Microgrid Energy Management Based on Sample-Efficient Reinforcement Learning

Microgrid is a small power system composed of distributed energy resources, and its energy management strategy (EMS) is of great significance for improving energy utilization efficiency and reducing energy waste. However, due to the complexity and uncertainty of microgrid, the traditional reinforcement learning algorithm has the problem of low sample efficiency when applied to this system. In order to solve this problem, a sample-efficient reinforcement learning algorithm is proposed in this paper. The algorithm trains and optimizes the model by establishing state-action-reward model and interacting with the simulation environment. In addition, the algorithm avoids the overfitting problem by resetting network parameters periodically. Through continuous iterative training, the system can gradually learn the optimal control strategy. The experimental results show that out proposed EMS can achieve efficient energy utilization and stable power supply. Compared with the traditional reinforcement learning algorithm, the proposed algorithm has significantly improved sample efficiency and performance. Therefore, this algorithm has important application value and popularization potential in microgrid energy management.