Real-time impact study of 2-wheeler EV charging on the distribution network
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Anirudha S. Marothiya
,
Pradyumn Chaturvedi
and
Madhuri A. Chaudhari
Abstract
Transportation is vital for modern societies, facilitating daily commutes and the movement of goods, but it is also a major contributor to pollution, second only to industrial emissions. While electric vehicles (EVs) offer a greener alternative, their charging demands-affected by factors such as vehicle type, location, battery state of charge (SoC), and charging rate (C-Rate) can place additional strain on power distribution networks and introduce non-linear loads, impacting network stability. This study investigates the effects of EV charging on the distribution network, focusing on changes in voltage, current, and total harmonic distortion (THD) at the point of common coupling (PCC). Using MATLAB environment, a 10 kVA single-phase distribution network was designed with various realistic loads, and EVs were modeled as rectifier-converter systems to assess their impact. The integration of EVs into the network is considered by taking variation with respect to changes in its location, battery SoCs and C-Rate at the time of charging. The research also examines the real-time impact of two-wheeler EVs, such as Bajaj Chetak, OLA S1-Pro, Okinawa, and Tunwal Lithino Li, with a focus on harmonic distortion injected in the grid current. The harmonic distortion events were captured via HIOKI PW-3900 Power Quality Meter (PQM). The findings provide useful insights for future decision-making regarding EV integration into power distribution networks.
Acknowledgments
The authors would like to thanks department of electrical engineering, VNIT-Nagpur (MH-India) and department of electrical engineering, Bajaj Institute of Technology-Wardha (MH-India) for providing the necessary support for the successful completion of this work.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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