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https://doi.org/10.5194/hess-2019-586
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-586
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 17 Mar 2020

Submitted as: research article | 17 Mar 2020

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This preprint is currently under review for the journal HESS.

A novel framework of deriving joint impoundment rules for large-scale reservoir system based on a classification-aggregation-decomposition approach

Shaokun He1,2,3, Shenglian Guo1, Chong-Yu Xu1,3, Kebing Chen1, Zhen Liao1, Lele Deng1, Huanhuan Ba1, and Dimitri Solomatine2,3 Shaokun He et al.
  • 1State Key Lab. of Water Resources & Hydropower Engineering Science, Wuhan Univ., 430072, Wuhan, China
  • 2Hydro-informatics Group, IHE Delft Institute for Water Education, Delft, the Netherlands
  • 3Water Resources Sect., Delft Univ. Technol., Delft, the Netherlands

Abstract. Joint and optimal impoundment operation of the large-scale reservoir system has become more crucial for modern water management. Since the existing techniques fail to optimize the large-scale multi-objective impoundment operation due to the complex inflow stochasticity and high dimensionality, we develop a novel combination of parameter simulation optimization and classification-aggregation-decomposition approach here to overcome these obstacles. There are four main steps involved in our proposed framework: (1) reservoirs classification based on geographical location and flood prevention targets; (2) assumption of a hypothetical single reservoir in the same pool; (3) the derivation of the initial impoundment policies by the non-dominated sorting genetic algorithm-II (NSGA-II); (4) further improvement of the impoundment policies via Parallel Progressive Optimization Algorithm (PPOA). The framework potential is performed on China's mixed 30-reservoir system in the upper Yangtze River. Results indicate that our method can provide a series of schemes to refer to different flood event scenarios. The best scheme outperforms the conventional operating rule, as it increases impoundment efficiency from 89.50 % to 94.16 % and hydropower generation by 7.70 billion kWh (or increase 3.79 %) while flood control risk is less than 0.06.

Shaokun He et al.

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Short summary
Aiming at cascade impoundment operation, we develop a classification-aggregation-decomposition method to overcome the curse of dimensionality and inflow stochasticity problem. It is tested with a mixed 30-reservoir system in China. The results show that our method can provide lots of schemes to refer to different flood event scenarios. The best scheme outperforms the conventional operating rule, as it increases impoundment efficiency and hydropower generation while flood control risk is less.
Aiming at cascade impoundment operation, we develop a classification-aggregation-decomposition...
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