{"id":"W4388991800","doi":"10.1016/j.jclepro.2023.139794","title":"Enhancing the resilience of zero-carbon energy communities: Leveraging network reconfiguration and effective load carrying capability quantification","year":2023,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; University of Connecticut","keywords":"Resilience (materials science); Control reconfiguration; Distributed generation; Grid; Adaptability; Computer science; Multi-objective optimization; Risk analysis (engineering); Pareto principle; Environmental economics; Distributed computing; Environmental resource management; Engineering; Business; Environmental science; Renewable energy; Operations management; Ecology; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007586437,0.0005897521,0.0003687292,0.0006618453,0.0004742145,0.001024635,0.0007759358,0.000585027,0.001634985],"category_scores_gemma":[0.00437096,0.0001963668,0.0002099459,0.0003304267,0.0008344353,0.002268756,0.001834731,0.0005950598,0.0001801291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006337554,"about_ca_system_score_gemma":0.00071448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001942153,"about_ca_topic_score_gemma":0.002639368,"domain_scores_codex":[0.99976,0.00005835386,0.000006669137,0.00006063907,0.00005089335,0.00006348754],"domain_scores_gemma":[0.9987422,0.0005566712,0.0002038098,0.0001641115,0.0001792232,0.0001540941],"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":[0.0001496289,0.0001156361,0.003615019,0.00006620697,0.00004326211,0.0001527074,0.0001099836,0.910201,0.0153468,0.02353307,0.0007522026,0.0459145],"study_design_scores_gemma":[0.00000787649,0.00006880084,0.0007110864,0.00001134721,0.00001129713,0.00002977186,0.00009584865,0.9725061,0.002714352,0.0232824,0.0005489321,0.00001223953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6008623,0.0003078241,0.3833345,0.001108307,0.0001123051,0.0000776356,0.0001205189,0.0005850507,0.0134915],"genre_scores_gemma":[0.9942784,0.00003553941,0.00523832,0.00002185517,0.000007853916,0.000008465925,0.00001562072,0.00001225867,0.0003816428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001942153,"threshold_uncertainty_score":0.005469561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227772875383418,"score_gpt":0.2253833278676824,"score_spread":0.2131055991138482,"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."}}