{"id":"W4234535881","doi":"10.32920/ryerson.14665608","title":"Load balancing for smart grid: centralized and distributed approaches","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Microgrid Control and Optimization","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; Load management; Jacobian matrix and determinant; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009872051,0.0002090021,0.0003086077,0.00002548565,0.00003460316,0.0001629346,0.00007906464,0.0001996935,0.00004338166],"category_scores_gemma":[0.00002844297,0.0002036244,0.00009220047,0.00004347464,0.00001111744,0.00004637858,0.0001010422,0.0001678193,8.471699e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000845438,"about_ca_system_score_gemma":0.00004058745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003506771,"about_ca_topic_score_gemma":0.00005283619,"domain_scores_codex":[0.9992031,0.00001208536,0.0002126572,0.0002639014,0.00007850304,0.0002297131],"domain_scores_gemma":[0.9996258,0.0000334665,0.00003057255,0.0001879981,0.00005596639,0.00006619267],"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":[0.00003991962,0.00003860829,0.0008805538,0.002009347,0.0004911714,0.000005137948,0.0004217142,0.9690561,0.001015248,0.000243755,0.007068845,0.01872964],"study_design_scores_gemma":[0.0007789346,0.000004852987,0.000540446,0.00007511791,0.00008319889,0.000003308345,0.0000955392,0.991003,0.0009099224,0.00008708031,0.006109301,0.0003093342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01375998,0.008952877,0.9734476,0.0001790944,0.001156113,0.0007838541,0.0003790291,0.0005081568,0.0008333562],"genre_scores_gemma":[0.9067572,0.003953264,0.07876041,0.0000935279,0.0007321652,0.0004588262,0.008844294,0.0001323969,0.0002678835],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8946871,"threshold_uncertainty_score":0.8303562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710076613167031,"score_gpt":0.1899068087248116,"score_spread":0.1728060425931413,"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."}}