{"id":"W2937879615","doi":"10.1049/iet-gtd.2018.5872","title":"Optimal allocation of distributed generation for planning master–slave controlled microgrids","year":2019,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Distributed generation; Distributed computing; Master/slave; Mathematical optimization; Operations research; Engineering; Electrical engineering; Mathematics; Renewable energy; Parallel computing","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":[0.0007356457,0.0008158424,0.0008392364,0.0003227379,0.000308463,0.001003097,0.0007478221,0.0006649329,0.003126726],"category_scores_gemma":[0.001117605,0.0004963086,0.0004856382,0.0004467825,0.0005467398,0.0008284848,0.0006258977,0.0007437787,0.0002029146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008661795,"about_ca_system_score_gemma":0.001434467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004649261,"about_ca_topic_score_gemma":0.005920674,"domain_scores_codex":[0.9997274,0.0001249441,0.000008822035,0.00005642122,0.00004744451,0.00003494864],"domain_scores_gemma":[0.9997676,0.0001284653,0.00003587802,0.00001418821,0.00003535366,0.00001844797],"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":[0.000021344,0.00001360101,0.0001048118,0.00003283496,0.000009758574,0.00002714629,0.00002044821,0.9873368,0.0003863954,0.006013085,0.0003821378,0.005651554],"study_design_scores_gemma":[0.000008325868,0.00001503524,0.00004075791,0.000003762582,0.000003278026,0.000005383758,0.00001121959,0.9965289,0.000152944,0.002767382,0.000460828,0.000002118205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02316404,0.0003130748,0.9672633,0.0002632978,0.00004502852,0.0001109466,0.0001294832,0.0001251111,0.008585649],"genre_scores_gemma":[0.8473218,0.0004100743,0.1451071,0.00008100991,0.00004907084,0.000307975,0.0001674479,0.00007439234,0.006481083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004649261,"threshold_uncertainty_score":0.01045996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564697820175585,"score_gpt":0.2216534401264374,"score_spread":0.2060064619246815,"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."}}