01f2k.optimum.design.of.Renewable.energy.systems.microgrid.and.Nature.grid.Methods

Optimum Design of Renewable Energy Systems:Microgrid and Nature Grid MethodsShinya ObaraKitami Institute of Technology, Japan

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Obara, Shinya. Optimum design of renewable energy systems : microgrid and nature grid methods / by Shinya Obara. pages cm Includes bibliographical references and index. ISBN 978-1-4666-5796-0 (hardcover) -- ISBN 978-1-4666-5797-7 (ebook) -- ISBN 978-1-4666-5799-1 (print & perpetual access) 1. Renewable energy sources. 2. Electric power distribution. 3. Electric power systems--Design and construction. 4. Energy storage--Equipment and supplies. I. Title. TJ808.O23 2014 621.042--dc23 2013051299 This book is published in the IGI Global book series Advances in Environmental Engineering and Green Technologies (AEEGT) (ISSN: 2326-9162; eISSN: 2326-9170)

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Optimum Design of Renewable Energy Systems Microgrid and Nature Grid MethodsShinyaObara(KitamiInstituteofTechnology,Japan)EngineeringScienceReferencecopyright2014303ppH/C(ISBN:9781466657960)US$235.00(ourprice)

Nuclear Power Plant Instrumentation and Control Systems for Safety and SecurityMichaelYastrebenetsky(StateScientificandTechnicalCentreforNuclearandRadiationSafety,Ukraine)andVyacheslavKharchenko(NationalAerospaceUniversity-KhAI,Ukraine,andCentreforSafetyInfrastructure-OrientedResearchandAnalysis,Ukraine)EngineeringScienceReferencecopyright2014470ppH/C(ISBN:9781466651333)US$265.00(ourprice)

Computational Intelligence in RemanufacturingBoXing(UniversityofPretoria,SouthAfrica)andWen-JingGao(MeiyuanMouldDesignandManufacturingCo.,Ltd,China)InformationScienceReferencecopyright2014348ppH/C(ISBN:9781466649088)US$195.00(ourprice)

Risk Analysis for Prevention of Hazardous Situations in Petroleum and Natural Gas EngineeringDavorinMatanovic(UniversityofZagreb,Croatia)NediljkaGaurina-Medjimurec(UniversityofZagreb,Croatia)andKatarinaSimon(UniversityofZagreb,Croatia)EngineeringScienceReferencecopyright2014433ppH/C(ISBN:9781466647770)US$235.00(ourprice)

Marine Technology and Sustainable Development Green InnovationsOladokunSulaimanOlanrewaju(UniversityMalaysiaTerengganu,Malaysia)AbdulHamidSaharuddin(UniversityMalaysiaTerengganu,Malaysia)AbSamanAbKader(UniversitiTeknologiMalaysia,Malaysia)andWanMohdNorsaniWanNik(UniversityMalaysiaTerengganu,Malaysia)InformationScienceReferencecopyright2014338ppH/C(ISBN:9781466643178)US$195.00(ourprice)

Sustainable Technologies, Policies, and Constraints in the Green EconomyAndreiJean-Vasile(PetroleumandGasUniversityofPloiesti,Romania)TurekRahoveanuAdrian(InstituteofResearchforAgriculturalEconomicsandRuralDevelopment,Romania)JonelSubic(InstituteofAgriculturalEconomics,Belgrade,Serbia)andDorelDusmanescu(PetroleumandGasUniversityofPloiesti,Romania)InformationScienceReferencecopyright2013390ppH/C(ISBN:9781466640986)US$180.00(ourprice)

Energy-Aware Systems and Networking for Sustainable InitiativesNaimaKaabouch(UniversityofNorthDakota,USA)andWen-ChenHu(UniversityofNorthDakota,USA)InformationScienceReferencecopyright2012469ppH/C(ISBN:9781466618428)US$180.00(ourprice)

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Table of Contents

Preface .................................................................................................................................................viii

Acknowledgment ................................................................................................................................. xv

Chapter 1Operating.Schedule.of.a.Combined.Energy.Network.System................................................................. 1

General.Introduction........................................................................................................................ 1Operating.Schedule.of.a.Combined.Energy.Network.System.with.Fuel.Cell................................. 2Fuel.Cell.Network.System.Considering.Reduction.in.Fuel.Cell.Capacity.Using.Load..Leveling.and.Heat.Release.Loss.................................................................................................... 18

Chapter 2Dynamic.Characteristics.of.a.Fuel.Cell.System.and.Microgrid............................................................ 38

General.Introduction...................................................................................................................... 38Load.Response.Characteristics.of.a.Fuel.Cell.Microgrid.with.Control.of.Number.of.Units........ 39Dynamic.Characteristics.of.a.PEFCL.System.for.Individual.Houses........................................... 53

Chapter 3Effective.Improvement.in.Generation.Efficiency.of.a.Fuel.Cell.Microgrid.......................................... 72

General.Introduction...................................................................................................................... 72Effective.Improvement.in.Generation.Efficiency.due.to.Partition.Cooperation.Management..of.a.Fuel.Cell.Microgrid................................................................................................................ 73Equipment.Plan.of.Compound.Interconnection.Microgrid.Composed.from.Diesel.Power..Plants.and.Proton-Exchange.Membrane.Fuel.Cell........................................................................ 87

Chapter 4Installation.Plan.of.a.Fuel.Cell.Cogeneration.System......................................................................... 103

General.Introduction.................................................................................................................... 103Installation.Plan.of.a.Fuel.Cell.Microgrid.System.Optimized.by.Maximizing.Power..Generation.Efficiency.................................................................................................................. 104Fuel.Cell.Network.with.Water.Electrolysis.for.Improving.Partial.Load.Efficiency.of.a.Residential.Cogeneration.System................................................................................................ 118

Chapter 5Fuel.Cell.Microgrid.with.Wind.Power.Generation.............................................................................. 136

General.Introduction.................................................................................................................... 136Analysis.of.a.Fuel.Cell.Microgrid.with.a.Small-Scale.Wind.Turbine.Generator........................ 137Power.Characteristics.of.a.Fuel.Cell.Microgrid.with.Wind.Power.Generation.......................... 154

Chapter 6Compound.Microgrid.of.City-Gas.Engine.and.Proton.Exchange.Membrane.Fuel.Cell..................... 167

General.Introduction.................................................................................................................... 167Amount.of.CO2.Discharged.from.Compound.Microgrid.of.Hydrogenation.City-Gas..Engine.and.Proton.Exchange.Membrane.Fuel.Cell..................................................................... 168Capacity.Optimization.of.PEFCL.and.Hydrogen.Mixing.Gas-Engine.Compound.Generator....... 181

Chapter 7Independent.Microgrid.Composed.of.Distributed.Engine.Generator.................................................. 198

General.Introduction.................................................................................................................... 198Energy.Cost.of.an.Independent.Microgrid.with.Control.of.Power.Output.Sharing.of.a..Distributed.Engine.Generator...................................................................................................... 199Improvement.of.Power.Generation.Efficiency.of.an.Independent.Microgrid.Composed.of.Distributed.Engine.Generators.................................................................................................... 216

Chapter 8Characteristics.of.PEFC./.Woody.Biomass.Engine.Hybrid.Microgrid.and.Exergy.Analysis.............. 237

General.Introduction.................................................................................................................... 237Dynamic.Characteristics.of.PEFC./.Woody.Biomass.Engine.Hybrid.Microgrid........................ 238Exergy.Analysis.of.the.Woody.Biomass.Stirling.Engine.and.PEFC.Combined.System.with.Exhaust.Heat.Reforming.............................................................................................................. 250Exergy.Analysis.of.a.Regional.Distributed.PEM.Fuel.Cell.System............................................ 263

Chapter 9Design.Support.Using.a.Neural.Network.Algorithm........................................................................... 282

General.Introduction.................................................................................................................... 282Dynamic.Operational.Scheduling.Algorithm.for.an.Independent.Microgrid.with..Renewable.Energy....................................................................................................................... 283Operation.Prediction.of.a.Bioethanol.Solar.Reforming.System.using.a.Neural.Network.......... 300

