  Electric power infrastructure has ignited several of the most destructive
wildfires in recent history. Preemptive power shutoffs are an effective tool to
mitigate the risk of ignitions from power lines, but at the same time can cause
widespread power outages. This work proposes a mathematical optimization
problem to help utilities decide where and when to implement these shutoffs, as
well as how to most efficiently restore power once the wildfire risk is lower.
Specifically, our model co-optimizes the power shutoff (considering both
wildfire risk reduction and power outages) as well as the post-event
restoration efforts given constraints related to inspection and energization of
lines, and 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 and forecasts from 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.
