Publication:
Optimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithm

Simple item page
dc.citedby0
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorNoor S.Z.M.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorMohamad H.en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid36806685300en_US
dc.contributor.authorid58643927500en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid36809989400en_US
dc.date.accessioned2024-10-14T03:18:05Z
dc.date.available2024-10-14T03:18:05Z
dc.date.issued2023
dc.description.abstractThe increasing prevalence of Electric Vehicles (EVs) has underscored the critical importance of establishing a comprehensive and effective charging station network. To sufficiently meet the energy demands of electric vehicles, it is imperative to establish a robust charging station infrastructure that can effectively cater to a substantial volume of electric automobiles. This infrastructure must be widely deployed to ensure widespread accessibility and usability. Many EVs� concurrent usage of electric charging stations may lead to potential unreliability in the distribution setup. Hence, it is imperative to strategically determine the placement and sizing of Fast Charging Stations (FCS) to achieve optimal functionality of the power grid. This paper proposes the Grasshopper Optimization Algorithm (GOA) as a technique for strategically locating FCS to minimize costs. GOA is a computational technique that addresses optimization challenges by formulating a mathematical model that emulates the collective behaviour observed in natural grasshopper swarms. The proposed methodology is evaluated on an IEEE 69-bus radial distribution system. The results indicate that the proposed methodology has successfully identified the most economically efficient location for FCS within a power distribution network compared to alternative optimization methods. � 2023 Seventh Sense Research Group�.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14445/23488379/IJEEE-V10I9P117
dc.identifier.epage189
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85173993412
dc.identifier.spage181
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85173993412&doi=10.14445%2f23488379%2fIJEEE-V10I9P117&partnerID=40&md5=5e8ce0bed325d6e00960b8568f14a37b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34131
dc.identifier.volume10
dc.pagecount8
dc.publisherSeventh Sense Research Groupen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofHybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleSSRG International Journal of Electrical and Electronics Engineering
dc.subjectAnt colony optimizer
dc.subjectCost minimization
dc.subjectDistribution system
dc.subjectMinimum voltage
dc.subjectPower loss minimization
dc.titleOptimal Location of Electric Vehicle Fast Charging Station Using Grasshopper Optimization Algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS