{"id":"W7106283972","doi":"10.1016/j.egyr.2025.11.057","title":"Using spatial-temporal flexibility of data center building in energy management of distribution grid coupled with multi-energy hubs and energy storage","year":2025,"lang":"en","type":"article","venue":"Energy Reports","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Data center; Flexibility (engineering); Grid; Energy consumption; Distribution center; Energy (signal processing); Energy storage; Energy management; Distribution (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003684606,0.0003452714,0.0005735615,0.0002881722,0.0000545034,0.00002805891,0.0002166271,0.0001916759,0.000008751172],"category_scores_gemma":[0.00001678997,0.0003365106,0.00005205034,0.0006724645,0.00009186717,0.0003469691,0.0002253428,0.00008789933,1.592027e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002780413,"about_ca_system_score_gemma":0.00007315523,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03209882,"about_ca_topic_score_gemma":0.008611863,"domain_scores_codex":[0.9974973,0.0001021354,0.001130515,0.0006341127,0.0003040716,0.0003318497],"domain_scores_gemma":[0.9982845,0.00002955415,0.0004032951,0.001046584,0.000165921,0.00007018282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008862811,0.0001664312,0.004660801,0.0002450777,0.000291596,0.0002525002,0.000024298,0.9783894,0.002233445,0.009476922,0.00009178514,0.004079062],"study_design_scores_gemma":[0.0007502337,0.00003578552,0.0009730835,0.0007179452,0.00007040369,0.0000572946,0.00005283804,0.9772155,0.01526472,0.0000818628,0.004467794,0.0003125783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1115017,0.0007508272,0.8863556,0.000006079037,0.0005910799,0.00005184486,0.00006797254,0.00010629,0.0005686735],"genre_scores_gemma":[0.9897534,0.0002424325,0.007909174,0.00001011542,0.00004431955,0.00002678758,0.001715633,0.00004936085,0.0002487383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8784464,"threshold_uncertainty_score":0.9999087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200246820932741,"score_gpt":0.2560596278798501,"score_spread":0.2360349457865761,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}