Solving the Economic Dispatch by Artificial Bees Colony (ABC) Algorithm

Authors

  • Abdulwahid A. khalleefah Department of Electrical and Electronic Technologies, Higher Institute of Sciences and Technology Alasabaa, Alasabaa, Libya Author
  • Abdurazq Elbaz Department of Electrical and Electronics Engineering, University of Tripoli, Tripoli, Libya Author
  • Abdelbaset M. Ihba Department of Electrical and Computer Engineering, School of Applied Science and Engineering, The Libyan Academy, Tripoli, Libya Author

DOI:

https://doi.org/10.65421/jshd.v1i2.45

Keywords:

Economic Dispatch, Artificial Bees Colony (ABC) algorithm, Optimization problem

Abstract

The goal of Economic Dispatch (ED), a basic optimization problem in power system operation, is to ascertain the ideal power output of producing units while satisfying system restrictions and minimizing overall fuel costs. Nonlinearity, nonconvex cost functions, and intricate operational restrictions like power balancing and generator limits are common challenges for conventional optimization techniques. This study applies the Artificial Bees Colony (ABC) algorithm, a swarm intelligence-based metaheuristic inspired by honey bee foraging behavior, to effectively solve the Economic Dispatch problem. In order to effectively handle nonlinear and limited optimization problems, the ABC algorithm uses employed bees, observer bees, and scout bees to explore and exploit the search space.

The proposed approach is implemented on a practical power system model consisting of three thermal generating units selected from the Libyan electrical network, namely Misurata, Al-Khums, and West Tripoli power plants. Simulation results demonstrate that the ABC algorithm successfully determines the optimal generation schedule while satisfying power balance and generator operating limits.

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Published

2025-12-25

Issue

Section

Articles

How to Cite

Solving the Economic Dispatch by Artificial Bees Colony (ABC) Algorithm. (2025). Journal of Scientific and Human Dimensions, 1(2), 324-333. https://doi.org/10.65421/jshd.v1i2.45