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An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times

Author

Listed:
  • Xiaoli Zhang

    (North China University of Water Resources and Electric Power)

  • Yong Peng

    (Dalian University of Technology)

  • Wei Xu

    (Chongqing Jiaotong University
    River Engineering, Sichuan University)

  • Bende Wang

    (Dalian University of Technology)

Abstract

To make full use of inflow forecasts with different lead times, a new reservoir operation model that considers the long-, medium- and short-term inflow forecasts (LMS-BSDP) for the real-time operation of hydropower stations is presented in this paper. First, a hybrid model, including a multiple linear regression model and the Xinanjiang model, is developed to obtain the 10-day inflow forecasts, and ANN models with the circulation indexes as inputs are developed to obtain the seasonal inflow forecasts. Then, the 10-day inflow forecast is divided into two segments, the first 5 days and the second 5 days, and the seasonal inflow forecast is deemed as the long-term forecast. Next, the three inflow forecasts are coupled using the Bayesian theory to develop LMS-BSDP model and the operation policies are obtained. Finally, the decision processes for the first 5 days and the entire 10 days are made according to their operation policies and the three inflow forecasts, respectively. The newly developed model is tested with the Huanren hydropower station located in China and compared with three other stochastic dynamic programming models. The simulation results demonstrate that LMS-BSDP performs best with higher power generation due to its employment of the long-term runoff forecast. The novelties of the present study lies in that it develops a new reservoir operation model that can use the long-, medium- and short-term inflow forecasts, which is a further study about the combined use of the inflow forecasts with different lead times based on the existed achievements.

Suggested Citation

  • Xiaoli Zhang & Yong Peng & Wei Xu & Bende Wang, 2019. "An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 173-188, January.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:1:d:10.1007_s11269-018-2095-1
    DOI: 10.1007/s11269-018-2095-1
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    References listed on IDEAS

    as
    1. P. Mujumdar & B. Nirmala, 2007. "A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(9), pages 1465-1485, September.
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    Cited by:

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    2. Yuri B. Kirsta & Ol’ga V. Lovtskaya, 2021. "Good-quality Long-term Forecast of Spring-summer Flood Runoff for Mountain Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 811-825, February.
    3. Priyanka Majumder & Mrinmoy Majumder & Apu Kumar Saha & Soumitra Nath, 2020. "Selection of features for analysis of reliability of performance in hydropower plants: a multi-criteria decision making approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 3239-3265, April.
    4. Xiaoling Ding & Xiaocong Mo & Jianzhong Zhou & Sheng Bi & Benjun Jia & Xiang Liao, 2021. "Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 645-660, January.
    5. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    6. Shu, Xingsheng & Ding, Wei & Peng, Yong & Wang, Ziru, 2024. "Value of long-term inflow forecast for hydropower operation: A case study in a low forecast precision region," Energy, Elsevier, vol. 298(C).

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