IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i12d10.1007_s11269-024-03889-7.html
   My bibliography  Save this article

Improved Multi-objective Butterfly Optimization Algorithm and its Application in Cascade Reservoirs Optimal Operation Considering Ecological Flow

Author

Listed:
  • Zhangling Xiao

    (Tianjin Research Institute for Water Transport Engineering, M.O.T.)

  • Mingjin Zhang

    (Tianjin Research Institute for Water Transport Engineering, M.O.T.)

  • Zhongmin Liang

    (Hohai University)

  • Jian Wang

    (Tianjin Research Institute for Water Transport Engineering, M.O.T.)

  • Yude Zhu

    (Tianjin Research Institute for Water Transport Engineering, M.O.T.)

  • Binquan Li

    (Hohai University)

  • Yiming Hu

    (Hohai University)

  • Jun Wang

    (Hohai University)

  • Xiaolei Jiang

    (Yangzhou University)

Abstract

Traditional reservoir operations often take power production as the main purpose. However, blindly maximizing power production will prompt the reservoirs to work continuously at high water levels, leading to lower water release and possible damage to the river ecosystem. In this paper, a multi-objective optimal operation model was established for a cascade reservoir system, with the goals of maximizing power production and ecological benefit. The ecological benefit was defined based on a suitable interval of ecological flow, which was calculated by the Tennant and flow duration curve methods. To efficiently solve the model, a multi-objective butterfly optimization algorithm was proposed by coupling the improved initial population strategy, dynamic switching probability strategy, archive elite solution-guided evolution, and polynomial mutation strategy. This algorithm was compared with three popular multi-objective optimization algorithms on benchmark functions and a cascade reservoir operation problem in the lower reaches of the Yalong River. Results showed that the proposed algorithm achieved the maximum hydropower production, with 82.5, 76.4 and 64.2 billion kW‧h in the wet, normal and dry years. It also obtained the highest ecological benefit values, which were 0.76, 0.80 and 0.86 in the wet, normal and dry years, respectively. The proposed algorithm has the potential to solve multi-objective optimization problems. Under different inflow scenarios, a certain competitive relationship between targets was witnessed. As the decrease of inflow, the competition tended to intensify. Graphical Abstract

Suggested Citation

  • Zhangling Xiao & Mingjin Zhang & Zhongmin Liang & Jian Wang & Yude Zhu & Binquan Li & Yiming Hu & Jun Wang & Xiaolei Jiang, 2024. "Improved Multi-objective Butterfly Optimization Algorithm and its Application in Cascade Reservoirs Optimal Operation Considering Ecological Flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4803-4821, September.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:12:d:10.1007_s11269-024-03889-7
    DOI: 10.1007/s11269-024-03889-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03889-7
    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/s11269-024-03889-7?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. 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.
    2. Kalyanmoy Deb & Nikhil Padhye, 2014. "Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms," Computational Optimization and Applications, Springer, vol. 57(3), pages 761-794, April.
    3. Hojat Karami & Sayed Farhad Mousavi & Saeed Farzin & Mohammad Ehteram & Vijay P. Singh & Ozgur Kisi, 2018. "Improved Krill Algorithm for Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3353-3372, August.
    4. J. Pablo Ortiz-Partida & Taher Kahil & Tatiana Ermolieva & Yuri Ermoliev & Belize Lane & Samuel Sandoval-Solis & Yoshihide Wada, 2019. "A Two-Stage Stochastic Optimization for Robust Operation of Multipurpose Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3815-3830, September.
    Full references (including those not matched with items on IDEAS)

    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. Marco van Dijk & Stefanus Johannes van Vuuren & Giovanna Cavazzini & Chantel Monica Niebuhr & Alberto Santolin, 2022. "Optimizing Conduit Hydropower Potential by Determining Pareto-Optimal Trade-Off Curve," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    2. Mohamed R. Torkomany & Hassan Shokry Hassan & Amin Shoukry & Mohamed Hussein & Chihiro Yoshimura & Mohamed Elkholy, 2023. "Investigation of Optimum Sustainable Designs for Water Distribution Systems from Multiple Economic, Operational, and Health Perspectives," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    3. Iman Ahmadianfar & Bijay Halder & Salim Heddam & Leonardo Goliatt & Mou Leong Tan & Zulfaqar Sa’adi & Zainab Al-Khafaji & Raad Z. Homod & Tarik A. Rashid & Zaher Mundher Yaseen, 2023. "An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems," Sustainability, MDPI, vol. 15(3), pages 1-28, January.
    4. Marzieh Mozafari & Alireza Zabihi, 2020. "Robust Water Supply Chain Network Design under Uncertainty in Capacity," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4093-4112, October.
    5. Almubaidin, Mohammad Abdullah Abid & Ahmed, Ali Najah & Malek, Marlinda Abdul & Mahmoud, Moamin A. & Sherif, Mohsen & El-Shafie, Ahmed, 2024. "Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios," Agricultural Water Management, Elsevier, vol. 293(C).
    6. 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.
    7. Xiaokuan Ni & Zengchuan Dong & Wei Xie & Shujun Wu & Mufeng Chen & Hongyi Yao & Wenhao Jia, 2022. "A Practical Approach for Environmental Flow Calculation to Support Ecosystem Management in Wujiang River, China," IJERPH, MDPI, vol. 19(18), pages 1-18, September.
    8. Arnaud Flori & Hamouche Oulhadj & Patrick Siarry, 2022. "QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 525-559, June.
    9. 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.
    10. Aryan Salvati & Alireza Moghaddam Nia & Ali Salajegheh & Parham Moradi & Yazdan Batmani & Shahabeddin Najafi & Ataollah Shirzadi & Himan Shahabi & Akbar Sheikh-Akbari & Changhyun Jun & John J. Clague, 2023. "Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2657-2690, September.
    11. Yin, Xiuxing & Zhao, Xiaowei & Lin, Jin & Karcanias, Aris, 2020. "Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations," Energy, Elsevier, vol. 202(C).
    12. Youngje Choi & Jungwon Ji & Eunkyung Lee & Sunmi Lee & Sooyeon Yi & Jaeeung Yi, 2023. "Developing Optimal Reservoir Rule Curve for Hydropower Reservoir with an add-on Water Supply Function Using Improved Grey Wolf Optimizer," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 2063-2082, March.
    13. Guang Yang & Shenglian Guo & Pan Liu & Xiaofeng Liu & Jiabo Yin, 2020. "Heuristic Input Variable Selection in Multi-Objective Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 617-636, January.
    14. Na Wei & Jiancang Xie & Kunming Lu & Shuni He & Yating Gao & Feng Yang, 2022. "Dynamic Simulation of Ecological Flow Based on the Variable Interval Analysis Method," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    15. 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.
    16. Tatiana Ermolieva & Petr Havlik & Yuri Ermoliev & Nikolay Khabarov & Michael Obersteiner, 2021. "Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model," Sustainability, MDPI, vol. 13(2), pages 1-16, January.

    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:38:y:2024:i:12:d:10.1007_s11269-024-03889-7. 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.