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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]

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]