A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts. (1st November 2015)

• Record Type: Journal Article
• Authors: Neaimeh, Myriam; Wardle, Robin; Jenkins, Andrew M.; Yi, Jialiang; Hill, Graeme; Lyons, Padraig F.; Hübner, Yvonne; Blythe, Phil T.; Taylor, Phil C.
• Is Part Of: Applied energy. Volume 157(2015:Nov. 01)
• Page Start: 688
• Page End: 698
• Publication Date: 2015-11-01
• Subjects: Electric Vehicle (EV) -- Smart meter -- Load profiles -- Spatial–temporal data -- Distribution network -- User behaviour; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.01.144
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Working with unique datasets of EV charging and smart meter load demand. Distribution networks are not a homogenous group with more capabilities to accommodate EVs than previously suggested. Spatial and temporal diversity of EV charging demand alleviate the impacts on networks. An extensive recharging infrastructure could enable connection of additional EVs on constrained distribution networks. Electric utilities could increase the network capability to accommodate EVs by investing in recharging infrastructure. Abstract: 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.