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<p>The low accuracy of sediment forecasting and simulation in sediment-laden rivers complicates and intensifies the uncertainty in multi-objective water and sediment (WS) operation for reservoirs, making it difficult to quantify. Therefore, this research develops a multi-objective optimization framework, considering the inherent uncertainties in WS forecasts, reservoir capacity curves, reservoir sediment discharge capacity, and channel deposition calculations. The model explores the influence of multiple uncertainties on the joint optimization of WS operation while optimizing for hydropower generation, reservoir sedimentation, and channel sedimentation objectives. This work uses the vine copula function to model the correlation patterns in WS forecast errors and employs the Monte Carlo (MC) sampling approach to generate scenario sets. Deterministic and stochastic multi-objective optimization operation models are established separately and solved using the NSGA-III optimization algorithm. The changes in Pareto front solutions under different uncertainty conditions are explored, quantifying the impact of multiple uncertainties on the results of WS operation. The proposed model is implemented in the Xiaolangdi Reservoir-Huayuankou Hydrological Station system in China’s lower Yellow River region. The primary results are as follows: (1) The vine copula coupled with the MC sampling method employed in this study effectively reproduces the spatiotemporal correlation of errors in WS forecasts. (2) The deterministic optimization results indicate a linear trade-off between reservoir and channel sedimentation, a concave trade-off between reservoir sedimentation and hydropower generation, and a concave positive correlation between channel sedimentation and hydropower generation. (3) The results of the uncertainty optimization indicate that the uncertainty in WS forecasts has a significant impact on operation. Thus, the uncertainties introduced by WS forecasts should be given considerable attention. These research results contribute theoretical insights to enhance the precision of WS operations and decision-making in sediment-laden rivers.</p>

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Study on multi-objective joint optimization of water and sediment operation considering multiple uncertainties: a case study in the lower yellow river

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Fangzheng Zhao,
  • Peng Yi,
  • Ping-an Zhong,
  • Xinyu Wan,
  • Jieyu Li,
  • Sen Wang,
  • Weiyi Shi,
  • Xinyu Wang,
  • Ke Zheng

摘要

The low accuracy of sediment forecasting and simulation in sediment-laden rivers complicates and intensifies the uncertainty in multi-objective water and sediment (WS) operation for reservoirs, making it difficult to quantify. Therefore, this research develops a multi-objective optimization framework, considering the inherent uncertainties in WS forecasts, reservoir capacity curves, reservoir sediment discharge capacity, and channel deposition calculations. The model explores the influence of multiple uncertainties on the joint optimization of WS operation while optimizing for hydropower generation, reservoir sedimentation, and channel sedimentation objectives. This work uses the vine copula function to model the correlation patterns in WS forecast errors and employs the Monte Carlo (MC) sampling approach to generate scenario sets. Deterministic and stochastic multi-objective optimization operation models are established separately and solved using the NSGA-III optimization algorithm. The changes in Pareto front solutions under different uncertainty conditions are explored, quantifying the impact of multiple uncertainties on the results of WS operation. The proposed model is implemented in the Xiaolangdi Reservoir-Huayuankou Hydrological Station system in China’s lower Yellow River region. The primary results are as follows: (1) The vine copula coupled with the MC sampling method employed in this study effectively reproduces the spatiotemporal correlation of errors in WS forecasts. (2) The deterministic optimization results indicate a linear trade-off between reservoir and channel sedimentation, a concave trade-off between reservoir sedimentation and hydropower generation, and a concave positive correlation between channel sedimentation and hydropower generation. (3) The results of the uncertainty optimization indicate that the uncertainty in WS forecasts has a significant impact on operation. Thus, the uncertainties introduced by WS forecasts should be given considerable attention. These research results contribute theoretical insights to enhance the precision of WS operations and decision-making in sediment-laden rivers.