Economic Dispatch Solution Using Hybrid Salp Swarm Algorithm and Simulated Annealing Approach

Authors

  • A. Dihem Laboratory of Electrical Engineering (LGEB), Biskra University, Algeria. University of Batna 2, Batna, Algeria
  • A. Salhi Laboratory of Electrical Engineering (LGEB), Biskra University, Algeria.
  • D. Naimi Laboratory of Electrical Engineering (LGEB), Biskra University, Algeria.
  • A. Bensalem Laboratory of Electrical Engineering (LGEB), Biskra University, Algeria. University of Batna 2, Batna, Algeria

Keywords:

Economic Dispatch, Meta-Heuristic Method, Salp Swarm Algorithm, Simulated Annealing,

Abstract

In this paper, a new optimization technique called SSA-SA approach has been suggested. This proposed technique, which is the hybridization of two meta-heuristic techniques named Salp Swarm Algorithm (SSA) and Simulated Annealing (SA) method aims to improve the global optimal solution of ED problem in electrical power systems, considering the various complexities of practical operational constraints, such as valve point effects, active transmission losses, Prohibited Operating Zones (POZ) and Ramp Rate Limits (RRL). The SSA algorithm is used as a global optimization approach, while the SA algorithm is employed to enhance the quality and the exploitation of the best global solution found at each iteration of the SSA. Three electrical test systems, which are the 06-units, 15-units and 40-units are implemented in order to investigate the performances of the SSA-SA approach. The simulation results using the proposed approach are compared to those of the basic SSA method and other optimization techniques, newly published in literature.

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Published

2020-12-22

How to Cite

Dihem, A., Salhi, A., Naimi, D., & Bensalem, A. (2020). Economic Dispatch Solution Using Hybrid Salp Swarm Algorithm and Simulated Annealing Approach. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 12(4), 57–63. Retrieved from https://jtec.utem.edu.my/jtec/article/view/5814

Issue

Vol. 12 No. 4: October - December 2020

Section

Articles

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