{"id":"W4412423599","doi":"10.1016/j.ijepes.2025.110858","title":"Adaptive frequency optimization control strategy of electric vehicles participation in energy storage considering user active response margin","year":2025,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Chongqing Three Gorges University; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Margin (machine learning); Automatic frequency control; Energy storage; Frequency regulation; Control (management); Electric vehicle; Computer science; Control theory (sociology); Energy (signal processing); Demand response; Engineering; Control engineering; Electric power system; Electrical engineering; Mathematics; Artificial intelligence; Telecommunications; Physics; Power (physics); Machine learning","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":[0.0003574476,0.0002104246,0.0004461262,0.001050924,0.00002710264,0.00005544306,0.0003149618,0.000188247,0.00002186234],"category_scores_gemma":[0.000193568,0.0001994656,0.0001242556,0.0008171842,0.0000258565,0.0003623752,0.0000146617,0.0003302214,3.425394e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006931854,"about_ca_system_score_gemma":0.0002611053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002082403,"about_ca_topic_score_gemma":0.00001773159,"domain_scores_codex":[0.9977391,0.0003102976,0.001007715,0.000164835,0.0004784784,0.0002995979],"domain_scores_gemma":[0.9982783,0.0004657363,0.0003930852,0.0001120832,0.0006719564,0.00007888469],"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":[0.001126574,0.00008369237,0.0004397977,0.000005942884,0.0004112942,0.0000827951,0.00005512353,0.9331079,0.04628228,0.01410339,0.0003835554,0.00391773],"study_design_scores_gemma":[0.002555481,0.0006023344,0.007788371,0.0002463766,0.00006145944,0.0001123152,0.00007727904,0.9542537,0.0313721,0.001551113,0.001080352,0.0002991349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5059363,0.006389229,0.4844705,0.0001654783,0.00119923,0.0001510171,0.00001931839,0.00005535512,0.001613538],"genre_scores_gemma":[0.9992474,0.0002368777,0.0002267133,0.00006677497,0.0001122495,0.00001083423,0.000003902633,0.00002438149,0.00007082922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4933111,"threshold_uncertainty_score":0.8133972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005705925619149186,"score_gpt":0.2302811030657102,"score_spread":0.224575177446561,"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."}}