Coordination for Multi-Energy Microgrids Using Multi-Agent Reinforcement Learning

Multi-energy microgrids (MEMGs) have significant potential to offer high energy utilization efficiency and system flexibility. The coordination of these MEMGs poses challenges due to the various system dynamics and uncertainties and the need to preserve privacy. This paper proposes a double auction (DA) market-based coordination framework. As such, MEMGs can not only schedule their own energy components but also trade energy with others in DA market. After that, we formulate this problem as Markov games and propose a multi-agent reinforcement learning method by making use of the DA market public information to enhance the stability with privacy perseverance. Case studies involving a real-world scenario validate the superior performance of the proposed method in reducing both energy costs and carbon emissions.