Optimal power scheduling strategy in power systems using swarm optimization technique

This study proposes a power scheduling strategy for power system networks by using PSO technique. This strategy searches for the optimal power for each generating unit in the system, without compromising the total power demands and constraints of each unit. The objective function aims to minimize th...

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Main Authors: Kamari N.A.M., Rahmat N.A., Musirin I.
Other Authors: 36680312000
Format: Article
Published: World Academy of Research in Science and Engineering 2023
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spelling my.uniten.dspace-248702023-05-29T15:28:08Z Optimal power scheduling strategy in power systems using swarm optimization technique Kamari N.A.M. Rahmat N.A. Musirin I. 36680312000 55647163881 8620004100 This study proposes a power scheduling strategy for power system networks by using PSO technique. This strategy searches for the optimal power for each generating unit in the system, without compromising the total power demands and constraints of each unit. The objective function aims to minimize the total generation cost. The amount of power loss is measured to determine the feasibility of the proposed technique. In addition, optimization processes using evolutionary programming (EP) and artificial immune system (AIS) are implemented. Five-and 30-bus power system networks are selected and processed using MATLAB. The simulation results indicate that PSO performs better than EP and AIS in determining the optimal power generation value with minimum generation cost and power loss. � 2019, World Academy of Research in Science and Engineering. All rights reserved. Final 2023-05-29T07:28:08Z 2023-05-29T07:28:08Z 2019 Article 10.30534/ijatcse/2019/3781.62019 2-s2.0-85078336237 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078336237&doi=10.30534%2fijatcse%2f2019%2f3781.62019&partnerID=40&md5=0dfad6c5ef4c680eb2eb784c9670252f https://irepository.uniten.edu.my/handle/123456789/24870 8 1.6 Special Issue 37 246 251 All Open Access, Bronze World Academy of Research in Science and Engineering Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description This study proposes a power scheduling strategy for power system networks by using PSO technique. This strategy searches for the optimal power for each generating unit in the system, without compromising the total power demands and constraints of each unit. The objective function aims to minimize the total generation cost. The amount of power loss is measured to determine the feasibility of the proposed technique. In addition, optimization processes using evolutionary programming (EP) and artificial immune system (AIS) are implemented. Five-and 30-bus power system networks are selected and processed using MATLAB. The simulation results indicate that PSO performs better than EP and AIS in determining the optimal power generation value with minimum generation cost and power loss. � 2019, World Academy of Research in Science and Engineering. All rights reserved.
author2 36680312000
author_facet 36680312000
Kamari N.A.M.
Rahmat N.A.
Musirin I.
format Article
author Kamari N.A.M.
Rahmat N.A.
Musirin I.
spellingShingle Kamari N.A.M.
Rahmat N.A.
Musirin I.
Optimal power scheduling strategy in power systems using swarm optimization technique
author_sort Kamari N.A.M.
title Optimal power scheduling strategy in power systems using swarm optimization technique
title_short Optimal power scheduling strategy in power systems using swarm optimization technique
title_full Optimal power scheduling strategy in power systems using swarm optimization technique
title_fullStr Optimal power scheduling strategy in power systems using swarm optimization technique
title_full_unstemmed Optimal power scheduling strategy in power systems using swarm optimization technique
title_sort optimal power scheduling strategy in power systems using swarm optimization technique
publisher World Academy of Research in Science and Engineering
publishDate 2023
_version_ 1806425978473283584
score 13.214268