–(d). The cost per house compared with an independent system decreases, so that the number of the houses connected to FEN from the result of Fig. 2.20(d) increases. In FEN planned using the energy demand pattern in February, 18%, 22%, and 25% of the total cost is reduced by the four-houses model, the five-houses model, and the nine-houses model compared with an independent system, respectively. When constructing FEN in the houses of four to nine buildings analyzed in this paper, the total cost per building can be reduced by optimizing the arrangement planning of the equipment as well as the system operation plan, so that there are many houses.

Fig. 2.20 Cost analysis results

2.7 Analysis Result

41

2.7.6 Consideration of Analysis Accuracy In order to investigate the difference in fitness values and the difference in operation plans, the solution (solution with a sufficient fitness value to the second term of Eq. (2.8)) of the second ranking of the four-building model is shown in Fig. 2.21 and Table 2.4. Figure 2.21 shows the cost analysis results of the optimal solution and the second ranking solution. Figure 2.21(a) expresses the analysis result of the operation cost of a representative day every month, Fig. 2.21(b) shows equipment cost, and Fig. 2.21(c) shows the result of the installation cost of Table 2.4 The results of the arrangement plan of the FEN for the four-houses model. These analysis results are the optimal solutions of the second ranking

Fig. 2.21 Four-houses model cost comparison of a different fitness value analysis result

42

2 Equipment Arrangement Planning of a Fuel Cell Energy Network

equipment. Figure 2.21(d) shows the total cost as a result of Fig. 2.21(c) from Fig. 2.21(a). The difference in the total cost of the optimal solution and the second ranking solution will be 0.1% in February and May, and will be 0.4% in August. When the results of Tables 2.3(a) and 2.4 are compared on a representative day, there will not be a big difference in the setting position or the output of the fuel cells, reformers, and boilers in February. However, on representative days of May and August, there will be a difference in the setting position and the output of the fuel cells and reformers. In the analysis of the operation plan using GA, when the difference in the value of the objective function is 0.4% or less, as shown in Tables 2.3(a) and 2.4, it differs.

2.8 Conclusions A computer program that optimizes the equipment arrangement of each building linked to a fuel cell network, and the route of the hot water piping network for supplying the exhaust heat of a fuel cell and a reformer to each house under the cost minimization objective has been developed. As a result of analyzing and optimizing the fuel cell network of four to nine houses using the average energy demand pattern of Sapporo, compared with a system that is not optimized, it clearly showed lower equipment costs and installation costs of equipment. If it is analyzed using the energy demand pattern of a house in February and outside temperature data, there will be 18% to 25% cost reduction by optimization. The heat release in a hot water piping network decreases, and it is for this reason that the route of the hot water piping and the arrangement of equipment are planned. In this study, further, the capacity of a heat storage tank, the arrangement planning of a boiler and capacity, and the quantity of flow of the hot water circulating pump were investigated, and the operation plan of each piece of equipment was described. In this study, although the route plan of a hot water piping network and the arrangement planning of equipment were investigated for a representative day in February, May, and August, respectively, in order to actually install these plan results, it is necessary to select the appropriate plan. It is necessary to select the optimal plan from the result of the route of a hot water piping network, and the arrangement of equipment for every month, and to investigate the results when conducting system management through the year from now on.

Chapter 3

Effective Improvement in Generation Efficiency due to Partition Cooperation Management

3.1 Introduction The effective reduction of greenhouse-gas emission is expected to be achieved with the micro-grid [21–23]. In particular, the micro-grid using a fuel cell is predicted to be a leading method of future energy supply. In order to effectively reduce greenhouse gases to a maximum, a micro-grid should be maintained and operated at the highest possible generation efficiency. It is necessary to optimize the operation plan of a micro-grid, as well as the capacity of the energy equipment based on the power demand pattern of the buildings connected to a grid. Therefore, to produce high generation efficiency of a micro-grid, a power generator that can maintain high efficiency is required over a large range from high load to low load. However, it is difficult to maintain high efficiency over a wide operating range with the solid polymer membrane-type fuel cell (PEM-FC) with a reformer. Then, the method of operating a water electrolyzer [24], and the method that divides a fuel cell and a reformer [25] were examined at the time of partial-load operation. The power load added to a micro-grid is determined by composing the power demand characteristics of two or more buildings. However, the example of consideration of the relation between the load patterns of a building and the power generation efficiency of a micro-grid is not reported. If the power demand pattern of a certain building reduces the generation efficiency of the whole micro-grid, this building sets the grid of another network with a group of other buildings, and overall generation efficiency may be improved. Therefore, the fuel cell micro-grid (FC micro-grid) is divided into multiple grids, and this chapter considers the independent management of each grid. Two or more divided grids can operate in cooperation with other grids to improve generation efficiency. In this paper, partition cooperation management of the FC micro-grid and its relationship to generation efficiency are investigated using the power demand pattern assuming typical buildings in Tokyo. In addition to this, the FC micro-grid that is equipped with the cooperation operation of the partition grid is effective at the “response to overload S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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44 3 Effective Improvement in Generation Efficiency due to Partition Cooperation Management

at the time of load fluctuation”, the “risk abatement at the time of accident due to distribution of power facilities”, and the “interruption of service not caused at the time of maintenance”.

3.2 System Configuration 3.2.1 Scheme of the FC Micro-grid There is an interconnection system (Fig. 3.1(a)) and an independent system (Fig. 3.1(b)) in a micro-grid. By increased power demand due to connection of a new building and large load fluctuations, when the grid cannot respond, an interconnection micro-grid connects to other grids. ( ⊗ of Fig. 3.1(a) is a systeminterconnection device.) Therefore, an interconnection micro-grid is a system that can respond to the power demand pattern of a grid flexibly. On the other hand, it is necessary to supply the entire power demand in a grid using an independent micro-grid. Therefore, the design of an independent micro-grid needs to sufficiently consider power demand and supply balance. The connection of a new building is difficult for this grid type, and flexibility is poor. However, the independent micro-grid is effective as a method of supplying electric power in areas where the transmission line infrastructure is not fixed. Therefore, in this chapter, as shown in Fig. 3.2, one independent micro-grid is divided into multiple grids (in Fig. 3.2, these are Grid A and Grid B). Usually, although each grid operates independently, if it is in the condition where generation efficiency is improved, each grid will be connected and it will perform cooperation operation. If the method of Fig. 3.2 is used, the independent micro-grid will be improved by the system that can respond flexibly to increases and decreases in a building, or increases and decreases in load fluctuation. Therefore, in this study, the relationship between the building (route of a micro-grid) connected to each of the divided grids (in the case of Fig. 3.2, these are Grid A and Grid B) and generation efficiency is investigated.

Fig. 3.1 Micro-grid model

3.2 System Configuration

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Fig. 3.2 Partition of a micro-grid

3.2.2 System Configuration The buildings in which power plants are installed (Fig. 3.3(a)) and other buildings (Fig. 3.3(b)) are connected to each of Grid A and Grid B of Fig. 3.2. A town gas reformer, PEM-FC, a boiler, a heat storage tank, and a system-interconnection device are installed in the building of Fig. 3.3(a). The boiler for heat supply and the system-interconnection device for receiving electric power from a grid are installed in the building of Fig. 3.3(b). Figure 3.3(a) shows the model of a building in which a power plant is installed as shown in Fig. 3.2. In this chapter, a power plant (Fig. 3.3(a)) connected to one independent grid (Grid A and Grid B, respectively) may be at one place. Buildings other than the power plant are buildings (Fig. 3.3(b)) in which a boiler is installed for heat supply, and a system-interconnection device. An actual FC micro-grid requires a town gas distribution network and exhaust heat distribution network other than an electric power supply network. This chapter does not describe the management of a town gas distribution network or an exhaust heat distribution network.

Fig. 3.3 Energy equipment model installed in a building

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3.2.3 Operating Method In the following, the operating method of the FC micro-grid system shown in Figs. 3.2 and 3.3 is described. In Fig. 3.3(a), town gas is supplied to a heat-source burner and a reformer, steam reforming of town gas is performed, and reformed gas with many hydrogen components is produced. Because there is a lot of water in reformed gas, the water is removed with a dryer. Furthermore, the carbon monoxide in the reformed gas is removed with a carbon monoxide oxidation system. Reformed gas is supplied to the fuel cell, and power and exhaust heat are output from the fuel cell. After storing exhaust heat in a heat storage tank and exchanging the heat of a thermal storage medium for tap water, tap water is supplied to an auxiliary boiler, which is output to the demand side. The power produced with the fuel cell is supplied to a grid through a DC–AC converter, an inverter, and a system-interconnection device. A fuel cell is operated so that the power load may be followed. The output control of a fuel cell adjusts and controls the amount of town gas supplied to a reformer. Figure 3.3(b) shows the equipment model of buildings other than the power plant shown in Fig. 3.2. In these buildings, power is obtained from a grid through a system-interconnection device. Heat is produced with a town gas fired boiler. Grid A and Grid B, shown in Fig. 3.2, usually supply power to buildings independently by each grid. However, when a load that exceeds capacity is added to one of the grids, power can be obtained from the other grids through the systeminterconnection device (⊗ in Fig. 3.2). In the partition cooperation management of the FC micro-grid described in the following, all the grids can deliver and receive other grids and power through the system-interconnection device.

3.3 Installation Planning of the FC Micro-grid 3.3.1 Generation Efficiency of the Micro-grid Figure 3.4 shows the cell performance curve for an operating temperature of 333 K, with the gas pressure at the anode and cathode being 0.1 MPa [26–29]. This characteristic is the model to be used when setting the reformer efficiency to be constant at 73%. Details of η RM are given below. Although steam reforming of the town gas at a flow rate of Q RM, t is conducted, and reformed gas is produced in the reformer, the heat source for the reforming reaction is burned and obtained using the town gas at a flow rate of Q B,t . The following equation defines the reformer efficiency, and the maximum of this value is 73% [29]. WH is the calorific value of the hydrogen included in reformed gas, and WB and WR are the calorific values of town gas at flow rates of Q BN1 , t and Q RM, t , respectively. For the power generation efficiency of a micro-grid it depends on the fuel cell output characteristics of Fig. 3.4. For example, the output characteristics of a fuel cell

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Fig. 3.4 The characteristics model of the load ratio of a PEM-FC with reformer, and power generation efficiency. The area of the electrode including the anode and cathode of the fuel cell stack is 1m2, respectively, and the reformer efficiency is 0.73

change with the operating method of a reformer. Care should be taken that the characteristics of Fig. 3.4 are not the result of representing a fuel cell system. η RM =

WH ⋅100 (WB + WR )

(3.1)

Figure 3.5 shows the model indicating the relation between the load pattern of a power demand model, and the generation efficiency of a fuel cell. As Fig. 3.4 shows, the generation efficiency of a fuel cell changes with the load ratio. Next, the operation method of a fuel cell when there are two or more power demand models is described. In Fig. 3.6, each power demand model of Buildings A, B, and C is shown. The following three methods of supplying power to these buildings from PEM-FC can be considered. (1) Installing a fuel cell in all the buildings (stand-alone system). (2) Connecting all the buildings to a micro-grid, and supplying power using one set of FC (central system). (3) Setting two or more micro-grids considering the power demand pattern of the buildings (partition cooperation system). The stand-alone system determines the capacity of the PEM-FC installed in each house so that it may exceed the maximum of each power load of Buildings A, B, and C of Fig. 3.3. In the central system, the power load of Buildings A, B, and C of Fig. 3.6 is added for every sampling time, and the capacity of one set of PEM-FC is determined to exceed the maximum. In addition, in the partition cooperation system, Building A and Building B of Fig. 3.6 are connected, for example, and Grid 1 operates. Building C is operated independently (Grid 2). The capacity of the PEM-FC installed in Grid 1 and Grid 2 is determined so that it may exceed the maximum of the power load of Building A and Building B, and the maximum of the power load of Building C. The stand-alone system does not need a grid; moreover, it does not need to transport exhaust heat to a neighboring building. Therefore, the heat loss of exhaust heat is small, and installation of a grid is un-

48 3 Effective Improvement in Generation Efficiency due to Partition Cooperation Management Fig. 3.5 Power demand model and power generation efficiency of a PEMFC system

Fig. 3.6 The electricity demand pattern of three buildings and the load model of a fuel cell system

necessary. However, there are many fuel cells that need to be installed, and it is necessary to install large-capacity fuel cells so that load fluctuation does not have an impact. Unlike the stand-alone system, the central system should just install FC in one building. Therefore, although it is advantageous with respect to equipment cost, the transport distance for the exhaust heat is long, and heat loss is a problem. Furthermore, the diversification of risks at the time of an accident and the extendibility of equipment are problems. In the central system, the power demands of the buildings are added, and the capacity of the fuel cell is optimized and determined to exceed the maximum. Therefore, if the number of the buildings connected to the grid and the pattern of the power demand model changes, the load ratio will change and generation efficiency will vary. On the other hand, the partition cooperation system can partially achieve each merit of the stand-alone system and the central system. Partition of the micro-grid in the partition cooperation system is optimized to maximize generation efficiency. For this reason, depending on the composition of the power demand model of the buildings in an urban area made into an analytical object, generation efficiency higher than that with the standalone system or the central system may be applicable.

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3.3.2 The Power Demand Model Figure 3.7 shows the power demand models of each building, and is the mean power load of each sampling time on a representative day in January (winter), May (mid-term), and August (summer) [30–33]. However, the actual power demand pattern is a mixture of loads that change rapidly over a short time, such as an inrush current. In addition, a power demand estimate for the house actually shown

Fig. 3.7 Power demand models

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in Fig. 3.7(a) is difficult, and a power demand estimate for the small offices and factories indicated in Figs. 3.7(d) and (e) is comparatively easy. Although load fluctuation is not taken into consideration in the power demand model in the analysis of this chapter, when accompanied by load fluctuations, system interconnection between the grids is expected to occur frequently. The power demand pattern of a house (Fig. 3.7(a)) has peaks in the morning and the afternoon. In the hotel of Fig. 3.7(b), demand is stabilized when the midnight-to-early-morning period is excluded, and the power demand is stable for 24 hours at convenience stores (Fig. 3.7(c)). The difference between the time zone with a small power demand from night to early morning and the time zone with great power demand from morning to evening is clear in small offices (Fig. 3.7(d)), factories (Fig. 3.7(e)), and small hospitals (Fig. 3.7(f)).

3.3.3 The Analysis Method The analysis flow of the generation efficiency of the FC micro-grid is shown in Fig. 3.8. First, a power demand model of the buildings that compose the urban area model is prepared. In the analysis, the power demand model shown in Fig. 3.7 is used. Although these power demand models are inputted into a program for every sampling time, the input is related to all the buildings. Next, all the routes (that is, a divided grid) of the grid are discovered. This route planning is obtained by calculating the permutation of the number of buildings. The capacity of the fuel cell installed in each route of the FC micro-grid is set up, and the power load and the load ratio of each route are calculated for every sampling time. The generation efficiency of a route is calculated by giving this load ratio to the approximation described in Fig. 3.4. Furthermore, the capacity of the fuel cell installed in each route of the FC micro-grid is changed, and the generation efficiency of a route is calculated using the same procedure. From the generation efficiency of all the routes obtained by calculation, the generation efficiency (average generation efficiency) in the entire FC micro-grid for a representative day can be calculated. The average generation efficiency of the stand-alone system, the central system, and the partition cooperation system is calculable using the analysis procedure described above.

3.4 Case Study

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Fig. 3.8 Calculation flow of generation efficiency for an FC micro-grid

3.4 Case Study Figure 3.9 shows the urban area model used for this analysis, and shows the type of power demand model of 23 buildings in this figure. In addition, the number given in this figure is the number of buildings, and shows the area and the maximum load of each building assumed in Table 3.1. Figure 3.9 shows a two-person family house and a six-person family house as well as a four-person family house (the power demand model is shown in Fig. 3.7(a)). Each power demand model compares and determines the number-of-persons rate of the model of Fig. 3.7(a). Moreover, although apartment houses are shown in Fig. 3.9, these power demand models are also determined relatively from the number-of-persons rate of the

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Fig. 3.9 Power demand model Table 3.1 Maximum power load of each buildings

model of Fig. 3.7(a). In this analysis, Fig. 3.9 is used as an urban area model. Moreover, Figs. 3.7(a)–(f) is used as a power demand model of each building that composes an urban area. The relation of the load ratio and generation efficiency of the PEM-FC installed in the model is shown in Fig. 3.4. Since the maximum generation efficiency of the model of Fig. 3.4 is 32%, the maximum generation efficiency of the micro-grid analyzed here is theoretically 32%.

3.5 Analysis Results and Discussion 3.5.1 Generation Efficiency of the Stand-alone System Figure 3.10 shows the analysis results of the average generation efficiency of a representative day of every month in the case of installing the stand-alone system in the urban area model of Fig. 3.9. Although the average generation efficiency differs each month, convenience stores with a small load fluctuation range of power throughout the year show about 30% at maximum. The average generation efficiency of a representative day in August, with high power consumption due to air-conditioners, is high in hotels and hospitals. The average generation efficiency of other buildings is less than 20%.

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Fig. 3.10 Average generation efficiency of the stand-alone system

Fig. 3.11 Introduced method and installed number

The system that distributes the PEM-FC system whose generation capacity is 1 kW or 2 kW to the urban area model of Fig. 3.9 is investigated. To install PEMFC (1 kW or 2 kW) using the stand-alone system, it is necessary to look for buildings that can install fuel cells of this capacity from among buildings that compose an urban area model. Figure 3.11 shows the analysis results of the number of buildings in which a fuel cell of each capacity can be installed using the standalone system, and the number of the fuel cells installing PEM-FC using the partition cooperation system. If equipment cost is taken into consideration from the difference in the installed number of PEM-FC, compared with the stand-alone system, the partition cooperation system is more advantageous.

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3.5.2 Generation Efficiency of the Central System Figure 3.12 shows the analysis results of the relationship between the capacity of PEM-FC and generation efficiency in the case of installing the central system in the urban area model. The results of the generation capacity of PEM-FC using the central system is 110 kW (the average generation efficiency of each representative day is 25.8% and 26.1%, respectively) on representative days in January and May, and is 160 kW (the average generation efficiency of a representative day is 24.8%) on a representative days in August. Therefore, it is required that a 160 kW PEMFC be installed in the central system in the urban area model, and be applied throughout the year. When a 160 kW PEM-FC is installed, the average generation efficiency of representative days in January and May is 20.6% to 20.9%. Consequently, the generation efficiency of an FC micro-grid when installing the central system is operated at 20.6% to 24.8%.

Fig. 3.12 Power generation efficiency of the central system

3.5.3 Generation Efficiency of the Partition Cooperation System (1) Operation of an FC Micro-grid in Which PEM-FC with Low Generation Capacity Is Installed Figures 3.13(a)–(c) show the analysis results of the partition cooperation system in which a PEM-FC whose generation capacity is 2 kW is installed. However, because two or more buildings that have the same power demand model are included as described in Fig. 3.9, when these are exchanged, it is expressed by the route difference from Figs. 3.13(a)–(c). The average generation efficiency of representative days in January, May, and August is 23%, 22.7%, and 20.4%, respectively.

3.5 Analysis Results and Discussion

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Fig. 3.13 Analysis results of the partition cooperation micro-grid system at the time of installing 2 kW PEM-FC

Because the space-cooling load of air-conditioners will be included in the power demand amount of a representative day in August, the load ratio is high compared with other months. Therefore, the average generation efficiency of a representative day in August is high. Because the power demand is high on a representative day in August compared with other months, these analysis results (the route of a grid, and the capacity of the fuel cells installed in the grids) can be applied to other months. The analysis result of the grid route of a representative day in August was given to the power demand model of a representative day in January, and the generation efficiency was analyzed. This result is shown in Fig. 3.14, where the generation efficiency of a representative day in January is low at 2.5% compared to the generation efficiency of the whole grid on a representative day in August, which was 19.9%. Therefore, when the PEM-FC where the capacity is 2 kW is installed in Fig. 3.9, the power is supplied to the 11 buildings by the 4 grids (that is, 4 fuel cells) of Grid A to Grid D, and the generation efficiency is about 20% throughout the year. As Fig. 3.10 describes, the generation efficiency in the case of supplying power to a house (two-person to six-person household) using the stand-alone system is about 20% at maximum. Moreover, compared with the number of PEM-FCs installed by the stand-alone system from the results of Fig. 3.11, the number of installations in the partition cooperation system is about 1/3 with a generation capacity of 2 kW. Compared with the stand-alone system,

56 3 Effective Improvement in Generation Efficiency due to Partition Cooperation Management Fig. 3.14 Analysis result in January when the same as analysis results of the grid in August. 2kW PEM-FC is installed in each grid. Average generation efficiency is 19.9%

the whole generation efficiency of the micro-grid, where a PEM-FC with small generation capacity is installed, is improved, and equipment cost is reduced. However, a maximum power demand greatly over 2 kW cannot be installed in this grid in buildings. (2) Operation of an FC Micro-grid Combined So That the Generation Efficiency of the Whole Grid Is Maximized Figures 3.15(a)–(c) show each representative day in the grid route obtained in the analysis, and show the analysis results at the time of combination, so that the generation efficiency of the whole grid is maximized. However, because two or more buildings that have the same power demand model in an urban area model are included as described in Fig. 3.9, if these are exchanged, the combination will become different from that in Figs. 3.15(a)–(c). Figure 3.16 shows the analysis results of the generation efficiency when giving the route result of a representative day in August (Fig. 3.15(c)) to the power demand model of a representative day in January. Compared with Fig. 3.15(c), the result of the generation efficiency of Fig. 3.16 falls due to Grid A to Grid C, and the average generation efficiency of the whole grid falls by 23.6% to 21.1%. Therefore, as for the FC micro-grid whose average generation efficiency in total comprises a combination of the highest grids, generation efficiency is operated at 21.1% to 27.6%. The range of this value exceeds the average generation efficiency (from 20.6% to 24.8%) when installing a 160 kW PEM-FC using the central system shown in Fig. 3.12. (3) Operation of an FC Micro-grid That Combines the Grid in Which the Average Generation Efficiency Exceeds 25% Figures 3.17(a)–(c) show the results of the combination for which the generation efficiency of a representative day is a grid route exceeding 25%, and the generation efficiency of the whole grid peaks every month. The grid route for which

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Fig. 3.15 Analysis results of the partition cooperation system which supplies the power to all the buildings Fig. 3.16 Analysis result in January when the same as analysis results of the grid routes in August. Average generation efficiency is 21.1%.

generation efficiency does not exceed 25% does not appear in Figs. 3.17(a)–(c). Consequently, there is no guarantee that all the buildings in an urban area model can be connected to any grid. Figure 3.18 shows the analysis results of the generation efficiency when installing the power demand model of a representative day in January into the analysis result (Fig. 3.17(c)) of the grid route of a representative day in August. Compared with Fig. 3.17(c), the generation efficiency of Fig. 3.18 falls compared to the other grids (Grid A and Grid B), and the average generation

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Fig. 3.17 The analysis results of the partition cooperation system when supplying the power to a building of 25% or more of average generation efficiency Fig. 3.18 Analysis result in January when the same as analysis results of the grid routes in August. Average generation efficiency is 23.2%.

efficiency of the whole grid falls by 19%, being 23.2%. Therefore, as for the FC micro-grid whose generation efficiency is composed from grids exceeding 25%, the generation efficiency is operated at 23.2% to 28.6%. If this system is installed, the FC micro-grid can be operated at the highest generation efficiency, but buildings that lower the generation efficiency of the whole grid are separated from this grid.

3.6 Conclusions

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3.6 Conclusions Even if an FC micro-grid is used for urban areas composed of buildings with various power demand patterns and supplies power to them, generation efficiency is generally reduced. Therefore, this chapter examined the generation efficiency of the following three power supply methods: (1) Installing fuel cells in all the buildings in an urban area model (stand-alone system); (2) connecting all the buildings to a micro-grid, and supplying power from one set of fuel cells (central system); and (3) dividing a micro-grid into multiple grids considering the power demand pattern of the buildings (partition cooperation system). The relationship between the capacity of the fuel cell to be installed and the generation efficiency became clear from the analysis results. Furthermore, in this chapter, generation efficiency is optimized to maximize generation efficiency to the grid route planning of the partition cooperation system. As a result, a system with a higher generation efficiency than the stand-alone system and the central system could be proposed. The average generation efficiency of the stand-alone system is 20% or less, and the average generation efficiency of the central system is 20.6% to 24.8%. On the other hand, the generation efficiency of the partition cooperation system proposed herein is 21.1% to 27.6%.

Chapter 4

Fuel Cell Network System Considering Reduction in Fuel Cell Capacity Using Load Leveling and Heat Release Loss

4.1 Introduction In order for installation of the fuel cell system to houses or a small-scale and middle-scale building to spread, it is necessary to reduce the equipment cost. Consequently, a fuel system network (hydrogen piping and oxygen piping) and an energy network (a power transmission line and hot water piping) of distribution fuel cells are proposed [24]. In this system, common auxiliary machinery is installed in a machinery room. In this chapter, in order to reduce the capacity of the fuel cell connected to the network, a method of leveling the load is proposed. By this method, hydrogen and oxygen are generated by water electrolysis at the time of low load with little power demand, and each gas is compressed and stored. On the other hand, the stored gas is supplied and generated to the fuel cell in a period of large power load. Experimental results show that the power generation characteristics improve greatly compared with air supply, when supplying oxygen to the fuel cell [34]. Therefore, if the oxygen generated when load is small can be used for a high-load period, the installed capacity of the fuel cell can be reduced. Moreover, the heat-energy network is hot water piping, and supplies heat to each building. Hot water piping distributes heat via each building. When there is heat excess with some buildings, it can also recover this heat through the hot water piping. In a heat-energy network, the hot water temperature in a building outlet changes with the heat consumed by each building and the fuel cell exhaust heat of each building. Therefore, the heat release of the overall network differs according to the outside air temperature, the piping distance, the starting point of the hot water supply, and the flow direction of the hot water. Consequently, to counteract the piping heat release loss of the heat-energy network, the minimum piping route is examined. In the analysis case, the capacity reduction effect of a fuel cell when installing load leveling using the water electrolyzer described above is investigated with respect to a local energy network that includes houses, a hospital, a factory, an S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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office, and a convenience store. Furthermore, the hot water piping route and the fuel cell capacity placed on each building when optimizing the system with the object of minimizing the hot water piping heat release are considered.

4.2 Load Leveling and the Arrangement Plan of the Fuel Cell 4.2.1 The Fuel Cell Network System The network model with the proton exchange membrane fuel cell (PEM-FC) installed that is assumed in this paper is shown in Figs. 4.1 and 4.2. As shown in each figure, the fuel system (hydrogen piping and oxygen piping), the power system (power transmission line), and the heat-energy network (hot water piping) between the fuel cells installed in each building are connected. A heat transfer medium (hot water) flows in hot water piping, the exhaust heat of a fuel cell is recovered, and heat is distributed to each building. The route of the hot water piping can be set up arbitrarily, and the flow direction is one way, as the arrow in each figure shows. Figure 4.1 shows the system that supplies the power to Buildings A to G from one set of the fuel cell installed in the machinery room, and is described as the R1 type below. A machinery room can be installed in an arbitrary building (Building A in Fig. 4.1). As shown in Fig. 4.1(c), the fuel cell (1: this number corresponds to that in Fig. 4.1), water electrolyzer (6), city gas reformer (7), hydrogen and oxygen compressor (9 and 11), cylinders (10 and 12), geothermal heat pump (13), heat storage tank (14), etc. are installed in the machinery room. The heat output by fuel cell exhaust heat, the heat storage tank, and the geothermal heat pump is distributed to each building through a heat transfer medium. The piping route can be planned arbitrarily and it is in the order of Building ABCFEDGA in the example of Fig. 4.1(a). As shown in Fig. 4.1(b), headers (4 and 5) are set in each building at a hot water gate. The heat of the radiator (3) and a heat exchanger connected to the header are used for space heating and hot water supply. Figure 4.2 shows the system that distributes a fuel cell in all the buildings, and this system is described as the R2 type below. Although the number of fuel cells increases and the equipment cost increases for the R2 type, heat release loss with heat transport is small. The hot water piping route of the R2 type and the building with a machinery room can be planned arbitrarily. In the example of Fig. 4.2(a), hot water is supplied in the order of Building ADGBFECA. The machinery room of Fig. 4.2(c) is installed in Building A. The equipment scheme installed in the building and machinery room in the R2 type is shown in Figs. 4.2(b) and (c). Ambient air is usually supplied to the fuel cell installed in R1 and R2 from a blower. However, both types can also supply oxygen through piping. Moreover, it is assumed that reformed gas of the city gas reformer and hydrogen of the cylinder can be supplied to the fuel cells at arbitrary times through the network.

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Fig. 4.1 Fuel cell network system model (R1 type)

Fig. 4.2 Fuel cell network system model (R2 type)

4.2.2 Power Generation Characteristics of the Fuel Cell Figure 4.3 shows the power generation characteristics when hydrogen and oxygen are supplied, and when supplying hydrogen and air by the results of the performance measurement of a PEM-FC. The differences in these power generation characteristics are considered to be due to the difference in oxygen partial pressure, the water balance inside the cell, and the electrical receptivity change of the ion exchange membrane. The power generation characteristics differ in supplying re-

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Fig. 4.3 Cell performance

Fig. 4.4 Output characteristics of a fuel cell

formed gas to a fuel cell, and supplying hydrogen. However, since there are few differences in the power generation characteristics of reformed gas or hydrogen, this difference is ignored. Figure 4.4 shows the characteristics of the power and heat output when supplying air or oxygen to a cathode using the same fuel cell (the electrode surface is 1 m2) as shown in Fig. 4.3. The maximum power output when supplying air to the cathode is E fca, max =1.05 kW, and it is E fco, max =1.9 kW in the supply of oxygen. In this way, if oxygen is supplied to the cathode, the power output will increase. Therefore, if oxygen is supplied and generated to a fuel cell when there is a high power demand, the fuel cell can be miniaturized compared with the design capacity by air supply. If the fuel cell with the characteristics shown in Fig. 4.4 is used with maximum output, the fuel cell facility capacity will decrease by the value of ( E fco, max − E fca, max ).

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65

4.2.3 Load Leveling Using Water Electrolysis Figure 4.5 shows the model indicating the power demand amount E need,t to which is added the power demand amount of each building in Fig. 4.1 or Fig. 4.2 for every sampling time t. E sep in this figure is the threshold value of the region of low load and high load. By using this threshold value, load leveling is attempted using the method described below. When E need,t is less than E sep , it generates electricity by supplying reformed gas and air to the fuel cell. However, the electricity production of the fuel cell is always E sep , it supplies power the value of which is the difference between E sep and E need,t to the water electrolyzer and produces hydrogen and oxygen (the black area in Fig. 4.5). After compressing these gases, they are stored in each cylinder. When E need,t exceeds E sep , it generates electricity by supplying the hydrogen and oxygen in the cylinders to the fuel cell through the network. In the proposed method of load leveling, it is necessary to determine E sep , where hydrogen and oxygen are produced at the time of low load, and the amount is consumed at the time of high load balance. Fig. 4.5 Fuel cell operation

4.2.4 Distribution of the Fuel Cell Figure 4.6 shows the model of (a) the hot water piping route, (b) the fuel cell capacity of each building, (c) the change of temperature of the hot water, and (d) the piping heat release per unit length of the R1 and R2 types. The machinery room of both types is installed in Building A, and hot water flows in the order of Building ABCDEFGA for the R1 type, and it flows in the order of ADGBFECA for the R2 type as shown in Fig. 4.6(a). As shown in Figs. 4.6(a) and (b), one fuel cell is installed in Building A (FA’) for the R1 type, and the fuel cell of the capacity of FA to FG is installed in Buildings A to G for the R2 type. Hot water of temperature TA,in, t is input into Building A in the R1 type. Heat is supplied for the hot water from the fuel cell exhaust heat (FA’), heat storage tank, and geo-thermal heat pump, and as shown in Fig. 4.6(c), hot water of temperature TA,out, t is output from Building A. After this, there is no heat input to the hot water, and hot water of

66

4 Fuel Cell Network System Considering Reduction in Fuel Cell Capacity

Fig. 4.6 Arrangement plan of fuel cell units

temperature TA,in, t returns to the machinery room of Building A due to the heat consumption of Buildings B to G, and piping heat release. The temperature falls as hot water of the R1 type progresses to Building G from Building A. Therefore, since the difference in temperature of the outside air and the hot water is small, as shown in Fig. 4.6(d), the piping heat release per unit length is small. On the other hand, in the R2 type, heat is supplied to hot water from distributed fuel cells. Therefore, the outlet hot water temperature of each building fluctuates, as shown in Fig. 4.6(c). As a result, the heat release per unit length of piping also fluctuates, as shown in Fig. 4.6(d).

