Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging

Abstract

Users charging the batteries of their electric vehicles in an uncoordinated manner can present energy systems with a challenge. One possible solution, smart charging, relies on the flexibility within each charging process and controls the charging process to optimize different objectives. Effective smart charging requires forecasts of energy requirements and parking duration at the charging station for each individual charging process. We use data from travel logs to create quantile forecasts of parking duration and energy requirements, approximated by upcoming trip distance. For this task, we apply quantile regression, multi-layer perceptrons with tilted loss function, and multivariate conditional kernel density estimators. The out-of-sample evaluation shows that the use of local information from the vehicle's travel data improves the forecasting accuracy by 13.7% for parking duration and 0.56% for trip distance compared to the data generated at the charging stations. In addition, the analysis of a case study shows that using probabilistic forecasts can control the interruption of charging processes more efficiently compared to point forecasts. Probabilistic forecasting leads up to 7.0% less interruptions, which can cause a restriction in drivers' mobility demand. The results show that charging station operators benefit from leveraging the driving patterns of electric vehicles. Thereby, smart charging and the application of the proposed models as benchmarks models for the related forecasting tasks is an improvement for the operators.