{"id":"W4323838514","doi":"10.1016/j.energy.2023.127133","title":"Assessing the potential of demand-side flexibility to improve the performance of electricity systems under high variable renewable energy penetration","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Mitacs","keywords":"Variable renewable energy; Software deployment; Renewable energy; Flexibility (engineering); Environmental economics; Electricity; Demand response; Electricity demand; Peak demand; Electricity system; Context (archaeology); Environmental science; Business; Natural resource economics; Electric power system; Computer science; Engineering; Electricity generation; Economics; Geography","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":[0.0006874301,0.0001741185,0.0002508358,0.0001383267,0.0001503358,0.00007671898,0.0002802753,0.0001372339,0.00001046511],"category_scores_gemma":[0.00004292472,0.0001162642,0.00005315714,0.001195551,0.00003425608,0.0002585435,0.00004210593,0.00009505432,0.000001918155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337443,"about_ca_system_score_gemma":0.00008779486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02243704,"about_ca_topic_score_gemma":0.0004422337,"domain_scores_codex":[0.9984894,0.0001693089,0.0005189222,0.0002092346,0.0003141692,0.000298951],"domain_scores_gemma":[0.9989892,0.0001260492,0.0001463998,0.0004754042,0.0002244415,0.00003848636],"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":[0.000008233787,0.000008649704,0.00001879361,0.00004909198,0.0000494894,2.749651e-7,0.00003716641,0.8420133,0.1491642,0.008021227,0.0004112725,0.0002183316],"study_design_scores_gemma":[0.0001023427,0.00004571193,0.0005281222,0.00004513461,0.00002257147,0.000002892284,0.0001495881,0.7856842,0.2128079,0.0001964087,0.0003098647,0.0001052247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5121505,0.0002019052,0.4830492,0.00003960071,0.001247671,0.0001280897,0.000008135989,0.0002190069,0.002955952],"genre_scores_gemma":[0.9981428,0.00006910366,0.0003179582,0.00002623671,0.0001759946,0.00007145778,0.00004478092,0.00003844724,0.00111327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4859923,"threshold_uncertainty_score":0.9840726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008052331504232126,"score_gpt":0.2145235829909882,"score_spread":0.2064712514867561,"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."}}