{"id":"W2924826474","doi":"10.1109/tsg.2019.2906823","title":"Hierarchical and Decentralized Stochastic Energy Management for Smart Distribution Systems With High BESS Penetration","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Energy management; Mathematical optimization; Markov decision process; Computer science; Energy management system; Partially observable Markov decision process; Markov process; Smart grid; Computational complexity theory; Markov chain; Engineering; Markov model; Energy (signal processing); Mathematics; Electrical engineering; Algorithm","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":[0.0004559822,0.0004887338,0.0005981101,0.0001909364,0.0004990991,0.000557334,0.0007761998,0.0003363178,0.001550856],"category_scores_gemma":[0.000703961,0.0002246992,0.0003653644,0.0002858189,0.000339431,0.0007221685,0.0006077399,0.0004918923,0.0001783866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005974493,"about_ca_system_score_gemma":0.000858317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003743458,"about_ca_topic_score_gemma":0.00699824,"domain_scores_codex":[0.9995779,0.00009436901,0.00002725884,0.0001009142,0.0001223971,0.00007708662],"domain_scores_gemma":[0.999631,0.0001197239,0.00008471993,0.00005061894,0.00008582352,0.00002806146],"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":[0.00008541807,0.00007054101,0.000799935,0.00008289875,0.00003243072,0.0001352982,0.00006819631,0.9275946,0.008204496,0.01554049,0.001198515,0.04618717],"study_design_scores_gemma":[0.000008123904,0.00002485158,0.000191667,0.000002018415,0.00000508765,0.00001930282,0.000008564398,0.9964259,0.0005081773,0.002465741,0.0003366195,0.000003926965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04510177,0.0001742022,0.9514351,0.0001400567,0.00002891712,0.00005930856,0.00005847288,0.0003402806,0.002661871],"genre_scores_gemma":[0.9717177,0.00007638308,0.02721389,0.00003244108,0.00002236784,0.00003362639,0.00005037277,0.00001151895,0.000841804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003743458,"threshold_uncertainty_score":0.007443368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005864645785266788,"score_gpt":0.1854539649386169,"score_spread":0.1795893191533501,"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."}}