{"id":"W3010250576","doi":"10.1109/icaee47123.2019.9014662","title":"Optimal Integration of Renewable Distributed Generation in Practical Distribution Grids based on Moth-Flame optimization Algorithm","year":2019,"lang":"en","type":"article","venue":"2019 International Conference on Advanced Electrical Engineering (ICAEE)","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Sizing; Distributed generation; Photovoltaic system; Computer science; Renewable energy; Mathematical optimization; Power (physics); Algorithm; Optimization algorithm; Stability (learning theory); Turbine; Automotive 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":[0.0007559104,0.0006879279,0.0008243014,0.0005319349,0.0003239364,0.0006341255,0.0006472604,0.0007472147,0.001527009],"category_scores_gemma":[0.001153988,0.0003646751,0.0005808112,0.0004844915,0.0003941593,0.0005216366,0.0005454788,0.0005204501,0.00016305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005598054,"about_ca_system_score_gemma":0.001000046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008175265,"about_ca_topic_score_gemma":0.005356826,"domain_scores_codex":[0.9998573,0.00004545921,0.000006299946,0.0000225741,0.00004630941,0.00002198192],"domain_scores_gemma":[0.99971,0.0001569826,0.00003731996,0.000008129175,0.0000715346,0.00001601995],"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":[0.0000360545,0.00001365382,0.0002174336,0.00001612655,0.000008906396,0.00002126891,0.00001110853,0.9904231,0.0003528766,0.001497091,0.0001726594,0.007229737],"study_design_scores_gemma":[0.000006542148,0.00001079276,0.0000228533,0.000001569376,0.00000126888,0.000002576688,0.000002151266,0.9995247,0.00007584137,0.000280399,0.00007023927,0.000001129183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0559813,0.0003087746,0.9371904,0.0002210767,0.00004845719,0.0001026704,0.00005121816,0.0002200072,0.005876227],"genre_scores_gemma":[0.7261602,0.0002328608,0.2698692,0.00007164438,0.00002990575,0.0002054795,0.000114386,0.00005311971,0.003263154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008175265,"threshold_uncertainty_score":0.01625532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141810857412042,"score_gpt":0.2510314696368036,"score_spread":0.2396133610626831,"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."}}