IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v37y2023i8d10.1007_s11269-023-03491-3.html
   My bibliography  Save this article

Cascade Hydropower System Operation Considering Ecological Flow Based on Different Multi-Objective Genetic Algorithms

Author

Listed:
  • Yubin Chen

    (Bureau of Hydrology, Changjiang Water Resources Commission)

  • Manlin Wang

    (Geological Survey of Jiangsu Province
    Key Laboratory of Earth Fissures Geological Disaster, Ministry of Natural Resources)

  • Yu Zhang

    (Nanjing Hydraulic Research Institute)

  • Yan Lu

    (Geological Survey of Jiangsu Province
    Key Laboratory of Earth Fissures Geological Disaster, Ministry of Natural Resources)

  • Bin Xu

    (Hohai University)

  • Lei Yu

    (Nanjing Hydraulic Research Institute)

Abstract

The main objective of most hydropower systems is to pursue the efficient use of water resources and maximize economic benefits. At the same time the protection of ecological environment should not be neglected. In this study, a coordination model of power generation and ecological flow for the operation of cascade hydropower system was first established using a multi-objective optimization method. Multi-objective genetic algorithms (MOGAs) are widely used to solve such multi-objective optimization problems because of their excellent performance in terms of convergence speed, diversity of solution set space and optimality seeking ability. However, the adaptability of MOGAs to a particular optimized operation problem sometimes varies widely. It is of great significance to investigate the adaptability of different algorithms for a new optimized operation problem and to recommend a more suitable solution algorithm. Three MOGAs namely NSGA-II, NSGA-III and RVEA are selected to solve the proposed optimized operation model. Numerical experiments were conducted to evaluate the performance of the algorithms using real-world data from a cascade hydropower system located in the lower Yalong River. The results show that the Pareto fronts corresponding to NSGA-II and NSGA-III significantly dominate the Pareto fronts corresponding to the RVEA. The Pareto fronts corresponding to the NSGA-III algorithm are slightly better than those of NSGA-II. In terms of the four performance metrics, NSGA-III has certain advantages over NSGA-II and RVEA. NSGA-III is recommended as the solution algorithm for the established coordination model of power generation and ecological flow.

Suggested Citation

  • Yubin Chen & Manlin Wang & Yu Zhang & Yan Lu & Bin Xu & Lei Yu, 2023. "Cascade Hydropower System Operation Considering Ecological Flow Based on Different Multi-Objective Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3093-3110, June.
  • Handle: RePEc:spr:waterr:v:37:y:2023:i:8:d:10.1007_s11269-023-03491-3
    DOI: 10.1007/s11269-023-03491-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-023-03491-3
    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/s11269-023-03491-3?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. 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.
    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. Favaro, Pietro & Dolányi, Mihály & Vallée, François & Toubeau, Jean-François, 2024. "Neural network informed day-ahead scheduling of pumped hydro energy storage," Energy, Elsevier, vol. 289(C).

    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. Zhong, Ruida & Zhao, Tongtiegang & Chen, Xiaohong, 2021. "Evaluating the tradeoff between hydropower benefit and ecological interest under climate change: How will the water-energy-ecosystem nexus evolve in the upper Mekong basin?," Energy, Elsevier, vol. 237(C).
    2. Tao Bai & Lei Li & Peng-fei Mu & Bao-zhu Pan & Jin Liu, 2023. "Impact of Climate Change on Water Transfer Scale of Inter-basin Water Diversion Project," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2505-2525, May.
    3. Ding, Ziyu & Fang, Guohua & Wen, Xin & Tan, Qiaofeng & Mao, Yingchi & Zhang, Yu, 2024. "Long-term operation rules of a hydro–wind–photovoltaic hybrid system considering forecast information," Energy, Elsevier, vol. 288(C).

    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:37:y:2023:i:8:d:10.1007_s11269-023-03491-3. 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.