Wei Hongtao, Chang Siyu, Zhang Jiaming
College of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2025 Jan 25;25(3):733. doi: 10.3390/s25030733.
With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids.
随着电力系统复杂性的增加和分布式能源的渗透，传统的电压控制方法难以应对动态变化和复杂工况。虽然现有的深度强化学习（DRL）方法推动了电网控制的发展，但在利用拓扑特征和确保计算效率方面仍存在挑战。为解决这些问题，本文提出一种结合图卷积网络（GCN）和软演员-评论家（SAC）的DRL方法，用于通过切负荷实现电压控制。该方法使用GCN提取电网的高阶拓扑特征，增强状态表示能力，而SAC在连续动作空间中优化切负荷策略，动态调整控制方案以平衡切负荷成本和电压稳定性。IEEE 39节点系统的仿真结果表明，所提方法显著减少了切负荷量，提高了电压恢复水平，并且在处理复杂干扰和拓扑变化时展现出强大的控制性能和鲁棒性。本研究为智能电网中的电压控制问题提供了一种创新解决方案。
