{"id":"W2419231748","doi":"10.1016/j.energy.2016.05.018","title":"Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty","year":2016,"lang":"en","type":"article","venue":"Energy","topic":"Electric Power System Optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Economic dispatch; Stochastic programming; Electricity market; Wind power; Electric power system; Electricity; Demand response; Mathematical optimization; Computer science; Interval (graph theory); Robust optimization; Operations research; Risk management; Reliability engineering; Engineering; Power (physics); Economics; Finance; Mathematics","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":[0.002495003,0.001366121,0.002629827,0.000550688,0.0005833925,0.002290831,0.0015513,0.002595774,0.002466759],"category_scores_gemma":[0.005363777,0.001436018,0.0008912478,0.0007626524,0.001610699,0.001833359,0.001597675,0.002875552,0.0002467315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001476794,"about_ca_system_score_gemma":0.002116333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01167462,"about_ca_topic_score_gemma":0.007150683,"domain_scores_codex":[0.9989455,0.0005031669,0.00004871889,0.0001585296,0.0002053038,0.0001387658],"domain_scores_gemma":[0.9976368,0.001514513,0.0002923745,0.00009093743,0.0002808236,0.0001846517],"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":[0.00001818274,0.000006878407,0.00005264473,0.000008401584,0.000009694383,0.00001686778,0.000006388529,0.9971199,0.0000505792,0.002179841,0.0001028686,0.0004278558],"study_design_scores_gemma":[0.00000328304,0.000005061498,0.00002196575,8.864169e-7,0.000001913215,0.000001309872,0.000001295879,0.9991809,0.00001291873,0.0007455582,0.0000232354,0.000001761661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09834038,0.000721918,0.8894004,0.001391874,0.0002423696,0.00009252683,0.0004103052,0.0002678844,0.009132375],"genre_scores_gemma":[0.9798266,0.0002181896,0.01418331,0.0001003588,0.00004977884,0.00008415113,0.0001674597,0.00004685485,0.005323283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01167462,"threshold_uncertainty_score":0.02321333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004602122078304065,"score_gpt":0.1781376304457637,"score_spread":0.1735355083674597,"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."}}