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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]

• 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 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]

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]

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]

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]