{"id":"W4406382426","doi":"10.1016/j.apenergy.2025.125297","title":"Analytical method for optimizing capacity expansion of existing hydropower plants in hydro-wind-photovoltaic hybrid system: A case study in the Yalong River basin","year":2025,"lang":"en","type":"article","venue":"Applied Energy","topic":"Power Systems and Renewable Energy","field":"Energy","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Science and Technology Program of Hubei Province; China National Funds for Distinguished Young Scientists; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Hydropower; Photovoltaic system; Structural basin; Environmental science; Hydroelectricity; Drainage basin; Civil engineering; Engineering; Environmental engineering; Geology; Geography; Geomorphology; Electrical engineering","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":[0.001032822,0.0006559173,0.0007083572,0.0009899272,0.0006465748,0.0009902192,0.0008399623,0.0007910297,0.002668482],"category_scores_gemma":[0.001648934,0.000420876,0.0005953489,0.0008584273,0.0003552712,0.0005017371,0.0004579107,0.0005388267,0.0002106558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001081874,"about_ca_system_score_gemma":0.001690865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01185808,"about_ca_topic_score_gemma":0.01500674,"domain_scores_codex":[0.999757,0.00009231551,0.000008943845,0.0000290015,0.00007348167,0.00003925467],"domain_scores_gemma":[0.9992237,0.0005054502,0.00004342261,0.00001728436,0.0001947871,0.00001526431],"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":[0.00004669688,0.00008961462,0.0006857787,0.0001711581,0.00001942971,0.0001792322,0.000109478,0.9463558,0.003728038,0.005998923,0.0009810308,0.04163485],"study_design_scores_gemma":[0.000005819174,0.00002332327,0.000160872,0.00001109539,0.000007847966,0.00001898865,0.00004365596,0.9978572,0.0006168116,0.0008092823,0.0004407042,0.00000454397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1718886,0.00133215,0.7939094,0.0004853184,0.00009845689,0.0002897678,0.0002209723,0.0005689014,0.0312064],"genre_scores_gemma":[0.8833125,0.0005120971,0.1104849,0.00005095442,0.00003150448,0.0001607313,0.00007268202,0.00008273661,0.005291887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01185808,"threshold_uncertainty_score":0.02357811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03495976090006069,"score_gpt":0.2966379117242931,"score_spread":0.2616781508242324,"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."}}