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Multi-objective optimal scheduling of cascade reservoirs in complex basin systems: Case study of the Jinsha River-Yalong River confluence basin in China
Study area: Cascade reservoir systems in the confluence basin of the Jinsha and Yalong rivers (CBJY), China. Study focus: Scheduling cascade reservoirs is a high-dimensional, nonlinear multi-objective optimization problem. In complex basins with multiple converging rivers, the ''dimensional catastrophe'' effect increases with more decision variables, requiring improved robustness and optimization of the scheduling algorithm. In this study, an improved multi-objective sparrow search algorithm (IMOSSA) is proposed to solve the problem, which overcome the classical SSA solution efficiency instability and easy to fall into the local optimal solution through Tent mapping, levy flight, Gaussian variation, and a strategy combining slime mold algorithm (SMA). Benchmark function tests demonstrate that IMOSSA outperforms others in terms of optimization capability and stability. New hydrological insights: The scheduling model and IMOSSA are applied to CBJY region involving 16 cascaded reservoir systems. The results show that IMOSSA obtains a more uniformly and widely distributed Pareto solution set, and the scheduling schemes derived from IMOSSA are significantly superior to other schemes. Taking very high flow year as an example, the total power generation of the multi-objective scheduling scheme is 4050 billion kW·h, the output is 32,040 MW, and the ecological discharge is 4739 m3/s. This scheduling approach can yield greater overall benefits, provide strong flood control capabilities, and ensure that the power generation distribution of the system better aligns with China's national requirements. This study offers valuable reference insights for the multi-objective optimization scheduling of cascaded reservoir systems in other complex watershed regions.
Multi-objective optimal scheduling of cascade reservoirs in complex basin systems: Case study of the Jinsha River-Yalong River confluence basin in China
Study area: Cascade reservoir systems in the confluence basin of the Jinsha and Yalong rivers (CBJY), China. Study focus: Scheduling cascade reservoirs is a high-dimensional, nonlinear multi-objective optimization problem. In complex basins with multiple converging rivers, the ''dimensional catastrophe'' effect increases with more decision variables, requiring improved robustness and optimization of the scheduling algorithm. In this study, an improved multi-objective sparrow search algorithm (IMOSSA) is proposed to solve the problem, which overcome the classical SSA solution efficiency instability and easy to fall into the local optimal solution through Tent mapping, levy flight, Gaussian variation, and a strategy combining slime mold algorithm (SMA). Benchmark function tests demonstrate that IMOSSA outperforms others in terms of optimization capability and stability. New hydrological insights: The scheduling model and IMOSSA are applied to CBJY region involving 16 cascaded reservoir systems. The results show that IMOSSA obtains a more uniformly and widely distributed Pareto solution set, and the scheduling schemes derived from IMOSSA are significantly superior to other schemes. Taking very high flow year as an example, the total power generation of the multi-objective scheduling scheme is 4050 billion kW·h, the output is 32,040 MW, and the ecological discharge is 4739 m3/s. This scheduling approach can yield greater overall benefits, provide strong flood control capabilities, and ensure that the power generation distribution of the system better aligns with China's national requirements. This study offers valuable reference insights for the multi-objective optimization scheduling of cascaded reservoir systems in other complex watershed regions.
Multi-objective optimal scheduling of cascade reservoirs in complex basin systems: Case study of the Jinsha River-Yalong River confluence basin in China
Zhaocai Wang (author) / Zhihua Zhu (author) / Hualong Luan (author) / Tunhua Wu (author)
2025
Article (Journal)
Electronic Resource
Unknown
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