Chapter 10Microgrid.with.Numerical.Weather.Information................................................................................. 321

General.Introduction.................................................................................................................... 321Compound.Microgrid.Installation.Operation.Planning.of.a.PEFC.and.Photovoltaics.with.Prediction.of.Electricity.Production.using.GA.and.Numerical.Weather.Information................. 322Energy.Supply.Characteristics.of.a.Combined.Solar.Cell.and.Diesel.Engine.System.with.a.Prediction.Algorithm.for.Solar.Power.Generation...................................................................... 335

Chapter 11SOFC-PEFC.Combined.Microgrid...................................................................................................... 352

General.Introduction.................................................................................................................... 352Power.Generation.Efficiency.of.Photovoltaics.and.a.SOFC-PEFC.Combined.Microgrid..with.Time.Shift.Utilization.of.the.SOFC.Exhaust.Heat.............................................................. 353Power.Generation.Efficiency.of.an.SOFC-PEFC.Combined.System.with.Time.Shift..Utilization.of.SOFC.Exhaust.Heat.............................................................................................. 367

Chapter 12Bioethanol.Solar.Reforming.System.for.Distributed.Fuel.Cell........................................................... 385

General.Introduction.................................................................................................................... 385Hydrogen.Production.Characteristics.of.a.Bioethanol.Solar.Reforming.System.with.Solar.Insolation.Fluctuations................................................................................................................. 386Efficiency.Analysis.of.a.Combined.PEFC.and.Bioethanol-Solar-Reforming.System.for.Individual.Houses........................................................................................................................ 399

Compilation of References................................................................................................................ 417

About the Contributors..................................................................................................................... 427

Index.................................................................................................................................................... 428

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Preface

Control of global warming is a common subject in the world. Therefore, the challengers of various fields are considering methods to control global warming. Microgrid technology is expected as a next-gener-ation energy supply system. However, since renewable energy is unstable, in many cases, it requires support by the conventional energy equipment. We are investigating the compound energy system from the following two viewpoints. One is the development of highly efficient energy storage equipment represented by a battery and heat-storage tank. Another is development of the operation optimization technology of the compound energy system including green energy. It is thought that the energy supply method shifts from the individual operation of large-scale plant to distribution of small equipment or microgrid. Moreover, a microgrid develops into a smart-grid by various added values with IT technol-ogy. On the other hand, it was predicted that the reduction technology of the greenhouse gas of a microgrid progressed sharply, and we named the nature-grid. A microgrid, a smart grid, and a nature-grid require fusion of energy technology and an information technology. For example, the operation in consideration of the green energy change with load prediction and weather prediction of a compound energy system can be planned. This book describes the operation optimization technology by compound utilization of a PEFC, PEFC-SOFC combined system, bio-ethanol solar reforming, wind-power generation, woody biomass engine, city-gas engine, diesel power plant, etc. The technology described in this book plays a large role in the development of a small-scale power-generation system, a microgrid, a smart-grid, and a nature-grid, which are introduced into individual houses, apartment houses or local area power sup-plies.

The book is organized into twelve chapters. A brief description of each of the chapters follows:Chapter 1 has described operating schedule of a combined energy network system. In the 1st section,

the chromosome model showing system operation pattern is applied to GA (genetic algorithm), and the method of optimization operation planning of energy system is developed. The optimization method of this operation planning was applied to the compound system of methanol steam reforming type fuel cell, geo-thermal heat pump and the electrolysis tank of water. The operation planning was performed for the energy system using the energy demand pattern of the individual residence of Sapporo in Japan. From analysis results, the amount of outputs of a solar module and the relation of the operation cost of the system which are changed by the weather were clarified. In the 2nd section, reduction in fuel cell capacity linked to a fuel cell network system is considered. An optimization plan is made to minimize the quantity of heat release of the hot water piping that connects each building. Such an energy network is analyzed assuming connection of individual houses, a hospital, a hotel, a convenience store, an office building, and a factory. Consequently, a reduction of 46% of fuel cell capacity is expected compared with the conventional system in the case study.

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Chapter 2 has described characteristics of a fuel cell system and microgrid. In the 1st section, the dy-namic characteristics and generation efficiency of a microgrid structured from 17 houses were examined. A gas engine generator with a power generation capacity of 3 kW installed in a house is made to correspond to the base load, and a proton-exchange membrane fuel cell (PECF) with a power generation capacity of 1 kW is installed in 16 houses. Moreover, when changing the load of a microgrid, the correspondence takes place by controlling the number of fuel cells. Using numerical analysis, the characteristics of the power quality of a fuel cell microgrid, and the generation efficiency of the fuel cell were examined. As a result, the relationship between the parameter of the controller and power quality and a fall in generation efficiency by a partial load were clarified. In the 2nd section, the method of determination of the control variables for a system controller, which controls the electric power output of a solid-polymer-membrane fuel cell system (PEFC) during electric power load fluctuations, was considered. The power load pattern of an individual house consists of loads usually moved up and down rapidly for a short time. This section investigates the relation of the control variables and power generation efficiency when adding change that simulates the load of a house to PEFC cogeneration. As a result, it was shown that an operation, minimally influenced by load fluctuations, can be performed by changing the control variables using the value of the electric power load of a system.

Chapter 3 has described effective improvement in generation efficiency of a fuel cell microgrid. The fuel cell microgrid is expected as a distributed power supply with little environmental impact. In the 1st section, a microgrid is divided into multiple and each is optimized for the purpose of maximization of power generation efficiency. In the cooperation management of a microgrid, large fluctuations in load, or increases and decreases in a building, can be followed with a grid using a system-interconnection device. The system proposed in this section obtained results with high generation efficiency (from 21.1% to 27.6%) compared with the central system (generation efficiency is 20.6% to 24.8%) of a fuel cell mi-crogrid. In the 2nd section, an independent microgrid that compounds and connects a diesel power plant generator (DEG) and a proton-exchange membrane fuel cell (PEFC) is proposed. The operation of DEG is controlled to correspond to the base load of whole CIM (Compound Interconnection microgrid), and, on the other hand, the operation of PEFC is controlled to follow the load fluctuation of CIM. A com-plex community model and residential area model were used for analysis. In this section, the microgrid concerning the urban area (18 buildings) in Tokyo was investigated. From the results of analysis, it was confirmed that CIM could be operated with a high generation efficiency of 27.1 to 29.9%.

Chapter 4 has described installation plan of a fuel cell cogeneration system. If energy-supplying microgrids can be arranged to operate with maximal efficiency, this will have a significant influence on the generation efficiency of the grid and will reduce greenhouse gas production. A means of opti-mizing the microgrid needs to be developed. In the 1st section, microgrids that use proton exchange membrane-type fuel cells (PEFC) may significantly reduce the environmental impact when compared with traditional power plants. The amount of power supplied to the grid divided by the heating value of the fuel is defined as the system generation efficiency. We find that when a set of PEFCs and a natural gas reformer are connected to the microgrid in an urban area, the annual generation efficiency of the system slightly exceeds 20%. When a PEFC follows the electricity demand pattern of a house, it operates at a partial load most of the time, resulting in a low efficiency of the microgrid. A method of improv-ing the generation efficiency of a fuel cell microgrid is proposed, where a supply system of power and heat with a high energy efficiency are constructed. In this section, a method of installing two or more microgrids is proposed (known as the partition cooperation system). The grids can be connected in an urban area to maximize generation efficiency. Numerical analysis shows that the system proposed in this

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section (which has an annual generation efficiency of 24.6 to 27.6%) has a higher generation efficiency than conventional PEFC systems (central generating systems have annual generation efficiencies of 20.6 to 24.8%). In the 2nd section, fuel cell energy network which connects hydrogen and oxygen gas pipes, electric power lines and exhaust heat output lines of the fuel cell cogeneration for individual houses, respectively is analyzed. As an analysis case, the energy demand patterns of individual houses in Tokyo are used, and the analysis method for minimization of the operational cost using a genetic algorithm is described. The fuel cell network system of an analysis example assumed connecting the fuel cell co-generation of five houses. If energy is supplied to the five houses using the fuel cell energy network proposed in this study, 9% of city gas consumption will be reduced by the maximum from the results of analysis. 2% included to 9% is an effect of introducing water electrolysis operation of the fuel cells, corresponding to partial load operation of fuel cell co-generation.

