Mitigating the Impacts of Wildfires on Electric Power Systems Through Stochastic Optimization
Abstract
Dry and windy weather conditions significantly increase the risk of wildfires, whose spread exacerbates the vulnerability of the grid and results in prolonged power outages. This tutorial introduces and reviews recent streams of studies on addressing this challenge through stochastic optimization approaches, including static, adaptive, dynamic, and distributionally robust models. In particular, we account for random failures of power lines, which depend not only on the ambient environment (such as temperature, wind speed, and fire) but also on the power flowing through the line, introducing decision-dependent uncertainty (DDU). We introduce the modeling of wildfire, power systems operations, and their interactions, as well as how stochastic optimization models can characterize DDU and mitigate the impacts of wildfires on electric power systems. As examples, we mention three models, ranging from long-term planning to short-term and dynamic reconfiguration of a power system amidst wildfire-prone conditions. For each model, we provide a numerical case study to demonstrate the value of modeling (e.g., DDU and dynamic reconfiguration) in mitigating the impacts of wildfires.
Funding: This work has been funded by the U.S. Department of Energy, Office of Electricity [DE-AC02-05CH11231]. R. Jiang was supported in part by the U.S. National Science Foundation [Grant ECCS-1845980].
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