Dynamic simulation studies are used to analyze the behavior of power systems after a disturbance has occurred. This type of simulation is essential when the system is operating close to its stability limits or its behavior is dictated by complex control and protection schemes modifying its trajectory. These simulations can be computationally very demanding, especially if performed over a time interval of several minutes. In this paper, new shared-memory parallel computing techniques to increase the performance of large-scale power system dynamic simulations are described. The algorithms presented achieve this by utilizing the parallel processing resources available in modern, inexpensive, multi-core machines. In addition, the localized response of power systems after a disturbance is exploited to further accelerate simulations without decreasing accuracy. The medium-scale model of a real power system and a realistic large-scale test system have been used for the performance evaluation of the proposed methods.
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