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Unlocking hydropower synergies: Multi-objective optimization drawdown operation of cascade reservoirs

Author

Listed:
  • Lin, Fanqi
  • Zhou, Yanlai
  • Ning, Zhihao
  • Liu, Pan
  • Chang, Fi-John
  • Xu, Chong-Yu

Abstract

Reservoirs' seasonal drought-limited water level (SDLWL) plays a pivotal role during drawdown periods, crucial for balancing energy demands, mitigating drought impacts, and sustaining ecological health. This study proposes an innovative framework for cascade reservoirs’ multi-objective drawdown operation, uniquely designed to optimize the interplay between hydropower generation, drought resilience, and ecological conservation. The proposed framework was validated through a case study involving seven cascade reservoirs in the upper Yangtze River. First, SDLWLs for the drought season were determined using a month-by-month moving calculation method, which explains seasonal streamflow variations and hydraulic interdependencies between reservoirs. Next, a SDLWL-based multi-objective drawdown operation model was built and optimized using the Non-dominated Sorting Genetic Algorithm II, generating a Pareto set of solutions. Last, the entropy weight method was employed to pinpoint the optimal solution, enhancing hydropower synergies. The results are compelling: the optimal solution boosts average annual power generation by 6.26 billion kW·h, reflecting a 5.7 % increase, while simultaneously reducing average annual drought risk by 5.1 % and decreasing the average amended annual proportional flow deviation by 0.27, a 5.2 % improvement. This study offers a groundbreaking approach to reservoir drawdown operations and provides decision-makers with practical strategies to optimize renewable energy generation during critical periods.

Suggested Citation

  • Lin, Fanqi & Zhou, Yanlai & Ning, Zhihao & Liu, Pan & Chang, Fi-John & Xu, Chong-Yu, 2025. "Unlocking hydropower synergies: Multi-objective optimization drawdown operation of cascade reservoirs," Renewable Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:renene:v:247:y:2025:i:c:s0960148125007591
    DOI: 10.1016/j.renene.2025.123097
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