Embedded adaptive mutation evolutionary programming for distributed generation management

Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to inc...

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Main Authors: Zulkefli, M.F.M., Musirin, I., Jelani, S., Mansor, M.H., Honnoon, N.M.S.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-128392020-07-07T04:44:06Z Embedded adaptive mutation evolutionary programming for distributed generation management Zulkefli, M.F.M. Musirin, I. Jelani, S. Mansor, M.H. Honnoon, N.M.S. Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced. © 2019 Institute of Advanced Engineering and Science. All rights reserved. 2020-02-03T03:27:11Z 2020-02-03T03:27:11Z 2019 Article 10.11591/ijeecs.v16.i1.pp364-370 en
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/
language English
description Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced. © 2019 Institute of Advanced Engineering and Science. All rights reserved.
format Article
author Zulkefli, M.F.M.
Musirin, I.
Jelani, S.
Mansor, M.H.
Honnoon, N.M.S.
spellingShingle Zulkefli, M.F.M.
Musirin, I.
Jelani, S.
Mansor, M.H.
Honnoon, N.M.S.
Embedded adaptive mutation evolutionary programming for distributed generation management
author_facet Zulkefli, M.F.M.
Musirin, I.
Jelani, S.
Mansor, M.H.
Honnoon, N.M.S.
author_sort Zulkefli, M.F.M.
title Embedded adaptive mutation evolutionary programming for distributed generation management
title_short Embedded adaptive mutation evolutionary programming for distributed generation management
title_full Embedded adaptive mutation evolutionary programming for distributed generation management
title_fullStr Embedded adaptive mutation evolutionary programming for distributed generation management
title_full_unstemmed Embedded adaptive mutation evolutionary programming for distributed generation management
title_sort embedded adaptive mutation evolutionary programming for distributed generation management
publishDate 2020
_version_ 1672614181799460864
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