Research on Charging Strategy Optimization of Electric Vehicle based on AGA

Bowen Xu

2019

Abstract

Because the charging load of electric vehicles is random in time and space, a large number of disorderly charging of electric vehicles will lead to the peak load of distribution network exceeding the limit of equipment, which will bring adverse effects on the operation of power grid. In order to smooth the daily load curve of distribution network, this paper establishes a solution model of intelligent charging control strategy for large-scale electric vehicle considering the charging demand constraints of electric vehicle users, and uses adaptive genetic algorithm (AGA) to solve the model. Taking IEEE33 bus distribution network as an example, based on Monte Carlo stochastic simulation of large-scale electric vehicle grid-connected scene, the impact of electric vehicle load on distribution network under two control modes of disorderly charging and intelligent charging is studied comparatively, and the effectiveness of this method is verified.

Paper Citation

in Harvard Style

Xu B. (2019). Research on Charging Strategy Optimization of Electric Vehicle based on AGA.In Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - Volume 1: ICVMEE, ISBN 978-989-758-412-1, pages 227-234. DOI: 10.5220/0008870302270234

in Bibtex Style

@conference{icvmee19,
author={Bowen Xu},
title={Research on Charging Strategy Optimization of Electric Vehicle based on AGA},
booktitle={Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - Volume 1: ICVMEE,},
year={2019},
pages={227-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008870302270234},
isbn={978-989-758-412-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - Volume 1: ICVMEE,
TI - Research on Charging Strategy Optimization of Electric Vehicle based on AGA
SN - 978-989-758-412-1
AU - Xu B.
PY - 2019
SP - 227
EP - 234
DO - 10.5220/0008870302270234