© 2020 The Society of Instrument and Control Engineers - SICE. The supply-demand adjustment market in Japan will be opened in 2021, and it is expected to contribute to a virtual power plant (VPP) to gather distributed energy resources even from residential sector. The problem is to integrate residential sector that respond differently depending on the characteristics of each household, with keeping protecting consumer privacy. This article proposes a hierarchical management method that realizes VPP in residential sector. The proposed framework meets the requirements to join a market. The proposed method gets evaluated in numerical experiments. We aim to aggregate 1 MW controllable electricity with 4, 000 detached household. The results show that the proposed method could achieve an amount of electricity of the procurement target in computing time of 10 minutes.

© ECOS 2020.All right reserved. The utilization of "100% renewable energy" has recently attracted considerable interest in view of the objective of achieving a low-carbon society. An approach that seems effective to realize this goal is the purchase of renewable electricity from the power grid. However, the amount of renewable electricity that the power grid can supply at various seasons and periods is limited. In view of this, it is necessary to consider the use of energy systems that can exploit renewable energy, such as the installation of photovoltaics in households. The purpose of this study is to investigate the feasibility of 100% renewable energy systems in residential areas and multi-dwelling buildings. The area and building examined are limited to a set of "all-electric houses" with electric vehicles, photovoltaics, and heat pump water heaters. In each case, the electricity self-sufficiency ratio and electricity self-consumption ratio resulting from the use of photovoltaics are derived. In this study, an operation that minimizes CO2 emissions during the evaluation period is developed. It is found that the use of 100% renewable energy in residential areas without relying on the renewable electricity from the power grid is possible as demonstrated by the high feasibility of using 100% renewable energy in rural areas. In multidwelling buildings, this feasibility is lower than that in residential areas. To achieve the utilization of 100% renewable energy using the renewable electricity from the power grid, the grid should increase its supply of renewable electricity at dawn and in the evening. In winter, the renewable electricity from the photovoltaic power alone is insufficient; hence, the power grid requires other renewable electricity sources, such as windgenerated power. To improve the electricity self-sufficiency ratio of multi-dwelling buildings, the use of an energy system that allows electricity sharing from the residential areas to multi-dwelling buildings is found effective.

© ECOS 2020.All right reserved. Reducing CO2emissions by the effective use of renewable energy is one of the most important issues globally. Therefore, research has focused on both the development of individual high-performance equipment and also district heating and cooling systems. In Japan, installations of the conventional district heating and cooling system, which utilizes steam and chilled water, are rare. In recent years, a promising alternative, the CO2network system, has been proposed by researchers at Swiss Federal Institute of Technology in Lausanne. The system uses the latent heat of vaporization of the CO2refrigerant to transport and exchange unused heat to specific areas via decentralized heat pumps on the demand-side. The proposed system is expected to significantly reduce energy consumption compared with the conventional system. In this study, we analyzed the energy-saving effects of introducing a CO2network system in Tokyo, with consideration of the environmental conditions and the typical energy demand profiles. The effects of modifying the central unit and the demand-side decentralized heat pump systems were examined in the context of Tokyo. When using the CO2network, the annual energy consumption resulting from equipment operation can be reduced by approximately 80% compared with the conventional system. This effect is almost of the same magnitude as the effect observed in previous research conducted in Switzerland. Furthermore, we examined the optimum connection pattern of the decentralized heat pumps and the optimum temperature of the CO2supplied to the network.

© ECOS 2020.All right reserved. The demand response (DR), which balances power supply and demand, is attracting attention as a method of absorbing fluctuations in renewable energy when output is unstable. Since the Great East Japan Earthquake in 2011, Japan has made progress toward the liberalization of electricity and the introduction of renewable energy. In 2021, a supply and demand adjustment market will be opened. Unlike companies and factories, which can control large amounts of power for long periods, household demand has a small capacity and is short term. However, household demand is expected to be able to respond quickly to emergencies and to secure capacity by consolidating multiple households via an aggregator. This study concerns two problems in assessing town-scale residential DR capability. First, we evaluate residential DR response capacity. This capacity is assumed to be pre-cooling heating by an air conditioner according to thermal insulation and heat capacity characteristics, and energy storage by a hot water storage tank and a power storage device. Next, we investigate implementing the optimization of the town-scale DR by an aggregator. Optimal town-scale aggregation proposals are often formulated in large-scale combinatorial optimization problems. The challenge is that the calculation tends to be time-consuming and difficult to solve. In this research, we propose a framework that can be solved at high speed using digital annealer. Finally, we evaluate the possibility of town-scale residential DR in specific areas of Tokyo.

