{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:6SXU36NUIZFKELS2VBZGDUSDTG","short_pith_number":"pith:6SXU36NU","schema_version":"1.0","canonical_sha256":"f4af4df9b4464aa22e5aa87261d24399965099aeee2d6cdb91b39802eefe05d3","source":{"kind":"arxiv","id":"1908.00787","version":2},"attestation_state":"computed","paper":{"title":"Day-ahead Operation of an Aggregator of Electric Vehicles via Optimization under Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"\\'Alvaro Porras, Juan Miguel Morales, Ricardo Fern\\'andez-Blanco, Salvador Pineda","submitted_at":"2019-08-02T10:19:14Z","abstract_excerpt":"We pose the aggregator's problem as a bilevel model, where the upper level minimizes the total operation costs of the fleet of EVs, while each lower level minimizes the energy available to each vehicle for transportation given a certain charging plan. Thanks to the totally unimodular character of the constraint matrix in the lower-level problems, the model can be mathematically recast as a computationally efficient mixed-integer program that delivers charging schedules that are robust against the uncertain availability of the EVs. Finally, we use synthetic data from the National Household Trav"},"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":false},"canonical_record":{"source":{"id":"1908.00787","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2019-08-02T10:19:14Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"7680b9f31c4661a0d63dcec57c8f9af22c1f46296b0d2c6c1852a6f384871983","abstract_canon_sha256":"51d9ac41d3509d2d26d25422bb633d330b66177d5ae971593103e8f634b9de2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:37:52.103398Z","signature_b64":"GmpWEXYP2LvCyQhgoBomOkF+QQcrWS3rDFgJlxxR9nNaBPD910saB57+O3NmVna+FfpzbZN2sQNf1cLQx3a9CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4af4df9b4464aa22e5aa87261d24399965099aeee2d6cdb91b39802eefe05d3","last_reissued_at":"2026-07-05T01:37:52.102995Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:37:52.102995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Day-ahead Operation of an Aggregator of Electric Vehicles via Optimization under Uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"\\'Alvaro Porras, Juan Miguel Morales, Ricardo Fern\\'andez-Blanco, Salvador Pineda","submitted_at":"2019-08-02T10:19:14Z","abstract_excerpt":"We pose the aggregator's problem as a bilevel model, where the upper level minimizes the total operation costs of the fleet of EVs, while each lower level minimizes the energy available to each vehicle for transportation given a certain charging plan. Thanks to the totally unimodular character of the constraint matrix in the lower-level problems, the model can be mathematically recast as a computationally efficient mixed-integer program that delivers charging schedules that are robust against the uncertain availability of the EVs. Finally, we use synthetic data from the National Household Trav"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.00787","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.00787/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1908.00787","created_at":"2026-07-05T01:37:52.103048+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.00787v2","created_at":"2026-07-05T01:37:52.103048+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.00787","created_at":"2026-07-05T01:37:52.103048+00:00"},{"alias_kind":"pith_short_12","alias_value":"6SXU36NUIZFK","created_at":"2026-07-05T01:37:52.103048+00:00"},{"alias_kind":"pith_short_16","alias_value":"6SXU36NUIZFKELS2","created_at":"2026-07-05T01:37:52.103048+00:00"},{"alias_kind":"pith_short_8","alias_value":"6SXU36NU","created_at":"2026-07-05T01:37:52.103048+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG","json":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG.json","graph_json":"https://pith.science/api/pith-number/6SXU36NUIZFKELS2VBZGDUSDTG/graph.json","events_json":"https://pith.science/api/pith-number/6SXU36NUIZFKELS2VBZGDUSDTG/events.json","paper":"https://pith.science/paper/6SXU36NU"},"agent_actions":{"view_html":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG","download_json":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG.json","view_paper":"https://pith.science/paper/6SXU36NU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.00787&json=true","fetch_graph":"https://pith.science/api/pith-number/6SXU36NUIZFKELS2VBZGDUSDTG/graph.json","fetch_events":"https://pith.science/api/pith-number/6SXU36NUIZFKELS2VBZGDUSDTG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG/action/storage_attestation","attest_author":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG/action/author_attestation","sign_citation":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG/action/citation_signature","submit_replication":"https://pith.science/pith/6SXU36NUIZFKELS2VBZGDUSDTG/action/replication_record"}},"created_at":"2026-07-05T01:37:52.103048+00:00","updated_at":"2026-07-05T01:37:52.103048+00:00"}