{"id":"W2979426993","doi":"10.1049/iet-rpg.2019.0583","title":"Optimal scheduling of bidirectional energy conversion units in energy and ancillary service markets for system restoration within MCESs","year":2019,"lang":"en","type":"article","venue":"IET Renewable Power Generation","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Scheduling (production processes); Computer science; Energy (signal processing); Operations research; Distributed computing; Mathematical optimization; Engineering; Physics; 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":[0.0003210898,0.0001634007,0.0001963943,0.0002387618,0.00005122616,0.00003640013,0.00007664031,0.0001269622,0.00001591294],"category_scores_gemma":[0.00001067925,0.0001859593,0.0000218607,0.0004259853,0.000009597265,0.0003193728,0.00003444119,0.0000358497,0.00000117944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001745377,"about_ca_system_score_gemma":0.00004988922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000695521,"about_ca_topic_score_gemma":0.001359432,"domain_scores_codex":[0.9989622,0.00005556885,0.0003457073,0.0002556559,0.0002055442,0.0001753652],"domain_scores_gemma":[0.9994408,0.00003937584,0.00009063046,0.0001841891,0.0001989029,0.00004605257],"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":[0.00005307261,0.00001023876,0.0004603679,0.0001736578,0.00003023296,9.084121e-7,0.0000983934,0.9344673,0.06197005,0.001788109,0.0008646191,0.00008302712],"study_design_scores_gemma":[0.0006319644,0.00004737318,0.000260777,0.00008870403,0.00001289859,0.000003778739,0.0002198215,0.8821415,0.1084578,0.000007621354,0.007955826,0.0001719697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189947,0.0006135925,0.07746582,0.00003507191,0.001627373,0.0001601556,0.00001206686,0.0001111498,0.0009801051],"genre_scores_gemma":[0.994492,0.00009695087,0.004628746,0.00005035935,0.0001636524,0.00005776515,0.0002279059,0.00003586266,0.0002467835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07549731,"threshold_uncertainty_score":0.7583199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177542924685594,"score_gpt":0.1898828608896261,"score_spread":0.1781074316427702,"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."}}