© ECOS 2020.All right reserved. The objective of the research is to develop an optimal management and control framework to contribute to improving the capacity factor of the geothermal power plant. As the initial consideration of the study, the article aims to identify the phenomenon that causes to decrease capacity factor from the analysis of a process data, to detect the phenomenon to support the decision-making of operators. For achieving the latter goal, we construct the framework that announces the operator to how much anomaly with a convolutional neural network model, which can capture non-stationary and nonlinearity of process data. Through the literature survey and the analysis of the process data, we confirmed that the steam flow rate and pressure of wellbore having two feed zones oscillate, and the wellbore reached the spurts stop of the steam due to oscillation growth. We confirmed that the pressure-drop events occurred six times in 2018, and it would be one of the causes to decrease capacity factor. As numerical results of the proposed framework, before the pressure-drop happens, they are increased that the amplitude of the second harmonic wave, the period of the first harmonic wave, and the anomaly score we derived. We could conclude that the pressure-drop, as mentioned above indexes made by the proposed method, could predict the pressure-drop at the one minute ahead, even in the case that pressure time-series have the nature of non-stationarity and nonlinearity.

© ECOS 2020.All right reserved. The article develops and demonstrates the fractional optimization to evaluate the environmental efficiency of the regional-scale energy system with heat pumping and thermal storage. The fractional optimization gets converted into the equivalent quadratic one via the proposed iterative algorithm. The proposed method makes converting a fractional equation into a different one with an auxiliary variable, and it makes us free from heuristic parameter tuning that makes balancing the weight of objective coefficient when the objective function is composed of some terms. We consider the operational planning problem of the mixed energy grid, including electricity and heat. It manages power generations and a heat pump with thermal storage as an application. The numerical studies take into account the open data measured by the most western region of Japan, where the region gets applied the installation/connection of variable renewable energy sources of 25.7 GW at the moment even not every capacity starts operation. As a result, the CO2conversion factor reaches 0.21 kg-CO2/kWh in the best case. It is confirmed that a heat pump with thermal storage can absorb the electrical output from variable renewable energy sources up to 2.72 TWh/y.

© 2019 IEEE. This study proposes a hot water demand prediction method for operational planning of polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process. An optimal operational plan according to household demand leads to further energy saving. Therefore, operational planning methods based on household demand prediction have received intense focus. In particular, the prediction of the amount of hot water demand is important for efficient operation. The authors have attempted to improve the hot water prediction method based on multivariate random forest (MRF), which uses the average of many decision trees' outputs as the prediction result. However, some experimental results show that a prediction strategy based on averaging the outputs of decision trees does not always lead to the best solution. In this study, the authors propose a novel prediction method utilizing the quantile of the estimation results derived in MRF. By setting the appropriate quantile, we can evade the demand underestimation, which has a higher negative impact on operational efficiency than overestimation. The usefulness of the proposed approach is evaluated via numerical simulations using real-world demand data.

© ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. All rights reserved. To reduce CO2 emissions in the residential sector, the installation of high-efficiency energy supply equipment and development of zero energy house are progressing in Japan. It is important to appropriately select energy system configuration, such as the number of installed equipment and capacity when designing the system configuration. There are various studies on the configuration of energy systems. The purpose of this study, we analyze the trends of the relationship between optimal configuration and household characteristics. Then, based on the derived configuration, we evaluate the system configuration from a long-term perspective. We assume a smart residential building consisting of multiple households that introduces photovoltaics (PV) and battery (BT) as common assets and has variations in the number of occupants and demand profile. In addition, we consider various thermal devices, which is a gas-fired water heater (GH), heat pump water heater (HPWH), and solid oxide fuel cell cogeneration systems (SOFC). Deriving the optimal configuration of energy systems over the year is difficult from the viewpoint of calculation load. Therefore, we select 7 representative days for each month and derive optimal configuration such as minimizing CO2 emissions in each evaluation period. Then, we aggregate the results and decide the optimal configuration of the residential building in the target year. As a result, when the CO2 conversion factor for electricity is 0.400 kg/kWh or more, installing SOFC to the few-person household is effective. On the other hand, in the case of the many-person household, the SOFC installation effect is lower than the HPWH from the viewpoint of CO2 emissions.

© ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. All rights reserved. For a daily-basis scheduling of an energy system, energy management system often enough to use not a global optimal scheduling but a near optimal scheduling. The article proposes an online scheduling framework without online optimization. The framework is built from two encoder-decoder architectures to extract features of time series; a multi-layer long short-term memory regression model for multi-step time-series forecasting, and multi-class and single-label classification model for on/off scheduling of a device. The models are estimated at offline, and return scheduling from historical time series as input, at online. We evaluate the accuracy of scheduling from the viewpoint of Kullback-Leibler divergence which measures the dissimilarity between two probability distributions. Through the numerical experiments, we demonstrate the usefulness of the proposed framework.

