Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood

Multi-area economic dispatch (MAED) is very important issue in power system operation. This study presents a Particle Swarm Optimization (PSO) approach to address the MAED problems. This approach will consider the flow limits between the areas and generation limits in order to minimize the generatio...

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Main Author: Wan Mahmood, Wan Nur Naimah
Format: Thesis
Language:English
Published: 2019
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/67253/2/67253.pdf
https://ir.uitm.edu.my/id/eprint/67253/
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spelling my.uitm.ir.672532023-01-20T02:59:04Z https://ir.uitm.edu.my/id/eprint/67253/ Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood Wan Mahmood, Wan Nur Naimah Production of electric energy or power. Powerplants. Central stations Electric power distribution. Electric power transmission Multi-area economic dispatch (MAED) is very important issue in power system operation. This study presents a Particle Swarm Optimization (PSO) approach to address the MAED problems. This approach will consider the flow limits between the areas and generation limits in order to minimize the generation cost. The proposed method is tested with three different case studies. The results show that PSO is capable to solve ED and MAED problems. These case studies show that the PSO gives better result compared to Genetic Algorithm (GA) and Lagrange multiplier in term of minimization the total cost. The results also show that when the flow limit is increased, the generation cost will be cheaper. 2019 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/67253/2/67253.pdf Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood. (2019) Degree thesis, thesis, Universiti Teknologi MARA (UiTM).
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Production of electric energy or power. Powerplants. Central stations
Electric power distribution. Electric power transmission
spellingShingle Production of electric energy or power. Powerplants. Central stations
Electric power distribution. Electric power transmission
Wan Mahmood, Wan Nur Naimah
Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood
description Multi-area economic dispatch (MAED) is very important issue in power system operation. This study presents a Particle Swarm Optimization (PSO) approach to address the MAED problems. This approach will consider the flow limits between the areas and generation limits in order to minimize the generation cost. The proposed method is tested with three different case studies. The results show that PSO is capable to solve ED and MAED problems. These case studies show that the PSO gives better result compared to Genetic Algorithm (GA) and Lagrange multiplier in term of minimization the total cost. The results also show that when the flow limit is increased, the generation cost will be cheaper.
format Thesis
author Wan Mahmood, Wan Nur Naimah
author_facet Wan Mahmood, Wan Nur Naimah
author_sort Wan Mahmood, Wan Nur Naimah
title Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood
title_short Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood
title_full Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood
title_fullStr Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood
title_full_unstemmed Economic dispatch in multi-area using particle swarm optimization (PSO) / Wan Nur Naimah Wan Mahmood
title_sort economic dispatch in multi-area using particle swarm optimization (pso) / wan nur naimah wan mahmood
publishDate 2019
url https://ir.uitm.edu.my/id/eprint/67253/2/67253.pdf
https://ir.uitm.edu.my/id/eprint/67253/
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score 13.209306