Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82345
Title: Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application
Authors: Lav, Agarwal
Wang, Peng
Lalit, Goel
Keywords: Electric Vehicle (EV); Vehicle-to-Grid (V2G); Aggregator; SOC; Driving pattern; Aggregate power capacity
Issue Date: 2014
Source: Lav, A., Wang, P., & Lalit, G. (2014). Probabilistic estimation of aggregated power capacity of EVs for vehicle-to-grid application. 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 1-6.
Abstract: Electric Vehicles (EVs) have emerged as a promising solution to reduce oil dependency and environmental impacts from the transportation segment. They can also be used as distributed energy resources providing ancillary services to the Grid through Vehicle-to-grid (V2G). EV availability estimation is the first step in determining the capacity for V2G operation. The main challenges in determining the Aggregate Power Capacity (APC) lies in the prediction of the vehicle availability and the plug-in probability. While the vehicle availability solely depends on the driving pattern of the EV owner, the plug-in probability depends on the availability of plugs at car park and plug-in human behavior. This paper models the stochastic mobility and plug-in probability of a fleet of EVs. The Aggregator model is realized using an infrastructure of contracted car parks at offices, recreational places and dispersed EVs at homes. Mobility is modeled using Trip Chaining and EV Driving patterns are profiled based on data from survey conducted, employment pattern and vehicular statistics. The Availability Probability Table (APT) is plotted to track the availability of each EV, considering EV reliability and traffic congestion index. The proposed models are tested and analyzed using Singapore data.
URI: https://hdl.handle.net/10356/82345
http://hdl.handle.net/10220/39955
DOI: 10.1109/PMAPS.2014.6960592
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/PMAPS.2014.6960592].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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