Placement and Sizing of EV Charging Stations According to Centrality of the Underlying Network

Parastvand, H, Bass, O, Masoum, MAS, Moghaddam, Zeinab, Lachowicz, S and Chapman, A 2020, Placement and Sizing of EV Charging Stations According to Centrality of the Underlying Network, in IETC 2020 : Proceedings of the Intermountain Engineering, Technology and Computing, IEEE, Piscataway, N,J., pp. 1-6, doi: 10.1109/IETC47856.2020.9249203.

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Title Placement and Sizing of EV Charging Stations According to Centrality of the Underlying Network
Author(s) Parastvand, H
Bass, O
Masoum, MAS
Moghaddam, ZeinabORCID iD for Moghaddam, Zeinab orcid.org/0000-0002-2296-915X
Lachowicz, S
Chapman, A
Conference name Intermountain Engineering, Technology and Computing Conference (1st : 2020 : Online from Orem, Utah)
Conference location Online from Orem, Utah
Conference dates 2020/10/02 - 2020/10/03
Title of proceedings IETC 2020 : Proceedings of the Intermountain Engineering, Technology and Computing
Publication date 2020
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N,J.
Keyword(s) Electric Vehicle
Charging Station
Graph Theory
Centrality
Placement
Sizing
Summary EV placement and sizing are the subject of ever increasing studies in the last decade mostly relying on optimization approaches. This study looks at the EV network as a complex network where the nodes are the potential locations of charging stations (CSs) and edges (links) represent the traffic flow. It then investigates the impacts of some graph properties on the solutions of the CS placement problem. In fact, the graph centrality and its variants are used to find the locations of CSs to reduce the average waiting times at the stations. It is shown that the centrality based analysis can lead to promising results for small and medium EV networks leaving the large networks to be addressed by more complicated approaches. Simulations are performed on the central (downtown) part of Perth City EV network, Western Australia scaled down by the real traffic information.
ISBN 9781728142913
Language eng
DOI 10.1109/IETC47856.2020.9249203
HERDC Research category E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30156525

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