{"id":"W4292573084","doi":"10.3390/sym14081735","title":"Review of Model Predictive Control of Distributed Energy Resources in Microgrids","year":2022,"lang":"en","type":"article","venue":"Symmetry","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Shandong Province; Shenzhen Fundamental Research Program; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Distributed generation; Renewable energy; Model predictive control; Computer science; Photovoltaic system; Reliability (semiconductor); Grid; Distributed computing; Wind power; Energy storage; Reliability engineering; Control (management); Engineering; Electrical engineering; Power (physics)","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":[0.0001715186,0.00007942779,0.0002648559,0.0001000645,0.00001602793,0.000001814538,0.0001240464,0.00002724641,0.0000381082],"category_scores_gemma":[0.00002446936,0.0000828824,0.00006562805,0.0003690538,0.00001860241,0.00003178132,0.00003396259,0.00009446044,1.935553e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005182949,"about_ca_system_score_gemma":0.00001517192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002755495,"about_ca_topic_score_gemma":0.000002498292,"domain_scores_codex":[0.9993164,0.00004927993,0.0003153278,0.00009057436,0.0001131618,0.0001152351],"domain_scores_gemma":[0.9997009,0.00003444359,0.00006915104,0.0001354886,0.00003856858,0.00002141975],"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":[0.00004701561,0.00005936143,0.0005082486,0.001080992,0.00006405891,0.00000128259,0.00007915087,0.987034,0.003341881,0.0002920192,0.002078507,0.005413548],"study_design_scores_gemma":[0.0007686996,0.00005066163,0.000214431,0.0004080552,0.0000394121,0.000001877047,0.0000522948,0.9921479,0.001207556,0.000112785,0.004901997,0.00009431618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02111917,0.6450904,0.3277333,0.0001851114,0.0002571346,0.0005283899,0.002691894,0.0001640296,0.002230559],"genre_scores_gemma":[0.9904777,0.008984298,0.0002356745,0.0001306667,0.00001481088,0.00004423303,0.00009049376,0.00001354645,0.000008564359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9693586,"threshold_uncertainty_score":0.3379847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003803738615951842,"score_gpt":0.1831732906734524,"score_spread":0.1793695520575005,"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."}}