Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management

• URL: http://arxiv.org/abs/2502.18321v2
• Date: Fri, 21 Mar 2025 15:16:16 GMT
• Title: Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
• Authors: Shuyi Chen, Ferdinando Fioretto, Feng Qiu, Shixiang Zhu,
• Abstract summary: We propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions.<n>Our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency.<n>Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.
• Score: 50.34345101758248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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