  Utilities in California conduct Public Safety Power Shut-offs (PSPSs) to
eliminate the elevated chances of wildfire ignitions caused by power lines
during extreme weather conditions. We propose Wildfire Risk Aware operation
planning Problem (WRAP), which enables system operators to pinpoint the
segments of the network that should be de-energized. Sustained wind and wind
gust can lead to conductor clashing, which could ignite surrounding vegetation.
The 3D non-linear vibration equations of power lines are employed to generate a
dataset that considers physical, structural, and meteorological parameters.
With the help of machine learning techniques, a surrogate model is obtained
which quantifies the risk of wildfire ignition by individual power lines under
extreme weather conditions. The cases illustrate the superior performance of
WRAP under extreme weather conditions in mitigating wildfire risk and serving
customers compared to the naive PSPS approach and another method in the
literature. Cases are also designated to sensitivity analysis of WRAP to
critical load-serving control parameters in different weather conditions.
Finally, a discussion is provided to explore our wildfire risk monetization
approach and its implications for WRAP decisions.
