Stochastic Power Flow Calculation of High-Density Correlation Input Power Grid Based on Hybrid Gaussian Model

Abstract

Abstract Traditional stochastic power flow calculation assumes that the input variables are uncorrelated and directly used to calculate high-density correlation input variables has certain errors. To this end, this paper proposes a stochastic power flow algorithm based on mixed Gaussian models that takes into account input correlation. First, a mixed Gaussian model is established based on load data, then uniform sampling and Naf transformation are used to process random variables, and the high-density correlation in the original space is converted into independent random variables, and the piecewise linear Monte Carlo method is used to carry out random power flow. Calculation can greatly reduce the calculation time and reduce the truncation error as much as possible. Finally, combined with the actual calculation examples of IEEE-30, the results show that the algorithm can simplify the actual calculation process, improve the calculation efficiency, and has certain practical value in engineering.