Analytic Interval Prediction of Power System Dynamic under Interval Uncertainty

Abstract With widespread access to renewable energy sources and active loads such as electric vehicles, uncertainty problems have gradually become a prominent problem in the power system. However, the conventional stochastic differential equation (SDE) model is not comprehensive in describing the randomness of disturbances, and the solution of novel models generally relies on numerical calculations. To improve the modeling accuracy and the calculation effectiveness, this paper utilizes intervals to model stochastic continuous disturbances and proposes an analytic method based on Taylor series expansion to predict the dynamic response of the power system under interval uncertainty, which may provide a reference for the small disturbance stability analysis of the power system. Furthermore, in order to apply to a more general situation, the case of continuous intervals is considered, and the analytic results are obtained, by which the superposition principle applicable to intervals is summarized. The comparison with the Monte Carlo method and the responses from actual wind power data verify the effectiveness and rationality of the proposed method.