IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i1d10.1007_s11269-018-2095-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-018-2095-1
    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/s11269-018-2095-1?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. 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.
    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. Crescenzo Pepe & Silvia Maria Zanoli, 2024. "Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review," Energies, MDPI, vol. 17(4), pages 1-35, February.
    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).

    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. Lisicki, Michal & Lubitz, William & Taylor, Graham W., 2016. "Optimal design and operation of Archimedes screw turbines using Bayesian optimization," Applied Energy, Elsevier, vol. 183(C), pages 1404-1417.
    2. Qiao-feng Tan & Guo-hua Fang & Xin Wen & Xiao-hui Lei & Xu Wang & Chao Wang & Yi Ji, 2020. "Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1589-1607, March.
    3. Wei Xu & Xiaoli Zhang & Anbang Peng & Yue Liang, 2020. "Deep Reinforcement Learning for Cascaded Hydropower Reservoirs Considering Inflow Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3003-3018, July.
    4. 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).
    5. Alcigeimes Celeste & Max Billib, 2010. "The Role of Spill and Evaporation in Reservoir Optimization Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(4), pages 617-628, March.
    6. H. Lu & G. Huang & G. Zeng & I. Maqsood & L. He, 2008. "An Inexact Two-stage Fuzzy-stochastic Programming Model for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(8), pages 991-1016, August.
    7. Qiuxiang Jiang & Tian Wang & Zilong Wang & Qiang Fu & Zhimei Zhou & Youzhu Zhao & Yujie Dong, 2018. "HHM- and RFRM-Based Water Resource System Risk Identification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 4045-4061, September.
    8. Li, W. & Li, Y.P. & Li, C.H. & Huang, G.H., 2010. "An inexact two-stage water management model for planning agricultural irrigation under uncertainty," Agricultural Water Management, Elsevier, vol. 97(11), pages 1905-1914, November.
    9. Liu, Yuan & Ji, Changming & Wang, Yi & Zhang, Yanke & Jiang, Zhiqiang & Ma, Qiumei & Hou, Xiaoning, 2023. "Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models," Energy, Elsevier, vol. 273(C).
    10. A. Lust & K.-H. Waldmann, 2019. "A general storage model with applications to energy systems," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 71-97, March.
    11. Katerina Spanoudaki & Panayiotis Dimitriadis & Emmanouil A. Varouchakis & Gerald A. Corzo Perez, 2022. "Estimation of Hydropower Potential Using Bayesian and Stochastic Approaches for Streamflow Simulation and Accounting for the Intermediate Storage Retention," Energies, MDPI, vol. 15(4), pages 1-20, February.
    12. Mengfei Xie & Suzhen Feng & Jinwen Wang & Maolin Zhang & Cheng Chen, 2022. "Impacts of Yield and Seasonal Prices on the Operation of Lancang Cascaded Reservoirs," Energies, MDPI, vol. 15(9), pages 1-11, April.
    13. Tan, Qiao-feng & Lei, Xiao-hui & Wen, Xin & Fang, Guo-hua & Wang, Xu & Wang, Chao & Ji, Yi & Huang, Xian-feng, 2019. "Two-stage stochastic optimal operation model for hydropower station based on the approximate utility function of the carryover stage," Energy, Elsevier, vol. 183(C), pages 670-682.

    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:33:y:2019:i:1:d:10.1007_s11269-018-2095-1. 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.