{"id":"W4409786295","doi":"10.1016/j.esr.2025.101712","title":"Penalty mechanism in transactive energy: A mechanism design approach for day-ahead markets","year":2025,"lang":"en","type":"article","venue":"Energy Strategy Reviews","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation de l’UQTR; Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Mechanism (biology); Transactive memory; Mechanism design; Energy (signal processing); Computer science; Risk analysis (engineering); Economics; Business; Environmental economics; Microeconomics; Mathematics; Knowledge management","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012917,0.0005799748,0.0008964339,0.0004100099,0.00009470259,0.00007342427,0.0005170876,0.0002400303,0.0000777448],"category_scores_gemma":[0.00002975609,0.0005574569,0.0002850475,0.0007531005,0.00002754146,0.0002274049,0.00004187011,0.0002080874,0.000005358971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000268024,"about_ca_system_score_gemma":0.00006993121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001160907,"about_ca_topic_score_gemma":0.0001762157,"domain_scores_codex":[0.9971694,0.0004083046,0.0008460753,0.0006733353,0.0002040002,0.0006988275],"domain_scores_gemma":[0.9989701,0.0001516046,0.00009519757,0.0006200531,0.0000423577,0.0001206804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006769712,0.0001551283,0.000001163568,0.0005402135,0.0002690251,0.00001538404,0.00007704029,0.2439309,0.001992557,0.6734416,0.007402666,0.07210666],"study_design_scores_gemma":[0.001774048,0.0001423779,0.00004402293,0.0003956977,0.0002108083,0.000005998854,0.0001165128,0.6532228,0.0286164,0.06010702,0.2542355,0.001128784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0000566768,0.008595356,0.955476,0.00004624445,0.0005627259,0.0007115154,0.000009593803,0.0002542752,0.03428761],"genre_scores_gemma":[0.8223542,0.03641542,0.1098633,0.001447834,0.0005274426,0.01391239,0.000321075,0.0004103462,0.01474793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8456126,"threshold_uncertainty_score":0.9996877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03127597932638341,"score_gpt":0.2441381761594641,"score_spread":0.2128621968330807,"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."}}