Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming

Recently, energy storage systems (ESSs) are becoming more important as renewable and microgrid technologies advance. ESSs can act as a buffer between generation and load and enable commercial and industrial end users to reduce their electricity expenses by controlling the charge/discharge amount. In this paper, to derive efficient charge/discharge schedules of ESSs based on time-of-use pricing with renewable energy, a combination of genetic algorithm and dynamic programming is proposed. The performance of the combined method is improved by adjusting the size of the base units of dynamic programming. We show the effectiveness of the proposed method by simulating experiments with load and generation profiles of various commercial electricity consumers.