{"id":"W4313129946","doi":"10.1109/tpwrs.2022.3228838","title":"MILP Model for Optimal Day-Ahead PDS Scheduling Considering TSO-DSO Interconnection Power Flow Commitment Under Uncertainty","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Simon Fraser University","funders":"","keywords":"Dispatchable generation; Interconnection; Integer programming; Linear programming; Scheduling (production processes); Mathematical optimization; Distributed generation; AC power; Computer science; Power flow; Electric power system; Voltage; Engineering; Power (physics); Reliability engineering; Electrical engineering; Renewable energy; Mathematics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001317578,0.001359484,0.001611056,0.0006977497,0.0006575017,0.002130474,0.001275316,0.001407546,0.003793713],"category_scores_gemma":[0.00182077,0.0009320119,0.001027404,0.001106354,0.0007890904,0.0009853101,0.001208441,0.001950686,0.0004713961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001582587,"about_ca_system_score_gemma":0.00243749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01230159,"about_ca_topic_score_gemma":0.009245029,"domain_scores_codex":[0.9992167,0.0002792442,0.00002951465,0.0001149284,0.0002140325,0.0001455623],"domain_scores_gemma":[0.9991651,0.0004550828,0.0001260205,0.00002980252,0.0001607259,0.00006337991],"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":[0.000007902257,0.000004090636,0.00004649977,0.000009954489,0.000005641396,0.00002177057,0.000007015697,0.9962986,0.00007073294,0.002521835,0.0001660688,0.0008399901],"study_design_scores_gemma":[0.000003353488,0.000005752101,0.00002017561,0.000002851893,0.000002577474,0.000002580986,0.000004559399,0.9982252,0.00004145448,0.001485098,0.000204272,0.000002108795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01299291,0.0003832951,0.9752597,0.0005248379,0.00007888753,0.0001048556,0.0004334679,0.0002534881,0.009968558],"genre_scores_gemma":[0.8393172,0.000736837,0.1490763,0.0002424273,0.0001062675,0.0007472017,0.0006278025,0.0001504007,0.008995584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01230159,"threshold_uncertainty_score":0.02445996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328195834327762,"score_gpt":0.2426219807537471,"score_spread":0.2193400224104695,"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."}}