4.2.5 Energy Balance Equation At sampling time t , the water electrolyzer installed in the machinery room and the fuel cells of M installed in M buildings are operating ( M = 1 in the R1 type). The power balance equation in this case is expressed with the following equation. M

∑ E f,m,t =

m =1

M

V

m =1

v =1

∑ E need,m,t + ΔE el,t + ΔE hp,t + ∑ ΔE sub,v,t

(4.1)

4.2 Load Leveling and the Arrangement Plan of the Fuel Cell

67

The left-hand side of Eq. (4.1) expresses the power output in the DC–AC converter outlet of the fuel cells of M . Moreover, the first term on the right-hand side is the power demand amount in each building, the second term expresses the power consumption of the water electrolyzer, the third term expresses the power consumption of the heat pump, and the fourth term expresses the power consumption of the auxiliary machinery (the pump of the hot water network, and the compressor of hydrogen and oxygen). The heat balance of the system is expressed below. M

M

M

m=1

m=1

m=1

∑ Hf,m,t + Hst,t + Hhp,t = ∑ Hneed,m,t + ∑ΔHhw,mm′,t

(4.2)

The first term on the left-hand side of Eq. (4.2) expresses the exhaust heat of the fuel cell of M , and the second and third terms express the heat output from the heat storage tank and the heat pump, respectively. The right-hand side of Eq. (4.2) expresses heat consumption, the first term is the heat demand of each building connected to the network, and the second term expresses the heat release of the hot water piping that connects each building. ΔH hw, mm′, t expresses the heat release of the hot water piping that connects Building m to Building m′ , and is calculated from Eq. (4.3).

ΔH hw, mm', t = h ⋅ π ⋅ D p ⋅ l mm' ⋅ (Tm,out, t − Tatm, t )

(4.3)

Equation (4.4) is the balance equation of hydrogen. The first term on the lefthand side of Eq. (4.4) expresses the quantity of hydrogen production of the water electrolyzer, the second term expresses the hydrogen quantity supplied to the network from the cylinder, and the third term expresses the quantity of hydrogen production of the reformer. Moreover, the right-hand side expresses the hydrogen consumption of the fuel cell of M . Equation (4.5) is a balance equation of oxygen. The first term on the left-hand side expresses the oxygen concentration of the water electrolyzer, the second term expresses the amount of oxygen supplied from the cylinder, and the third term expresses the amount of oxygen in the air supply of the blower. The right-hand side is the amount of oxygen consumed with the fuel cell.

Q el, H 2 , t + Q a, H 2 , t + Q r, H 2 , t = Q el,O 2 , t + Q a, O 2 , t + Q bw, O 2 , t =

M

∑ Q f,m, H ,t

m =1

2

(4.4)

M

∑ Q f,m,O ,t

m =1

2

(4.5)

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4 Fuel Cell Network System Considering Reduction in Fuel Cell Capacity

4.2.6 Operating Method of the System The exhaust heat of each fuel cell connected to the network is used for buildings in which a fuel cell is installed, which is given priority. The surplus heat of each building is recovered in the hot water network. On the other hand, when the heat of a certain building runs short, heat is received from the hot water piping. Moreover, when the heat of the overall network system runs short, heat is supplied to the network from the heat storage tank and the heat pump. When the network has excess heat, surplus heat is stored in the heat storage tank. The heat pump is operated when the heat of the heat storage tank is insufficient.

4.3 Analysis Method 4.3.1 Procedure of Analysis The analysis follows steps (1) to (3). (1) Load Leveling Using Water Electrolysis and Calculation of Esep The load-leveling method using water electrolysis is employed in the R1 type (the R2 type also uses the same procedure). In order to determine E sep in Fig. 4.5, an initial value is decided at random concerning the given power demand pattern. At this time, the amount of production of hydrogen and oxygen in a low-load period is calculated, and hydrogen and oxygen consumption in a high-load period are also calculated. The balance is calculated from the amount of production and consumption of hydrogen and oxygen. The value of E sep is changed, and it is repeatedly calculated until the balance of hydrogen and oxygen becomes sufficiently small. In the analysis case in following section, the time of less than 1% of the balance error was adopted. Balance equation (4.1) of the power, balance equation (4.4) of hydrogen and oxygen, and Eq. (4.5) are used for the calculation of the balance of hydrogen and oxygen. Figures 4.4 and 4.7 are used as the power generation char-

Fig. 4.7 Characteristics of the water electrolysis device

4.3 Analysis Method

69

acteristics of the fuel cell and the characteristics of the water electrolyzer. When the fuel cell capacity in the analysis exceeds that of Fig. 4.4, it is assumed that the relation of Fig. 4.4 can be extrapolated. (2) Calculation of Heat Release from the Hot Water Piping Figure 4.8 shows the heat release model of the hot water piping. The fuel cell is installed in four houses, Buildings A to D. Each building is connected with piping, and hot water returns to Building A. The machinery room is set in Building A, and the heat outputs of the heat storage tank and the heat pump installed in this machinery room are H st, t and H hp, t . There is heat demand of H need,A,t to H need,D, t in Buildings A to D, respectively. In the fuel cell installed in each building, there is exhaust heat power output of H f,A,t to H f,D, t . Therefore, the heat balance of Buildings A to D is calculable from Eq. (4.2). Moreover, the heat release (from ΔH hw,AB,t to ΔH hw,DA,t ) of the hot water piping that connects each building is calculated using Eq. (4.3). Tatm,t in Eq. (4.3) employs the outside air temperature in Tokyo, as shown in Fig. 4.9 [35].

Fig. 4.8 Heat energy network model

Fig. 4.9 Outside air temperature

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4 Fuel Cell Network System Considering Reduction in Fuel Cell Capacity

(3) Route Planning of Hot Water Piping Considering Heat Release Loss Since a fuel cell is placed in each building, for the R2 type it is necessary to determine the capacity of each fuel cell. The outlet hot water temperature of a certain building is decided by the heat balance in the building, and the heat release of the hot water piping is calculated from the difference between the outlet hot water temperature and the outside air temperature. Therefore, the heat release of the overall network differs according to the capacity of the fuel cell installed in each building. In this chapter, as shown in Fig. 4.10, information on the capacity of the fuel cell installed in each building and the piping route is expressed with genes, and these are installed into a genetic algorithm. It is evaluated as a solution with high fitness, so that there are few values in Eq. (4.6) showing heat release from the hot water piping. The calculation is iterated, chromosomes are evolved, and a solution with high fitness is sought. In the last generation’s chromosomes, a solution with the highest fitness is determined as an optimal solution. From the information on the optimum chromosome, the capacity of the fuel cell installed in each building and the piping route are determined.

F=

Period M

∑ ∑ ΔHhw,l,t

(4.6)

t =1 l =1

Fig. 4.10 The chromosome model

4.3.2 Solution Parameters As parameters of the genetic algorithm employed in the analysis in following section, the population is 10,000, the generation number is 20, and the crossover probability is 0.5. The gene manipulation of mutation is not added. The search of the hot water piping route in the R1 type is also analyzed using the genetic algorithm.

4.4 Case Study

71

4.4 Case Study 4.4.1 Energy Demand Pattern and Network System In this case study, an energy network composed of seven buildings is investigated. The energy need pattern in winter (January), mid-term (May), and summer (August) of each building is shown in Fig. 4.11 [30, 36, 37]. These energy-demand patterns are assumed to be in Tokyo. Space-cooling power in summer is included in the power demand shown in Fig. 4.11, and hot water supply and space heating are included in the heat demand. However, the heat for convenience stores, offices, and factories is supplied from an electric heat pump. Figure 4.12 shows the sum of the power demand amount of these seven buildings. The arrangement of the buildings is shown in Fig. 4.13. Moreover, the broken line in Fig. 4.13 is the hot water piping route of the shortest distance. In these analyses, in order to make the hot water flow rate in the piping 1 m/s or less, the inside diameter of the piping is set at 60 mm. The hot water piping is equipped with a 40 mm thick polystyrenefoam system heat insulating mold. Moreover, the overall heat transfer coefficient on the surface of the heat insulating mold is set at 8.0 W/m2K. Under the conditions described above, reduction in fuel cell capacity using load leveling, and the route of the hot water piping for minimum heat release are investigated.

Fig. 4.11 Energy demand patterns

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4 Fuel Cell Network System Considering Reduction in Fuel Cell Capacity

Fig. 4.12 Demand patterns of total power

Fig. 4.13 Location model of hot water piping

4.4.2 Reduction Effect of the Fuel Cell Facility Capacity When threshold value E sep of a low-load region and a high-load region is calculated according to the procedure of the previous section, a representative day in January is 109 kW and a representative day in May and August is 125 kW. By installing E sep into the load leveling described in previous section, the fuel cell is made to follow the power load pattern of Fig. 4.12. Figure 4.14 shows the fuel cell exhaust heat in this case. In the heat balance on a representative day in January shown in Fig. 4.14, since heat runs short in the 7:00 to 17:00 period, the heat pump is operated. On the other hand, the heat supply and demand on representative days in May and August show much heat surplus. Moreover, Fig. 4.15 shows the calculation result of the electrode surface of the fuel cell at the time of installing Esep and performing load leveling. The fuel cell electrode surface in Fig. 4.15 expresses the fuel cell capacity. The data enclosed within the broken line in Fig. 4.15 are power generation using hydrogen and oxygen. These gases are produced using the power generated with reformed gas and air. The fuel cell generated with reformed gas and air is operated at times other than the broken-line region in Fig. 4.15. At the time of high load from 12:00 to 16:00 on a representative day in August, about 180 m2 electrode surface was conventionally taken. If load leveling using water electrolysis is employed, the fuel cell can be reduced to a 120 m2 electrode surface, which is equivalent to 2/3 at the peak at 20:00.

4.4 Case Study

73

Fig. 4.14 Demand patterns of total heat energy

Fig. 4.15 Results for the electrode area

4.4.3 Route Planning Result of the Hot Water Piping The result of the outlet hot water temperature of each building that composes the network is described. The outlet hot water temperature differs according to the R1 type or the R2 type. Moreover, since the heat release of the hot water piping differs according to the outside air temperature, the sampling time is different. The results of 4:00 and 16:00 on representative days in January and August are shown in Fig. 4.16. As Fig. 4.12 shows, the sum of the power demand of each building connected to the network at 4:00 on representative days in January and August is small. On the other hand, this value is large at 16:00. The horizontal axis in Fig. 4.16 is the route order (Nos. 1–7) of the hot water piping. Letters A to G in Fig. 4.16 correspond to the building number shown in Fig. 4.13. For example, in the analysis results at 4:00 and 16:00 for the R1 type on a representative day in January, hot water flows in the order of GFDCABE. The optimal path on a representative day in January for the R1 type is GFDCABE, and the optimal path on a representative day in August is BEGFDCA. This way, the starting points of the hot water differ according to each month. Moreover, the route of the representa-

74

4 Fuel Cell Network System Considering Reduction in Fuel Cell Capacity

Fig. 4.16 Hot water temperature of outlet piping of buildings in January and August Fig. 4.17 Hot water temperature of outlet piping of buildings

tive days of both months for the R2 type is GEBACDF. The routes GFDCABE, BEGFDCA, and GEBACDF are the same as a result of the shortest route shown in Fig. 4.13. However, GFDCABE and BEGFDCA are clockwise rotations, and GEBACDF is counterclockwise. The outlet hot water temperature of each building differs in the starting point of the hot water piping, route and the flow direction, as shown in Fig. 4.17. Figure 4.17 shows the result of the hot water temperature when setting the starting point of the hot water piping as B, E, or G. Figure 4.18 shows the result of the hot water piping heat release relevant to the piping routes in Fig. 4.17. Figure 4.19 shows the result of the piping heat release in the network on a representative day every month. Under these analysis conditions, the difference in the heat release for the R1 type and R2 type on a representative day is less than 3% every month. Considering the analysis error of the genetic algorithm, these can be estimated as the same value. Therefore, if the heat release of the R1 type and R2 type is optimized, it will converge at almost the same value. However, since the R1 type in this case assumes that the starting point of the hot water piping is movable to arbitrary buildings according to the month, it is not realistic.

4.4 Case Study

75

Fig. 4.18 Quantity of heat loss of piping between buildings

Fig. 4.19 Waste heat of hot water supply

4.4.4 Result of the Fuel Cell Arrangement Plan Figure 4.20 shows the result of the fuel cell arrangement plan for the R2 type. The fuel cell capacity installed in each building is a circle of the broken line in Fig. 4.20. When the electrode surface of each building shown in Fig. 4.20 is added, it is 97 m2. The electrode surface when installing the capacity reduction by load leveling is 120 m2. Furthermore, if the optimum arrangement plan of a fuel cell is installed, the electrode surface will be reduced to 97 m2. When load leveling using water electrolysis and optimization of fuel cell distribution are installed, the fuel cell electrode surface is reduced by 46% compared to the conventional system.

76

4 Fuel Cell Network System Considering Reduction in Fuel Cell Capacity

Fig. 4.20 Installation capacity of the fuel cell

4.5 Conclusions A load-leveling method that supplies air and water electrolysis oxygen to the cathode of the fuel cell has been proposed for the fuel cell energy network system. Furthermore, the optimum operation plan of the hot water network has been proposed, and the fuel cell capacity of each building, position of the machinery room, piping route, and hot water flow direction have been investigated. The fuel cell energy network composed of individual houses, a hospital, a hotel, a convenience store, an office building, and a factory has been analyzed, and the following conclusions obtained. (1) If the load-leveling method is used, the installed capacity of by fuel cell will be reduced by 34% compared with the conventional system. (2) Moreover, when fuel cell distribution is optimized, in accordance with the effectiveness of (1), there is a 46% reduction compared with the conventional system.

Chapter 5

Equipment Plan of a Compound Interconnection Micro-grid Composed of Diesel Power Plants and a Fuel Cell

5.1 Introduction Installation of the fuel cell micro-grid in an urban area is the technology of spreading the utilization of hydrogen energy. For example, hydrogen production using green energy and reforming technology of natural gas can be introduced. Generally, as for the introduction of a micro-grid in a city area, the following points are expected: (a) The distance for the heat supply is short and effective use of exhaust heat is possible [38, 39]; (b) load leveling of existing large-sized power generation equipment is possible; and (c) since a facility suitable for the energy demand characteristics of a community can be installed, it is expected to be a technology in which energy efficiency is high and environmental impact is low [21, 22, 40]. However, the proton exchange membrane-type fuel cell (PEM-FC) has expensive electrode material (catalyst material and a solid polymer membrane). Furthermore, since the system is complex, it is difficult to commercialize it immediately. Thus, reduction of the number of installations of the expensive fuel cell by connecting PEM-FC to a micro-grid, and supplying power to two or more buildings is considered in this chapter. However, the subject of this system is the frequent partialload operation with low efficiency, when power is supplied to two or more buildings using a large-capacity fuel cell (FC). As technology to solve this issue, a fuel cell is divided into small-capacity units, and as the method of increasing the load factor of each unit is used [25]. However, by this method the number of fuel cell units increases greatly, and facility costs increase. Consequently, the base load of a micro-grid is supplied using a diesel engine power generator (DEG), and how to install and interconnect two or more PEM-FC grids is examined. The compound grid of DEG and PEM-FC is interconnected in this chapter. This micro-grid is termed CIM (compound interconnection micro-grid). In CIM there are an interconnection system and an independent system. The interconnection system is connected with other grids, such as commercial power, and operated. Although achieving an independent system is predicted to be difficult compared with the S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

77

78

5 Equipment Plan of a Compound Interconnection Micro-grid

interconnection system, it is considered that the effect of (b) and (c) described above will be large. Thus, this chapter examines the independent micro-grid system built by two or more FC grids in which system interconnection is possible. To date there have been many cases of DEG being installed as cogeneration, and the characteristics, such as the power generation efficiency, facility costs, and power cost due to a number of achievements, can be estimated. Although it is expected that the micro-grid using DEG has high potential, it is accompanied by the problem of carbon dioxide discharge. Consequently, DEG is introduced as generating equipment corresponding to the base load of the whole CIM, and operation near the maximum efficiency point is examined. On the other hand, the dynamic characteristics at the time of load fluctuation and carbon dioxide emissions of PEM-FC are good compared with DEG [41]. PEM-FC linked to CIM is controlled to operate correspondingly to the load fluctuation of the grid. However, the load factor of PEM-FC changes with the power demand patterns of each building linked to CIM in this system. As a result, the power generation efficiency of the whole micro-grid may improve by dividing a micro-grid into two or more parts, and determining the buildings connected to each grid (grid route) with the object of maximizing the power generation efficiency. In this chapter, an independent micro-grid with a high power generation efficiency is planned by optimizing the capacity of DEG and PEM-FC, and selecting the grid routes.

5.2 Compound Interconnection Micro-grid 5.2.1 The Micro-grid Model This chapter examines two or more FC grids and the independent micro-grid built by DEG operated according to a base load. However, herein only the power system is investigated, and the exhaust heat of the generating equipment is not touched upon. Figure 5.1 shows the CIM model that introduced seven FC grids into 61 buildings. DEG is installed in the buildings of any grid and outputs constant power corresponding to the base load of all the FC grids. The power demand patterns of each building in a city area differ. Therefore, the power load pattern of each FC grid changes with the route of the building linked to the grid. Consequently, as shown in Fig. 5.1(a), the building linked to each FC grid is selected and arranged with the object of maximizing the power generation efficiency. Figure 5.1(b) shows the model of the FC grid (FC grids A to G) in part (a), and power supply-and-demand is possible for each grid through a system interconnection device (CP1–CP7). The system interconnection between the grids is effective when supplying power from another system in the case of an accident, maintenance, etc., and when there is large load exceeding a certain grid capacity.

5.2 Compound Interconnection Micro-grid

Fig. 5.1 The CIM (compound interconnection micro-grid) model

79

80

5 Equipment Plan of a Compound Interconnection Micro-grid

5.2.2 The CIM Model Figure 5.1(c) shows the model of the FC grid; an interconnection device is shown in parts (a) and (b). In CIM, DEG of with a capacity of Pc,DEG is installed and PEM-FC with a capacity from Pc,FC,A to Pc,FC,G is installed from FC Grids A to G, respectively. Each grid can change over and interconnect the system interconnection device of CP1 to CP7. DEG is operated correspondingly to the base load of the city area model shown in Fig. 5.1(a). DEG is operated by a constant load. The load fluctuation, power is supplied from FC Grids A to G. As shown in Fig. 5.1(c), all FC Grids A to G are connectable with DEG.

5.2.3 Facility Scheme Figure 5.2(a)–(c) shows the facility scheme installed in the building linked to CIM shown in Fig. 5.1. Figure 5.2(a) shows the facility scheme of a building where DEG is installed, and Fig. 5.2(b) shows the facility scheme of a building where PEM-FC is installed. A building with the installed DEG facility shown in Fig. 5.2(a) is connected to any one grid, and a building with the installed facility shown in Fig. 5.2(b) is connected to all the FC grids. Figure 5.2(c) shows the facility scheme of a building in which DEG or PEM-FC is not installed. Generating equipment composed from a diesel engine, a power generator, a boiler, a heat storage tank, a system interconnection device, etc., is installed in Fig. 5.2(a). Moreover, the generating equipment composed from a town gas reformer, PEMFC, a boiler, a heat storage tank, a system interconnection device, etc., is installed in Fig. 5.2(b). In a reformer, reformed gas is produced on a catalyst by making the combustion gas of town gas into a heat source. Since there is a large amount of water in reformed gas generated by steam reforming, reformed gas is cooled by the air supply of a blower with a dryer, and the water is condensed and separated. In order for the carbon monoxide concentration in the reformed gas in a fuel cell stack entrance to be several ppm, the carbon monoxide oxidization part is prepared. In the carbon monoxide oxidization part, carbon monoxide is burned on a catalyst and it changes into carbon dioxide. Reformed gas is supplied to a fuel cell stack from the carbon monoxide oxidization part, and it generates electricity. The generated DC power is changed into an alternating current of constant frequency through an inverter, and is supplied to a system interconnection device. Moreover, the boiler for heat supply and the system interconnection device for obtaining power from a grid are installed in Fig. 5.2(c).

5.2 Compound Interconnection Micro-grid

Fig. 5.2 Equipment model installed in a building and an operation plan

81

82

5 Equipment Plan of a Compound Interconnection Micro-grid

5.2.4 The CIM Operating Method The model of the operating method of CIM is shown in Fig. 5.2(d). The power load of a representative day is divided into the base load of the constant load, and other loads as shown in the figure. In operating and generating DEG at the base load, other loads correspond by the power generation of FC. Although DEG is one set, the FC grid consists of two or more sets. Since the FC grid corresponds to load fluctuation, it may operate at partial load with low efficiency, but DEG can be operated by the constant load of the maximum efficiency point.

5.3 Equipment Characteristics 5.3.1 Diesel Engine Power Generator The output characteristics result of the cogeneration system using DEG is shown in Fig. 5.3(a). This result is the relation among the calorific heat of the kerosene fuel supplied to DEG, the engine-cooling-water heating value and the engine exhaust gas heating value, and the production of electricity. The engine specifications of the cogeneration system of Fig. 5.3(a) are shown in Table 5.1(a). Moreover, the specifications of a synchronous power generator are shown in Table 5.2(b). The fuel of a diesel engine is kerosene and uses two cylinders and four cycles. A power generator is of a single-phase synchronous type, and power is transmitted through a belt from the power shaft of the diesel engine. If the amount of kerosene fuel is increased, the production of electricity and the exhaust gas heating value increase, but the engine-cooling water heating value decreases. The maximum power generation output is 3 kW, and the kerosene supply heating value at this time is 9.8 kW. Figure 5.3(b) shows the production of electricity of DEG and the relation of power generation efficiency obtained by the test. Although the power generation efficiency changes with the number of engine rotations, since this difference is small, the approximated curve shown in Fig. 5.3(b) is used in the analysis of this research. Moreover, the relation between the load factor and power generation efficiency shown in Fig. 5.3(b) should be maintained even if the capacity of DEG changes.

5.3 Equipment Characteristics

Fig. 5.3 The power generator model

83

84

5 Equipment Plan of a Compound Interconnection Micro-grid

Table 5.1 Engine specifications

Table 5.2 Generator specifications

5.3.2 The Proton Exchange Membrane-type Fuel Cell The output characteristics of the fuel cell stack used in the analysis of this chapter are shown in Fig. 5.3(c). The maximum power generation efficiency of PEM-FC shown in Fig. 5.3(c) is 32%. The supply of town gas has a system of changing it into reformed gas, and a system of supplying it to the burner used as a heat source of the reforming reaction. The power generation efficiency is calculated by dividing the production of electricity of PEM-FC by the calorific power adding these two town gas systems [26–29]. The electrode area of an anode and cathode of the examined PEM-FC is 1.0 m2, respectively. Moreover, the cost calculated by dividing the calorific power of hydrogen in the reformed gas by the calorific power of the two town gas systems described above is defined as the reformer efficiency.

5.4 Analysis Method 5.4.1 Route Plan of the Compound Interconnection Grid Equation (5.1) is an expression of total power generation efficiency η total,t in sampling time t, and calculates for all the grid routes that compose CIM. η total,t is calculated from power generation efficiency η DEG,t and load E DEG,t of DEG,

5.4 Analysis Method

85

and power generation efficiency η FC,n, t of the FC grid of route n ( n = 1, 2, ..., N R ) and load E FC,n, t . Equation (5.2) is the objective function. Objective function FO, Day is equal to total power generation efficiency η total,Day of a representative day, and is obtained using Eq. (5.1). In the analysis of this chapter, the route of the FC grid and the generation capacity of DEG and FC in case FO, Day is the maximum and has been determined as the optimal solution. E DEG, t ⋅ η DEG, t + η total,t =

NR

∑ (E FC,n,t ⋅ η FC,n,t ) n =1

E total, t

(5.1)

23

FO, Day = η total,Day = ∑ (η total, t )

(5.2)

t =0

When there is the same pattern among the power-demand patterns of each building introduced into a city area model, two or more grid routes considered to be optimal appear.

5.4.2 Analysis Flow The analysis flow that searches for the optimal solution of CIM is shown in Fig. 5.4. First, the power demand data of each building that composes a city area model is given to the analysis program. Next, the base load of a micro-grid is calculated from the power demand data, and the capacity of DEG is determined. Regarding all the FC grid routes, the power generation efficiency for every sam-

Fig. 5.4 Calculation flow of the generation efficiency for an FC micro-grid

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5 Equipment Plan of a Compound Interconnection Micro-grid

pling time is calculated. However, the FC grid route is given to a program widely assuming the number of grids and the capacity of PEM-FC installed in each grid, and calculates the power generation efficiency for all the FC grid routes. By adding and equalizing these results, the average generation efficiency on a representative day is obtained. The power generation efficiency of DEG and PEM-FC is used to calculate the load ratio from the power demand of all the buildings connected to a grid and the capacity of DEG and PEM-FC that were set up beforehand, and these are calculated by inserting them into Figs. 5.3(b) and (c). The power generation efficiency of DEG and each route of FC grid is given to Eq. (5.1), and the route of FC grid in case FO, Day of Eq. (5.2) is the largest value, and the capacity of DEG and PEM-FC are decided on as the optimal solutions.

5.4.3 The Power Demand Model Figure 5.5 shows the power demand model of each building in Tokyo used in the analysis, and is the mean power load of each sampling time of the representative day in January (winter), May (mid-term), and August (summer) [30–32]. However, the actual power demand pattern is a meeting of the load that changes rapidly in a short time, such as an inrush current. In Tokyo, the annual average temperature for the past five years was 289 K. The average temperature in January is 25.8 K, and the highest and the lowest temperatures on a representative day in January are 283 K and 25.4 K, respectively. The average temperature in May is 292 K, and the highest and the lowest temperatures on a representative day in May are 296 K and 288 K, respectively. The highest and the lowest temperatures on a representative day in August for the past five years have been 302 K and 296 K, respectively, and the average temperature is 298 K [20]. There is a high power demand on a representative day in August compared with other months, including the space-cooling load. The power demand estimate of the family household shown in Fig. 5.5(a)–(d) is difficult, and the power demand estimate of the small offices and factories indicated in Figs. 5.5(g) and (h) is regular, and comparatively easy to estimate. Although load fluctuation in a short time is not taken into consideration for the power demand model in the analysis of this chapter, when accompanied by load fluctuation, it is necessary to investigate the dynamic characteristics of the grid. The power demand pattern of a family household (Fig. 5.5(a)–(d)) shows peaks in the morning and the afternoon. The demand of hotels (Fig. 5.5(e)) stabilized when midnight to early morning was excluded, and there is continuous power demand at convenience stores (Fig. 5.5(f)) that are open around the clock. The difference in the time zone of night to early morning with little power demand and the time zone from morning to evening with a high power demand is clear in offices (Fig. 5.5(g)), factories (Fig. 5.5(h)), and hospitals (Fig. 5.5(i)).

5.4 Analysis Method

Fig. 5.5 Power demand models

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5 Equipment Plan of a Compound Interconnection Micro-grid

5.5 Case Study 5.5.1 The Urban Area Model Figure 5.6 shows the urban area model used for analysis. The number shown in the figure is the building number, and also shows the type of each building. The urban area model is composed from 20 buildings, and Table 5.3 shows the type of each building. The analysis investigates each case of Table 5.3(a) and (b). Case A is a model (complex community) assuming an urban area that consists of various buildings, and Case B is a model (residential area) assuming a residential street. In addition, each power demand pattern uses Fig. 5.5. For example, building numbers 1 and 2 are family households (two persons), and each power demand model used for analysis is shown in Fig. 5.5(b). Therefore, the grid route of building numbers 1 and 2 is exchangeable. The power demand model of Fig. 5.5 and the urban area model of Fig. 5.6 are installed into the analysis program described in Fig. 5.4, and the efficiency of the CIM system is verified in analysis. However, in the analysis, the power demand model of a representative day in May of Fig. 5.5 is used. The analysis using the power demand model of the representative days of other months is the same as that of the example of a representative day in May; other months are not analyzed in this chapter. Table 5.3 Energy demand for the urban model

Fig. 5.6 Urban area model

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89

5.5.2 Complex Community (1) Grid Route and Generation Capacity of FC and DEG

Figure 5.7(a) shows the rate of the power demand of a representative day in May in the urban area model of Case A of Table 5.3(a). A representative day shows the greatest power demand to be for convenience stores (two buildings), followed by hotels, factories, and small hospitals, in that order. As Fig. 5.5 describes, the difference in the power demand for day and night is comparatively small with convenience stores, hotels, and small hospitals, and is large for small offices and factories. There is a difference between family households and apartments in the amount demanded from midnight to early morning, and daytime. In order to maintain the high power generation efficiency of the whole micro-grid, it is necessary to plan the path of the FC grid containing convenience stores, hotels, factories, small hospitals, etc., with a large power demand. The grid route shown in

Fig. 5.7 Analysis results in Case A

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5 Equipment Plan of a Compound Interconnection Micro-grid

Fig. 5.7(b) was obtained from the results of analyzing the power generation efficiency of each grid route. Figure 5.7(b) consists of three FC grids: Grid A, Grid B, and Grid C. Figure 5.7(c) shows the analysis results of the generation capacity of FC installed in each FC grid, and the generation capacity of DEG corresponding to the base load. PEM-FC of 10 kW, 15 kW, and 47 kW is connected to each of Grid A, Grid B, and Grid C, respectively. In addition, 57 kW DEG is installed and it corresponds to the base load of the whole grid. (2) Result of Power Generation Efficiency

Figure 5.7(d) shows the analysis results of the electric energy to be outputted on a representative day in May by each FC grid. In each grid, there is base power supplied from DEG and power corresponding to the load fluctuation supplied from FC. The base load of each grid distributes the power outputted by DEG. The output of DEG is larger than FC, removing Grid B by the building composition of Case A. In addition, the power supply of a representative day has more DEG than the sum total of each PEM-FC. Figure 5.7(e) shows the analysis results of the power generation efficiency of FC of each FC grid, and the power generation efficiency of the whole grid. Although the power generation efficiency of Grid A, Grid B, and Grid C is 19.2%, 15.3%, and 18.6%, respectively, a base load operation is added due to DEG, and the power generation efficiency of the whole grid improves to 27.1%. Because there are two or more buildings with the same power demand model in the urban area model, the grid routes shown in Fig. 5.7(b) differ, but there is a case where Figs. 5.7(c), (d), and (e) show the same results. Moreover, one set of DEG or one set of PEM-FC is installed into the conditions of the urban area model of Case A, and the analysis result of the power generation efficiency of the system that supplies the power demand of all the buildings (central system) is shown in Table 5.4(a). The power generation efficiency of the DEG central system and the FC central system is 22.4% and 26.2%, respectively. Therefore, the CIM system of power generation efficiency (27.1%) proposed in this chapter is larger. The result of the load distribution of the whole micro-grid of Case A is shown in Fig. 5.8(a). In this figure, allocation of the load of DEG and the load of the FC grid (Grid A, Grid B, and Grid C) is shown. The magnitude of the load during the time zone from midnight to early morning, and others, differs greatly, and the power generation efficiency of the FC grid and the total efficiency of the micro-grid (equal to CIM efficiency) are influenced by this.

