{"id":"W2125135194","doi":"10.1109/tpwrs.2012.2213309","title":"DG allocation for benefit maximization in distribution networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":293,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Distributed generation; Renewable energy; Installation; Maximization; Reliability engineering; Reliability (semiconductor); Computer science; Mathematical optimization; Upgrade; Reduction (mathematics); Integer programming; Power (physics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001458422,0.0002056887,0.0001982045,0.0001183679,0.00008704677,0.0001069814,0.0001071143,0.000207502,0.00004987897],"category_scores_gemma":[0.000004232142,0.0002298765,0.00008058843,0.0003504692,0.00001706339,0.0004225029,4.644855e-7,0.0001753711,0.0001089818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004413998,"about_ca_system_score_gemma":0.00001061958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000123799,"about_ca_topic_score_gemma":0.00004488408,"domain_scores_codex":[0.9988266,0.00002369462,0.0004237304,0.0002345051,0.0001554301,0.0003360599],"domain_scores_gemma":[0.9994282,0.00005773486,0.00004704904,0.0002489909,0.0001375523,0.00008049934],"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":[0.00001472574,0.00006138789,0.00002647555,0.00004601836,0.00002324755,2.767691e-7,0.00004683982,0.996324,0.0005847684,0.0003092959,0.001466144,0.001096791],"study_design_scores_gemma":[0.0005576634,0.0001004718,0.0009471596,0.00008515677,0.00001850843,0.000004741198,0.00007851831,0.9947639,0.001627026,0.00003739252,0.001515987,0.0002635519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02339356,0.0001217069,0.9717346,0.0000585522,0.002652674,0.001271461,0.0002179253,0.0003423577,0.000207165],"genre_scores_gemma":[0.9983321,0.00003193422,0.0001337595,0.0000122368,0.000025598,0.0009546456,0.0003325909,0.00004423152,0.0001328753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9749386,"threshold_uncertainty_score":0.9374091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006693980631605308,"score_gpt":0.1936410292391505,"score_spread":0.1869470486075452,"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."}}