We consider the procurement of electricity for a large fleet of electric vehicles operating in electricity markets. Due to uncertain regulating prices, this problem has typically been modelled as a stochastic program. In this study, we address the issue of generating scenario trees, ie, simplified representations of the uncertainty necessary to solve the corresponding stochastic programs. A trade-off between accurate descriptions of the uncertainty and tractability of the stochastic program is sought. Based on data describing electric mobility and electricity prices in Denmark, general-purpose scenario generation strategies are tested and compared. Such strategies include state-of-the-art property matching methods and time-series analysis. The results show that the co-dependence between the regulating prices at different hours of the day plays a crucial role when generating scenario trees for these problems, making copulas an important property to consider. This information can help decision makers to achieve better (cheaper) electricity procurement by accurately preprocessing the uncertainty in the regulating prices.