An Unscented Transformation Based Probabilistic Power Flow for Autonomous Hybrid AC/DC Microgrid with Correlated Uncertainty Sources

Due to highly correlated uncertainties of wind, solar radiation and power demand, it is imperative that probabilistic power flow (PPF) is a reliable option to handle the corresponding issues of a hybrid AC/DC microgrid (MG) in islanding operation mode. A Nataf transformation based unscented transformation (UT) is employed to conduct the PPF analysis for an autonomous hybrid AC/DC MG in this paper. The method is able to deal with various random variables, including wind speed, solar radiation and loads, in which both symmetric and asymmetric distributions can be used to model their probabilistic characteristics. Amongst them, the Weibull distribution is used to represent the DC load demand in terms of plug-in hybrid electric vehicles rather than the normal distribution. More importantly, the correlation between the random variables is also taken into consideration and tackled properly, in particular given that the inherent strong correlation between uncertainty sources in MG. Via case study, the accuracy, efficiency and robustness of the proposed method are validated by a set of tests in a modified hybrid AC/DC MG system.