{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DOIIXUCKDHLV4723QWJFECI4C6","short_pith_number":"pith:DOIIXUCK","schema_version":"1.0","canonical_sha256":"1b908bd04a19d75e7f5b859252091c178c05d9ae4e04007367009a0f5d5264b8","source":{"kind":"arxiv","id":"2509.07218","version":5},"attestation_state":"computed","paper":{"title":"Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Ana Colacelli, Le Xie, Matt Lee, Xiaoyang Wang, Xin Chen","submitted_at":"2025-09-08T20:55:54Z","abstract_excerpt":"The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key char"},"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":"2509.07218","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SY","submitted_at":"2025-09-08T20:55:54Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"f83e6b5ac19cfaa696a9dd097f43a4085103b8a9d02fcc458063d65ab78b4314","abstract_canon_sha256":"7fffa6f49e9a60eae87467c9f1247a45c5e2cf5dea97b2d11789b78dcc2512ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T00:15:49.379419Z","signature_b64":"0Uvieja0SZsnX5IWb/aOsXAA7dfMmXmJwl9tm0qCv+alfKVzFxYRivbXzK4cyvgdYYjOpn8YSym+rP4NcHunAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b908bd04a19d75e7f5b859252091c178c05d9ae4e04007367009a0f5d5264b8","last_reissued_at":"2026-07-07T00:15:49.378406Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T00:15:49.378406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Ana Colacelli, Le Xie, Matt Lee, Xiaoyang Wang, Xin Chen","submitted_at":"2025-09-08T20:55:54Z","abstract_excerpt":"The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key char"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.07218","kind":"arxiv","version":5},"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/2509.07218/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":"2509.07218","created_at":"2026-07-07T00:15:49.378570+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.07218v5","created_at":"2026-07-07T00:15:49.378570+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.07218","created_at":"2026-07-07T00:15:49.378570+00:00"},{"alias_kind":"pith_short_12","alias_value":"DOIIXUCKDHLV","created_at":"2026-07-07T00:15:49.378570+00:00"},{"alias_kind":"pith_short_16","alias_value":"DOIIXUCKDHLV4723","created_at":"2026-07-07T00:15:49.378570+00:00"},{"alias_kind":"pith_short_8","alias_value":"DOIIXUCK","created_at":"2026-07-07T00:15:49.378570+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":22,"internal_anchor_count":22,"sample":[{"citing_arxiv_id":"2606.25098","citing_title":"Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2606.22590","citing_title":"Dynamic Resilience Assessment of Power Systems With Data Center Load Events Using Physics-Informed Neural Networks","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2606.21536","citing_title":"Stability Enhancement of Centralized UPS Data Center Systems Under Weak-Grid Conditions","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2606.21064","citing_title":"AI Data Centers and Power System Sustainability: Understanding the Sustainability Implications of AI-Driven Data Centers on Power Systems","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2606.19262","citing_title":"Detecting Hidden ML Training With Zero-Overhead Telemetry","ref_index":46,"is_internal_anchor":true},{"citing_arxiv_id":"2606.09617","citing_title":"Powering the Future of AI: Navigating the Trade-offs for Europe's Energy Transition and Net-Zero Goals","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"2606.00941","citing_title":"Power Grid Infrastructure for AI Data Centers","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2606.30206","citing_title":"The Many-Body Problem of the Data Centre","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2606.10440","citing_title":"ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2605.23244","citing_title":"Convex Optimization for Alignment and Preference Learning on a Single GPU","ref_index":105,"is_internal_anchor":true},{"citing_arxiv_id":"2602.10900","citing_title":"AI Infrastructure Sovereignty","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2603.00415","citing_title":"Grid Integration of AI Data Centers: A Critical Review of Energy Storage Solutions","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2604.06198","citing_title":"Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2605.14109","citing_title":"Grid Integration of Gigawatt-Scale AI Data Centers under Connect-and-Manage","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2605.14105","citing_title":"Battery-Assisted Operation of Hyperscale AI Data Centers under Connect-and-Manage Interconnection Practices","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27207","citing_title":"Regime-Adaptive Weighted Ensemble Learning for Computing-Driven Dynamic Load Forecasting in AI Data Centers","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2605.05519","citing_title":"OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00769","citing_title":"Voltage Ride-Through in Large Loads- A Dual PQ Approach","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00681","citing_title":"Deployment-Efficient Short-Term Load Forecasting in AI Data Centers via Sequence-to-Point Knowledge Distillation","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2604.19933","citing_title":"Cross-Atlantic Research Agenda for Scalable Grid Architectures and Distributed Flexibility","ref_index":51,"is_internal_anchor":true},{"citing_arxiv_id":"2604.07345","citing_title":"Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2604.21072","citing_title":"Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6","json":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6.json","graph_json":"https://pith.science/api/pith-number/DOIIXUCKDHLV4723QWJFECI4C6/graph.json","events_json":"https://pith.science/api/pith-number/DOIIXUCKDHLV4723QWJFECI4C6/events.json","paper":"https://pith.science/paper/DOIIXUCK"},"agent_actions":{"view_html":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6","download_json":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6.json","view_paper":"https://pith.science/paper/DOIIXUCK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.07218&json=true","fetch_graph":"https://pith.science/api/pith-number/DOIIXUCKDHLV4723QWJFECI4C6/graph.json","fetch_events":"https://pith.science/api/pith-number/DOIIXUCKDHLV4723QWJFECI4C6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6/action/storage_attestation","attest_author":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6/action/author_attestation","sign_citation":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6/action/citation_signature","submit_replication":"https://pith.science/pith/DOIIXUCKDHLV4723QWJFECI4C6/action/replication_record"}},"created_at":"2026-07-07T00:15:49.378570+00:00","updated_at":"2026-07-07T00:15:49.378570+00:00"}