{"id":"W2621489503","doi":"10.1109/tii.2017.2712652","title":"Optimal Energy Management and Marginal-Cost Electricity Pricing in Microgrid Network","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Microgrid; Electricity market; Mathematical optimization; Electricity; Computer science; Marginal cost; Smart grid; Electricity generation; Renewable energy; Electricity pricing; Demand response; Distributed generation; Energy management; Energy (signal processing); Power (physics); Economics; Microeconomics; Engineering; Mathematics; Electrical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001555189,0.0001700794,0.00019225,0.0001682247,0.000287277,0.0002104525,0.0001635009,0.0001612237,0.00001673068],"category_scores_gemma":[0.000002357514,0.0001785092,0.00004290111,0.0001498874,0.00003122684,0.0003972989,0.000002921757,0.0003308824,0.000006496207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009939441,"about_ca_system_score_gemma":0.0000128345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000049872,"about_ca_topic_score_gemma":0.00004332027,"domain_scores_codex":[0.999078,0.00001276787,0.000395758,0.00008028703,0.0001125973,0.0003205421],"domain_scores_gemma":[0.9995511,0.00002856622,0.00008887067,0.0002446008,0.00001870392,0.00006815776],"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":[0.0000431502,0.0000171868,0.00004248384,0.00001806104,0.0000458404,0.000002549272,0.0000883536,0.8362044,0.00001113894,0.00003843583,0.0004525055,0.1630359],"study_design_scores_gemma":[0.002606464,0.00006777426,0.0002316621,0.0001184463,0.00006616982,0.00001110784,0.00007253429,0.9803299,0.002798087,0.00001786452,0.0133541,0.0003259457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04445483,0.0001520993,0.9502608,0.00005390202,0.001296922,0.0005172584,0.00002135741,0.0001646214,0.003078214],"genre_scores_gemma":[0.9914602,0.002070473,0.005950962,0.00007737918,0.0002075846,0.00007166811,0.000007622968,0.00002641257,0.0001277213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9470053,"threshold_uncertainty_score":0.7279396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01565635964164344,"score_gpt":0.2055296919370003,"score_spread":0.1898733322953568,"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."}}