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Aggregation-Decomposition Coupling Drawdown Rule and Progressive Optimal Algorithm for Optimization of Large-Scale Reservoirs

Author

Listed:
  • Jiahui Sun

    (Shandong University)

  • Chao Wang

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research)

  • Hao Wang

    (Shandong University
    State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research)

  • Yunke Xiao

    (Changjiang River Scientiffic Research Institute)

  • Xiaohui Lei

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research)

  • Zhongzheng He

    (Nanchang University)

  • Peibing Song

    (China Renewable Energy Engineering Institute)

  • Pengyu Jin

    (Hohai University)

Abstract

With the increased construction reservoirs, hydropower systems are becoming larger and more complex, which brings challenges of optimal operation of large-scale reservoirs to improve the power generation. To address this efficiently, we propose an aggregation-decomposition method based on cascade reservoir drawdown rule. Based on a two-stage method, we analyze the monotonicity of power generation increment of cascade reservoirs and propose the drawdown rule, which we used to guide the drawdown order of cascade reservoirs. On this basis, we propose an aggregation-decomposition coupling drawdown rule and progressive optimal algorithm (ADDR-POA) method of large-scale reservoirs. To confirm the viability of the proposed approach, we selected 29 series–parallel-mixed reservoirs in the upper Yangtze River Basin in China as the study subjects and optimized them with the goal of maximizing the total power generation. Results show that compared to conventional mathematical optimization method and heuristic algorithm, ADDR-POA can effectively express the compensation effect between reservoirs and has a good performance in improving the total power generation of the basin and reducing iteration times, which presents a novel approach for solving the problem of drawdown operation of large-scale reservoirs.

Suggested Citation

  • Jiahui Sun & Chao Wang & Hao Wang & Yunke Xiao & Xiaohui Lei & Zhongzheng He & Peibing Song & Pengyu Jin, 2024. "Aggregation-Decomposition Coupling Drawdown Rule and Progressive Optimal Algorithm for Optimization of Large-Scale Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(14), pages 5463-5483, November.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:14:d:10.1007_s11269-024-03863-3
    DOI: 10.1007/s11269-024-03863-3
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    References listed on IDEAS

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    1. Richard E. Rosenthal, 1981. "A Nonlinear Network Flow Algorithm for Maximization of Benefits in a Hydroelectric Power System," Operations Research, INFORMS, vol. 29(4), pages 763-786, August.
    2. Deepti Rani & Maria Moreira, 2010. "Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1107-1138, April.
    3. Chun-Tian Cheng & Wen-Chuan Wang & Dong-Mei Xu & K. Chau, 2008. "Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(7), pages 895-909, July.
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