Application of Black-Winged Differential-Variant Whale Optimization Algorithm in the Optimization Scheduling of Cascade Hydropower Stations

Mi Zhang, Zixuan Liu, Rungang Bao, Shuli Zhu, Li Mo and Yuqi Yang ()
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Mi Zhang: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zixuan Liu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Rungang Bao: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Shuli Zhu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Li Mo: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yuqi Yang: China Yangtze Power Co., Ltd., Yichang 443000, China

Sustainability, 2025, vol. 17, issue 3, 1-27

Abstract: Hydropower is a vital strategic component of China’s clean energy development. Its construction and optimized water resource allocation are crucial for addressing global energy challenges, promoting socio-economic development, and achieving sustainable development. However, the optimization scheduling of cascade hydropower stations is a large-scale, multi-constrained, and nonlinear problem. Traditional optimization methods suffer from low computational efficiency, while conventional intelligent algorithms still face issues like premature convergence and local optima, which severely hinder the full utilization of water resources. This study proposed an improved whale optimization algorithm, the Black-winged Differential-variant Whale Optimization Algorithm (BDWOA), which enhanced population diversity through a Logistic-Sine-Cosine combination chaotic map, improved algorithm flexibility with an adaptive adjustment strategy, and introduced the migration mechanism of the black-winged kite algorithm along with a differential mutation strategy to enhance the global search ability and convergence capacity. The BDWOA algorithm was tested using test functions with randomly generated simulated data, with its performance compared against five related optimization algorithms. Results indicate that the BDWOA achieved the optimal value with the fewest iterations, effectively overcoming the limitations of the original whale optimization algorithm. Further validation using actual runoff data for the cascade hydropower station optimization scheduling model showed that the BDWOA effectively enhanced power generation efficiency. In high-flow years, the average power generation increased by 8.3%, 6.5%, 6.8%, 4.1%, and 8.2% compared to the five algorithms while achieving the shortest computation time. Significant improvements in power generation were also observed in normal-flow and low-flow years. The scheduling solutions generated by the BDWOA can adapt to varying inflow conditions, offering an innovative approach to solving complex hydropower station optimization scheduling problems. This contributes to the sustainable utilization of water resources and supports the long-term development of renewable energy.

Keywords: power optimization scheduling; cascade reservoir joint scheduling; whale optimization algorithm; sustainable development; water resources (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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