IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v34y2020i3d10.1007_s11269-019-02435-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02435-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-019-02435-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hong Pan & Chenyang Hang & Fang Feng & Yuan Zheng & Fang Li, 2022. "Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    2. 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).
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amir Hatamkhani & Mojtaba Shourian & Ali Moridi, 2021. "Optimal Design and Operation of a Hydropower Reservoir Plant Using a WEAP-Based Simulation–Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1637-1652, March.
    2. Zhiqiang Jiang & Zhengyang Tang & Yi Liu & Yuyun Chen & Zhongkai Feng & Yang Xu & Hairong Zhang, 2019. "Area Moment and Error Based Forecasting Difficulty and its Application in Inflow Forecasting Level Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4553-4568, October.
    3. Jiang, Zhiqiang & Li, Anqiang & Ji, Changming & Qin, Hui & Yu, Shan & Li, Yuanzheng, 2016. "Research and application of key technologies in drawing energy storage operation chart by discriminant coefficient method," Energy, Elsevier, vol. 114(C), pages 774-786.
    4. Majid Mohammadi & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2019. "Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4767-4782, November.
    5. Zhe Yang & Kan Yang & Hu Hu & Lyuwen Su, 2019. "The Cascade Reservoirs Multi-Objective Ecological Operation Optimization Considering Different Ecological Flow Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 207-228, January.
    6. Mohammad Reza Sharifi & Saeid Akbarifard & Kourosh Qaderi & Mohamad Reza Madadi, 2021. "Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 385-406, January.
    7. 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.
    8. Yufei Ma & Ping-an Zhong & Bin Xu & Feilin Zhu & Yao Xiao & Qingwen Lu, 2020. "Multidimensional Parallel Dynamic Programming Algorithm Based on Spark for Large-Scale Hydropower Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3427-3444, September.
    9. Behrang Beiranvand & Parisa-Sadat Ashofteh, 2023. "A Systematic Review of Optimization of Dams Reservoir Operation Using the Meta-heuristic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3457-3526, July.
    10. Vijendra Kumar & S. M. Yadav, 2018. "Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4375-4391, October.
    11. Mahdi Valikhan-Anaraki & Sayed-Farhad Mousavi & Saeed Farzin & Hojat Karami & Mohammad Ehteram & Ozgur Kisi & Chow Ming Fai & Md. Shabbir Hossain & Gasim Hayder & Ali Najah Ahmed & Amr H. El-Shafie & , 2019. "Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    12. Qiang Zou & Li Liao & Hui Qin, 2020. "Fast Comprehensive Flood Risk Assessment Based on Game Theory and Cloud Model Under Parallel Computation (P-GT-CM)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1625-1648, March.
    13. Francesco Macchione & Gianluca De Lorenzo & Pierfranco Costabile & Babak Razdar, 2016. "The Power Function for Representing the Reservoir Rating Curve: Morphological Meaning and Suitability for Dam Breach Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4861-4881, October.
    14. Ziyu Ding & Guohua Fang & Xin Wen & Qiaofeng Tan & Xiaohui Lei & Zhehua Liu & Xianfeng Huang, 2020. "Cascaded Hydropower Operation Chart Optimization Balancing Overall Ecological Benefits and Ecological Conservation in Hydrological Extremes Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1231-1246, February.
    15. Avesani, Diego & Zanfei, Ariele & Di Marco, Nicola & Galletti, Andrea & Ravazzolo, Francesco & Righetti, Maurizio & Majone, Bruno, 2022. "Short-term hydropower optimization driven by innovative time-adapting econometric model," Applied Energy, Elsevier, vol. 310(C).
    16. 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).
    17. 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.
    18. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2018. "Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm," Energy, Elsevier, vol. 153(C), pages 706-718.
    19. Zaher Mundher Yaseen & Mohammad Ehteram & Md. Shabbir Hossain & Chow Ming Fai & Suhana Binti Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Jaafar & Haitham Abdulmohsin Afan & Lai Sai Hin & Nuratiah, 2019. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems," Sustainability, MDPI, vol. 11(7), pages 1-28, April.
    20. Chengjun Wu & Guohua Fang & Tao Liao & Xianfeng Huang & Bo Qu, 2020. "Integrated Software Development and Case Studies for Optimal Operation of Cascade Reservoir within the Environmental Flow Constraints," Sustainability, MDPI, vol. 12(10), pages 1-16, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:34:y:2020:i:3:d:10.1007_s11269-019-02435-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.