The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A data-driven approach for characterising the charging demand of electric vehicles: A UK case study
Open accessAbstract
As the number of electric vehicles increases, the impact of their charging on distribution networks is being investigated using different load profiles. Due to the lack of real charging data, the majority of these load impact studies are making assumptions for the electric vehicle charging demand profiles. In this paper a two-step modelling framework was developed to extract the useful information hidden in real EVs charging event data. Real EVs charging demand data were obtained from Plugged-in Midlands (PiM) project, one of the eight ‘Plugged-in Places’ projects supported by the UK Office for Low Emission Vehicles (OLEV). A data mining model was developed to investigate the characteristics of electric vehicle charging demand in a geographical area. A Fuzzy-Based model aggregates these characteristics and estimates the potential relative risk level of EVs charging demand among different geographical areas independently to their actual corresponding distribution networks. A case study with real charging and weather data from three counties in UK is presented to demonstrate the modelling framework.
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Cites
Neural network load forecasting with weather ensemble predictions
Taylor, J. W., & Buizza, R. (2002) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.800906 · Cited by 459
Distribution grid impacts of smart electric vehicle charging from different perspectives
Veldman, E., & Verzijlbergh, R. A. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2355494 · Cited by 356
Assessment of electric vehicle charging scenarios based on demographical data
Steen, D., Tuan, L. A., Carlson, O., & Bertling, L. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2195687 · Cited by 145
Cited by
A review of consumer preferences of and interactions with electric vehicle charging infrastructure
Hardman, S., Jenn, A., Tal, G., Axsen, J., Beard, G., Daina, N., Figenbaum, E., Jakobsson, N., Jochem, P., Kinnear, N., et al. (2018) · Transportation Research Part D: Transport and Environment · doi:10.1016/j.trd.2018.04.002 · Cited by 835
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
Al-Ogaili, A. S., Tengku Hashim, T. J., Rahmat, N. A., Ramasamy, A. K., Marsadek, M. B., Faisal, M., & Hannan, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2939595 · Cited by 303
Daily electric vehicle charging load profiles considering demographics of vehicle users
Zhang, J., Yan, J., Liu, Y., Zhang, H., & Lv, G. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115063 · Cited by 259
Quantitive analysis of electric vehicle flexibility: A data-driven approach
Sadeghianpourhamami, N., Refa, N., Strobbe, M., & Develder, C. (2018) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2017.09.007 · Cited by 219
Metadata
- Authors
- Xydas, E., Marmaras, C., Cipcigan, L. M., Jenkins, N., Carroll, S., & Barker, M.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 162
- Pages
- 763-771
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2152012918
- Cited by
- 229
- References
- 3
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Xydas, E., Marmaras, C., Cipcigan, L. M., Jenkins, N., Carroll, S., & Barker, M. (2016). A data-driven approach for characterising the charging demand of electric vehicles: A UK case study. Applied Energy. https://doi.org/10.1016/j.apenergy.2015.10.151
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