{"id":"W2023865237","doi":"10.1109/tsg.2014.2302245","title":"Game-Theoretic Demand-Side Management With Storage Devices for the Future Smart Grid","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":235,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Stackelberg competition; Smart grid; Nash equilibrium; Computer science; Game theory; Energy storage; Mathematical optimization; Schedule; Profit (economics); Energy consumption; Uniqueness; Energy management; Demand response; Operations research; Mathematical economics; Energy (signal processing); Electricity; Microeconomics; Economics; Power (physics); Engineering; Mathematics; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004396103,0.0004603064,0.0003187782,0.0002313431,0.0003702405,0.0001196725,0.0004695183,0.0001073865,0.00009938859],"category_scores_gemma":[0.000001502564,0.0003355492,0.0002017789,0.000376139,0.0001064087,0.0001926263,0.000004548245,0.0003278355,0.0001223873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001382769,"about_ca_system_score_gemma":0.00001048832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001858867,"about_ca_topic_score_gemma":0.000632809,"domain_scores_codex":[0.9981244,0.00006888845,0.0003500334,0.0004599617,0.0004141882,0.0005825706],"domain_scores_gemma":[0.9986197,0.0002570386,0.00005894995,0.0008685682,0.00005498893,0.0001407659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001154135,0.00008169739,0.00004438517,0.0003306302,0.0006137721,0.000008779261,0.0001481323,0.9755339,0.0000353942,0.001630438,0.006939584,0.01451785],"study_design_scores_gemma":[0.002128245,0.0003902762,0.003838653,0.0001872116,0.0008928702,0.0000265762,0.0004966257,0.1569892,0.003669947,0.0002126509,0.8302293,0.0009383868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01155267,0.00021411,0.9722295,0.0007287168,0.008668281,0.0009881662,0.00004873654,0.00071521,0.004854613],"genre_scores_gemma":[0.992622,0.0004535655,0.002465442,0.0006376109,0.001571322,0.001105892,0.00002275492,0.0001647927,0.0009566735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9810693,"threshold_uncertainty_score":0.9999096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005398316040496996,"score_gpt":0.1888385152619778,"score_spread":0.1834401992214808,"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."}}