Fast Deterministic Sampling for Mean and Covariance Estimation in Stochastic Load Flow

This paper proposes a new method for stochastic load flow (SLF) by using deterministic sampling based on sigma-point selection. In stead of conducting time-consuming Monte Carlo simulation, the proposed method can efficiently estimate the mean and covariance of state variables and branch power flow by conducting only 2k+ chosen deterministic power flow for a power system with k normally distributed uncertain parameters. The benefits of the proposed method are: 1) it has accuracy at least to the second order of truncated Taylor series; 2) it needs no derivatives; 3) it is not limited to the size of uncertain parameters. The effectiveness of the proposed method is demonstrated in an example of IEEE 14-bus power system.