Long-term optimal reservoir operation with tuning on large-scale multi-objective optimization: Case study of cascade reservoirs in the Upper Yellow River Basin

• DOI: 10.1016/j.ejrh.2022.101000
• Publication Date: 2022-01-20T02:20:20Z
• Authors: Hongyi Yao; Zengchuan Dong; Dayong Li; Xiaokuan Ni; Tian Chen; Mufeng Chen; Wenhao Jia; Xin Huang
• Keywords: Physical geography; QE1-996.5; Upper Yellow River Basin; 0208 environmental biotechnology; Weight optimization framework; Geology; 02 engineering and technology; Grouping mechanism; Constraints handling method; 6. Clean water; GB3-5030; Large-scale multi-objective evolutionary algorithm

Study region: Reservoir system on the Upper Yellow River Basin (UYRB), China. Study focus: A multipurpose reservoir system with multi-year regulation capacity calls for new optimization with high efficiency owing to the curse of dimensionality. This paper presents a state-of-the-art large-scale multi-objective evolutionary algorithm (LSMOEA), called the weight optimization framework (WOF) with Non-dominated Sorting Genetic Algorithm II (NSGAII) optimizer, to alleviate the problem, and improve its performance by determining applicable grouping mechanism based on inflow features. A novel constrains handle method named dual progressive repair is used to ensure search progress in feasible decision space. New hydrological insights for the region: Compared to classic NSGA2, WOF with NSGA2 optimizer (WOF-NSGA2 herein) shows better performance on diversity, convergence, and convergence rate. The tuning method, along with the repair method, makes WOF-NSGA2 outperform in all parameter combinations, and produces satisfying operation schedule in the case of multi-objective reservoir operation in the UYRB. The tuning and repair method could be used widely for the large-scale multi-objective reservoir system operation.