A Cooperative Game-Based Low-Carbon Optimal Operation Strategy for Multi-Microgrids Based on Multi-Agent Deep Reinforcement Learning

Distributed integrated energy microgrids support the low-carbon transition of regional energy systems. However, multi-agent trading among microgrids still faces insufficient cross-market coordination, weak low-carbon incentives, and difficulties in fair benefit allocation. To address these issues, this paper proposes a cooperative-game-based low-carbon optimal operation strategy for multi-microgrids using multi-agent deep reinforcement learning. First, an energy-carbon-green certificate peer-to-peer coordinated trading mechanism and a green-carbon offsetting-based dual-incentive model are developed to link energy exchange, carbon quota adjustment, and green certificate circulation. Second, a Nash bargaining-based cooperative game model is formulated for multi-commodity P2P trading to maximize coalition benefits and ensure a fair allocation of surplus. Finally, the cooperative game is transformed into a Markov decision process, and a centralized training and decentralized execution framework with homogeneous agents is constructed based on the multi-agent soft actor-critic algorithm. Case studies using data from the Yangtze River Delta region of China show that the proposed method achieves a 1.25% optimality gap compared with the centralized MILP benchmark and reduces the coalition operating cost by 8.19% relative to independent operation. The carbon trading costs of the three microgrids are reduced by 37.27%, 40.13%, and 33.82%, respectively, verifying the economic applicability of the proposed method.