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Study for Optimal Energy Management System in Houses Collectively Receiving Electricity at Low Voltage
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ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2017) 000–000 479–483 EnergyProcedia Procedia141 00 (2017) www.elsevier.com/locate/procedia
4th International Conference on Power and Energy Systems Engineering, CPESE 2017, 25-29 4th International Conference September on Power and Energy Systems Engineering, CPESE 2017, 25-29 2017, Berlin, Germany September 2017, Berlin, Germany
The 15th International Symposium on District Heating and in Cooling Study for Optimal Energy Management System Houses Study for Optimal Energy Management System in Houses Collectively Receiving Electricity at Low Voltage Assessing the feasibility ofElectricity using the heat demand-outdoor Collectively Receiving at Low Voltage a b Eijifor Nishiura , Ryuji Matsuhashi temperature function a long-term district *heat demand forecast Eiji Nishiuraa, Ryuji Matsuhashib* a
Department of Electric Engineering and Information System, the University of Tokyo, Tokyo, Japan
a,b,c a a b c Department and Information System, the ., University of Tokyo, Tokyo, Japan I. Andrić *, ofA.Electric PinaEngineering , P. Ferrão , J. Fournier B. Lacarrière , O. Le Correc ab
a
b Department of Electric Engineering and Information System, the University of Tokyo, Tokyo, Japan IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract Currently collective electricity receiving and its trade among houses are promoted as a method of energy saving. Previous Abstractcollective Currently electricity receivingof and its tradereceiving among houses promoted as apower. method saving. researches investigated the possibility collective of highare voltage electric In of thisenergy research, we Previous evaluate researches investigated the possibility of collective receiving high voltage electric power. In cells. this research, we evaluate collective electricity receiving at low voltage and its trade amongofhouses with photovoltaics and fuel In this system, power District heating networks areatcommonly addressed in the literature one photovoltaics of theis most effective solutions forsystem, decreasing the collective electricity receiving low voltage and itsalthough trade among houses with and cells. this receiving facility for high voltage is not necessary, the price ofaselectricity higher. Wefuel develop aInMixed Integerpower Nongreenhouse gas(MINLP) emissions from of the building sector. These the systems high which are returned through the heat receiving facility for high model voltage isthe not necessary, although thewheeling pricerequire of rate electricity is higher. develop aThen Mixed Integer NonLinear Problem system, introducing as a investments benefit of theWe aggregator. we adopted the sales. Problem Due to (MINLP) the changed climate and renovation heatBy in the could decrease, Linear model of theconditions introducing the wheeling as ahouses. benefit ofdemand the aggregator. Then the concept of Nucleolus in game theory tosystem, maximize thebuilding minimum benefitrate ofpolicies, all doing this, we future designwe a adopted system that prolonging the investment return period. concept of profits Nucleolus game house. theory to maximize the minimum benefit of all houses. By doing this, we design a system that distributes fairlyinamong main scope of this paper ishouse. to Elsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand distributes fairly among ©The 2017 Theprofits Authors. Published by Ltd. ©forecast. 2017 The Authors. Published by Elsevier Ltd. district of Alvalade, in Lisbon (Portugal), was2017. used as a case study. The district is consisted of 665 © 2017 TheThe Authors. Published by Ltd. Peer-review under responsibility of Elsevier thelocated organizing committee of CPESE Peer-review under responsibility of the scientific of theThree 4th International Conference Power and Energy buildings that vary in both construction periodcommittee and typology. weather scenarios (low,onmedium, high) and three district Peer-review under responsibility of the organizing committee of CPESE 2017. Systems Engineering. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: collective electricity receiving, low voltage, electricity trade among houses, Nucleolus, MINLP; comparedcollective with results fromreceiving, a dynamic demand model, previously developed and validated Keywords: electricity lowheat voltage, electricity trade among houses, Nucleolus, MINLP; by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.(the Introduction scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1. Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Now global warming phenomenon by a greenhouse gas and exhaustion of a fossil fuel are becoming a social decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Now global warming phenomenon by aisgreenhouse gas and exhaustion of areducing fossil fuel becoming a social problem, so building of low carbon society very important. When we consider the are emission of GHG, it is renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the problem, so building of which low carbon society is very important. When we consider reducing the emission of GHG, it is very important to see way of power generation (such as thermal power generation or renewable power coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and very important to see which way of power generation (such as thermal power generation or renewable power improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +81-3-5841-6736 Cooling.
