The Impact of Plug-in Electric Vehicles on Distribution Network

Feng，Kaida1; Zhong，Yanling2; Hong，Binzhuo1; Wu，Xiaomei2; Lai，Chun Sing3; Bai，Chenchen4

10.1109/ISC251055.2020.9239073

2020-09-28

2687-8852

978-1-7281-8295-7

2020 IEEE International Smart Cities Conference, ISC2 2020

1-7

28 Sept.-1 Oct. 2020

Piscataway, NJ, USA

With concerned environmental problem, a large number of electric vehicles (EVs) has been adopted to replace the oil-fueled vehicles. If electric vehicles are charged simultaneously on a large-scale, it may cause peak load increase. Therefore, it is of great practical significance to study the influence of controlled charging behavior of electric vehicles on power grid. Firstly, Gaussian Mixture Model is used to modeling electric vehicles. Secondly, Monte Carlo method is studied to determine the charging load of electric vehicles, and the influence of uncontrolled charging of electric vehicles on the power grid is analyzed. Then the peak and valley hours are divided according to the membership function and the time-of-use pricing to minimize the difference between peak and valley load. Furthermore, the influence of controlled charging of EVs on power grid is analyzed. Finally, the model is applied to simulate and analyze the distribution network of Yangjiang, a coastal city in South China. The case study shows that the uncontrolled charging of EVs will increase the peak load of the power grid. The proposed controlled charging strategy can effectively transfer the charging load of EVs and lessen peak load demand.

controlled charging electric vehicles (EVs) membership function Monte Carlo method time-of-use pricing

其他

英语

EI

20204909588659

Electric power system control ; Gaussian distribution ; Costs ; Vehicle-to-grid ; Plug-in electric vehicles ; Electric power transmission networks ; Charging (batteries) ; Membership functions

Secondary Batteries:702.1.2 ; Electric Power Systems:706.1 ; Electric Power Transmission:706.1.1 ; Control System Applications:731.2 ; Cost and Value Engineering; Industrial Economics:911 ; Mathematics:921 ; Probability Theory:922.1 ; Mathematical Statistics:922.2

2-s2.0-85097174237

Scopus

1.Yangjiang Power Supply Bureau,Guangdong Power Grid Co. Ltd,Yangjiang,China
2.Guangdong University of Technology,Department of Electrical Engineering,Guangzhou,China
3.Brunel Institute of Power Systems,Brunel University London,London,United Kingdom
4.Southern University of Science and Technology,Department of Mathematics,Shenzhen,China

Feng，Kaida,Zhong，Yanling,Hong，Binzhuo,et al. The Impact of Plug-in Electric Vehicles on Distribution Network[C],2020:1-7.