📝 SummarXiv

Abstract

System restoration is critical for power system resilience, nonetheless, its growing reliance on artificial intelligence (AI)-based load forecasting introduces significant cybersecurity risks. Inaccurate forecasts can lead to infeasible planning, voltage and frequency violations, and unsuccessful recovery of de-energized segments, yet the resilience of restoration processes to such attacks remains largely unexplored. This paper addresses this gap by quantifying how adversarially manipulated forecasts impact restoration feasibility and grid security. We develop a gradient-based sparse adversarial attack that strategically perturbs the most influential spatiotemporal inputs, exposing vulnerabilities in forecasting models while maintaining stealth. We further create a restoration-aware validation framework that embeds these compromised forecasts into a sequential restoration model and evaluates operational feasibility using an unbalanced three-phase optimal power flow formulation. Simulation results show that the proposed approach is more efficient and stealthier than baseline attacks. It reveals system-level failures, such as voltage and power ramping violations that prevent the restoration of critical loads. These findings provide actionable insights for designing cybersecurity-aware restoration planning frameworks.