{"id":"W2343495502","doi":"10.1109/tsg.2015.2499264","title":"Optimal ESS Allocation for Benefit Maximization in Distribution Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Waterloo","funders":"Government of Canada","keywords":"Sizing; Probabilistic logic; Maximization; Computer science; Smart grid; Reliability (semiconductor); Peaking power plant; Renewable energy; Reliability engineering; Mathematical optimization; Operations research; Distributed generation; Engineering; Power (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002207137,0.0001915186,0.0001686114,0.000105327,0.00007556694,0.00004745241,0.0001022205,0.0001732297,0.00001379062],"category_scores_gemma":[0.000008531739,0.0002277451,0.000077329,0.000382394,0.00002367724,0.0003377848,7.433238e-7,0.0002043774,0.00003271093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005099639,"about_ca_system_score_gemma":0.00002558503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003131177,"about_ca_topic_score_gemma":0.0001054099,"domain_scores_codex":[0.9989555,0.00001865319,0.0003129114,0.0002282288,0.0001697385,0.0003149315],"domain_scores_gemma":[0.999465,0.00004344062,0.00003274774,0.0002029767,0.0001356762,0.0001201792],"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":[0.00009726565,0.00008393579,0.00003639362,0.00001603862,0.00001673344,7.202565e-7,0.00004654259,0.9937361,0.0001459547,0.00009081582,0.00146626,0.004263185],"study_design_scores_gemma":[0.001079306,0.0001462947,0.0005135895,0.00003190472,0.00003233832,0.000004628198,0.00004428672,0.9872661,0.008039463,0.00004464791,0.002543048,0.0002544226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06837128,0.00004613976,0.9277738,0.0000976422,0.002454519,0.0004655011,0.0004065558,0.0002950287,0.00008952808],"genre_scores_gemma":[0.996834,0.00003685731,0.00156154,0.00001928443,0.0001610777,0.000252935,0.001053181,0.00003881754,0.0000423109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9284627,"threshold_uncertainty_score":0.9287175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620210299193571,"score_gpt":0.2261103407964321,"score_spread":0.2099082378044964,"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."}}