Shin’ya Obara is a Professor of Department of Electrical and Electronic Engineering at Kitami Institute of University, Hokaido, Japan. He received B.S. in mechanical engineering from Nagaoka University of Technology, Japan in 1987, M.S. in mechanical system from Nagaoka University of Technology, in 1989 and a Ph.D. in mechanical science from Hokkaido University in 2000, while he was working in companies and academic organizations. He worked as a researcher in Department of Mechanical Science of Hokkaido University from 2000-2001. Dr. Shin’ya Obara joined Tomakomai National College of Technology in 2001 after an eight-year period in industry (as engineer and assistant manager for researches, in two different companies, namely Takasago Thermal Engineering Co., Ltd. and Aisin AW Co., Ltd. in Japan). He was associate professor of Department of Mechanical Engineering of Tomakomai National College between 2001-2007. Moreover, he was professor of Department of Mechanical Engineering of Tomakomai National College in 2008 and Professor of Department of Electrical and Electronic Engineering at Kitami Institute of Technology between 2008 to data. His research has been involved with power and heat energy and operation optimization analyses of energy compound systems and energy efficiency, microgrid technology, energy network system with renewable energy sources. Dr. Obara is author and co-author of over 90 papers on a national and international journal. Arif Hepbasli, having the work life philosophy which is to attain Industry (I), Profession Institutions (P) and University (U), Balanced IPU simultaneously, is a Full Professor of Mechanical Engineering at Ege University, Izmir, Turkey. He received B.S. (first class honor) in mechanical engineering from Selcuk University (SU), Turkey in 1980, M.S. from Istanbul Technical University of Istanbul (studied one year at the German-Preparation Class of Institution of Higher Education for Foreign Languages, Technical University of Istanbul), Turkey in 1985 and a Ph.D. in mechanical engineering from Selcuk University in 1990, while he was working in industry. He worked as a research assistant in ME Department of Selcuk University from 1982-1986. Dr. Hepbasli joined Ege University in 1996 after a ten-year period in industry (as head engineer, assistant manager for investments and maintenance manager, in three different companies, namely DESA Combustion Systems Manufacturer, AKZO-KEMIPOL Paint Factory and SIMPLOT & BESIKCIOGLU French-fried Processor in Izmir) and a one-year period at Izmir Branch Office of Chamber of Mechanical Engineers as a consultant. He was Vice Director of Solar Energy Institute of Ege University between 1997-2000 and EBILTEM (Ege University Science Technology Application and Research Center) between 2000-2001. His research has been involved with energy, exergy and exergoeconomic analyses of energy systems, energy efficiency and management, fluidized bed combustion systems, ground-source heat pumps, and utilization and potential of renewable energy sources. Dr. Hepbasli is the first Certified Industrial Energy Manager of Turkey (having a certificate from JICA, Japan) as a university lecturer, while he is author and co-author of over 320 papers on a national and international basis. He has chaired and co-chaired many national and international conferences, symposia, workshops and technical meetings. He worked as a Visiting Professor between 2004-2005 at the University of Ontario Institute of Technology in Canada. He has served as a consultant in cases involving his research area and is also a member in the International Advisory Board of five journals on the SCI basis, namely “Energy Sources (Taylor & Francis)”, “International Journal of Green Energy”, “International Journal of Energy Research (Wiley)”, “International Journal of Exergy” and “Energy and Buildings (Elsevier) “.

Compound Energy Systems

Optimal Operation Methods

By Shin’ya Obara, Arif Hepbasli

The Royal Society of Chemistry

Copyright © 2010 Shin’ya Obara and Arif Hepbasli
All rights reserved.
ISBN: 978-1-84973-031-0

