{"id":"W2033109453","doi":"10.1109/psce.2006.296349","title":"Optimal Power Flow with Complementarity Constraints","year":2006,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Mathematical optimization; Complementarity (molecular biology); Mixed complementarity problem; Electric power system; Power flow; Complementarity theory; Computer science; Interior point method; Operating point; Benchmark (surveying); Optimization problem; Economic dispatch; Mathematics; Power (physics); Engineering; Nonlinear system","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":[0.0008466996,0.0008175806,0.0008429161,0.0005914276,0.0004419357,0.001073021,0.000621959,0.0007296893,0.003637799],"category_scores_gemma":[0.001956853,0.0004804252,0.0006052721,0.0008105726,0.0009604151,0.001317908,0.0006977005,0.0008920234,0.0003006507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206044,"about_ca_system_score_gemma":0.001759726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006382025,"about_ca_topic_score_gemma":0.003517998,"domain_scores_codex":[0.9995052,0.000188317,0.00001619959,0.00007473805,0.0001444269,0.00007113106],"domain_scores_gemma":[0.9995348,0.0003067155,0.00005258916,0.00002462253,0.00006656152,0.00001465493],"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":[0.00001445663,0.00001692097,0.0000760172,0.00002378228,0.000004882328,0.00002824108,0.00001895481,0.9346808,0.0003461054,0.05841267,0.0004143835,0.005962733],"study_design_scores_gemma":[0.000008886455,0.00001411032,0.00004015167,0.000005148445,0.000002390394,0.000009318781,0.000006607778,0.9726484,0.0003113498,0.02589071,0.001059314,0.000003548829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01222733,0.00009917897,0.9762656,0.0001960544,0.00001679963,0.00004874207,0.0000725099,0.00007127527,0.01100255],"genre_scores_gemma":[0.6905991,0.0003553287,0.2959155,0.0001309388,0.00005320426,0.0003735187,0.0002079887,0.0001063059,0.01225813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006382025,"threshold_uncertainty_score":0.01268977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004802236468634928,"score_gpt":0.1891944526791192,"score_spread":0.1843922162104843,"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."}}