Optimally Scheduling Public Safety Power Shutoffs
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In an effort to reduce power system-caused wildfires, utilities carry out public safety power shutoffs (PSPS) in which portions of the grid are de-energized to mitigate the risk of ignition. The decision to call a PSPS must balance reducing ignition risks and the negative impact of service interruptions. In this work, we consider three PSPS scheduling scenarios, which we model as dynamic programs. In the first two scenarios, we assume that N PSPSs are budgeted as part of the investment strategy. In the first scenario, a penalty is incurred for each PSPS declared past the Nth event. In the second, we assume that some costs can be recovered if the number of PSPSs is below N while still being subject to a penalty if above N. In the third, the system operator wants to minimize the number of PSPS such that the total expected cost is below a threshold. We provide optimal or asymptotically optimal policies for each case, the first two of which have closed-form expressions. Lastly, we establish the applicability of the first PSPS model’s policy to critical-peak pricing, and obtain an optimal scheduling policy to reduce the peak demand based on weather observations.
Antoine Lesage-Landry is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, QC, Canada. He is also a member of the Group for Research in Decision Analysis (GERAD) and an associate academic member of Mila — Québec’s Artificial Intelligence Institute. He received the B.Eng. degree in Engineering Physics from Polytechnique Montréal, in 2015, and the Ph.D. degree in Electrical Engineering from the University of Toronto, ON, Canada, in 2019. From 2019 to 2020, he was a Postdoctoral Scholar in the Energy & Resources Group at the University of California, Berkeley, CA, USA. His research interests include optimization, machine learning and their application to power systems with renewable generation.
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