Chapter 5 has described fuel cell microgrid with wind power generation. Since the output of renew-able energy is unstable, other energy equipment needs to cover the stability of output. Thus, in the 1st section, the operating conditions of an independent microgrid that supplies power with natural power sources and fuel cells are investigated. If electric power is supplied using an independent microgrid connected to renewable energy, it can flexibly match the energy demand characteristics of a local area. The output of wind power generation and fuel cells is controlled by proportional-integral control of an independent microgrid for rapid power demand change. An independent microgrid that connects with renewable energy has the potential to reduce energy costs, and reduce the amount of greenhouse gas dis-charge. However, the frequency and voltage of a microgrid may not be stable over a long time due to the input of unstable renewable energy, and changes in short-period power load that are difficult to predict. Thus, when planning the installation of a microgrid in the 2nd section, it is necessary to investigate the dynamic characteristics of the power. About the microgrid composed from ten houses, a 2.5kW proton exchange membrane fuel cell is installed in one building, and this fuel cell operated corresponding to a base load is assumed. A 1kW PEFC is installed in other seven houses, in addition a 1.5kW wind turbine generator is installed. The microgrid to investigate connects these generating equipments, and supplies the power to each house. The dynamic characteristics of this microgrid were investigated in numerical analysis, and the cost of fuel consumption and efficiency was also calculated. Moreover, the stabiliza-tion time of the microgrid and its dynamic characteristics accompanied by wind-power-generation and fluctuation of the power load were clarified.

Chapter 6 has described CO2 discharged from compound microgrid of hydrogenation city-gas engine and fuel cell. The independent microgrid is considered to be a technology in which maximum distributed energy is realizable. However, there are many subjects, such as the stability of the dynamic character-istics of power and development of an optimal design method. If the fuel cell system of the capacity corresponding to a load peak is installed, equipment cost will be high and energy cost will not be able to get any profile commercially. By increasing the hydrogen concentration at the time of low load, the power-generation efficiency of a city-gas-engine-generator (NEG) improves, and carbon dioxide emis-sions decrease. So, in the 1st section, a microgrid composed from a PEFC and a hydrogenation city gas engine was investigated using numerical simulation. The system with a small load factor of NEG and with a large load factor of PEFC system has few CO2 emissions. The system which combined base-load operation of PEFC and load fluctuation operation of hydrogenation city gas engine is the most advantageous for the comprehensive evaluation of equipment cost, power generation efficiency, and CO2 emissions. When the optimal system was installed into the urban area model of 20 buildings and analyzed, power generation efficiency was 25% and CO2 emissions were 1,106 kg/Day. Distribution of

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the independent energy source can be optimized with regionality in mind. The 2nd section examines the independent power supply system relating to hydrogen energy. Generally speaking, the power demand of a house tends to fluctuate considerably over the course of a day. Therefore, when introducing fuel cell cogeneration into an apartment house, etc., low-efficiency operations in a low-load region occur frequently in accordance with load fluctuation. Consequently, the hybrid cogeneration system (HCGS) that uses a proton-exchange membrane fuel cell fuel cell (PEFC) and a hydrogen mixture gas engine (NEG) together to improve power generation efficiency during partial load of fuel cell cogeneration is proposed. In this section, HCGS is introduced into 10 household apartments in Tokyo, and the power generation efficiency, carbon dioxide emissions and optimal capacity of a boiler and heat storage tank are investigated through analysis. Analysis revealed that the annual average power generation efficiency when the capacity of PEFC and NEG is 5 kW was 27.3%. Meanwhile, the annual average power genera-tion efficiency of HCGS is 1.37 times that of the PEFC independent system, and 1.28 times that of the NEG independent system respectively.

Chapter 7 has described independent microgrid composed of distributed engine generator. In the 1st section, small kerosene diesel-engine power generators are introduced into an independent microgrid (IMG) that connects 20 houses, and power and heat are supplied to them. A 3 kW engine generator is installed in six houses, and a boiler and a heat storage tank are also installed, and exhaust heat to make up for insufficiency is supplied. The boiler is installed in the house that does not install the engine generator, and heat is supplied to the demand side. Partial load operation of the engine generator has a large influence on power generation efficiency. Therefore, this section discusses the system that controls the power of the engine generator by the power distribution control system using the genetic algorithm (GA), and the control system that changes the number of operations of the engine generators accord-ing to the magnitude of the power load. As a case study, the energy-demand model of the 20 houses in Sapporo was analyzed. As a result, the annual energy cost of the number of operations system and the power distribution control system is reducible with 16% and 8% compared with the conventional method, respectively. However, it depends for this cutback effect on the heat demand characteristic greatly, and when the proposed system is introduced into a community with little heat demand, effectiveness will decrease greatly. In the 2nd section, the power generation efficiency and power cost of an independent microgrid that distributes the power from a small diesel engine power generator was investigated using numerical analysis. The independent microgrid built using one to six sets of 20 average houses in Sap-poro and the distributed engine generators were examined using these test results. When a diesel engine power generator is distributed, since the power generation capacity per set decreases compared with the central system, the load factor of each engine generator rises. As a result, the operation of an engine at partial load with low efficiency can be reduced. When the number of distributions of the engine genera-tor increases as a result of numerical analysis, the cost of the fuel decreases.

Chapter 8 has described characteristics of PEFC / woody biomass engine hybrid microgrid and exergy analysis. The combustion exhaust heat of woody biomass engine using Stirling cycle is high temperature. So, in the 1st section, this exhaust heat is used for the city gas reforming reaction of a proton exchange membrane fuel cell (PEFC) system. The woody biomass engine generator has the characteristic that the greenhouse gas amount of emission with power generation is greatly reducible. In this study, the microgrid system that introduces PEFC / woody biomass engine hybrid cogeneration (PWHC) is pro-posed. It depends on the dynamic characteristics of the grid for the power quality at the time of load fluctuation being added to the microgrid. Especially, the dynamic characteristics of the independent microgrid are important on security of power quality. So, in this section, the response characteristic of

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PEFC and woody biomass engine was investigated by the experiment and the numerical analysis. Fur-thermore, the response characteristic of the PWHC independent microgrid including auxiliary machinery was investigated by the numerical simulation. Moreover, an improvement of dynamic characteristics is proposed using the method of adding proportional-plus-integral control to PWHC. If woody biomass engine is introduced into a house, 10.2s will be required to stabilize power quality at the maximum. On the other hand, when woody biomass engine corresponds to a base load and PEFC corresponds to the load exceeding the base load, settling time is less than 1.6 s. In this study, relation between the system configuration of the PWHC microgrid and the dynamic characteristices of the power was clarified. The woody biomass Stirling engine (WB-SEG) is an external combustion engine that outputs high-temperature exhaust gases. It is necessary to improve the exergy efficiency of WB-SEG from the viewpoint of energy cascade utilization. In the 2nd section, a combined system that uses the exhaust heat of WB-SEG for the steam reforming of city gas and that supplies the produced reformed gas to a proton exchange membrane fuel cell (PEFC) is proposed. The energy flow and the exergy flow were analyzed for each WB-SEG, PEFC, and WB-SEG / PEFC combined system. Exhaust heat recovery to preheat fuel and combustion air was investigated in each system. In the 3rd section, the exergy flow and exergy efficiency of a 3kW PEFC were investigated, and the regional characteristic of the distributed energy system was considered. In the environmental temperature range of 263K to 313K, the difference of the total efficiency of the proposed system was 6%. On the other hand, the difference of the exergy total efficiency of the same temperature range was 30%. Moreover, as a result of examining how to improve the exergy efficiency of this system, certain improvement methods were proposed. (a) Preheat the city-gas and air supplied to the system using exhaust heat, and raise the combustion temperature, (b) Preheat the water supplied to the system using exhaust heat, (c) Change the catalyst material of each unit and reduce the amount of cooling of the reformed gas, (d) Examination of combined cycle power generation. The exergy ef-ficiency, in the case of introducing the proposed system into individual homes in Sapporo, Tokyo, and Kagoshima in Japan was evaluated. Consequently, when the system was introduced into a community with low outside air temperatures, exergy efficiency increased compared with communities with high outside air temperatures.

