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This article presents an optimal demand response (DR) control for a residential photovoltaic (PV) storage system using energy pricing limits. Unlike the existing literature, both energy buying and selling prices are considered in DR application for reducing the energy cost of the system. A method for determining buying price and selling price limits of the day using day-ahead predictions of energy price, load demand, and PV power profiles is proposed. To further reduce the energy cost of the system subbuying price limit is considered. A rule-based DR control for minimizing the energy cost of the system is proposed. The rules are formulated such that energy buying price and selling prices are limited to their corresponding price limits of the day with the flexible day-to-day management of battery energy storage system. For obtaining optimal results with rule-based control, required control inputs are determined optimally using genetic algorithm. The proposed method is tested in MATLAB. It is observed that energy cost savings of 34.09% and 5.4% are obtained on the day of more PV energy availability and less PV energy availability, respectively, based on the day-ahead operation. Further, the impact of uncertainties on the proposed DR control is discussed for real-time operation of the system.
Manojkumar et al. (Tue,) studied this question.