© 2019 IEEE. This study proposes an operation planning method for polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process. An appropriate operation plan according to household demand will lead to further energy saving. The authors have discussed a framework for estimating expected operation costs of individual operation plans to select an appropriate candidate that minimizes the cost. In this scheme, the expected cost under each corresponding operation was independently estimated. This approach can be expected to provide accurate prediction of costs under individual operations. However, one open issue remains; it may not work well in estimating relative goodness compared to other operations. In this study, the authors propose a new cost estimation approach, focusing on relative appropriateness in candidate plans based on a multiple output prediction. The usefulness of the proposed approach is proved via a numerical simulation.

© 2018 University of Minho. All rights reserved. There is a high demand that aggregating Home Energy Management System (HEMS) as demand response service. Aggregation scheme is divided into the centralized and decentralized approach. On the one hand, in a centralized fashion, a controller manages all device. On the other hand, in a decentralized fashion, a local controller manages own devices and exchanges information for achieving the global optimum. Our previous work has proposed decentralized HEMS aggregation with Alternating Direction Method of Multipliers (ADMM) to address scalability and privacy issue. The main idea of this method is that we decompose the large-scale aggregated scheduling problem into individual HEMS scheduling problem by introducing local upper limit which stands for the maximum purchasable electricity from an electrical grid. This article reports that we extend the previous decentralized HEM method to asynchronous fashion in order to improve convergence time efficiency from the practical implementation perspective. We propose the waiting ratio which represents the minimum HEMS number for updating the next local upper limit. We also evaluate the algorithm processing time with changing waiting ratio and the number of households. As a result, it is confirmed that though the result shows 53% of acceleration at the most accompanied by barely 2% increase cost, the asynchronous process does not always accelerate the processing time. It may be reasonable to suppose that the degree of acceleration is decreased, as the degree of asynchrony increases, because iteration number until fulfilling converging criteria is increased.

The introduction of photovoltaic power systems is being significantly promoted. This paper proposes the implementation of a distributed energy management framework linking demand-side management systems and supply-side management system under the given time-of-use pricing program for efficient utilization of photovoltaic power outputs; each system implements a consistent management flow composed of forecasting, operation planning, and control steps. In our framework, demand-side systems distributed in the electric distribution network manage individual energy consumption to reduce the residential operating cost by utilizing the residential photovoltaic power system and controllable energy appliances so as not to inconvenience residents. On the other hand, the supply-side system utilizes photovoltaic power maximally while maintaining the quality of electric power. The effectiveness of the proposed framework is evaluated on the basis of an actual Japanese distribution network simulation model from both the supply-side and demand-side viewpoints.

As demand-side energy management system (EMS) has not only a function to maximize owner's utility by automatic control but also a potential capability to respond a demand activation, demand-side EMS is expected to fulfill the key role for intelligent control of community energy management. To achieve the stable control result of EMS against the uncertain environment, it needs approach from both forecast and operation sides. The objective of this study is to construct a control scheme for a home EMS from a practicable implementation perspective and to evaluate the control performance of the scheme proposed. The scheme is composed of operational planning with receding horizon and energy demand forecasting. The goal of the scheme is to maximize householder's utility only by retrofitting the home EMS to a residential household. Throughout numerical results, it is revealed that the better control performance comes from the conservative operational strategy derived from the multiple scenarios.

© 2017 IMEKO As communication standard, such as SEP 2.0 and ECHONET Lite, for digital communication of home appliances has been established, the research of networked control system is progressing in the domain of residential house. Instead of building new power station to fulfil new electricity requirement, such as electric vehicle charging, the concept, that an aggregator coordinates massive amount of home energy management system (HEMS) to supply negawatt power, is proposed. The aggregator requires to manipulate electricity consumption through massive amount of HMES as networked control system. As fundamental study for control algorithm of distributed architecture, this paper constructed one of the networked supervisory control system formulated as stochastic model predictive control scheme, and analysed primary energy consumption sensitivity against update interval of energy demand forecasting and operational strategy in addition to changing number of scenarios. The mainly results are concluded that: as the energy saving ratio in the case of 6 hours update interval has enough high point, which is 9.5% in comparison with the case of 24 hours update interval, the stochastic model predictive control scheme proposed in the case using six scenarios can downsample until 6 hours update interval.

© 2017 IMEKO In order to reduce energy consumption, the installation of combined heat and power (CHP) has been promoted. However, when the heat to power ratio of CHP and the energy demand do not match, performance of CHP is depreciated. As one of the solutions to improve performance of the CHP and to reduce the energy consumption, energy interchanges have attracted attention and gradually increased in importance in the commercial sector. The objective of this study was to analyze the impact of the difference between demand profiles in buildings employing power and heat interchanges on energy saving. Specifically, we formulated the optimal planning problem of building energy systems by Mixed Integer Linear Programming (MILP) from the viewpoint of energy consumption minimization. The analysis was applied to three model areas with different demand profiles by changing the component ratio of each building in a city with the same total floor area. The main results showed that the energy reduction effects of three model areas with different demand profiles were due to power and heat interchanges. We also found that employing power and heat interchanges increased both the installed capacity of CHPs in buildings and the contributing ratio of CHPs for electricity demand.

