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Optimal Placement of Electric Vehicle Charging Stations

Lookup NU author(s): Dr Anurag SharmaORCiD

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Abstract

© 2024 IEEE.In response to global climate change, the energy landscape is undergoing a major transformation. To reduce dependence on fossil fuels and limit greenhouse gas (GHG) emissions, renewable energy sources like solar, wind, and geothermal are increasingly being adopted. Countries such as Singapore are shifting away from internal combustion engine (ICE) vehicles toward electric vehicles (EVs) to foster a more sustainable energy future. As the adoption of EVs grows, ensuring sufficient availability of electric vehicle charging stations (EVCS) becomes crucial, highlighting the need to optimize their placement and sizing. This paper presents an approach for optimizing the placement of EVCS within the IEEE-33 Bus system. The system is divided into five distinct regions to accommodate varying load requirements based on different load types. Using the Particle Swarm Optimization (PSO) algorithm, the optimal placement of EVCS is determined, assuming two cars per AC charger and one car per DC charger. Three scenarios are analyzed, with results showing that as EV penetration rates and numbers rise, total power losses and voltage deviations in the system also increase. However, the application of PSO effectively minimizes these power losses and deviations, demonstrating the algorithm's ability to enhance overall system performance.


Publication metadata

Author(s): Dong J, Chiaki T, Sharma A, Kumar DS, Sivaneasan B

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2024 IEEE 11th Power India International Conference (PIICON)

Year of Conference: 2024

Online publication date: 15/05/2025

Acceptance date: 02/04/2024

ISSN: 2642-5289

Publisher: Institute of Electrical and Electronics Engineers Inc.

URL: https://doi.org/10.1109/PIICON63519.2024.10995160

DOI: 10.1109/PIICON63519.2024.10995160

Library holdings: Search Newcastle University Library for this item

ISBN: 9798350367072


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