用机器学习加速电力线路停电决策，降低火灾风险。
Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
- 结合机器学习与电力系统知识，快速生成停电方案
- 在加州仿真系统上比传统方法快且解质量高
- 适合需要高频决策的电网防火应用场景
为降低极端野火点火风险，电力公司会切断高风险区域的输电线路。最优断电（OPS）问题通过优化线路通断状态，在减少负荷损失的同时控制火灾风险。该问题属于计算复杂的混合整数线性规划（MILP），需在运行中快速频繁求解。针对特定电网，不同OPS实例具有相同结构但参数（如火灾风险、负荷、可再生能源出力）不同，这为利用机器学习挖掘共性模式提供了可能。本文提出一种机器学习引导框架，通过扩展已有ML求解MILP的方法，并融入领域知识（如通电/断电线路数量限制），快速生成高质量断电决策。在基于加州的大规模合成测试系统上，所提方法在速度和解质量上均优于传统优化方法。
原文摘要 · Abstract (English)
To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to manage wildfire ignition risks through de-energizations while reducing load shedding. OPS problems are computationally challenging Mixed-Integer Linear Programs (MILPs) that must be solved rapidly and frequently in operational settings. For a particular power system, OPS instances share a common structure with varying parameters related to wildfire risks, loads, and renewable generation. This motivates the use of Machine Learning (ML) for solving OPS problems by exploiting shared patterns across instances. In this paper, we develop an ML-guided framework that quickly produces high-quality de-energization decisions by extending existing ML-guided MILP solution methods while integrating domain knowledge on the number of energized and de-energized lines. Results on a large-scale realistic California-based synthetic test system show that the proposed ML-guided method produces high-quality solutions faster than traditional optimization methods.
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