Optimal Control Method for Microgrid Distributed Generation Based on Multi-Agent Adaptive Decision-Making

In addressing the challenges of strong power generation fluctuation, complex load variation, and dynamic topology changes in microgrid operation, this paper puts forward a proposal for optimal control methodology for distributed generation based on multi-agent adaptive decision-making. The initial conception of the dynamic strategy optimization module is to facilitate online learning and continuous optimization of agent decision-making capabilities. This objective is realized through the construction of a topology correlation matrix, which is based on microgrid operation data. The subsequent generation of preliminary control actions is facilitated by a multi-agent adaptive decision network. Concurrently, a safety optimization model founded upon the barrier Lyapunov function has been developed. The model has been designed to facilitate collaborative correction and safety constraint verification of the preliminary actions, thereby producing final control actions that satisfy global optimization and safe operation requirements. On this basis, an enhanced execution control module is employed, incorporating adaptive ramp rate limitations to calculate the target power reference, achieving high precision and fast tracking of power commands. The findings from both simulation and experimental studies demonstrate that, in complex scenarios such as load fluctuations and topology reconfiguration, the proposed method maintains voltage fluctuations within 1.6 V, achieves frequency recovery within 0.01 s, ensures a rapid power sharing response with a small steady-state error, and improves system operational economy by approximately 8.2% compared to conventional distributed control methods. This enhancement of dynamic adaptability, operational safety, and economic performance of the microgrid is therefore significant.