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• Aggregator model of demand-side energy resources are proposed. • Procurement cost is minimized for day-ahead market and an imbalance pricing system using DSRs. • The impact of PV forecast error on procurement cost is found to be largest. • Control saturates as the proportion of controllable demand-side energy resources increases. We propose a tool for aggregating a large number of buildings' electricity demand and photovoltaic (PV) generation, controllable heat pump water heaters (HPWH) and electric vehicles (EV) to minimize the procurement cost in a market transaction consisting of a day-ahead market and an imbalance pricing system. The novelty of this research is that the aggregator's strategy can be modeled including various uncertainties, and the value of multiple demand-side resources (DSRs) can be quantified within realistic market rules. The model shows that the ability to control the imbalance through real-time control of DSRs has significant economic benefits. In the control of HPWH hot water storage operation, the procurement cost can be reduced by 13% compared to nighttime operation when the same number of units are installed as the aggregate target rooftop PV and residential demand. The electric vehicle storage battery charge/discharge control can reduce the total cost by 38% compared to nighttime charging under the same conditions. The impact of the PV-prediction error on the procurement cost was found to be generally larger than those of other parameters, and the market-price-prediction error was found to be considerably large during EV charging and discharging optimization. Furthermore, the simulation confirms that the value of control saturates as the proportion of controllable DSRs increases compared to uncontrollable demand and generation. [ABSTRACT FROM AUTHOR]

This study examines the economic effects of demand response management using heat pump water heaters (HPWHs) and batteries with residential photovoltaic (PV) systems. A model was developed for optimizing the operation of HPWHs using 24 h advance forecasting of electricity consumption and PV generation. The goal was to minimize electricity charges by optimizing the operation of actual HPWHs. Annual electricity consumption data from 357 houses fitted with both PV and an HPWH were used in the study. Optimal operation of an HPWH was shown to yield average cost savings of $58 or 7% of the electricity cost in conventional operation. HPWH electricity consumption was reduced by 11% on average, compared with conventional operation, because running the heat pumps during the daytime increases their efficiency. A simple modification, in which the start of HPWH operation depended on the level of available sunlight, yielded a comparable average saving of $53 per year. A comparison was made with the use of battery systems. The self-consumption ratio increased from 32% in conventional operation to 45% by optimizing the operation of the HPWH, which was similar to the effect of introduction of a 2–4 kW h battery. The simulation results show that the demand response of HPWHs contributes to the expansion of self-consumption of PV systems and energy conservation. [ABSTRACT FROM AUTHOR]

This study assesses the effects of sending home energy reports utilizing the Home Energy Management System (HEMS) data to more than 1600 households in Japan. The treatment effect was verified using a panel data regression random effects model comparing the electricity consumption of a treatment group to which the report was sent with that of a control group that was not sent. The report was effective in winter and led to a 3.4% reduction in electricity consumption compared to the previous year in the average household. A further reduction of 5.4% for the households with higher electricity consumption for whom a significant reduction of 11.4% in the use of space heating was also observed. Although the treatment effect was not significant in summer for the average household, larger households reduced consumption by an overall average of 2%, with reductions of 6.8% and 7% in terms of space cooling and hot water use, respectively, from the previous month. In contrast, smaller households increased their space cooling consumption by more than 10% on average, which might be considered an undesirable boomerang effect. The accumulative treatment effect in a detached house group was also confirmed. Additionally, an accumulative two-year winter consumption reduction of 7.5% demonstrated the effectiveness of continual intervention. [ABSTRACT FROM AUTHOR]

This paper examines seasonal panel data analysis at a higher resolution using commercial home energy management system data to identify the parameters that determine Japanese household electricity consumption. The electricity consumption data from 532 detached houses and 208 apartment-style houses is aggregated by use and time period and regressed with explanatory variables to indicate house and occupant attributes. Predictable significant impact factors such as outdoor temperature, floor area, household size, presence of a central air conditioning system, and a variety of appliances are estimated quantitatively. The differences due to appliance possession are estimated as 844 kWh/year for a water server, 885 kWh/year for an additional refrigerator, 491 kWh/year for a portable humidifier, and 443 kWh/year for an air purifier. We found previously unknown correlation between variables such as central air conditioning and hot water demand through this model. We also identified several important parameters that explain electricity demand by data collecting at different sites and time periods. Obtained knowledge will contribute to promoting further energy efficiency program in the Japanese residential sector. Households in our study were newer houses and additional data are required for a more stable and reliable model. In addition, a reverse estimation of household attributes from electricity load dynamics is an issue for future study. [ABSTRACT FROM AUTHOR]

