Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder

Zhixin Pan, Jianming Wang, Wenlong Liao, Haiwen Chen, Dong Yuan, Weiping Zhu, Xin Fang and Zhen Zhu
Additional contact information
Zhixin Pan: State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210029, China
Jianming Wang: Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China
Wenlong Liao: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Haiwen Chen: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Dong Yuan: State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210029, China
Weiping Zhu: State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210029, China
Xin Fang: Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China
Zhen Zhu: State Grid Wuxi Power Supply Company, Wuxi 214062, China

Energies, 2019, vol. 12, issue 5, 1-15

Abstract: Although the penetration of electric vehicles (EVs) in distribution networks can improve the energy saving and emission reduction effects, its random and uncertain nature limits the ability of distribution networks to accept the load of EVs. To this end, establishing a load profile model of EV charging stations accurately and reasonably is of great significance to the planning, operation and scheduling of power system. Traditional generation methods for EV load profiles rely too much on experience, and need to set up a power load probability distribution in advance. In this paper, we propose a data-driven approach for load profiles of EV generation using a variational automatic encoder. Firstly, an encoder composed of deep convolution networks and a decoder composed of transposed convolution networks are trained using the original load profiles. Then, the new load profiles are obtained by decoding the random number which obeys a normal distribution. The simulation results show that EV load profiles generated by the deep convolution variational auto-encoder can not only retain the temporal correlation and probability distribution nature of the original load profiles, but also have a good restorative effect on the time distribution and fluctuation nature of the original power load.

Keywords: electric vehicles; load profiles; data-driven; variational automatic encoder (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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