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Fig. 5.8 Load distribution of the entire micro-grid Table 5.4 Result of power generation efficiency

5.5.3 The Residential Area Model (1) Grid Route and Generation Capacity of FC and DEG

Figure 5.9(a) shows the rate of the power demand of a representative day in May in the urban area model of Case B in Table 5.3(b). In Case B, family households account for 18 buildings and convenience stores account for 2 buildings. However, the power demand rate of convenience stores is 84%, and the power demand rate of family households is 16%. The grid route shown in Fig. 5.9(b) was obtained from the analysis result of the power generation efficiency of each grid route. Figure 5.9(b) consists of two FC grids: Grid A and Grid B. Figure 5.9(c) shows the analysis result of the generation capacity of FC installed in each FC grid, and the generation capacity of DEG corresponding to the base load. PEM-FC of 8 kW is connected to each of Grid A and Grid B, and DEG of 33 kW is operated as a base load of the whole grid.

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5 Equipment Plan of a Compound Interconnection Micro-grid

Fig. 5.9 Analysis results in Case B

(2) Result of Power Generation Efficiency

Figure 5.9(d) shows the analysis result of the electric energy to be outputted on a representative day in May by each FC grid. From the analysis result of Fig. 5.9(d), with the composition of the buildings of Case B, the output of DEG is overwhelmingly larger than FC, and the power supply of a representative day has more DEG than the sum total of each PEM-FC. Figure 5.9(e) shows the analysis result of the power generation efficiency of FC connected to each FC grid, and the power generation efficiency of the whole grid. Although the power generation efficiency of Grid A and Grid B is 19.5% and 14.5%, respectively, a base load operation is added due to DEG, and the power generation efficiency of the whole grid improves to 29.9%. In addition, one set of DEG, or one set of PEM-FC, is installed into the conditions of the urban area model of Case B, and the analysis result of the power generation efficiency of the system (central system) that supplies the power demand of all the buildings is shown in Table 5.4(b). The power generation

5.6 Conclusions

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efficiency of the DEG central system and the FC central system is 23.2% and 29.1%, respectively. Therefore, the CIM system (29.9%) of power generation efficiency proposed in this chapter is larger. The result of the load distribution of the whole micro-grid of Case B is shown in Fig. 5.8(b). There is little load distribution of the FC grid compared with Case A, and the difference in the load at each sampling time is small. This is why the power generation efficiency (equal to the power generation efficiency of CIM) of the whole micro-grid is high.

5.6 Conclusions CIM, which divides the grid of an independent micro-grid into multiple units and interconnects between grids, was proposed in this chapter. Although there are many examples of introducing DEG (diesel power plant generator) as cogeneration until now, there has been the problem of carbon dioxide emission. Therefore, in this chapter, DEG was installed as generating equipment corresponding to the base load of whole CIM, and the method of operating a proton exchange membrane-type fuel cell (PEM-FC) so that it may correspond to the load fluctuation of the grid was investigated by numerical analysis. The generation capacity and the grid route of DEG and PEM-FC in the case of maximum power generation efficiency of the whole micro-grid were sought. From the results of this analysis, an independent micro-grid with high power generation efficiency was planned. As a result, compared with the method of installing DEG or PEM-FC into a micro-grid independently (central system), the power generation efficiency is confirmed to have improved. Moreover, it has been verified that the power generation efficiency of a micro-grid, the number of FC grids introduced, and the capacity of DEG and PEM-FC change with the types of building that compose an urban area. In the complex community model and the residential area model investigated in this chapter, the power generation efficiency of the whole micro-grid (CIM) was 27.1% and 29.9%, respectively. Although the micro-grid that combines DEG and PEM-FC is advantageous with respect to power generation efficiency and carbon dioxide emission, studies including those on the increase in equipment costs are now required.

Chapter 6

The Effective-use Method of Exhaust Heat for Distributed Fuel Cells

6.1 Introduction When a proton exchange membrane fuel cell cogeneration system with a town gas reformer is distributed to each building and energy is supplied, the transportation loss of the heat supply is small. However, if the fuel cell system is installed in each building, a drop in energy efficiency is predicted due to the imbalance between the rate of heat supply and electric power supply. Although the installation of a battery and a heat storage tank is effective, using these methods, the system and the operational plan become complicated, and serve to raise the equipment costs. In this study, the fuel system (the hydrogen piping network), the electric power system (the power line network), and the heat-power system (the hot water piping network) of the fuel cells installed in each building are connected, and the best method of satisfying two or more of electric power and heat loads of the buildings in cooperation is examined. When two or more fuel cells are connected in a network, then the method of cooperation and control of the electric power output and the heat power output is called a fuel-cell-energy-network (FEN) in this chapter. In an FEN, it is possible to stop a fuel cell, which represents the operational status of a partial load with low efficiency in the cooperative operational control by FEN, to utilize the electric power generated by other fuel cells and to improve the efficiency of the system as a whole. Since being charged, from now on according to the amount of discharge of carbon dioxide gas is also considered, connecting renewable energy equipment and unused energy equipment to the FEN, which is composed of tens of buildings, is also considered. Compared to a conventional system, which is installed as an independent system in each building, the FEN energy supply system, which is comprised of common auxiliary machinery (a heat storage tank, an auxiliary heat source, a reformer, etc.) confined to one machinery room, reduces the overall facility costs. A reformer installed in the machinery room produces reformed gas with a high hydrogen concentration from town gas. The existing town gas network can be used as the hydrogen piping netS. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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6 The Effective-use Method of Exhaust Heat for Distributed Fuel Cells

work for supplying reformed gas to the fuel cell in each building. Using the existing power line network, the exchange of electric power, based on supply and demand, produced by the fuel cells in each building between each other is possible between the buildings connected to the FEN. However, a hot water piping network must be newly installed for the construction of an FEN, and piping needs to be connected to all the buildings; therefore the heat loss from the piping is an important issue. It is expected that the total amount of heat released in a hot water piping network varies between networks depending on the path of the piping. In this chapter, a scheme for determining and predicting the optimal piping path for minimizing the heat release from a hot water piping network has been developed. For planning a hot water piping network, the program uses a genetic algorithm (GA) [14, 16], and a detailed analysis has been performed in this study. With respect to optimization using GA, previous reports have explored the piping path and the layout of equipment [42–44]. However, there have been no studies on the analysis of an energy network using fuel cells and other energy equipment. Sapporo City in Japan is used as an urban area model for the path planning program of the hot water network in this study, and the optimal path and the amount of heat release of the hot water piping network in FEN are investigated. It is expected that the actual electric power loads and heat loads of buildings deviate sharply from the average energy demand patterns. Therefore, in this chapter, the optimal path and the amount of heat released in a hot water piping network in the presence of random load fluctuations for an average electricity demand pattern are also investigated.

6.2 Outline of the Fuel Cell Energy Network System 6.2.1 System Outline An example of applying the hot water piping network by FEN to two or more buildings is shown in Fig. 6.1. S1 to S7 in Fig. 6.1 indicate the buildings in the network, and L Si − (i +1) is the length of the hot water piping that connects Si and

Fig. 6.1 Hot water piping network of FEN

6.2 Outline of the Fuel Cell Energy Network System

97

Si +1 . The hot water piping path shown in Fig. 6.1 is in an order that connects each building by the shortest distance. When the paths of the hot water piping differ, the amounts of heat released from the network also differ. Therefore, effective use of the fuel cell exhaust heat requires a path plan that minimizes the amount of heat released from the hot water piping network.

(1) The Machinery Room Figure 6.2(a) presents a machinery room scheme showing the installation of common facilities of an FEN. It is assumed that the machinery room is installed in the building S m linked to the FEN. This machinery room is the starting point of the hot water piping network. A town gas reformer, a heat storage tank, a radiator, and backup heat source equipment are installed in this machinery room. Town gas, with the quantity of flow Q f,rm,t , and water are supplied to a reformer, and reformed gas with high hydrogen concentration is produced from the town gas by a steam reforming reaction using a catalyst. Steam reforming is an endoergic reaction and the heat source of this reaction is a supply of town gas of the quantity of

Fig. 6.2 Equipment of the FEN system

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6 The Effective-use Method of Exhaust Heat for Distributed Fuel Cells

flow Q f ,cb,t to a burner. The composition of hydrogen and steam in reformed gas varies considerably. The reformed gas is then cooled with atmospheric air and a dryer is used to condense and remove moisture. Carbon monoxide with a concentration of several percent is typically present in the reformed gas when it comes out of the dryer. If this reformed gas containing carbon monoxide is supplied to a fuel cell, the reaction in an electrode catalyst will be blocked due to poisoning, and the power generation performance will fall. Therefore, CO oxidization is carried out using oxidation equipment so that the concentration of the carbon monoxide gas in the reformed gas can be reduced to 10 ppm or less. The exhaust heat output H rm,t from the reformer is stored in the heat storage tank using a heat medium. The heat in the heat storage tank can be supplied to each building through the hot water piping network. However, when the quantity of heat, including H rm,t , H in,Sm , t of the return of the hot water piping network, and H st, t of thermal storage, exceeds the overall heat storage capacity, the excess heat H ra,t is emitted out of the system via a radiator. On the other hand, when the quantity of heat obtained by adding H rm,t , H in,Sm , t , and H st, t is less than the heat demand in each building, a quantity of heat H bh, t is supplied to the heat storage tank using auxiliary heat sources, such as a boiler and a heat pump. (2) Individual Buildings

At first, after supplying the heat output from a fuel cell installed in a building to the heat demand of the building, excess heat (deficiency) is output (input) from the hot water piping network. As shown in Fig. 6.2(b), a fuel cell, an inverter, and a heat exchanger, which can output or input heat from or to the hot water piping network, are installed in each building linked to the FEN. When the building in Fig. 6.2(b) is set to Si , it has a hot water input with quantity of heat H in,Si −1 , t at temperature Tin,Si , t from the hot water piping network. Moreover, when the heat demand in Si is set to H n,Si , t and the exhaust heat output of a fuel cell is set to H f,Si , t , the hot water output from Si is the quantity of heat H out,Si , t = H in,Si −1 , t + (H f,out,Si , t − H n,Si , t ) at temperature Tout,Si , t . The electric power generated by a fuel cell is E out,Si , t , and the electric power after changing into the regulation frequency of exchange with an inverter is E sys,Si , t . Although the electric power, excluding the electricity demand E n,Si , t from E sys,Si , t , can be supplied to any building in the FEN through the power line network, this is not taken into consideration in the case analysis described later.

6.2.2 The Path and the Amount of Heat Release from the Hot Water Piping A system that includes only one fuel cell installed in a machinery room and that supplies electric power in each building is called a centralized system. Figures 6.3(a)–(e) show the model of the FEN and a centralized system. Figure 6.3

6.2 Outline of the Fuel Cell Energy Network System

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Fig. 6.3 The model of the FEN and a centralized system showing the relationship between the allocation of distributed fuel cells and heat release

shows (a) the installation of the hot water piping, (b) the path of the hot water piping, (c) the fuel cell capacity in each building, (d) the change in hot water temperature, and (e) the amount of heat release from the piping per unit length in the FEN. In the model shown in Fig. 6.3, the machinery room in the FEN and a centralized system is installed in Building S1 , and hot water for heat supply flows in the order of the Buildings S1 , S 2 , S3 , S 4 , S5 , S6 and S7 , as shown in Fig. 6.3(b). However, as shown in Fig. 6.3(a), the hot water piping is equipped with heat insulating material, and all the piping is exposed to the open air and connects each building. Moreover, as shown in Fig. 6.3(c), in a centralized system,

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only one fuel cell is installed in the machinery room of Building S1 , and a fuel cell is installed in the FEN based on the capacity required for supplying the maximum electric load for all buildings. Figure 6.3(d) shows the model for the hot water temperature as a function of the building location. In a centralized system, the hot water input into S1 at temperature Tin,S1 , t is output at temperature Tout,S1 , t at the exhaust of a fuel cell. In this system, there is no heat source heating the hot water after this point, and due to the heat demand in each building and the heat released from the piping connecting each building, the hot water temperature falls to Tin,S1 , t , and the hot water returns to S1 . With a centralized system, since the hot water temperature falls sequentially as the hot water progresses from Building S1 to S7 , the difference in temperature between the outside temperature and the hot water gradually becomes smaller, as shown in Fig. 6.3(e), and it is assumed that the amount of heat release per hot water piping unit length becomes sequentially smaller as well. On the other hand, the fuel cells are installed in all the buildings by the FEN, and the outlet hot water temperature for each building is determined by the balance relation of the quantity of heat of the hot water input into each building, the amount of exhaust heat of the fuel cell installed in the building, and the heat demand in the building. Therefore, the outlet hot water temperature of the buildings will be changed upwards or downwards as shown in Fig. 6.3(d), and the hot water goes to each building. As shown in Fig. 6.3(e), as a result, the heat release per unit length of piping changes in an upwards or downwards direction. The amounts of heat released in the whole piping will differ based on the course plan of the hot water piping of the FEN, and the piping path is likely to affect the system-wide energy efficiency.

6.2.3 Heat Energy Balance Assuming that the buildings with fuel cells M are connected to the FEN, and that the fuel cells M generate electricity at a sampling time t, the heat balance of the FEN is then expressed using Eq. (6.1). M

M

M

∑ Hf,S ,t + Hrm,t + Hbh,t + Hst,t = ∑ Hn,S ,t + ∑ Hr,S i =1

i

i =1

i

i =1

i −(i +1) , t

+ H ra,t

(6.1)

The left-hand side of Eq. (6.1) shows the heat outputs, and the right-hand side shows the consumption of heat. The first term of the left-hand side is the exhaust heat output by the fuel cell M , the second term expresses the exhaust heat of a reformer, the third term expresses heat supply from an auxiliary heat source, and the fourth term expresses the heat output from the heat storage tank. The first term of the right-hand side is the heat demand in each building, the second term is the amount of heat released by the hot water piping that connects Si and Si +1 , and the third term expresses the amount of heat released in the radiator installed in the machinery room.

6.2 Outline of the Fuel Cell Energy Network System

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6.2.4 The Heat Release Amount for a Hot Water Network H r,Si − (i +1), t in Eq. (6.1) is calculated by the method described below. Figure 6.4(a) shows the model for heat input and output from the hot water piping that connects Buildings Si and Si +1 , and the amount of heat released in the piping is set to H r,Si − (i +1) , t . The hot water temperature is Tin,Si , t and the quantity of heat H in,Si , t is input into building Si through the network. When carrying out the power generation operation of the fuel cell installed in Si so that the electricity demand E n,Si , t in sampling time t may be satisfied, the exhaust heat output of the fuel cell is H f,Si , t . Moreover, H n,Si , t is the heat demand in Si . The quantity of hot water heat H out,Si , t output from Si is H in,Si , t + H f,Si , t − H n,Si , t . Here, the values of H f,Si , t vary based on the value of E n,Si , t , as described in the next section. Although the temperature of the hot water output from Si is Tout,Si , t , by the time the hot water reaches Si +1 , there will be heat release H r,Si − (i +1) , t from the piping. Therefore, for the hot water input into Si +1 , the temperature falls to Tin,Si +1 , t , and the quantity of heat is H in,Si +1 , t = (H out,Si , t − H r,Si − (i +1) , t ) . Furthermore, in Building Si +1 , since power generation operation of the fuel cell is performed so that the electricity demand E n,Si +1 , t is satisfied, the exhaust heat H f,Si +1 , t is output. This calculation is repeated for the following condition for all buildings: For a heat demand of H n,Si +1 , t in Si +1 , the quantity of heat Si +1 of the hot water output from H out,Si +1 , t is H in,Si +1 , t + H f,Si +1 , t − H n,Si +1 , t . Figure 6.4(b) shows the model of the hot water piping connecting Si and Si +1 . The bore diameter of the hot water piping is expressed as D i,Si − (i +1) , the outside diameter is expressed as D o,Si − (i +1) , and the outside diameter of the heat insulating material, with which the piping is

Fig. 6.4 Heat model for the hot water piping network

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equipped, is set to D c,Si − (i +1) . The heat conductivity of the piping material and thermal insulation are set to k p and k c , respectively. The coefficient of overall heat transmission K Si − (i +1) between the hot water and the surface of heat insulating material is determined as follows. ⎧⎪ Do,Si−(i+1) 1 1 K Si−(i+1) = 1 ⎨ ln + ⎪⎩ h w,Si−( i+1) ⋅ Di,Si−(i+1) 2 ⋅ k p Di,Si−(i+1) +

1 2 ⋅ kc

ln

Dc,Si−(i+1) Do,Si−( i+1)

+

1 h ∞ ,Si−( i+1) ⋅ Dc,Si−(i+1)

⎫⎪ ⎬ ⎪⎭

(6.2)

In the analysis example in this chapter, although the hot water piping is installed above the ground, K Si − (i +1) can also be obtained by similar calculations for piping buried underground. In the analysis example herein, the hot water piping used is assumed to be a hard vinyl pipe with an outside diameter D o,Si − (i +1) = 50 mm, and a bore diameter D i,Si − (i +1) = 40 mm, and it is assumed that the piping is covered with a 30 mm thick polystyrene-foam system heat insulating material ( D c,Si − (i +1) = 110 mm). Moreover, the heat transfer coefficient h w of the hot water in the piping is set to 3.5 kW/m2･K, and the heat transfer coefficient h∞ between the heat-insulating material surface and atmospheric air is 11.6 W/m2K. These values are assigned to Eq. (6.2), and as a result, K S, t is calculated to be 0.05 W/m2･K. The outlet hot water temperature for Building Si is Tout,Si , t , and the outdoor air temperature in sampling time t is T∞, t . The amount of heat released H r,Si − (i +1), t in the piping of length L Si − (i +1) that connects Si and Si +1 for this time is calculated by the following equation. H r,Si − (i +1), t = K Si − (i +1) ⋅ D o,Si − (i +1) ⋅ π ⋅ L Si − (i +1) ⋅ ( Tout,Si , t − T∞ , t )

(6.3)

6.3 Model of the Fuel Cell 6.3.1 Characteristics of Electric Power and Heat Output The relationship between the ratio of electric power load to the power generation capacity of a fuel cell, defined as the load factor, and the ratio of heat output to the electric power output, calculated from the results of the power generation of a proton exchange membrane fuel cell stack, is shown in Fig. 2.7 [45]. This essentially represents the performance of the fuel cell stack. The characteristics of Fig. 2.7 are the measurement results of the electric power output at an AC–DC converter exit, and the heat output in a fuel cell stack exit. The load factor for the electric power load E n,Si , t of the fuel cell in Building Si is calculated, and the heat output H f,Si , t of the fuel cell can be calculated using the plot in Fig. 2.7.

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6.3.2 Energy Demand Pattern and Capacity of the Fuel Cell The urban area considered in the model is assumed to be Sapporo city in Japan, where the FEN is introduced as shown in Fig. 6.5. The total number of buildings in the urban area model is 15. The use of the buildings is shown in Fig. 6.5. The buildings include an individual house of a single-person household (SF in Fig. 6.5), a two-person household (DF), a three-person or four-person household (F), two households living together (DH) consisting of five or more persons, a small-scale office (SO), and an apartment house (AP1). Also, in Fig. 6.5, the shortest path of the hot water piping going via all the buildings is also shown. The electric power and thermal energy demand patterns for each building in winter (February), summer (August), and mid-term (May) as used in the analysis in the following section is shown in Fig. 6.6 [31]. The power generation capacity of the fuel cell linked to FEN makes the maximum value of the annual power consumption in each building nearly 1.2 times. The power generation capacity of the fuel cell used in the analysis in the following section is shown in Table 6.1. Table 6.1 Fuel cell capacity of the buildings

Fig. 6.5 The FEN urban area model for Sapporo in Japan

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Fig. 6.6 Energy demand patterns

6.4 Case Analysis 6.4.1 Weather Conditions in Sapporo In Sapporo city in Japan, which is in a cold and snowy area, the annual mean temperature for the past five years has been 282 K. The average temperature in Febru-

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Fig. 6.7 Outside temperature model for Sapporo

ary is 270 K, and the highest and lowest temperatures on a representative day in February are 273 K and 266 K, respectively. Moreover, the average number of snowy days for February is 25 days. On the other hand, the average temperature for a representative day in July day for the past five years has been 293 K, and the highest and the lowest temperatures 298 K and 290 K, respectively. The average temperatures in Sapporo for the sampling time on representative days in February, May, and August are shown in Fig. 6.7 [46]. There is no summer cooling load in Sapporo. Electricity demand includes household appliances and electric light, and heat demand comes from heating, the hot water supply, and baths. Thermoelectricity ratios for a representative day in February and August are 0.90:0.1 and 0.5:0.5, respectively. The area of an average individual house (three-person or four-person household) in Sapporo is 140 m2, the number of stories is two, and the houses are made of wood.

6.4.2 Analysis Method (1) The Genetic Algorithm (GA)

In this chapter, a program for the hot water piping path using the traveling salesman problem (TSP) [19] has been developed. The path of the hot water piping is described by the chromosome model used by the genetic algorithm. Crossovers and mutations are added to many chromosome models, and an analysis program evaluates the value of the objective function of each chromosome model. When the value of the objective function of a certain chromosome model fills the objective in a better fashion, this indicates that the “adaptive value” is high. In an analysis program, a chromosome model with a high adaptive value is made to survive with high probability, and other chromosome models become extinct. The model with the highest adaptive value is the optimal solution for the chromosome models, which are repeated and calculated for gene manipulation by crossovers and mutations of the chromosome models, and survive with the last generation number decided beforehand. However, if the problem in this chapter is analyzed by a general GA, many chromosome models showing a path passing through the same building two or more times will be generated. For this reason, it is necessary to extinguish many chromosome models, and the calculation efficiency will then

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fall significantly. Therefore, in this chapter, the view of order expression of the path by Dewdney is introduced [9]. The gene model in a chromosome model does not express the number of an actual building with the view of order expression of the path of Dewdney. A gene model expresses the number of the new buildings list devised so that the same building is not passed two or more times. Since the chromosome model, which must be canceled by devising the order expression of a path does not appear, the efficiency of the path planning analysis program improves. A chromosome model with a smaller amount of heat release from the hot water piping on a representative day is evaluated as an individual model with a high adaptive-value. The objective function FO is shown in the following equation. Day M

FO = ∑∑ H r,Si − (i +1) , t

(6.4)

t =1 i =1

(2) Analysis Flow

The analysis procedure of planning the path of a hot water piping network is described below. First, the coordinates of each ridge of the urban area model shown in Fig. 6.5, the energy demand pattern of the building shown in Fig. 6.6, and the outside temperature data of the representative day for each month in Sapporo shown in Fig. 6.7, are input into the program developed in this chapter. Next, N cr chromosome models showing the path order of the hot water piping are prepared at random. For each of these chromosome models, the objective function shown by Eq. (6.4) is evaluated. The top R cr % of the high chromosome model of the adaptive value is made to survive, and the remaining chromosome models are excluded. Crossover and mutations are added to the surviving chromosome models using the probabilities rcs and rmu . Furthermore, N ge generation numbers decided beforehand repeat the calculation for evaluating the adaptive value of the chromosome models and selecting the individuals with a low adaptive value. The hot water piping path obtained for the highest adaptive value individual, is made into an optimal path in the previous generation’s chromosome models. In the calculation of Eq. (6.4), the heat H r,Si − (i +1) , t released in the piping, which connects each building in the equation, is calculated using the following procedures. From the electricity demand pattern in Building i , the electricity demand E n,Si , t in the sampling time t is found. The power generation capacities of the fuel cells installed in each building are shown in Table 6.1, and the characteristics of electric power and heat output are obtained from Fig. 2.7. The load factor of the electric power can then be calculated by the power generation capacity of a fuel cell, and E n,Si , t and H f,Si , t can be obtained by applying the load factor and E n,Si , t to the curve in Fig. 2.7. The heat storage capacity is decided beforehand, and when the heat input exceeds this capacity, it is assumed that H ra,t of the quantity concerned is emitted

6.5 Analysis Results

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from a radiator. The amount of heat released from the hot water piping ( H r,Si − (i +1) , t ) in the sampling time t can be calculated by the ability to give H f,Si , t , H rm,t , H bh, t , H st, t , H n,Si , t , and H ra,t to Eq. (6.1). However, the exhaust heat temperature of a fuel cell is set to 353 K, and the temperature of the hot water returning from the hot water piping network to the machinery room is assumed to be nearly 333 K. (3) The GA Analysis Parameters

The number N cr of chromosome models is 3000, and R cr = 40% is made to survive in the high order of the adaptive value during selection. However, a chromosome model determined at random is generated and added instead of the 60% that became extinct. From the results of maintaining the diversity of the chromosome models, and trial and error, the crossover probability rcs is assigned to be 70%, and the mutation probability rmu is assigned to be 1%. The generation number N ge is 100. If the results calculated using these parameters are compared with the results calculated using several different parameters, the values generated for Eq. (6.4) are in agreement within several percent.

6.5 Analysis Results 6.5.1 The Optimal Path and the Amount of Heat Release from a Hot Water Piping Network Figure 6.8 shows the results of analyzing the planned route of the hot water piping using the urban area model shown in Fig. 6.5. Although the path of the shortest distance, which goes via all the buildings, was also shown in Fig. 6.5, the path analysis results in Figs. 6.8(a), (b), and (c) are the same as the path of the shortest distance in Fig. 6.5. In Figs. 6.8(a), (b), and (c), the total distance of the network, the hot water flow direction, and the starting point of the hot water network are shown. Figure 6.8(d) shows the calculation results of the heat demand of the FEN on a representative day of each month, and the amount of heat release in a hot water piping network. Also, Fig. 6.8(e) shows the ratio of the heat released in the hot water piping network to the heat demand on a representative day of each month. As shown in Fig. 6.8(d), the amount of heat release decreases in the order of winter, mid-term, and summer. The reason for this is that the difference in temperature between the hot water, which flows through the inside of piping, and the open air becomes smaller, and the amount of piping heat release becomes less, when the outdoor air temperature becomes higher. On the other hand, the ratio of the amount of heat release in the hot water piping network to the heat demand for

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Fig. 6.8 The path planning results for minimum heat. The arrangement of the buildings is the pattern shown in Fig. 6.5.

every monthly representative day shown in Fig. 6.8(e) increases in the order of winter, mid-term, and summer. Although the quantity of heat transported by the hot water in a season or time in a hot water piping network differs, this means that the amount of change of network heat release is smaller than the amount of change of the quantity of heat in the hot water. Therefore, in an FEN with various heat demands, the rate of the amount of the heat demand in hot water piping is small, and fuel cell exhaust heat is used effectively. In the analysis in Fig. 6.8, calculations were performed using several different energy demand patterns. In the following section, using the arrangement of the buildings shown in Fig. 6.5, the relationship between the energy demand pattern and the hot water path is investigated.

6.5.2 Optimal Path of the Energy Demand Pattern and the Hot Water Piping Figure 6.9(a) shows the urban area model of Fig. 6.5, and the path analysis results for the hot water piping assuming that the energy demand pattern of all the buildings is for a two-person household (DF). The path in the analysis results for representative days in February and May is the same as the shortest path shown in Fig. 6.5, and the starting point building is the same as that in Fig. 6.8. However, in contrast, the path of the hot water piping for a representative day in August is not the shortest one. Moreover, although the starting point building was also S15 on representative days in February and May, the starting point for a representative day in August is S12 . The heat demand of a building connected to the FEN for a

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Fig. 6.9 Path planning results for minimum heat release. The case which installed a different pattern into the same energy demand pattern

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representative day in August is extremely small compared to the quantity of the fuel cell exhaust heat output from each building. As a result, in the hot water piping network of FEN on a representative day in August, excess heat is released, and the results of path planning calculations for the hot water piping will in this case become unsuitable. The cause for this is analysis inaccuracy. Figure 6.9(b) shows the results for the hot water piping path for the energy demand pattern only for Building S11 assuming a small-scale office (SO). By the energy demand pattern for the SO, a representative day in August also has a large heat demand when compared to DF, and, as a result, unlike the hot water piping paths for a representative day in August shown in Fig. 6.9(a), there is no crossover of the piping. The heat demand and the amount of heat released in a hot water network, and the ratio of the amount of heat release to the heat demand for each representative day in Fig. 6.9(b) do not show a large difference. Figure 6.9(c) shows the results for the hot water piping path for the energy demand pattern of Building S8 when it is an apartment (AP2), and setting the energy demand patterns of other buildings to DF. In the analysis results of the hot water piping path for each monthly representative day, the optimal piping path differs from the path in Figs. 6.9(a) and (b). The electric power and heat demands for AP2 are very large compared to the demand for DF. The amount of heat released in a hot water piping network changes for the same reason as explained for Fig. 6.3, with the capacity of the fuel cell arranged for each building and paths of hot water piping. Although the heat demand for each monthly representative day increases compared with Figs. 6.9(a) and (b), there are few ratios that the amount of heat released in the hot water piping network to the heat demand, compared with Figs. 6.9(a) and (b). As described in the foregoing paragraph, the change in the amount of piping heat released is smaller than the change in the hot water quantity of heat transported in the hot water piping network. It is thought that the optimal path of the hot water piping network in an FEN is based on the energy demand pattern in each building and the power generation capacity of the fuel cells installed in each building.

6.5.3 The Influence of Load Fluctuations Figure 6.10(a) shows the results for the hot water piping path for the energy demand pattern of Building S8 when it is a store (CV), for a time period of 24 hours, and by setting the energy demand pattern of other buildings to DF. For the energy demand pattern of CV, since there is a 24-hour electricity demand, the exhaust heat output of the fuel cell is large, and the path planning results of the hot water piping for each monthly representative day differs from the path of the shortest distance. The path of the hot water piping, the starting point building, and the flow direction of the hot water are also the same for a representative day for each month in Fig. 6.10(a). The path planning results of hot water piping in the presence of ±30% and ±60% random load fluctuations to the electricity demand pattern in

6.6 Conclusions

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Fig. 6.10 The energy demand pattern of all the buildings other than S8, which is a building for a two-person household (DF). The energy demand pattern of S8 is of a store of day-long business (CV)

each ridge of the system shown in Fig. 6.10(a) are shown in Figs. 6.10(b) and (c). The path planning results of the hot water piping shown in Fig. 6.10(b) is the same as the results when there is no load fluctuation (Fig. 6.10(a)). However, when load fluctuations of electric power is ±60%, as shown in Fig. 6.10(c), the path planning results of the hot water piping differs from Figs. 6.10(a) and (b). These results demonstrate that when planning the hot water piping network in an FEN, it is necessary to take the load fluctuations of electricity demand into consideration.

6.6 Conclusions For FEN, cooperative control of each operation of the fuel cell installed in two or more buildings was carried out, and an effective utilization approach of the exhaust heat of the fuel cells was considered. In this chapter, path planning at the time of supplying exhaust heat of the fuel cell is linked through the FEN to each building through hot water piping was investigated. In consideration of the difference in temperature of the open air and the hot water in the hot water piping network, a program that searches for the path of hot water piping for minimal piping heat release was developed. As a result of conducting a case analysis, the optimal path of the hot water piping network in an FEN confirmed the energy demand pattern in each building, and the difference in the power generation capacity of the fuel cell installed in each building. Furthermore, if the fluctuation of the electricity demand in each ridge linked to the FEN becomes large, the optimal path of the hot water piping network will be influenced.

