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.
Highlights
The analysis of electrical vehicle (EV) load characteristics is essential for the planning and operation of charging infrastructures and V2G, it has been studied widely in recent years
A total of 1588 charging stations including bus charging stations (BCS), highway charging stations (HCS), and urban public charging stations (UPCS) are selected as the research objects, including a total of more than 5.8 million charging records collected over a period of two years
Data error may be generated when an EV is connected to a charging point and during upload owing to unsuccessful charging, failure of measuring components, packet
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Data-driven methods are less restricted by hypotheses and have gradually received attention in the study of EV load characteristics Historical data of both traffic and weather were analyzed in [7] to obtain different traffic scenarios, and EV charging behaviors were classified using decision tree algorithm. This study set out to explore the general method for analyzing charging load characteristics based on massive data and provide suggestions to the construction and operation of charging infrastructures. For this purpose, this paper utilizes more than five million pieces of electrical energy supply data from various charging operators in a provincial administrative area collected over a period of two years.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
