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Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm

Author

Listed:
  • Hu Hu

    (Hohai University)

  • Kan Yang

    (Hohai University)

  • Lang Liu

    (Bureau of Water Resources of Jurong)

  • Lyuwen Su

    (Hohai University)

  • Zhe Yang

    (Hohai University)

Abstract

Short-term hydropower generation scheduling (STHGS), a highly complicated nonlinear optimization problem with various equality and inequality constraints, plays an important role in the utilization of hydropower and water resources. To overcome the complexity and nonlinearity of STHGS problem effectively, an improved cloud adaptive quantum-inspired binary social spider optimization (ICAQBSSO) algorithm is proposed in this paper. Quantum bit (q-bit) and quantum rotation gate are used to improve its code mode and search mode and enable it to optimize discrete problems. The improved cooperative operators of ICAQBSSO overcome the problem of unreasonable parameters and elements in its original cooperative operators. With the heuristic strategies for repairing minimum uptime/downtime constraint and spinning reserve capacity constraint, the ICAQBSSO algorithm is coupled with an optimal stable load distribution table (OSLDT) to optimize the sub-problems of STHGS, unit commitment (UC) and economic load dispatch (ELD). In the case study of the STHGSs for Three Gorges hydropower station, corresponding to 75 m, 88 m and 107 m water heads, the results of the proposed algorithm and other intelligent algorithms show the feasibility and effectiveness of the proposed algorithm for obtaining near-optimal solutions in less time.

Suggested Citation

  • Hu Hu & Kan Yang & Lang Liu & Lyuwen Su & Zhe Yang, 2019. "Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2357-2379, May.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:7:d:10.1007_s11269-018-2138-7
    DOI: 10.1007/s11269-018-2138-7
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    References listed on IDEAS

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    Cited by:

    1. Shengli Liao & Yan Zhang & Jie Liu & Benxi Liu & Zhanwei Liu, 2021. "Short-Term Peak-Shaving Operation of Single-Reservoir and Multicascade Hydropower Plants Serving Multiple Power Grids," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 689-705, January.
    2. José García & Paola Moraga & Matias Valenzuela & Hernan Pinto, 2020. "A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    3. Shuangquan Liu & Pengcheng Wang & Zifan Xu & Zhipeng Feng & Congtong Zhang & Jinwen Wang & Cheng Chen, 2023. "Hydropower Unit Commitment Using a Genetic Algorithm with Dynamic Programming," Energies, MDPI, vol. 16(15), pages 1-13, August.
    4. Yang, Zhe & Wang, Yufeng & Yang, Kan, 2022. "The stochastic short-term hydropower generation scheduling considering uncertainty in load output forecasts," Energy, Elsevier, vol. 241(C).
    5. José García & Gino Astorga & Víctor Yepes, 2021. "An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics," Mathematics, MDPI, vol. 9(3), pages 1-20, January.
    6. 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.
    7. Liao, Shengli & Liu, Zhanwei & Liu, Benxi & Cheng, Chuntian & Wu, Xinyu & Zhao, Zhipeng, 2021. "Daily peak shaving operation of cascade hydropower stations with sensitive hydraulic connections considering water delay time," Renewable Energy, Elsevier, vol. 169(C), pages 970-981.

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