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Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model

Author

Listed:
  • Xiaoling Ding

    (Huazhong University of Science and Technology
    Changjiang River Scientific Research Institute of Changjiang Water Resource Commission)

  • Xiaocong Mo

    (Changjiang River Scientific Research Institute of Changjiang Water Resource Commission)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology)

  • Sheng Bi

    (Changjiang River Scientific Research Institute of Changjiang Water Resource Commission)

  • Benjun Jia

    (Huazhong University of Science and Technology)

  • Xiang Liao

    (Hubei University of technology)

Abstract

Forecasted inflow is one of the most important input information for reservoir operation planning. However, current inflow prediction accuracy is difficult to meet the needs of long-term operation. Therefore, it is of great significance to consider the uncertainty of inflow forecast and study its influence on reservoir operation decisions. In this paper, a long-term scheduling framework considering inflow forecast uncertainty based on a temporal disaggregation method is proposed. First, the uncertainty of forecast is described from two aspects. Gaussian distribution is used to simulate the annual forecast error, and an Adaptive Nearest Neighbor Gaussian sampling method (A-NGS) is proposed to decompose annual inflow into temporal series. Based on the implicit scheduling model, the sample set of generation scheduling plans, actual dispatching schemes and theoretical optimal results are constructed. On this basis, a series of indexes are presented to evaluate the inflow simulation performance and the scheduling benefits. A case study of the Xiluodu-Xiangjiaba cascade reservoirs is conducted to analyze the effects of forecast uncertainty on operation benefits, and the effectiveness of forecast information is identified. Compared with the deterministic fragment method, the inflow processes simulated by A-NGS achieve better precision and behave more conducive to the scheduling. Although the uncertainty of forecast errors will bring some hydropower generation losses, a certain degree of forecast accuracy is effective to improve scheduling benefits when in the electricity market.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:2:d:10.1007_s11269-020-02748-5
    DOI: 10.1007/s11269-020-02748-5
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    References listed on IDEAS

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    1. 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.
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