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Continuous-time random disturbances from the renewable generation pose asignificant impact on power system dynamic behavior. In evaluating this impact,the disturbances must be considered as continuous-time random processes insteadof random variables that do not vary with time to ensure accuracy. Monte Carlosimulation (MCs) is a nonintrusive method to evaluate such impact that can beperformed on commercial power system simulation software and is easy for powerutilities to use, but is computationally cumbersome. Fast samplings methodssuch as Latin hypercube sampling (LHS) have been introduced to speed upsampling random variables, but yet cannot be applied to sample continuousdisturbances. To overcome this limitation, this paper proposes a fast MCsmethod that enables the LHS to speed up sampling continuous disturbances, whichis based on the It\^{o} process model of the disturbances and the approximationof the It\^{o} process by functions of independent normal random variables. Acase study of the IEEE 39-Bus System shows that the proposed method is 47.6 and6.7 times faster to converge compared to the traditional MCs in evaluating theexpectation and variance of the system dynamic response.

Fast Monte Carlo Simulation of Dynamic Power Systems Under Continuous Random Disturbances