E-mail address:author. [email protected] * Corresponding Tel.: +81-3-5841-6736 E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review the organizing committee 1876-6102 ©under 2017responsibility The Authors. of Published by Elsevier Ltd. of CPESE 2017. Peer-review under responsibility of the organizing committee of CPESE 2017.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 4th International Conference on Power and Energy Systems Engineering. 10.1016/j.egypro.2017.11.062
480
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
resources) is used. On the other hand, the energy saving of demand side is also important. While energy saving is promoted by various ways, we focus on energy saving by residential sector. These days, private power generators such as photovoltaics(PV) and fuel cells(FC) are introduced in houses. PV is the system that generates electric power by solar power. It is renewable energy, so is unlimited and clean resource. FC produces electricity and thermal energy through the chemical reaction of hydrogen and oxygen, and is very efficient when both of electricity and thermal power are used[1]. Households can control these energy resources optimally by the introduction of HEMS (Home Energy Management System) and it can apply for demand response, so it is considered to develop from now on [2]. As another way of energy saving by residents, houses collectively receiving electricity at high voltage are promoted in Japan. Collective receiving is the system that aggregated households buy the high-voltage electric power collectively and share it. In general, high-voltage electric power is less expensive than low-voltage power, so the system has economic advantage [3]. However, voltage transformation is necessary for houses which use at low voltage, and it needs the initial cost for voltage transformer. In this research, we introduce collective receiving system of low-voltage electricity. An aggregator buys the low voltage electricity collectively and supplies it to every household, and this system has a feature that voltage transformation cost is not necessary while they can’t buy inexpensive electric power of the high voltage. We also introduce the electric power trading system to send surplus electricity to the house which wants it, and it allows the flexible work of energy equipment [4]. However, there is a problem that the aggregator can’t make a profit. In the high voltage collective receiving system, aggregators can buy inexpensive high-voltage electricity and sell electricity to each household, so they can make a profit by the difference. On the other hand, aggregators in low voltage collective receiving system buy only low-voltage electricity, so they need to make a profit by other ways. Then, we introduce the wheeling rate, which is paid to the aggregator by the households who accommodate electricity with other households. It will be the sources of aggregator’s profit. We simulate the flow of energy in the houses which have electricity trading system, design optimal electricity rate, and evaluate the economy and energy saving. 2. Simulation model The optimal operation of the photovoltaics and a fuel cells and the optimal charge system were decided by solving optimization problem.
Figure 1. The flow of energy in the house PV and FC installed
The data used in the model is the electric power consumption, the heat consumption and the photovoltaic generation amount of 5 households. All households have both of fuel cell and photovoltaic. The flow of energy is shown in Figure 1. A fuel cell consumes hydrogen made from gas and generates the electric power and heat. The electricity is consumed at the own house or sent to other houses, and heat is stored in a hot water storage tank. The demand of heat is supplied from storage tank or back-up heater which consumes gas and produces heat. FC is
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
481
operated depending on electricity and heat demand, and the efficiency of FC is high when the output is large. In addition, the electric power generated by photovoltaics is consumed in houses or can be sold to power company. In Japan, feed-in-tariff is introduced and the power generated by renewable resources can be sold at high rate. The houses are to sell as much power from PV as possible when the electricity demand is supplied sufficiently from fuel cell. The objective function is the total cost of all households as COST_all in equation (1) and we minimize it. It includes the running cost “COSTrun(p)” such as electricity charge and gas charge, wheeling cost ”WheelingCost(p)”, and income from PV selling “PVincome(p)” is subtracted. The demand-and-supply balance of the energy and the specification of the energy equipment such as efficiency and capacity were set as the constraint equations. The balanced demand-and-supply of the energy means that there is no surplus and shortage of the energy, and energy equipment is operated optimally. We set as a variable the amount of each energy flow and the wheeling rate. There is an equation "the wheeling rate * wheeling amount" in the constraints, and it is a nonlinear equation. In addition, the state whether a fuel cell works or stops and whether the power generated by photovoltaic is being sold or not is included as a variable, and it is binary variable. Therefore, this optimization problem is Mixed Integer Non-Linear Problem (MINLP).
