Year: 2024
Type: article
Abstract: The threat of wildfire ignitions from electric power equipment has led utilities to increasingly turn to preemptive power shutoffs, which, while effective in reducing grid-induced wildfire risk, can cause significant load loss.Undergrounding power lines is an alternative strategy for preventing grid-induced wildfires.However, undergrounding lines is costly, so an effective undergrounding plan must balance the cost of load loss with the cost of undergrounding lines.We propose a robust optimization model to identify power lines to underground to maximize load served while limiting wildfire risk across a range of wildfire risk and weather scenarios.Since solving this problem may be computationally heavy for large power systems and many operating scenarios, we present a delayed constraint generation algorithm to iteratively add scenarios until an optimal solution is found.We evaluate the performance of this robust optimization framework on the RTS-GMLC system with scenarios representing a year of operating conditions.
Author: Taylor, S [University of Wisconsin-Madison]; Musselman, A E [Lawrence Livermore National Laboratory]; Roald, L A [University of Wisconsin-Madison]; Watson, J P [Lawrence Livermore National Laboratory]
Institution: University of Wisconsin–Madison
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4393201281