Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations
Charging (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; Mark...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-24879 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-248792023-05-29T15:28:15Z Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations Al-Ogaili A.S. Tengku Hashim T.J. Rahmat N.A. Ramasamy A.K. Marsadek M.B. Faisal M. Hannan M.A. 57189511897 55241766100 55647163881 16023154400 26423183000 57215018777 7103014445 Charging (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 networks The 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. Final 2023-05-29T07:28:15Z 2023-05-29T07:28:15Z 2019 Article 10.1109/ACCESS.2019.2939595 2-s2.0-85077988994 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077988994&doi=10.1109%2fACCESS.2019.2939595&partnerID=40&md5=388be3fc66bdb7d4b6b025e812082aa8 https://irepository.uniten.edu.my/handle/123456789/24879 7 8825773 128353 128371 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus |
| 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/ |
| description |
Charging (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 networks |
| author2 |
57189511897 |
| author_facet |
57189511897 Al-Ogaili A.S. Tengku Hashim T.J. Rahmat N.A. Ramasamy A.K. Marsadek M.B. Faisal M. Hannan M.A. |
| format |
Article |
| author |
Al-Ogaili A.S. Tengku Hashim T.J. Rahmat N.A. Ramasamy A.K. Marsadek M.B. Faisal M. Hannan M.A. |
| spellingShingle |
Al-Ogaili A.S. Tengku Hashim T.J. Rahmat N.A. Ramasamy A.K. Marsadek M.B. Faisal M. Hannan M.A. Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations |
| author_sort |
Al-Ogaili A.S. |
| title |
Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations |
| title_short |
Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations |
| title_full |
Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations |
| title_fullStr |
Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations |
| title_full_unstemmed |
Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations |
| title_sort |
review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: challenges and recommendations |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806426427154759680 |
| score |
13.214268 |