Abstract
The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW.
Highlights
BosscheThe Intergovernmental Panel on Climate Change (IPCC) has further confirmed that climate is warming up due to human activities which are the principal source of carbon dioxide emissions in the atmosphere [1]
The charging sessions have been recorded in a hospital semi-public charging site which consists of six chargers containing two Type 2 connectors of 22 kW each
The electric vehicles (EV) charging sessions follow a constant pattern with a high stochastic behavior, as shown in Figure 4, where quarter-hours of the year are summarized in mean values, first and third quartile values, and the maximum values
Summary
The Intergovernmental Panel on Climate Change (IPCC) has further confirmed that climate is warming up due to human activities which are the principal source of carbon dioxide emissions in the atmosphere [1] To reduce such emissions, new technologies are being massively implemented such as wind energy, solar energy, and electric vehicles (EV). In order to mitigate these problems, electric vehicles can be charged intelligently by spreading or shifting the charging demand over time, according to user and electricity system needs. This coordination of EV charging provides a complex optimization problem that could benefit from EV charging demand forecast. This research focuses on developing accurate EV charging demand forecasters, which can be used by energy management systems for coordinated EV charging
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