{"id":"W2520206052","doi":"10.1049/iet-gtd.2016.0303","title":"Sensitivity‐based relaxation and decomposition method to dynamic reactive power optimisation considering DGs in active distribution networks","year":2016,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Independent Electricity System Operator","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"AC power; Mathematical optimization; Sensitivity (control systems); Relaxation (psychology); Computer science; Linear programming; Integer programming; Control theory (sociology); Electric power system; Power control; Power-flow study; Power (physics); Voltage; Mathematics; Electronic engineering; Engineering; Control (management)","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":[0.001418659,0.001384288,0.001044123,0.0006824079,0.0003608847,0.0008143481,0.000641154,0.0007950284,0.002695769],"category_scores_gemma":[0.002125762,0.0006502929,0.001458154,0.0008093999,0.0006624287,0.0007976381,0.0009459484,0.001792745,0.0003450609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006539632,"about_ca_system_score_gemma":0.001373232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006427536,"about_ca_topic_score_gemma":0.003370336,"domain_scores_codex":[0.9995974,0.0002101342,0.00001535384,0.00004972562,0.00008167473,0.00004581585],"domain_scores_gemma":[0.9991202,0.0006638,0.0000528452,0.00002890091,0.000100623,0.00003363562],"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":[0.00003054042,0.00002605354,0.0001421544,0.00007090007,0.00002967374,0.00004486228,0.00003723409,0.9753692,0.00106215,0.007292508,0.0006765723,0.0152182],"study_design_scores_gemma":[0.000002272551,0.000006453258,0.00001964557,0.000003007582,0.000002087256,0.000003447394,0.000002576334,0.9988319,0.0001019338,0.0008722843,0.0001523908,0.000001965229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00561181,0.0002770677,0.9917142,0.0001080692,0.00003871759,0.0000360525,0.00003252138,0.0001015885,0.002079925],"genre_scores_gemma":[0.5120485,0.001441425,0.4791275,0.0002109938,0.0001741216,0.0004409987,0.0002970362,0.0002209905,0.006038269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006427536,"threshold_uncertainty_score":0.01278025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008602034486430219,"score_gpt":0.2577911859521703,"score_spread":0.2491891514657401,"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."}}