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Solving economic dispatch problem using particle swarm optimization

This project presents a new approach to solve Economic Dispatch (ED) using Particle Swarm Optimization (PSO) technique with consideration of several generators constraints to search the optimal solution and the minimum of total generation operating cost. Conventional optimization methods assume gene...

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Main Author: Syed Jamalil, Syed Akhmal
Format: Thesis
Language:English
English
English
Published: 2013
Subjects:
Q350-390 Information theory
Online Access:http://eprints.uthm.edu.my/6836/1/24p%20SYED%20AKHMAL%20SYED%20JAMALIL.pdf
http://eprints.uthm.edu.my/6836/2/SYED%20AKHMAL%20SYED%20JAMALIL%20COPYRIGHT%20DECLARATION.pdf
http://eprints.uthm.edu.my/6836/3/SYED%20AKHMAL%20SYED%20JAMALIL%20WATERMARK.pdf
http://eprints.uthm.edu.my/6836/
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spelling my.uthm.eprints.68362022-03-28T01:29:47Z http://eprints.uthm.edu.my/6836/ Solving economic dispatch problem using particle swarm optimization Syed Jamalil, Syed Akhmal Q350-390 Information theory This project presents a new approach to solve Economic Dispatch (ED) using Particle Swarm Optimization (PSO) technique with consideration of several generators constraints to search the optimal solution and the minimum of total generation operating cost. Conventional optimization methods assume generator cost curves to be continuous and monotonically increasing, but modern generators have a variety of nonlinearities in their cost curves making this assumption inaccurate, and the resulting approximate dispatches cause a lot of revenue loss. In PSO technique, the movement of a particle is governed by three behaviors namely, inertial, cognitive, and social. The cognitive behavior helps the particle to remember its previously visited best position. This technique helps to explore the search space very effectively. The proposed method considers the nonlinear characteristics of a generator such as ramp rate limits, power balance constraints with maximum and minimum operating limits and prohibited operating zone for actual power system operation. The practicality of the proposed method was demonstrated for different cases on 6-unit generation system and 15-unit generation system based on IEEE standard operation. The PSO algorithms with the proposed objective function are being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The proposed function approach was first tested on some less complex systems and then the effectiveness of the PSO was compared with the research studies from several references of studied papers. 2013-06 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/6836/1/24p%20SYED%20AKHMAL%20SYED%20JAMALIL.pdf text en http://eprints.uthm.edu.my/6836/2/SYED%20AKHMAL%20SYED%20JAMALIL%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/6836/3/SYED%20AKHMAL%20SYED%20JAMALIL%20WATERMARK.pdf Syed Jamalil, Syed Akhmal (2013) Solving economic dispatch problem using particle swarm optimization. Masters thesis, Universiti Tun Hussein Malaysia.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
English
English
topic Q350-390 Information theory
spellingShingle Q350-390 Information theory
Syed Jamalil, Syed Akhmal
Solving economic dispatch problem using particle swarm optimization
description This project presents a new approach to solve Economic Dispatch (ED) using Particle Swarm Optimization (PSO) technique with consideration of several generators constraints to search the optimal solution and the minimum of total generation operating cost. Conventional optimization methods assume generator cost curves to be continuous and monotonically increasing, but modern generators have a variety of nonlinearities in their cost curves making this assumption inaccurate, and the resulting approximate dispatches cause a lot of revenue loss. In PSO technique, the movement of a particle is governed by three behaviors namely, inertial, cognitive, and social. The cognitive behavior helps the particle to remember its previously visited best position. This technique helps to explore the search space very effectively. The proposed method considers the nonlinear characteristics of a generator such as ramp rate limits, power balance constraints with maximum and minimum operating limits and prohibited operating zone for actual power system operation. The practicality of the proposed method was demonstrated for different cases on 6-unit generation system and 15-unit generation system based on IEEE standard operation. The PSO algorithms with the proposed objective function are being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The proposed function approach was first tested on some less complex systems and then the effectiveness of the PSO was compared with the research studies from several references of studied papers.
format Thesis
author Syed Jamalil, Syed Akhmal
author_facet Syed Jamalil, Syed Akhmal
author_sort Syed Jamalil, Syed Akhmal
title Solving economic dispatch problem using particle swarm optimization
title_short Solving economic dispatch problem using particle swarm optimization
title_full Solving economic dispatch problem using particle swarm optimization
title_fullStr Solving economic dispatch problem using particle swarm optimization
title_full_unstemmed Solving economic dispatch problem using particle swarm optimization
title_sort solving economic dispatch problem using particle swarm optimization
publishDate 2013
url http://eprints.uthm.edu.my/6836/1/24p%20SYED%20AKHMAL%20SYED%20JAMALIL.pdf
http://eprints.uthm.edu.my/6836/2/SYED%20AKHMAL%20SYED%20JAMALIL%20COPYRIGHT%20DECLARATION.pdf
http://eprints.uthm.edu.my/6836/3/SYED%20AKHMAL%20SYED%20JAMALIL%20WATERMARK.pdf
http://eprints.uthm.edu.my/6836/
_version_ 1738581541449105408
score 13.211869

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