  Electric grid faults can cause catastrophic wildfires, particularly in
regions with high winds and low humidity. In real-time operations, electric
utilities are often left with few options for wildfire risk mitigation, leading
to use of disruptive measures such as proactive de-energization of equipment,
frequently referred to as public safety power shut-offs. Such power shut-offs
have significant impacts on customers, who experience power cuts in an attempt
to protect them from fires. This work proposes the optimal power shut-off
problem, an optimization model to support short-term operational decision
making in the context of extreme wildfire risk. Specifically, the model
optimizes grid operations to maximize the amount of power that can be
delivered, while proactively minimizing the risk of wildfire ignitions by
selectively de-energizing components in the grid. This is the first
optimization model to consider how preventive wildfire risk measures impact
both wildfire risk \emph{and} power systems reliability at a short-term,
operational time-frame. The effectiveness of the method is demonstrated on an
augmented version of the RTS-GMLC test case, located in Southern California,
and compared against two approaches based on simple risk thresholds. The
proposed optimization-based model reduces both wildfire risk and lost load shed
relative to the benchmarks.
