Abstract:Wildfire risk poses a growing challenge for electric utilities, as powerline failures can ignite wildfires while large fires can disrupt grid operations. Utilities increasingly rely on operational interventions such as Public Safety Power Shutoffs (PSPS) and fast-trip protection to mitigate ignition risk, but these measures can cause widespread service disruptions if deployed indiscriminately. Infrastructure planning decisions--such as feeder sectionalization and protection configuration--play a key role in determining how effectively these interventions can be targeted. We develop a planning framework for wildfire resilience that jointly optimizes long-term infrastructure configuration and short-term operational response under uncertain ignition risk. The problem is formulated as a tri-level optimization model capturing the interaction between infrastructure planning, wildfire risk realization, and adaptive operational decisions. To represent system-wide ignition uncertainty, we construct a data-driven uncertainty set that combines segment-level prediction intervals with group-level uncertainty budgets. Leveraging the model structure, we reformulate the problem as a tractable robust optimization model and develop a scalable column-and-constraint generation algorithm. Synthetic experiments and a large-scale case study on an investor-owned utility distribution system show that coordinated planning of sectionalization and operational mitigation strategies can substantially reduce wildfire risk while maintaining service reliability.
| Comments: | V4 updated reference with correction and minor fixes to outdated links |
| Subjects: | Optimization and Control (math.OC) |
| Cite as: | arXiv:2604.01232 [math.OC] |
| (or arXiv:2604.01232v4 [math.OC] for this version) | |
| https://doi.org/10.48550/arXiv.2604.01232 arXiv-issued DOI via DataCite |
From: Shuyi Chen [view email]
[v1]
Sat, 21 Mar 2026 19:57:54 UTC (4,080 KB)
[v2]
Sun, 5 Apr 2026 20:29:48 UTC (5,236 KB)
[v3]
Sun, 12 Apr 2026 17:17:00 UTC (5,235 KB)
[v4]
Thu, 21 May 2026 19:26:58 UTC (5,235 KB)
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