  Large-scale power outages caused by extreme weather events are one of the
major factors weakening grid resilience. In order to prevent the critical
infrastructure from cascading failure, power lines are often proactively
de-energized under the threat of a progressing wildfire. In this context, the
potential of microgrid (MG) functioning in islanded mode can be exploited to
enhance the resiliency of the power grid. However, there are numerous
uncertainties originating from these types of events and an accurate modeling
of the MG is required to harness its full potential. In this paper, we consider
the uncertainty in line outages depending on fire propagation and reduced solar
power generation due to the particulate matter in wildfire smoke. We formulate
a two-stage stochastic MG optimal power flow problem by utilizing a
second-order cone relaxation of the DistFlow model. Leveraging an effective
approximation of the resistive heat gain, we separate the complicating
constraints of dynamic line rating from the resulting optimization problem.
Extensive simulation results corroborate the merits of our proposed framework,
which is tested on a modified IEEE 22-bus system.