Contents

Chapter 1 Background Shin’ya Obara, 1,
Chapter 2 Operation Analysis of a Compound Energy System – Exhaust Heat Use Plan when Connecting Solar Modules to a Fuel Cell Network Shin’ya Obara, 5,
Chapter 3 Operation of Compound Energy System – Fuel Cell Network System Considering Reduction in Fuel Cell Capacity Shin’ya Obara, 31,
Chapter 4 Power-Independent House Using PEFC – Operation Plan of a Combined Fuel Cell Cogeneration, Solar Module, and Geothermal Heat Pump System Shin’ya Obara, 52,
Chapter 5 PEFC/Engine Generator Compound Energy System (1) – CO2 Discharge Characteristic of PEFC/ Hydrogen-Gas-Engine Hybrid Cogeneration Shin’ya Obara, 74,
Chapter 6 PEFC/Engine Generator Compound Energy System (2)–Power-Generation E.ciency of an Independent Microgrid Composed of Distributed Engine Generators Shin’ya Obara, 102,
Chapter 7 PEFC/Green Energy Compound System (1) – Operation Planning of a PEFC and Photovoltaics with Prediction of Electricity Production Using GA and Numerical Weather Information Shin’ya Obara, 125,
Chapter 8 PEFC/Green Energy Compound System (2) – Overall E.ciency of a PEFC with a Bioethanol Solar Reforming System for Individual Houses Shin’ya Obara, 145,
Chapter 9 PEFC/Green Energy Compound System (3) – Fuel Cell Microgrid with Wind-Power Generation Shin’ya Obara, 169,
Chapter 10 Solar Cell/Diesel Engine Compound System with Production-of-Electricity Prediction Shin’ya Obara, 185,
Chapter 11 Dynamic Characteristics of Power for PEFC Compound System Shin’ya Obara, 208,
Chapter 12 Performance Analysis and Assessment of Compound Energy Systems Using Exergy Analysis Method Arif Hepbasli, 230,
Subject Index, 260,

CHAPTER 1

Background

SHIN’YA OBARA

1.1 Distributed Energy System

Distributed energy systems with sustainable energy operation have been widely discussed recently from the point of view reducing the environmental impact of society. In these setups, the operation optimization program installed in the controller of a combined system is the most important aspect of the technology for determining the performance of the system. However, because an output prediction for the green energy contribution to the system is required, the dynamic operation plan of a system that combines conventional energy equipment (for example, a diesel engine, a gas engine, a fuel cell, etc.) and green-energy equipment can be very difficult to design. In this work, we use a neural network (NN) to obtain output predictions for a solar cell. Weather data from the past 14 years (amount of solar radiation and outside temperature) is fed into the learning process of the NN. This NN production-of-electricity prediction algorithm (PAS) was developed by the author and is described in ref. 5. In this book, details of a compound energy system with the power prediction algorithm of green energy like the PAS are described.

Power fluctuations are known to occur in systems that utilize green energy on an independent microgrid and that experience large or rapid changes in load. Given this, power storage equipment must be introduced and the dynamic characteristics of the microgrid must be improved. Due largely to the proliferation of hybrid vehicles and the like, the cost and performance of batteries have recently improved remarkably. With this in mind, this book investigates algorithms for the operation planning of a microgrid that combines conventional energy equipment, a solar cell and a battery. Since a microgrid is typically built up of two or more energy systems, we have to solve a nonlinear problem with many variables. Therefore, this book shows the operation condition of generating equipment in chromosome code, and describes how to optimize operation for a compound energy system using a genetic algorithm (GA).

1.2 Independent Microgrid

The introduction to an urban area of a microgrid 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 microgrid, the scale of equipment for distributing electricity is small. Furthermore, (d) Connecting renewable energy considering regionality is expected to be an advanced system in microgrid technology. At present, the method of a microgrid interconnecting with commercial power, etc. is investigated (interconnect microgrid).10 However, in order to achieve the advantages of (a) to (d) described above, it is necessary to operate a microgrid independently. The subjects of the independent microgrid are backup in the case of overload, and securing 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 microgrid. An improvement in power-generation efficiency is expected from the independent microgrid using a fuel cell compared with conventional electric power-supply technology. However, for the moment, fuel cells are expensive, and whether they will spread is not clear. As for a fuel-cell-independent microgrid, 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. The hydrogenation technology of a city gas engine is effective concerning efficiency falls and increases in carbon-dioxide emissions at the time of partial load. The power-generation system using a city gas engine with generator (NEG) is cheap compared with the fuel cell. Therefore, this book describes the investigation method of the power-generation efficiency and carbon-dioxide emissions in case of connecting NEG and PEFC (proton-exchange membrane fuel cell) to a microgrid.

