Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application
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 estimati...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/82345 http://hdl.handle.net/10220/39955 |
| _version_ | 1826118260649623552 |
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| author | Lav, Agarwal Wang, Peng Lalit, Goel |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lav, Agarwal Wang, Peng Lalit, Goel |
| author_sort | Lav, Agarwal |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:40:26Z |
| format | Conference Paper |
| id | ntu-10356/82345 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:40:26Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/823452020-03-07T13:24:44Z Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application Lav, Agarwal Wang, Peng Lalit, Goel School of Electrical and Electronic Engineering 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) Electric Vehicle (EV); Vehicle-to-Grid (V2G); Aggregator; SOC; Driving pattern; Aggregate power capacity 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. Accepted version 2016-02-11T03:42:02Z 2019-12-06T14:53:45Z 2016-02-11T03:42:02Z 2019-12-06T14:53:45Z 2014 Conference Paper 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. https://hdl.handle.net/10356/82345 http://hdl.handle.net/10220/39955 10.1109/PMAPS.2014.6960592 en © 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]. application/pdf |
| spellingShingle | Electric Vehicle (EV); Vehicle-to-Grid (V2G); Aggregator; SOC; Driving pattern; Aggregate power capacity Lav, Agarwal Wang, Peng Lalit, Goel Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application |
| title | Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application |
| title_full | Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application |
| title_fullStr | Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application |
| title_full_unstemmed | Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application |
| title_short | Probabilistic Estimation of Aggregated Power Capacity of EVs for Vehicle-to-Grid Application |
| title_sort | probabilistic estimation of aggregated power capacity of evs for vehicle to grid application |
| topic | Electric Vehicle (EV); Vehicle-to-Grid (V2G); Aggregator; SOC; Driving pattern; Aggregate power capacity |
| url | https://hdl.handle.net/10356/82345 http://hdl.handle.net/10220/39955 |
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