{"paper":{"title":"Large-Scale Resilience Planning for Wildfire-Prone Electricity-System via Adaptive Robust Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A tri-level optimization model lets utilities plan grid sectionalization and targeted shutoffs to cut wildfire ignition risk while preserving service reliability.","cross_cats":[],"primary_cat":"math.OC","authors_text":"Ramteen Sioshansi, Shixiang Zhu, Shuyi Chen","submitted_at":"2026-03-21T19:57:54Z","abstract_excerpt":"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 "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"coordinated planning of sectionalization and operational mitigation strategies can substantially reduce wildfire risk while maintaining service reliability","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The data-driven uncertainty set combining segment-level prediction intervals with group-level uncertainty budgets accurately represents system-wide ignition uncertainty, and the tri-level model correctly captures interactions between infrastructure planning, risk realization, and adaptive operational decisions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A tri-level adaptive robust optimization framework jointly optimizes infrastructure planning and operational responses to mitigate wildfire ignition risk in electricity distribution systems while preserving service reliability.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A tri-level optimization model lets utilities plan grid sectionalization and targeted shutoffs to cut wildfire ignition risk while preserving service reliability.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"702d72a31cfd0882330abebd531b6619320296e5913c5e25420a7b8aa35854ab"},"source":{"id":"2604.01232","kind":"arxiv","version":4},"verdict":{"id":"5659278d-21cd-440a-8eb1-d89f0401c1c3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T06:33:35.921473Z","strongest_claim":"coordinated planning of sectionalization and operational mitigation strategies can substantially reduce wildfire risk while maintaining service reliability","one_line_summary":"A tri-level adaptive robust optimization framework jointly optimizes infrastructure planning and operational responses to mitigate wildfire ignition risk in electricity distribution systems while preserving service reliability.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The data-driven uncertainty set combining segment-level prediction intervals with group-level uncertainty budgets accurately represents system-wide ignition uncertainty, and the tri-level model correctly captures interactions between infrastructure planning, risk realization, and adaptive operational decisions.","pith_extraction_headline":"A tri-level optimization model lets utilities plan grid sectionalization and targeted shutoffs to cut wildfire ignition risk while preserving service reliability."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.01232/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}