{"id":"W2162884667","doi":"10.1109/tpwrs.2005.846171","title":"Optimal Power Flow With Complementarity Constraints","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Electric Power Systems and Control","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Power flow; Complementarity (molecular biology); Mixed complementarity problem; Mathematical optimization; Complementarity theory; Electric power system; Maximum flow problem; Operating point; Maximum power principle; Computer science; Control theory (sociology); Mathematics; Power (physics); Engineering; Voltage; Electronic engineering; Electrical engineering; Nonlinear system; Physics","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":[0.0006446295,0.0007531092,0.0009325068,0.0005681967,0.0004737311,0.001363866,0.0006831475,0.0009443059,0.004727196],"category_scores_gemma":[0.00214776,0.0006472989,0.0005506885,0.0009339253,0.0008775974,0.001589892,0.0008703919,0.0009816109,0.0005126638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059815,"about_ca_system_score_gemma":0.001458251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005023409,"about_ca_topic_score_gemma":0.003164604,"domain_scores_codex":[0.9995707,0.0001659773,0.00001240599,0.00006523151,0.0001046574,0.00008114787],"domain_scores_gemma":[0.9996254,0.000238891,0.00004102571,0.00001718959,0.00005612718,0.00002134848],"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":[0.00002885312,0.00002707728,0.00008549362,0.00006323136,0.000008332863,0.00007103722,0.00003541317,0.8194352,0.0006324219,0.1666698,0.001959621,0.01098347],"study_design_scores_gemma":[0.0000169577,0.00001739942,0.0000538361,0.00001285188,0.000004322781,0.00001997776,0.00001301779,0.9215628,0.0003157264,0.07544388,0.002532541,0.000006698538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0106997,0.0002136318,0.9624378,0.0004311115,0.00005033278,0.00004423446,0.00009679476,0.00009080391,0.02593544],"genre_scores_gemma":[0.744435,0.000998767,0.2279009,0.0003082814,0.0001665351,0.0003896009,0.0002352734,0.000153078,0.02541248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005023409,"threshold_uncertainty_score":0.01581401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006627572279855933,"score_gpt":0.1947850479219204,"score_spread":0.1881574756420644,"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."}}