COST _ all COSTrun( p) WheelingCost ( p) PVincome( p)
(1)
p
3. Results 3.1. Reduction rate and power sources We evaluate this system by comparing the collective low-voltage electricity receiving model and the standard model (which the collective receiving system is not introduced to). We can reduce the residential cost, CO2 emission and consumption of primary energy as shown in Table 1. Table 1. Reduction rate of each topic
Topic Reduction Rate Annual Cost 12.8% CO2 Emission 1.9% Consumption of Primary Energy 1.8% We focus on the sources of electricity supply in each house. By showing this, it is possible to find out how energy equipment works. Figure 2 shows the house where electric power demand is small(a) and is large(b). During the day, the small demand house has surplus electricity and send it to the large demand house. It means that the small demand house can drive fuel cell at high efficiency (about 0.6kWh) and the large demand house can reduce the amount of electricity which is bought from power company grid.
(a)the small demand house
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
482
(b)the large demand house Figure 2. Sources of electricity supply
3.2. Analysis of each house Until here the charge of power trade among houses isn't considered, and this makes the difference of each house’s profit. This is unfair system so it is necessary to adjust the profit. The house which sends electricity to other houses has to receive the revenue from the power received house. Then power trade charge “E_tr” is introduced. Corrected profit “BENE_new_resident (p)” is calculated by adding “E_tr * Trade_net (p) (net power trade amount)" to the profit of each house “BENE_resident(p)” as equation (2). The net power trade amount “Trade_net (p)” is calculated by pulling the amount of received power from that of sent power.
BENE _ new _ resident( p) BENE _ resident( p) E _ tr * Wheeling _ net ( p)
(2)
We adopt a concept of nucleolus for a profit adjustment. Nucleolus is the concept mainly used in a game theory, and aims to minimize the biggest dissatisfaction in the group and the equal profit distribution is performed [5]. In this model, the minimum profit of the households is maximized. In other words, we maximize the function “MIN(p,BENE_new_resident(p))” by correcting power trade charge (E_tr). Figure 3 shows the profit of each house before and after correction by nucleolus. The house which received electricity gets the positive profit and the house which sends electricity gets minus profit before correction. After correction, the profits of all houses become positive and this means all households can get benefit. Calculated most suitable power trade charge (E_tr) is 21 [yen/kWh].
Figure 3. profit of each house before and after correction by nucleolus
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
483
4. Conclusion We introduce the collective receiving system of low-voltage electric power to houses with photovoltaics and fuel cells and we can show that this system has economic and ecological advantages. We also introduce wheeling rate, set it as a variable, and simulate the work of energy equipment by solving MINLP. In addition, we introduce the concept of nucleolus as a method of adjusting the difference of each house’s profit. As a result, all houses which have small and large electricity demand can get benefits fairly. We consider MINLP, which is very complex and is hard to solve. Therefore, we set the case that proposed system is consisted of a small number of households and make easy problem to solve. It is necessary to improve the system, which can be applied to larger scale. Moreover, the demand pattern of houses is very important factor, so we want to analyze about the various demand in future. References [1] Hirohisa Aki, Tetsuya Wakui, Ryohei Yokoyama, “Development of an energy management system for optimal operation of fuel cell based residential energy systems”, International Journal of Hydrogen Energy, Volume 41, Issue 44, Pages 20314-20325, 2016. [2] Yusuf Ozturk , Datchanamoorthy Senthilkumar , Sunil Kumar , Gordon Lee, “An Intelligent Home Energy Management System to Improve Demand Response”, IEEE Transactions on Smart Grid, Vol.4, 694-701, 2013. [3] Sho Aihara,”Simulation Analysis on Operation Model of Household Power Generation and Storage”, 3rd International Conference on Renewable Energy Research and Applications, 2014. [4] Taro Kakutani, ”Optimal Vehicle-to-Home Energy Systems for Houses with Fuel Cells and Solar Panels”, Journal of Environmental Information Science, Vol.42, No.5, 2014. [5] Akira Okada, Game Theory, Tokyo, Japan, 1996.