Demand-side energy management systems (EMSs) are expected to be used as a method for further balancing the supply and demand of power systems with high levels of renewable energy generation, such as photovoltaic (PV) power. Energy demand and solar radiation must be predicted to realize the optimal operation scheduling of demand-side appliances by a home energy management system (HEMS), including heat pump water heaters, PV systems, and solar-powered water heaters. This paper presents a HEMS model that controls a residential battery system connected to a rooftop PV system taking into account of energy load and PV generation forecast errors. Forecast accuracy is verified by real HEMS data from 160 households; the impact of forecast errors on household economics is examined. Furthermore, the contribution to the entire power system is examined using the model under the dynamic pricing system. [ABSTRACT FROM AUTHOR]

Japan's energy consumption in 2018 was about 2.5 times that in 1975, with the increase in the household sector being the largest at 28%. Most of primary energy is still fossil fuel, and it is urgent to reduce energy consumption in the household sector. The purpose of this paper was to identify ways to reduce household energy consumption without compromising the quality of life in residence. However, the reduction methods vary by region, building specifications, household type, equipment specifications, season, and weather. The value of this paper is based on a systematic analysis of home energy management systems (HEMS) data from about 50,000 households under various conditions. We are analyzing ways to reduce energy consumption. Few studies have analyzed this much back-up data, which is likely to lead to a reduction in CO2 emissions across the household sector. To explore ways to reduce energy consumption in this sector, the company has introduced and provided services for home energy management systems (HEMS) since 2011 and is currently collecting HEMS data for up to 50,000 households. In order to grasp the actual state of energy consumption in each household, HEMS data are systematically analyzed, necessary conditions for energy reduction and self-sufficiency rate (SSR) improvement are analyzed, and energy consumption under certain conditions is estimated using storage batteries (SB) and heat pump water heaters (HPWH). In addition, energy consumption was investigated by actual measurement and simulation for several hundred households. Since power generation and consumption vary greatly depending on the region, building specifications, household type, equipment specifications, season, weather, etc., it is necessary to analyze these factors systematically. As a conclusion, in order to improve SSR, it is necessary to (1) reduce surplus power consumption and energy consumption of heat pump water heaters (HPWHs), (2) increase solar power generation, and (3) increase the size of SB. This study contributes to the spread of advanced housing and the reduction of CO2 emissions in the household sector. [ABSTRACT FROM AUTHOR]

The increase in the number of electric vehicles (EVs) has led to increased global expectations that the application of this technology may result in the reduction of CO2 emissions through the replacement of conventional petrol vehicles and ensure the flexibility of power systems such as batteries. In this paper, we propose a residential demand response (DR) evaluation model that considers the degradation mechanism of the EV battery and examines the effective battery operation. We adopted the already-proposed NiMnCo battery degradation model to develop an EV DR evaluation model. In this model, the battery operation is optimized to minimize the electricity and degradation costs affected by ambient temperature, battery state of charge (SOC), and depth of discharge. In this study, we evaluated the impact of the relevant parameters on the economics of the DR of EV batteries for 10 all-electric detached houses with photovoltaic system assuming multiple EV driving patterns and battery (dis)charging constraints. The results indicated that the degradation costs are greatly affected by the SOC condition. If a low SOC can be managed with a DR strategy, the total cost can be reduced. This is because the sum of the reduction of purchased cost from the utility and calendar degradation costs are higher than the increase of the cycle degradation cost. In addition, an analysis was conducted considering different driving patterns. The results showed that the cost reduction was highest when a driving pattern was employed in which the mileage was low and the staying at home time was large. When degradation costs are included, the value of optimized charging and discharging operations is more apparent than when degradation costs are not considered. [ABSTRACT FROM AUTHOR]

The environmental impact of battery electric vehicles (BEVs), which are an integral part of climate change mitigation measures, depends on charging strategies, such as uncontrolled charging, controlled charging (V1G), and discharging (V2G). However, the impact is unclear because power system operation involves the cost of carbon dioxide (CO 2) emissions and flexibility requirements for imbalances caused by renewable energies. To clarify the robustness of the environmental impact of BEVs, we assessed the interactions between CO 2 prices and flexibility requirements in Japan in 2030 using a production cost model. The results showed that uncontrolled charging increases CO 2 emissions, whereas V1G or V2G reduces them. Further, the impact of BEVs was non-monotonic with respect to CO 2 prices, implying that policymakers should carefully evaluate CO 2 prices to achieve decarbonization as BEVs do not always reduce CO 2 emissions. This study provides useful insights for policymakers in countries with low-cost, inflexible, and high-emission power plants. [ABSTRACT FROM AUTHOR]

