Optimal ESS scheduling for peak shaving of building energy using accuracy-enhanced load forecast

This paper proposes an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load forecast method is applied to a real building energy prediction and it is shown that the deep learning-based method leads to an accuracy-enhanced load forecast. Second, an optimization problem is formulated in order to devise an ESS scheduling. In the optimization problem, the objective function and constraints are defined such that the peak load is reduced; the cost for electricity is minimized; and the ESS’s lifetime is elongated considering the accuracy-enhanced load forecast, real-time electricity price, and the state-of-charge of the ESS. For the purpose of demonstrating the effectiveness of the proposed ESS scheduling method, it is implemented using a real building load power and temperature data. The simulation results show that the proposed method can reduce the peak load and results in smooth charging and discharging, which is important for the ESS lifetime.