ScienceDirect ScienceDirect Available onlineatatwww.sciencedirect.com www.sciencedirect.com Available online Energy Procedia 00 (2017) 000–000 Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2017) 000–000 479–483 EnergyProcedia Procedia141 00 (2017) www.elsevier.com/locate/procedia
4th International Conference on Power and Energy Systems Engineering, CPESE 2017, 25-29 4th International Conference September on Power and Energy Systems Engineering, CPESE 2017, 25-29 2017, Berlin, Germany September 2017, Berlin, Germany
The 15th International Symposium on District Heating and in Cooling Study for Optimal Energy Management System Houses Study for Optimal Energy Management System in Houses Collectively Receiving Electricity at Low Voltage Assessing the feasibility ofElectricity using the heat demand-outdoor Collectively Receiving at Low Voltage a b Eijifor Nishiura , Ryuji Matsuhashi temperature function a long-term district *heat demand forecast Eiji Nishiuraa, Ryuji Matsuhashib* a
Department of Electric Engineering and Information System, the University of Tokyo, Tokyo, Japan
a,b,c a a b c Department and Information System, the ., University of Tokyo, Tokyo, Japan I. Andrić *, ofA.Electric PinaEngineering , P. Ferrão , J. Fournier B. Lacarrière , O. Le Correc ab
a
b Department of Electric Engineering and Information System, the University of Tokyo, Tokyo, Japan IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract Currently collective electricity receiving and its trade among houses are promoted as a method of energy saving. Previous Abstractcollective Currently electricity receivingof and its tradereceiving among houses promoted as apower. method saving. researches investigated the possibility collective of highare voltage electric In of thisenergy research, we Previous evaluate researches investigated the possibility of collective receiving high voltage electric power. In cells. this research, we evaluate collective electricity receiving at low voltage and its trade amongofhouses with photovoltaics and fuel In this system, power District heating networks areatcommonly addressed in the literature one photovoltaics of theis most effective solutions forsystem, decreasing the collective electricity receiving low voltage and itsalthough trade among houses with and cells. this receiving facility for high voltage is not necessary, the price ofaselectricity higher. Wefuel develop aInMixed Integerpower Nongreenhouse gas(MINLP) emissions from of the building sector. These the systems high which are returned through the heat receiving facility for high model voltage isthe not necessary, although thewheeling pricerequire of rate electricity is higher. develop aThen Mixed Integer NonLinear Problem system, introducing as a investments benefit of theWe aggregator. we adopted the sales. Problem Due to (MINLP) the changed climate and renovation heatBy in the could decrease, Linear model of theconditions introducing the wheeling as ahouses. benefit ofdemand the aggregator. Then the concept of Nucleolus in game theory tosystem, maximize thebuilding minimum benefitrate ofpolicies, all doing this, we future designwe a adopted system that prolonging the investment return period. concept of profits Nucleolus game house. theory to maximize the minimum benefit of all houses. By doing this, we design a system that distributes fairlyinamong main scope of this paper ishouse. to Elsevier assess the feasibility of using the heat demand – outdoor temperature function for heat demand distributes fairly among ©The 2017 Theprofits Authors. Published by Ltd. ©forecast. 2017 The Authors. Published by Elsevier Ltd. district of Alvalade, in Lisbon (Portugal), was2017. used as a case study. The district is consisted of 665 © 2017 TheThe Authors. Published by Ltd. Peer-review under responsibility of Elsevier thelocated organizing committee of CPESE Peer-review under responsibility of the scientific of theThree 4th International Conference Power and Energy buildings that vary in both construction periodcommittee and typology. weather scenarios (low,onmedium, high) and three district Peer-review under responsibility of the organizing committee of CPESE 2017. Systems Engineering. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: collective electricity receiving, low voltage, electricity trade among houses, Nucleolus, MINLP; comparedcollective with results fromreceiving, a dynamic demand model, previously developed and validated Keywords: electricity lowheat voltage, electricity trade among houses, Nucleolus, MINLP; by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.(the Introduction scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1. Introduction The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Now global warming phenomenon by a greenhouse gas and exhaustion of a fossil fuel are becoming a social decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Now global warming phenomenon by aisgreenhouse gas and exhaustion of areducing fossil fuel becoming a social problem, so building of low carbon society very important. When we consider the are emission of GHG, it is renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the problem, so building of which low carbon society is very important. When we consider reducing the emission of GHG, it is very important to see way of power generation (such as thermal power generation or renewable power coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and very important to see which way of power generation (such as thermal power generation or renewable power improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +81-3-5841-6736 Cooling.
