Quasi Second-Order Stochastic Dominance Model for Balancing Wildfire Risks and Power Outages due to Proactive Public Safety De-Energizations
Jinshun Su, Saharnaz Mehrani, Payman Dehghanian, Miguel A. Lejeune
Abstract
Faults on overhead power line infrastructures in electric power distribution systems (DSs) can potentially ignite catastrophic wildfires, especially in areas exposed to high wind regimes, low humidity, and dense vegetation. The common practice adopted by electric utilities to build resilience against such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">electrically-induced wildfires</i> is called public-safety power-shutoff (PSPS): strategies to intentionally and proactively de-energize power line infrastructures to prevent wildfire risks. Using a quasi second-order stochastic dominance (Q-SSD) measure, this article proposes an optimization model to generate an optimal PSPS plan which mitigates the risk of costly wildfires while keeping the intentional power outages minimal. This objective is achieved by the strategic deployment of transportable energy backup technologies in the DS, i.e., mobile power sources (MPSs). The proposed model is a stochastic mixed-integer nonlinear programming (S-MINLP) capturing the uncertainties in wildfire consequences under different weather realizations. We derive a tractable linearization procedure to reformulate the S-MINLP model as an equivalent mixed-integer linear problem. Numerical studies on the IEEE 33-node test system demonstrate the efficiency of the resulting PSPS actions in balancing the wildfire risks and the power outage consequences, and highlight the promising performance of the proposed modeling approach compared to the state-of-the-art and benchmark formulations.