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

Jiankai Gao, Yang Li (), Bin Wang and Haibo Wu
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Jiankai Gao: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Yang Li: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Bin Wang: State Grid Jining Power Supply Company, Jining 272000, China
Haibo Wu: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Energies, 2023, vol. 16, issue 7, 1-21

Abstract: 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 an MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes an 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 energy 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’s operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency.

Keywords: multi-microgrid; collaborative optimization; multi-agent deep reinforcement learning; automated machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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