Predictive Dynamic Simulation for Large-Scale Power Systems through High-Performance Computing

Power system dynamic simulation solves a set of differential-algebraic equations to determine the time-series trajectory when the system is subject to disturbances such as a short-circuit fault, generator tripping, or line switching. Due to computational inefficiency, dynamic simulation, though widely used for off-line studies, has not been used in real-time operation. That limits the ability to operate a much-evolved power system with significant dynamic and stochastic behaviors introduced by the increasing penetration of renewable generation and the deployment of smart grid technologies. The need for performing dynamic simulation in real-time or faster than real-time for power grid operation becomes apparent. And such predictive dynamic simulation can enable many new power grid operation functions such as real-time path rating. To improve the computational efficiency of dynamic simulation requires parallel computing implementation of the solution methods, as computers no longer have only a single core. This paper examines the equations and implements a parallel version of power system dynamic simulation. The testing results clearly show a significant improvement in performance. Dynamic simulation of a largescale power system with a size equivalent to the Western U.S. power grid achieves a performance of three times faster than real time for the first time. This makes the simulation predictive in time. Applying such predictive dynamic simulation for real-time path rating is discussed as well.