{"id":"W2248709188","doi":"10.1109/epec.2015.7379993","title":"Impacts of binding constraints on the planning process of renewable DG in distribution systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Renewable energy; Mathematical optimization; Overhead (engineering); Computer science; Sizing; Probabilistic logic; Distributed generation; Linear programming; Integer programming; 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":[0.0004102329,0.00008707355,0.0001533921,0.00005056248,0.00001183801,0.00001268016,0.00009250619,0.00005887755,0.000009420069],"category_scores_gemma":[0.0001540577,0.00006473305,0.00001962717,0.0002650826,0.00004613923,0.0001006798,0.0000102417,0.00008219692,0.000005383441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001402143,"about_ca_system_score_gemma":0.00003722503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001647677,"about_ca_topic_score_gemma":0.000008872995,"domain_scores_codex":[0.9992918,0.00002135611,0.0002636313,0.00007525656,0.0001776436,0.0001703604],"domain_scores_gemma":[0.9996424,0.00005941225,0.00005983988,0.00011749,0.00007296284,0.0000479495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004897806,0.00005842013,0.01653434,0.0003568862,0.00003414811,0.000003786529,0.0007825804,0.9613375,0.01509102,0.002407183,0.003226391,0.0001187502],"study_design_scores_gemma":[0.002382956,0.0005628922,0.01052254,0.00289105,0.00004059027,0.00002720621,0.01837345,0.452238,0.5117638,0.0002803728,0.0002836142,0.000633585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871078,0.00007269788,0.005577999,0.00001514578,0.0001544219,0.0001863436,0.0001448486,0.00005516114,0.006685565],"genre_scores_gemma":[0.9998658,0.000001657488,0.00001617666,0.000001392393,0.00001216249,0.000007448034,0.00008047927,0.000007831075,0.000007029528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5090995,"threshold_uncertainty_score":0.2639737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02570969900297204,"score_gpt":0.2603572554497329,"score_spread":0.2346475564467609,"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."}}