Chapter 7

Load Response Characteristics of the Fuel Cell for Individual Cold-region Houses

7.1 Introduction In order to introduce and apply small fuel cell cogeneration to a building, it is necessary to investigate the response characteristics of the power and heat power with load fluctuations. In particular, the power demand pattern of an individual house is a load that has usually gone up and down rapidly for a short time. If a system is controlled to follow such a load, the difference in the response and load increases. As a result, the power quality (voltage and frequency) of this power system may worsen. “Fluctuation in a short period, such as an inrush current”, and “fluctuation in a long period to cause in change of demand” are included in the power load. “Change over a long period” means a step change in the power demand. With the change factor of the transient power demand, such as with an inrush current, there is a change over a long period in the demand. When “transient power demand” is defined as load fluctuation and “change over a long period” is defined as demand fluctuation, the power load changes of an individual house have large fluctuations of both. If the transient response of a single cell of a fuel cell is examined, it seems that stable response characteristics are acquired for the load fluctuation characteristics of the household appliance items used in common homes. However, the details of the transient response characteristics when putting together a reformer, etc., are not known [47–50]. In the reformer, a shift catalyst and oxidation catalyst for removing carbon monoxide, other than a reforming catalyst, are also provided. In order to operate each catalyst with high efficiency, it is necessary to control each in the appropriate temperature range. For this reason, the transient response characteristics of the reforming gas outputted by a reformer are slow compared with the transient response characteristics in a fuel cell [23, 26, 27, 51]. The control variables of the controller that controls the power and heat power is determined from the transient response characteristics, such as the settling time (this chapter defines in a period converged on less than ±5% of the target value), overshooting, rising time, and steady-state error. The response S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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characteristics of a system change greatly with the value of the control variables. When introducing a fuel cell system into a house, it is necessary to consider the two transient response characteristics: electric power and heat power. However, there have been no reports on the details of the transient response characteristics of the fuel cell system composed of a reformer, a fuel cell, an inverter, a system interconnection device, etc. Similarly, there are examples reporting the transient response characteristics of the heat supply of a fuel cell system composed of fuel cell exhaust heat, an auxiliary heat source, a heat storage tank, etc. So, in this chapter, the transient response characteristics of the power in the proton exchange membrane type (PEM) fuel cell cogeneration with a town gas reformer and heat output are investigated. However, “town gas” is natural gas of a methane principal component. An overall transient response characteristic is investigated by numerical analysis using the response characteristic of each instrument obtained in the experiment. Furthermore, the dynamic characteristics of the system when introducing the energy demand pattern of an individual cold-region house into the fuel cell system in which a geo-thermal heat pump is installed as an auxiliary heat source are clarified.

7.2 System Configuration 7.2.1 System Outline The system outline figure of the PEM fuel cell examined in this chapter is shown in Fig. 7.1. Further, the assumed specifications of the system are shown in Table 7.1. The main equipment of the system is a town gas reformer, a PEM fuel cell, a dryer, a carbon monoxide oxidization system, an inverter including a DC/AC inverter, a heat pump, a heat storage tank, and a radiator. The dynamic characteristics of the heat storage tank are not taken into consideration in the analysis described below. The dynamic characteristics of a heat output examine only the geo-thermal heat pump. If a complex system is installed in an individual house, the time to recover facility costs becomes long. However, the cost of a PEM fuel cell system may be greatly reduced due to advances in material development and manufacturing techniques. The output characteristics of the fuel cell stack used in this chapter are shown in Fig. 7.2. The maximum power generation efficiency of the fuel cell stack shown in Fig. 7.2 is 48% [28, 29]. The town gas supplied to the system is of the quantity reformed by the reformer ( Q f,rm,t , t is sampling time), and the quantity consumed by the burner as a heat source of the reformer ( Q f,cb,t ). The relationship to the supply adding these two amounts of town gas ( Q f,rm,t + Q f,cb,t ) and the power outputted by a fuel cell system ( E Inverter,t ) is shown in Fig. 7.3 [28, 29]. However, Fig. 7.3 shows the characteristics when the electrode area of the anode cathode is 0.5 m2. Here, the value dividing the calorific power of the hydrogen in the reforming gas generated by the

7.2 System Configuration

Fig. 7.1 The PEM fuel cell co-generation system

Fig. 7.2 Cell performance with reformed gas and air. The operating temperature is 333 K

Fig. 7.3 The electric power output of the system outlet. The areas of the electrode of the anode and the cathode of the fuel cell stack are 0.5m2, respectively. Reformer efficiency is 73%

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7 Load Response Characteristics of the Fuel Cell for Individual Cold-region Houses

Table 7.1 System specifications

reformer by the calorific power of town gas supply ( Q f,rm,t + Q f,cb,t ) is defined as reformer efficiency η RM, t . In this analysis η RM, t is 73% [23, 26]. The power that can be supplied to the demand side from a PEM fuel cell is a maximum of 1.0 kW, and the maximum heat power of the heat pump is 15 kW.

7.2.2 Method of Power Generation In a reformer, reforming gas is produced in the catalyst using the combustion gas of town gas as a heat source. There is a large amount of water in the reforming gas generated by steam reforming. Therefore, a dryer is prepared, reforming gas is cooled by air supply from a blower, and the water is condensed and dissociated. In order for the carbon monoxide concentration in the reforming gas in a fuel cell stack entrance to be several ppm, carbon monoxide oxidization equipment is prepared. With carbon monoxide oxidization equipment, carbon monoxide is burned in a catalyst and changes into carbon dioxide. In this chapter, about 2% of hydrogen in reforming gas burns with carbon monoxide oxidization equipment. Therefore, the efficiency of carbon monoxide oxidization equipment is 98%. These values refer to past experiments [52]. Reforming gas is supplied to a fuel cell stack from carbon monoxide oxidization equipment, and after changing the DC power generated by the fuel cell into AC power of a fixed frequency through an inverter, the demand side is supplied from a system interconnection device. It is possible to switch and supply the power generated by the fuel cell system and commercial power to the demand side. Moreover, commercial power is used for the operation of the heat pump.

7.2 System Configuration

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7.2.3 Method of Heat Supply The exhaust heat of a PEM fuel cell and a reformer is stored in a heat storage tank (ST). Tap water is supplied to the heat exchanger installed in the heat storage tank, exhaust heat and the heat of the tap water are exchanged, and hot water is supplied to the demand side. When the exhaust heat exceeds the heat demand, the surplus heat is stored. Moreover, a heat pump is operated when there is high heat demand and exhaust heat and thermal storage output are insufficient. However, the response time of the exhaust heat output in the reformer and fuel cell stack is long compared with the response time of the heat output in the thermal storage and heat pump. Priority is given to the heat output of the thermal storage and heat pump when the heat demand requires a quick response. When there is a large amount of exhaust heat and it exceeds the capacity of the heat storage tank, valves VA2 and VA3 in Fig. 7.1 are operated, and excessive heat is released out of the system. It is assumed that this system will be used in a cold region. A heat exchanger is buried in the ground, and the heat source of the heat pump is obtained from the soil. With the circulation pump PP2 in Fig. 7.1, it circulates through a heat transfer medium between a subterranean heat exchanger and the low-temperature-side heat exchanger of the heat pump, and heat is obtained from soil. The heat power in a trial production geo-thermal heat pump and the test result of the coefficient of performance (COP) introduced into this system are shown in Fig. 7.4. Fig. 7.4 Characteristics of heat output of geo-thermal heat pump system

7.2.4 Controller and Auxiliary Machinery Two controllers are installed in the proposed system. As shown in Fig. 7.1, Controller 1 controls the power generation system and Controller 2 controls the heat supply system. In each Controller, each control variable of a proportional action (P), an integral action (I), and a derivative action (D) can be set up, and the output is put close to the target value by feedback control. In Controller 1, the data of the temperature ( T2 ) of a fuel cell stack and the amount of electricity demand ( E need,t ) is inputted, and in Controller 2, the data of the heat-transfer-medium

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temperature in the heat storage tank ( T1 ) and the heat amount demanded ( H need,t ) are inputted, and operation commands are outputted to each blower, valve, and pump. The power generated by the fuel cell system and commercial power can be supplied to the demand side through a system interconnection device. However, the auxiliary machinery in Fig. 7.1 described below is operated with commercial power. The auxiliary machinery operated with commercial power is a blower for a reformer burner (BW1), a blower for a dryer (BW2), a blower for a fuel cell cathode (BW3), a exhaust heat extraction pump (PP1) of a fuel cell stack, a circulating pump for a geo-thermal heat pump (PP2), and an electric motor for a heat pump (MT). By the command of Controller 1 and Controller 2, the switch of SW1, SW2, SW3, SW4, and SW5 is operated, and each of these auxiliary machines are started and stopped. Moreover, the number of rotations of MT is controlled by the command of Controller 2.

7.2.5 Model of Operation Control Figure 7.5 shows the operation model of the fuel cell system of Fig. 7.1. System operation when giving the power load shown in Fig. 7.5(a) and the heat load shown in Fig. 7.5(b) to a fuel cell system is considered. Figure 7.5(c) shows the

Fig. 7.5 System operation model

7.3 The Time Constant of Each Piece of Equipment

119

model of operation of the switch of a system and valves that are controlled by the controller. The symbols in this figure correspond to the symbols shown in Fig. 7.1. In starting up a system, in order to supply air to the burner for the heat source and the dryer of a reformer, switches SW1 and SW2 are turned ON by the command of Controller 1, and blowers BW1 and BW2 are operated. At time t1 , in order to supply air to the cathode of the fuel cell, a command is given to SW3 from Controller 1, and BW3 is operated. A control command is sent so that Controller 1 to valve VA1 may be opened simultaneously. Figure 7.5(d) shows a fuel cell in operation following the power load. The transient response characteristics, such as the reformer, fuel cell stack, and inverter, affect the response characteristics of the system. Therefore, compared with the input characteristics shown in Fig. 7.5(a), time delay, overshooting, etc., produce the response characteristics of Fig. 7.5(d). Immediately after start-up, a heat pump with large heat output is the main heat source. By the control command of Controller 2, the electric motor MT of heat pump, SW5, and circulating pump PP2 are operated at time t1 . If the operating time of a system passes and temperature T2 of the fuel cell stack increases, SW4 will be turned ON by the command of Controller 1 exhaust heat-taking pump PP1 of the fuel cell stack will be operated. If operating time passes and the exhaust heat output of the fuel cell stack and reformer increases, the heat output of the heat pump can be reduced. When the amount of exhaust heat of the fuel cell stack and reformer satisfies the heat amount demanded, the heat pump is stopped by the command of Controller 2. Figure 7.5(e) shows a model of these operations. Furthermore, when the two amounts of exhaust heat exceed the heat amount demanded, as shown in Fig. 7.5(f), excessive heat is stored in the heat storage tank. Moreover, as shown in Fig. 7.5(g), when the exhaust heat exceeds the capacity of the heat storage tank, valves VA2 and VA3 are operated by the command of Controller 2, and surplus heat is released from the system. Figure 7.5(h) shows a model of the amount of consumption of town gas in the system. The consumption of town gas has the same characteristics as shown in Fig. 7.5(d) depending on the production of electricity of the fuel cell. The output characteristics of Fig. 7.5(d) differ greatly from the control variables set as Controller 1. The characteristics of the town gas consumption of the system also change with the value of the control variable set up by Controller 1.

7.3 The Time Constant of Each Piece of Equipment In order to investigate the transient response characteristics of the power and the heat of the system shown in Fig. 7.1, the transient response characteristics of each piece of equipment of the fuel cell, reformer, heat pump, inverter, and system interconnection device are expressed by a primary delay system. The time constant of each piece of equipment is determined as described below.

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7.3.1 The Time Constant of the Fuel Cell Stack The heat output characteristics of a fuel cell stack are calculated by the enthalpy transportation of the off-gas discharged from the cathode obtained using the test system of Fig. 7.6. In the test system of Fig. 7.6, the initial operation temperature of the fuel cell stack installed in a case that is thermally insulated can be set up freely. From the examination results of this test system, the power generation efficiency of a fuel cell is considered depending on the operation temperature. According to an experimental result, when the operating temperature of the fuel cell changes from 288 K to 358 K, there is about 30% difference in the power generation efficiency. In this chapter, the temperature of the fuel cell in operation is a constant 333 K. Figure 7.7 shows the transient response characteristics of the heat output of a fuel cell stack. This figure was prepared from the experimental results of the test system using the power and the heat output of the fuel cell stack. As shown in Fig. 7.7, the strong production of electricity depends on the transient response characteristics of the heat output of the fuel cell stack. In this chapter, as shown in Fig. 7.7, approximated curves are prepared for each production of electricity, and transfer functions are also prepared. Time constants a and b of the

Fig. 7.6 Fuel cell stack test system

Fig. 7.7 The model of air exhaust-gas temperature of the fuel cell stack at the time of changing a load

7.3 The Time Constant of Each Piece of Equipment

121

transfer function of the fuel cell shown in Table 7.2 are arranged using the production of electricity of the fuel cell ( E f, t ), as shown in Figs. 7.8 and 7.9. Therefore, the values of a and b are determined by giving the amount of power outputs of the fuel cell to the approximate expression in Figs. 7.8 and 7.9. The transfer function of the heat output in the fuel cell stack is determined by giving a and b to the transfer function of Table 7.2. Figure 7.10(a) shows the examination result of an experiment on a trial production fuel cell stack using the test system of Fig. 7.16. This fuel cell stack is 100 W and capacity differs from the fuel cell stack (1.1k W) shown in Fig. 7.11. Therefore, in this chapter, it is assumed that the “capacity factor” of a fuel cell is the same characteristic. In the examination of Fig. 7.10(a), a load of 70% of the capacity factor was inputted into the fuel cell stack in a step-wise manner, and the transient response characteristics were obtained. Here, the value that divided “the output of the equipment” by “the equipment capacity” is defined as the capacity Table 7.2 Transfer function of a heat output

Fig. 7.8 The approximate equation for the time constant of the transfer function

Fig. 7.9 The approximate equation for the constant of the transfer function

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7 Load Response Characteristics of the Fuel Cell for Individual Cold-region Houses

Fig. 7.10 Dynamic characteristics of a test PEM fuel cell stack

Table 7.3 Transfer function of an electric power output

factor. As a result, the transient response characteristics shown in Fig. 7.10(b) are obtained, and the formula expressing these characteristics with the transfer function of a primary delay system is shown in Table 7.3. To be exact, the transfer function depends on the capacity factor. However, according to the examination results of this chapter, the influence of the capacity factor on the transfer function is small and is not taken into consideration.

7.3 The Time Constant of Each Piece of Equipment

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Fig. 7.11 Dynamic characteristics model of the reformer

7.3.2 Town Gas Reformer Figure 7.11(a) shows the response characteristic when giving the step input to a town gas reformer. The result shown in this figure changed the capacity factor between 100%–80%, and between 100%–50%. Figure 7.11(b) shows the transient response characteristics acquired from this result. This characteristic is expressed with the transfer function of a primary order system and is described in Table 7.3. A transfer function influences a capacity factor exactly like a fuel cell stack. In this chapter, within the range described above, there is no large difference, and this influence is not taken into consideration. Figure 7.12 shows the response characteristic model of the exhaust heat of the heat-source burner installed in the reformer when inputting a capacity factor of the reformer from 50% to 100% in a step-wise manner [23, 26, 27]. As shown in this figure, unlike the heat output characteristics of the fuel cell stack and heat pump described later, the response characteristics of the exhaust heat of the heat-source burner installed in the reformer show an ‘S’ shape. Then, an approximated curve is independently prepared for the transfer function of the response characteristics of Fig. 7.12 for each range of A1 and A2. The transfer function of the reformer shown in Table 7.2 is a formula for the A2 range.

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Fig. 7.12 Model of the burner exhaust gas heat at the time of a load going up from 50% to 100%

7.3.3 Inverter and System Interconnection Device A cheap voltage control type inverter was used. Although the power inputted into the inverter is changed and outputted to the voltage and the frequency of the requirement (less than ±5% of the regulation in this chapter), 120 ms is taken [53]. When the transfer function of an inverter is expressed with a primary delay system, it is the formula shown in Table 7.3. System interconnection equipment is used to change the power of single-phase 100 V to a network. It takes about 10 μs to change the system interconnection equipment [53]. The operation time of the model of the system interconnection equipment assumed in this chapter is set at 12 ms [53]. The transfer function of the system interconnection equipment by a primary delay system is set up using the formula shown in Table 7.3.

7.3.4 The Time Constant of the Heat Pump Figure 7.13 shows the experimental result of the heat output characteristics of a geo-thermal heat pump when inputting a step-wise load so that it operates at maximum output in an instant from the unloaded condition [54]. The hightemperature-side heat exchanger of the trial production geo-thermal heat pump was installed in the water tank, and this experimental result was obtained from the water temperature change. On the other hand, the black dot in the figure is an approximation of the examination result of the transient response characteristics of the heat output of the heat pump. From the approximated curve, the transfer function of the heat pump shown in Table 7.2 was prepared.

7.4 Analysis Method

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Fig. 7.13 Characteristics of transient response of the geo-thermal heat pump

7.4 Analysis Method Figure 7.14 is a control block diagram of the system shown in Fig. 7.1. If the load of the power and heat is given to Fig. 7.14, a transient response characteristic can be investigated. Random fluctuation is added for every sampling time supposing the actual power load. There are two controllers: Controller 1, which controls a production-of-electricity network, and Controller 2, which controls the heat supply network in this system. Controller 2 is contained in Subsystem 0 in Fig. 7.14, and Fig. 7.15 shows the details of Subsystem 0. Immediately after Controller 1 and Controller 2, a limiter is provided so that extremely large overshooting does not occur. Subsystem 1–8 is a transfer function of the response characteristics of the exhaust heat output in the fuel cell stack, and Fig. 7.16 shows this information. a and b of the transfer function of Fig. 7.16 express the time constant and the constant part in a primary delay system transfer function, respectively. a and b can decide to give the production of electricity E f, t of a fuel cell to Fig. 7.8 and Fig. 7.9, as the section “The Time Constant of the Fuel Cell Stack” described. The exhaust heat of a fuel cell stack is a non-linear output to the power load, as Fig. 7.7 shows. So, in this chapter, the relation between the power load and the exhaust heat output of a fuel cell is divided into eight ranges for the power load. The relation between the power load and the exhaust heat output of each range is approximated by the transfer function formula shown by Subsystems 1–8. The dynamic characteristics of a heat storage tank are not taken into consideration in analysis for simplification. Moreover, operation of SW4 and PP1 shown in Fig. 7.1 and the time delay of the heat pump are not taken into consideration, either. The transient response characteristics of the power and heat output of the system of Fig. 7.14 is analyzed by MATLAB® (Ver.7.0)/Simulink® (Ver.6.0) of The MathWorks Corporation. In the solver to be used, the Runge–Kutta method is installed, and the sampling time of analysis is calculated automatically and determined so that the error may be less than 0.1%.

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Fig. 7.14 System control block diagram

Fig. 7.15 Block diagram of Subsystem 0 Fig. 7.16 Block diagram of Subsystems 1–8

7.5 Results and Discussion 7.5.1 Control Variables of the Controller The analysis results of the response characteristics of the control variables set up by Controller 1 and Controller 2, the power output of the system, and the heat output of the geo-thermal heat pump are shown in Figs. 7.17 and 7.18, respectively. Figures 7.17(a), (b), and (c) inputted the power load (0.2 kW, 0.6 kW, and

7.5 Results and Discussion

Fig. 7.17 Characteristics of electric power output of the system

Fig. 7.18 Characteristics of heat output of the geo-thermal heat pump system

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1.0 kW) at t = 5 s , respectively. The control variables of action P and action I set up by Controller 1 were calculated by changing them arbitrarily, as shown in Fig. 7.17. When action P is used independently in the control of the system by Controller 1, steady-state error is large. Moreover, with the use of action D, such as D control, PD control, and PID control, the response characteristics of the system become extremely unstable, and we decided not to use action D for this analysis. The optimal values of the control variables installed into Controller 1, which controls the production-of-electricity network of a fuel cell system, differ in the volume of the power load from each result of Figs. 7.17(a), (b), and (c). However, the response characteristics do not depend on the control variables in the range of the control variables of the P action and the I action shown in Fig. 7.17(c) by 1.0 kW of the maximum production of electricity of the system. On the other hand, the response characteristics of the heat pump of Fig. 7.18 are the result of inputting a heat load (5.0 kW and 15.0 kW) in a step-wise manner at t = 5 s . The optimal value of the control variables set up by Controller 2 from this result changes with the volume of the heat load. Therefore, in order to improve the transient response characteristics of the heat pump, it is necessary to change the control variables of the controller in the magnitude of the heat load.

7.5.2 Step Response Characteristics of the System (1) Power Response Results

Figure 7.19 shows the analysis results of the transient response when inputting a power load of 0.2 kW, 0.5 kW, and 1.0 kW into the system of Fig. 7.14 in a stepwise manner. The step input is performed at t = 5 s . Figure 7.19(a) shows the result of calculating the response of each power load. In these calculations, the control variables of the P action and the I action control were set as the values shown in this figure. Control variables for the P action and the I action that considered that a transient response is good for every power load were chosen (offset and overshooting is small, settling time is short), and the result shown in Fig. 7.19(a) was obtained. However, as Fig. 7.17(c) shows, the response characteristics of a 1.0 kW power load do not depend on the control variables of P and I. The response characteristics of 1.0 kW with the largest power load has a long settling time compared with other results in Fig. 7.19(a). When the response of the system has a large power load, the response speed is low, and with the maximum power output, it is the latest response. When a power load of 1.0 kW of maximum output is put into the system of unloaded condition, the settling time is about 10 s In order to shorten the settling time of the system, it is necessary to improve the reformer with the slowest response speed.

7.5 Results and Discussion

Fig. 7.19 Analysis results of the fuel cell system with a geo-thermal heat pump

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(2) Heat Response Results

Figure 7.19 (b) shows the analysis result of the heat load response of the system. Since the response speed of the exhaust heat in the fuel cell stack and reformer is lower than the heat pump, as shown in the figure, it starts the heat supply using the heat pump. Gradually, the exhaust heat of the fuel cell stack and the reformer increases, and the heat output of the heat pump are controlled to reduce the quantity concerned. Figure 7.19(c) shows the analysis result of the power consumption of the heat pump when the heat loads are 5 kW, 10 kW, and 15 kW in a 1.0 kW power load. If the operation time passes as Fig. 7.19(b) describes, the exhaust heat output of the fuel cell stack and reformer will increase, and the electric power consumption of the heat pump will decrease with time. (3) Amount of Town Gas Consumption and Power Generation Efficiency

The cold start of a reformer takes about 10 minutes to 1 hour [8, 27]. It is assumed in this analysis that the reformer operates when having already finished the warmup. In this case, the analysis result of the amount of town gas consumed by the reformer is shown in Fig. 7.19(d). The analysis result in the case of a power load 0.2 kW, 0.5 kW, and 1.0 kW is shown in this figure. For the town gas consumption amount it depends on the response characteristic of power load. Moreover, the settling time of 1.0kW of power loads is the longest (Fig. 7.19(a)). The response characteristics of a reformer will be good from now on due to modification of the control method and the improvement of the heat transfer performance.

7.5.3 Step Response Characteristics with Power Load Fluctuation (1) Electricity Production of the System

Figure 7.20 shows the analysis result of the response characteristics of the system when considering load fluctuations to the step input of the power load. Figure 7.20(a) shows the load pattern that added ±10% of fluctuation to a 1.0 kW power load. As shown in Fig. 7.20(a), a step load was inputted at t = 5 s , and load fluctuations were given at random within a range given beforehand for every sampling time. Figures 7.20(b) and (c) show the error analysis result of the step response when adding ±10% of load fluctuation to the base power load (0.5 kW and 1.0 kW). The main causes of the error shown in Figs. 7.20(b) and (c) are a time delay of the system and the influence of overshooting. Figure 7.21 shows the analysis result of the difference in the step load and system response with load fluctuation when operating the system for 200 s. There are fewer response results of the system than loading when the load fluctuation is 0%, and if load fluctuation is added, there is an increase compared to loading. In

7.5 Results and Discussion Fig. 7.20 Analysis results of electric power output of the fuel cell system with load fluctuations

Fig. 7.21 Balance of production of electricity

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Fig. 7.22 Percentage of the integrated value of a response result to a load integrated value if a sampling time period is operating for 200 s

Fig. 7.22, the response result at the time of 0% load fluctuation is a negative value. This main reason is the time delay of the response. On the other hand, when the response result is a positive value, an overshoot follows the response. (2) Heat Power of the System

Figure 7.22 shows the analysis result of the difference in the amount of heat load and the heat output of the heat pump when inputting a step load with a load fluctuation of power for an operating time of 200 s. When load fluctuation increases, there are fewer heat outputs of the heat pump than the heat load. As Fig. 7.21 describes, when load fluctuation becomes large, the power output of the system will increase. From this, when load fluctuation becomes large, the amount of exhaust heat of the fuel cell stack and the reformer will increase, and the heat output of heat pump will decrease. Moreover, if the heat load becomes large, the heat output of the heat pump is approximated to the amount of heat load. This is because the ratio of the exhaust heat output of the fuel cell stack and reformer to the heat output of the heat pump is small when the heat load is large. When power load increases, the amount of exhaust heat of the fuel cell stack and the reformer will increase. Therefore, the rate of heat output of the heat pump to heat load falls. As a result, the power consumption of the heat pump decreases, so that the power load is large and the load fluctuation is larger. Figure 7.23 shows the analysis results of the response of the heat pump output when changing the PI control parameter set up with the controller (Controller 2) of the heat pump shown in Fig. 7.15. When the analysis results of the transient response characteristics are compared in the difference in control parameters, Ph =200.0 and Ih =0.05, which has the best evaluation of settling time, overshooting, and steady-state error. So, the control parameters of the controller of the heat pump are set at Ph =200.0 and Ih =0.05 in the following analyses.

7.5 Results and Discussion

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Fig. 7.23 Heat output of the geo-thermal heat pump system

7.5.4 Application to an Individual Cold-region House In Sapporo in Japan, which is a cold and snowy area, the annual mean temperature for the past five years has been 282 K. The average temperature in February is 270 K, and the highest and lowest temperatures on a representative day in February are 273 K and 266 K, respectively [14]. Moreover, the average number of snowy days in February is 25. On the other hand, the average temperature for a representative July day has been 293 K for the past five years, and the highest and the lowest temperatures are 298 K and 290 K, respectively. There is no cooling load in the summer in Sapporo. Electricity demand includes household appliances and electric lights, and heat demand comes from heating, hot water supply, and baths.

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Fig. 7.24 Calculation results of heat system in Sapporo

Figures 7.24 and 7.25 show the analysis results for the heat output and power output in the system at the time of installing a PEM fuel cell under the load pattern of an average individual house in Sapporo [8]. The horizontal axis of Figs. 7.24 and 7.25 is the sampling time of the analysis. Real time is also displayed on the horizontal axis of Fig. 7.24(a). Since the computation time is enormously long, real-time analysis is performed by shortening the real time to 1/180 in this chapter. A model of the heat and power load pattern of the representative time of winter (February), mid-term (May), and summer (August) of an individual house in Sapporo is shown in Figs. 7.24(a) and 7.25(a). The heat load for hot water supply and baths is included in the heat load pattern. Figure 7.24(b) shows the analysis result of the heat response of the heat pump. From this result, the response speed of the heat output of the heat pump is sufficiently rapid, and there is no problem in following the heat load. Figure 7.24(c) shows the analysis result of the heat response that added the exhaust heat of the heat-source burner of the reformer to the exhaust heat of the fuel cell stack. The time constant of the exhaust heat output of the

7.6 Conclusions

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Fig. 7.25 Calculation results of electric power system in Sapporo

fuel cell and the exhaust heat output of the heat-source burner of the reformer are large. As shown in Fig. 7.24(c), the heat output does not converge within this sampling time. Figure 7.24(d) shows the analysis result of the heat dissipation from the system through a radiator. However, this heat is recoverable if the heating storage capacity is extended. On the other hand, Fig. 7.25(b) shows the analysis results of the response characteristics of the power when controlling the PEM fuel cell to follow the power load pattern of Fig. 7.25(a). The electric power needs model of Fig. 7.25(a) is an average in each time. It consists of many dynamic peaks of transient power demand. The electric power demand of a time average was used for analysis. This way, the response speed of the power of the system is very fast compared with the response speed of the exhaust heat of the fuel cell and reformer. From this result, in order to supply heat demand only with the exhaust heat of the fuel cell and the reformer, improvement of the heat response is required. In order to supply early morning heat demand, to recover the exhaust heat of the fuel cell and the reformer, waste heat recovery of a long period is required. Therefore, the heat dissipation loss of the heat storage tank is predicted to have a large influence on the overall efficiency of the system.

7.6 Conclusions The dynamic characteristics of a fuel cell system for individual houses in cold regions have been investigated using numerical analysis. This chapter examined the transient response characteristics of the power and heat output of proton exchange membrane type (PEM) fuel cell cogeneration with a town reformer. Furthermore, from experiments, etc., the transient response characteristics of each piece of equipment of the fuel cell, the reformer, the heat pump, the inverter, and

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the system interconnection equipment was expressed by a primary delay system, and a control block diagram of the system was prepared. This control block figure was analyzed, the control variables were set up with the controller of the system, and the response characteristics of the power and heat were clarified. As a result, the following conclusions were obtained. (1) By changing the control variables of the controllers according to the magnitude of the power load and the heat load, the response characteristics (settling time, time delay, overshooting) of the system improve. (2) In order to improve the response characteristics of the power of the system, it is necessary to improve the response speed of the reformer. (3) The production of electricity of the system is sometimes small compared with the integrated value of step load. This causes a time delay of the system. However, when a power load with fluctuations is put into the system, the production of electricity is large compared with the integrated value of the step load due to the response overshooting. (4) The dynamic characteristics of the system when installing a fuel cell system with a geo-thermal heat pump into the energy-demand pattern of an individual cold-region house were investigated. From these analysis results, the system operation for every sampling time and the relation to the response characteristics were clarified.

Chapter 8

Load Response Characteristics of a Fuel Cell Micro-grid with Control of the Number of Units

8.1 Introduction The micro-grid is expected to reduce the discharge of carbon dioxide gas, to cut the peak of an electric power plant, and to supply backup power in an emergency [21–23]. A micro-grid technique connects energy equipment, such as an engine generator and a fuel cell, and power is supplied by each cooperating piece of equipment. In forming a micro-grid, the coordinated grid system with commercial power, etc., and the independent grid system should be considered. In the coordinated grid type, the supply and demand of power with a commercial system are possible, and the peak cut of an electric power plant and the buying and selling of power are possible. If the exhaust heat of the generating equipment linked to a micro-grid needs to be conveyed only a short distance, it can be supplied to the consumer with small radiation loss. On the other hand, in the exhaust heat of the large-scale conventional power plant, long distance transport has many heat losses, and utilization of exhaust heat is limited. One of the problems predicted by the construction of a micro-grid is that power quality deteriorates when the power demand and supply balance of the grid do not balance. The deterioration of the power quality described in this chapter means that fluctuation of voltage and frequency because the dynamic characteristics of an electric power supply do not meet the demand. Power quality can be maintained comparatively easily by controlling the voltage and frequency of a grid by a coordinated grid type to synchronize with another network, such as commercial power [51]. On the other hand, in an independent grid type, the reference of the power is determined to be any power generator linked to a grid. In addition, other voltages and frequencies of generators are controlled to synchronize with this reference electrode. Therefore, if the power quality of the power generator made into a reference is not stabilized, the power quality of the whole grid may deteriorate. In order to stabilize the power quality of a micro-grid, the method of connecting a battery to a micro-grid can be used. However, when facility cost and the maintenance cost of a facility are taken S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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into consideration, the introduction of a common battery is difficult. If the number is controlled and the distributed power generator is made to correspond so that a rapid change of electricity demand may be followed, the response characteristics of each power generator will influence the power quality of the grid. In this chapter, the dynamic characteristics of a highly independent micro-grid that connects a gas-engine power generator (EG) and a solid-polymer-membrane-type fuel cell (PEMFC) as a distributed energy system are investigated for the possibility of achieving this. Generally, in order to maintain high generation efficiency by EG, it is necessary to extract the area of an operation point (load and number of rotations) as much as possible. Moreover, if large load fluctuation is added to EG, tens of seconds are required to stabilize the power [56]. Therefore, EG is used for base load operation in this chapter, and the fuel cell distributed in each house examines the micro-grid that controls the number according to the amount demanded. EG and PEMFC connected to a grid cooperate according to the power demand and supply the power. In this case, although the exhaust heat of EG and PEMFC is supplied to each house, the characteristics of the heat supply are not dealt with in this chapter. One method of connecting a fuel cell with the grid is to install one set of large-capacity PEMFC (centralization system), and another method is placing PEMFC in each house (distributed system). Because the centralization system should only install one set of PEMFC, equipment and installation costs are low compared with the distributed system. However, if PEMFC is operated in a time zone with small electricity demand, a low-efficiency partial-load operation will produce a centralization system. On the other hand, when distributing PEMFC and controlling the number, the fuel cell operated with a partial load is one set of distributed fuel cells. Therefore, the decrease in generation efficiency is predicted to be smaller in the distributed system than in the centralization system. Thus, an engine generator with a generation capacity of 3 kW is installed in a house, and it is made to correspond to the operation of the base load as an analysis example of this chapter. In addition, the dynamic characteristics of a micro-grid and the generation efficiency of fuel cells when a fuel cell system with a generation capacity of 1 kW is distributed and installed in 16 houses, and when one PEMFC with a generation capacity of 16 kW installed (centralization system) are examined.

