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Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

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dc.citedby0
dc.contributor.authorAbdullah A.en_US
dc.contributor.authorMusirin I.en_US
dc.contributor.authorOthman M.M.en_US
dc.contributor.authorRahim S.R.A.en_US
dc.contributor.authorMansor M.H.en_US
dc.contributor.authorShaaya S.A.en_US
dc.contributor.authorAminuddin N.en_US
dc.contributor.authorGoel L.K.en_US
dc.contributor.authorid57197864035en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid35944613200en_US
dc.contributor.authorid11639107900en_US
dc.contributor.authorid56372667100en_US
dc.contributor.authorid16022846200en_US
dc.contributor.authorid57211493660en_US
dc.contributor.authorid7005345158en_US
dc.date.accessioned2025-03-03T07:41:39Z
dc.date.available2025-03-03T07:41:39Z
dc.date.issued2024
dc.description.abstractWith the rise in load demand, improving the voltage profile and reducing line loss is vital to ensure reliable power delivery to the customer. However, increasing power plant generation capacity is limited by environmental and economic factors, requiring a careful assessment of locally optimal options. Distributed Generation (DG) installation into the electricity grid is one of the reliable remedial actions to ensure a smooth power delivery. Installation of DG requires an optimization process to identify the appropriate placement and sizing. Inaccurate sizing and placement of the DG installation may result to over-compensation or under-compensation phenomena. This paper proposes a novel approach termed the Integrated Immune Moth Flame Evolutionary Programming technique (IIMFEP) for optimizing the installation of DG sources in distribution systems. It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. Two cases were considered in this study termed Case 1 and Case 2. Results in Case 1 discovered that the optimal sizing and placement of four Type III DGs exhibit the lowest power loss worth 2.74 kW (98.78 % reduction) for the 69-Bus RDS, while for the 118-Bus RDS the power loss is 319.89 kW (75.36 % reduction). In Case Study 2, the combination of DG Types I and III provided the highest power loss reduction. With one DG Type I and two DG Type III units installed, power loss was reduced by 97.15 % to 6.41 kW for the 69-Bus RDS and by 62.01 % to 493.21 kW for the 118-Bus RDS. The proposed IIMFEP managed to alleviate the setback experienced in the traditional EP, AIS and MFO which found to be stuck at local optimum. The IIMFEP method is compared to Moth Flame Optimization, Artificial Immune System and Evolutionary Programming and validated using the IEEE 69-Bus and 118-Bus Radial Distribution Systems, resulting in outstanding performance. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2024.09.006
dc.identifier.epage914
dc.identifier.scopus2-s2.0-85204057752
dc.identifier.spage895
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85204057752&doi=10.1016%2fj.aej.2024.09.006&partnerID=40&md5=c0cd022c926b70b19be94e37338fa351
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36236
dc.identifier.volume107
dc.pagecount19
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.subjectElectric power system planning
dc.subjectPower distribution planning
dc.subjectArtificial Immune System
dc.subjectEvolution programming
dc.subjectEvolutionary programming techniques
dc.subjectGeneration types
dc.subjectMoth flame optimization
dc.subjectOptimisations
dc.subjectPower delivery
dc.subjectPower loss reduction
dc.subjectPower loss reduction percentage
dc.subjectPowerloss
dc.subjectDistributed power generation
dc.titleEffect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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