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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]

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]

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]