E-mail address:author. [email protected] * Corresponding Tel.: +81-3-5841-6736 E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review the organizing committee 1876-6102 ©under 2017responsibility The Authors. of Published by Elsevier Ltd. of CPESE 2017. Peer-review under responsibility of the organizing committee of CPESE 2017.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 4th International Conference on Power and Energy Systems Engineering. 10.1016/j.egypro.2017.11.062
480
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
resources) is used. On the other hand, the energy saving of demand side is also important. While energy saving is promoted by various ways, we focus on energy saving by residential sector. These days, private power generators such as photovoltaics(PV) and fuel cells(FC) are introduced in houses. PV is the system that generates electric power by solar power. It is renewable energy, so is unlimited and clean resource. FC produces electricity and thermal energy through the chemical reaction of hydrogen and oxygen, and is very efficient when both of electricity and thermal power are used[1]. Households can control these energy resources optimally by the introduction of HEMS (Home Energy Management System) and it can apply for demand response, so it is considered to develop from now on [2]. As another way of energy saving by residents, houses collectively receiving electricity at high voltage are promoted in Japan. Collective receiving is the system that aggregated households buy the high-voltage electric power collectively and share it. In general, high-voltage electric power is less expensive than low-voltage power, so the system has economic advantage [3]. However, voltage transformation is necessary for houses which use at low voltage, and it needs the initial cost for voltage transformer. In this research, we introduce collective receiving system of low-voltage electricity. An aggregator buys the low voltage electricity collectively and supplies it to every household, and this system has a feature that voltage transformation cost is not necessary while they can’t buy inexpensive electric power of the high voltage. We also introduce the electric power trading system to send surplus electricity to the house which wants it, and it allows the flexible work of energy equipment [4]. However, there is a problem that the aggregator can’t make a profit. In the high voltage collective receiving system, aggregators can buy inexpensive high-voltage electricity and sell electricity to each household, so they can make a profit by the difference. On the other hand, aggregators in low voltage collective receiving system buy only low-voltage electricity, so they need to make a profit by other ways. Then, we introduce the wheeling rate, which is paid to the aggregator by the households who accommodate electricity with other households. It will be the sources of aggregator’s profit. We simulate the flow of energy in the houses which have electricity trading system, design optimal electricity rate, and evaluate the economy and energy saving. 2. Simulation model The optimal operation of the photovoltaics and a fuel cells and the optimal charge system were decided by solving optimization problem.
Figure 1. The flow of energy in the house PV and FC installed
The data used in the model is the electric power consumption, the heat consumption and the photovoltaic generation amount of 5 households. All households have both of fuel cell and photovoltaic. The flow of energy is shown in Figure 1. A fuel cell consumes hydrogen made from gas and generates the electric power and heat. The electricity is consumed at the own house or sent to other houses, and heat is stored in a hot water storage tank. The demand of heat is supplied from storage tank or back-up heater which consumes gas and produces heat. FC is
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
481
operated depending on electricity and heat demand, and the efficiency of FC is high when the output is large. In addition, the electric power generated by photovoltaics is consumed in houses or can be sold to power company. In Japan, feed-in-tariff is introduced and the power generated by renewable resources can be sold at high rate. The houses are to sell as much power from PV as possible when the electricity demand is supplied sufficiently from fuel cell. The objective function is the total cost of all households as COST_all in equation (1) and we minimize it. It includes the running cost “COSTrun(p)” such as electricity charge and gas charge, wheeling cost ”WheelingCost(p)”, and income from PV selling “PVincome(p)” is subtracted. The demand-and-supply balance of the energy and the specification of the energy equipment such as efficiency and capacity were set as the constraint equations. The balanced demand-and-supply of the energy means that there is no surplus and shortage of the energy, and energy equipment is operated optimally. We set as a variable the amount of each energy flow and the wheeling rate. There is an equation "the wheeling rate * wheeling amount" in the constraints, and it is a nonlinear equation. In addition, the state whether a fuel cell works or stops and whether the power generated by photovoltaic is being sold or not is included as a variable, and it is binary variable. Therefore, this optimization problem is Mixed Integer Non-Linear Problem (MINLP).
