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Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions

Author

Listed:
  • Qiao-feng Tan

    (Hohai University
    Sichuan University)

  • Guo-hua Fang

    (Hohai University)

  • Xin Wen

    (Hohai University)

  • Xiao-hui Lei

    (China Institute of Water Resources and Hydropower Research)

  • Xu Wang

    (China Institute of Water Resources and Hydropower Research)

  • Chao Wang

    (China Institute of Water Resources and Hydropower Research)

  • Yi Ji

    (Northeast Agricultural University)

Abstract

Bayesian stochastic dynamic programming (BSDP) has been widely used in hydropower generation operation, as natural inflow and forecast uncertainties can be easily determined by transition probabilities. In this study, we propose a theoretical estimation method (TEM) based on copula functions to calculate the transition probability under conditions of limited historical inflow samples. The explicit expression of the conditional probability is derived using copula functions and then used to calculate prior and likelihood probabilities, and the prior probability can be revised to the posterior probability once new forecast information is available by Bayesian formulation. The performance of BSDP models in seven forecast scenarios and two extreme conditions considering no or perfect forecast information is evaluated and compared. The case study in the Ertan hydropower station in China shows that (1) TEM can avoid the shortcomings of empirical estimation method (EMM) in calculating the transition probability, so that the prior and likelihood probability matrices can be distributed more uniformly with less zeros, and the problem that the posterior probability cannot be calculated can be avoided; (2) there is a positive correlation between operating benefit and forecast accuracy; and (3) the operating policy considering reliable forecast information can improve hydropower generation. However, an incorrect decision may be made in the case of low forecast accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:5:d:10.1007_s11269-019-02449-8
    DOI: 10.1007/s11269-019-02449-8
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    References listed on IDEAS

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    Cited by:

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    2. Xinyi Zhang & Guohua Fang & Jian Ye & Jin Liu & Xin Wen & Chengjun Wu, 2022. "Risk Control in Optimization of Cascade Hydropower: Considering Water Abandonment Risk Probability," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    3. 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.
    4. 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.
    5. Rosalva Mendoza Ramírez & Maritza Liliana Arganis Juárez & Ramón Domínguez Mora & Luis Daniel Padilla Morales & Óscar Arturo Fuentes Mariles & Alejandro Mendoza Reséndiz & Eliseo Carrizosa Elizondo & , 2021. "Operation Policies through Dynamic Programming and Genetic Algorithms, for a Reservoir with Irrigation and Water Supply Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1573-1586, March.
    6. 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).
    7. 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).
    8. Ding, Ziyu & Wen, Xin & Tan, Qiaofeng & Yang, Tiantian & Fang, Guohua & Lei, Xiaohui & Zhang, Yu & Wang, Hao, 2021. "A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system," Applied Energy, Elsevier, vol. 291(C).

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