Latest revision as of 22:45, 28 January 2021

Abstract

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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• http://dx.doi.org/10.1016/j.apenergy.2015.10.151 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1016/j.apenergy.2015.10.151 under the license http://creativecommons.org/licenses/by/4.0

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• https://doi.org/10.1016/j.apenergy.2015.10.151 under the license http://creativecommons.org/licenses/by/4.0

• http://orca.cf.ac.uk/80940,

https://www.sciencedirect.com/science/article/pii/S0306261915013938,

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https://ideas.repec.org/a/eee/appene/v162y2016icp763-771.html,

https://EconPapers.repec.org/RePEc:eee:appene:v:162:y:2016:i:c:p:763-771,

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https://academic.microsoft.com/#/detail/2152012918 under the license cc-by

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https://api.elsevier.com/content/article/PII:S0306261915013938?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.apenergy.2015.10.151

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