Chapter 9 has described the design support using a neural network algorithm. A microgrid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent microgrid, green energy, which is typically thought of as unstable, can be utilized effectively by introducing a battery. In the past study, the production-of-electricity prediction algorithm (PAS) of the solar cell was developed. In 1st section, a layered neural network is made to learn based on past weather data and the operation plan of the compound system of a solar cell and other energy systems was examined using this prediction algorithm. In this study, a dynamic operational scheduling algorithm is developed using a neural network (PAS) and a genetic algorithm (GA) to provide predictions for solar cell power output. We also do a case study analysis in which we use this algorithm to plan the operation of a system that connects nine houses in Sapporo to a microgrid composed of power equipment and a polycrystalline silicon solar cell. In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the microgrid was clarified. Moreover, we found that operating the microgrid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data. In the 2nd section, the bioethanol reforming system (FBSR) using sunlight as a heat source is de-scribed. FBSR is a fuel production system for fuel cells with little environmental impact. An operation prediction program of the FBSR using a layered neural network (NN) with the error-correction learning

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method has been developed. The weather pattern (the amount of global solar radiation and the outside air temperature) and energy-demand pattern for the past one year are inputted into the NN. Moreover, training signals are calculated by a genetic algorithm. The training signals are given to the NN, and the operation pattern of the FBSR is made to learn. As a result of analyzing using the developed algorithm, when 20% or less of power load fluctuation occurred, the operation plan was analyzable in 14% or less of error span. On the other hand, in operation prediction when 50% or less of fluctuation is added to the outside temperature and global solar radiation, there was 16% or less analysis error.

Chapter 10 has described Microgrid with Numerical Weather Information. A fuel cell microgrid with photovoltaics effectively reduces greenhouse gas emission. A system operation optimization technique with photovoltaics and unstable power is important. In the 1st section, the optimal operation algorithm of this compound microgrid is developed using numerical weather information (NWI) that is freely available. A GA (genetic algorithm) was developed to minimize system fuel consumption. Furthermore, the relation between the NWI error characteristics and the operation results of the system was clarified. As a result, the optimized operation algorithm using NWI reduced the energy cost of the system.The production of electricity from the solar cells continues to attract interest as a power source for distrib-uted energy generation. It is important to be able to estimate solar cell power to optimize system energy management. The 2nd section proposes a prediction algorithm based on a neural network (NN) to pre-dict the electricity production from a solar cell. The operation plan for a combined solar cell and diesel engine generator system is examined using the NN prediction algorithm. Two systems are examined in this section: one with and one without a power storage facility. Comparisons are presented of the results from the two systems with respect to the actual calculations of output power and the predicted electricity production from the solar cell. The exhaust heat from the engine is used to supply the heat demand. A back-up boiler is operated when the engine exhaust heat is insufficient to meet the heat demand. Elec-tricity and heat are supplied to the demand side from the proposed systems, and no external sources are used. When the NN production-of-electricity prediction was introduced, the engine generator operating time was reduced by 12.5% in December and 16.7% for March and September. Moreover, an operation plan for the combined system exhaust heat is proposed, and the heat output characteristics of the back-up boiler are characterized.

Chapter 11 has described SOFC-PEFC Combined Microgrid. In the 1st section, the combined sys-tem of a solid-oxide fuel cell (SOFC) and a proton-exchange membrane fuel cell (PEFC) is examined. The proposed system consists of a SOFC-PEFC combined system and a photovoltaic system (PV) as the energy supply to a microgrid. The exhaust heat of the SOFC is used for the steam reforming of the bio-ethanol gas with time shift utilization of the exhaust heat of the SOFC in optional time. The SOFC-PEFC combined system with the PV was introduced in a microgrid of 30 residences in Sapporo, Japan. The operation plan of the system has three cases: without solar power, with 50% and with 100% of solar output power. Moreover, three types of system operation of using the SOFC independent opera-tion, PEFC independent operation and SOFC-PEFC combined system are used to supply the demand side. A comparative study between the types of system operation is presented. The power generation efficiency is investigated for different load patterns: average load pattern, compressed load pattern and extended load pattern. This study reported that the power generation efficiencies of the proposed sys-tem in consideration of these load patterns are 27% to 48%. In the 2nd section, a microgrid, with little environmental impact, is developed by introducing a combined SOFC and PEFC system. This section is investigated the operation of a SOFC-PEFC combined system, with time shift operation of reformed gas, into a microgrid with 30 houses in Sapporo, Japan. The SOFC is designed to correspond to base

xiv

load operation, and the exhaust heat of the SOFC is used for production of reformed gas. This reformed gas is used for the production of electricity for the PEFC, corresponding to fluctuation load of the next day. The relation between operation method, power generation efficiency, and amount of heat storage of the SOFC-PEFC combined system to the difference in power load pattern was investigated. The average power generation efficiency of the system can be maintained at nearly 48% on a representative day in February (winter season) and August (summer season).

Chapter 12 has described bioethanol solar reforming system for distributed fuel cell.The 1st section has described hydrogen production of a bioethanol solar reforming system for dis-

tributed fuel cell. The development of a bioethanol steam reforming system (FBSR) is considered as a means of distributing energy using PEFCs. Small-scale solar collectors (collection areas on the order of several m2) are installed in a house as a method for applying the FBSR. However, the temperature distribution of a reforming catalyst fluctuates under conditions of unstable solar insolation. Therefore, heat transfer analysis applied in reforming the catalyst layer of the reactor and the temperature distribu-tion and transient response characteristics of the gas composition of the process were investigated. In the 2nd section, the development of a bioethanol reforming system for fuel cells (FBSR) using sunlight as a heat source was investigated. The system was investigated using the experimental result of catalyst performance, and numerical analysis. The overall efficiency of the production of electricity and heat power of this system was determined by examining its thermal output characteristic. The FBSR was introduced into standard individual houses in Sapporo, Japan for analysis. The amount of hydrogen production, the production-of-electricity characteristic, and the thermal output characteristic were examined using meteorological data on representative days in March and August. As a result, the overall efficiency of the system, defined as the rate of power and heat output compared to the amount of solar heat collected, was calculated to be 47.4% and 41.9% on the representative days in March and August, respectively.

xv

Acknowledgment

Special thanks also go to the publishing team at IGI Global, Ms. Jan Travers, Allyson Gard, Ms. Chris-tine Smith, Ms. Erika L. Carter, Ms. Emily E. Golesh, and Mr. Mike Killian, whose contributions throughout the whole process from inception of the initial idea to final publication have been invaluable. In particular to Jan Travers, who assisted in keeping this project on schedule.

Shinya Obara Kitami Institute of Technology, Japan October 2013

1

Copyright 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Chapter 1

DOI: 10.4018/978-1-4666-5796-0.ch001

Operating Schedule of a Combined Energy

Network System

ABSTRACT

This chapter consists of two sections, Operating Schedule of a Combined Energy Network System with Fuel Cell and Fuel Cell Network System Considering Reduction in Fuel Cell Capacity Using Load Leveling and Heat Release Loss. The chromosome model showing system operation pattern is applied to GA (genetic algorithm), and the method of optimization operation planning of energy system is devel-oped in the 1st section. In the case study, the operation planning was performed for the energy system using the energy demand pattern of the individual residence of Sapporo, Japan. Reduction in fuel cell capacity linked to a fuel cell network system is considered in the 2nd section. Such an energy network is analyzed assuming connection of individual houses, a hospital, a hotel, a convenience store, an office building, and a factory.

