Enhancing Infrastructure Resilience by Using Dynamically Updated Damage Estimates in Optimal Repair Planning: The Power Grid Case

Abstract

Besides robustness, a crucial aspect of power grid resilience is the postdisruption restoration of transmission capacity. Conventionally, grid repair planning is initiated when damage assessment is complete. With the current communication bandwidth and the role of drones in inspection, damage assessment is an increasingly dynamic process. Early damage estimates can serve preliminary repair planning. Subsequent replanning is then performed as updated damage assessments come in, thus mitigating the impact of restoration uncertainties. The present work examines the gains from starting grid recovery using preliminary damage estimates and replanning repair. A receding horizon approach, model predictive control (MPC), is applied to the IEEE-39 bus system. The benefits are expressed by the integral loss of service (ILOS), measuring the power demand not served over time. In the baseline, repair planning is not performed before definitive repair estimates are delivered. In this study, MPC reduces the maximum ILOS by up to 57%. In terms of computation, three prediction steps are sufficient for the receding horizon to decrease the maximum ILOS by at least 37%.