Research on Configuration Optimization of AC/DC Charging Equipment under Large Scale Access of Electric Vehicles Considering Disordered Charging
- DOI
- 10.2991/msbda-19.2019.38How to use a DOI?
- Keywords
- Electric vehicle, Charging law, Monte Carlo simulation, AC/DC charging equipment, Configuration optimization
- Abstract
In order to meet the demand of large-scale charging of electric vehicles effectively, it is urgent to carry out research on configuration optimization of AC/DC charging equipment. For this reason, the charging law is modeled on the basis of the charging characteristic data of a small-scale electric vehicle in this paper, with which the charging behavior of the large-scale electric vehicles is deduced by adopting Monte Carlo simulation method. Then, some model assumptions are given and the equivalent load curve is acquired. Furtherly, the optimization objective function of AC/DC charging equipment configuration for minimizing the equipment investment and the peak-to-valley difference of disordered charging load is constructed. Finally, the optimal solutions, i.e., referential allocation quantities of various charging equipment are obtained by solving the optimization problem with the related constraints, which can provide certain theoretical basis for the configuration planning of AC/DC charging equipment under connection of large-scale electric vehicles.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Lingyi Li AU - Tie Chen PY - 2019/08 DA - 2019/08 TI - Research on Configuration Optimization of AC/DC Charging Equipment under Large Scale Access of Electric Vehicles Considering Disordered Charging BT - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019) PB - Atlantis Press SP - 249 EP - 253 SN - 2352-538X UR - https://doi.org/10.2991/msbda-19.2019.38 DO - 10.2991/msbda-19.2019.38 ID - Li2019/08 ER -