1.3 Distribution Plan of Energy System

PEFC and SOFC (solid-oxide fuel cell) may develop as a power plant. These fuel cells have the advantage that they are highly efficient and have little environmental impact. However, these fuel cells are expensive, and the system is complex. It may be possible to reduce the number of expensive fuel cells that need to be installed by connecting the fuel cell to a microgrid and supplying power to two or more buildings. If the energy of the overall grid is supplied by one set of fuel cells (central system), the facility costs will be reduced considerably. Past work has examined the method of supplying power to a water electrolyzer and hydrogen and oxygen fuel storage methods. Another study looked at controlling the number of units that divide a fuel cell and a reformer, finding that the system efficiency falls when operated at partial load. In addition, energy-storage methods, such as batteries and flywheels, have been considered, though this equipment is not introduced in this book. Energy storage methods must consider power fluctuations in the microgrid affecting how the fuel cell is controlled. Although this is an important topic, for simplicity, this book focuses on other issues related to microgrids. There are no examples of the effect of the power demand pattern of buildings linked to a microgrid on the generation efficiency of a fuel cell system. This book will examine how the overall generation efficiency is affected by connecting a building to another grid. In this book, the fuel cell microgrid (FC microgrid) is installed in an urban area and divided into multiple grids. The system efficiency is improved over the case where each grid is connected independently (partition cooperation system). By dividing the grid and increasing the load factor of PEFC linked to each grid, the proposed method improves the generation efficiency of the overall grid. This book describes the generation efficiency of the FC microgrid using the power-demand model assuming typical buildings (individual houses, apartments, hotels, convenience stores, small offices, factory, and small hospitals).

CHAPTER 2

Operation Analysis of a Compound Energy System – Exhaust Heat Use Plan when Connecting Solar Modules to a Fuel Cell Network

SHIN’YA OBARA

2.1 Introduction

In order to utilize effectively the power and the thermal energy produced in nuclear power plants and thermal power plants, it is necessary to reduce the transportation loss of energy. In particular, large heat release follows the transmission of thermal energy. Therefore, utilization of the exhaust heat in a distant place is not economical. From the viewpoint of energy transport, the distribution of a small energy-generation system is economical. This is because loss of energy transport decreases by installing an energy-generation system near the demand side. Furthermore, it is necessary to consider the introduction of small fuel cells and renewable energy from the viewpoint of the environment. In this chapter, the fuel system (hydrogen piping network), the electrical power system (power line network), and the heat-power system (hot-water piping network) of the fuel cells installed in each building are connected, and the best method of simultaneously generating two or more electrical power and heat loads of the buildings is examined. When two or more fuel cells are connected in a network, then the method involving cooperation and control of the electrical and heat power outputs is labeled a fuel-cell-energy network (FEN). In an FEN, two or more fuel cells operated with a partial load with low efficiency can be stopped compulsorily. If fewer fuel cells are made to respond to these loads, decline in efficiency by a partial load may decrease.

The existing commercial power system can be used for a power network of FEN. Moreover, the existing town gas network can be used for a hydrogen piping network. However, a hot-water piping network must be newly installed. Since hot-water piping involves piping to all buildings, heat release is a problem. However, if a large-scale FEN is built in a cold district with considerable heat demand, the heat-release loss in a hot-water piping network would be predicted to represent the overall efficiency. When the paths of the hot-water piping differ, the amounts of heat released from the network also differ. Therefore, using the fuel cell exhaust heat effectively requires a path plan to minimize the amount of heat loss from the hot-water piping network. In this section, a program simulating the TSP analysis method (traveling salesman problem), using the genetic algorithm (GA) for the path plan of the hot-water piping network, is developed and investigated. Since charging according to the volume of carbon dioxide gas discharged is being considered, connecting renewable energy and unused energy equipment to the FEN, which comprises tens of buildings, is also being investigated. For optimization using GA, there are previous reports that have explored the piping path and the equipment layout. However, to the best of our knowledge, there have been no studies concerning the analysis of an energy network using fuel cells with a solar module accompanied by output change. Therefore, when connecting a solar module accompanied by output change to FEN, the path plan of the hot-water piping network, optimized to ensure minimal heat release, is investigated.

