Distributed energy resources (DER) systems introduce uncertainties in the electrical grid that cannot be addressed by classical deterministic methods. Power system analytic tools, such as Load Flow (LF), should be revisited to address such uncertainties and dependencies. Probabilistic Load Flow (PLF) provides a solution to this problem by handling these uncertainties as random variables, which addresses the rising need for fast and accurate sampling methods. Among the existing methods, the Unscented Transform (UT) has provided reliable and fast estimations. In this paper, three variants of PLF based on the UT method, with the effects of their weighting and scaling parameters are described, analyzed and compared in the IEEE 30 test case.