GENERAL INTRODUCTION

The summary of the 1st section is as follows. The optimization method of this operation planning was applied to the compound system of methanol steam reforming type fuel cell, geo-thermal heat pump and the electrolysis tank of water. In the case study, the operation planning was performed for the energy system using the energy demand pattern of

the individual residence of Sapporo, Japan. From analysis results, the amount of outputs of a solar module and the relation of the operation cost of the system which are changed by the weather were clarified. The representation day in February of the ratio of the operation cost in case of (0% of output rates) the rainy weather to the time of fine weather (100% of output rates) is 1.12. And the representation day in July is 1.71. Furthermore, the

2

Operating Schedule of a Combined Energy Network System

optimal capacity of accumulation-of-electricity and thermal storage was estimated, and they are 308MJ and 23MJ, respectively.

The summary of the 2nd section is as follows. When the power demand of the whole network is small, some of the electric power generated by the fuel cell is supplied to a water electrolysis device, and hydrogen and oxygen gases are generated. Both gases are compressed with each compres-sor and they are stored in cylinders. When the electric demand of the whole network is large, both gases are supplied to the network, and fuel cells are operated by these hydrogen and oxygen gases. Furthermore, an optimization plan is made to minimize the quantity of heat release of the hot water piping that connects each building. Such an energy network is analyzed assuming connection of individual houses, a hospital, a hotel, a con-venience store, an office building, and a factory. Consequently, compared with the conventional system, a reduction of 46% of fuel cell capacity is expected.

OPERATING SCHEDULE OF A COMBINED ENERGY NETWORK SYSTEM WITH FUEL CELL

Introduction

Until now, various energy devices with individual controls have been used in buildings. However, renewable energy and unused energy are positively utilized from the viewpoint of global environment problems from now on. In order to utilize renew-able energy and unused energy, it is necessary to use active energy device for stabilization of an energy output. The object of study is to develop the method of the operation plan and optimum design of the combined system of active energy device and unutilized energy. The energy network is structured using an electric power system, a hot water system, and a fuel system. For the opera-tional plan of the energy network that conducts the

cooperative operation of complex energy devices, it is necessary to solve the nonlinear problem of many variables with objective functions provided beforehand. In the optimization calculations of system operational planning of a complex energy system, linear approximation calculations based on the mixed-integer plan-making method was used (Ito, Shibata, & Yokoyama, 2002). However, to analyze the operational planning of a complex energy system with high accuracy, it is necessary to solve the nonlinear problem with many variables. Until now, the operation planning of an energy system has been managed as a linear problem. So, in this Section, the method of analyzing a compound energy system by many variables and nonlinearity is developed.

A genetic algorithm (Goldberg, 1989) is there-fore introduced to analyze operational planning in this Section. Previously, an analysis method of a large-scale energy system that combined a genetic algorithm and an annealing algorithm (Hongmei et al., 2000) was developed (Srinivas & Patnaik, 1994; Fujiki. et al., 1997; Yu et al., 2000). However, an analysis method that optimizes the operational pattern with the application of a genetic algorithm (GA) to the compound system built using an active energy device, a renewable enegy device, and an unutilized energy device has not yet been developed. In this Section, a GA analysis method for a compound energy system is developed as a preliminary survey of the energy network that conducts cooperative operation. The analysis software using GA developed in this Section is introduced in an individual house in Sapporo, Japan, which is a cold, snowy area, and the operational plan is investigated. The opera-tional planning of the compound energy system is analyzed using the minimization of operational costs and the maximization of renewable energy use, and the operational planning of an active energy device is considered. Although operation costs and facility costs need to be considered for a feasibility study of the system, the facility costs of a proton exchange membrane (PEFC) fuel cell

3


are changing greatly. Since estimating facility costs is difficult, the analysis in this Section only considers operation costs. Furthermore, the device capacity for the accumulation of electricity and thermal storage is estimated.

System Description

Network of Distributed Energy Devices

In a dispersed arrangement of small energy devices, a reduction in power transmission loss and heat dissipation loss is expected. Since the discharge of carbon dioxide is predicted, renewable energy devices and unutilized energy devices are connected along with established active energy devices in an energy network, and research on sup-plying energy to two or more houses is required. A network model of the fuel cell cogeneration (CGS) installed in individual houses, as assumed for the final target of this research, is shown in Figure 1(a). The fuel cell CGS installed in each house is connected with hydrogen gas system piping, an electric system power line, and hot water piping of an exhaust heat system. The hot water system recovers heat from fuel cells and supplies thermal energy to individual houses. Hot water flows in one direction, as shown by the arrows in Figure 1(a). The energy devices are connected to the electric power and thermal energy network, and the operational planning of a system that fulfills the energy demands of individual buildings is considered. The energy devices installed in each house were controlled by autonomous distribution. The objective of an energy network is to control the devices linked to the network cooperatively, and to obtain a better effect than conventional autonomous distribution control.

Figure 1(b) shows a model of the cooperative operational control of an energy network. The control device of the energy network is composed of a computer, a communication device, and a LAN that communicates control information for

each energy device. In this system, the opera-tional state of each energy device linked to LAN, weather information and maintenance information can be communicated to the outside.

The Combined Energy System

A feasibility study of the operational planning analysis method of the energy network with cooperative control, shown in Figure 1(a), is the target of this Section. The analysis method in the case of operating the compound energy system consisting of an active energy device, a renew-able energy device, and an unutilized energy device has been developed. Figure 1(c) shows the model of the compound energy system. The analysis method for operational planning using GA has been developed to minimize costs, and the estimated device capacities and an operational plan for a complex energy system are determined.

The Combined Energy System to be Assumed

A block diagram of the energy system for houses adopted in this study is shown in Figure 1(a). In this system, methanol fuel is stored, and its distributed power supply is also possible in the residential areas of local cities where the city gas piping networks are not well developed. In individual houses and apartments, load changes are sharp and abrupt, and partial load operation of the energy system increases (Obara et al., 2005). Therefore, to improve energy efficiency, a dynamic operational plan for the energy system is required, wherein thermal storage and electric energy storage are introduced. A water electrolytic bath and gas tanks are added to the electric power storage device, and electric power generated with a fuel cell and a solar cell is supplied to the water electrolysis bath. Since the output of a solar cell changes according to the weather, the operational plan is defined by considering the amount of power

4


Figure 1. Combined energy network system

5


generation as a variable. Furthermore, when in-stalling the fuel cell cogeneration system in houses in cold regions, since the supply of heat energy is insufficient, there is additional combined use of the geothermal heat pump system.

Operation Method of Combined Energy System

Figure 2(a) shows the compound energy system for individual houses, and is investigated by this study. Active energy device is a fuel cell with a methanol reformer, and unutilized energy device is a solar module and a geo-thermal heat pump. This Section does not describe the equipment cost of the system of Figure 2(a). The equipment cost of PEFC and unutilized energy device is not commercially realized in the present condition. However, such equipment costs may be reduced rapidly from now on. Therefore, it is necessary to investigate the operation plan of a compound energy system. Methanol fuel (mole ratio of methanol/water = 1.0/1.4), which is contained in the methanol tank (3), is supplied to the reformer (2), and hydrogen and carbon dioxide are formed. The methanol reformer is always warmed up. The piping system of hydrogen and reformed gas assumes use of a stainless steel tube. The specifi-cation of the reformer and the fuel cell stack was shown in Table 1. The heat source of the reformer drives the catalytic combustion of methanol, and the air for combustion is supplied by the blower. The reformed gas generated by the methanol steam reformer is sent to the anodes, air is supplied to the cathodes by a blower, and electricity is gener-ated by the PEFC (1). The energy supply path for this system is shown in Figure 2(b). The electric power generated by the fuel cell is supplied us-ing one of the following methods: (a) Alternating current electric power is generated by the DC/

AC converter (8), and demand is fulfilled; (b) Hydrogen and oxygen are generated in the water electrolyzer (5) and stored in a hydrogen tank (6) and an oxygen tank (7), respectively; (c) Electric power is changed into heat by an electric heater (9) in a thermal storage tank (10). It is possible to drive a fuel cell at any time using the stored hydrogen and oxygen. Selecting the appropriate energy supply path among (a) to (c) above is also possible for electric power generated by the solar cell. Because the capacity of a water electrolyzer and a thermal storage tank differs in the opera-tion plan of a system, they are determined from analysis output. However, electric power from the system to the demand side is supplied only via one of the following systems, without multiple supply sources: (a) Methanol fuel is reformed to generate hydrogen, which is supplied to a fuel cell, and electric power is generated; (b) Electric power is generated by the solar cell; (c) Stored hydrogen and oxygen, formed by water electroly-sis, are used in the fuel cell to generate electric power. In order to reduce the discharge of carbon dioxide, methanol fuel is not used to the extent possible. Therefore, as many renewable energy supply sources as possible are used with priority set in decreasing order to be (b), (c) and (a).

