{"id":"W2889617750","doi":"10.3390/en11092381","title":"Optimal Scheduling of Microgrid with Distributed Power Based on Water Cycle Algorithm","year":2018,"lang":"en","type":"article","venue":"Energies","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology; Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Microgrid; Mathematical optimization; Computer science; Operating cost; Distributed generation; Renewable energy; Convergence (economics); Scheduling (production processes); Algorithm; Engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00004296799,0.0001176801,0.0001256898,0.00005682656,0.00004051801,0.00002329778,0.00007731967,0.00004519168,0.0001129859],"category_scores_gemma":[0.000003190215,0.00008111391,0.000032615,0.00008111117,0.00005681242,0.0000654524,0.00001202163,0.00005407976,0.00001752422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001684484,"about_ca_system_score_gemma":0.000007420588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007851899,"about_ca_topic_score_gemma":0.000001748943,"domain_scores_codex":[0.9994921,0.000008567031,0.0001077894,0.0001101367,0.00009164458,0.0001897655],"domain_scores_gemma":[0.9997249,0.00001273727,0.00001624766,0.0001501755,0.00006614333,0.00002983744],"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":[0.00003212102,0.00002075272,0.00003163987,0.000008476358,0.00002533876,0.000002207939,0.00008153355,0.9811842,0.01501236,0.00001040334,0.0001016787,0.003489323],"study_design_scores_gemma":[0.0004214173,0.0001062023,0.0001231137,0.00002585899,0.00001014435,0.00000114775,0.00003347383,0.632574,0.3646516,0.000002190782,0.001943414,0.0001074036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8521938,0.0002201168,0.1466065,0.00005748733,0.000189916,0.00006826776,0.00004559489,0.0002207905,0.0003975156],"genre_scores_gemma":[0.9472738,0.00001502407,0.05247419,0.00002461902,0.00009248703,0.000007257379,0.00007426381,0.00002429112,0.00001409079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3496392,"threshold_uncertainty_score":0.3307729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002187710904616886,"score_gpt":0.1670814573272079,"score_spread":0.164893746422591,"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."}}