{"id":"W4236816715","doi":"10.1109/pesgm46819.2021.9638223","title":"Integrating Net Benefits Test for Demand Response into Optimal Power Flow Formulation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Power & Energy Society General Meeting (PESGM)","topic":"Electric Power System Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Procurement; Demand response; Power flow; Payment; Test (biology); Commission; Environmental economics; Order (exchange); Business; Microeconomics; Economics; Power (physics); Finance; Electric power system; Engineering; Electrical engineering; Marketing; Electricity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004441486,0.001506856,0.001482993,0.0009890058,0.0004564882,0.001738203,0.001376682,0.001925958,0.01191356],"category_scores_gemma":[0.01118712,0.0008320121,0.00101088,0.0006351265,0.001252012,0.001977728,0.001368848,0.002806669,0.0007987455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001319644,"about_ca_system_score_gemma":0.002832784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008001396,"about_ca_topic_score_gemma":0.006552394,"domain_scores_codex":[0.9986141,0.0007576625,0.0000302809,0.0001069035,0.0003022549,0.0001888469],"domain_scores_gemma":[0.9972075,0.002109999,0.0001129189,0.00005814185,0.0004327166,0.00007877537],"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":[0.00004508865,0.00004680955,0.0002140525,0.00006612261,0.00001592323,0.00008394389,0.00002821971,0.953158,0.0002528182,0.03813677,0.00207147,0.005880798],"study_design_scores_gemma":[0.000009620412,0.00002102434,0.00003151393,0.00001169392,0.000002569629,0.000006071419,0.00001016124,0.9901695,0.00007083693,0.00881954,0.0008434691,0.00000401647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008330422,0.0004186892,0.9555843,0.001226349,0.0003206362,0.0003351693,0.0001989524,0.0001924161,0.03339311],"genre_scores_gemma":[0.6667829,0.0007061443,0.3016388,0.001422352,0.000444869,0.001711241,0.0006326674,0.0005330844,0.02612801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01191356,"threshold_uncertainty_score":0.03985488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009788778924891068,"score_gpt":0.2273988463125864,"score_spread":0.2176100673876953,"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."}}