The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Spatial and Temporal Model of Electric Vehicle Charging Demand
Abstract
This paper presents a spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M/M/s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M/M/s queueing theory with the arrival rate of discharged vehicles. This mathematical model of charging demand may allow grid's distribution planners to anticipate a charging demand profile at a charging station. A numerical example shows that the proposed model is able to capture the spatial and temporal dynamics of charging demand in a highway charging station.
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Citations
Cites
Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks
Pieltain Fernandez, L., Gomez San Roman, T., Cossent, R., Mateo Domingo, C., & Frias, P. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2049133 · Cited by 1,245
Cited by
Yilmaz, M., & Krein, P. T. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2012.2212917 · Cited by 3,118
Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces
Yilmaz, M., & Krein, P. T. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2012.2227500 · Cited by 1,024
Acharige, S. S. G., Haque, M. E., Arif, M. T., Hosseinzadeh, N., Hasan, K. N., & Oo, A. M. T. (2023) · IEEE Access · doi:10.1109/access.2023.3267164 · Cited by 590
A spatial–temporal model for grid impact analysis of plug-in electric vehicles
Mu, Y., Wu, J., Jenkins, N., Jia, H., & Wang, C. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.10.006 · Cited by 424
Electric vehicle charging demand forecasting model based on big data technologies
Arias, M. B., & Bae, S. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.080 · Cited by 346
Self-citation
Metadata
- Authors
- Bae, S., & Kwasinski, A.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2012-03
- Volume
- 3
- Issue
- 1
- Pages
- 394-403
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2047902637
- Cited by
- 474
- References
- 1
- FWCI
- 11.14
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
