Multi-Dimensional Analysis of Load Characteristics of Electrical Vehicles Based on Power Supply Side Data and Unsupervised Learning Method

Abstract

This study set out to extract the charging characteristics of an electrical vehicle (EV) from massive real operating data. Firstly, an unsupervised learning method based on self-organizing map (SOM) is developed to deal with the power supply side data of various charging operators. Secondly, a multi-dimensional evaluation index system is constructed for charging operation and vehicle-to-grid (V2G). Finally, according to more than five million pieces of charging operating data collected over a period of two years, the charging load composition and characteristics under different charging station types, daily types and weather conditions are analyzed. The results show that bus, high-way, and urban public charging loads are different in concentration and regulation flexibility, however, they all have the potential to synergy with power grid and cooperate with renewable energy. Especially in an urban area, more than 37 GWh of photovoltaic (PV) power can be consumed by smart charging at the current penetration rate of EVs.

Keywords

• power supply side data
• unsupervised learning
• electric vehicle (EV)
• multi-scenario