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Study on reservoir optimal operation based on coupled adaptive ε constraint and multi strategy improved Pelican

Ji He1, Xiaoqi Guo1, Songlin Wang2

  • 1School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, 450011, China.

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This study introduces an improved Pelican optimization algorithm (ε-IPOA) for complex reservoir group operations. The new method effectively reduces peak flood flow rates, enhancing reservoir flood control strategies.

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Area of Science:

  • Optimization algorithms
  • Water resource management
  • Flood control engineering

Background:

  • Reservoir group operation is a complex, multi-stage optimization challenge.
  • Existing algorithms struggle with high-dimensional, constrained problems in flood control.

Purpose of the Study:

  • To develop an enhanced optimization algorithm for optimal reservoir operation.
  • To improve flood control efficiency in cascade reservoirs.

Main Methods:

  • Coupling the Pelican optimization algorithm with adaptive ε constraint methods.
  • Incorporating population initialization, reverse differential evolution, and t-distribution perturbation.
  • Applying the improved algorithm (ε-IPOA) to a peak-cutting model for Sanmenxia and Xiaolangdi reservoirs.

Main Results:

  • The ε-IPOA algorithm demonstrated strong optimization ability and stable performance on test functions.
  • Applied to cascade reservoirs, ε-IPOA achieved a 44% peak shaving rate at Huayuankou control point.
  • Effectively reduced peak flow to 12,319 m³/s, well below the safe overflow limit of 22,000 m³/s.
  • Outperformed ε-POA and ε-DE algorithms, which failed to find valid solutions.

Conclusions:

  • The proposed ε-IPOA algorithm offers a novel and effective approach for optimizing cascade reservoir flood control.
  • This method provides a significant advancement in managing complex water resource systems for enhanced safety.