DocumentCode :
79207
Title :
Quantifying the Long-Term Impact of Electric Vehicles on the Generation Portfolio
Author :
Shortt, Aonghus ; O´Malley, Mark
Author_Institution :
Sch. of Electr., Electron. & Commun. Eng., Univ. Coll. Dublin, Dublin, Ireland
Volume :
5
Issue :
1
fYear :
2014
fDate :
Jan. 2014
Firstpage :
71
Lastpage :
83
Abstract :
Electric Vehicles (EVs) charged in a manner that is optimal to the power system will tend to increase the utilization of the lowest cost power generating units on the system, which in turn encourages investment in these preferable forms of generation. Were these gains to be substantial, they could be reflected in future charging tariffs as a means of encouraging EV ownership. However, where the impact of EVs is being quantified, much of the system benefit can only be observed where generator scheduling is performed by unit-commitment based methods. By making use of a rapid, yet robust unit-commitment algorithm, in the context of a capacity expansion procedure, this paper quantifies the impact of EVs for a variety of demand and wind time-series, relative fuel costs and EV penetrations. Typically, the net-cost of EV charging increases with EV penetration and CO2 cost, and falls with increasing wind. Frequently however these relationships do not apply, where changes in an input often lead to step-changes in the optimal plant mix. The impact of EVs is thus strongly dependent on the dynamics of the underlying generation portfolio.
Keywords :
electric vehicles; power generation dispatch; power generation scheduling; tariffs; time series; EV charging; EV penetrations; capacity expansion; charging tariffs; electric vehicles; generation portfolio; generator scheduling; investment; long-term impact; optimal plant mix; power generating units; power system; relative fuel costs; robust unit commitment; wind time-series; Biological system modeling; Computational modeling; Generators; Power systems; Production; Schedules; Vehicles; Electric vehicles; power generation planning; wind power generation;
fLanguage :
English
Journal_Title :
Smart Grid, IEEE Transactions on
Publisher :
ieee
ISSN :
1949-3053
Type :
jour
DOI :
10.1109/TSG.2013.2286353
Filename :
6654292
Link To Document :
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