Co-optimization of power line shutoff and restoration for electric grids under high wildfire ignition risk

Electric power infrastructure has ignited several of the most destructive wildfires in recent history. Preemptive power shut-offs are an effective tool to mitigate the risk of ignitions from power lines, but at the same time can cause widespread power outages. Electric utilities are thus faced with the challenging trade-off of where and when to implement these shut-offs, as well as how to most efficiently restore power once the wildfire risk is reduced. This work proposes a mathematical optimization problem to help utilities make these decisions. Our model co-optimizes the power shut-off (considering both wildfire risk reduction and power outages) as well as the post-event inspection and energization of lines. It is implemented as a rolling horizon optimization problem that is resolved whenever new forecasts of load and wildfire risk become available. We demonstrate our method on the IEEE RTS-GMLC test case using real wildfire risk data US Geological Survey, and investigate the sensitivity of the results to the forecast quality, decision horizon and system restoration budget. The software implementation is available in the open source software package PowerModelsWildfire.jl