© 2017 IMEKO As a replacement for conventional sources, which cause CO2 emissions, the installation of photovoltaics (PV) has increased extensively in residential sectors. However, large-scale PV installation causes voltage violations of low-voltage distribution grids, particularly in the residential sector. According to previous studies, energy storage systems and power interchange methods are effective in utilizing PV surplus electricity and saving energy. The objective of this study is to derive the optimal configuration of energy systems in multiple households under power interchange for the minimization of total CO2 emissions. Two methods for solving this complicated and large-scale problem are proposed. First, we considered a calculation of the optimal configuration of energy systems in multiple households as an optimal unit sizing of energy systems in a single household. Second, we examined the operational planning problem as mixed integer linear programming (MILP) with an enumeration method. We found that a heat pump water heater (HP) can be installed as the heat supply equipment regardless of the PV installation ratio by the year 2030 in Japan, when the CO2 conversion factor of the electrical grid will be 0.37 kg/kWh. In addition, the maximum impact of power interchange on CO2 emissions will lead to its reduction by 85 kg, at 75 % of PV installation ratio.

In order to reduce CO2 emissions in the residential sector, the installation of photovoltaics (PV) has been increasing extensively. However, such large-scale PV installations cause problems in the low-voltage distribution grid of the residential sector, such as PV related voltage surges. In this study, the utilization of suppressed PV output through energy storage devices was proposed. Using demand side energy storage devices reduces voltage surge, transmission loss, and CO2 emissions from the residential buildings. The objective of this study was to add voltage constraints of the low-voltage distribution grid to an operational planning problem that we developed for the residential energy systems, and to quantitatively evaluate the potential of heat pump water heater (HP) to utilize the PV surplus electricity, while considering the electrical grid constraints based on the minimization of CO2 emissions. We found that when a 4.5 kW HP with 370 L storage, which utilizes PV output, was added to the system, the reduction in CO2 emissions was more than twice compared with that in the case of adding 4 kWh battery (BT) to a PV and gas fired water heater configuration. Further, the effect of utilizing the suppressed PV electricity by HP was almost equivalent to that by the BT. Therefore, the potential of HP in utilizing PV surplus electricity is higher than that of the BT in terms of CO2 emissions reduction in the residential sector.

In Japan, a feed-in tariff scheme has been implemented to promote the use of photovoltaic power generators in combination with electric batteries. Although many researchers have assessed the operation of grid-connected photovoltaic systems with electric batteries, little attention has been paid to the impact of the electric batteries' degradation characteristics. This paper evaluates the operation of a grid-connected photovoltaic/electric-battery system in a house in terms of cost savings and energy savings. We constructed a long-term operational optimization model considering the degradation characteristics of the electric battery. This model contains two objective functions: energy savings and operating costs. We create a scenario of power rates and photovoltaic/battery system configurations. To reveal the optimal operational strategy for the photovoltaic/battery system, the multi-objective optimization problem was solved. As a result, Pareto-optimal solutions were obtained, and trade-off relationships between cost and energy savings are presented. In this study, the utilization of a grid-connected photovoltaic system with an electric battery was expected to be most effective in energy-saving priority operation. (C) 2015 Elsevier B.V. All rights reserved.

© 2016 University of Ljubljana. The degree of operational freedom of home appliances is increasing with introducing controllable demand-side appliances by using Home Energy Management System with some communication standard. This situation increases the demands on methodology achieving customer utility maximization, which is able to plan operational strategies of a variety of appliances under various exogenous variables such as energy demand, outlet temperature, and power output of Photovoltaic power generator. Energy demand and photovoltaic output have some uncertainty or variation in the parameter. The objective of this study is to analyze a solution behavior of an operational planning problem adapting Mixed Integer Second-Order Cone Programming extended from traditional Mixed Integer Linear Programming model. The objective function of the mathematical optimization involves mean and its variance by extracting naturally Markowitz model. Above proposed model is intended to sophisticate robust operational planning method, and is also evaluated on the EMS simulation platform that we are developing. As a result, it is found that involving variance to objective function affects to computational time meaning computational load rather than robustness of planned schedule. Additionally, using several scenario results in more robust schedule than using plausible one scenario.

The large-scale introduction of renewable energy resources will cause instability in the power supply. Residential energy management systems will be even more important in the near future. An important function of such systems is visualization of appliance-wise energy consumption; residents will be able to consciously avoid unnecessary consumption behavior. However, visualization requires sensors to measure appliance-wise energy consumption and is generally a costly task. In this paper, an unsupervised method for non-intrusive appliance load monitoring based on a semi-binary non-negative matrix factorization model is proposed. This framework utilizes the total power consumption patterns measured at the circuit breaker panel in a house, and derives disaggregated appliance-wise energy consumption. In the proposed approach, the energy consumption of individual appliances is estimated by considering the appliance-specific variances based on an aggregated energy consumption data set. The authors implement the proposed method and evaluate disaggregation accuracy using real world data sets.

