{"id":"W3106692822","doi":"10.3390/app10238585","title":"Minimization of Global Adjustment Charges for Large Electricity Customers Using Energy Storage—Canadian Market Case Study","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electricity; Energy storage; Environmental economics; Revenue; Deferral; Arbitrage; Context (archaeology); Grid; Sizing; Computer science; Electricity market; Minification; Business; Reliability engineering; Economics; Power (physics); Engineering; Finance; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001532704,0.00009483632,0.0001330513,0.00005627059,0.0001476165,0.0000261325,0.0001131804,0.00003296926,0.0000284418],"category_scores_gemma":[0.00001026999,0.00009218515,0.00002524241,0.0006173923,0.00002171259,0.00007480074,0.00001099002,0.00002330306,5.402702e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008633729,"about_ca_system_score_gemma":0.00008352426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001625809,"about_ca_topic_score_gemma":0.004133549,"domain_scores_codex":[0.9992813,0.00001358607,0.0001570447,0.0001791236,0.0001223777,0.0002465228],"domain_scores_gemma":[0.9997419,0.00001908799,0.00004119596,0.00005546139,0.00003195016,0.000110364],"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":[0.00004324234,0.0001067702,0.002323098,0.00006151974,0.00009138565,0.00002234238,0.001379528,0.9684534,0.002601682,0.003452531,0.001956129,0.01950835],"study_design_scores_gemma":[0.0004629621,0.00006954554,0.0001949458,0.000001851911,0.00003245079,0.00000673177,0.001455498,0.9963827,0.0004791857,0.00001298424,0.000781047,0.0001201621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8298463,0.001392853,0.1631812,0.0001291367,0.0003804194,0.001282627,0.0001757679,0.0001742801,0.003437387],"genre_scores_gemma":[0.9980425,0.00002094911,0.001725493,0.0001158954,0.00005767421,0.00002432156,0.00000455586,0.000006526047,0.000002038463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1681962,"threshold_uncertainty_score":0.3759201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01434419537268636,"score_gpt":0.225773274793173,"score_spread":0.2114290794204867,"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."}}