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Risk Analysis for Short-Term Operation of the Power Generation in Cascade Reservoirs Considering Multivariate Reservoir Inflow Forecast Errors

Author

Listed:
  • Yueqiu Wu

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Liping Wang

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Yi Wang

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Yanke Zhang

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Jiajie Wu

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Qiumei Ma

    (School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China)

  • Xiaoqing Liang

    (Department of Physics and Hydropower Engineering, Gansu Normal College for Nationalities, Hezuo, Gansu 747000, China)

  • Bin He

    (National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China)

Abstract

In the short-term operation of the power generation of cascade reservoirs, uncertainty factors such as inflow forecast errors could cause various types of risks. The inflow to a downstream reservoir is not only affected by inflow forecast errors from upstream reservoirs but also the forecast errors associated with inflow to the stream segment between the reservoirs, such as from a tributary. The inflow forecast errors of different forecast periods may also be correlated. To address this multivariate problem, the inflow forecast error variables were jointly fitted in this study using the Gaussian mixture model (GMM) and a t -Copula function based on the analysis of the error distribution characteristics in different forecast periods. Therefore, a stochastic model that coupled with the GMM and t -Copula to calculate inflow forecast errors in multiple forecast periods was established. Furthermore, according to the simulation results of the stochastic model and the predicted runoff series, a set of simulated runoff processes were obtained. Then they were combined with the existing power generation plan to carry out the risk analysis for short-term operation of the power generation in a cascade reservoir. The approach was evaluated using the Jinguan cascade hydropower system within the Yalong River basin as a case study. For this case study, the risk analysis for short-term operation of the power generation was analyzed based on stochastic simulation of the inflow forecast errors; the results show the feasibility and effectiveness of the proposed methods.

Suggested Citation

  • Yueqiu Wu & Liping Wang & Yi Wang & Yanke Zhang & Jiajie Wu & Qiumei Ma & Xiaoqing Liang & Bin He, 2021. "Risk Analysis for Short-Term Operation of the Power Generation in Cascade Reservoirs Considering Multivariate Reservoir Inflow Forecast Errors," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3689-:d:524509
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    References listed on IDEAS

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