Smart Grid Coordination with Electric Vehicles for Frequency Regulation Using V2G Technology

Authors

  • Sohail Khan Author
  • Dr. Syed Sheraz Ul Hasan Mohani Author

DOI:

https://doi.org/10.63075/jej9mh19

Keywords:

Vehicle-to-Grid (V2G), Electric Vehicles (EVs), Frequency Regulation, Smart Grid, Distributed Energy Resources (DERs), Renewable Energy, Grid Stability, Energy Efficiency, Battery Degradation, Cost Savings

Abstract

This paper will explore how Vehicle-to-Grid (V2G) technology can support grid frequency regulation by leveraging the Electric Vehicles (EVs) as distributed energy resources (DERs). With the higher demand of renewable energy, the intermittent nature of sources such as solar and wind becomes a serious problem to the stability of the grid, requiring creative solutions to issues of frequency regulation. V2G technology also makes EVs provide power to the grid when high demands occur and to absorb extra power when the demands are low because it enables bi-directional energy flow between the grid and EVs. Simulations have shown that V2G systems significantly improve the grid frequency deviation significantly, with a potential reduction of the operational costs of a 25 percent compared to traditional approach. Beside that, also in the study, we find efficiency of V2G in relation to the amount of energy it supplies to the system compared to the amount it uses on the grid, and the EVs used supply more than they use. Nonetheless, obstacles like battery degradation and an optimized schedule were found. However, despite these issues, the introduction of EVs into grid services has presented a flexible, cost-effective, and sustainable frequency-supporting solution, resulting in a more resilient energy system. This study highlights the significance of expanding charging infrastructure and developing battery technology further in order to maximize the advantages of V2G systems.

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Published

2025-07-30

How to Cite

Smart Grid Coordination with Electric Vehicles for Frequency Regulation Using V2G Technology. (2025). Annual Methodological Archive Research Review, 3(7), 627-647. https://doi.org/10.63075/jej9mh19

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