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

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  • Mi Zhang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital River Basin Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Institute of Water Resources and Hydropower, 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
    Hubei Key Laboratory of Digital River Basin Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Institute of Water Resources and Hydropower, 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
    Hubei Key Laboratory of Digital River Basin Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Institute of Water Resources and Hydropower, 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
    Hubei Key Laboratory of Digital River Basin Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Institute of Water Resources and Hydropower, 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
    Hubei Key Laboratory of Digital River Basin Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    Institute of Water Resources and Hydropower, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yuqi Yang

    (China Yangtze Power Co., Ltd., Yichang 443000, China
    Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, Yichang 443000, China)

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.

Suggested Citation

  • Mi Zhang & Zixuan Liu & Rungang Bao & Shuli Zhu & Li Mo & Yuqi Yang, 2025. "Application of Black-Winged Differential-Variant Whale Optimization Algorithm in the Optimization Scheduling of Cascade Hydropower Stations," Sustainability, MDPI, vol. 17(3), pages 1-27, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1018-:d:1577923
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    References listed on IDEAS

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    1. Kun Yang & Kan Yang, 2021. "Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3771-3790, September.
    2. Kun Yang & Kan Yang, 2022. "Improved Whale Algorithm for Economic Load Dispatch Problem in Hydropower Plants and Comprehensive Performance Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5823-5838, December.
    3. Wen-jing Niu & Zhong-kai Feng & Yu-rong Li & Shuai Liu, 2021. "Cooperation Search Algorithm for Power Generation Production Operation Optimization of Cascade Hydropower Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2465-2485, June.
    4. Yousif H. Al-Aqeeli & Omar M. A Mahmood Agha, 2020. "Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3099-3112, August.
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