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.
Other Authors: 57212927977
Format: Article
Published: Institute of Advanced Engineering and Science 2023
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spelling my.uniten.dspace-243802023-05-29T15:23:07Z Embedded adaptive mutation evolutionary programming for distributed generation management Zulkefli M.F.M. Musirin I. Jelani S. Mansor M.H. Honnoon N.M.S. 57212927977 8620004100 57193388570 56372667100 57210749614 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. Final 2023-05-29T07:23:07Z 2023-05-29T07:23:07Z 2019 Article 10.11591/ijeecs.v16.i1.pp364-370 2-s2.0-85077489022 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077489022&doi=10.11591%2fijeecs.v16.i1.pp364-370&partnerID=40&md5=d94d9d496984a241d7b93569ffc22308 https://irepository.uniten.edu.my/handle/123456789/24380 16 1 364 370 All Open Access, Hybrid Gold Institute of Advanced Engineering and Science Scopus
institution Universiti Tenaga Nasional
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collection Institutional Repository
continent Asia
country Malaysia
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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.
author2 57212927977
author_facet 57212927977
Zulkefli M.F.M.
Musirin I.
Jelani S.
Mansor M.H.
Honnoon N.M.S.
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_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
publisher Institute of Advanced Engineering and Science
publishDate 2023
_version_ 1806424556851691520
score 13.222552