Research on Energy Management in Hydrogen–Electric Coupled Microgrids Based on Deep Reinforcement Learning
Tao Shi, Tao Shi, Hangyu Zhou, Tianyu Shi, Tianyu Shi, Minghui Zhang
Abstract
Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion, utilization, and storage technologies with distributed wind and photovoltaic power generation techniques, it is possible to achieve complementary utilization and synergistic operation of multiple energy sources in the form of microgrids. However, the diverse operational mechanisms, varying capacities, and distinct forms of distributed energy sources within hydrogen-coupled microgrids complicate their operational conditions, making fine-tuned scheduling management and economic operation challenging. In response, this paper proposes an energy management method for hydrogen-coupled microgrids based on the deep deterministic policy gradient (DDPG). This method leverages predictive information on photovoltaic power generation, load power, and other factors to simulate energy management strategies for hydrogen-coupled microgrids using deep neural networks and obtains the optimal strategy through reinforcement learning, ultimately achieving optimized operation of hydrogen-coupled microgrids under complex conditions and uncertainties. The paper includes analysis using typical case studies and compares the optimization effects of the deep deterministic policy gradient and deep Q networks, validating the effectiveness and robustness of the proposed method.