8.2 The Micro-grid Model 8.2.1 Power Quality of the Micro-grid The energy network and micro-grid that are installed in an urban area are shown in Fig. 8.1. Figure 8.1(a) shows the micro-grid of the system linked with a commercial power network, and Fig. 8.1(b) shows the micro-grid of the independent system that does not connect with other grids. An energy network consists of a hot water piping network used for the waste heat recovery of energy equipment, and

8.2 The Micro-grid Model

139

Fig. 8.1 Fuel cell micro-grid system

the heat supply to each house, a city gas network that supplies city gas to the reformer for fuel cells, and a gas-engine power generator, and a micro-grid that supplies electric power to each house. The power transported by the independent micro-grid shown in Fig. 8.1(b) needs to control the voltage and frequency of each power generator on the basis of the power of any one power generator. On the other hand, it is controllable by the micro-grid connected with the other power grids shown in Fig. 8.1(a) to synchronize with the network voltage and the frequency that were connected. Even if a rapid load fluctuation is added to a grid, and a difference occurs in the production of electricity and the amount demanded, the power quality of the coordinated grid type is stabilized by synchronizing with the connected network. With the independent grid type, if rapid load fluctuation is added to a grid, and a difference occurs in the production of electricity and the amount demanded of a power generator, it is considered that a long time is required to stabilize the power quality. If the load fluctuation is not appropriately predicted, in an independent grid type, the power quality of the grid is expected to be low over a long time.

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System Scheme Figure 8.2(a) is a system configuration of an independent micro-grid that sets one set of EG as base load operation and connects 16 sets of fuel cell systems to a grid. In the independent grid type, the power quality of the power generator operated as a base load is a standard of the power quality of the whole grid, and is very significant. EG connects the synchronous power generator to a gas engine, and obtains exhaust heat from a water jacket and an exhaust gas heat exchanger. If a rapid load fluctuation is added to EG, the power quality of a grid is affected, but in this system, EG is operated at the base load of the fixed load. On the other hand, adjustment of the power supply when changing the load of a grid corresponds to the number control of the fuel cells installed in each house. The number of fuel cells installed in House Q from House B shown in Fig. 8.2(a) is expressed by F/C (1) to F/C (16). Figure 8.2(b) shows the parts of House A in which an engine generator is installed, and Fig. 8.2(a) shows House B and House C in which a fuel cell of the system is installed. The block expressing the response characteristics of an EG, a city gas reformer, a fuel cell, an inverter, and an interconnection device with the primary-delay-system and the secondary-delay-system is shown in Fig. 8.2(b). u in this figure expresses the power load, and v2–v4 expresses the output in the block (from Action 1 to Action 3) that branches at the value of u. Moreover, h0– h3 expresses the generation capacity of the engine generator and the fuel cell installed in House B and House C. In this system, when the value of u exceeds capacity h0 of the engine generator corresponding to operation of the base load, the fuel cell (capacity h1) of House B is operated first. When the production of electricity (h0 + h1) is less than the value of u, the fuel cell installed in House C is operated. Thus, the number of fuel cells is controlled by the volume of the load added to the grid. The power of the generating equipment installed in House C from House A can be supplied to any house through a micro-grid. As shown in Fig. 8.2(b), in analysis, the load pattern of power is given to a system and Action 1–Action 3 are selected by an IF conditional branch according to the volume of load (u) and the capacity (h0, h1, h2) of the generating equipment installed in each house. In Action 1–Action 3, as shown in Fig. 8.2(c), the production of electricity of each power generator is calculated and outputted. As shown in Fig. 8.2(b), the dynamic characteristics of each power generator are expressed with a PI controller (Controller 0–Controller 2), output limitation equipment (Limiter 0–Limiter 2), and a transfer function. Capacity of the Engine Generator and the Fuel Cell In the system in Fig. 8.2, an engine generator with a generation capacity of 3 kW is installed in House A, and the base load operation is performed. Furthermore, a fuel cell system with a generation capacity of 1 kW is installed in 16 buildings of House B to House Q, and electric power and heat are supplied to each house. The

8.2 The Micro-grid Model

Fig. 8.2 System block diagram

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Fig. 8.3 Power demand model for 17 houses in February in Sapporo

number of fuel cells installed in a distributed system is determined from the electricity demand pattern shown in Fig. 8.3 used in the analysis. The electricity demand pattern of Fig. 8.3 is a measurement result of an individual house for representative days in February in Sapporo, Japan [6]. On the horizontal axis of this figure, the sampling time of analysis and the time assumed (assumed time) are written together. In this electricity demand pattern, the minimum load is determined as a value of the base load (in the example in Fig. 8.3, it could be 3 kW). In the case of a centralization system, there is one set of installed fuel cells, and, in the case of a distributed system, a fuel cell of 1 kW maximum output is installed in each house. Moreover, let the fuel cell capacity of the centralization system be a value where the maximum load added to a grid is satisfied. In the distributed system, the fuel cells of F/C (1) to F/C (16) all have the same capacity and dynamic characteristics. The electric transmission loss of a micro-grid is considered in this analysis.

8.3 Response Characteristics of System Configuration Equipment 8.3.1 Generation Characteristics of the Engine Generator The model of the generation characteristics of an engine generator installed in the independent micro-grid assumed in this chapter is shown in Fig. 8.4(a) [56]. This model represents the response characteristics when adjusting the engine supply fuel to control the production of electricity, and outputting power to a grid through an interconnection device. The settling time when converging in the range of ±5% of the targeted output value of power by adjusting the control parameter of the PI (proportionality-integration) controller can be set at about 15 seconds. The value of the PI control parameter and transfer function is shown in Fig. 8.4(a).

8.3 Response Characteristics of System Configuration Equipment

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Fig. 8.4 Response characteristics of system configuration equipment

8.3.2 Generation Characteristics of the Fuel Cell Figure 8.4(b) shows the test results of the response characteristics when inputting a step load of 70% of a load factor into the PEM fuel cell trial production as an experiment [57]. The response characteristics shown in Fig. 8.4(c) are obtained from the results of Fig. 8.4(b), and require the transfer function of a primarydelay-system so that these characteristics may be approximated. The equations for the transfer function are shown in Fig. 8.4(c). To be exact, although the transfer function is considered to depend on the load factor, it is not taken into consideration because this difference is small as a result of examination. The settling time of a fuel cell when generating 1 kW maximum output is about 3 seconds [57].

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8.3.3 Output Characteristics of the City Gas Reformer Figure 8.4(d) shows the model of the step input given to the city gas reformer of a load factor when it is between 100% and 80%, and between 100% and 50% [57, 58]. Figure 8.4(e) shows the response result of the load input to Fig. 8.4(d), and the transfer function of a primary-delay-system shows this response characteristic in this figure. In addition to fuel cells, although it is considered that a transfer function influences the load factor directly, since there is no big difference, the result of Fig. 8.4(f) is used in the range managed in this chapter.

8.3.4 Inverter and Interconnection Device An inverter uses a cheap voltage control form, and converts and outputs input power to regular voltage and frequency. An inverter requires 120 ms to stabilize voltage and frequency within 95% of a regular value [58]. Figure 8.4(f) expresses the transfer function of this inverter with the primary-delay-system. When switching the power of the single phase 100 V by an interconnection device, the duration of the change is about 10 μs. However, since it is necessary to synchronize the frequency by control, the interconnection device assumed in this chapter sets the change time to 12 μs. As a result, the transfer function of the interconnection device by the primary-delay- system is the value of Fig. 8.4(f).

8.3.5 Generation Efficiency of the Fuel Cell System Figure 8.5 shows the output characteristics of a fuel cell and a city gas reformer obtained in the experiments. Figure 8.5(a) shows the relation between the load factor of a fuel cell and the generation efficiency assumed in this chapter, and Fig. 8.5(b) shows the characteristics of the amount of hydrogen supplied to a fuel cell, and generation and exhaust heat output. Figure 8.5(c) shows the relation between the load factor of a city gas reformer and reformer efficiency. Here, the value that divides the calorific power of the hydrogen contained in the reforming gas by the calorific power of the city gas supplied to a reformer is defined as the reformer efficiency. The city gas supplied to a reformer has the object of producing reforming gas and the object of being the heat source of a reformer. In analysis, a load factor is calculated from the capacity of a fuel cell, and the quantity of the load, and the generation efficiency is calculated using the relation shown in Fig. 8.5(a). Moreover, the amount of exhaust heat of a fuel cell is obtained by giving a power load to Fig. 8.5(b). Because the load factor of a reformer is calculable from the load and capacity, reformer efficiency is determined if this value is given to Fig. 8.5(c).

8.4 Control Variables and Analysis Method

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Fig. 8.5 Output characteristics of a fuel cell system with city gas reformer

8.4 Control Variables and Analysis Method The response characteristics that inputted step load into the independent microgrid shown in Fig. 8.2(a) for 0.2, 0.6, and 1.0 kW loads are shown in Figs. 8.6(a)– (c) [57, 58]. The response characteristics of a fuel cell changes with the control parameter set up with a controller, and changes and analyzes the parameters of PI control in Fig. 8.6. As shown in Fig. 8.6(c), the result of the 1.0 kW loads does not depend on the rise time and the settling time of the control parameter. In the result of the 0.2 kW loads, although the rise time of P = 12.0, I = 1.0 is short; as for the

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8 Load Response Characteristics of a Fuel Cell Micro-grid

Fig. 8.6 Characteristics of electric power output of the system

settling time, P = 1.0 and I = 1.0 are short. P = 12.0 and I = 1.0 is compared with P = 1.0 and I = 1.0, as for the rise time, P = 12.0 and I = 1.0 is shorter, and the settling time is almost the same, and the overshooting is large. Moreover, in the result of P = 5.0 and I = 1.0, the steady-state error of low load is large, and is not suitable as a control variable. Therefore, in the analysis example of the following section, the control parameter of a fuel cell is analyzed as P = 1.0, I = 1.0 and P = 12.0, I = 1.0. The dynamic characteristics of a micro-grid are analyzed using MATLAB® (Ver.7.0) and Simulink® (Ver.6.0) of The MathWorks Corporation. However, in the analysis example of the following section, the solver to be used is made into the Runge–Kutta method; it is calculated, and the sampling time of the analysis is determined so that error is less than 0.01%.

8.5 Load Response Characteristics of the Micro-grid

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8.5 Load Response Characteristics of the Micro-grid 8.5.1 Step Response Characteristics The response characteristics when inputting a step load into the micro-grid is shown in Fig. 8.7(a). The parameter of the PI controller of all the fuel cell systems was set to P = 1.0 and I = 1.0. Figure 8.7(b) shows the result of a response of a micro-grid, Fig. 8.7(c) shows the result of a response of an engine generator, Figs. 8.7(d)–(f) show the results of a response of fuel cell Nos. 1, 8, and 12, respectively. When the response results of an engine generator (Fig. 8.7(c)) is compared with the response results of a fuel cell (Figs. 8.7(d)–(f)), there is little output vibration and the settling time is short in a fuel cell. If the load added to a grid increases, the number of fuel cells to be operated is increased corresponding to the load. According to the load of a grid, a larger number of fuel cells is operated such as Nos. 1, 8, 12 (Fig. 8.7(d)–(f)). How the dynamic characteristics of a micro-grid would change according to the difference in the control parameter of the PI controller of a fuel cell system was investigated. The results of investigating the response characteristics of the grid for the control parameter with P = 12.0 and I = 1.0 are shown in Fig. 8.8. Figure 8.8(a) shows P = 12.0 and I = 1.0 and is a response result when inputting the step load of Fig. 8.7(a) into a micro-grid. When Fig. 8.7(b) is compared with Fig. 8.8(a), it shows that the overshooting in Fig. 8.8(a) is larger. The analysis results of the power load and settling time when the control parameters of a fuel cell system are

Fig. 8.7 Step response characteristics of the system. The control parameter of the fuel cell system is P = 1.0 and I = 1.0

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Fig. 8.8 Analysis results of the step input for micro-grid system

P = 1.0, I = 1.0 and P = 12.0, I = 1.0 are shown is Fig. 8.8(b). The period until convergence of less than ±5% of the load by the power supplied to a grid is defined as the settling time. From the result of Fig. 8.8(b), a load does not depend on the settling time of a control parameter of 15 kW or less. However, when a load exceeds 16 kW, control parameter P = 12.0, I = 1.0 of a fuel cell system is about 5 seconds short compared with P = 1.0, I = 1.0. Figure 8.8(c) shows the analysis result concerning the difference in the power load of a micro-grid, and the power supplied to a grid from EG and PEMFC. The difference in the power load and electric power supply is due to a time delay in the response of EG and the fuel cell. The result of Fig. 8.8(c) shows that the difference in the control parameter of a fuel cell does not affect the difference in the power load or the electric power supply. However, the control parameter of a fuel cell system strongly affects the settling time and overshooting.

8.5.2 Application of the Electric Power Demand Pattern of a House The electric power demand pattern of an individual house in Sapporo is inputted into the micro-grid shown in Fig. 8.2(a), and response characteristics are investigated. The response results of having inputted the electric power demand pattern

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Fig. 8.9 Response results analyzed using an electricity demand pattern of a house in Sapporo in February. The control parameter of the fuel cell system are P = 1.0 and I = 1.0

Fig. 8.10 Response results analyzed using an electricity demand pattern of a house in Sapporo in February. The control parameters of the fuel cell system are P = 12.0 and I = 1.0

of Fig. 8.3 into the micro-grid are shown in Figs. 8.9(a) and 8.10(a). In the analysis of Figs. 8.9 and 8.10, the control parameter of the PI control device of a fuel cell system was set up with P = 1.0, I = 1.0 and P =12.0, I = 1.0, respectively. As for overshooting, in these analysis results, P = 12.0, I = 1.0 are larger than P = 1.0, I = 1.0. Moreover, immediately after startup (past [0:00]), the reason for the many fluctuation elements is based on the starting characteristics of an engine generator, as shown in Figs 8.9(b) and 8.10(b). The response results of fuel cell Nos. 1, 3, 5, and 9 are shown in (f) from (c) of Figs. 8.9 and 8.10. The analysis results of Figs. 8.9 to 8.10 have large overshooting when load fluctuation is added to a fuel cell and an engine generator. As for the analysis results of the sufficiency ratio of power supply to the power demand

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Fig. 8.11 Difference in the amount power demanded and the amount of power supply

amount of a micro-grid, P = 1.0, I = 1.0 is shown in Fig. 8.11(a), and P = 12.0, I = 1.0 is shown in Fig. 8.11(b). The time when the difference in the sufficiency ratio of power supply and demand is large; it will be the assumed time in Figs. 8.11(a) and 8.11(b) at 05:00 and 17:00. As Fig. 8.3 shows, at such times, load fluctuation is large compared with other times. A supply-and-demand difference occurs at two or more peaks exceeding 100% in the analysis result of Fig. 8.11(b) compared with Fig. 8.11(a). The analysis result of the sufficiency ratio of the power supply and demand in all of the representative days is 99.63% in Fig. 8.11(b) and 99.74% in Fig. 8.11(a). Even if it searches by trial and error for the control parameter of a controller, the characteristics of Fig. 8.11 do not change. In order to change the characteristics of Fig. 8.11, it is necessary to improve the time constant of each piece of equipment.

8.5.3 Generation Efficiency of the Fuel Cell The analysis results of generation efficiency when supplying power by the fuel cell micro-grid of a centralization system and a distributed system to 17 houses is shown in Fig. 8.12. The average value of the generation efficiency of each fuel cell under operation defines the generation efficiency of the fuel cell in a distrib-

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Fig. 8.12 Fuel cell efficiency results analyzed using an electricity demand pattern of 17 houses in February in Sapporo. The control parameters of the fuel cell system are P = 12.0 and I = 1.0

uted system. The generation efficiency of a centralization system and a distributed system has the following differences. (1) Although a change of the generation efficiency of a centralization system is smooth, the efficiency of a distributed system has a large fluctuation. (2) Moreover, because the efficiency fluctuation of a distributed system is large, there are large overshoots. (3) The change of the generation efficiency through a representation day of a distributed system is larger than a centralization system. When the electric power demand pattern of the representative day shown in Fig. 8.3 is introduced into a micro-grid, the generation efficiency of a fuel cell of a centralization system is 25.5% and of a distributed system it is 28.2%. The distributed system has about 3% higher efficiency than the centralization system. This is because the fall area of the generation efficiency by the partial-load operation of PEMFC is small in the distributed system with number control compared with the centralization system. Therefore, the distributed system with number control of the

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generation efficiency of the micro-grid using PEMFC is more advantageous than the centralization system.

8.6 Conclusions An engine generator with a power generation capacity of 3 kW, corresponding to the base load, was installed in a house. The dynamic characteristics of the microgrid at the time of installing a fuel cell system with a power generation capacity of 1 kW in 16 houses were investigated by numerical analysis. As a result, the following conclusions were obtained. (1) The settling time (period to converge at ±5% of range of the output target) and overshooting of a micro-grid can be changed by parameter setting of the controller of a fuel cell. The settling time of this system was 10 to 15 seconds. (2) The cause of the supply-and-demand difference in the power of a micro-grid is a response delay of the generating equipment, and the control parameter of the controller is not related. It is necessary to improve the time constant of each generator. (3) The fall in the generation efficiency of PEMFC by partial-load operation can be reduced by a distributed system with the control of a number of units compared with the centralization system. This is because the fall area of the generation efficiency by the partial-load operation of PEMFC is small in a distributed system with number control compared with a centralization system.

Chapter 9

Dynamic Characteristics of a PEM-FC/ Woody Biomass Engine Hybrid Micro-grid

9.1 Introduction If the micro-grid is introduced into an urban area, it will be expected that the energy cost of a distributed power supply and emission of greenhouse gas can be reduced. To date, authors have investigated the operating method that connects distributed proton exchange membrane fuel cells (PEM-FC) in a power network and cooperates with it [41, 59]. Although the generation efficiency of a PEM is high, greenhouse gas discharges by the reforming reaction of city gas. On the other hand, micro-combined heat and power (micro-CHP) using a small-scale Stirling engine generator (SEG) has been examined in the UK as an energy system for individual houses [60, 61]. By using woody biomass so that carbon dioxide may circulate, the greenhouse gas amount of emission of a power generation system can be decreased. Therefore, the introduction of SEG using woody biomass is effective in emission control of greenhouse gas [62–64]. However, compared with an internal combustion engine or a fuel cell, the generation efficiency, volume efficiency, equipment cost, etc. of the conventional SEG are small. The energy supply system using the micro-grid can reduce equipment cost compared with the method of introducing generating equipment into each house. Moreover, the energy equipment linked to the micro-grid can help, for example to minimize the amount of greenhouse gas emission. Thus, in this chapter, the dynamic characteristics of the power of the independent micro-grid using hybrid cogeneration (PWHC) of PEM-FC and SEG using woody biomass are investigated. The control response characteristic of SEG depends on the engine structure, the configuration of the combustion chamber, the heat transmission characteristic of the heat source, etc. Until now, optimization of the combustion chamber configuration and the heat transmission characteristic of combustion gas have been investigated [65, 66]. Commonly, the power demand pattern of a house or an apartment house consists of many peaks changed for a short time. Since such a power load is followed, a rapid control response characteristic is required of the generating equipS. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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ment. In order to manage the power quality of the micro-grid, it is necessary to clarify the dynamic characteristics of the power with load fluctuation. So, in this chapter, the dynamic characteristics of the PWHC micro-grid are clarified by a numerical analysis using the results of an investigation of the SEG test machine and PEM-FC.

9.2 System Scheme 9.2.1 The Hybrid Micro-grid Figure 9.1 shows the model of the independent micro-grid that introduces two-set PWHC (PWHC (1) in House (1), and PWHC (2) in House (5)). The micro-grid of this model consists of eight buildings of House (1) to House (8). The heat supply of the exhaust heat of PWHC, a heat storage tank, and a boiler is separated into the group of House (1) to House (4), and the group of House (5) to House (8). The power of two-set PWHC is supplied to each building through the power grid. The system interconnection device is installed in the contact point of PWHC and a power grid. Moreover, the power of PWHC is changed into 100 V and 50 Hz with an inverter. On the other hand, the exhaust heat of PWHC, the heat of a heat storage tank, and a boiler is supplied to each building through hot water piping (1) and (2). However, this study is limited to the dynamic characteristics of the power for the micro-grid. Figure 9.2 shows the energy flow and chemical reaction of each component of the proposed system. Chip fuel is supplied to a woody biomass engine (SEG), and power is transmitted to an alternating current synchronous power generator. The heat output of SEG is the high-temperature exhaust gas of the combustion cham-

Fig. 9.1 Independent hybrid micro-grid model with PEM-FC and a woody biomass engine

9.2 System Scheme

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Fig. 9.2 The PWHC power supply system

ber, and engine-cooling hot water. Moreover, as the heat output of PEM-FC, there is fuel cell stack exhaust heat and reformer exhaust heat. In the proposed system, the combustion chamber high-temperature exhaust gas of SEG is supplied to the heat exchanger of the reformer. With a catalyst in the reformer, city gas is changed into reformed gas with a high hydrogen concentration with a reaction temperature of 970 K to 1070 K using this exhaust heat. Reformer exhaust heat is the remaining heat after providing heat to the catalyst through the heat exchanger. In the case study, exhaust heat that can be supplied to the demand side is taken as the reformer exhaust heat and SEG cooling water. Moreover, the demand side is supplied after changing the power of SEG and PEM-FC into an alternating current of constant frequency. Outline of SEG Testing Tables 9.1 and 9.2 show the operating conditions and specifications of SEG and the power generator that are examined in this chapter. Although the maximum output of SEG is 3.7 kW, the maximum power load examined according to restricTable 9.1 SEG specifications

Table 9.2 Power generator specifications

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tions of the combustion chamber capacity, etc., is 1.6 kW. Figure 9.3 shows a general view of the test equipment. Chip fuel (woody biomass) is fed into the hopper of the combustion chamber. Chips are mixed with preheated air before entering the combustion chamber. The rate of feed of chip fuel is controllable by the fuel feed system installed in the lower part of the hopper. Power is transmitted to the power generator shown in Table 9.2 by a belt from the power shaft of SEG. Since the test SEG is a single cylinder, its vibration is large. Consequently, the combustion chamber is connected with the engine by a buffer duct so that the vibration of the engine does not spread to the combustion chamber. The exhaust gas of the combustion chamber is discharged from the system through a duct. The quantity of heat of the exhaust gas Q Ex and cooling water Q Ey is obtained from the value of the temperature sensor and the flow meter by calculating the transport volume of enthalpy. Moreover, the amount of heat radiation on the combustion chamber surface ( Q Ez ) is measured by heat flow rate sensor q , and the heatmedium pressure is measured using sensor Pg . Figure 9.4 shows the experimental results of the energy flow of the test SEG. The energy flow is separated into auxiliary machinery loss, cooling water quantity of heat, exhaust gas quantity of heat, production of electricity, and other losses. Fig. 9.3 The test woody biomass engine (SEG)

Fig. 9.4 Examination results of SEG energy flow

9.3 Control Response Characteristics of PEM-FC and SEG

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Other losses of the energy flow are mechanical loss of radiation of heat and friction of SEG, vibration, etc. Other losses decrease, so that the production of electricity of SEG is large. The power generation efficiency of SEG improves by reducing other losses that hold a large part of the energy flow at the time of low load. The quantity of exhaust gas heat holds the largest part in the energy flow, and it is always large compared to the cooling water quantity of heat. Since there is large exhaust gas heat, the development of a compound cycle of operating a steam turbine using the high-temperature exhaust gas of SEG, for example, is possible. Auxiliary machinery loss holds very few parts in the whole energy flow.

9.2.2 The Micro-grid System Operating Method Figure 9.5 shows the PWHC operation model on a representation day. In this operation pattern, SEG is operated in a range smaller than the base load set up beforehand. In addition to SEG, PEM-FC is operated in a larger load range than the base load. When a load exceeds the base load, SEG can be operated at a maximum efficiency point. However, when a load is less than the base load, a load following operation is required of SEG. Fig. 9.5 The PWHC operation model

9.3 Control Response Characteristics of PEM-FC and SEG 9.3.1 The Control Block Diagram Figures 9.6(a) and (b) are the block diagram of the feedback control on the microgrid by SEG and PEM-FC, respectively. Proportional-plus-integral control (PI control) is introduced into the control of each system. PEM-FC and SEG are controlled by the controller. Each controller is controlled based on the PI control parameters ( P and I ) set up beforehand. The power generated by SEG and PEMFC is supplied to the demand side through an inverter and a system interconnection device. The transfer functions of each equipment shown in Figs. 9.6(a) and

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(b) describe the determination method in the sections “Response Characteristics of PEM-FC” and “Response Characteristics of SEG”. The control block diagram in the case of one-set SEG operating corresponding to a base load, and corresponding to the load exceeding the base load by one-set PEM-FC is shown in Fig. 9.6(c). SEG supplies the power to the load below the

Fig. 9.6 Control block diagram of power supply

9.3 Control Response Characteristics of PEM-FC and SEG

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base load set up beforehand. PEM-FC is also operated when the load of the microgrid exceeds the base load. The control block diagram in the case of one-set PEMFC operating corresponding to the base load, and corresponding to the load exceeding the base load by one-set SEG is shown in Fig. 9.6(d). The control block diagram of the PWHC micro-grid in the case of one-set SEG operation corresponding to the base load, and corresponding to the load exceeding the base load with multiple generators is shown in Fig. 9.6(e). In Fig. 9.6(e), SEG (1) operates corresponding to the base load, and operates SEG (2), PEM-FC (1), and PEM-FC (2) according to the magnitude of a load. When supplying power to the micro-grid from the combined cycle system, the dynamic characteristics of the micro-grid are determined with the transfer functions of each piece of equipment. So, in this chapter, the transfer function and control parameters of PEM-FC, SEG, an inverter, and a system interconnection device in Fig. 9.6 are determined by the method described in the sections “Response Characteristics of PEM-FC” and “Response Characteristics of SEG”. The transient response characteristics of the power output of the SEG, PEM-FC, the auxiliary machine, and the PWHC micro-grid have been analyzed by MATLAB® (Ver.7.0)/Simulink® (Ver.6.0) of The MathWorks Corporation. In the solver to be used, the Runge–Kutta method is installed, and the sampling time of analysis is calculated automatically and determined so that error may be less than 0.1%.

9.3.2 Response Characteristics of PEM-FC Table 9.3 shows the result of the investigation of the transfer function in the previous study about the fuel cell stack, the reformer, the inverter, and the system interconnection device [57, 64]. The transfer function of the fuel cell stack was determined from the experimental result, and the transfer function of other equipment was decided from references [27, 47–51, 67, 68]. In the further last study, the optimal value of the parameters of the PI control introduced into the controller of PEM-FC was also investigated. The transfer function and control parameters on the PEM-FC of the control block diagram shown in Fig. 9.6 introduce each value of Table 9.3. Table 9.3 The transfer function of a power output

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Fig. 9.7 The PI control step response result of the PEM-FC with a reformer (P = 12.0, I = 1.0)

Figure 9.7 shows the results of the step response of 0.2 kW, 0.4 kW, 0.6 kW, 0.8 kW, 1.0 kW of the PEM-FC with a reformer [57, 64]. In the analysis of Fig. 9.7, the control block diagram of Fig. 9.6(b) was used. The control parameter with a short settling time and small overshooting was investigated by numerical analysis, and P = 12.0 and I = 1.0 were determined. The response time of a system converging on ±5% of a target value is defined as the settling time.

9.3.3 Response Characteristics of SEG Figure 9.8 shows the experimental result of the step response of 0.2 kW, 0.4 kW, 0.6 kW, 0.8 kW, and 1.0 kW of the testing SEG. As shown in Fig. 9.8, the step response of the testing SEG has large overshooting, and its settling time is long compared with PEM-FC. The heat transmission characteristics between the combustion gas of a chip and the heat exchanger of SEG is considered to influence the settling time greatly. However, it is difficult to improve the rate of heat transfer of the combustion gas of a chip, so that the load fluctuation of the power can be followed. So, in order to shorten the settling time of SEG as much as possible and to reduce overshooting, PI control is added to the operation of SEG. Figure 9.9 shows the example as a result of a step response obtained in the operating experiment of SEG (Fig. 9.4). The model of the transfer function that simulated this step response is shown in Fig. 9.9. The settling time of the testing SEG exceeds 10 s. Therefore, when SEG is operated so that the fluctuating load

Fig. 9.8 Step response result of SEG when not adding PI control

9.4 Results of Dynamic Characteristics Analysis of the PWHC Micro-grid

161

Fig. 9.9 Step response result of the test SEG, and response model

Fig. 9.10 Step response results of 2 kW SEG model

may be followed, the unstable time of voltage and frequency is long. Figure 9.10 shows the analysis results of a step response when adding PI control to the system using the transfer function in Fig. 9.9. The control block diagram used in this analysis is Fig. 9.6(a), and the control parameters of SEG introduced P = 0.1 and I = 0.001 . Moreover, a response result in the case without PI control is also shown in Fig. 9.10. The settling time becomes short by adding PI control to SEG, and an overshoot does not appear. For example, the settling time of the 2 kW step response that does not use PI control is about 16 s. However, if PI control is added, it will improve at about 6 s.

9.4 Results of Dynamic Characteristics Analysis of the PWHC Micro-grid 9.4.1 Power Response Characteristics of PWHC The 1 kW PWHC micro-grid consists of 0.5 kW SEG and 0.5 kW PEM-FC. Figure 9.11 shows the analysis results of the step response of 0.2 kW, 0.4 kW, 0.6 kW,

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Fig. 9.11 Step response result of 1kW PWHC

0.8kW, and 1.0kW of this system. Results in the case when SEG corresponds to the base load and PEM-FC follows the fluctuating load exceeding the base load are shown in Figs. 9.11(a) and (b). The control block diagram used in the analysis in Figs. 9.11(a) and (b) is shown in Fig. 9.6(c). However, the values in Figs. 9.7 ( P = 12.0 and I = 1.0 ) and 9.10 ( P = 0.1 and I = 0.001 ) were used for the control parameter of the analysis in Fig. 9.11(a). The speed of response of the PEM-FC shown in Fig. 9.7 is quick compared with the speed of response of SEG shown in Fig. 9.10. From the difference in this speed of response, as shown in the step response of 0.8 kW and 1.0 kW in Fig. 9.11(a), the response of a quick response part and a late response part appears. Consequently, the control parameters of PEM-FC with a quick speed of response are changed, and an improvement of the response characteristics of the PWHC micro-grid is tried. Figure 9.11(b) shows the response characteristics at the time of changing the control parameters of PEM-FC into P = 0.95 and I = 1.1 . These control parameters were decided by trial and error. Two response parts, 0.8 kW and 1.0 kW in Fig. 9.11(a), have improved. Step response results in the case when PEM-FC corresponds to the base load and SEG follows the fluctuating load exceeding the base load are Figs. 9.11(c) and (d). In the analysis in Figs. 9.11(c) and (d), the control block diagram shown in

9.4 Results of Dynamic Characteristics Analysis of the PWHC Micro-grid

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Fig. 9.6(d) was used. In Fig. 9.11(c), the control parameters of PEM-FC are P = 12.0 and I = 1.0 , and the control parameters of SEG are P = 0.1 and I = 0.001 . Since the overshoot of the response shown in Fig. 9.11(c) is large, the control parameters of PEM-FC are changed and an improvement is tried. Figure 9.11(d) shows the response characteristics at the time of changing the control parameters of PEM-FC into P = 0.95 and I = 1.1 . These control parameters were determined by trial and error. Compared with the response of Fig. 9.11(c), the response of Fig. 9.11(d) has small overshooting, and its settling time is short.

