用强化学习优化电网拓扑，降低损耗且防崩溃
Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control
- 引入掩码拓扑动作空间，让智能体自主探索降损策略
- 20种场景下均降低电力损耗，有效防止电网黑启动
- 适合电网自动化与能源系统智能管理研究者参考
随着分布式能源生产者（prosumers）增多及清洁能源需求上升，电力系统管理日益复杂，亟需创新方法保障稳定与效率。本文提出一种新型无模型强化学习框架，无需先验知识即可优化电网运行。通过引入掩码拓扑动作空间，结合状态逻辑指导行动选择，使智能体在20个不同场景的5变电站仿真环境中自主探索降损策略。实验表明，该方法在保持电网稳定性、预防潜在黑启动的同时，持续降低功率损耗。结果证明，动态观测形式化与对手式训练相结合具有显著有效性，为现代能源系统的自主管理提供可行路径，或可成为该领域基础模型的构建基础。
原文摘要 · Abstract (English)
The increasing complexity of power grid management, driven by the emergence of prosumers and the demand for cleaner energy solutions, has needed innovative approaches to ensure stability and efficiency. This paper presents a novel approach within the model-free framework of reinforcement learning, aimed at optimizing power network operations without prior expert knowledge. We introduce a masked topological action space, enabling agents to explore diverse strategies for cost reduction while maintaining reliable service using the state logic as a guide for choosing proper actions. Through extensive experimentation across 20 different scenarios in a simulated 5-substation environment, we demonstrate that our approach achieves a consistent reduction in power losses, while ensuring grid stability against potential blackouts. The results underscore the effectiveness of combining dynamic observation formalization with opponent-based training, showing a viable way for autonomous management solutions in modern energy systems or even for building a foundational model for this field.
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