【摘要】 针对主动配电网多主体协同优化调度面临的隐私保护和信任不足问题，提出了基于多主体博弈和联邦强化学习算法的日前-日内协同优化调度策略。首先，建立含分布式电源运营商、主动配电网运营商、储能运营商等不同主体的协同优化架构。在该架构下，提出了以最大化综合收益和最小化调整量为目标的多主体日前-日内优化调度模型。然后，在日前阶段采用考虑有限理性的信任演化博弈方法得到日内调度计划，在日内阶段则采用联邦自然策略梯度算法进行滚动校正，在满足运行约束的同时，避免了调度过程中产生的隐私信息泄露问题。最后，通过仿真分析验证了所提模型的经济性和算法的有效性。更多还原

【Abstract】 To address the problems of privacy preservation and trust deficiency in multi-agent collaborative optimal scheduling of active distribution networks(ADNs), this paper proposes a day-ahead and intra-day collaborative optimization strategy based on multi-agent game and federated reinforcement learning(FRL). First, a collaborative optimization framework is established,involving various agents such as distributed generator operators, ADN operators, and energy storage operators. Within this framework, a multi-agent day-ahead and intra-day optimal scheduling model is formulated with dual objectives of maximizing overall revenue and minimizing operational adjustment. In the day-ahead stage, a trust evolution game approach considering bounded rationality is employed to generate preliminary scheduling plans, while an intra-day rolling correction mechanism is implemented using a federated natural policy gradient algorithm. This strategy ensures operation constraint compliance and effectively mitigates privacy leakage risks during information exchange. Finally, the economic feasibility of the proposed model and the effectiveness of the algorithm are verified through simulation analysis.更多还原