{"id":"W3211701738","doi":"10.1109/tec.2021.3116234","title":"Optimal Sizing and Scheduling of Mobile Energy Storage Toward High Penetration Levels of Renewable Energy and Fast Charging Stations","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Energy Conversion","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Windsor","funders":"King Abdulaziz University","keywords":"Sizing; Photovoltaic system; Renewable energy; Energy storage; Automotive engineering; Operating cost; Scheduling (production processes); Computer science; Electric power system; Wind power; Reliability engineering; Simulation; Engineering; Mathematical optimization; Electrical engineering; Power (physics); Mathematics; Waste management","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":[0.0002690876,0.0006894506,0.0005627214,0.0003252584,0.0002802796,0.0006899209,0.0005719036,0.0004618493,0.002679117],"category_scores_gemma":[0.000564757,0.0004116283,0.0004099501,0.0004773754,0.0003267786,0.0005856736,0.0002952013,0.0004232768,0.0001978196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008236769,"about_ca_system_score_gemma":0.00118695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023351,"about_ca_topic_score_gemma":0.01399108,"domain_scores_codex":[0.9998831,0.00004166371,0.000003791406,0.00002242444,0.00002395569,0.00002503674],"domain_scores_gemma":[0.9998311,0.00009219275,0.00002932302,0.000007614074,0.0000257773,0.00001402112],"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":[0.0000148383,0.00000492072,0.0000961626,0.00001141237,0.000003229196,0.00001341661,0.000006210776,0.9962409,0.0003084893,0.0009074227,0.0001137661,0.002279264],"study_design_scores_gemma":[0.000005411561,0.00001752363,0.000065303,0.000002071333,0.000003592624,0.000003894687,0.000008170021,0.9987755,0.0002281345,0.0007176607,0.0001710245,0.00000168678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1953277,0.000536298,0.7812313,0.0003778128,0.00005597741,0.0002147483,0.0004988056,0.0004084523,0.02134897],"genre_scores_gemma":[0.9675021,0.0001876537,0.02950024,0.00002293198,0.000008014514,0.0000965024,0.00008622909,0.00002633352,0.002569963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023351,"threshold_uncertainty_score":0.02034789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00770137621558218,"score_gpt":0.1854276660645149,"score_spread":0.1777262898489327,"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."}}