9.4.2 Response Characteristics of SEG and the PEM-FC Micro-grid Using the Power Load Pattern for Houses (1) Response Result of SEG The response characteristics in the case of power supplied to the micro-grid from SEG or PEM-FC are investigated. However, the power load pattern added to the micro-grid assumes two houses on a representative day in February in Sapporo. The power load pattern consists of time average values of the load consumed by the household appliances and electric lights [8]. Space cooling and heating loads are not included in this power load pattern. Therefore, the power load pattern does not have a large difference every month. Figure 9.12 shows the analysis results of a load response at the time of supplying the power to the micro-grid using 2 kW SEG. The control block diagram used in the analysis of Fig. 9.12 is that of Fig. 9.6(a). Moreover, the control parameters of SEG are P = 0.1 and I = 0.001 as well as Fig. 9.10. The horizontal axis of Fig. 9.12 is the representative time of analysis. Real time is also displayed on the horizontal axis of Fig. 9.12. Since the calculation time is enormously long, the real-time analysis is performed by shortening the real time to 1/180 in this chapter. Figure 9.12(a) shows the results of a load input and the system response, and Fig. 9.12(b) shows the results of the error of a load input and a response. As for the broken-line part shown in Fig. 9.12(b), the error of the load and the response is over ±5%. A large rising error occurs immediately after 0:00 in Fig. 9.12(b). Actually, since the system is operated continuously, this rising error does not exist. Figure 9.12(c) shows the analysis results of the time period for the error of the load and the response to exceed ±5%. Accordingly, the results of Fig. 9.12(c) express settling times. The settling time when installing SEG into the micro-grid from the result of Fig. 9.12(c) is 10.2 s at the maximum. When the micro-grid is composed from SEG, the unstable period of voltage and a frequency is 10.2 s at the maximum.

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Fig. 9.12 Dynamic characteristics analysis results of the micro-grid at the time of installing the power demand model of two houses in Sapporo. One set of 2 kW SEG. P = 0.1 and I = 0.001

(2) Response Result of PEM-FC Figure 9.13 shows the analysis result of a load response at the time of installing 2 kW PEM-FC into the micro-grid. The control block diagram used in the analysis of Fig. 9.13 is that of Fig. 9.6(b). The control parameters set up with the controller are P = 12.0 and I = 1.0, as well as Fig. 9.7. The settling time in the case when PEM-FC composes the micro-grid from the result of Fig. 9.13(c) is 1.6 s or less. However, rising parts are excluded. The power supply due to PEM-FC has a short settling time compared with SEG. Therefore, the dynamic characteristic of the power of the PEM-FC micro-grid is good compared with SEG micro-grid.

9.4 Results of Dynamic Characteristics Analysis of the PWHC Micro-grid

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Fig. 9.13 Dynamic characteristics analysis results of the micro-grid at the time of installing the power demand model of two houses in Sapporo. The power is supplied to the grid from 2 kW PEM-FC of one set. PEM-FC of P = 12.0 and I = 1.0

9.4.3 Response Characteristics of the PWHC Micro-grid Using the Power Load Pattern for Houses Figure 9.14 shows the analysis results of a load response of the micro-grid composed from 8 kW PWHC. Eight houses are connected to the micro-grid. Woody biomass engine generators installed into the micro-grid are 2 kW SEG (1) and 2 kW SEG (2), in addition 2 kW PEM-FC (1) and 2 kW PEM-FC (2) are installed. Moreover, the control block diagram used in the analysis of Fig. 9.14 is that of Fig. 9.6(e). The control parameters set up with the controller of PEM-FC are P = 0.95 and I = 1.1 as well as Fig. 9.11(d), and the SEG parameters are P = 0.1 and I = 0.001 . Since the speed of response of SEG is slow, the dynamic characteristics of SEG (2) have a large influence on the micro-grid. It is because SEG (2) is

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Fig. 9.14 Dynamic characteristics analysis results of the micro-grid at the time of installing the power demand model of eight houses in Sapporo. The power is supplied to the grid from 2 kW SEG of two sets and 2 kW PEM-FC of two sets. PEM-FC of P = 0.95 and I = 1.1, and SEG of P = 0.1 and I = 0.001

followed and operated. As a result, the settling time becomes long as shown in Fig. 9.14(c). Consequently, installation of SEG shall be one set corresponding to the base load. With respect to the load exceeding the base load, it corresponds by installing two-set PEM-FC. Figure 9.15 shows the analysis results of the load response of the micro-grid composed from a one-set of 2 kW SEG and a twoset of 2.5 kW PEM-FC. Eight houses are connected to the micro-grid. This system was analyzed by modifying the control block shown in Fig. 9.6(e). The control parameters set up with the controller of PEM-FC are P = 12.0 and I = 1.0 , and the SEG parameters are P = 0.1 and I = 0.001 . The error analysis results in Figs. 9.14(b) and 9.15(b) are similar. However, as shown in Fig. 9.15(c), the settling time of the micro-grid becomes very short compared with that shown

9.5 Conclusions

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Fig. 9.15 Dynamic characteristics analysis results of the micro-grid at the time of installing the power demand model of eight houses in Sapporo. The power is supplied to the grid from 2 kW SEG of one set and 2.5 kW PEM-FC of two sets. PEM-FC of P = 12.0 and I = 1.0, and SEG of P = 0.1 and I = 0.001

in Fig. 9.14(c). The system of Fig. 9.15 is the PWHC micro-grid stabilized dynamically.

9.5 Conclusions The load response characteristics were investigated using the testing Stirling engine power generator (SEG) that uses woody biomass as a fuel. The transfer function was determined from these results, and the dynamic characteristics of the power of the micro-grid composed from SEG were investigated. Moreover, hybrid cogeneration (PWHC) that uses the combustion exhaust heat of SEG for the heat

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source of the reformer of PEM-FC was proposed. The dynamic characteristics of the power when composing the micro-grid from this PWHC were investigated by numerical analysis. Moreover the control parameters installed into each controller of SEG and PEM-FC were examined from the results of numerical analysis. The load response characteristics of the micro-grid using PWHC were investigated, and the following conclusions were obtained. (1) The settling time of the SEG micro-grid for the power supply to houses exceeds 10 s at the maximum. Therefore, in the SEG independent micro-grid, voltage and frequency may often be instable for a long time. (2) The micro-grid composed from PEM-FC has a short settling time at the time of load fluctuation compared with the power supply due to SEG. Therefore, the dynamic characteristics of the power of the PEM-FC micro-grid are good compared with SEG. (3) The micro-grid system that combined the base load operation of SEG and the load following operation of PEM-FC was proposed. The settling time of the proposed micro-grid with eight houses is 1.6 s or less. The micro-grid that installs the proposed system is stable, and there are small amounts of emission of greenhouse gas. Waveform distortion of a higher harmonic wave (about 10–4 to 10–2 s), voltage fluctuation (about 10–2 to 100 s), frequency change (about 100 to 102 s), and an overvoltage/undervoltage (about 102 s or more) are the dynamic characteristics that should be secured with respect to the power supply of the independent microgrid. In this chapter, the influence of a period longer than a voltage fluctuation grade (10–2 s) was investigated. In addition, the details of the greenhouse gas emission characteristics and the economic evaluation of the proposed system are reported elsewhere [69].

Chapter 10

A Fuel Cell and Hydrogenation Engine Hybrid System Considering Efficiency Improvement for Partial-load Operation

10.1 Introduction Cogeneration (CGS) using a solid polymer membrane-type fuel cell (PEM-FC) is hoped to become a next-generation distributed power supply. However, the short lifetime, high price, complex control, and decreased generation efficiency at partial load are issues that are still to be solved [52, 70]. Among these, the life and price of a fuel cell (FC) may improve greatly by advances in material development and recycling technology. Some examples report that the lowered generation efficiency at partial load operation is a resolvable issue, which can be seen from the results of a demonstration of PEM-FC for houses with a reformer [52, 70]. The causes of lowered efficiency at partial load operation can be separated into those concerning the reformer and those concerning the cell stack. A cell stack is divided into multiple stacks, and the method of controlling the number of operations according to the magnitude of the load has been examined in the past [25]. By this method, the load factor (the load added to the cell stack/capacity of the cell stack) of each cell stack rises. However, complications in the fuel cell system cause increasing energy unit costs. Thus, this chapter examines the hybrid PEM-FC and gas engine generator (NEG) system (HCGS). The operation of this system has the advantages of PEM-FC and NEG. Since the history of engine technology is long, NEG cogeneration is reliable and cheap compared with PEM-FC. However, the maximum generation efficiency and noise show low performance compared with PEM-FC. On the other hand, the efficiency of exhaust cleanup and improvement at partial load operation by the hydrogenation technology of NEG have been studied [71–74]. From these research findings, improvement in emission cleanup and brake thermal efficiency was confirmed by increasing the amount of hydrogen mixtures of a fuel at the time of low load operation [71]. Thus, in this chapter, the output characteristics of the hybrid cogeneration of PEM-FC and hydrogenation NEG are investigated by numerical simulation. A single-cylinder gas engine is used as the NEG. It is known from past examination results that hydrogen supply S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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in the engine has an advantageous mean effective pressure of 0.8 MPa or less. When the mean effective pressure exceeds this value, the hydrogen supply in the engine has low efficiency. As hybrid operations of PEM-FC and NEG, there is (a) the method of operating NEG in a low-load region and operating PEM-FC in a high-load region. Furthermore, since the maximum generation efficiency of PEMFC is higher than NEG, there is (b) the operating method of the base load response of PEM-FC, corresponding to the fluctuating load of NEG. In this chapter, a system operation map was prepared from the test data of PEMFC and NEG that was evaluated in the past [71, 28, 29]. A system operation map expresses the city gas calorific power supplied to HCGS (it consists of PEM-FC, NEG, and a boiler), and the relation between power generation and the heat output of HCGS. In order to compare systems, this chapter investigates: (a) The hydrogenation NEG individual-operation system (OM-A); (b) the PEM-FC individualoperation system (OM-B), (c) the hybrid system (OM-C) that combines the operation of the low-load region of NEG, and operation of the high-load region of PEM-FC, and (d) the hybrid system (OM-D) that combines the base load operation of PEM-FC and the fluctuating load following operation of NEG. These systems were installed into the load pattern of an apartment building composed of ten family apartment houses in Tokyo, and fuel consumption, carbon dioxide emissions, amount of heat storage, generation efficiency, and total efficiency were analyzed. As a result, OM-D regarding fuel consumption, generation efficiency, and total efficiency was understood to be the best. On the other hand, OM-A has the least carbon dioxide emissions. A load pattern changes greatly according to different times of the day. Therefore, fuel cell cogeneration that includes a NEG hydrogenation method with sufficient low-load characteristics is dramatically advantageous. The total efficiency of OM-A on a representative day in January, May, and August was 66%, 75%, and 61%, respectively, and it was 65%, 68%, and 63% in OM-B. On the other hand, the total efficiency of OM-C was 70%, 79%, and 71%, respectively, and the total efficiency of OM-D was 88%, 94%, and 73%, respectively.

10.2 System Scheme 10.2.1 The HCGS Model A block diagram of HCGS proposed in this chapter is shown in Fig. 10.1. City gas (CH4) is supplied to a reformer and a heat-source burner, and reformed gas is produced. Equations (10.1) and (10.2) express the reactions of natural gas reforming. Since Eq. (10.2) is an endothermic reaction, the heat of city gas burning shown in Eq. (10.3) is supplied. After removing some of the water in the reformed gas with a gas cooler, the fuel is supplied to the PEM-FC and NEG system. The hydrogen in reformed gas after removing water is about 80% of the volume ratio.

10.2 System Scheme

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Fig. 10.1 System configuration

Moreover, 1% or less carbon monoxide is contained in reformed gas. Carbon monoxide reduces the performance of a fuel cell. Thus, when reformed gas is supplied to PEM-FC, the CO concentration is reduced to several ppm through a carbon monoxide oxidation system. The power of PEM-FC is changed with a DCAC converter and an inverter into the normal frequency (50 Hz) of 100 V alternating current. City gas and reformed gas can be mixed and supplied to NEG. The power generator linked to NEG is a single-phase synchronous type and controls the frequency with an inverter like a fuel cell. The amount of reformed gas supplied to PEM-FC and the amount of city gas and reformed gas that are supplied to NEG are controlled by the command of a controller. The power load is monitored by the controller, and the flow rate of the city gas and reformed gas is controlled by the magnitude of the load.

CH 4 + H 2 O → CO+ 3 H 2 − 206 [kJ/mol]

(10.1)

CO + H 2 O → CO 2 + H 2 + 41 [kJ/mol]

(10.2)

CH 4 + 2 O 2 → CO 2 + 2 H 2 O+ 802 [kJ/mol]

(10.3)

10.2.2 Operation Method of the System Figure 10.2 shows the operation model using the HCGS shown in Fig. 10.1. OMA, OM-B, OM-C and OM-D are the operation models shown in Figs. 10.2(a), (b), (c) and (d), respectively, where C 'N and C 'F express the maximum load of NEG and PEM-FC, respectively. It is necessary to decide the capacity ( C N ， C F ) of NEG and PEM-FC installed for each operation model with a value exceeding C 'N and C 'F , which are shown in the figures. OM-A follows the load pattern by a NEG independently, and OM-B follows the load pattern by a PEM-FC independently.

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Fig. 10.2 System operation Fig. 10.3 Efficiency characteristics model of OM-C

Figure 10.3 illustrates the model showing the relation between the load factor and the generation efficiency of hydrogenation NEG and PEM-FC. In the low-load operation region of NEG, an improvement in emission cleanup and brake thermal efficiency is expected by increasing the hydrogen concentration of the fuel [71]. However, there is no advantage of hydrogenation in the high-load operation region of NEG. Moreover, as shown in Fig. 10.3, compared with PEM-FC, the maximum efficiency of NEG is low. Therefore, operation in a low-load region of NEG is advantageous, and operation in a high-load region of PEM-FC is advantageous. Thus, at OM-C, PEM-FC is changed to hydrogenation NEG according to the magnitude of the load. However, compared with the capacity of NEG and PEMFC installed by OM-A and OM-B, although NEG decreases, PEM-FC does not change. From Figs. 10.2(a) and (c), the capacity of NEG installed into OM-C

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becomes less rather than OM-A. However, the expensive fuel cell installed into OM-C is the same capacity, as seen in Figs. 10.2(b) and (c). Therefore, the equipment cost of OM-C increases compared with OM-A and OM-B. Thus, in order to use OM-C, the energy unit cost needs to decrease significantly compared to OM-A and OM-B. On the other hand, when using PEM-FC for a base load, FC can always be operated at a maximum efficiency point. So, with OM-D, the load fluctuation region where low load frequently occurs is followed by hydrogenation NEG. The capacity of NEG and PEM-FC installed in OM-D decreases compared with OM-A and OM-B.

10.3 Equipment Characteristics 10.3.1 Output Characteristics of NEG (1) Output Characteristics The output characteristics of NEG with hydrogenation take into account past evaluation results. The specifications of the engine examined and those of the power generator assumed in this chapter are shown in Table 10.1. Figure 10.4(a) shows the examination results of the hydrogenation rate and the brake thermal efficiency of the gas engine in Table 10.1(a). This examination results are data from [71]. The effective range of hydrogenation is 0.8 MPa of the mean engine effective pressure. On the other hand, in the range in which the mean effective pressure exceeds 0.8 MPa, even if hydrogen is not added, high thermal efficiency can be obtained. Figure 10.4(b) shows the relation between the hydrogenation rate, the mean effective pressure, and the brake thermal efficiency [71]. The broken line in the figure is the hydrogenation rate to express maximum thermal efficiency. Figure 10.5 shows Table 10.1 Specifications of NEG

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Fig. 10.4 Output characteristics of NEG Fig. 10.5 Amount of CH4 and hydrogenation when obtaining maximum thermal efficiency

the relation of the city gas (CH4) consumption, the amount of hydrogenation, and the electricity production of NEG at the time of maximum thermal efficiency. The characteristics of Fig. 10.5 were calculated from the hydrogenation rate based on the examination results of Figs. 10.4(a) and (b). In Fig. 10.5, when the production of electricity exceeds 14 kW, the amount of hydrogenation is zero. This is because high thermal efficiency can be obtained even if there is no hydrogenation in the large range of engine power, as Fig. 10.4(a) describes. (2) Efficiency Figure 10.6 shows the relation between the load factor (load added to NEG/the capacity of NEG), and the generation efficiency. Reformer efficiency is included in the generation efficiency shown in Fig. 10.6. Here, Eq. (10.4) defines the reformer efficiency. ⎛q ⎞ ηR = ⎜ H2 ⎟ × 100 [%] q CH 4 ⎠ ⎝

(10.4)

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Fig. 10.6 Relation between a production of electricity and gross power generation efficiency

Fig. 10.7 Output model of NEG

q H2 in Eq. (10.4) is the calorific power of hydrogen in reformed gas, and q CH 4 expresses the calorific power of the city gas supplied to a reformer. In this chapter, η R was set at 73% [26, 67, 75]. Moreover, in the following analysis case, 10 kW NEG is installed into OM-A of Fig. 10.2(a), and 5 kW NEG is installed into OM-C

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of Fig. 10.2(c) and OM-D of Fig. 10.2(d). Generally, the engine thermal efficiency increases, so this capacity grows. The NEG generation efficiency characteristics of 5 kW and 10 kW are shown in Fig. 10.6. The difference in the maximum generation efficiency shown in Fig. 10.6 is about 3%. Figure 10.7 shows the result of the power of NEG (5 kW and 10 kW), the heat output, and the total efficiency. The heat outputs are engine exhaust, cooling hot water, and reformer exhaust heat. However, it is assumed that the total efficiency of Fig. 10.7 consumes all power and heat produced by NEG. The difference in the total efficiency of NEG of 10 kW and 5 kW is less than 1%. Under conditions where all exhaust heat is consumed, the difference in total efficiency by the capacity of NEG is small. (3) Carbon Dioxide Emissions The hydrogen quantity supplied to NEG can be determined from Fig. 10.5. CH4 supplied to a reformer, and the amounts of carbon dioxide discharged by a reforming reaction are calculable using Eqs. (10.1), (10.2), and (10.3). The sampling time is expressed as t . When adding hydrogen to NEG, the amount of carbon dioxide discharged by the reforming reaction, and the heat-source burner is expressed as g N,R, t and g N,B,t , respectively. Moreover, the amount of carbon dioxide discharged by city gas burning in NEG is expressed as g N,I,t . The amount g N, t of carbon dioxide discharged by the power generation of NEG is calculated by Eq. (10.5).

g N, t = g N,R, t + g N,B,t + g N,I,t

(10.5)

Figure 10.8 shows the carbon dioxide emission characteristics of NEG of Table 10.1. This figure shows the relation between the load factor, and CO2 emissions are also shown. The characteristics of the total CO2 emissions differ by about 60% of the load factor. The fuel supply rate to NEG is due to the fact that there is a large quantity of hydrogen in a low-load region, and there is much city gas in a high-load region. In a low-load region, a large amount of CO2 is discharged by reforming of the city gas reforming, which produces hydrogen, as well as reformer burners. Moreover, in a high-load region, there is a large amount of CO2 where city gas is burned in engines. The characteristics of Fig. 10.8 change with the capacity of NEG, as well as with the generation efficiency. In the calculation of the carbon dioxide emissions of the analysis case, 5 kW NEG was set up compared with 10 kW NEG, with a maximum increase of 3%.

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Fig. 10.8 CO2 emission characteristics of NEG

10.3.2 Output Characteristics of PEM-FC (1) Output Characteristics and Efficiency

Figure 10.9 shows the output characteristics of 5 kW PEM-FC and the model of total efficiency with a city gas reformer [26, 28, 29, 67, 75]. The model of Fig. 10.9 was prepared from the analysis results in this chapter, and the literature on PEM-FC [26, 28, 29, 67, 75]. The heat outputs are the exhaust heat of the reformer and the cell stack. Moreover, the power output is the value of the inverter outlet. Total efficiency is assumed when all power and heat is consumed. Figure 10.10 shows a model of the carbon dioxide emissions and generation efficiency of PEM-FC of Fig. 10.9. The carbon dioxide discharged by the operation of PEM-FC is the city gas burning of a reformer burner (Eq. (10.3)), and the reforming reaction (Eqs. (10.1) and (10.2)). The generation efficiency η F of Fig. 10.10 was calculated using Eq. (10.6). E F, t on the right-hand side of Eq. (10.6) is the power of the inverter outlet of PEM-FC. q R,CH 4 , t is the calorific power of CH4 supplied to a reformer, and q B,CH 4 , t expresses the calorific power of CH4 supplied to the heat-source burner of the reformer. The maximum generation efficiency of the fuel cell shown in Fig. 10.10 is 32%.

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Fig. 10.9 Output model of the 5 kW PEM-FC system

Fig. 10.10 Characteristics model of the load factor of a PEM-FC with reformer, and power generation efficiency. The area of the electrode including the anode and cathode of the fuel cell stack is 1m2, respectively, and the reformer efficiency is 73%

⎧E ⎫ η F, t = ⎨ F, t ⎬ × 100 ( ) + q q R, CH B, CH , t 4 4 ⎩ ⎭

(10.6)

(2) Emission Characteristics of Carbon Dioxide

The amount of CO2 discharged by the reforming reaction and a heat-source burner is expressed as g F,R, t and g F,B, t , respectively. The amount g F, t of CO2 discharged by the power generation of PEM-FC is calculated from Eq. (10.7). g F, t = g F,R, t + g F,B, t

(10.7)

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The changing ratio of the CO2 emissions of NEG is large in a high-load region, as Fig. 10.8 shows. On the other hand, the CO2 emissions of PEM-FC are large in a low-load region, as Fig. 10.10 shows. From the difference among these CO2 emission characteristics, the operation of NEG is advantageous in a low-load region, and operation of PEM-FC is advantageous in a high-load region.

10.4 Power and Heat Output Characteristics of HCGS 10.4.1 Output Characteristics of NEG and PEM-FC (1) The Rate of Heat Output to the Production of Electricity

The chart showing the relation between the calorific power of the city gas supplied to HCGS, the production of electricity, and the heat output is described as the operation map of HCGS. The city gas calorific power of an operation map expresses the fuel consumption of HCGS. Figure 10.11 shows the operation map of OM-A (10 kW hydrogenation NEG). Operation Area A of this figure is a region of the production of electricity and exhaust heat power when operating NEG. Area B is a region of the production of electricity and the heat output (the amount of exhaust heat, and the boiler power) when operating NEG with a boiler. In the figure, the city gas calorific power (described as city gas consumption in the following) supplied to a system is also shown. Furthermore, the boiler efficiency of Area B is 90%. Figure 10.12 shows the operation map of OM-B (10 kW PEM-FC). When OM-A is compared with OM-B, Area A with OM-A is wider. Furthermore, in Area A with OM-A, if the production of electricity increases, the exhaust heat

Fig. 10.11 Relation of fuel supply and output of 10 kW NEG with a boiler (OM-A)

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Fig. 10.12 Relation of fuel supply and output of 10 kW PEM-FC with a boiler (OM-B)

power will also increase. However, with OM-B, even if the production of electricity increases, the increase in heat output is lost. PEM-FC has the feature that the rate of heat output to production of electricity becomes low when the production of electricity increases. (2) City Gas Consumption at the Time of Load Fluctuation

The city gas consumption of OM-A and OM-B in the same heat output range (0–50 kW) as the same power generation range (2–10 kW) are 11–73 kW, and 17–69 kW, respectively, as shown in Figs. 10.11 and 10.12. The difference of the city gas consumption by Area A of OM-A and OM-B is large. For example, in the power generation range of 2–10 kW, city gas consumption of OM-A is 11–44 kW. On the other hand, city gas consumption of OM-B is 17–31 kW.

10.4.2 Method Operating FC or NEG with the Threshold Value of the Load (OM-C) Figure 10.13 shows the operation map of OM-C. In OM-C, NEG is operated in a low-load region, and PEM-FC is operated in a high-load region. The efficiency of exhaust cleanup and improvement at partial load by the hydrogenation of NEG is known. So, in OM-C, NEG is operated in a low-load region. The operation map of 5 kW NEG and 10 kW PEM-FC is sketched in Fig. 10.13. Areas A and B are the operation maps of NEG, and Areas C and D are the operation maps of PEM-FC.

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Fig. 10.13 Relation of fuel supply and output of 5 kW NEG and 10 kW PEM-FC hybrid operation (OM-C)

In OM-C, a system with lower city gas consumption per production of electricity is selected, and NEG or PEM-FC is operated. The operation of switching NEG and PEM-FC makes a threshold value of 3.9 kW production of electricity. By switching the operation of NEC and PEM-FC, OM-C shows the load characteristics of OM-A and OM-B.

10.4.3 Operation Method Using PEM-FC Corresponding to a Base Load (OM-D) Figure 10.14 shows the operation map of 5 kW NEG and 5 kW PEM-FC operated by OM-D. In OM-D, 5 kW PEM-FC is operated corresponding to a base load, and 5 kW-NEG is operated according to the fluctuating load. Since the base load does not change, PEM-FC can always be operated at maximum efficiency. The maximum efficiency range of PEM-FC is near the 5 kW production of electricity of Fig. 10.14(b). On the other hand, NEG operates corresponding to the load fluctuation, and low-load operations occur frequently. The low-load operation of NEG has a high generation efficiency compared with PEM-FC. Therefore, it is expected that the generation efficiency of OM-D is high compared with OM-A and OM-B.

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Fig. 10.14 Relation between fuel supply and output of OM-D

10.5 Case Study 10.5.1 Power and Heat Demand Model Figure 10.15 shows the power and heat demand model of ten family apartment houses in Tokyo, and is used for analysis. The power and heat demand model are the average loads of each sampling time of a representative day in January (winter), May (mid-term), and August (summer) [30–33]. However, the actual power demand pattern is an assembly of the load that changes rapidly in a short time, such as an inrush current. Space cooling, space heating, and household appliances are power loads, and hot water supply and baths are heat loads. In Tokyo, the

10.5 Case Study

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Fig. 10.15 Power and heat demand model for ten family apartment houses

annual average temperature for the past five years has been 289 K. The average temperature in January is 279 K, and the highest and the lowest temperatures on a representative day in January are 283 K and 275 K, respectively. The average temperature in May is 292 K, and the highest and the lowest temperatures on a representative day in May are 296 K and 288 K, respectively. The highest and the lowest temperature on a representative in day August for the past five years have been 302 K and 296 K, respectively, and the average temperature is 298 K [46]. Since the cooling load of air-conditioners will be included in the power demand amount of a representative day in August, the power demand is high compared with other months. The capacity of NEG and PEM-FC of OM-A to OM-D was determined so that the power demand of Fig. 10.15(a) may be satisfied. The maximum value of the power demand of Fig. 10.15 is 9 kW. Therefore, OM-A and OM-B that operate NEG or PEM-FC independently set the power generation capacity to 10 kW. Moreover, OM-C set NEG to 5 kW, and set PEM-FC to 10 kW. OM-D set NEG and PEM-FC to 5 kW, respectively. The capacity of NEG of OMC, NEG of OM-D, and PEM-FC can be changed into other values. In this chapter, in order to simplify the analysis, NEG and PEM-FC may be 5 kW and 10 kW.

10.5.2 Analysis Method The operation map of HCGS shown in Figs. 10.11–10.12 is installed into a power and heat demand model (Fig. 10.15), and the city gas consumption and the amount of exhaust heat power are obtained. When the exhaust heat power exceeds the heat demand, surplus heat is stored in a heat storage tank. On the other hand, when the power is lower than the heat demand, heat is supplied from a heat storage tank. If heat still runs short, it is output by a boiler. City gas consumption and the amount of heat storage are calculated using all the sampling times of a representative day every month. The generation efficiency of a representative day is calculated by dividing the “power demand amount” by “the consumption calorific power of city gas”. The total efficiency is calculated by dividing “the value adding production of

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electricity and the amount of heat output” by “the consumption calorific power of city gas”. The load factor is given in Figs. 10.8–10.10, and the carbon dioxide emissions for every sampling time are obtained. The amount of emission on a representative day is calculated by adding these carbon dioxide emissions.

10.6 Results and Discussion 10.6.1 City Gas Consumption Figures 10.16(a)–(c) show the analysis results of city gas consumption using each operation method. With OM-B the change in fuel consumption in every month is small compared with other operation methods. The reason for this can be explained from the operation map of OM-B shown in Fig. 10.12. In Area A of OMB, it is because the city gas consumption with a fluctuating production of electricity has a change smaller than other operation methods. On the other hand, a change in the fuel consumption of each month with OM-A is large compared with the other operation methods. This is because the change from city gas consumption to the production of electricity is large in Area A of the NEG operation map (Fig. 10.11). With OM-D the least amount of fuel consumption will be in January

Fig. 10.16 Analysis results of fuel consumption

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185

and May. Figure 10.16(d) shows the analysis results of city gas consumption for every month on a representative day. There are few operation methods of OM-D for fuel consumption. Since August has a high power demand due to the cooling load, there is more city gas consumption with OM-A and OM-B than in other months. However, in January there will be more city gas consumption with OM-C than in August. This is because in August the generation efficiency of PEM-FC operated in the high-load region of OM-C is higher than in January.

10.6.2 Generation Efficiency and Total Efficiency Figure 10.17 shows the analysis results of the generation efficiency and the total efficiency of each operation method. OM-D has the highest generation efficiency and total efficiency every month. The total efficiency on a representative day in May with OM-D is the highest. This is because there is little fuel consumption, as Fig. 10.16(d) shows. However, a representative day in August has a high load factor of NEG due to the cooling load, and the generation efficiency and total efficiency of OM-D are lower than in other months. The generation efficiency and total efficiency of OM-C are high following OM-D. OM-C has particularly high generation during a load peak in August when the power demand is high. This is because the hours of operation of 10 kW PEM-FC are long, and the load factor at the time of operation is higher in August than in other months. Furthermore, the generation efficiency of OM-B is strongly influenced by the power demand amount compared with other operation methods. As described in the section “Capacity of the Heat Storage Tank”, heat output characteristics differ according to each operation method of OM-A to OM-D. However, the heat demand considering the heat output characteristics of each operation method is required for the total efficiency shown in Fig. 10.17(b).

Fig. 10.17 Analysis results of efficiency

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10.6.3 Carbon Dioxide Emissions Figures 10.18(a)–(c) are the analysis results of the carbon dioxide emissions for every sampling time in each operating method. Compared with the relation between each operation method of Fig. 10.16(a)–(c) and city gas consumption, the result of Fig. 10.18 shows a great difference according to the operation method. The characteristics of the carbon dioxide emissions of NEG and PEM-FC shown in Figs. 10.8 and 10.10 are due to the fast that the difference is large between low load and high loads. For this reason, fuel consumption (Fig. 10.16(d)), generation efficiency (Fig. 10.17(a)) and total efficiency (Fig. 10.17(b)), and the relation of carbon dioxide emissions are not in a simple proportional relationship. This relationship is remarkable when power demand with load fluctuation and low load operation is used. The carbon dioxide emissions of each operation method are dependent on the operation map of each operation method shown from Figs. 10.11–10.14. When the operating point of the system has been determined, the fuel consumption will be obtained from the operation map. Generation efficiency and carbon dioxide emissions are calculable from this value. Therefore, the relationship of fuel consumption, generation efficiency, and carbon dioxide emission can be predicted by preparing the operation map of the system. OM-B has the largest amount of carbon dioxide emission on a representative day every month. Figure 10.8(d) shows the analysis results of the carbon dioxide emission on a representative day. OM-A has the lowest carbon dioxide emission, followed by

Fig. 10.18 Analysis results of carbon dioxide emissions

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187

OM-C, OM-D, and OM-B. From this, it is considered that the operation of NEG with hydrogenation is most suitable with respect to the carbon dioxide emission of the power demand pattern with the large load fluctuation shown in Fig. 10.15(a).

