用强化学习优化电网拓扑，提升复杂环境下的运行效率。
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
- 基于强化学习构建电网拓扑动态调整策略
- 通过基准竞赛数据对比验证方法有效性
- 适合电力系统智能化研究者参考
随着可再生能源接入比例上升，电力系统运行日益复杂，亟需更灵活的控制策略。强化学习（RL）因其在动态不确定环境中增强决策能力的潜力，成为电力网络控制（PNC）的重要方向。L2RPN竞赛提供了标准化基准与问题定义，推动了基于RL方法的快速发展。本文系统综述了强化学习在电网拓扑优化中的应用，对现有技术进行分类，分析关键设计选择，并揭示当前研究的空白。此外，通过对比实验评估常见RL方法的实际效果，提供实践洞察。本综述旨在整合已有成果，明确开放挑战，为未来基于强化学习的电力系统优化研究奠定基础。
原文摘要 · Abstract (English)
Power grid operation is becoming increasingly complex due to the rising integration of renewable energy sources and the need for more adaptive control strategies. Reinforcement Learning (RL) has emerged as a promising approach to power network control (PNC), offering the potential to enhance decision-making in dynamic and uncertain environments. The Learning To Run a Power Network (L2RPN) competitions have played a key role in accelerating research by providing standardized benchmarks and problem formulations, leading to rapid advancements in RL-based methods. This survey provides a comprehensive and structured overview of RL applications for power grid topology optimization, categorizing existing techniques, highlighting key design choices, and identifying gaps in current research. Additionally, we present a comparative numerical study evaluating the impact of commonly applied RL-based methods, offering insights into their practical effectiveness. By consolidating existing research and outlining open challenges, this survey aims to provide a foundation for future advancements in RL-driven power grid optimization.
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