Volume 9, Issue 4 (December 2013)                   IJEEE 2013, 9(4): 246-252 | Back to browse issues page

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Subramanian R, Thanushkodi K, Prakash A. An Efficient Meta Heuristic Algorithm to Solve Economic Load Dispatch Problems. IJEEE 2013; 9 (4) :246-252
URL: http://ijeee.iust.ac.ir/article-1-559-en.html
An Efficient Meta Heuristic Algorithm to Solve Economic Load Dispatch Problems
R Subramanian , K Thanushkodi , A Prakash
Abstract:   (15024 Views)
The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA), Differential Evolution (DE), Particle swarm optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.
Keywords: Artificial Bee Colony optimization , Bio geography-Based Optimization , Economic Load Dispatch , Firefly Algorithm , Genetic Algorithm , Particle Swarm Optimization
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Type of Study: Research Paper | Subject: Power Systems Operation
Received: 2013/03/10 | Revised: 2014/09/24 | Accepted: 2013/12/22
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.