TY - GEN
T1 - Optimal Fast Charging Station Development Model Using Real Time Traffic Information
AU - Asna, Madathodika
AU - Shareef, Hussain
AU - Prasanthi, Achikkulath
N1 - Funding Information:
Author Contributions: conceptualization, M. Asna and H.Shareef ; methodology, M. Asna and H.Shareef; software, M. Asna and H.Shareef; validation, M.Asna; formal analysis, M.Asna .; resources, A.Prasanthi; writing—original draft preparation, M.Asna ; writing—review and editing, H.Shareef and A.Prasanthi; supervision, H.Shareef; funding, H.Shareef; Funding: This research was funded by the United Arab Emirates University with fund code 31R224-RTTSC(1)-2019.
Publisher Copyright:
© 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
PY - 2021
Y1 - 2021
N2 - Carbon emissions from conventional vehicles have significantly damaged the ecosystem balance, and have pushed the transportation sector to switch towards cleaner fuel sources that can power vehicles more efficiently. Electric vehicles (EV) offer an excellent substitute for fossil fuel-fired vehicles; however, the availability of publicly accessible charging stations is essential for wider adoption and acceptance of electric vehicles by customers. This paper presents an optimal fast charging station planning method to determine station location and station capacity. The method aims to minimize the overall station cost that includes investment costs, operation costs, and EV charging costs without compromising the EV user benefits and distribution network performance constraints. In addition, queuing theory based station capacity estimation is employed to ensure adequate station efficiency. Furthermore, this planning methodology uses Google Map Services to estimate the average time a user is required to access the charging station from the demand point, taking into account real road traffic flow information. The developed model is solved and optimized using a Binary Particle Swarm Optimization algorithm. The simulation result validates the novelty and feasibility of the developed charging station model.
AB - Carbon emissions from conventional vehicles have significantly damaged the ecosystem balance, and have pushed the transportation sector to switch towards cleaner fuel sources that can power vehicles more efficiently. Electric vehicles (EV) offer an excellent substitute for fossil fuel-fired vehicles; however, the availability of publicly accessible charging stations is essential for wider adoption and acceptance of electric vehicles by customers. This paper presents an optimal fast charging station planning method to determine station location and station capacity. The method aims to minimize the overall station cost that includes investment costs, operation costs, and EV charging costs without compromising the EV user benefits and distribution network performance constraints. In addition, queuing theory based station capacity estimation is employed to ensure adequate station efficiency. Furthermore, this planning methodology uses Google Map Services to estimate the average time a user is required to access the charging station from the demand point, taking into account real road traffic flow information. The developed model is solved and optimized using a Binary Particle Swarm Optimization algorithm. The simulation result validates the novelty and feasibility of the developed charging station model.
KW - Distribution network
KW - Electric vehicle
KW - Fast charging station
KW - Queuing theory
KW - Traffic flow
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M3 - Conference contribution
AN - SCOPUS:85125778153
T3 - ZEMCH International Conference
SP - 437
EP - 445
BT - ZEMCH 2021 - 8th Zero Energy Mass Custom Home International Conference, Proceedings
A2 - Tabet Aoul, Kheira Anissa
A2 - Shafiq, Mohammed Tariq
A2 - Attoye, Daniel Efurosibina
PB - ZEMCH Network
T2 - 8th Zero Energy Mass Custom Home International Conference, ZEMCH 2021
Y2 - 26 October 2021 through 28 October 2021
ER -
