Deep reinforcement learning method for energy management in fast charging station

Electric vehicle (EV) fast charging stations often experience high peak charging loads and strong fluctuations. Therefore, developing an energy management strategy for the energy storage system is crucial to improve load characteristics and reduce peak power. Addressing these challenges, this paper proposes a fast-charging station energy management strategy based on deep reinforcement learning. Firstly, the load characteristics of the charging station are analyzed. Subsequently, a mathematical optimization model is established, aiming to minimize the daily electricity purchase cost of the fast charging station while considering peak power reduction as a constraint. The control strategy is designed using a deep deterministic policy gradient algorithm. To validate the efficacy of the proposed deep reinforcement learning control strategy, a case study is conducted. The results demonstrate the significant reduction in peak load power, which validates the efficacy of the strategy.