Risk-Averse Resilient Operation of Electricity Grid Under the Risk of Wildfire

TL;DR

This paper introduces a robust optimization approach for resilient electricity grid operation under wildfire risk, balancing power line de-energization and customer service amid climate-induced extreme weather.

Contribution

It formulates a two-stage robust optimization model incorporating wildfire ignition risk and renewable energy penetration, solved with a column-and-constraint generation algorithm.

Findings

The model improves the balance between wildfire risk mitigation and customer power supply.

Higher renewable penetration levels can reduce wildfire-related operational risks.

The algorithm's effectiveness is validated on multiple test cases.

Abstract

Wildfires and other extreme weather conditions due to climate change are stressing the aging electrical infrastructure. Power utilities have implemented public safety power shutoffs as a method to mitigate the risk of wildfire by proactively de-energizing some power lines, which leaves customers without power. System operators have to make a compromise between de-energizing of power lines to avoid the wildfire risk and energizing those lines to serve the demand. In this work, with a quantified wildfire ignition risk of each line, a resilient operation problem is presented in power systems with a high penetration level of renewable generation resources. A two-stage robust optimization problem is formulated and solved using column-and-constraint generation algorithm to find improved balance between the de-energization of power lines and the customers served. Different penetration levels of renewable generation to mitigate the impact of extreme fire hazard situations on the energization of customers is assessed. The validity of the presented robust optimization algorithm is demonstrated on various test cases.