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Utilization of EV Charging Station in Demand Side Management Using Deep Learning Method

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journal contribution
submitted on 2024-02-12, 07:08 and posted on 2024-02-12, 07:08 authored by Abdul Hafeez, Rashid Alammari, Atif Iqbal

Conventional 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.

Other Information

Published in: IEEE Access
License: http://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1109/access.2023.3238667

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU