Two-Stage Stochastic Optimal Power Flow for Microgrids With Uncertain Wildfire Effects

TL;DR

This paper develops a two-stage stochastic optimal power flow model for microgrids that accounts for wildfire-induced uncertainties, enhancing grid resilience through improved modeling and optimization techniques.

Contribution

It introduces a novel two-stage stochastic optimization framework for microgrid operation under wildfire uncertainties, incorporating second-order cone relaxation and dynamic line rating separation.

Findings

The proposed model improves microgrid resilience during wildfires.

Simulation results demonstrate effective handling of wildfire uncertainties.

The framework outperforms traditional methods in reliability and efficiency.

Abstract

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.