Proceedings of the 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)

Highway Electric Vehicle Charging Load Prediction and its Impact on the Grid

Authors
Huiyi Wang, Xueliang Huang, Lixin Chen, Yuqi Zhou
Corresponding Author
Huiyi Wang
Available Online January 2016.
DOI
https://doi.org/10.2991/ifeea-15.2016.23How to use a DOI?
Keywords
highway, electric vehicles, load prediction, Floyd algorithm, Monte Carlo method, the impact on grid.
Abstract
In this passage, a new method is proposed to predict the load of electric vehicles (EVs) on highway. In this method, a simplified highway network containing charging stations is established, based on which we can generate an adjacency matrix. Then the origin and destination (OD) of vehicles are extracted according to the probability density function (PDF) built by the statistic of highway toll station. Floyd algorithm is used to determine the routine of EVs. Departure time, state of charge (SOC), battery capacity, driving speed and other parameters are all extracted according to PDF. Monte Carlo method is attached to simulate large-scale EV load on highway. Highway network of Jiangsu province is proposed as an example, and impact of EV load on power grid is evaluated by voltage deviation and loss.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)
Part of series
Advances in Engineering Research
Publication Date
January 2016
ISBN
978-94-6252-153-7
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifeea-15.2016.23How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Huiyi Wang
AU  - Xueliang Huang
AU  - Lixin Chen
AU  - Yuqi Zhou
PY  - 2016/01
DA  - 2016/01
TI  - Highway Electric Vehicle Charging Load Prediction and its Impact on the Grid
BT  - 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeea-15.2016.23
DO  - https://doi.org/10.2991/ifeea-15.2016.23
ID  - Wang2016/01
ER  -