Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm

However, due to the inherent uncertainty of renewable energy, the integration of multiple renewable energy sources in the form of microgrid (MG) has played a significant role in promoting the consumption of renewable energy [3, 4, 5]. As technology advances, connecting multiple microgrids (MGs) within the same power distribution area can unlock the potential of various flexible resources, enabling the complementary utilization of multi-microgrid (MMG) energy [6]. In addition, this approach further promotes the consumption of various renewable energy sources, which has emerged as a new trend in development [7, 8]. However, the energy interaction between multiple MGs involves complex transaction relationships, leading to significant challenges in system regulation. In this case, it is of great significance to investigate the collaborative optimal dispatch of MMG with electric energy interaction to fully exploit the potential of renewable energy sources and ensure efficient system regulation. Existing research has made significant progress in addressing the complexity of managing MMG energy. Ref. [9] proposes optimal scheduling of MMG based on federated learning and reinforcement learning.