Energy Storage Assisted Conventional Unit Load Frequency Control Strategy Using Deep Reinforcement Learning

The traditional load frequency control systems suffer from long response time lag of thermal power units, low climbing rate, and poor disturbance resistance ability. By introducing energy storage participation in secondary frequency regulation and a deep reinforcement learning technique, a new load frequency control strategy is proposed. Firstly, the rules for two operating modes of the energy storage, i.e., adaptive frequency regulation and energy storage self-recovery, are designed. Then, a deep reinforcement learning load frequency controller is designed to dynamically adjust the outputs of the energy storage system and the conventional unit. To improve the exploration efficiency of the deep reinforcement learning algorithm, a random network distillation technique is used. A multi-objective reward function containing an external reward and an additional internal reward is designed. Finally, simulation results show that, compared with the traditional load frequency control strategy, the proposed control strategy can achieve optimal performance in frequency regulation.