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Economic Load Dispatch - A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO. / Vlachogiannis, Ioannis (John); Lee, KY.

In: I E E E Transactions on Power Systems, Vol. 24, No. 2, 2009, p. 991-1001.

Publication: Research - peer-reviewJournal article – Annual report year: 2009

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Vlachogiannis, Ioannis (John); Lee, KY / Economic Load Dispatch - A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO.

In: I E E E Transactions on Power Systems, Vol. 24, No. 2, 2009, p. 991-1001.

Publication: Research - peer-reviewJournal article – Annual report year: 2009

Bibtex

@article{d9ff256dc6a840a7b3bdea9bf4449893,
title = "Economic Load Dispatch - A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO",
publisher = "I E E E",
author = "Vlachogiannis, {Ioannis (John)} and KY Lee",
note = "Copyright: 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE",
year = "2009",
doi = "10.1109/TPWRS.2009.2016524",
volume = "24",
number = "2",
pages = "991--1001",
journal = "I E E E Transactions on Power Systems",
issn = "0885-8950",

}

RIS

TY - JOUR

T1 - Economic Load Dispatch - A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO

A1 - Vlachogiannis,Ioannis (John)

A1 - Lee,KY

AU - Vlachogiannis,Ioannis (John)

AU - Lee,KY

PB - I E E E

PY - 2009

Y1 - 2009

N2 - In this paper an improved coordinated aggregation-based particle swarm optimization (ICA-PSO) algorithm is introduced for solving the optimal economic load dispatch (ELD) problem in power systems. In the ICA-PSO algorithm each particle in the swarm retains a memory of its best position ever encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly. Moreover, the population size is increased adaptively, the number of search intervals for the particles is selected adaptively and the particles search the decision space with accuracy up to two digit points resulting in the improved convergence of the process. The ICA-PSO algorithm is tested on a number of power systems, including the systems with 6, 13, 15, and 40 generating units, the island power system of Crete in Greece and the Hellenic bulk power system, and is compared with other state-of-the-art heuristic optimization techniques (HOTs), demonstrating improved performance over them.

AB - In this paper an improved coordinated aggregation-based particle swarm optimization (ICA-PSO) algorithm is introduced for solving the optimal economic load dispatch (ELD) problem in power systems. In the ICA-PSO algorithm each particle in the swarm retains a memory of its best position ever encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly. Moreover, the population size is increased adaptively, the number of search intervals for the particles is selected adaptively and the particles search the decision space with accuracy up to two digit points resulting in the improved convergence of the process. The ICA-PSO algorithm is tested on a number of power systems, including the systems with 6, 13, 15, and 40 generating units, the island power system of Crete in Greece and the Hellenic bulk power system, and is compared with other state-of-the-art heuristic optimization techniques (HOTs), demonstrating improved performance over them.

U2 - 10.1109/TPWRS.2009.2016524

DO - 10.1109/TPWRS.2009.2016524

JO - I E E E Transactions on Power Systems

JF - I E E E Transactions on Power Systems

SN - 0885-8950

IS - 2

VL - 24

SP - 991

EP - 1001

ER -