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AuthorHafeez, Abdul
AuthorAlammari, Rashid
AuthorIqbal, Atif
Available date2023-05-21T08:32:43Z
Publication Date2023
Publication NameIEEE Access
ResourceScopus
URIhttp://dx.doi.org/10.1109/ACCESS.2023.3238667
URIhttp://hdl.handle.net/10576/43074
AbstractConventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO2 emissions. Research shows that by 2030, EVs can reduce CO2 emissions by 28%. However, two major obstacles affect the widespread adoption of electric vehicles: the high cost of EVs and the lack of charging stations. This paper presents a comprehensive data-driven approach based demand-side management for a solar-powered electric vehicle charging station connected to a microgrid. The proposed approach utilizes a solar-powered electric vehicle charging station to compensate for the energy required during peak demand, which reduces the utilization of conventional energy sources and shortens the problem of fewer EVCS in the current scenario. PV power stations, commercial loads, residential loads, and electric vehicle charging stations were simulated using the collected real-time data. Furthermore, a deep learning approach was developed to control the energy supply to the microgrid and to charge the electric vehicle from the grid during off-peak hours. Furthermore, two different machine learning approaches were compared to estimate the state of charge estimation of an energy storage system. Finally, the proposed framework of the demand management system was executed for a case study of 24 hours. The results reflect that peak demand has been compensated with the help of an electric vehicle charging station during peak hours. 2013 IEEE.
SponsorThis publication was made possible by Qatar University Research grant# [QUCP-CENG-2020-2] from Qatar University, Qatar. The APC for the article is funded by the Qatar National Library, Doha, Qatar.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCO2 emission
data-driven approach
deep learning
demand-side management
electric vehicle charging station
peak clipping
TitleUtilization of EV Charging Station in Demand Side Management Using Deep Learning Method
TypeArticle
Pagination8747-8760
Volume Number11
dc.accessType Open Access


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