Achieving the economical and stable operation of Multi-microgrids (MMG) systems is vital. However, there are still some challenging problems to be solved. Firstly, from the perspective of stable operation, it is necessary to minimize the energy fluctuation of the main grid. Secondly, the characteristics of energy conversion equipment need to be considered. Finally, privacy protection while reducing the operating cost of an MMG system is crucial. To address these challenges, a Data-driven strategy for MMG systems with Shared Energy Storage (SES) is proposed. The Mixed-Attention is applied to fit the conditions of the equipment, additionally, Multi-Agent Soft Actor-Critic(MA-SAC) and (Multi-Agent Win or Learn Fast Policy Hill-Climbing)MA-WoLF-PHC are proposed to solve the partially observable dynamic stochastic game problem. By testing the operation data of the MMG system in Northwest China, following conclusions are drawn: the R-Square (R2) values of results reach 0.999, indicating the neural network effectively models the nonlinear conditions. The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746.5kW in 24 hours and achieve a cost reduction of 16.21% in the test. Finally, the superiority of the proposed algorithms is verified through their fast convergence speed and excellent optimization performance.

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.