Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach

Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users' behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users' behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV's initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

Automated summary

The penetration growth of electric vehicles (EVs) is crucial for reducing transportation-related local pollutants. Most countries are rapidly developing charging infrastructure to accommodate the increasing EV energy demand. However, forecasting power demand for EV charging is challenging due to various factors, such as the limited number of EVs, diverse user behaviors, heterogeneous charging infrastructure and EV population, and uncertainty regarding the initial state of charge (SOC) distribution. This paper proposes a forecasting method that considers all these factors and predicts users' behavior and initial SOC through statistical patterns. Machine learning techniques are employed to develop a battery-charging behavioral model, which accounts for different EV models. The results demonstrate significant differences in power demand across various parking scenarios.