{"id":"W3175609235","doi":"10.32920/ryerson.14665608.v1","title":"Load balancing for smart grid: centralized and distributed approaches","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Load balancing (electrical power); Smart grid; Computer science; Distributed computing; Grid; Scheduling (production processes); Electric power system; Distributed generation; Jacobian matrix and determinant; Load management; Demand response; Electric power; Power (physics); Mathematical optimization; Electrical engineering; Electricity; Engineering; Renewable energy; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008291526,0.0006100467,0.000836466,0.000392461,0.0004562852,0.001904779,0.001044486,0.00110768,0.002855217],"category_scores_gemma":[0.001003193,0.0002858508,0.0004238286,0.0009871878,0.0008718058,0.002076694,0.001128108,0.001205117,0.000523911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008763795,"about_ca_system_score_gemma":0.0007610146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139635,"about_ca_topic_score_gemma":0.00123414,"domain_scores_codex":[0.9993668,0.0001880065,0.00002633667,0.0001411496,0.000227541,0.00005012323],"domain_scores_gemma":[0.9996754,0.0001146013,0.00004698588,0.00005954005,0.00008323186,0.00002039013],"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":[0.00009062816,0.0001053057,0.0004807691,0.0004230005,0.00006793597,0.0001621494,0.0001517617,0.4666216,0.004637979,0.2813504,0.007208304,0.2387003],"study_design_scores_gemma":[0.00002236955,0.00005147629,0.0001565376,0.0000358236,0.0000172277,0.00006865479,0.00005613807,0.8853365,0.0009746727,0.1014813,0.01178464,0.00001479844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004790509,0.004901038,0.9763105,0.0009514063,0.0002128721,0.00004714406,0.00002564944,0.0001908461,0.01257007],"genre_scores_gemma":[0.8047765,0.008611031,0.1702082,0.0004669242,0.001255391,0.0001878407,0.0001195122,0.0001380135,0.01423652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002855217,"threshold_uncertainty_score":0.009551644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02752856296852878,"score_gpt":0.2030694143410832,"score_spread":0.1755408513725545,"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."}}