Including uncertainties in harmonic hosting capacity calculation of a fast EV charging station utilizing Bayesian statistics and harmonic correlation
Naser Nakhodchi, Hamed Bakhtiari, Math Bollen, Sarah Rönnberg
Abstract
The harmonic emission from an electric vehicle fast charger depends on factors like charger topology, EV type, initial state of charge of EV battery, as well as supply voltage and background distortion. This paper presents the results from harmonic current measurement of a fast charger for a period of one month in Sweden that has charged a variety of EVs from different brands under different state of charge and background distortion. Besides the common harmonic emission pattern, a high level of variation in emission is observed that can affect the aggregation of the emission from multiple chargers. To include such uncertainties, the harmonic hosting capacity is obtained for a fast EV charging station in a stochastic way. A new method, based on Bayesian statistics and the correlation between harmonic magnitude and fundamental magnitude, is proposed for the generation of stochastic samples. It is shown that the proposed method, to a high extent, can model the stochastic behavior of harmonic emission from a fast charger. Furthermore, the results show that neglecting the correlation between harmonic magnitude and fundamental magnitude can underestimate the harmonic hosting capacity.