  The implementation of a multi-microgrid (MMG) system with multiple renewable
energy sources enables the facilitation of electricity trading. To tackle the
energy management problem of a MMG system, which consists of multiple renewable
energy microgrids belonging to different operating entities, this paper
proposes a MMG collaborative optimization scheduling model based on a
multi-agent centralized training distributed execution framework. To enhance
the generalization ability of dealing with various uncertainties, we also
propose an improved multi-agent soft actor-critic (MASAC) algorithm, which
facilitates en-ergy transactions between multi-agents in MMG, and employs
automated machine learning (AutoML) to optimize the MASAC hyperparameters to
further improve the generalization of deep reinforcement learning (DRL). The
test results demonstrate that the proposed method successfully achieves power
complementarity between different entities, and reduces the MMG system
operating cost. Additionally, the proposal significantly outperforms other
state-of-the-art reinforcement learning algorithms with better economy and
higher calculation efficiency.
