{"id":"W2321414982","doi":"10.1109/tsg.2016.2543264","title":"Scalable Optimization Methods for Distribution Networks With High PV Integration","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":170,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Laboratory Directed Research and Development; Division of Electrical, Communications and Cyber Systems; University of Minnesota; National Science Foundation","keywords":"Mathematical optimization; Photovoltaic system; Computer science; Quadratic equation; AC power; Scalability; Power flow; Nonlinear system; Quadratic programming; Power (physics); Control theory (sociology); Electric power system; Voltage; Control engineering; Engineering; Mathematics; Electrical engineering","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":[0.0009821961,0.001061837,0.0008561172,0.0004483581,0.0004726296,0.0008164859,0.000896818,0.0006706269,0.003848586],"category_scores_gemma":[0.002285868,0.0005293064,0.0005782626,0.0007257593,0.000587987,0.00108211,0.001382648,0.001544086,0.0006432008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008488796,"about_ca_system_score_gemma":0.001196198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004812721,"about_ca_topic_score_gemma":0.00629427,"domain_scores_codex":[0.9996479,0.0001146222,0.00001298822,0.00006289662,0.0001299058,0.0000317726],"domain_scores_gemma":[0.999324,0.000425396,0.00006209204,0.00005788012,0.0001057035,0.00002502921],"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":[0.00001524197,0.00002206631,0.0001203417,0.00005484006,0.00001676529,0.00003090445,0.00001822036,0.957491,0.0005495611,0.01712378,0.001248405,0.02330893],"study_design_scores_gemma":[0.000005910643,0.000004857402,0.00002013873,0.000003434452,0.000001521183,0.000004165765,0.000003758646,0.9930022,0.00007270544,0.006188125,0.0006916565,0.000001432946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003278183,0.0003358713,0.9921581,0.0001936842,0.00003753308,0.00003830191,0.00005686675,0.0001979521,0.003703478],"genre_scores_gemma":[0.37015,0.001194161,0.6187685,0.0001735362,0.0001987172,0.0005474098,0.0003478482,0.0003180909,0.008301713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004812721,"threshold_uncertainty_score":0.01287478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00938960024598681,"score_gpt":0.2442615278006634,"score_spread":0.2348719275546766,"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."}}