Trust-Region Approximation of Extreme Trajectories in Power System Dynamics

In this work we present a novel technique, based on trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power system dynamic simulations given bounded set that represents parametric uncertainty. We show how method, while remaining computationally efficient compared with sampling-based techniques, overcomes limitations previous sensitivity-based techniques approximate bounds trajectories, when local approximation loses validity because nonlinearity. addition, method can be adapted account for those cases in which initial conditions depend uncertain parameter. To conclude, several numerical experiments showcase accuracy scalability including demonstration IEEE New England test system.