10.6.4 Capacity of the Heat Storage Tank Figures 10.19(a)–(d) show the analysis results of the heat balance of the heat storage tank for every sampling time of each operation. VS,1 – VS,4 shown in Fig. 10.19(a) express the heat output in a heat storage tank. The operation method with the greatest excess heat balance is OM-D, and OM-C shows the least excess heat balance. When OM-A is compared with OM-B, there is a particular difference in the heat output in January and May, but this is due to the difference in the exhaust heat characteristics of NEG and PEM-FC. According to Figs. 10.19(a)– (d), VS determines the largest value to be the heat storage tank capacity. The result of the heating storage capacity obtained in the analysis is shown in Table 10.2. The heat storage capacity of the equipment with a lower rate of heat output to power output such as PEM-FC is larger, and it is so large that the peak of the heat load is high. The capacity of the heat storage tank installed in OM-D from the analysis results of Table 10.2 is the smallest.

Fig. 10.19 Analysis results of heat balance

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Table 10.2 Capacity of the heat storage tank

10.7 Conclusions The city gas-engine cogeneration facility is cheap compared with PEM-FC. However, it is inferior with respect to maximum generation efficiency, exhaust gas composition, and efficiency lowering at partial load and noise. Therefore, attention has been focused on the gas-engine generator (NEG) with hydrogenation for improving efficiency and exhaust gas emission. In hydrogenation NEG, emission cleanup and brake thermal efficiency improvement is confirmed by increasing the hydrogen rate of a fuel at the time of low load operation. Therefore, this study investigated the output characteristics of the hybrid cogeneration (HCGS) of PEM-FC and NEG by numerical analysis. These output characteristics were incorporated into the power and heat demand model of ten family apartment houses in Tokyo, and the following conclusions were obtained. (1) Compared with the conventional method (the power supply at one set of PEM-FC, or one set of NEG), an operation method where generation efficiency and total efficiency are improved was proposed. These methods are the operation method that switches hydrogenation NEG to PEM-FC in the magnitude of load, and the method of combining the base load operation of PEM-FC and the load fluctuation operation of NEG. (2) The carbon dioxide emissions characteristics of NEG with hydrogenation and PEM-FC show a large difference between low load operation and high load operation. For this reason, the carbon dioxide emissions of a system and its fuel consumption, generation efficiency, and total efficiency are not in a simple proportional relationship. (3) The exhaust heat characteristics of HCGS and the capacity of the heat storage tank were investigated. Since exhaust heat characteristics differ depending on the operation method, in order to maintain total efficiency, the heat demand corresponding to the heat output characteristics for every operation method is used. (4) The total efficiency of the method of combining the base load operation of PEM-FC and the load fluctuation operation of NEG is the highest. Moreover, under a load pattern with large fluctuation of power demand, individual NEG operation with hydrogenation has the lowest carbon dioxide emissions.

Chapter 11

CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine and a Fuel Cell

11.1 Introduction The introduction to a urban area of a micro-grid has the following advantages: (a) The heat transport distance is short, and effective use of the exhaust heat of the generating equipment is possible; (b) the optimal facility for the energy demand characteristic of a community is installed, and a system having small environmental impact can be built; and (c) with an independent micro-grid, the scale of equipment for distributing electricity is small [21–23]. Furthermore, (d) connecting renewable energy considering regionality is expected to become an advanced system in micro-grid technology. At present, the method of a micro-grid interconnecting with commercial power, etc., is being investigated (interconnect microgrid) [23]. However, in order to achieve the advantages of (a)-(d) described above, it is necessary to operate a micro-grid independently. The subjects of the independent micro-grid are back-up in the case of overload and securing of power quality (voltage and frequency). Furthermore, it is necessary to clarify the power generation efficiency, the carbon dioxide emissions, and the power cost of an independent micro-grid. An improvement in power generation efficiency is expected from the independent micro-grid using a fuel cell compared with conventional electric power-supply technology. However, at the moment, fuel cells are expensive and whether they will spread is not clear. As for a fuel cell independent micro-grid, power-generation efficiency and carbon dioxide emissions are expected to be advantageous compared with existing generating equipment. However, because the fuel cell is expensive, it is difficult to install the capacity corresponding to a load peak. Consequently, there is a case of operation that limits operation of a fuel cell to a highly efficient load region [76]. The hydrogenation technology of a city gas engine is effective concerning efficiency falls and increases in carbon dioxide emission at the time of partial load operation [71–74]. The power-generation system using a city gas engine with a generator (NEG) is cheap compared with the fuel cell. Thus, this chapter examines the powerS. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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11 CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine

generation efficiency and carbon dioxide emission when connecting NEG and the proton exchange membrane fuel cell (PEM-FC) to an independent micro-grid. In this chapter, the load of an independent micro-grid is divided into a base load region and a fluctuating load region, and the system that compounds NEG and PEM-FC is examined. Below, the independent micro-grid that compounds the NEG and PEM-FC systems is described as IMPE.

11.2 System Scheme 11.2.1 The IMPE Model A micro-grid model is shown in Fig. 11.1. Figure 11.1(a) shows a system-interconnection micro-grid. This system is interconnected with commercial power, etc. Power Pc is delivered and received between other grids, and Power Pg is supplied to a micro-grid with the generating equipment installed in the machinery room of Building 5 in the urban area model of Fig. 11.1(a). The power quality (frequency, voltage) of the system-interconnection micro-grid is dependent on other grids for interconnection. Therefore, even if a large load is added to this grid, power quality is stabilized in a short time. On the other hand, Fig. 11.1(b) shows an independent micro-grid that does not interconnect with other grid systems. The method that supplies the power of an independent micro-grid by a one-set powergeneration system is defined as a centralized system. Two sets of NEG or PEMFC systems are introduced, and how to divide them into a base load operation and a fluctuating load operation, and supply power is defined as a base load-sharing system. However, the base load-sharing system corresponds to a base load and fluctuating load using either FC or NEG. For example, how to correspond baseload operation by NEG and correspond to a fluctuating-load operation by the

Fig. 11.1 The micro-grid model

11.2 System Scheme

191

PEM-FC system is defined as an IMPE system. By the IMPE system, the kinds of generating equipment of the base-load operation and the fluctuating-load operation differ.

11.2.2 Operation Method of the Micro-grid Figure 11.2 shows the power load pattern of the independent micro-grid shown in Fig. 11.1(b). The load pattern of Fig. 11.1(b) is separated into a base load region and a fluctuating load region in systems other than the centralized system. As Fig. 11.1(b) shows, the PEM-FC system of capacity PF,l is installed in building 5 linked to Grid A, and NEG of capacity PE,m is installed in building 19 linked to Grid B. Grids A and B can deliver and receive the power by system interconnection equipment CP. Therefore, the PEM-FC system of building 5 is made to correspond to the base load region of Fig. 11.2, and NEG of building 19 is made to correspond to a fluctuating load region.

Fig. 11.2 Output share model of load power

11.2.3 Equipment Scheme Figure 11.3 shows an example of equipment schemes of the building connected to IMPE shown in Fig. 11.1(b). Figure 11.3(a) shows the equipment scheme of Building m linked to an NEG central system. The generating equipment installed with a centralized system is any one set of FC or NEG. Figure 11.3(a) shows the equipment scheme of the central system using NEG, where NEG, a boiler, a heat storage tank, an interconnection device, etc., are installed. Although city gas ( Q E ) is supplied to NEG, at the time of low load, hydrogen ( Q Er ) is supplied through reformed gas piping. However, the equipment cost can also be reduced by installing a city gas reformer in the same building, m , as NEG. NEG and PEM-FC systems are installed in Building l ; and the equipment scheme of the IMPE system corresponding to the base load or the fluctuating load is shown in Fig. 11.3(b).

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11 CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine

The hydrogen produced by the reformer is supplied to NEG at the time of low load, and PEM-FC stack, NEG, PEM-FC stack, a city gas reformer, a boiler, a heat storage tank, an interconnection device, etc., are installed in the building shown in Fig. 11.3(b). City gas ( Q S ) is a heat source, and city gas ( Q R ) produces reformed gas with the fuel for reforming. Furthermore, in order to reduce the CO concentration in the reformed gas in a fuel cell stack entrance to several ppm, a carbon monoxide oxidization section is provided. In the carbon monoxide oxidation section, carbon monoxide is burned on a catalyst and it changes into carbon dioxide. The direct current power generated by the fuel cell stack is changed into an alternating current of fixed frequency through an inverter, and is supplied to an interconnection device. Figure 11.3(c) shows the equipment scheme of Building n in which NEG or PEM-FC system is not installed. The power demand of Building n is received from a micro-grid through an interconnection device. Moreover, heat supply is obtained by city gas ( Q B ) burning of a boiler. Carbon dioxide emissions are calculated from the city gas supplied to a reformer ( Q R and Q S ) and NEG ( Q E ).

Fig. 11.3 Energy equipment model

11.3 Equipment Characteristics

193

11.3 Equipment Characteristics 11.3.1 Output Characteristics of the Gas Engine Power Generator Figure 11.4(a) shows the examination results of the hydrogenation rate and brake thermal efficiency of a one-cylinder city gas engine [71]. The engine mean effective pressure of hydrogenation is effective in a range that is less than 0.8 MPa. Thermal efficiency with a large mean effective pressure without hydrogenation in a range exceeding 0.8 MPa can be obtained. Figure 11.4(b) shows the relation between the mean effective pressure and the brake thermal efficiency, and the hydrogenation rate [71]. The broken line in the figure is the hydrogenation rate indicating the maximum thermal efficiency. Figure 11.4(c) shows the analysis results of the production of electricity of NEG, city gas consumption, and the amount of hydrogenation calculated from the model of Figs 11.4(a) and (b). The

Fig. 11.4 Output characteristics of NEG

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Table 11.1 Specifications of NEG

Fig. 11.5 CO2 emission characteristics of NEG

amount of hydrogenation of Fig. 11.4(c) is the result when obtaining the maximum thermal efficiency. The specifications of a city gas engine and a power generator are shown in Tables 11.1(a) and (b). Hydrogen consumption is zero when the production of electricity exceeds 14 kW, as shown in Fig. 11.4(c). This is because high thermal efficiency can be obtained even if there is no hydrogenation in the large range of engine power, as Fig. 11.4(a) describes. Figure 11.4(d) shows the relation of the production of electricity and the generation efficiency of NEG. Although reformed gas is supplied to NEG, the generation efficiency of Fig. 11.4(d) includes reformer efficiency. The reformer is as common as the PEMFC system described below. Details of reformer efficiency are given in the section “The PEM-FC System”. Figure 11.5(a) shows the relation between carbon dioxide emission and the production of electricity of NEG and engine hydrogenation. This model was calculated from the characteristics of the thermal efficiency described in Figs. 11.4 and

11.3 Equipment Characteristics

195

Fig. 11.6 Output characteristics of the PEM-FC system

the equations (Eq. (11.1)-(11.3)) described below. The fuel supplied to NEG has many hydrogen rates in a low-load region, and there are many rates of city gas in a high-load region. Therefore, there are many rates of carbon dioxide discharged with a reforming reaction and a reformer burner in a low-load region, and there are many rates of carbon dioxide discharged by the engine burning of city gas in a high-load region. Figure 11.5(b) shows the model of a load factor and CO2 emissions calculated from Fig. 11.5(a). In Region A in this figure, NEG is mainly operated using reforming gas. In this region, CO2 emissions decrease slightly with the rise of a load factor. It is because reformer efficiency will improve when a load factor rises as described in the section “The PEM-FC System” and Fig. 11.6.

11.3.2 Carbon Dioxide Emissions of NEG Equation (11.1) is a steam-reforming reaction equation of city gas (CH4). Since this equation is an endothermic reaction, the heat for advancing its response is produced using the combustion reaction of CH4 shown in Eq. (11.2). Moreover, Eq. (11.3) is an equation that changes the carbon monoxide of Eq. (11.1) into carbon dioxide and hydrogen. If the hydrogen quantity supplied to NEG and the

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11 CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine

PEM-FC stack is determined, the amount of city gas supplied to a reformer and the carbon dioxide to be discharged are calculable using Eqs. (11.1)–(11.3). The CH4 quantity supplied to an engine is calculable using Eqs. (11.2), and Figs. 11.4(a) and (b).

CH 4 + H 2 O → CO+ 3 H 2 − 206 [kJ/mol]

(11.1)

CH 4 + 2 O 2 → CO 2 + 2 H 2 O+ 802 [kJ/mol]

(11.2)

CO + H 2 O → CO 2 + H 2 + 41 [kJ/mol]

(11.3)

Equation (11.4) expresses the amount of carbon dioxide discharged by NEG. G E,p, t is the carbon dioxide emission when burning CH4 with engine p . G R, p, t is the amount of carbon dioxide discharged by a reforming reaction required for engine hydrogenation. G S, p, t is the carbon dioxide emission of a heat-source burner. N R is the installed number of NEG, and in the NEG centralized system and a NEG base-load IMPE system, it is one set, and it is two sets in the NEG base load-sharing system. G NEG, t =

NE

∑ (G E,p,t + G R,p,t + G S,p,t )

(11.4)

p =1

11.3.3 The PEM-FC System Figure 11.6 shows the model of the generation efficiency of PEM-FC system and the city gas reformer efficiency [77]. Moreover, generation efficiency η F of Fig. 11.6(a) was calculated using Eq. (11.5). When the sampling time is expressed with t , E F, t of Eq. (11.5), the right-hand side is the power in the inverter outlet of a PEM-FC system. Q R,F, t expresses the calorific power of CH4 for reforming, and Q S,F, t expresses the calorific power of CH4 supplied to a heat-source burner. The maximum generation efficiency of the fuel cell shown in Fig. 11.6 is 31%. Moreover, the reformer efficiency in Fig. 11.6(a) improves with the increase in a load factor. Figure 11.6(b) shows the CO2 emissions of the PEM-FC system. Figure 11.6(b) shows the result of calculating based on the power-generation efficiency and reformer efficiency in Fig. 11.6(a). At the time of the hydrogen supply to the PEM-FC stack, the amount of CO2 discharged by a reforming reaction is expressed with G R, F,t , and the quantity discharged by a heat-source burner is expressed with G S, F, t . Therefore, the amount G F,t of CO2 discharged by the generation of the PEM-FC system is calculated by Eq. (11.6). ⎧E ⎫ η F, t = ⎨ F, t (Q R,F,t + QS,F,t )⎬⎭ ×100 ⎩

(11.5)

G F, t = G R,F, t + G S, F,t

(11.6)

11.4 Case Study

197

As Figure 11.5(b) shows, there is a large amount of CO2 emission of NEG in a high-load zone, but there is a large amount of CO2 emissions of a fuel cell in a low-load zone (Fig. 11.6(b)). From the difference in CO2 emission characteristics, NEG is advantageous in the operation of a partial load, and the PEM-FC system is advantageous in steady operation at high load.

11.4 Case Study 11.4.1 The Urban Area Model The urban area model analyzed in this chapter is shown in Fig. 11.7. The number of buildings is shown in this figure and the application for each building is shown in Table 11.2. The number of buildings of an urban area model is 20. The urban area model can consider various patterns. This chapter examines the characteristics of the carbon dioxide emission of the compound grid of NEG and the PEMFC system from the case of Fig. 11.7. Table 11.2 Power demand model for an urban area

Fig. 11.7 The urban area model

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11 CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine

11.4.2 The Power Demand Model Figure 11.8 shows the power demand model of each building in Tokyo, and is the mean power load of each sampling time of a representative day in January (winter), May (mid-term), and August (summer) [30–33]. However, the actual power demand pattern is an assembly of the load that changes rapidly in a short time, such as an inrush current. A power demand estimate of the houses shown in Figs. 11.8(a)–(d) is difficult. On the other hand, the power demand of the small offices of Fig. 11.8(g) and the factories of Fig. 11.8(h) is regular and easy to predict. The power demand pattern of a house has a peak in the morning and in the afternoon. When midnight to early morning is excluded, hotels (Fig. 11.8(e)) have a stable demand and convenience stores (Fig. 11.8(f)) have a 24-hour power demand. In small offices (Fig. 11.8(g)), factories (Fig. 11.8(h)), and small hospitals (Fig. 11.8(i)), there is small power demand at night to early morning, and there is much power demand from morning until evening. In the case study, the CO2 emission of August representation days, which have the largest power demand, are calculated. Figure 11.9 shows the heat demand model in August of each building described at the top of the figure [30–33]. However, in a convenience store, an office, and a factory, because the heat pump is introduced, heat demand is not taken into consideration.

11.4.3 Analysis Flow The analysis flow of the centralized system, the base load-sharing system, and the IMPE system is shown in Fig. 11.10. First, the power demand data of each building are given to the analysis program, and the base load of the whole micro-grid is calculated. Next, the power plant capacity installed into a micro-grid is given, and the power generation efficiency and carbon dioxide emission are calculated for every sampling time concerning all the grid routes of an urban area model. By adding all these, the total power generation efficiency and the total carbon dioxide emission in the operation period, and the capacity of a power plant are determined. The load factor is calculated from the capacity and power load of a power plant. A load factor is given to the approximation of Fig. 11.4(d) or Fig. 11.6, and the power generation efficiency is determined. The carbon dioxide emission of a system are calculated by giving a load factor to the approximation of Figs. 11.5 or 11.6.

11.4 Case Study

Fig. 11.8 Power demand models

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11 CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine

Fig. 11.9 Heat demand models in August Fig. 11.10 Calculation flow

11.5 Results and Discussion 11.5.1 Power Load of the Micro-grid Figure 11.11(a) shows the result of the power load pattern of a representative day in August of the urban area model. As the result of the time change of the power

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201

demand, Fig. 11.11(a) shows that the power plant capacity of a fluctuating load is 100 kW, and the power plant capacity of a base load is 66 kW. Figure 11.11(b) shows the result of the rate of a base load and a fluctuating load. The base load is 1.32 times larger. Figure 11.11(c) shows the composition of the power demand added to a micro-grid. The largest load component is convenience stores (two buildings), which takes 36% of the whole load. Because there is a 24-hour power demand in convenience stores (Fig. 11.8(f)) and hotels (Fig. 11.8(e)), it is a component that smoothes the whole load added to the micro-grid. Factories and small offices of the ratio of the whole load are large. However, the demand difference during the day and at night is large, and is a component for which the fluctuating load region of a micro-grid is made to increase.

Fig. 11.11 Results of the load pattern on a representative day in May

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11.5.2 Capacity of the Power Plant The analysis results of a representative day in August are shown in Fig. 11.12. Figure 11.12(a) shows the results of the capacity of the power plant installed in each micro-grid system. (A)–(E) in Fig. 11.12 expresses the power supply method described in the figure. In a centralized system ((A) and (B)), one set of the 11.5 kW power plant is connected to a micro-grid. On the other hand, in the base load-sharing system ((C) and (D)) and the IMPE system ((E) and (F)), the power plant capacity corresponding to a base load and load fluctuation is 66 kW and 100 kW, respectively.

11.5.3 Power Generation Efficiency Figure 11.12(b) shows the analysis results of the total power generation efficiency of the system of (A)–(F). Total power generation efficiency is high at (A), (C), (E) and (F). Most these systems are a method of corresponding to base-load operation by FC. Figure 11.12(c) shows the distribution of the power-generation efficiency of the system of (C)–(F), except for the centralized system. In FC base-load operation, it can operate at the maximum power-generation efficiency shown in Fig. 11.6(a). The maximum power-generation efficiency of PEM-FC system is higher than the efficiency of NEG shown in Fig. 11.4(d). Therefore, the total power generation efficiency of the system of (A), (C), and (E) using FC to baseload operation is high.

11.5.4 Carbon Dioxide Emissions Figure 11.12(d) shows the analysis result of the amount of carbon dioxide discharged from each system. (C) and (E) have small CO2 emission, and these are PEM-FC system base load operations. Moreover, (F) (NEG base load and FC load fluctuation operation) also has small CO2 emission. When Fig. 11.5(b) is compared with Fig. 11.6(b), the change of NEG CO2 emissions is larger than the PEM-FC system. As Figs. 11.5(b) and Fig. 11.6(b) show, when the load factor of PEM-FC system is large and the load factor of NEG is small, CO2 emission will decrease. Therefore, there is little CO2 emission of (C) and (E). Although (F) is the NEG base load operation, because it corresponds to load fluctuation by the large capacity PEM-FC system (100kW), there is less CO2 emission than in the system composed only from NEG ((B) and (D)). (E’) in Fig. 11.12(d) is CO2 emission of NEG without hydrogenation. When the hydrogenation of NEG is introduced, compared with the method that does not add hydrogen, about 15% of CO2 emission will reduce. Finally, the order with little CO2 emission is (C), (F), (E), (E’),

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203

Fig. 11.12 Analysis results

(D), (A), and (B). The order ((A) and (C), (E), (F), (B) and (D)) of the powergeneration efficiency described above differs from this order. Furthermore, when facility cost is taken into consideration, the smallest possible system of fuel cell capacity is advantageous. Power-generation efficiency is high, there is little CO2 emission, and a system with cheap facility cost is the best. Therefore, system (E) is proposed in this chapter.

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11 CO2 Discharged from a Compound Micro-grid of a Hydrogenation City Gas Engine

The energy supply by commercial power and a kerosene boiler is defined as the conventional method. The amount of greenhouse gas discharge of the conventional system is calculable based on “the investigative commission report of the calculation method of the amount of greenhouse gas discharge (the Ministry of Environment, Japan, August, 2003)”. The commercial power of a greenhouse gas discharge factor is 0.331 kg-CO2/kWh, and a kerosene boiler is set up at 0.0685 kg-CO2/MJ, 9.5 kg-CH4/TJ, and 0.57 kg-N2O/TJ. As a result, as shown in Fig. 11.1(d), the CO2 emission of system (E) decreases slightly more than with the conventional method. It depends on the amount of discharge of CO2 on the magnitude of a power load factor. Therefore, the amounts of discharge of the system (A) differ greatly compared with the system (C).

11.5.5 Heat Demand and Exhaust Heat Output Figure 11.13 shows the analysis results of the exhaust heat output of the base load operation and the load fluctuation operation of each system for representative days in August. The exhaust heat of each system of (A) to (F) exceeds the heat amount demanded of the urban area model in Fig. 11.7, as shown in Fig. 11.13. When an exhaust heat network is introduced into a micro-grid, and exhaust heat is distributed to each building, the boiler shown in Fig. 11.3 will become unnecessary.

Fig. 11.13 Heat demand and exhaust heat output on representative days in August

11.6 Conclusions Compared with the present power supply method, power-generation efficiency may improve and carbon dioxide emission may decrease in independent microgrids using a fuel cell. However, if a fuel cell micro-grid is introduced into an urban area with great load fluctuation, it becomes a very expensive facility. Thus, for most representative days in August, this chapter examined the independent micro-grid of power demand that introduce a hydrogenation city gas engine and

11.6 Conclusions

205

the PEM-FC system, and are operated. The following conclusions were obtained as a result of installing this system into an urban area model composed from the power load patterns of 20 buildings, such as houses, offices, and hospitals. (1) The total power generation efficiency of the centralization system, base-load sharing system, the IMPE system using PEM-FC and NEG was in the range of 19%–30%. Especially, power-generation efficiency has a high introduction of the PEM-FC system base load operation. (2) The load factor of the PEM-FC system is large, and the system with a small load factor of NEG has little CO2 emission. There is little CO2 emission with the PEM-FC base-load sharing system as a result of analysis. Further, there is little CO2 emission with the IMPE system of PEM-FC and NEG. Moreover, when hydrogen is added to NEG, CO2 emission will be reduced by 15%. (3) A PEM fuel cell base load and the system operating a hydrogenation city gas engine in a load fluctuation region are the most advantageous under the overall evaluation of facility cost, power generation efficiency, and CO2 emissions. When the urban area model was analyzed using the highest system of an overall evaluation, 25% of power-generation efficiency and CO2 emissions were 1,106 kg/day. The amount of CO2 emission is influenced by the magnitude of the power-demand fluctuation (load factor) of the micro-grid. The maximum effect is expected by reflecting making an energy-demand characteristic in the planning of a micro-grid. The relation between the locality of an energy demand characteristic and the optimal design of a micro-grid will be investigated hereafter.

Chapter 12

Development of a Fast Operation Algorithm of a Fuel Cell System with Solar Reforming

12.1 Introduction There are many methods to produce the hydrogen gas supplied to a fuel cell system [78–81]. Moreover, the production method for hydrogen fuel depends on the emission characteristic of the fuel cell greenhouse gas. For this reason, a reforming method using sunlight as the heat source is examined [82, 83]. If solar reforming equipment can be developed, the distribution of the small-scale fuel cell system will be accelerated. To date, there have been cases involving study of the solar reforming system using methane gas [82, 83]. On the other hand, the authors are investigating steam reforming of bioethanol [84]. Within the proposed system, hydrogen is produced by steam reforming of the bioethanol and used to operate the proton exchange membrane fuel cell (PEM-FC). The heat of the solar collector with condensing is then used for vaporization and steam reforming of the bioethanol fuel. Bioethanol is predominantly carbon-free and the heat source of this system uses renewable energy. The bioethanol solar reforming system proposed in this chapter is described as FBSR. Although FBSR is clean compared with hydrogen production methods using fossil fuel, the amount of hydrogen production fluctuates depending on the weather, time, season and installation location, etc. Previous weather patterns (outside air temperature and global solar radiation) are put into the neural network (NN), and the training signal is analyzed via the input of the genetic algorithm (GA). This method was used to investigate optimization of the collecting area of the solar collector with condensing, and the capacity of the reforming gas cylinder and the heat storage tank [84]. In this chapter, the algorithm of NN, which analyzes the dynamic operation of the arbitrary days of the FBSR at high speed, is developed. If the dynamic operation method of the system is determined within a short time, it can be optimized by installing this algorithm in the control device. As for the dynamic operation plan in the non-linear system of many variables, the method of approximating a linear problem [85] and that of using the GA [86] are proposed. By the approximation method of a linear probS. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

lem, analytic accuracy changes with approximate expressions. However, by the method using GA, despite the positive analytic accuracy, considerable computation time is required. With this in mind, the dynamic operation result of the system using the GA is used as the training signal, and these data are used for the learning process of the proposed algorithm. In addition, the dynamic operation method of the system, several hours later, is obtained in the form of data input (the meteorological data and the energy-demand data) into the learned NN. The details of the developed algorithm are clarified in this chapter, and the analysis error of the operation prediction is also considered.

12.2 System Configuration 12.2.1 The Fuel Cell System with Bioethanol Solar Reforming (FBSR) Figure 12.1 is a block diagram of the fuel cell system with the bioethanol solar reforming system (referred to as FBSR) [84]. In FBSR, two paraboloid rotating mirrors (described as condensing solar collectors) that have a solar tracking system are used. The high-density solar energy in condensing solar collector A is used for vaporization of the bioethanol fuel. The solar energy in condensing solar collector B is used as the heat source to reform the fuel steam. When solar energy cannot be used, it generates electricity using the reformed gas stored in the cylinder, or commercial power is supplied. In order to reduce CO, a shifter and CO oxidation equipment are installed. Reformed gas is supplied to the fuel cell and the power is converted into regular voltage and a regular frequency with a DC–DC converter and an inverter. This power is supplied to an individual house or a

Fig. 12.1 Fuel cell system with bioethanol solar reforming system (FBSR)

12.2 System Configuration

209

power grid through an interconnection device. The exhaust heat of the cooler, the fuel cell, and the CO oxidization equipment shown, respectively, in Fig. 12.1 is supplied to the heat storage tank, and this heat is supplied to the demand side.

12.2.2 Installation Method of FBSR As shown in Fig. 12.2, introducing FBSR into a micro-grid is considered. Expected benefits from the micro-grid are backup power supply in an emergency, reduction of environmental impact, and peak cut of the power plant [87]. Moreover, effective use of exhaust heat is possible and total efficiency is improved compared with the conventional power generation system. However, in the case study of this chapter, the example of introducing FBSR into one house is investigated. This case is the simplest system.

Fig. 12.2 Fuel cell micro-grid with FBSR

12.2.3 Control of Reformed Fuel The S/C (mole ratio of steam to ethanol) of the steady-state value of the reformed fuel supplied to the vaporizer equipment is 3.0. Moreover, the SV value (space velocity) in the catalyst layer in the solar reformer unit is 3000 hour–1, the conversion ratio of the reformed gas is 95%, the methanization rate is 5%, and the CO generation percentage is 10% [88]. When there is little solar radiation, the SV value is controlled to maintain the reaction temperature of the reforming unit at 673 K or more.

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

12.3 Energy and Mass Balance 12.3.1 Energy Balance Equations (12.1) and (12.2) express the balance of the power and heat, respectively. The left-hand side of each equation expresses inputs, and the right-hand side expresses outputs. φ IT and φ CT in Eq. (12.1) are the efficiency of the inverter and the DC–DC converter, respectively. Moreover, ΔE AUX expresses the  Loss on the right-hand side of Eq. (12.2) is the demand of auxiliary machinery. ΔH heat loss of the system. Power balance φ IT ⋅ φ CT ⋅ E CS + E CC = E SYS + ΔE AUX

(12.1)

 CL + H  CS + H  OU + H  ST + H  BL = H  SYS + ΔH  Loss H

(12.2)

Heat balance

12.3.2 Mass Balance Equation (12.3) expresses the mass balance of hydrogen, and Eq. (12.4) expresses the reaction formula of the steam reforming of ethanol. Hydrogen is calculable  ) of bioethanol based on Eq. (12.4). However, the from the supply flow rate ( Q BE  rate converted into hydrogen among Q BE is decided by the methane conversion ratio of bioethanol. Hydrogen balance  +Q      Q SR HC + Q EX = Q OU + ΔQ OU − ΔQ SU

(12.3)

Steam reforming reaction of bioethanol

C 2 H 5 OH + 3H 2 O → 2CO 2 + 6H 2 − 173kJ/mol

(12.4)

12.4 Dynamic Operation Prediction of SRF 12.4.1 Analysis Procedure of the Operation Prediction Algorithm Figure 12.3 shows the preparation procedure of the operation prediction algorithm of the SRF developed in this chapter. In the operation prediction using the NN, as

12.4 Dynamic Operation Prediction of SRF

211

Fig. 12.3 Procedure of the SRF prediction algorithm

shown in Fig. 12.3, it is necessary to perform the learning process first. The training signal used for the learning calculation is previously calculated by the GA. The training signal is the optimal solution of the dynamic operation plan on a representative day. When predicting operation, the input data given to the NN is the meteorological data (the amount of global solar radiation, and the outside air temperature) and the energy-demand data (power and heat demand), both of which were previously measured. The results obtained by the proposed NN are the amount of hydrogen production and the amount of exhaust heat output for every sampling time in the representative day to be predicted. Based on these results, the quantity of hydrogen and exhaust heat to be stored for every sampling time in the representative day to predict can be known.