We propose a model for the integration of electric vehicles (EVs) into the grid power system in Japan. The potential of the switchover from conventional vehicles to EVs and the incurred charging loads for the EV fleet were evaluated based on the results of a Japanese road traffic census. Furthermore, an EV battery operation model was incorporated into the production cost analysis model, which is capable of determining the optimal electricity supply and demand, considering the existing interconnector power flows. The potential economic and environmental contributions of EV charge and discharge controls, with the ultimate goal of realizing the introduction of a massive renewable energy source in the future, were also evaluated. We found that EVs can greatly contribute to expanding the use of renewable energy and reducing system cost by charging and discharging not only at the owner's home but also at his/her workplace. [ABSTRACT FROM AUTHOR]

The evaluation of trade-offs between technologies and policies for mitigation of environmental problems requires a systematic investigation of effects over the entire region under consideration. When attempting to model such large complex systems, issues such as usability, maintenance, and computing efficiency often become major modeling barriers. In this work a software prototype for integrating the services of computational models over the Internet, called DOME (distributed object-based modeling environment) is used to facilitate the construction of virtual Tokyo a simulation platform for evaluating holistically the trade-offs between various technologies for reducing the emissions of greenhouse gases. In making steps toward this ultimate goal, two models have been developed that use data defining spatial land-use distributions and the flows of goods expressed as an input-output table to provide information on the spatial and temporal characteristics of an urban region. Integrated, these models form a preliminary virtual Tokyo model when applied to Tokyo-specific databases. Given this platform, process models are applied to examine the effectiveness of using photovoltaic (PV) modules on the demand side to reduce conventional electric power generation and, thereby, also reduce carbon dioxide emissions. The results of introducing PV modules on the rooftops of buildings in Tokyo under various installation conditions are presented as a working example of the prototype. For full deployment on usable rooftop space, PV power generation could reduce carbon dioxide emissions from electric power generation by more than 12%. Future work will use the same methods as presented in this paper to examine cost, a critical determinant in the actual feasibility of PV module installation. [ABSTRACT FROM AUTHOR]

To develop a decarbonized society, two contradictory requirements must be met: (1) reducing energy demand and (2) creating flexibility in energy demand in order to respond to fluctuations in renewable electricity generation. To help meet these requirements, conventional energy efficiency studies should be extended to incorporate "energy demand science." This paper presents a definition of "energy demand science" and then reviews the related history and research questions of energy demand science in the context of the residential sector. It then examines three key areas that must be integrated into the next-generation energy demand science: (1) energy demand measurement with detailed granularity and analysis using cutting-edge technology, (2) energy demand modeling that helps clarify the formation mechanism of energy demand, and (3) identification of the factors that influence people's decision making, which represents typical human-dimension research. Energy demand science consists of technical, human, natural environment, demographic, and land-use dimensions, and their integration is key for the establishment of a decarbonized society. • Energy demand must be reduced and have the flexibility to establish decarbonization. • Knowledge integration is needed for planning, design, and operation of energy systems. • As energy demand data becomes more granular, data analysis methods must do the same. • High spatiotemporal modeling and consideration of occupant behavior is necessary. • Identification of factors influencing behavior is important to a human-dimension study. [ABSTRACT FROM AUTHOR]

SUMMARY In recent years, there has been a growing interest in ecofriendly technologies such as residential photovoltaic (PV) systems and electric vehicles (EVs). PV systems and EVs will contribute to reducing CO2 emissions in the residential sector and the transportation sector, respectively. In spite of that, high penetration of PV systems into the power grid can cause grid voltage and frequency stability problems. Also, the growth of the EV market will create an extra electricity load (for charging the EV fleet), leading to an increase in power utility fuel costs. In this research, we proposed the usage of the PV-EV system as a method of mitigating the impact the spread of residential PV systems and EV on the power grid. We built an PV-EV system simulation model and investigated the PV-EV system contribution to the balance of power supply and demand and to reducing the total cost of the household under different electricity pricing scenarios. We also evaluated the effect of uncertainty in the forecasting of load and PV output on the performance of the PV-EV system. [ABSTRACT FROM AUTHOR]

SUMMARY To assure power system stability when variable photovoltaic (PV) power generation is deployed on a large scale, accurate irradiance forecasting will be important in the near future. A substantial amount of PVs will be generating power at numerous locations in the power service area, and it will be necessary to predict the total PV output for supply/demand balancing control. Therefore, irradiance forecasting accuracy should be verified using the total irradiance at numbers of sites equivalent to areas with large-scale PV deployment. This paper reports an investigation of the accuracy of an approach to area total irradiance prediction based on regional numerical weather prediction data from the Japan Meteorological Agency. The forecast area total irradiance is compared to the assumed total area irradiance for numbers of sites equivalent to large-scale PV deployment. [ABSTRACT FROM AUTHOR]