A Predictive Control Strategy for Battery Energy Storage Systems to combine Peak Shaving with Primary Frequency Control

Battery energy storage systems (BESS) are one of the key technologies for a successful energy turnaround in Germany. Several studies have shown that they are only economically efficient combining different applications. In this paper, a real-time control strategy is presented, to provide peak shaving for intensive energycustomers to achieve reduced network fees in Germany as the primary application. This application is combined with the ancillary service primary frequency control to optimize the economic efficiency of the battery energy storage system. The performance of the combined applications control strategy is determined by the accuracy of short-term load forecasting, which is done by using an artificial neural network. The objective of this paper is trying to achieve an optimal design of a control strategy for peak shaving and primary frequency control, and the considered constraints include state-of-charge, rated power and power gradient. The control strategy is modelled and simulated using MATLAB Simulink R2015b.