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Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations

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dc.citedby105
dc.contributor.authorAl-Ogaili A.S.en_US
dc.contributor.authorTengku Hashim T.J.en_US
dc.contributor.authorRahmat N.A.en_US
dc.contributor.authorRamasamy A.K.en_US
dc.contributor.authorMarsadek M.B.en_US
dc.contributor.authorFaisal M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorid57189511897en_US
dc.contributor.authorid55241766100en_US
dc.contributor.authorid55647163881en_US
dc.contributor.authorid16023154400en_US
dc.contributor.authorid26423183000en_US
dc.contributor.authorid57215018777en_US
dc.contributor.authorid7103014445en_US
dc.date.accessioned2023-05-29T07:28:15Z
dc.date.available2023-05-29T07:28:15Z
dc.date.issued2019
dc.descriptionCharging (batteries); Electric utilities; Electric vehicles; Forecasting; Fossil fuels; Learning systems; Machine learning; Probability; Scheduling; Smart power grids; Charging strategies; clustering; Electric vehicle charging; Electric Vehicles (EVs); Global energy demand; Local distributions; Market penetration; Smart grid applications; Electric power transmission networksen_US
dc.description.abstractThe usage and adoption of electric vehicles (EVs) have increased rapidly in the 21st century due to the shifting of the global energy demand away from fossil fuels. The market penetration of EVs brings new challenges to the usual operations of the power system. Uncontrolled EV charging impacts the local distribution grid in terms of its voltage profile, power loss, grid unbalance, and reduction of transformer life, as well as harmonic distortion. Multiple research studies have addressed these problems by proposing various EV charging control methods. This manuscript comprehensively reviews EV control charging strategies using real-world data. This review classifies the EV control charging strategies into scheduling, clustering, and forecasting strategies. The models of EV control charging strategies are highlighted to compare and evaluate the techniques used in EV charging, enabling the identification of the advantages and disadvantages of the different methods applied. A summary of the methods and techniques for these EV charging strategies is presented based on machine learning and probabilities approaches. This research paper indicates many factors and challenges in the development of EV charging control in next-generation smart grid applications and provides potential recommendations. A report on the guidelines for future studies on this research topic is provided to enhance the comparability of the various results and findings. Accordingly, all the highlighted insights of this paper serve to further the increasing effort towards the development of advanced EV charging methods and demand-side management (DSM) for future smart grid applications. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8825773
dc.identifier.doi10.1109/ACCESS.2019.2939595
dc.identifier.epage128371
dc.identifier.scopus2-s2.0-85077988994
dc.identifier.spage128353
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85077988994&doi=10.1109%2fACCESS.2019.2939595&partnerID=40&md5=388be3fc66bdb7d4b6b025e812082aa8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24879
dc.identifier.volume7
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleReview on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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