{"id":"W3157915181","doi":"","title":"Bilevel models for demand response in smart grids","year":2020,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bilevel optimization; Mathematical optimization; Demand response; Leverage (statistics); Computer science; Optimization problem; Flexibility (engineering); Anticipation (artificial intelligence); Smart grid; Electricity; Mathematics; 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":[0.001321012,0.001313146,0.002163679,0.0007180069,0.0005890794,0.002988092,0.001178767,0.001908192,0.00746183],"category_scores_gemma":[0.005505356,0.0008679169,0.001491053,0.001486285,0.0008844904,0.002222941,0.002254213,0.002390681,0.000981682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00119842,"about_ca_system_score_gemma":0.001289537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008277554,"about_ca_topic_score_gemma":0.008434362,"domain_scores_codex":[0.9991666,0.0003522765,0.00004044527,0.0001317293,0.0001688646,0.0001399593],"domain_scores_gemma":[0.9981769,0.001269405,0.0001363492,0.00007829499,0.0002568048,0.00008228415],"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":[0.00004880808,0.00002664328,0.0003241816,0.00009970704,0.00003733177,0.00003441858,0.0000558916,0.9662234,0.0002397241,0.02161824,0.0006515193,0.01064017],"study_design_scores_gemma":[0.000007305039,0.00001256388,0.00004916137,0.00001395044,0.000005841473,0.000007158986,0.00002070897,0.9814808,0.00009529679,0.01713536,0.001166414,0.000005491855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02269301,0.001912321,0.9616643,0.0009051129,0.0001579891,0.00005133901,0.0003232859,0.0003052911,0.01198739],"genre_scores_gemma":[0.807627,0.003362977,0.1540537,0.0003193742,0.0002011809,0.000339902,0.0009214174,0.0003224139,0.03285194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008277554,"threshold_uncertainty_score":0.02496225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02089551195150685,"score_gpt":0.2280817420961219,"score_spread":0.2071862301446151,"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."}}