Collaborative Optimization of Multi-microgrids System with Shared Energy
  Storage Based on Multi-agent Stochastic Game and Reinforcement Learning
Yijian Wang Yang Cui Yang Li Yang Xu
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.