Multi-Agent Reinforcement Learning-based Two-stage Coordinated Scheduling and Operation Optimization

As the randomness and intermittency brought by the increasing renewable energy sources and emerging loads intensify, traditional scheduling plans and intraday dispatch methods struggle to fully address the source and load fluctuations. Achieving coordinated optimization between economic efficiency and system robustness has become a key research direction. This paper employs the Value-at-Risk (VaR) metric to quantify the potential risks posed by system uncertainties. In the day-ahead scheduling stage, the dispatch objectives include minimizing the day-ahead cost of generating units, interconnection line costs, reserve costs and system risk costs, and reserve availability was verified to ensure the economic efficiency and robustness of day-ahead scheduling plan. In the intraday dispatch stage, a multi-agent real-time intraday dispatch model is proposed, adopting the MAPPO framework. The primary objective is to minimize the redispatch cost of generating units and the penalty costs for curtailing wind, solar power, and load shedding. Finally, the feasibility and effectiveness of the proposed model and methodology are evaluated through a devised IEEE 39-bus case.