A thermal storage tank has the following three heat input sources: (a) Exhaust heat from a fuel cell and the reformer; (b) Heat conversion of the electric power generated by the fuel cell and the solar cell; (c) Heat generated by the geothermal heat pump (11). The low-temperature source of heat pump is obtained from a bore hole with a depth of 30m installed in soil. And maximum output is 12 kW and COP is 3. However, when the heat input exceeds the thermal storage capac-ity, some of the surplus is released. After the heat from the thermal storage tank heats city water via the heat medium inside the thermal storage tank,

6


it is supplied to the demand side. A thermal stor-age medium is water and the maximum tempera-ture of thermal storage is 353 K.

For the specifications of other system compo-nent devices, we used the values shown in Table 1, which are typical for houses in cold regions such as Sapporo. Since a geothermal heat pump was used, the capacity of a fuel cell was set at 4.2 kW. With respect to device costs, installing a complex energy system such as shown for individual houses in Figs. 2(a) and 2(b) is difficult.

Analysis

Analysis Method Using Genetic Algorithm (GA)

1. Indication of Device Operation by the Chromosome ModelFigure 3(a) shows the chromosome model intro-duced in GA, and expresses information on elec-tric energy output E

D ti k, for each time t

k of

device Di, heat output H

D ti k,, amount of electric

Figure 2. Outline of the proposal system

7


energy storage SE D ti k, ,

, amount of thermal storage

Sst D ti k, ,

, and device selection switch SW D ti k, ,

using the gene model with 0 and 1 (Obara and Kudo, 2003). When two or more devices do not yield a simultaneous energy supply, S

W D ti k, , is

introduced in order to select the device that sup-plies energy. As the chromosome model deter-mined above expresses the operational pattern of the device from t

k to t

k+1 . As shown in Figure 3(b), sets of the chromosome model of each sam-pling time of k R= 0 1 2, , , , represent all the operational patterns for operational period R .

Although a number of chromosome models Ndv'

are created as an initial generation, either 0 or 1 is selected. If the value of the random number is less than 0.5, the gene model is set at 0, and 1 is selected if the random number is 0.5 or more.

2. Production, Selection and ReproductionThe fitness values of the number of N

dv' chromo-

some model groups (they indicate the patterns of the system operation) of an initial generation are calculated, and proliferation or selection is judged based on the values. The combination method is introduced in the calculation of the ranking selec-

Table 1. Energy device initial specifications

8


tion (Baker, 1985) and roulette selection (Gold-berg, 1989). In the first reproduction calculation, the chromosome models of the initial generation are selected based on the number of N

dv (here,

Ndv' >N

dv), and these chromosome models are

used in subsequent calculations.

3. Crossover and Mutation of the Chromo-some ModelThe calculation process of crossover and mutation is given to the chromosome model group, and the diversity of genes is maintained. Using the cal-culation for the last generation, the chromosome model with the best fit is determined as the opti-mal operational pattern. However, the number of generations in the analysis is decided beforehand. When using the chromosome model group with the crossover process, only a specific chromosome model evolves beyond a certain generation, and a model with high fitness cannot be found beyond

it. In the calculation of crossover, two parent chromosomes are chosen by probabilityP

cros,

parent chromosomes are combined, and one child chromosome is generated in the intersection posi-tion decided at random. Subsequently, the calcu-lation process of the mutation described below is added. In the mutation, parent chromosome mod-els are chosen at random using probabilityP

mut,

and the number and the position of the genes of the parent chromosomes are also decided at ran-dom. If the original value of a gene is 1, it has to change to 0, and if it is 0, it has to change to 1. In order to progress to the next generation, the fitness value is again evaluated with respect to all the operational patterns of number N

dv with added

crossover and mutation. Proliferation and selection are performed using these results. The above analysis is repeated up to the number of the last generation, and the gene arrangement of the model that has the highest fitness value in the

Figure 3. Chromosome model

9


chromosome model group of the last generation is decoded, and the optimal operational pattern is decided.

4. Analysis FlowThe flow of calculation of the operational plan-ning analysis of the complex energy system using GA is shown in Figure 4(a). First, N

dv' chromo-

some model groups described in previous Section are generated at random. The fitness values for each chromosome model are calculated, and the

chromosome models of Ndv higher ranks are

determined by the combination of ranking selec-tion and roulette selection. Furthermore, the calculation of production and selection described in previous is added to these N

dv chromosome

models, and a chromosome model with a large fitness value is obtained, maintaining diversity by the calculation of crossover and mutation described in previous Section. This calculation is repeated, and the chromosome model with the highest fitness value when reaching the number

Figure 4. Analysis flow, energy demand pattern, and output characteristics of equipment

10


of the last generation, decided beforehand, is determined as an optimal model. Operational planning of all energy devices for each sampling time is decided by decoding the optimal model.

Cold-Region Houses

1. Characteristics of Weather in Sapporo in JapanSapporo is a cold, snowy region, and the annual average temperature for the past five years is 282 K (National Astronomical Observatory of Japan, 2003). The average temperature in February is 270 K, and the highest and the lowest tempera-tures on a representative day are 273 K and 266 K, respectively. Moreover, there is an average 25 days of snowfall in February. On the other hand, the highest and the lowest temperature on a representative July day for the past five years are 298 K and 290 K, respectively, and the aver-age temperature is 293 K. Since air heat-source heat pumps cannot be used in winter, the use of a geothermal heat pump is assumed in this Section. Air conditioning is not needed during summer.

2. Characteristics of Individual Houses in SapporoThe average individual house in Sapporo is a 2-story wooden house with a 140-m2 living area (Narita, 1996). The model of the average electric power and thermal energy demand of the representative February day and July day for individual houses in Sapporo is shown in Figure 4(b). The thermoelectric ratio of representative days is 0.90:0.1 in February and 0.5:0.5 in July.

Characteristics of Energy Devices

1. Fuel Cell CogenerationFigure 4(c) is the result of examining the rela-tionship between electric power and thermal energy output, and the fuel amount of supply. The characteristic curve is divided into two or more regions, and each region is approximated

by the least-squares method with an equation of secondary order. The characteristics of electric power shown in Figure 4(c) are for a model that includes the power consumption of blowers and electric power loss of the DC/AC converter. Moreover, the values of the joule heat of the fuel cells, the battery reaction heat, and the exhaust heat of the reformer have been included in the heat characteristics in the figure. Methanol fuel using a burner for the heat sources of the reformer is also included on the horizontal axis of Figure 4(c). In addition, to start the fuel cell system, consumption of methanol fuel equivalent to 900 kJ (250 Wh) is considered (Takeda, 2004). In order to collect the hydrogen and oxygen generated by water elec-trolysis, tanks are installed in the electric energy storage device. For fuel cell systems using not the gas obtained by steam reforming of methanol but the hydrogen and oxygen in each tank, the power generation efficiency is 0.75 and the heat output is set at 0.05 (Obara et al., 2005).

2. Geothermal Heat PumpBased on the examination results of hydrocarbon binary vapor (HC-TECH Inc, 1997), we simplify the analysis by setting the temperature TL

( )= 277 K of the low-temperature heat source

and the condensation temperatureTH

( )= 347 K

to be constant, and the coefficient of pump COPtk

at 3 0. .

