203 / 2021-10-31 22:34:22
Research on Temporal and Spatial Distribution of Electric Vehicle (EV) Charging Load Based on Real Data & Simulation
electric vehicle,charging load forecasting,data mining,simulation
Final Paper
Zezhao Chen / UESTC
Jierui Zhang / UESTC
Yalong Guo / UESTC
Jialin Du / UESTC
Zongxing Xin / UESTC
Qianyu Li / Xi'an Jiaotong University
Changhua Zhang / UESTC
Xiaohao Xu / UESTC
To solve the problems of over-theorization and lack of real data in the current research, this paper proposes a data-driven EV charging load demand forecasting model. The model is based on analysis of residents’ travel patterns hided in EV travel data and single EV charging & discharging model considering its related characteristics. The results of a calculation example in Chengdu show that the proposed model can effectively predict the temporal and spatial distribution characteristics of EV charging load in different urban functional areas and in different time ranges. This provides a basis for the construction of charging stations and charging load management after EV have been applied in large scale.

 
Important Date
  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST