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.
Bibliographical noteFunding Information:
The Switch EV project was co-funded by Innovate UK and the former regional development agency ONE North East. The Switch EV project partners are: Nissan, Future Transport Systems, ONE NE, Smiths EV, AVID EV, Simon Bailes Peugeot, Liberty EV and Newcastle University. EPSRC funded projects also supported the EV trials and subsequent analysis (namely SiDE Digital Hub (EP/G066019/1); Transforming Utilities’ Conversion Points (no. EP/J005649/1); and iBuild (no. EP/K012398/1)). The Authors gratefully acknowledge the contributions and support of the Switch EV Partners and the EPSRC.
The Customer-Led Network Revolution (CLNR) project is funded by the Office of Gas and Electricity Markets (Ofgem) under the Low Carbon Network Fund. The collaborating CLNR project partners are: Northern Powergrid, British Gas plc, EA Technology Ltd, Durham University and Newcastle University. The Authors gratefully acknowledge the contributions and support of the CLNR partners.
© 2015 The Authors.
Copyright 2017 Elsevier B.V., All rights reserved.
- Distribution network
- Electric Vehicle (EV)
- Load profiles
- Smart meter
- Spatial-temporal data
- User behaviour