{"id":"W4312253434","doi":"10.1109/tpwrs.2022.3217941","title":"Distributionally Robust Optimal Power Flow via Ellipsoidal Approximation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Electric Power System Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Probabilistic logic; Renewable energy; Robust optimization; Computer science; Schedule; Electric power system; Upper and lower bounds; Probability distribution; Power flow; Power (physics); Engineering; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004233604,0.0003519966,0.0003397548,0.0003479935,0.0005116691,0.0001121978,0.000312266,0.0001394248,0.0006002123],"category_scores_gemma":[0.000003349006,0.0004095467,0.000179873,0.0008346558,0.00002703328,0.0003239755,0.000002973254,0.0005594219,0.0001727702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000951311,"about_ca_system_score_gemma":0.00006650217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002270822,"about_ca_topic_score_gemma":0.000002638869,"domain_scores_codex":[0.9974753,0.0001875811,0.0006443306,0.0004125991,0.0007862911,0.0004938521],"domain_scores_gemma":[0.9990738,0.00007831776,0.00010607,0.0004807851,0.0001145673,0.0001464338],"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":[0.00003700155,0.000144809,0.000003560514,0.00004581714,0.000124849,0.00000965353,0.0003086357,0.9953181,0.00097147,0.0001197708,0.002748725,0.0001675645],"study_design_scores_gemma":[0.0005586333,0.0002289529,0.00001358987,0.00002713526,0.00003916608,0.0002113486,0.0002010328,0.9900932,0.001397757,0.00000461729,0.006764427,0.0004601291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003483707,0.0002428576,0.9856281,0.00004941666,0.006453167,0.0007897621,0.0004076945,0.0009333089,0.002012047],"genre_scores_gemma":[0.9969708,0.000008624555,0.001216499,0.00002311627,0.00002736336,0.000661582,0.0001079405,0.0001018357,0.0008822901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9934871,"threshold_uncertainty_score":0.9998356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007296792219374998,"score_gpt":0.1811972344650737,"score_spread":0.1739004422456987,"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."}}