用神经微分方程统一预测与决策，提升电网抗灾能力。
Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
- 构建决策感知的神经微分方程模型，同步优化停电预测与干预策略。
- 在真实和合成数据上，停电预测一致性提升37%，电网韧性显著增强。
- 适合电力系统韧性管理、灾害响应优化的研究者与工程师。
野火和飓风等极端灾害对电力系统威胁加剧，引发大范围停电并破坏关键服务。近年来，预测-再优化方法在电网运行中日益流行，即先生成系统功能预测，再用于下游决策。然而，这种两阶段方法常导致预测目标与优化目标不一致，造成资源分配不佳。为此，本文提出预测-全量-全局优化（PATOG）框架，将停电预测与全局优化干预相结合。核心是全局决策导向（GDF）神经微分方程模型，在捕捉停电动态的同时以决策意识优化韧性策略。相比传统方法，该模型实现时空一致的决策，同时提升预测准确性和运营效率。在合成与真实数据集上的实验表明，停电预测一致性显著提高，电网韧性得到明显增强。
原文摘要 · Abstract (English)
Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services. Recently, predict-then-optimize approaches have gained traction in grid operations, where system functionality forecasts are first generated and then used as inputs for downstream decision-making. However, this two-stage method often results in a misalignment between prediction and optimization objectives, leading to suboptimal resource allocation. To address this, we propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions. At its core, our global-decision-focused (GDF) neural ODE model captures outage dynamics while optimizing resilience strategies in a decision-aware manner. Unlike conventional methods, our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency. Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.
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