{"id":"W4235578714","doi":"10.32920/ryerson.14660970","title":"AC-DC Microgrid Optimal Power Flow","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Microgrid; Reliability (semiconductor); Electricity generation; Electricity; Grid; Battery (electricity); Peaking power plant; Power (physics); Dual (grammatical number); Mathematical optimization; Converters; Deregulation; Computer science; Reliability engineering; Electrical engineering; Distributed generation; Renewable energy; Engineering; Economics; Mathematics; Voltage","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":[0.0002701117,0.0005930112,0.0005980377,0.0003660849,0.0004576609,0.001417436,0.0004062394,0.000598717,0.01012675],"category_scores_gemma":[0.0007148175,0.0003148933,0.0002953609,0.0005430738,0.0002868109,0.0005400108,0.000513311,0.0005213979,0.0009738884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009653075,"about_ca_system_score_gemma":0.001163593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01071234,"about_ca_topic_score_gemma":0.01060458,"domain_scores_codex":[0.999925,0.00002377305,0.000003364033,0.00001877141,0.00001570979,0.00001332129],"domain_scores_gemma":[0.9998835,0.00004488097,0.00001314078,0.000007894456,0.00004021807,0.00001042939],"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":[0.00007932841,0.00003747352,0.0003643569,0.0001218542,0.00001418495,0.0000640436,0.0000320307,0.9503184,0.001114527,0.02330986,0.004910445,0.01963346],"study_design_scores_gemma":[0.00001211199,0.0000200624,0.0001482144,0.00001074904,0.000005344581,0.000009928428,0.00003088,0.9904981,0.0002647567,0.007327299,0.001669527,0.000003052581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1102446,0.001820367,0.6285614,0.002282131,0.0002976003,0.0003934087,0.001706494,0.0006103644,0.2540836],"genre_scores_gemma":[0.9197952,0.0008226513,0.05208552,0.0001119612,0.00006375367,0.0001824332,0.0003487777,0.00007629806,0.02651345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01071234,"threshold_uncertainty_score":0.03387731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004369103044328974,"score_gpt":0.1801927267008907,"score_spread":0.1758236236565618,"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."}}