{"id":"W4312532452","doi":"10.1109/tsg.2022.3228636","title":"Physics-Shielded Multi-Agent Deep Reinforcement Learning for Safe Active Voltage Control With Photovoltaic/Battery Energy Storage Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Reinforcement learning; Scalability; AC power; Computer science; Photovoltaic system; Energy storage; Electric power system; Battery (electricity); Engineering; Control engineering; Voltage; Power (physics); Artificial intelligence; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005518313,0.0005324934,0.0005347244,0.0001582292,0.0002273434,0.0004124596,0.0006530791,0.0004890458,0.000908813],"category_scores_gemma":[0.00112409,0.000274051,0.000291856,0.0001411986,0.0004533089,0.0004470037,0.0006418818,0.0009110725,0.0001087671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006180292,"about_ca_system_score_gemma":0.0008535912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006460352,"about_ca_topic_score_gemma":0.005093154,"domain_scores_codex":[0.9998631,0.00003822607,0.00000666498,0.0000257883,0.00003590089,0.00003024427],"domain_scores_gemma":[0.9996833,0.0001604438,0.00004451195,0.00002335161,0.00006148599,0.00002691509],"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":[0.00003094094,0.00002350948,0.0003518973,0.0000177805,0.00001183278,0.0000300182,0.00001652174,0.9828682,0.0007857045,0.00225161,0.0002412577,0.01337083],"study_design_scores_gemma":[0.000002477648,0.000006869569,0.00001833556,6.050264e-7,9.016587e-7,0.000001516659,7.177483e-7,0.9994217,0.00009264654,0.0004086198,0.00004501207,6.116611e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06645954,0.0003208785,0.929296,0.0002963588,0.00005889144,0.00003023404,0.00002583486,0.000471808,0.00304047],"genre_scores_gemma":[0.9720419,0.00006479531,0.02654803,0.00008671232,0.00001263763,0.00003429949,0.00002588977,0.00001731318,0.001168388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006460352,"threshold_uncertainty_score":0.01284546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01057924438012842,"score_gpt":0.2011182880561035,"score_spread":0.190539043675975,"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."}}