Impact of EV Charging Load Uncertainty as Gaussian Mixtures Model on System Performance

This paper proposes a method to assess the impact of the uncertainty in electric vehicle (EV) charging loads on system static performance reflected through the resulting uncertainty in system states, i.e., bus voltage magnitude and phase-angles. Historical EV charging data is fit to a Gaussian mixtures model (GMM), which can capture generic probability distributions. The GMM is then propagated through a linearized power flow model to yield a probabilistic characterization of the voltage magnitudes and phase-angles. As a direct consequence of the linear model approximation, the resulting characterization is also a GMM, the parameters of which can be computed in closed form. Numerical simulations involving the IEEE 33-bus distribution test system demonstrate the effectiveness of the proposed method to assess uncertainty in system states.