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Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models

Author

Listed:
  • Daohong Wei

    (College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Chunpeng Feng

    (College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Dong Liu

    (College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

Abstract

In order to enhance the overall power generation efficiency of cascade hydropower, it is essential to conduct modelling optimization of its in-plant operation. However, existing studies have devoted minimal attention to the detailed modelling of turbine operating performance curves within the in-plant economic operation model. This represents a significant challenge to the practical application of the optimization results. This study presents a refined model of a hydraulic turbine operating performance curve, which was established by combining a particle swarm optimization (PSO) algorithm and a backpropagation (BP) neural network. The model was developed using a cascade small hydropower group as an illustrative example. On this basis, an in-plant economic operation model of a cascade small hydropower group was established, which is based on the principle of ’setting electricity by water’ and has the goal of maximizing power generation. The model was optimized using a genetic algorithm, which was employed to optimize the output of the units. In order to ascertain the efficacy of the methodology proposed in this study, typical daily operational scenarios of a cascade small hydropower group were selected for comparison. The results demonstrate that, in comparison with the actual operational strategy, the proposed model and method enhance the total output by 3.38%, 2.11%, and 3.56%, respectively, across the three typical scenarios. This method enhances the efficiency of power generation within the cascade small hydropower group and demonstrates substantial engineering application value.

Suggested Citation

  • Daohong Wei & Chunpeng Feng & Dong Liu, 2025. "Research on Economic Operation of Cascade Small Hydropower Stations Within Plants Based on Refined Efficiency Models," Energies, MDPI, vol. 18(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:964-:d:1593210
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    References listed on IDEAS

    as
    1. 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.
    2. Jing, Zhiqiang & Wang, Yimin & Chang, Jianxia & Wang, Xuebin & Zhou, Yong & Li, Liang & Tian, Yuyu, 2024. "Benefit compensation of hydropower-wind-photovoltaic complementary operation in the large clean energy base," Applied Energy, Elsevier, vol. 354(PA).
    3. Zhao, Mingzhe & Wang, Yimin & Wang, Xuebin & Chang, Jianxia & Chen, Yunhua & Zhou, Yong & Guo, Aijun, 2022. "Flexibility evaluation of wind-PV-hydro multi-energy complementary base considering the compensation ability of cascade hydropower stations," Applied Energy, Elsevier, vol. 315(C).
    4. Haugen, Mari & Blaisdell-Pijuan, Paris L. & Botterud, Audun & Levin, Todd & Zhou, Zhi & Belsnes, Michael & Korpås, Magnus & Somani, Abhishek, 2024. "Power market models for the clean energy transition: State of the art and future research needs," Applied Energy, Elsevier, vol. 357(C).
    5. Xiaoyu Wang & Kan Yang & Liu Yang, 2018. "Application Research of Inner-plant Economical Operation by Multi-colony Ant Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4275-4295, October.
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