{"id":"W4404564609","doi":"10.1109/eeeic/icpseurope61470.2024.10751245","title":"Electric Vehicle Flexibility Harnessing Through Local Energy Community Operation Optimization: Maximizing Local Energy Utilization","year":2024,"lang":"en","type":"article","venue":"","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Flexibility (engineering); Energy (signal processing); Electric vehicle; Computer science; Automotive engineering; Engineering; Power (physics); Economics","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":[0.0007254999,0.0009046152,0.0007381535,0.000452166,0.0004215843,0.001164883,0.001011721,0.0006456139,0.002705275],"category_scores_gemma":[0.001200896,0.0003225813,0.0004852902,0.0005483679,0.0005038854,0.001191524,0.001268223,0.0006025953,0.0002590263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006849275,"about_ca_system_score_gemma":0.0006458287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003180518,"about_ca_topic_score_gemma":0.003833656,"domain_scores_codex":[0.9997217,0.00009009347,0.000006950775,0.00005399974,0.00004534204,0.00008194011],"domain_scores_gemma":[0.9996749,0.0001297669,0.00005636276,0.00001909824,0.00005910694,0.00006072859],"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":[0.00006609263,0.00004221361,0.0005607044,0.0000426613,0.00002875675,0.0001052426,0.00003481163,0.9836194,0.00125149,0.003682753,0.0005404679,0.01002542],"study_design_scores_gemma":[0.000007320801,0.0000324511,0.0001278967,0.000004778368,0.000009388626,0.00001536573,0.00004363513,0.9977197,0.0002237284,0.001565411,0.0002460623,0.000004171759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2402064,0.0006855291,0.7400476,0.0005079606,0.00005533061,0.0001260391,0.0001191249,0.000305849,0.01794621],"genre_scores_gemma":[0.9917942,0.00007890344,0.006575281,0.0000288571,0.00000615976,0.00003586878,0.00002755925,0.00001957474,0.001433617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003180518,"threshold_uncertainty_score":0.009050071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02246256561232464,"score_gpt":0.2425963818941105,"score_spread":0.2201338162817858,"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."}}