The subject of this study is to propose a power interchange system in a collective housing with residential solid oxide fuel cells (SOFCs) and a robust operation planning method for integrated SOFCs against uncertain energy demand forecast. In this method, the future operation plan for multiple SOFCs is optimized and determined to minimize the expected total primary energy consumption in the collective housing considering uncertainty in demand forecast. If the forecast energy demand includes forecast errors, the result of SOFCs operation will corrupt from the viewpoint of the primary energy consumption. Thus, the output decision problem for SOFCs is formulated by considering the corruption caused by forecast errors, so that the decided SOFC outputs have the robustness against uncertainty in demand forecast. The validity of the proposed method is examined based on numerical simulations from the perspective of the robustness.

<p>PMVを用いて熱的快適性を考慮した上で、窓・日射遮蔽ブラインド・断熱ブラインドなどの窓システムとエアコンを協調制御した場合とエアコンのみを単体制御した場合のシミュレーション解析を行い、エアコン単体制御に対する協調制御の電力消費量削減分を求めた。各制御システムに対して、それぞれ異なる気象条件下で解析、PMVの快適範囲を変化させた解析を行った。結果として、熱的快適性と省エネルギー性を両立した窓システムとエアコンの協調制御の有効性を示した。</p>

This paper proposes an optimal predictive control of 0.75 kW PEM fuel-cell cogeneration with home appliances. This paper also models fuel cell system for design and operation evaluation of building equipment based on actual measurement of residential fuel cell system on sale. As one application of constructed model and proposed control method, this paper discusses concerning home EMS for efficient PV utilization.

Compensating the voltage within the appropriate range becomes difficult when a large number of photovoltaic (PV) systems are installed. As a solution to this problem, the installation of a low-voltage regulator (LVR) has been studied. In this paper, we propose a method for rapidly and accurately determining the line drop compensator method (LDC) parameters as a part of a voltage management scheme, which consists of prediction, operation, and control. In the proposed method, candidates of the appropriate LDC parameters are selected with low computational cost by using classifiers that learns the relation between power series data and the properness of LDC parameters. We performed numerical simulations to evaluate the validity from the viewpoints of computational time and classification accuracy for determination of the LDC parameters, and verified the voltage control performance of the proposed method.

PV system recently attracts much attention on the back of environmental problems, antinuclear power movements and energy problems. Therefore, a large scale introduction of PV system is expected in the near future. On the other hand, a lot of PV systems connected to the power system bring on some problems. For example, a system voltage often drifts from the norm when the reverse power flow increase. Accordingly, it is necessary to perform an optimal system operation in order to utilize a solar energy to the maximum by installing energy buffers, e.g. storage batteries. Especially, the forecast information i.e., the reliability as well as predicted solar irradiance is essential for the effective operation. In this paper, we propose a way to estimate the prediction interval of the solar irradiance as an index of reliability by using Just-In-Time Modeling (JIT Modeling). We consider the accuracy of the prediction interval under many conditions and derive the high-precision estimation method. In addition, we also talk about the future outlook of this study.

Residential houses are in the process of introducing power generators such as photovoltaic (PV) power generators and fuel cell cogeneration systems. Under current laws in Japan, surplus electricity from a residential PV system can be sold by feeding it back into the electrical grid. However, when a lot of neighboring power generators make and feed back electricity at the same time, there is an issue of an upper voltage violation of the provisions of the laws and regulations relating to the Electricity Business Act in the distribution system of the electrical grid. The issue has been solved by stopping power generators when they reach an upper voltage limit, 107V. Another solution is to acknowledge demand response signal from electrical grid operator to store electricity in residential batteries using Home Energy Management System (HEMS). The research question is that how to collaborate HEMS and Grid Energy Management System (GEMS). This paper developed an evaluation framework for the cooperative behavior between HEMS and GEMS. Using this evaluation framework, this paper demonstrated the optimal operational strategy of HEMS including PV in the case that HEMS is informed voltage profile from GEMS, and also assessed amount of PV suppression quantitatively in residential sector in practical aspect.

