DocumentCode
3665382
Title
Electric vehicle capacity forecasting model with application to load levelling
Author
Bowen Zhou;Tim Littler;Aoife Foley
Author_Institution
School of Electronics, Electrical Engineering and Computer Science, Queen´s University Belfast, UK
fYear
2015
fDate
7/1/2015 12:00:00 AM
Firstpage
1
Lastpage
5
Abstract
There are many uncertainties associated with forecasting electric vehicle charging and discharging capacity due to the stochastic nature of human behavior surrounding usage and intermittent travel patterns. This uncertainty if unmanaged has the potential to radically change traditional load profiles. Therefore optimal capacity forecasting methods are important for large-scale electric vehicle integration in future power systems. This paper develops a capacity forecasting model considering eight particular uncertainties under three categories to overcome this issue. The model is then applied to a UK summer scenario in 2020. The results of this analysis demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale electric vehicle integration.
Keywords
"Uncertainty","Forecasting","System-on-chip","Load modeling","Predictive models","Batteries","Electric vehicles"
Publisher
ieee
Conference_Titel
Power & Energy Society General Meeting, 2015 IEEE
ISSN
1932-5517
Type
conf
DOI
10.1109/PESGM.2015.7285829
Filename
7285829
Link To Document