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
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spelling	my-uthm-ep.68362022-03-28T01:29:47Z Solving economic dispatch problem using particle swarm optimization 2013-06 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 http://eprints.uthm.edu.my/6836/ http://eprints.uthm.edu.my/6836/1/24p%20SYED%20AKHMAL%20SYED%20JAMALIL.pdf text en public http://eprints.uthm.edu.my/6836/2/SYED%20AKHMAL%20SYED%20JAMALIL%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6836/3/SYED%20AKHMAL%20SYED%20JAMALIL%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
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
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
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
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
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
_version_	1747831092134543360

Solving economic dispatch problem using particle swarm optimization

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