Residential houses are in the process of introducing power generators such as photovoltaic (PV) power generators and fuel cell cogeneration systems. Under current laws in Japan, surplus electricity from a residential PV system can be sold by feeding it back into the electrical grid. However, when a lot of neighboring power generators make and feed back electricity at the same time, there is an issue of an upper voltage violation of the provisions of the laws and regulations relating to the Electricity Business Act in the distribution system of the electrical grid. The issue has been solved by stopping power generators when they reach an upper voltage limit, 107V. Another solution is to acknowledge demand response signal from electrical grid operator to store electricity in residential batteries using Home Energy Management System (HEMS). The research question is that how to collaborate HEMS and Grid Energy Management System (GEMS). This paper developed an evaluation framework for the cooperative behavior between HEMS and GEMS. Using this evaluation framework, this paper demonstrated the optimal operational strategy of HEMS including PV in the case that HEMS is informed voltage profile from GEMS, and also assessed amount of PV suppression quantitatively in residential sector in practical aspect.

Demand-side electricity saving is an important factor in the reduction of the installed capacity of power-supply facilities. In order to save electricity automatically while maintaining comfort levels, home energy management systems (HEMS) have attracted attention. These systems can control residential energy equipment cooperatively to reduce electricity consumption while considering benefits to consumers. Although many researchers have evaluated HEMS, no one has conducted a study which considers the control of various types of residential energy equipment in real time along with the uncertainties of energy demands. This paper proposes a single HEMS method which connects prediction, operational planning and control steps and enables the evaluation of operational planning methods of HEMS connected with many kinds of residential energy equipment currently in use in Japan while considering the uncertainties. The purpose of this study is to evaluate the economic potential of residential energy systems based on the proposed method with the uncertainties of energy demands and photovoltaic (PV) output under time-of-use prices. The results allowed us to establish a framework to quantitatively evaluate the operational planning methods of HEMS with the uncertainties of energy demands and PV output. In addition, the usability of the proposed method was confirmed by comparing the operational costs to those of a reference method.

An optimal operational planning problem of residential energy system has been formulated by Mixed Integer Linear Programming (MILP). The decision variables of optimal operational planning problem are energy and mass flows, equipment's operating statuses, and energy level of storage. Many kinds of energy supply equipment are available for householders in Japan. Of course, in operational planning problem, the increase of integer variables which means equipment's on/off status, is linked to the increase of calculation time. It is important to assess the impact of introducing an energy system for a house based on the suitable planning horizon of this problem. Energy storage brings the energy in the form of hot water and electricity to the next d a y. The operational strategy of energy system including storage should be evaluated through few days toward various energy demand. The optimal planning problems become large scale because many pieces of equipment to introduce and long evaluation period are required. This paper analyzes characteristics of energy systems caused by planning horizon. Additionally, we propose a hierarchical method as heuristic method for solving large MILP problem easily, and the proposed method is tested the effectiveness. Our finding shows that the proposed search method for better feasible solution has good performance in comparison with default settings of conventional MILP solver in terms of calculation time.

The multi-stage renewal planning problem is studied here for an energy supply system installed into a hospital from the long- Term economic viewpoint by applying the mathematical optimization method. This problem is mathematically formulated as a large-scale mixed-integer linear programming one, and the average annual total cost during the evaluation period is set as the objective function to be minimized. In this problem, by taking the deterioration of performance efficiency of the existing pieces of equipment, the renewal system is assumed to be composed by installing new pieces of equipment with improved efficiency by reflecting technical progress. By optimizing the system's operational policy for hourly and seasonally changing energy demands through years of the evaluation period, the renewal years and the system's configurations at each multi-stages are determined optimally. A numerical study is carried out for a really existing hospital with total floor area of 25000m2, and many comprehensive and useful results are obtained by comparing 13 alternative renewal plans by using the CPLEX solver.

The purpose of this study is to evaluate the maximum energy-saving potential of residential energy supply systems consisting of a solid oxide fuel cell (SOFC) cogeneration system (CGS) combined with a solar cell (SC) and a battery (BT), compared with a reference system (RS). This study applies an optimization theory into an operational planning problem to measure actual energy demands over the course of 1 year. Eight different types of energy supply system were compared with each other by changing the components of the SOFC-CGS, SC, BT, and RS. Meaningful numerical results are obtained, indicating the maximum potential energy savings.

In this paper, the authors propose an electric power interchange system in collective housing with fuel cells (FCs) and a determination method of operation plan for FCs in the collective housing. In the method, the operation plan for FCs is determined from an evaluation point, primary energy consumption of all houses in the collective housing, based on an enumeration method and particle swarm optimization (PSO) which is one of non-liner optimization methods. In order to examine the validity of the determination method, numerical simulations are carried out for the collective housing model, primary energy consumptions of the model are compared to those of a standard system (without FCs), and the reduction effects are evaluated taking uncertainty into consideration by using 10,000 demand patterns which are represented by 40 observational demand data based on the bootstrap method. In addition, the reduction effects by reducing introduced FCs by half are also evaluated from the point of primary energy consumptions, operating efficiency and running efficiency.

