{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007463807,0.0006091862,0.0007903234,0.0003443873,0.0004110172,0.001600733,0.0008936772,0.000938906,0.002437172],"category_scores_gemma":[0.000881363,0.0002737274,0.0004082293,0.0008394415,0.0008113729,0.001633153,0.001014938,0.0010903,0.0004204377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007996327,"about_ca_system_score_gemma":0.0007090651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0011573,"about_ca_topic_score_gemma":0.001215274,"domain_scores_codex":[0.9994841,0.0001659848,0.00002105463,0.0001132514,0.0001750998,0.00004044119],"domain_scores_gemma":[0.999741,0.00009132829,0.0000398824,0.00004668767,0.0000645326,0.00001651094],"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":[0.00007803171,0.00008657147,0.0004402659,0.0003960423,0.00006306887,0.0001307408,0.0001245745,0.5526959,0.003890123,0.2498633,0.006021051,0.1862103],"study_design_scores_gemma":[0.00001690869,0.00004592521,0.0001498373,0.00003020207,0.00001430318,0.0000515784,0.00004586752,0.9041388,0.0007701223,0.08625831,0.008465867,0.00001229512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005186942,0.005126726,0.9763877,0.0008787907,0.0001860025,0.00003864928,0.00002482089,0.0001503936,0.01201999],"genre_scores_gemma":[0.8240243,0.008678271,0.1530727,0.0003861002,0.001090573,0.000162883,0.0001042094,0.0001245859,0.01235646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002437172,"threshold_uncertainty_score":0.008153141,"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."}}