A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts
By M. Neaimeh, R. Wardle, Andrew M. Jenkins, Jialiang Yi, G. Hill, Padraig F. Lyons, Y. Huebner, P. Blythe, P. Taylor. 2015
187 citations recorded by the source corpus.
This work uses a probabilistic method to combine two unique datasets of real world electric vehicle charging profiles and residential smart meter load demand. The data was used to study the impact of the uptake of Electric Vehicles (EVs) on electricity distribution networks. Two real networks representing an urban and rural area, and a generic network representative of a heavily loaded UK distribution network were used. The findings show that distribution networks are not a homogeneous group with a variation of capabilities to accommodate EVs and there is a greater capability than previous studies have suggested. Consideration of the spatial and temporal diversity of EV charging demand has been demonstrated to reduce the estimated impacts on the distribution networks. It is suggested that distribution network operators could collaborate with new market players, such as charging infrastructure operators, to support the roll out of an extensive charging infrastructure in a way that makes the network more robust; create more opportunities for demand side management; and reduce planning uncertainties associated with the stochastic nature of EV charging demand.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/d56b582dc3977605018eb48dfa36b799b1520046
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In Automotive Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Electric vehicles: Battery capacity, charger power, access to charging and the impacts on distribution networks (2020, methodology): “In [37], data from Newcastle University’s SwitchEV project is used to build a stochastic simulation of the impact of EV charging on a real distribution network feeder.”