In Japan a feed-in tariff scheme has promoted the utilization of photovoltaic power generators in combination with electric batteries. Although many researchers have assessed the operations of grid-connected photovoltaic systems with electric batteries, little attention has been paid to the impact of the electric batteries' degradation characteristics. This paper presents an evaluation of the operation of a grid-connected photovoltaic/electric battery system in a house from energy saving and economy viewpoints. We constructed a long-term operational optimization model considering the degradation characteristics of the electric battery. This model contains two objective functions: energy saving and operating cost. We set a scenario of power rate and photovoltaic/battery system configurations. In order to reveal the optimal operational strategy of the photovoltaic/battery system, the multi-objective optimization problem was solved. As a result, pareto optimal solutions were obtained, and trade-off relationships between economy and energy saving are presented. In this study, the utilization of the grid-connected photovoltaic system with an electric battery was expected to be effective in an energy saving priority operation.

Various residential cogeneration systems have been studied and developed as parts of distributed energy supply system. A residential fuel cell cogeneration system is composed of three units: a fuel cell unit, a thermal storage unit and a back-up boiler. Those three-parts are necessary to compensate a gap between system output with a rigid heat-to-power ratio and heat and power demand. It is difficult to operate the fuel cell system with frequent starts and stops. The peak thermal load is far greater than the maximum thermal output capacity of the fuel cell unit alone. Generally, a cogeneration system performs effectively when all of the recovered heat was utilized in a day. The common issues in promoting residential fuel cell cogeneration systems are the unit price and size. The size must be reduced for use in apartment houses, which constitute about half of the households in Japan. The objectives of this study are to evaluate the rational hot water tank capacity for individual households in order to provide a guide for hot water tank capacity sizing. Sensitivity analysis on the hot water tank capacity from energy saving and economic viewpoints based on an optimal operational planning problem was carried out. Additionally, residential energy demand profiles with different climate conditions are analysed. Our findings show that, in Japan, commutative duration of hot water demand is about 10% or less of the time in one year, and domestic hot water demand rarely observed above 4 kWh in any quarter of an hour. Annual primary energy consumption and operating costs rarely fell when the hot water tank capacity was 7 kWh or more. Additionally, reducing the hot water tank capacity to 5 kWh had only a small effect on the objective values. This suggests that the hot water tank could be smaller than that currently in use. The daily hot water demand gives an excellent estimation of the maximum stored heat of the hot water tank. It was found that the hot water tank capacity can be estimated from the regression lines, or from the fraction of daily hot water demand.

The main objective of this study is to evaluate energy saving potentials of polymer electrolyte fuel cell cogeneration systems integrated with solar cell and battery installed into residential houses. There exist many alternative operational policies in the above systems, and the mathematical planning approach is taken to derive the rational systems operational solution based on the mixed-integer linear planning method. In the numerical analysis of this study, energy saving characteristics of 12 alternative systems are compared respectively, and as one of main obtained results, it become clear that the energy saving effects to install battery into the systems depend largely on the operational policy of the system together with the evaluation criterion of adverse electricity from the solar cell to the electric grid. In other words, in the case of giving high priority to the self-consumption of electricity from the solar cell within the house, the energy saving potential to install battery increases largely, but few effects are noticed in other cases.

Analysis and modeling of electric energy demand is indispensable for power planning, operation, facility investment, and urban planning. Because of recent development of renewable energy generation systems oriented for households, there is also a great demand for analysing the electricity usage and optimizing the way to install electricity generation systems for each household. In this study, employing statistical techniques, a method to model daily consumption patterns in households and a method to extract a small number of their typical patterns are presented. The electricity consumption patterns in a household is modeled by a mixture of Gaussian distributions. Then, using the symmetrized generalized Kullback-Leibler divergence as a distance measure of the distributions, typical patterns of the consumption are extracted by means of hierarchical clustering. The statistical modeling of daily consumption patterns allows us to capture essential similarities of the patterns. By experiments using a large-scale dataset including about 500 houses' consumption records in a suburban area in Japan, it is shown that the proposed method is able to extract typical consumption patterns. © 2010-2012 IEEE.

The operational degree of freedom in a residential energy system has dramatically increased recently because an energy system tends to be composed of many devices including some energy buffers. It is challenging to construct the framework of the operational planning problems, which update the plan based on the short-span predictions. This framework has to plan the operation before uncertain demand and PV output are realized; hence the plan is based on ex-ante decisions. The aim of this paper is to apply a stochastic programming framework to the operational planning of a residential energy system considering the prediction, and is to show basic directions for the planning. The residential energy system includes a fuel cell cogeneration system with a hot water tank, the photovoltaic system with an electrical battery. The parameters of the numerical experiments are three kinds of temporal precision and the number of predicted scenarios. The operation means the timing of the fuel cell's start-stop, and the stored energy levels of the battery and the hot water tank. As a result, the expected value based on scenarios was 21% greater than the minimized value based on perfect information in terms of daily primary energy consumption. The number of predicted scenarios was reasonable around 10 at 15-min temporal precision, because the great number of input scenarios made a decision not to operate the fuel cell cogeneration system for the entire day, and needed a great deal of the computational time.

