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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
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    References listed on IDEAS

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    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.
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    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).

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