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Optimization of hydropower energy generation by 14 robust evolutionary algorithms.

Mohammad Reza Sharifi1, Saeid Akbarifard2, Mohamad Reza Madadi3

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Moth swarm algorithm (MSA) significantly boosted Karun-4 hydropower reservoir energy generation by 65%. This study evaluated 14 evolutionary algorithms (EAs) for optimal hydropower operation, with MSA outperforming others.

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Area of Science:

  • Engineering
  • Computational Intelligence
  • Renewable Energy Systems

Background:

  • Evolutionary algorithms (EAs) show promise for complex engineering optimization.
  • Optimal operation of hydropower reservoirs is crucial for efficient energy generation.
  • Karun-4 hydropower reservoir serves as a case study for evaluating optimization techniques.

Purpose of the Study:

  • To investigate the capability of 14 recently introduced robust EAs for optimizing energy generation in the Karun-4 hydropower reservoir.
  • To identify the most effective EA for maximizing energy output while minimizing operational variability and computational time.
  • To compare the performance of various EAs in the context of hydropower reservoir management.

Main Methods:

  • Evaluated 14 recently introduced robust evolutionary algorithms (EAs).
  • Optimized energy generation for the Karun-4 hydropower reservoir.
  • Assessed algorithms based on objective function (energy generation), standard deviation (SD), coefficient of variation (CV), and CPU usage time.

Main Results:

  • The Moth Swarm Algorithm (MSA) achieved the highest energy generation (19,311,535 MW), a 65.088% increase over the actual generation.
  • MSA demonstrated excellent performance with low SD (0.0029) and CV (0.0192).
  • Search Group Algorithm (SGA), Water Cycle Algorithm (WCA), Symbiotic Organism Search (SOS), and Coyote Optimization Algorithm (COA) also improved energy generation by over 65%, but ranked below MSA.

Conclusions:

  • The Moth Swarm Algorithm (MSA) is highly effective for optimizing energy generation in hydropower reservoirs.
  • Several other EAs, including SGA, WCA, SOS, and COA, show significant potential for similar applications.
  • Some EAs, such as GOA, DA, ALO, and WOA, did not yield satisfactory results, highlighting the importance of algorithm selection for hydropower optimization.