{"paper":{"title":"Robust Capacity Expansion under Wildfire Ignition Risk and High Renewable Penetration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A robust optimization model finds optimal battery storage placements and transmission line undergrounding to handle worst-case wildfire de-energization combined with renewable variability.","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Jean-Paul Watson, Ryan Piansky, Tom\\'as Tapia, Yury Dvorkin","submitted_at":"2026-05-08T15:33:10Z","abstract_excerpt":"In power systems, the risk of wildfire ignition has increased significantly in recent years. The impact and severity of these events on energy dispatch, as well as their societal ramifications, make wildfire prevention critical for power system planning and operation. A common intervention by system operators is to de-energize transmission lines to mitigate the risk of fire caused by equipment failures. With the growing integration of variable renewable generation, managing and preparing the system to de-energization under wildfire risk has become even more challenging. In this context, mitiga"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This paper addresses (i) the worst-case realization of ignition risk leading to the de-energization of transmission lines, combined with the worst-case realization of renewable energy availability, and (ii) the optimal investment decisions for energy storage capacity and undergrounding of transmission lines that are exposed to ignition risk.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That representative weeks and uncertainty sets sufficiently capture the temporal relationships and extreme scenarios of ignition risk and renewable availability.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A robust optimization model optimizes battery storage locations and transmission line undergrounding under worst-case wildfire de-energization combined with renewable uncertainty, formulated as MILP and solved via column-and-constraint generation on the San Diego power system.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A robust optimization model finds optimal battery storage placements and transmission line undergrounding to handle worst-case wildfire de-energization combined with renewable variability.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ef1024ed8ca08a336ce66c32bd00b3641ae3f1a7af3adaa0e6a096b3e3b07cad"},"source":{"id":"2605.07880","kind":"arxiv","version":2},"verdict":{"id":"672689e5-9c17-44f7-a318-fb02a7b82354","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T02:11:06.850690Z","strongest_claim":"This paper addresses (i) the worst-case realization of ignition risk leading to the de-energization of transmission lines, combined with the worst-case realization of renewable energy availability, and (ii) the optimal investment decisions for energy storage capacity and undergrounding of transmission lines that are exposed to ignition risk.","one_line_summary":"A robust optimization model optimizes battery storage locations and transmission line undergrounding under worst-case wildfire de-energization combined with renewable uncertainty, formulated as MILP and solved via column-and-constraint generation on the San Diego power system.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That representative weeks and uncertainty sets sufficiently capture the temporal relationships and extreme scenarios of ignition risk and renewable availability.","pith_extraction_headline":"A robust optimization model finds optimal battery storage placements and transmission line undergrounding to handle worst-case wildfire de-energization combined with renewable variability."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.07880/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T10:02:13.684551Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-20T04:47:44.106296Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T15:31:18.484787Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T11:26:56.411239Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"08d7ab2c299d547847db0376a8ddc187d2aed9fef3fb10dda0952a4b4b75dddf"},"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"}