3. Water ElectrolyzerIf direct current power is supplied to a water electrolyzer, hydrogen can be produced at a rate of Equation (1). E

EL tk,indicates the amount of

electric energy supplied to the water electrolysis bath, and

EL expresses the efficiency of the

charge. The flow rate of hydrogen QH tk2 ,

gener-

ated from sampling time tk to t is calculated

by the equation below. The oxygen flow rate is also determined by the same calculation. In a

11


report on the water electrolysis bath for hydrogen generation, efficiency

EL of charge is given as

0.85 (Kosaka et al., 2000). Here, Ec, Fd, and E

V

shows the chemical equivalent, Faraday constant and voltage, respectively.

QE E

F EH tEL t c

d VELk

k

2 ,

,=

(1)

4. Thermal Storage TankSSt,max

is the maximum thermal energy storage, and T

St,max is the maximum temperature of the

heat medium. Equations (2) and (3) are restrictions for thermal storage. V is the capacity of a thermal storage medium volume (calcium chloride is as-sumed), C

p is the specific heat and T is the air

temperature outside the thermal storage tank. The thermal storage temperature during sampling time tk is calculated by:

T S C VSt t St t pk k, ,

/ ( ).=

0 S SSt t Stk, ,max

(2)

T T Tt St t Stk k

, , ,max (3)

The following equation is an expression of the thermal energy storage between time t

k andt .

S S H HSt t St t St in t St out tk k k k, , , , , ,

{ = 1

St p St t tC V T T tk k( )}, , (4)

HSt in tk, ,

and HSt out tk, ,

show the input and output

heat energies of the thermal storage tank, respec-tively, and the loss of thermal storage is the 3rd term within { } on the right-hand side of Equation

(4) when it depends on open-air temperature Ttk,.

ST and shows the efficiency of thermal stor-

age and the density of thermal storage medium, respectively. In this Section, thermal storage loss at time t

k of the representative day will be con-

sidered as 1% of the value on the left-hand side of Equation (4) in July and 2% in February. These heat losses were calculated and determined from the difference of temperature of ambient air and a thermal storage medium.

5. Solar ModulesFigure 4(d) shows the results for a solar cell in Sapporo in winter (representative days in Febru-ary) (Obara & Kudo, 2003; Nagano, 2002). The solar cell is a roof installation-type device installed perpendicularly so that it does not become covered with snow. The characteristics for the represen-tative days in July shown in the figure are the predicted results. Each characteristic curve is the amount of power generated during fair weather, and power generation falls during cloudy or rainy weather. Using the output characteristic perfor-mance of the solar cell as 0% in snowfall, 50% under cloudy conditions and 100% in fair weather in Figure 4(d), the operational planning for the representative day for each month is analyzed.

Objective Function and Energy Equation

1. Objective FunctionOnly methanol is used as the fuel supplied to the system as shown in Figure 4(b). Therefore, op-erational planning to minimize costs requires an operational pattern where the methanol fuel con-sumption is minimum for each sampling time t

k.

Di represents the energy device and the subscript

i ( i M= 1 2 3, , ,..., and M is the total number of devices) corresponds to the device number used here. The operational costs of device D

i during

sampling time tk to t are equal to fuel input

12


flow FD ti k,

of the device multiplied by unit fuel

price Cfuel

. The operational costs of the whole system are estimated using Equation (5). There-fore, the total operational costs of all working periods of a system are calculated using Equation (6) and called the best-fit solution, so that the value of Equation (6) should be small. In the example of the application of operational planning in the following Section, C

fuel of methanol fuel

is calculated to be 0.463 $/kg (Energy and Indus-trial Technology Development Organization in Japan, 1999))

C C F tSystem t fuel D t

i

M

k i k, ,= ( )

=

1

(5)

C CSystem day System t

i

M

tk

k

, ,=

==

10

23

(6)

2. Energy BalanceEquations (7) and (8) are the electric power and thermal energy balance equations of this system, respectively.

E EFS t SL tk k, ,+

= + + +E E E ESystem t EL t HP t H tk k k k, , , ,

(7)

FS FS t FSF

k

,( )1

+ + +H H HHP t H t St tk k k, , ,

= + +H H HSystem t St t Rad tk k k, , ,

(8)

The left-hand sides of Equations (7) and (8) correspond to the output energy from the system, and the right-hand sides correspond to the amount of consumption energy of the system. E

System tk,

and HSystem tk,

are decided on the basis of energy

demand patterns. The left-hand side of Equation (7) shows electric power output from the fuel cell (E

FS tk,) and solar cell (E

SL tk,). The right-hand

side of Equation (7) shows the electric power consumption of the water electrolyzer (E

EL tk,),

electric power consumption of the heat pump (E

HP tk,), and electric power converted into heat

by the electric heater (EH tk,

), respectively. The

left-hand side of Equation (8) shows thermal energy output from the fuel cell heat exhaust, heat pump (H

HP tk,), electric heater (H

H tk,) and thermal

storage tank (HSt tk,

), respectively. FS

, FFS tk,

,

and FS shows the calorific power of methanol

fuel, the quantity of methanol fuel mass flow and the fuel cell stack efficiency.

FS is calculated

from the relations between the amount of supply of a methanol fuel, and a power output shown in Figure 4(c). The right-hand side of Equation (8) shows heat loss from the thermal storage tank (H

St tk,) and heat release from the radiator (

HRad tk,

), respectively.

Operation Planning

1. Analysis Method of System Operational PlanningThe mixed-integer plan-making method has been studied to analyze the operational planning of an energy system (Ito K, Shibata T, & Yo-koyama R., 2002). In this method, the nonlinear input-and-output characteristics of energy de-vices are expressed as a linear model and analyzed. An example of the test results of electric power and thermal energy output characteristics of a fuel cell with a reformer is shown in Figure 4(c). If the nonlinear characteristics of an energy device can be made to fit a linear approximation problem, an increase in analysis error is predicted. In the mixed-integer plan-making method, the charac-

13


teristics of the electric power output are approxi-mated by three straight lines l

1 to l

3 in Figure

4(c), and heat output is approximated by four straight lines l

4 to l

7. Generally, since the output

of small energy equipment is nonlinear, we should use the nonlinear model for analysis. In the analysis of the operational planning of the system with a number of energy devices, many variables associated with each device operation are used. Therefore, if many variables can be calculated simultaneously, the efficiency of the analysis will increase. A genetic algorithm, where simultaneous calculations of many variables and the calculation of a nonlinear problem are possible, is introduced in the software developed in this study. However, neither the application of a GA to a small-scale energy system nor a design method that opti-mizes the operational pattern and device capac-ity has been studied previously. In particular, no research reports on the optimization of the op-erational plan for a compound system of an active energy device, a renewable energy device and unutilized energy device or their optimal capac-ity can be found in the literature.

2. Operation of a Chromosome ModelThe chromosome model operated by the GA calculation needs to satisfy the energy balance in Equations (7) and (8). However, the chromo-some model must also satisfy conditions (a) and (b) described below:

1. A quantity that excludes electric energy consumption (sum of all E ) from the amount of electric energy output of the fuel cell and the solar cell satisfies the electric energy demand.

2. A quantity excluding heat loss (H HRad t St tk k, ,

+ ) from the sum total of the

exhaust heat of the reformer and fuel cell, heat pump, electric heater, and the heat energy output of the thermal storage tank should satisfy the heat energy demand.

When an operational pattern (chromosome model) that does not fulfill one of these condi-tions arises, it is forced to a very low value of fit so that it cannot proceed to the next generation. Similarly, a low fitness value is given for an op-erational pattern that does not satisfy the energy balance of Equations (7) and (8).

