{"id":"W4390234906","doi":"10.3390/en17010123","title":"Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University; University of Calgary","funders":"Mitacs","keywords":"Wind power; Electric power system; Automotive engineering; Renewable energy; Power system simulation; Scheduling (production processes); Electric vehicle; Electricity generation; Engineering; Computer science; Electrical engineering; Power (physics); Operations management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006990623,0.0001264088,0.0001733685,0.000215222,0.00004877305,0.00003704038,0.00004880353,0.00006684702,0.000002293861],"category_scores_gemma":[0.000004169559,0.00010165,0.00001123159,0.0005398556,0.00002289972,0.00008899158,0.00001892999,0.00007607514,2.140424e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000339119,"about_ca_system_score_gemma":0.00001768923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005474132,"about_ca_topic_score_gemma":0.0002205206,"domain_scores_codex":[0.9994152,0.00002259977,0.0001596265,0.0001266264,0.00009407711,0.0001819015],"domain_scores_gemma":[0.9997986,0.0000283529,0.00003012416,0.00008298959,0.00002794412,0.00003198328],"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":[0.00001069123,0.000003032742,0.005505426,0.00004814619,0.00003165222,0.000005167219,0.0001699775,0.9348463,0.05720278,0.0008169264,0.0001139308,0.001245911],"study_design_scores_gemma":[0.0004004976,0.0001337335,0.007127267,0.00009274192,0.00001327846,0.00001633936,0.001010692,0.8006018,0.1898582,0.00002008665,0.0005537353,0.000171695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961802,0.002566057,0.0008295721,0.00001244581,0.00002871028,0.00003946901,0.000003060047,0.0001586989,0.0001817749],"genre_scores_gemma":[0.9984629,0.000418291,0.0009550657,0.0000059159,0.00002719785,0.00001143187,0.00001681205,0.00002160831,0.00008084856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1342446,"threshold_uncertainty_score":0.4145167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006509238151149704,"score_gpt":0.1818047780697311,"score_spread":0.1752955399185814,"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."}}