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

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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.

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Acknowledgements

Special thanks are given to the anonymous reviewers and editors for their constructive comments.

Funding

This work is supported by the Fundamental Scientific Research Business Expenses for Central Public Welfare Research Institutes (No.CKSF2019433/SZ), the National Natural Science Foundation of China (No.51709012, U1865202, 51809097), and the National Public Research Institutes for Basic R&D Operating Expenses Special Project (No. CKSF2019209).

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Authors and Affiliations

  1. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Xiaoling Ding, Jianzhong Zhou & Benjun Jia

  2. Changjiang River Scientific Research Institute of Changjiang Water Resource Commission, Wuhan, 430074, China

    Xiaoling Ding, Xiaocong Mo & Sheng Bi

  3. School of Electrical & Electronic Engineering, Hubei University of technology, Wuhan, 430074, China

    Xiang Liao

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  1. Xiaoling Ding
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  2. Xiaocong Mo
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  3. Jianzhong Zhou
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  4. Sheng Bi
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  5. Benjun Jia
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  6. Xiang Liao
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Contributions

Xiaoling Ding and Jianzhong Zhou conceived of the study, supervised the experiments and Xiaoling Ding wrote the manuscript. Xiaocong Mo provided some instructions on experiments. Sheng Bib, Benjun Jia a and Xiang Liao helped to process some data and produce some figures.

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Correspondence to Jianzhong Zhou.

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Ding, X., Mo, X., Zhou, J. et al. Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model. Water Resour Manage 35, 645–660 (2021). https://doi.org/10.1007/s11269-020-02748-5

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