For the chromosome models of the initial generation for the power generation of a fuel cell, the total power generated by the fuel cell is decided at random within the electric power capacity. In addition, the total power generated is distributed to the amounts of electric power output (E

FS tk,), the quantity supplied to a water

electrolyzer and stored as electricity (EEL tk,

),

and the quantity conducting heat conversion (EH tk,

) in an electric heater at random. The total

amount of power generated in fine weather in sampling time t

k in a solar cell is decided as

shown in Figure 5(a). The total power generated from the solar cell is distributed to the electric power supplied to the electric power output (ESL tk,

), the amount of accumulated electricity (

EEL tk,

) and amount of electric power supplied

to a heater (EH tk,

) for every chromosome mod-

el at random. Moreover, the power consumption (E

HP tk,) of the geothermal heat pump for every

chromosome model is estimated from the amount of heat output that was decided at random within the limits of the device capacity and coefficient of performance (COP).

Case Study

Analysis Conditions

Operational period R of a system is split into 23 parts, and:

tk( , , ,....., )k = 0 1 2 23

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Figure 5. Output characteristics of solar power generation and fuel cell, and system device cost

15


defines the sampling time. Moreover, the number of devices M is set at five including the fuel cell with the reformer, solar cell, geothermal heat pump, water electrolyzer, and thermal storage tank. In the analysis described below, the number of initial-generation chromosome model groups Ndv' is set at 3000, and the number of chromosomes

operational after reproduction Ndv is set at 2500.

The last generation is analyzed as 100 generations. Moreover, considering the maintenance of the diversity of gene models, the number of intersec-tions is selected randomly. That is, with one in-tersection, 0.2% of the total number of chromo-somes is extracted as parent chromosomes, at maximum. The frequency of mutations governed is about 4% of the genes in a mutated chromosome model, at maximum. These parameters of the SGA confirm that the value of the optimal solution is in agreement within several percent, as a result of trial calculations with two or more parameters.

The minimum operational costs for every generation when performing an operational plan by GA to minimize the operational costs with the application of the energy demand pattern of rep-resentative days in July and February are shown in Figure 5 (a). In this calculation, we assumed fair weather and the electric power output of a solar module to be 100% (same as in Figure 4(d)). Although the fitness value of the representative day for both months decreases rapidly, the op-erational costs, to almost ten generations, shows a gradual change in successive generations. For the best-fitness solution after 10 generations, the mutation calculation is important. However, if analysis conditions are changed, the number of generations for convergence changes.

Objective of Analysis Calculation

The operational planning of a system is analyzed using the energy demand pattern of the represen-tative days in February and July in Sapporo. We assume that the active energy device (fuel cell

cogeneration with a methanol reformer) is already installed in individual houses in Sapporo in this analysis. The renewable energy device (solar cell), unutilized energy device (geothermal heat pump), electricity accumulation device (water electrolyz-er), and thermal storage tank are connected to an active energy device, and the complex independent energy system shown in Figure 4(b) is built. It is difficult to introduce such a complex system into individual houses because of the device costs. This section investigates the energy network system of the distributed energy device. This Section also examines, the operational planning method of the active energy device with the maximum use of renewable energy using the information obtained from the analysis. Furthermore, the capacity of devices to accumulate electricity and thermal storage is estimated.

Results and Discussion

Results of Operational Planning

Figure 5(b) shows the analytical results of op-erational planning assuming fair weather with the energy demand pattern for the representative day in each month, and it shows the device energy output for every time period. However, the output results of the fuel cell include the output values of both the electric energy and the heat energy. The breakdown of the energy output of the fuel cell is shown in Figure 5(c). Hydrogen has been formed by the steam reformation of methanol in the time period with high thermal output (0:00 and 1:00 on the representative day for July and 0:00~9:00 and 19:00 and 21:00 on the representative day for February). A thermal output is small when sup-plying and generating the hydrogen and oxygen produced by water electrolysis to the fuel cell. The reason for this is that the use of renewable energy (solar cell) is a top priority. Therefore, if the amount of power generated by a solar cell exceeds the electricity demand, the fuel cell will not operate by driving the reformer. If, from night

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to early morning, power generation from a solar cell is not conducted, the fuel cell operates by the driving reformer. On a representative day, power from a fuel cell by the operation of the reformer is generated from 0:00 at 9:00 in February, and the heat pump is operated using this electric power. From the analysis results of the operational plan, thermal storage of the heat generated by the heat pump occurs, and the stored thermal energy is used to conduct the time shift and to fulfill heat demand during the daytime.

Figure 5(d) shows the analytical results of the system operational costs for conducting opera-tional planning in which the output proportion of the solar cell is a variable, for the representative day in each month. Using the characteristic output performance of the solar cell of 0% in snowfall, 50% under cloudy conditions and 100% in fair weather in Figure 4(d), operational planning for the representative day in each month is analyzed. The total value of one day of electric power and thermal energy demand is set at 100. The repre-sentative days in February and July of the total electric power obtained in the solar cell for fine weather are 95 and 28, respectively. On the other hand, from Figure 5(d), the system operation costs during fine weather when setting the operation costs in case of rainy weather and snowfall at 100 for February and July representative days are 88 and 29, respectively. The difference of the opera-tion cost of every month is equal to the difference of the consumption of a methanol fuel. Therefore, the difference of operation cost is the difference of a carbon-dioxide discharge.

The difference in the operational costs of the system according to the difference in weather for the representative days in February (100-88 = 12) is larger than the difference in solar cell output (100-95 = 5) due to the weather. The main reasons for this are that fuel cell efficiency improves by reducing operation because of low loads in Figure

4(c), and because the driving period to operate a fuel cell by generating hydrogen and oxygen using a solar cell is long. On the other hand, for the representative July day, the generation of hy-drogen and oxygen is conducted by a solar cell, and operation of a fuel cell in most periods is performed with high energy demand. Therefore, the difference in the output of a solar cell due to the weather directly affects the operational costs of the system.

Design of the Capacity of the Water Electrolyzer and Thermal Storage

Figure 6(a) shows the results of the operational planning of the amount of thermal storage, and the accumulation of electricity for a represen-tative February day. The largest quantity of thermal storage and electric energy storage in the analytical results approximates the design capacity of each device in fair weather because it increases as the output proportion of the solar cell increases. Figure 6(b shows the analytical results of the thermal storage quantity and electric energy quantity where the output proportion of the solar cell was 100% in the energy demand pattern for the representative day in each month. From these results, the maximum value of the thermal storage quantity is 308 MJ (representative February day), and the maximum value of electric energy storage quantity is 23 MJ (representative July day). Thermal storage capacity is reduced by lengthening the operational period of a fuel cell with the reformer and the heat pump.

Conclusions

Analysis software for operational planning, when two or more energy devices were introduced in an individual house, was developed. A genetic algorithm (GA), which can analyze nonlinear

17


problems and many variables at a time, was used for the analysis software for the operational plan-ning of the complex energy system developed in this Section.

The operational planning to introduce an active energy device (fuel cell cogeneration), a renewable energy device (solar cell), and an unutilized energy device (geothermal heat pump) in an individual house in Sapporo, Japan, which is a cold, snowy region, as an analysis example was conducted using

the developed software. Furthermore, the device capacity of accumulation for an electricity storage device and a thermal storage tank were estimated. From the results of the analysis, a fuel cell with a reformer operates from night to early morning when a solar module is not operational. A heat pump also operates from night to early morning. The thermal storage of heat generated by the heat pump is conducted, and this heat is supplied to the heat load during the daytime.

Figure 6. Quantity of energy storage

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FUEL CELL NETWORK SYSTEM CONSIDERING REDUCTION IN FUEL CELL CAPACITY USING LOAD LEVELING AND HEAT RELEASE LOSS

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 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 (Obara & Kudo, 2005). 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, the 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 sup-plied and generated to the fuel cell in a period of large power load. The experimental result shows that the power generation characteristics improve greatly compared with air supply, when supplying oxygen to the fuel cell (Badami &Caldera, 2002). 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 outside air temperature, 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 regarding a local energy network that includes houses, a hospital, a factory, an 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.

Load Leveling and Arrangement Plan of Fuel Cell

Fuel Cell Network System

The network model with the proton exchange membrane fuel cell (PEFC) installed that is as-sumed in this chapter is shown in Figure 7 and Figure 8. 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) is flowed 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. Fi

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