{"id":"W3105321426","doi":"10.1109/iecon43393.2020.9255140","title":"Optimal Dynamic Pricing and Rewarding for Electric Vehicle Charging Scheme in High Penetration Photovoltaic Microgrid","year":2020,"lang":"en","type":"article","venue":"IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Microgrid; Photovoltaic system; Dynamic pricing; Electric vehicle; Computer science; Automotive engineering; Energy storage; Grid; State of charge; Mathematical optimization; Interval (graph theory); Scheduling (production processes); Voltage; Battery (electricity); Electrical engineering; Engineering; Mathematics; Business","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":[0.0007979829,0.0005178045,0.000783044,0.0003098864,0.0003835314,0.0009098606,0.000732689,0.0008058501,0.001528137],"category_scores_gemma":[0.001945289,0.0003963798,0.0003749334,0.0003442247,0.0004668723,0.0006833539,0.000553064,0.000646972,0.0001166076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009971709,"about_ca_system_score_gemma":0.000988839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004961572,"about_ca_topic_score_gemma":0.004727956,"domain_scores_codex":[0.9996607,0.0001632889,0.00001079588,0.0000449671,0.00004693373,0.00007326671],"domain_scores_gemma":[0.9995716,0.0002207878,0.00007114054,0.000019799,0.00006711356,0.00004951022],"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":[0.00005327024,0.00003264665,0.0003608715,0.00002239532,0.00000996573,0.0000497439,0.00001821456,0.9892163,0.000462866,0.003065989,0.0003060566,0.006401754],"study_design_scores_gemma":[0.000005467876,0.00001812746,0.0000659401,0.000001471023,0.0000031267,0.000005978749,0.00000838815,0.9991304,0.00007516685,0.000618244,0.00006574602,0.000001972803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2750991,0.0007547271,0.7117118,0.0007407885,0.0001172455,0.0001426782,0.00009316969,0.0002294223,0.01111103],"genre_scores_gemma":[0.9872273,0.00007497239,0.01180502,0.00002164555,0.000008748979,0.00002220112,0.00001460421,0.000009026548,0.0008164813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004961572,"threshold_uncertainty_score":0.009865403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01457988846556164,"score_gpt":0.2106355892381299,"score_spread":0.1960557007725683,"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."}}