Sage Journals: Discover world-class research

Abstract

With the rapid growth of the number of electric vehicles, the impact of the electric vehicle charging load on the grid has gradually increased, which has become an urgent issue to be solved. To reduce the impact of large-scale electric vehicles connecting to the grid, accurate load forecasting is particularly critical. In this paper, the influencing factors of the EV charging load were studied, the charging load characteristics of different types of EVs were analyzed, calculation models of different EV charging loads in a certain area were constructed, a program simulation was performed, a prediction curve of the future charging load was drawn, and the regional EV charging load was established. The future charging load levels of EVs in a certain area were calculated and analyzed to establish the basis for future optimization control schemes.

Keywords

electric vehicle charging load model Monte Carlo simulation charging load calculation

Get full access to this article

View all access options for this article.

References

Husain

Ozpineci

Islam

, et al. Electric drive technology trends, challenges, and opportunities for future electric vehicles. Proc IEEE 2021; 109(6): 1039–1059. DOI: 10.1109/JPROC.2020.3046112.

Aghajan-Eshkevari

Azad

Nazari-Heris

, et al. Charging and discharging of electric vehicles in power systems: an updated and detailed review of methods, control structures, objectives, and optimization methodologies. Sustainability 2022; 14(4): 2137. DOI: 10.3390/su14042137.

Zhao

Wang

Shen

ZJM

, et al. Data-driven framework for large-scale prediction of charging energy in electric vehicles. Appl Energy 2021; 282: 116175. DOI: 10.1016/j.apenergy.2020.116175.

Yin

. Orderly charging strategy of electric vehicle based on improved PSO algorithm. Energy 2023; 271: 127088. DOI: 10.1016/j.energy.2023.127088.

Zhang

Yin

Zhang

, et al. A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm. Energy Rep 2022; 8: 14578–14594. DOI: 10.1016/j.egyr.2022.10.442.

Ghasemi-Marzbali

. Fast-charging station for electric vehicles, challenges and issues: a comprehensive review. J Energy Storage 2022; 49: 104136. DOI: 10.1016/j.est.2022.104136.

Tao

Huang

Chen

, et al. Orderly charging strategy of battery electric vehicle driven by real-world driving data. Energy 2020; 193: 116806. DOI: 10.1016/j.energy.2019.116806.

Shi

, et al. Data-driven spatial-temporal prediction of electric vehicle load profile considering charging behavior. Electr Power Syst Res 2020; 187: 106469. DOI: 10.1016/j.epsr.2020.106469.

Feng

Chang

Fan

, et al. Electric vehicle charging load prediction model considering traffic conditions and temperature. Processes 2023; 11(8): 2256. DOI: 10.3390/pr11082256.

10.

Elahe

Kabir

Mahmud

, et al. Factors impacting short-term load forecasting of charging station to electric vehicle. Electronics 2022; 12(1): 55. DOI: 10.3390/electronics12010055.

11.

Zheng

Shao

Zhang

, et al. A systematic methodology for mid-and-long term electric vehicle charging load forecasting: the case study of Shenzhen, China. Sustain Cities Soc 2020; 56: 102084. DOI: 10.1016/j.scs.2020.102084.

12.

Xing

Sun

, et al. Multi-type electric vehicle load prediction based on Monte Carlo simulation. Energy Rep 2022; 8(S9): 966–972. DOI: 10.1016/j.egyr.2022.05.264.

13.

Amara-Ouali

Goude

Massart

, et al. A review of electric vehicle load open data and models. Energies 2021; 14(8): 2233. DOI: 10.3390/en14082233.

14.

Zhang

Yan

Liu

, et al. Daily electric vehicle charging load profiles considering demographics of vehicle users. Appl Energy 2020; 274: 115063. DOI: 10.1016/j.apenergy.2020.115063.

A study on the charging load model of electric vehicles in medium-sized cities

Abstract

Keywords

Get full access to this article

References