{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FX5OJPAAL46B62YVT3RYE4EWUK","short_pith_number":"pith:FX5OJPAA","schema_version":"1.0","canonical_sha256":"2dfae4bc005f3c1f6b159ee3827096a2ad34aeaa4f32aeb92b46c93e734723d2","source":{"kind":"arxiv","id":"2510.22048","version":4},"attestation_state":"computed","paper":{"title":"PF$\\Delta$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The PFΔ benchmark provides 859,800 power flow instances to test solvers and ML methods under load, generation, topology, and contingency variations.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alvaro Carbonero, Ana K. Rivera, Anvita Bhagavathula, Priya Donti","submitted_at":"2025-10-24T22:09:09Z","abstract_excerpt":"Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running these calculations at operational timescales or across large evaluation spaces remains a major computational bottleneck. Additionally, growing uncertainty in power system operations from the integration of renewables and climate-induced extreme weather also calls for tools that can accurately and ef"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":true},"canonical_record":{"source":{"id":"2510.22048","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-24T22:09:09Z","cross_cats_sorted":[],"title_canon_sha256":"79e471d4629d323ad2d3287aff1df8062287dde12814991253e1894bf3ea3aad","abstract_canon_sha256":"9f9517b1c845033c0c8c90a14d01efdec30e43e3e01b6a6d2a7471e21a4ae0e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:14:30.288365Z","signature_b64":"py9bFYHqn5hja/TbTWZGTXhAA+imA86BSrMwoTEdBXmIAt2GxQ3mzcN9exJ/pT9mNx3oR+TwKimBQ58PHqfvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dfae4bc005f3c1f6b159ee3827096a2ad34aeaa4f32aeb92b46c93e734723d2","last_reissued_at":"2026-06-05T01:14:30.287926Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:14:30.287926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PF$\\Delta$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The PFΔ benchmark provides 859,800 power flow instances to test solvers and ML methods under load, generation, topology, and contingency variations.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alvaro Carbonero, Ana K. Rivera, Anvita Bhagavathula, Priya Donti","submitted_at":"2025-10-24T22:09:09Z","abstract_excerpt":"Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running these calculations at operational timescales or across large evaluation spaces remains a major computational bottleneck. Additionally, growing uncertainty in power system operations from the integration of renewables and climate-induced extreme weather also calls for tools that can accurately and ef"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"PFΔ contains 859,800 solved power flow instances spanning six bus system sizes, three contingency types (N, N-1, N-2), and close-to-infeasible cases near steady-state voltage stability limits; evaluations of traditional solvers and GNN-based methods highlight key areas where existing approaches struggle.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The synthetic variations in load, generation, and topology together with the chosen contingency scenarios and near-infeasible points are sufficiently representative of real-world power system uncertainties to serve as a useful benchmark for ML methods.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PFΔ is a benchmark dataset of 859,800 power flow solutions across six bus system sizes with N/N-1/N-2 contingencies and close-to-infeasible cases to evaluate traditional solvers and GNN methods.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The PFΔ benchmark provides 859,800 power flow instances to test solvers and ML methods under load, generation, topology, and contingency variations.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c14e304ebc341be58593d268d3ba2f7498c0d013a9b5dc718b71f25077d23b7e"},"source":{"id":"2510.22048","kind":"arxiv","version":4},"verdict":{"id":"11336eb3-5275-4596-96ef-f921f00a3c8d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T04:05:22.957619Z","strongest_claim":"PFΔ contains 859,800 solved power flow instances spanning six bus system sizes, three contingency types (N, N-1, N-2), and close-to-infeasible cases near steady-state voltage stability limits; evaluations of traditional solvers and GNN-based methods highlight key areas where existing approaches struggle.","one_line_summary":"PFΔ is a benchmark dataset of 859,800 power flow solutions across six bus system sizes with N/N-1/N-2 contingencies and close-to-infeasible cases to evaluate traditional solvers and GNN methods.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The synthetic variations in load, generation, and topology together with the chosen contingency scenarios and near-infeasible points are sufficiently representative of real-world power system uncertainties to serve as a useful benchmark for ML methods.","pith_extraction_headline":"The PFΔ benchmark provides 859,800 power flow instances to test solvers and ML methods under load, generation, topology, and contingency variations."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.22048/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":2,"snapshot_sha256":"34909c6150894f601e8f9b9a93f4a6ca43b5b59b6b0201ca23314bebbf23ded0"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2510.22048","created_at":"2026-06-05T01:14:30.287987+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.22048v4","created_at":"2026-06-05T01:14:30.287987+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.22048","created_at":"2026-06-05T01:14:30.287987+00:00"},{"alias_kind":"pith_short_12","alias_value":"FX5OJPAAL46B","created_at":"2026-06-05T01:14:30.287987+00:00"},{"alias_kind":"pith_short_16","alias_value":"FX5OJPAAL46B62YV","created_at":"2026-06-05T01:14:30.287987+00:00"},{"alias_kind":"pith_short_8","alias_value":"FX5OJPAA","created_at":"2026-06-05T01:14:30.287987+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK","json":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK.json","graph_json":"https://pith.science/api/pith-number/FX5OJPAAL46B62YVT3RYE4EWUK/graph.json","events_json":"https://pith.science/api/pith-number/FX5OJPAAL46B62YVT3RYE4EWUK/events.json","paper":"https://pith.science/paper/FX5OJPAA"},"agent_actions":{"view_html":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK","download_json":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK.json","view_paper":"https://pith.science/paper/FX5OJPAA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.22048&json=true","fetch_graph":"https://pith.science/api/pith-number/FX5OJPAAL46B62YVT3RYE4EWUK/graph.json","fetch_events":"https://pith.science/api/pith-number/FX5OJPAAL46B62YVT3RYE4EWUK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK/action/storage_attestation","attest_author":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK/action/author_attestation","sign_citation":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK/action/citation_signature","submit_replication":"https://pith.science/pith/FX5OJPAAL46B62YVT3RYE4EWUK/action/replication_record"}},"created_at":"2026-06-05T01:14:30.287987+00:00","updated_at":"2026-06-05T01:14:30.287987+00:00"}