图强化学习驱动的主动配电网动态重构与无功补偿协同优化方法OA

To address the challenges of security,stability,and renewable energy integration caused by high-penetration distributed generators in active distribution networks,this study proposes a multi-time-scale collaborative optimization method for active distribution network dynamic reconfiguration and reactive power compensation based on graph reinforcement learning.The method aims to overcome the limitations of traditional approaches in handling high-dimensional mixed-integer nonlinear stochastic optimization problems,such as high parameter sensitivity,low computational efficiency,and insufficient utilization of grid topological information.A two-stage collaborative optimization model is constructed,integrating a stepwise renewable energy curtailment strategy to achieve fine-grained control of both slow-and fast-time-scale devices.Simulation results on a modified IEEE 3 3-node system demonstrate that the proposed method improves the reward function value by 78.17％compared to the original network,while reducing network losses and voltage deviations by 56.24％and 44.48％,respectively.The findings indicate that the method enables deep coupling of dynamic reconfiguration and reactive power compensation without requiring precise grid parameters,leveraging graph-structured features and multi-time-scale coordination mechanisms.This provides a theoretically innovative and practically viable solution for high-renewable-penetration scenarios,effectively balancing operational efficiency,voltage stability,and renewable energy utilization.