Optimization of Coordinated Operation of Multi-Microgrid System Based on Multi-Agent Reinforcement Learning Method

With the advancement of renewable energy and distributed power generation technology, microgrids, as a platform for efficient resource integration and management, play a key role in improving energy efficiency, reducing transmission losses and supporting grid stability. Microgrids can not only optimize energy utilization, reduce transmission losses, and improve energy efficiency at the local level, but also support stable operation and reliable power supply through interactions with other power grids. This interaction involves multiple levels such as two-way flow of energy, information exchange, and operational coordination, and has an important impact on enhancing the resilience and disaster prevention capabilities of the power grid. Therefore, studying the optimal operation control of microgrids and the interaction mechanism of microgrids is extremely important to formulate strategies to promote the sustainable development of energy systems. This article discusses the application of SAC algorithm in multi-microgrid systems and highlights its advantages in system performance optimization. Through a series of experiments and analysis, the effectiveness of the SAC algorithm in improving energy utilization efficiency, reducing operating costs, and enhancing system power supply reliability was verified. Especially in dealing with renewable energy supply volatility and load demand uncertainty, the SAC method shows excellent adaptability and flexibility.