Deep reinforcement learning‐based optimal data‐driven control of battery energy storage for power system frequency support
Ziming Yan, Yan Xu, Yu Wang, Xue Feng
Abstract
A battery energy storage system (BESS) is an effective solution to mitigate real‐time power imbalance by participating in power system frequency control. However, battery aging resulted from intensive charge–discharge cycles will inevitably lead to lifetime degradation, which eventually incurs high‐operating costs. This study proposes a deep reinforcement learning‐based data‐driven approach for optimal control of BESS for frequency support considering the battery lifetime degradation. A cost model considering battery cycle aging cost, unscheduled interchange price, and generation cost is proposed to estimate the total operational cost of BESS for power system frequency support, and an actor–critic model is designed for optimising the BESS controller performance. The effectiveness of the proposed optimal BESS control method is verified in a three‐area power system.