12.4.2 Structure of the Neural Network The structure of the layered neural network introduced in this chapter is shown in Fig. 12.4. This neural network consists of three layers, the input, the medium, and the output. All the neurons between each layer of the NN are connected with networks. Each neuron is outputted to output layer So,k, j according to the magnitude of the input. Input–output between the neurons of n − 1 layer and n layer is shown in Fig. 12.5. Input x nj of neuron j in n layer is calculable using output out nk −1 and weight w n,j, nk −1 of neuron k of the n − 1 layer, as shown in Eq. (12.5): where j = 1, " , L n and k = 1, " , L n −1 . x nj =

L n −1

∑ w n,j, nk−1 ⋅ out nk −1 k =1

(12.5)

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

Fig. 12.4 Layered neural network of the proposed system Fig. 12.5 Input and output of the neuron

Output out nj of neuron j in n layer is given with the sigmoid function of input x nj , as shown in Eq. (12.6). out nj =

1 1 + exp − x nj

(

)

(12.6)

12.4.3 Learning Calculation (1) The Learning Method −1 All weights w n,n in the NN shown in Fig. 12.4 using the error-correction learnj,k ing method are determined. So, the past weather pattern and the energy-demand

12.4 Dynamic Operation Prediction of SRF

213

pattern in a building are given to the NN. Training signals are solutions of the operation plan on the representative day previously calculated by the GA. When w n,j, nk −1 is decided by learning of the NN, the error of the training signal and the data of the output layer can be evaluated from Eq. (12.7). This error is expressed as ErrN , and the NN is made to learn, changing weight w n,j, nk−1 so that ErrN may approach zero. ErrN =

∑(

1 Ln ⋅ y j − out Nj 2 j=1

)

2

(12.7)

(2) Modification of Weights

Corrected weight w n,j, nk−1 is expressed with Eq. (12.8) using the weight before modification w n,j, kn −1 and the amount of modifications Δw n,j, nk −1 . Amount of modifications Δw n,j, nk−1 in Eq. (12.9) is expressed with Eq. (12.10). The term of the partial differential of the right-hand side of Eq. (12.9) is calculable by Eqs. (12.10) and (12.11) [12]. w n,j, kn −1 = w n,j, kn −1 + Δw n,j, nk −1 Δw n,j, nk −1 = −η

∂ErrN ∂w n,j, nk −1

= −η

(12.8)

∂ErrN out nj −1 ∂x nj

(12.9)

In the case of n = N

(

)

(

∂ErrN = − y j − x Nj ⋅ out Nj ⋅ 1 − out Nj ∂x nj

)

(12.10)

In the case of n < N ⎫⎪ ∂ErrN ⎧⎪ L n ∂ErrN =⎨ ⋅ w l,n +j 1, n ⎬ ⋅ out nj ⋅ 1 − out nj n n 1 + ∂x j ⎪⎩ l =1 ∂x j ⎪⎭

∑

(

)

(12.11)

(3) Analysis Flow of the Learning Process

Figure 12.6 shows the analysis flow of the learning process of the NN. All weights w n,j, nk−1 are first determined at random, and learning rate η in Eq. (12.9) is given to the program. Next, input data x nj described in the section “Analysis Procedure of the Operation Prediction Algorithm” and training signal y j described at the bottom section are input into the program, and input x nj and output out nj of each

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

Fig. 12.6 Analysis flow of the NN learning process

neuron are calculated. Equation (12.10) is calculated using out nj and y j , and Eq. (12.11) is calculated further These results are introduced into Eq. (12.9), and Δw n,j, nk −1 is calculated. When this value is given to Eq. (12.8), the weight of each neuron can be updated. The analysis error is calculated using Eq. (12.7), and calculation will finish if this value is smaller than the value set up previously. On the other hand, when the analysis error is larger than the value set up previously, as shown in Fig. 12.6, the process is returned and calculated repeatedly.

12.4.4 The Operation Prediction Process When the current time is t11 , as shown in Fig. 12.7 for example, the operation of the system in time t14 is predicted. In this case, by time t 0 to t10 , the weather measurement data and energy demand data of the same day are input into the learned NN described previously. Moreover, the meteorological data measured on ′ , and t15 ′ to t ′23 . The ′ and t13 the previous day is used for the input value of t12 input data given to the NN at the prediction time t14 should give the predictor of the best possible accuracy. In this chapter, the monthly average value at the same time of the previous year is input.

12.5 Preparation of the Training Signal Using a GA

215

Fig. 12.7 Input data to the neural network program

12.5 Preparation of the Training Signal Using a GA 12.5.1 Dynamic Operation Plan of a Representative Day A GA can investigate optimization of the non-linear problem of many variables. However, determination of the solution parameter (the population of chromosomes, selection, mutation probability, crossover probability, generation number) concerning genetic manipulation requires many trials. Moreover, because a GA has the characteristic of random search, the analysis time will be long when the design variables increase in number. Therefore, when the operation plan of the FBSR is analyzed by a GA, a very long trial time is required. On the other hand, with the NN, although based on the network structure of the neurons, the trial time is short compared with GA calculation. So, in this chapter, the training signal of the NN is prepared using a GA, and actual operation prediction is analyzed only by the NN.

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

12.5.2 Chromosome Model and Analysis Flow The amount of hydrogen production of the FBSR is expressed with the chromosome code shown in Fig. 12.8. The chromosome code is a 13-bit genetic code of 0 or 1. Figure 12.9 shows the analysis flow for obtaining training signals using the GA. Many chromosome codes are generated, and the adaptive values (objective function) described in the following section for all the chromosomes are calculated. Chromosome groups with a low adaptive value are exchanged for chromosome groups newly generated at random. Moreover, handling of crossover and mutation are added to chromosomes, and diversity is maintained. These calculations are repeated by the number of generation numbers set up previously. In the last generation, it is decided that the chromosome with the highest adaptive value is the optimal solution. The amount of the hydrogen production is decided by decoding this chromosome code. The amount of hydrogen storage, the output of exhaust heat and thermal storage, etc., are calculated by introducing this solution into Eqs. (12.1)–(12.6). Fig. 12.8 The chromosome code

Fig. 12.9 Analysis flow of the training signal using GA

12.5 Preparation of the Training Signal Using a GA

217

12.5.3 System Operation Figure 12.10 shows the case of the power-demand pattern in a building and the operation pattern of the system. In this figure, solar radiation is obtained with a condensing solar collector in period R dh from 06:00 to 12:00. In this case, start time t st of the system operation is 06:00. In insufficient time zones, solar radiation generates electricity using the reformed gas stored during the day. In period R of time t gs to time t ge shown in Fig. 12.7, energy is supplied from the fuel cell. Although t gs and t ge can be determined arbitrarily, in the case study in the section “Case Study”, it is set at 23:00 and 07:00. This time zone is set as midnight power in many domestic electric power companies. Fig. 12.10 Operation plan of a representative day

12.5.4 Objective Function and Adaptive Value Equation (12.12) expresses the sum total of the difference in the power of the system and demand on a representative day. When the cylinder capacity of the reformed gas and the capacity of the heat storage tank are expressed as VH 2 and Vhw , respectively, objective function f o of the system is given in Eq. (12.13). w o,1 , w o,2 , and w o,3 in Eq. (12.13) express the weight of each term. In this chapter, it is decided that the adaptive value in the GA is a high solution, so that

218

12 Development of a Fast Operation Algorithm of a Fuel Cell System

the value of f o is small. Moreover, the reformed gas cylinder and the heat storage tank are designed to be as small as possible. Errday =

t ge

∑

E N − E SYS

(12.12)

t gs

f o = w o,1 ⋅ Errday + w o,2 ⋅ VH 2 + w o,3 ⋅ Vhw

(12.13)

12.6 Case Study 12.6.1 Analysis System Operation of the system when the FBSR shown in Fig. 12.1 is introduced into an individual house is predicted. The energy demanded of the individual house used as input data introduces the data of the reference [8]. Moreover, the database of the Japanese Meteorological Agency is used concerning outside air temperature and global solar radiation. Operation of the system is taken as the pattern shown in Fig. 12.10 where, t gs = 08:00 and t ge = 21:00. The data input into the NN by the learning calculation are the weather pattern in 2006 and the energy-demand pattern of a house [90]. The operation prediction of the FBSR on arbitrary days is calculable by giving the learning NN the weather pattern and the energy-demand pattern.

12.6.2 Characteristics of the System The single-cell performance of the PEM-FC is shown in Fig. 12.11. This performance is obtained from the experiments in the references [28, 29]. The maximum output point of the single cell is decided as 100% of a load factor in this chapter.

Fig. 12.11 Cell stack power output

12.6 Case Study

219

Fig. 12.12 Relation between load factor and power output of the cell stack

Table 12.1 Conditions of the system components

Figure 12.12 shows the relation between the load factor and the power density prepared from single-cell performance. The characteristics shown in Fig. 12.12 are divided into three areas, and approximate expressions are shown in this figure. When the production of electricity of the PEM-FC is decided, the generation efficiency, the power density, and the amount of exhaust heat will be decided from Figs. 12.11 and 12.12. The conditions of the system components used in analysis are shown in Table 12.1.

12.6.3 Analysis Condition The parameters introduced into the analysis of the GA and the NN are shown in Tables 12.2 and 12.3, respectively. These values were determined by applying a trial-and-error method for many analyses. When operation of the FBSR is analyzed by the NN using the conditions in Table 12.3, the computation time is 240 times faster compared with the GA. The analytical object is an individual house in Sapporo with the weather pattern and the energy-demand pattern in 2006 shown in Fig. 12.13. There is no cooling load in the summer season, and hot water supply, baths, and the space heating load

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

Fig. 12.13 Meteorological pattern and energy demand pattern in Sapporo in 2006 Table 12.2 Parameters of the GA

Table 12.3 Parameters of the NN

are included in the heat demand. Moreover, the load of electric lights and household appliances is included in the power demand, and there is no large monthly difference. Figure 12.14 shows the measurement data of the outside air temperature for each day in January and August, 2007. The NN is made to learn using the weather pattern in 2006. Operation on each representative day in January and August, 2007 (J1, J2, A1, A2) is predicted using this NN. In the operation plan in this chapter, the priority of design variables is in order of the power balance, the miniaturization of the cylinder capacity of the reformed gas, and the miniaturization of the capacity of the heat storage tank. Then, weights w o,1 , w o,2 and w o,3 , of the objective function shown in Eq. (12.13) are set at 0.7, 0.2, and 0.1, respectively.

12.7 Results and Discussion

221

Fig. 12.14 Meteorological data in 2007

12.7 Results and Discussion 12.7.1 Energy Supply by FBSR Figure 12.15 shows the result of the optimizing operation of the FBSR using the GA based on the weather pattern in 2006. Figure 12.15(a) shows the analysis results of the collecting area of the solar collector with condensing on a representative day every month. Moreover, Fig. 12.15(b) shows the result of the rate, whereby the energy supplied by FBSR takes to the amount demanded. The power supplied from the FBSR is 67% to 76% of the demand. However, the heat supplied from the FBSR is 3%–47%, differing greatly according to the season. Supplying energy for the power demand from t gs to t ge is included in the objective function (Eqs. (12.12) and (12.13)) of the FBSR. Therefore, the power supply rate from the FBSR is high compared to demand.

Fig. 12.15 Analysis results of the operation plan using GA in 2006

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

12.7.2 Analytic Accuracy of the Operation Prediction (1) Meteorological Data

Figure 12.16 shows the meteorological data measured on January 4 and 5, 2007 (representative day J1 in Fig. 12.14(a)). When the present is 3:00 on January 5, operation of the system of 3 hours after (6:00) is predicted. In this case, the measurement data on January 5 in Figs. 12.16(a) and (b) is used for the input data from 0:00 to 3:00 to the NN. Moreover, the measurement data on January 4 in Figs. 12.16(a) and (b) are used for the input data 4:00 and 5:00, and 7:00 to 23:00. Moreover, concerning the input data to the NN at 6:00 for prediction, the average data of the sampling time for every month of 2006 is used. Fig. 12.16 The meteorological data in point J1

(2) Integration Error in Forecasts

Figure 12.17 shows the operation plan showing the amount of hydrogen production by the NN, and the amount of exhaust heat thermal storage. In the same figure, the operation result calculated by the GA, which is considered to be the optimal solution, is also shown. Figures 12.17(a)–(c) represent the prediction results of the current time, up to 1 hour after, 3 hours after, and 6 hours after, respec-

12.7 Results and Discussion

223

tively. When “the amount of hydrogen production and the amount of exhaust heat thermal storage” are negative, the purchased power and operation of the boiler are required. Figures 12.18(a)–(c) show errors of the analysis result of the NN and GA. The integrated value is defined for the error in the forecast for one day shown in Fig. 12.18 as the integration error in the forecast in this chapter. Compared with the result of Fig. 12.17(a), the integration error in the forecast of Figs. 12.17(b) and 12.17(c) is 1% and 7% of the increase in hydrogen production, and 2% and 11% of the increase in thermal storage respectively. Accordingly, analytic accuracy deteriorates, so that the time to predict operation is the future.

Fig. 12.17 Analysis result of the FBSR dynamic operation in point J1

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12 Development of a Fast Operation Algorithm of a Fuel Cell System

Fig. 12.18 Prediction error in point J1

(3) The Maximum Forecast Error

The maximum values in terms of analytical error concerning hydrogen production shown in Fig. 12.17(a)–(c) are 9%, 10% and 11%, respectively. However, the maximum errors concerning heat storage are 13%, 14%, and 15%, respectively.

12.7 Results and Discussion

225

12.7.3 Analysis Error of the Heat Storage Prediction Figures 12.19–12.21 show the analysis results of the meteorological data and the operation prediction concerning the representative day J2 (January 12, 2007) in Fig. 12.14(a). Figure 12.20 shows the operation plan of the amount of hydrogen production, and the amount of exhaust heat storage, while Fig. 12.21 shows the error of the operation result in the NN and the GA. In this chapter, the monthly average outside-air-temperature data in 2006 (Fig. 12.19) are used for the learning process of the NN. Therefore, when the outside-air-temperature characteristic (Fig. 12.19) of a prediction day differs from the outside-air-temperature data in the monthly average in 2006, the analysis error of operation prediction will be considered to increase. When the characteristics of the “monthly average and representative day” in Figs. 12.16(a) and 12.19(a) are compared, Fig. 12.16(a) is larger and when the difference of “monthly average” and “representative day”, as shown in Figs. 12.16(a) and 12.19(a), is compared, Fig. 12.16(a) is larger. Consequently, the

Fig. 12.19 The meteorological data in point J2

226 Fig. 12.20 Analysis results of the FBSR dynamic operation in point J2

Fig. 12.21 Prediction error in point J2

12 Development of a Fast Operation Algorithm of a Fuel Cell System

12.7 Results and Discussion

227

analysis error of heat storage operation, as shown in Fig. 12.18, is significant compared with Fig. 12.21, and with respect to the analysis error of heat storage operation, this is also dependent on the magnitude of heat demand. Therefore, the analysis error of the heat storage operation in August (Figs. 12.22 and 12.23) is smaller than that in January (Figs. 12.18 and 12.21).

12.7.4 Relationship Between the Difference in Weather Characteristics and the Operation Prediction Error Figures 12.22 and 12.23 show the analysis results of the meteorological data and the operation prediction on the representative day A1 (August 15, 2007) and the representative day A2 (August 25, 2007) in Fig. 12.14(b), respectively. Figures 12.22(c) and 12.23(c) show the operation plan of the amount of hydrogen production, and the amount of exhaust heat storage, while Figs. 12.22(d) and 12.23(d) show the error of the operation result in the NN and the GA. Compared with Fig. 12.23(a), the difference between “the outside-air-temperature characteristic in the monthly average in 2006” and “the measurement outside-airtemperature (representative day) input into the NN” is larger in Fig. 12.22(a). However, the difference in the global-solar-radiation characteristic on these representative days is small compared with that in the outside-air-temperature characteristic. In this chapter, the monthly average outside-air-temperature data in 2006, as shown in Figs. 12.22(a) and 12.23(a), are used for the learning process of the NN. According to the difference of “the monthly average outside-air-temperature characteristic in 2006”, and “the temperature characteristic used for prediction”, as shown in Figs. 12.22(d) and 12.23(d), the magnitude of the analysis error changes.

228 Fig. 12.22 The prediction results of 6 hours after in August 15, 2007 (point A1)

12 Development of a Fast Operation Algorithm of a Fuel Cell System

12.7 Results and Discussion Fig. 12.23 The analysis results of 6 hours after in August 25, 2007 (point A2)

229

230

12 Development of a Fast Operation Algorithm of a Fuel Cell System

12.8 Conclusions The operation prediction algorithm of a fuel cell system with a bioethanol solar reforming system using a neural network (NN) was developed. In this algorithm, the operation plan of a building analyzed using a genetic algorithm (GA) is introduced as a training signal. When input data (weather pattern and energy-demand pattern) are given to the learning program, the operation method of the system (the amount of hydrogen production, and the amount of exhaust heat thermal storage) on arbitrary days can be obtained. A proposal algorithm was formed to learn about use of the meteorological data in Sapporo in 2006, and a dynamic operation plan was analyzed for two or more representative days in January and August, 2007. These results were then compared with the optimal solution by the GA. Any error in the operational analysis depends on the difference between the outside-airtemperature pattern used for the learning process of the NN and the outside-airtemperature pattern of the prediction day. Moreover, the analysis error of operation prediction increases, meaning there is previous prediction time. The operation prediction of the system, based on past weather patterns, is rapidly analyzable over a constant error range with the proposal algorithm.

Chapter 13

Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation

13.1 Introduction A micro-grid technique is predicted to be effective with respect to backup power supply in an emergency, peak cut of power plants, and exhaust heat utilization. Furthermore, when renewable energy is connected to a micro-grid, there is potential to reduce the amount of greenhouse gas discharge [21, 22, 91]. A micro-grid has an interconnection system with commercial power, etc., and the independence supplying system of the power. The micro-grid with an interconnection system outputs and inputs the power between other grids. Therefore, the dynamic characteristic of the grid is influenced by the grid of a connection destination. When a micro-grid and a large-scale grid such as a commercial power system are interconnected, the dynamic characteristics of the power depend on the commercial power system. For this reason, in the micro-grid of the interconnection type, the option regarding the equipment to connect is large. On the other hand, since the micro-grid can reduce transportation loss of power and heat, this technique may become the major energy supply. The method of connecting two or more smallscale fuel cells and renewable energy equipment by a micro-grid, and supplying power to the demand side is effective in respect to environmental problems. Thus, this chapter examines the independent micro-grid that connects fuel cells and wind power generation. In order to follow load fluctuation with an independent grid system, a method of installing a battery and a method of controlling the output of power generators can be used. Since batteries are expensive, in this chapter, it corresponds to load fluctuation by controlling the power output of the fuel cell. The output adjustment of the fuel cell has the method of controlling the production of electricity of each fuel cell, and the method of controlling the number of operations of the fuel cell. However, adjustment of the production of electricity of each fuel cell connected to the micro-grid may operate some fuel cell with a partial load with low efficiency. Thus, herein, the number of operations of fuel cells is controlled to follow fluctuations in the electricity demand. S. Obara, Fuel Cell Micro-grids, © 2009 Springer, Power Systems Series

231

232

13 Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation

In an independent micro-grid, a certain fuel cell connected to the micro-grid is chosen and this is considered as a power basis. The power (voltage and frequency) of the other fuel cells is controlled to synchronize with this base power. Therefore, if the fuel cell that outputs the base power is unstable, the power quality of the whole grid will deteriorate. Fuel cells other than the base load operation are controlled to synchronize with the base power. The power quality (voltage and frequency) of the micro-grid depends on the difference in the demand-and-supply balance. A 2.5 kW fuel cell is installed in one house of the micro-grid formed from ten houses. This fuel cell is operated corresponding to a base load. A 1 kW fuel cell is installed in seven houses, and a 1.5 kW wind power generator is connected to the micro-grid. According to the difference in the electricity demand of the grid and the power produced by the wind power generator, the number of operations of 1 kW fuel cells is controlled. A city gas reformer is installed in houses in which fuel cells are installed, and hydrogen is produced by city gas reforming. By adding random fluctuation to an average power load pattern, the power demand of a general residence is simulated and used for analysis. The dynamic characteristics of the micro-grid and the efficiency of the system that are assumed in this chapter are investigated by numerical analysis.

13.2 The Micro-grid Model Figure 13.1 shows the fuel cell independent micro-grid model investigated in this chapter. There is a network of power and city gas in this micro-grid. Although

Fig. 13.1 Fuel cell micro-grid system with a wind power generator

13.3 Response Characteristics of System Configuration Equipment

233

a power network connects all houses, a city gas network connects houses in which a fuel cell is installed. The fuel cell installed in each house is a proton exchange membrane type (PEM-FC). The output of a 2.5 kW fuel cell is determined to be a base power of the micro-grid. Moreover, PEM-FC of 1 kW power is installed in seven houses. However, the fundamental dynamic characteristics of all the fuel cells are the same, and a fuel cell and a city gas reformer are installed as a pair. One set of wind power generators is installed, and the power produced by wind force is supplied to a micro-grid through an inverter and an interconnection device. The power supply of the micro-grid assumes 50 Hz of the single-phase 200 V.

13.3 Response Characteristics of System Configuration Equipment 13.3.1 Power Generation Characteristics of the Fuel Cell Figure 13.2(a) shows the measurement results of a step-wise input of a load of 45 W into the testing equipment of PEM-FC (maximum output 100 W). In the test, the ambient temperature was set to 293 K, and reformed gas and air were supplied to an anode and a cathode, respectively. An approximated curve was prepared from the result of the measurement in Fig. 13.2(a), and the transfer function of a primary delay was obtained. Strictly speaking, although a transfer function is considered depending on the load factor, it is not taken into consideration because this difference is small in the test results.

13.3.2 Output Characteristics of the City Gas Reformer Figure 13.2(b) shows the output model that step-wise inputted a load of a 100% load factor into the city gas reformer [4, 26, 27, 67, 92]. An approximated curve was prepared from the result of the measurement, and the transfer function of the primary delay of the city gas reformer was obtained. Although the transfer function of a city gas reformer influences the magnitude of the load significantly, since there is no large difference, the result of Fig. 13.2(b) was used as a fuel cell. Compared with the condition of the steady operation of the reformer, the characteristics of startup and shutdown differ greatly. Cold start and shutdown operations require about 20 minutes each. In the analysis of this chapter, it is assumed that the startup of the methanol reformer is always a hot start.

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13 Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation

Fig. 13.2 Response characteristics of the system configuration equipment

13.3.3 Power Generation Characteristics of Wind Power Generation The model of power obtained by wind power generation is decided at random between 0 to 1.5 kW for every sampling time, as shown in Fig. 13.3(a). The power of the wind power generator is supplied to a micro-grid through an inverter and a system interconnection device. Figure 13.3(b) shows the output model of the wind power generator through an inverter and a system-interconnection device. The output of wind power generation is 0.75 kW ± 0.25 kW, as shown in Fig. 13.3(b). This is because the influence of the dynamic characteristics of the inverter and the system-interconnection device is significant. The dynamic characteristics of the inverter and system interconnection device significantly influence the power output characteristics of wind power generation.

13.3 Response Characteristics of System Configuration Equipment

235

Fig. 13.3 Output model of the wind power generator

13.3.4 Generation Efficiency of the Fuel Cell System Figure 13.4 shows a model of the relation between the load factor of a fuel cell and the generation efficiency [52, 93]. Power generation efficiency is obtained by dividing “the power output of the fuel cell system” by “the city gas calorific power supplied to the system”. This model was prepared from the results of the power output when attaching the fuel cell show in Fig. 13.2(a) to the city gas reformer

236

13 Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation

Fig. 13.4 Output characteristics of PEMFC with a city gas reformer

show in Fig. 13.2(b). If the load of a fuel cell is given to Fig. 13.4, power generation efficiency is calculable. The maximum efficiency of one set of a fuel cell system is 32%.

13.3.5 Inverter and System Interconnection Device It is assumed that an inverter of a voltage control type is used, and 120 ms is required to output power on regular voltage and frequency (in this chapter, it is less than 95%) [53]. Figure 13.5(a) expresses the transfer function of such an inverter with primary delay. When changing the power with a system interconnection device, the change takes about 10 μ s [8]. However, there is the operation of taking the synchronism of the frequency between systems, and the model of the system interconnection device sets the change time to 12 ms. The transfer function of the system interconnection device by primary delay is shown Fig. 13.5(b).

Fig. 13.5 Transfer function of an inverter and an interconnection device

13.4 Control Parameters and Analysis Method

237

13.4 Control Parameters and Analysis Method The response characteristics of the 1 kW fuel cell system when inputting 0.13.10, and a 1.0 kW load step-wise is shown in Fig. 13.6. The response characteristics of a fuel cell system changes by the control parameters set up with the controller. As shown in Fig. 13.6(c), with 1 kW step input, the rising time and settling time (time to converge on ±5% of the target output) are not based on control parameters. With a 0.2 kW step input, the rise time of P = 12.0, I = 1.0 is short, and the settling time of P = 1.0, I = 1.0 is short. With a 0.6 kW step input, P = 12.0, I = 1.0

Fig. 13.6 Characteristics of the electric power output of the system

238

13 Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation

and P = 1.0, I = 1.0 have almost the same settling time. Moreover, overshooting is large although the rise time of P = 12.0, I = 1.0 is short. Considering the following load fluctuations, the control parameters of the fuel cell are analyzed by P = 12.0, I = 1.0. The dynamic characteristics of a micro-grid are analyzed using MATLAB® (Ver.7.0) and Simulink® (Ver.6.0) of The MathWorks Corporation. However, in the analysis, the solver used is the positive Runge–Kutta system, and this determines the sampling time from calculation converged to less than 0.01% by error.

13.5 Load Response Characteristics of the Micro-grid 13.5.1 Step Response The response results when applying the step-wise input of 2, 4, 6 or 8 kW to the micro-grid at intervals of 30 seconds are shown in Fig. 13.7(a). The left-hand part of Fig. 13.7(a) shows the result of not installing a wind power generator. The right-hand side of the figure shows the result of installing a wind power generator. The maximum power by overshooting and settling time (time to converge on ±5%

Fig. 13.7 Results of step response

13.5 Load Response Characteristics of the Micro-grid

239

of the target output) are described in the left-hand part of Fig. 13.7(a). Moreover, the maximum power due to overshooting is described in the right-hand part of the figure. The settling time when not installing a wind power generator has the longest period of step input of 6 kW and 8 kW for 3.9 seconds. If a wind power generator is connected to the micro-grid, many fluctuations in the system response characteristics will occur in a short period. If the power produced by wind power generation is supplied to the micro-grid, the dynamic characteristics of power of the micro-grid will be influenced. Figure 13.7(b) shows the analysis results of the response error corresponding to Fig. 13.7(a). If a wind power generator is connected to the grid, the response error will become large as the load of the grid becomes small. It is expected that the power range of the fluctuation of the microgrid will increase as the output of the wind power generation grows. Therefore, when the load of a micro-grid is small compared with the output of a wind power generator, and the power supply of the independent micro-grid becomes unstable.

13.5.2 Load Response Characteristics of Cold-region Houses Fig. 13.8(a) shows the power demand pattern of a micro-grid formed from ten individual houses in Sapporo, Japan, and assumes a representative day in February. This power demand pattern is the average value of each hour, and the sampling time of analyses and the assumption time are written together on the horizontal axis. As a base load of the power demand pattern shown in Fig. 13.8(a), F/C (0) is considered as operation of 2.5 kW constant load. Figures 13.8(b) and (c) are the power demand patterns when adding load fluctuations (±1 kW and ±3 kW) to Fig. 13.8(a) at random. The variation of the load was determined at random within the limits of the range of fluctuation for every sampling time. Figure 13.9 shows the response results of F/C (0) to F/C (6) when wind power generation is connected to the micro-grid and the power load has ±1 kW fluctuations. F/C (0) assumed operation with 2.5 kW constant output, with the result that the response of F/C (0) is much less than 2.5 kW in less than the sampling time of 100 s, as shown in Fig. 13.9(a). This is because F/C (0) was less than 2.5kW with the power of wind power generation. Although the micro-grid assumed in this chapter controlled the number of operations of F/C (1) to F/C (7) depending on the magnitude of the load, since the power supply of wind power generation existed, there was no operating time of F/C (7).

240

13 Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation

Fig. 13.8 480 s demand model for 10 houses in Sapporo in February

13.5 Load Response Characteristics of the Micro-grid

241

Fig. 13.9 Response results of each fuel cell

13.5.3 Power Generation Efficiency Figure 13.10 shows the analysis results of the average power generation efficiency of fuel cell systems for every sampling time. The average efficiency of a fuel cell system is the value averaging the efficiency of F/C (0) to F/C (7) operated at each sampling time. However, the fuel cell system to stop is not included in average power generation efficiency. The average power generation efficiency of Fig. 13.10(a) is 13.4%, and that of Fig. 13.10(b) is 14.3%. The difference in average efficiency occurs in the operating point of a fuel cell system shifting to the efficient side, when load fluctuations are added to the micro-grid. Thus, if load fluctuations are added to the micro-grid, compared with no load fluctuations, the load factor of the fuel cell system shown in Fig. 13.4 will increase. Figure 13.11 shows the power generation efficiency of each fuel cell in the case of connecting wind power generation to the micro-grid of ±1.0 kW of the load fluctuation. F/C (0) operated corresponding to a base load has maximum power generation efficiency at all sampling times. Since the number of operations of a

242

13 Power Characteristics of a Fuel Cell Micro-grid with Wind Power Generation

Fig. 13.10 Results of micro-grid average efficiency

fuel cell is controlled by the magnitude of the load added to the micro-grid, the operating time falls in the order of F/C (1) to F/C (6). Moreover, there is no time to operate F/C (7) in this operating condition.

13.6 Conclusions A 2.5 kW fuel cell was installed in a house linked to a micro-grid, operation corresponding to a base load was conducted, and the dynamic characteristics of the grid when installing a 1 kW fuel cell system in seven houses were investigated by

13.6 Conclusions

243

Fig. 13.11 Results of efficiency for each fuel cell

numerical analysis. A wind power generator outputted to a micro-grid at random within 1.5 kW was installed, and the following conclusions were obtained. (1) Although the settling time (time to converge on ±5% of the target output) of the micro-grid differs with the magnitude of the load and the parameters of the controller, it is about 4 seconds. (2) When connecting a wind power generator to the micro-grid, the instability of the power of the grid due to supply-and-demand difference is an issue. This issue is remarkable when the load of an independent micro-grid is small compared to the production of electricity of unstable wind power generation. (3) When wind power equipment is connected to the micro-grid with load fluctuation, the operating point of the fuel cell system may shift and power generation efficiency may improve.

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Index

demand model 21 heat demand model 22, 182, 183, 188, 198 power demand model 48–52, 54–57, 86, 88, 90, 198 efficiency 1 boiler efficiency 5, 8, 29, 179 brake thermal efficiency 172, 173, 188, 193 engine thermal efficiency 176 generation efficiency 17, 43, 44, 46–48, 50, 52, 54–59, 78, 82, 84, 86, 89–93, 114, 120, 138, 144, 150–153, 157, 169, 170, 172, 174, 176, 177, 181, 183, 185, 186, 188–190, 194, 196, 198, 202–205, 219, 235, 241 reformer efficiency 46, 84, 116, 144, 174, 194–196 thermal efficiency 13, 169, 173, 174, 193, 194 total efficiency 8, 90, 170, 176, 177, 183, 185, 186, 188, 209 fuel cell CGS 1 back-up boiler 3 catalyst burner 2, 3 change over switch 3 CO oxidation unit 1

CO oxidization unit 2, 5 DC/AC converter 3 electric heater 3 evaporation unit 2 evaporator 2 high-speed-changeover switch 3 radiator 3 reformer unit 2 shift unit 1, 2, 6 shifter unit 5 thermal storage tank 3 micro-grid 17 independent micro-grid 44, 78, 93, 138–140, 142, 153, 154, 168, 189–191, 204, 231, 232, 239, 243 interconnection micro-grid 44, 77, 190 system operation 6 central system 47, 48, 50, 54, 56, 59, 90, 92, 93, 191 cooperation system 47, 48, 50, 53–55, 59 dynamic operation 207, 208, 211, 230 fluctuation operation 204 load fluctuation operation 188, 202 partial-load operation 43, 138, 151, 152, 169, 189 stand-alone system 47, 48, 50, 52, 53, 55, 59

251