ORC Seminar: Modeling Adversarial Wildfires for Power Grid Disruption

Matthew Brun ORC Best Student Paper Award Winner MIT Abstract: Electric power infrastructure faces increasing risk of damage and disruption due to wildfire. Operators of power grids in wildfire-prone regions must consider the potential impacts of unpredictable fires. However, traditional wildfire models do not effectively describe worst-case, or even high-impact, fire behavior. To address this issue, we propose a mixed-integer conic program to characterize an adversarial wildfire that targets infrastructure while respecting realistic fire spread dynamics. We design a wind-assisted fire spread set based on the Rothermel fire spread model and propose principled convex relaxations of this set, including a new relaxation of the inner product over Euclidean balls. We present test cases derived from the recent Park, Eaton, and Palisades fires in California and solve models to identify the minimum time-to-outage of multiple-element contingencies and the maximum load shed associated with a sequence of element outages caused by a realistic wildfire. We use the minimum time-to-outage values to screen contingencies and construct security-constrained optimal power flow models that promote operational resilience against wildfire. Bio: Matthew Brun is a final-year PhD student at the MIT Operations Research Center, advised by Dr. Andy Sun. His research improves the design and operation of energy systems by solving large-scale, nonconvex optimization problems. He studies algorithms, decompositions, relaxations, and heuristics for diverse energy applications, including power grid operations, recycling supply chains for energy assets, and infrastructure resilience against natural disasters. He holds a B.A. in Operations Research from Rice University and is a recipient of the INFORMS Undergraduate Operations Research Prize. Beyond OR, he plays trumpet and has performed with the MIT Symphony Orchestra, the Rice University Campanile Orchestra, and Mariachi Luna Llena.