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Optimal Scheduling of Reservoir Flood Control under Non-Stationary Conditions

Author

Listed:
  • Chongxun Mo

    (College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
    Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China
    Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China)

  • Changhao Jiang

    (College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
    Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China
    Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China)

  • Xingbi Lei

    (College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
    Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China
    Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China)

  • Weiyan Cen

    (College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
    Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China
    Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China)

  • Zhiwei Yan

    (College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
    Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China
    Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China)

  • Gang Tang

    (Guangxi Water & Power Design Institute Co., Ltd., Nanning 530023, China)

  • Lingguang Li

    (Guangxi Water & Power Design Institute Co., Ltd., Nanning 530023, China)

  • Guikai Sun

    (College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
    Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China
    Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China)

  • Zhenxiang Xing

    (School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150000, China)

Abstract

To improve reservoir flood control and scheduling schemes under changing environmental conditions, we established an adaptive reservoir regulation method integrating hydrological non-stationarity diagnosis, hydrological frequency analysis, design flood calculations, and reservoir flood control optimization scheduling and applied it to the Chengbi River Reservoir. The results showed that the peak annual flood sequence and the variation point of the annual maximum 3-day flood sequence of the Chengbi River Reservoir was in 1979, and the variation point of the annual maximum 1-day flood sequence was in 1980. A mixed distribution model was developed via a simulated annealing algorithm, hydrological frequency analysis was carried out, and a non-stationary design flood considering the variation point was obtained according to the analysis results; the increases in the flood peak compared to the original design were 4.00% and 8.66%, respectively. A maximum peak reduction model for optimal reservoir scheduling using the minimum sum of squares of the downgradient flow as the objective function was established and solved via a particle swarm optimization algorithm. The proposed adaptive scheduling scheme reduced discharge flow to 2661 m 3 /s under 1000-year flood conditions, and the peak reduction rate reached 60.6%. Furthermore, the discharge flow was reduced to 2661 m 3 /s under 10,000-year flood conditions, and the peak reduction rate reached 65.9%.

Suggested Citation

  • Chongxun Mo & Changhao Jiang & Xingbi Lei & Weiyan Cen & Zhiwei Yan & Gang Tang & Lingguang Li & Guikai Sun & Zhenxiang Xing, 2023. "Optimal Scheduling of Reservoir Flood Control under Non-Stationary Conditions," Sustainability, MDPI, vol. 15(15), pages 1-22, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11530-:d:1202435
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    References listed on IDEAS

    as
    1. Lu, Shibao & Sun, Huaping & Sun, Dongying & Guo, Min & Bai, Xiao, 2020. "Assessment on reservoir flood resources utilization of Ankang Reservoir, China," Resources Policy, Elsevier, vol. 68(C).
    2. Suiling Wang & Zhiqiang Jiang & Yi Liu, 2022. "Dimensionality Reduction Method of Dynamic Programming under Hourly Scale and Its Application in Optimal Scheduling of Reservoir Flood Control," Energies, MDPI, vol. 15(3), pages 1-17, January.
    3. P. C. D. Milly & R. T. Wetherald & K. A. Dunne & T. L. Delworth, 2002. "Increasing risk of great floods in a changing climate," Nature, Nature, vol. 415(6871), pages 514-517, January.
    4. A. N. Pettitt, 1979. "A Non‐Parametric Approach to the Change‐Point Problem," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(2), pages 126-135, June.
    Full references (including those not matched with items on IDEAS)

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