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Continuous-time random disturbances (also called stochastic excitations) dueto increasing renewable generation have an increasing impact on power systemdynamics; However, except from the Monte Carlo simulation, most existingmethods for quantifying this impact are intrusive, meaning they are not basedon commercial simulation software and hence are difficult to use for powerutility companies. To fill this gap, this paper proposes an efficient andnonintrusive method for quantifying uncertainty in dynamic power systemssubject to stochastic excitations. First, the Gaussian or non-Gaussianstochastic excitations are modeled with an It\^{o} process as stochasticdifferential equations. Then, the It\^{o} process is spectrally represented byindependent Gaussian random parameters, which enables the polynomial chaosexpansion (PCE) of the system dynamic response to be calculated via an adaptivesparse probabilistic collocation method. Finally, the probability distributionand the high-order moments of the system dynamic response and performance indexare accurately and efficiently quantified. The proposed nonintrusive method isbased on commercial simulation software such as PSS/E with carefully designedinput signals, which ensures ease of use for power utility companies. Theproposed method is validated via case studies of IEEE 39-bus and 118-bus testsystems.

Nonintrusive Uncertainty Quantification of Dynamic Power Systems Subject to Stochastic Excitations