Research on microgrid resilience in highway service areas based on federated multi-agent deep reinforcement learning

针对频繁极端天气事件对高速公路服务区微电网系统安全稳定运行带来的严峻挑战,本文提出了基于联邦多智能体深度强化学习(FMADRL)的综合微电网韧性优化方法。首先,通过将生成对抗网络(GAN)与蒙特卡洛模拟整合,构建了一个高精度极端天气场景生成模型。通过融合长期和短期气候数据,生成了1个时空相关的情景样本,有效捕捉极端事件的动态特征。其次,设计了一种基于联邦学习架构的多智能体协作调度机制,在保护数据隐私的同时,高效优化五个分布式服务区微电网。 随后,引入了涟漪扩散算法(RSA),建立了多目标优化框架,在经济效率、可靠性和响应等维度上产生了000个帕累托最优解,确保了稳健的决策。 最后,大规模模拟实验表明,该方法在综合系统韧性指标上得分为285.88,平均故障恢复时间从3.46分钟缩短至6.8分钟,年运营成本降低4.69%(相当于3.3万日元),实现年碳排放减少747吨。这种方法为增强分布式微电网在极端天气事件中的韧性提供了创新解决方案。To address the severe challenge posed by frequent extreme weather events to the safe and stable operation of expressway service area microgrid systems, this paper proposes a comprehensive microgrid resilience optimization method based on Federated Multi-Agent Deep Reinforcement Learning (FMADRL). First, a high-precision extreme weather scenario generation model is constructed by integrating Generative Adversarial Network (GAN) and Monte Carlo Simulation. By fusing long-term and short-term climate data, one spatio-temporally correlated scenario sample is generated, which effectively captures the dynamic characteristics of extreme events. Second, a multi-agent collaborative scheduling mechanism based on federated learning architecture is designed, which efficiently optimizes five distributed service area microgrids while protecting data privacy. Subsequently, the Ripple Spread Algorithm (RSA) is introduced to establish a multi-objective optimization framework, generating 000 Pareto optimal solutions across dimensions including economic efficiency, reliability and response to ensure robust decision-making. Finally, large-scale simulation experiments show that the proposed method scores 285.88 on the comprehensive system resilience index, reduces the average fault recovery time from 3.46 minutes to 6.8 minutes, cuts the annual operating cost by 4.69% (equivalent to 33,000 Japanese Yen), and achieves an annual carbon emission reduction of 747 tons. This method provides an innovative solution for enhancing the resilience of distributed microgrids in the face of extreme weather events.