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

Jiahui Sun, Chao Wang (), Hao Wang, Yunke Xiao, Xiaohui Lei, Zhongzheng He, Peibing Song and Pengyu Jin
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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
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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 14, No 3, 5463-5483

Abstract: 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.

Keywords: Large-scale reservoirs; Aggregation-decomposition; Drawdown rule; Progressive optimal algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03863-3

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