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Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem.

Barzan Hussein Tahir1, Tarik A Rashid1, Hafiz Tayyab Rauf2

  • 1Department of Computer Science and Engineering, University of Kurdistan Helwer, Erbil, Iraq.

Computational Intelligence and Neuroscience
|July 21, 2022
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Summary
This summary is machine-generated.

This study introduces an enhanced Fitness-Dependent Optimizer (FDO) to solve the economic load dispatch problem, achieving lower fuel costs, emissions, and transmission losses in power systems.

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Area of Science:

  • Power Systems Engineering
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • Economic Load Dispatch (ELD) is crucial for minimizing power system operating costs, environmental impact, and conserving energy.
  • Swarm-based algorithms offer solutions for ELD but often suffer from premature convergence.
  • The Fitness-Dependent Optimizer (FDO) is a novel swarm-based algorithm inspired by bee swarming behavior.

Purpose of the Study:

  • To apply the Fitness-Dependent Optimizer (FDO) to solve the Economic Load Dispatch (ELD) problem, focusing on reducing fuel cost, emission allocation, and transmission loss.
  • To enhance the FDO algorithm with novel population initialization and dynamic weight factor selection for improved performance.

Main Methods:

  • An enhanced Fitness-Dependent Optimizer (FDO) variant was developed, incorporating quasi-random Sabol sequence for population initialization.
  • Dynamically employed sine maps were used to select the weight factor for guiding search agents during exploitation and exploration.
  • The enhanced FDO was tested on a standard 24-unit power system under various load demands.

Main Results:

  • The enhanced FDO demonstrated superior performance in minimizing fuel cost, emission allocation, and transmission loss compared to the conventional FDO.
  • The study achieved a record low transmission loss of 7.94E-12 using the enhanced FDO.
  • Standard estimations confirmed the stability and effectiveness of the enhanced FDO in both exploitation and exploration phases.

Conclusions:

  • The enhanced Fitness-Dependent Optimizer (FDO) effectively addresses the Economic Load Dispatch (ELD) problem, offering significant improvements over conventional methods.
  • The novel enhancements, including quasi-random initialization and dynamic sine map-based weight selection, contribute to superior optimization capabilities.
  • The FDO algorithm shows promise for efficient and stable power system operation optimization.