2.2 The Fuel Cell Energy Network with Solar Modules

2.2.1 Urban Area Model

An example where the hot-water piping network is applied to two or more buildings in Sapporo in Japan using the FEN is shown in Figure 2.1. S1 to S12 in Figure 2.1 indicate the buildings in the network, while [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the length of the hot-water piping connecting them. The urban area considered in the model is assumed to be Sapporo city in Japan, wherein the FEN is introduced as shown in Figure 2.1. The buildings include two-person households (TH), 3- or 4-person households (FH), 2 households living together with five or more persons (DH), a small-scale office (SO) and an apartment house (AP). The electrical power and thermal energy-demand patterns for each building in winter (February), summer (August), and mid-term (May) are shown in Figure 2.2. The power capacity of the fuel cell installed in each building is decided at the maximum value of the power load of each building shown in Figure 2.2.

The average temperatures in Sapporo for the sampling time on representative days in February, May, and August are shown in Figure 2.3. There is no cooling load during the summer in Sapporo. Electricity demand includes household appliances and electric lighting, and heat demand comes from heating, the hot-water supply, and a baths. The area of an average individual house (3- or 4-person household) in Sapporo is 140 m2, with 2 storeys and the houses are made of wood.

2.2.2 Characteristic of the Solar Module

The power-generation area of a solar module is 24 m2, and the maximum production of electricity is 3 kW. The experimental results when using this solar module in Sapporo City is shown in Figure 2.4. The solar module is a single-crystal type and the installation angle is 60 degrees. The surface is fixed southward. Although it snows in winter (February), the modular surface is treated and snow can be further melted with an electric heater so that snow does not lie on the solar module. The production of electricity of the solar module influences the production of electricity of the fuel cell as described in Section 2.4. If the installation angle and capacity of the solar module are changed, the amount of exhaust heat of the fuel cell is affected. If the amount of exhaust heat of the fuel cell changes, the temperature of the hot water that recovers the exhaust heat also changes. After all, a change in the power output of a solar module influences the heat release of the hot-water piping.

In the analysis example of Section 2.4, the maximum electrical power output of the solar module installed in each building is rendered the same as the capacity of the fuel cell installed in each building. The electrical power generated by the solar module of each building is supplied to meet the electricity demand of the building, once this module has been preferentially installed. When the electrical power remains within a certain building, surplus electrical power can be supplied to each building through a power line network. In the analysis example of Section 2.4, the output characteristics of the solar module installed in each building mean they are treated relatively the same as shown in Figure 2.4.

2.2.3 Hot-Water Piping Network

Suppose reforming gas and power line networks within existing town gas and power line networks are used, respectively. The hot-water piping path shown in Figure 2.1 is of an order that connects each building with the shortest distance (517 m). When the specification of the hot-water piping connecting each building are the same, assuming the heat release of the hot-water piping is dependent only on the piping length, the hot-water piping path intended to minimize heat release will always be the shortest path. The path of hot-water piping influences the heat release of the whole hot-water network. Details of the relationship between the piping path and heat release are presented in Section 2.3. Therefore, in order to use the exhaust heat or fuel cells effectively, a path plan of piping that minimizes the heat release in a hot-water piping network is required.

2.2.4 Facility Scheme

Figure 2.5 shows the model that connects hot-water piping to Si+2 from Building Si. The heat output in the fuel cell installed in each building conveys the surplus and deficiencies through the hot-water piping, once the heat demand in the building has been satisfied. 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 Figure 2.6(a), a fuel cell, solar module, inverter, and heat exchanger, able to output or input heat from or to the hot-water piping network, respectively, are installed in each building linked to the FEN. When the building in Figure 2.6(a) is set to Si, it includes a hot-water input with a quantity of heat [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] at temperature [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] from the hot-water piping network. Moreover, when the heat demand in Si is set to [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and the exhaust heat output of a fuel cell is set to [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], the hot-water output from Si is the quantity of heat [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] at temperature [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. The electrical power generated by a fuel cell is [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], that generated by the solar module is [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], and that after changing into the regulation frequency of exchange with an inverter is [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. The electrical power generated by a solar module is preferentially supplied to meet the electricity demand in the building currently installed. When electricity production [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] of a solar module is lacking and insufficient to meet the electricity demand [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], this shortfall is compensated via the operation of a fuel cell ([MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]). Although the electrical power, excluding the electricity demand [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] from [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], 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.

(Continues…)Excerpted from Compound Energy Systems by Shin’ya Obara, Arif Hepbasli. Copyright © 2010 Shin’ya Obara and Arif Hepbasli. Excerpted by permission of The Royal Society of Chemistry.
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