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Electric grid faults can cause catastrophic wildfires, particularly inregions with high winds and low humidity. In real-time operations, electricutilities are often left with few options for wildfire risk mitigation, leadingto use of disruptive measures such as proactive de-energization of equipment,frequently referred to as public safety power shut-offs. Such power shut-offshave significant impacts on customers, who experience power cuts in an attemptto protect them from fires. This work proposes the optimal power shut-offproblem, an optimization model to support short-term operational decisionmaking in the context of extreme wildfire risk. Specifically, the modeloptimizes grid operations to maximize the amount of power that can bedelivered, while proactively minimizing the risk of wildfire ignitions byselectively de-energizing components in the grid. This is the firstoptimization model to consider how preventive wildfire risk measures impactboth wildfire risk \emph{and} power systems reliability at a short-term,operational time-frame. The effectiveness of the method is demonstrated on anaugmented version of the RTS-GMLC test case, located in Southern California,and compared against two approaches based on simple risk thresholds. Theproposed optimization-based model reduces both wildfire risk and lost load shedrelative to the benchmarks.

Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offs