Microgrid Operation Optimization Strategy Based on CMDP-D3QN-MSRM Algorithm

This paper addresses the microgrid operation optimization challenges arising from the variability in and uncertainty and complex power flow constraints of distributed power sources. A novel method is proposed, based on an improved Dual-Competitive Deep Q-Network (D3QN) algorithm, which is enhanced by a multi-stage reward mechanism (MSRM) and formulated within a Constrained Markov Decision Process (CMDP) framework. First, the reward mechanism of the D3QN algorithm is optimized by introducing a redesigned MSRM, enhancing the training efficiency and the optimality of trained agents. Second, the microgrid operation optimization problem is modeled as a CMDP, thereby enhancing the algorithm’s capacity for handling complex constraints. Finally, numerical experiments demonstrate that our method reduces operating costs by 16.5%, achieves a better convergence performance, and curtails bus voltage fluctuations by over 40%, significantly improving the economic efficiency and operational stability of microgrids.