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The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

Author

Listed:
  • Zhe Yang

    (Hohai University)

  • Kan Yang

    (Hohai University)

  • Lyuwen Su

    (Hohai University)

  • Hu Hu

    (Hohai University)

Abstract

The short-term economical operation (STEO) in hydropower station is nonlinear mixed integer problem, satisfying complex hydrologic constraints simultaneously. In this paper, we decomposed STEO into unit commitment (UC) and economical load dispatch (ELD) sub-problems. In terms of premature convergence and inefficient search performance of conventional shuffled frog leaping algorithm (SFLA), we proposed chaotic normal cloud model based discrete SFLA (CNCM-DSFLA). The CNCM-DSFLA incorporates novel population initialization based on renewed chaotic logistic mapping, three frog sub-populations including leader frog, follower frog and mutation frog, heuristic frog activation mechanism and frog mutation based on normal cloud model. The CNCM-DSFLA is applied to solve UC sub-problem and ELD sub-problem is handled based on optimal economic load distribution table. Moreover, reserve capacity supplement and repair, and minimum startup and shutdown repair strategies are introduced to deal with multiple hydrologic and electrical constraints. Finally, CNCM-DSFLA is verified by a case in Three Gorges hydropower station. Simulation results demonstrate that CNCM-DSFLA gets higher-quality solutions with less total water consumption and shorter computation time in comparison with other methods. Thus, validity and superiority of CNCM-DSFLA are verified and it can provide novel effective way for solving STEO problem in complex hydropower station system.

Suggested Citation

  • Zhe Yang & Kan Yang & Lyuwen Su & Hu Hu, 2020. "The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 905-927, February.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:3:d:10.1007_s11269-019-02435-0
    DOI: 10.1007/s11269-019-02435-0
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    References listed on IDEAS

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    1. Mohammad Azizipour & Vahid Ghalenoei & M. H. Afshar & S. S. Solis, 2016. "Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3995-4009, September.
    2. Guohua Fang & Yuxue Guo & Xin Wen & Xiaomin Fu & Xiaohui Lei & Yu Tian & Ting Wang, 2018. "Multi-Objective Differential Evolution-Chaos Shuffled Frog Leaping Algorithm for Water Resources System Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 3835-3852, September.
    3. Ping Sun & Zhi-qiang Jiang & Ting-ting Wang & Yan-ke Zhang, 2016. "Research and Application of Parallel Normal Cloud Mutation Shuffled Frog Leaping Algorithm in Cascade Reservoirs Optimal Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1019-1035, February.
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    Cited by:

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