COST _ all COSTrun( p) WheelingCost ( p) PVincome( p)
(1)
p
3. Results 3.1. Reduction rate and power sources We evaluate this system by comparing the collective low-voltage electricity receiving model and the standard model (which the collective receiving system is not introduced to). We can reduce the residential cost, CO2 emission and consumption of primary energy as shown in Table 1. Table 1. Reduction rate of each topic
Topic Reduction Rate Annual Cost 12.8% CO2 Emission 1.9% Consumption of Primary Energy 1.8% We focus on the sources of electricity supply in each house. By showing this, it is possible to find out how energy equipment works. Figure 2 shows the house where electric power demand is small(a) and is large(b). During the day, the small demand house has surplus electricity and send it to the large demand house. It means that the small demand house can drive fuel cell at high efficiency (about 0.6kWh) and the large demand house can reduce the amount of electricity which is bought from power company grid.
(a)the small demand house
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
482
(b)the large demand house Figure 2. Sources of electricity supply
3.2. Analysis of each house Until here the charge of power trade among houses isn't considered, and this makes the difference of each house’s profit. This is unfair system so it is necessary to adjust the profit. The house which sends electricity to other houses has to receive the revenue from the power received house. Then power trade charge “E_tr” is introduced. Corrected profit “BENE_new_resident (p)” is calculated by adding “E_tr * Trade_net (p) (net power trade amount)" to the profit of each house “BENE_resident(p)” as equation (2). The net power trade amount “Trade_net (p)” is calculated by pulling the amount of received power from that of sent power.
BENE _ new _ resident( p) BENE _ resident( p) E _ tr * Wheeling _ net ( p)
(2)
We adopt a concept of nucleolus for a profit adjustment. Nucleolus is the concept mainly used in a game theory, and aims to minimize the biggest dissatisfaction in the group and the equal profit distribution is performed [5]. In this model, the minimum profit of the households is maximized. In other words, we maximize the function “MIN(p,BENE_new_resident(p))” by correcting power trade charge (E_tr). Figure 3 shows the profit of each house before and after correction by nucleolus. The house which received electricity gets the positive profit and the house which sends electricity gets minus profit before correction. After correction, the profits of all houses become positive and this means all households can get benefit. Calculated most suitable power trade charge (E_tr) is 21 [yen/kWh].
Figure 3. profit of each house before and after correction by nucleolus
Eiji Nishiura et al. / Energy Procedia 141 (2017) 479–483 Eiji Nishiura et al. / Energy Procedia 00 (2017) 000–000
483
4. Conclusion We introduce the collective receiving system of low-voltage electric power to houses with photovoltaics and fuel cells and we can show that this system has economic and ecological advantages. We also introduce wheeling rate, set it as a variable, and simulate the work of energy equipment by solving MINLP. In addition, we introduce the concept of nucleolus as a method of adjusting the difference of each house’s profit. As a result, all houses which have small and large electricity demand can get benefits fairly. We consider MINLP, which is very complex and is hard to solve. Therefore, we set the case that proposed system is consisted of a small number of households and make easy problem to solve. It is necessary to improve the system, which can be applied to larger scale. Moreover, the demand pattern of houses is very important factor, so we want to analyze about the various demand in future. References [1] Hirohisa Aki, Tetsuya Wakui, Ryohei Yokoyama, “Development of an energy management system for optimal operation of fuel cell based residential energy systems”, International Journal of Hydrogen Energy, Volume 41, Issue 44, Pages 20314-20325, 2016. [2] Yusuf Ozturk , Datchanamoorthy Senthilkumar , Sunil Kumar , Gordon Lee, “An Intelligent Home Energy Management System to Improve Demand Response”, IEEE Transactions on Smart Grid, Vol.4, 694-701, 2013. [3] Sho Aihara,”Simulation Analysis on Operation Model of Household Power Generation and Storage”, 3rd International Conference on Renewable Energy Research and Applications, 2014. [4] Taro Kakutani, ”Optimal Vehicle-to-Home Energy Systems for Houses with Fuel Cells and Solar Panels”, Journal of Environmental Information Science, Vol.42, No.5, 2014. [5] Akira Okada, Game Theory, Tokyo, Japan, 1996.