In this study, a renewal planning problem of energy supply system is formulated as a large scale mixed-integer linear programming problem, in which the objective function to the minimized is the average value of annual total cost during system's evaluation period. By adopting the programming language AMPL and CPLEX solver, a numerical study is carried out for a hospital, where electrical(e.g., heat pump) and gas(e.g., gas engine cogeneration) systems are compared together with arbitrary combination one, which is composed of electrical and gas driven pieces of equipment, by focusing particularly on the influence of initial system's difference. The main results obtained are as follows: (a) If the initial system is gas one, it is better to renew it to the electrical one as soon as possible due to relatively low energy efficiency of gas utilizing pieces of equipment, the high price of gas input energy and so on. (b) If the initial system is electrical one, the optimal renewal year becomes relatively later year, because it is economically better to use the initially installed high efficiency system as long as possible. (c) Theoretically, the arbitrary combination system is of course the best renewal one. However, there is no economic difference between the arbitrary combination system and the electrical one. © 2013 The Japan Society of Mechanical Engineers.

When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, the authors aim to extract basic time-series demand patterns from two kinds of measured demand (electricity and domestic hot water), and also aim to reveal effective demand patterns for primary energy saving. Time-series demand data are categorized with a hierarchical clustering method using a statistical pseudo-distance, which is represented by the generalized Kullback-Leibler divergence of two Gaussian mixture distributions. The classified demand patterns are built using hierarchical clustering and then a comparison is made between the optimal operation of a polymer electrolyte membrane fuel cell co-generation system and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the appropriately built demand profiles. Our results show that basic demand patterns are extracted by the proposed method, and the heat-to-power ratio of demand, the amount of daily demand, and demand patterns affect the primary energy saving of the co-generation system. © 2013 by the authors
licensee MDPI, Basel, Switzerland.

Considering recent trends in energy technology development, consumer's energy demand could be influenced by the renewable energy supply in any way. A simple extension of pattern sequence-based forecasting (PSF) enables us to predict demand curves based on the correlated bidimensional time-series by using co-occurrence patterns of energy supply and demand. However, prediction accuracy of PSF deeply depends on the clustering result, which is used for pattern matching. In this paper, a promising clustering method based on nonnegative tensor factorization is applied for this task and evaluated experimentally from the viewpoint of prediction accuracy.

Electricity consumption in households varies dependent on a lot of possible reasons such as lifestyle, family configuration, and weather. It is of great importance to optimize the electricity generation system to install for each household. In our previous work, we proposed a clustering approach for extracting a small number of basic electricity consumption patterns in a household. In this study, we apply the method to a larger dataset with many households. In the previous work, we determined the number of basic patterns in a heuristic manner. In this work, we use gap statistics to automatically determine an appropriate number of basic patterns, and we obtained a reasonable result on a large-scale data.

This paper proposes a computation method to determine a optimal planning and operation of residential Photovoltaic Power Generation systems (PV systems) and Battery Energy Storage Systems (BESS) based on the weather forecast to each customer. In this proposed method, the optimal planning and operation are decided by changing the amount of charge in the BESS in midnight when the electric rate is cheap. This proposed method can determine the optimal planning (determination of optimal rating capacities of PV and BESS) and the optimal operation (determination of three values of the amount of charge at midnight on sunny day, cloudy day and rainy day) so as to minimize the annual cost of the household. In order to check the effectiveness of the proposed method, the optimal planning and the operation are compared on the data of two years.

Residential energy demand varies widely in terms of time-series behaviors, amounts consumed between families, and even within one family. Residential energy demand profiles have a high degree of uncertainty in their essentials because the demand profile is entirely based on the occupant-driven load. When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, in order to clarify rational energy system selection guidelines and rational operation strategies, authors aim to extract basic demand time-series patterns from two kinds of measured demand (electricity and domestic hot water), measured over 26307 days of data in Japan. Authors also aim to reveal the relationship between primary energy consumption and demand patterns. Demand time-series data are categorized by means of a kind of "unsupervised" learning, which is a hierarchical clustering method using a statistical pseudo-distance. The statistical pseudo-distance is calculated from the generalized Kullback-Leibler divergence with the Gaussian mixture distribution fitted to the demand time-series data. The classified demand patterns are built using a hierarchical clustering and then a comparison is performed between the optimal operation of the two systems (a polymer electrolyte membrane fuel cell co-generation system, and a CO2 heat pump system) and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the demand profiles appropriately built. Our results show that basic demand patterns are extracted by the proposed method. The demand patterns, the amount of daily demand and heat-to-power ratio of demand affect the primary energy reduction ratio of the polymer electrolyte membrane fuel cell co-generation system.