Recently, energy management systems (EMSs) and storage equipment have become increasingly important for optimizing operating schedules to reduce operating costs. Although many previous studies aimed to optimize the operating schedule of energy systems, some considered only perfect predictions for demand and photovoltaic (PV) power generation. In practice, however, predicted values are often different from actual phenomena. Thus, a practical method that can specify how to control each component under the uncertainty is needed. In this paper, we proposed a new optimization strategy to handle the uncertainty and called it the two-time steps recalculation strategy (TtsR). TtsR proposes a framework for problem formulation and functions in combination with an optimization method. Epsilon differential evolution (εDE) was adopted to handle efficiency nonlinear and constraint conditions. TtsR was compared to the all-time steps recalculation strategy (AtsR), which is considered an ideal strategy because it can obtain accurate solutions. The results showed that TtsR required lower computational time than AtsR to obtain a quasi-optimal solution under conditions of unpredicted changes in the power generation and demand; meanwhile, TtsR was able to maintain computational accuracy.
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