Probabilistic load flow computation considering dependence of wind powers and using quasi-Monte Carlo method with truncated regular vine copula

Modeling high-dimension dependence is a challenging problem since it involves too many parameters. In this paper, aquasi-Monte Carlo (QMC) method based probabilistic load flow computation algorithm, which uses truncated regular vine copula and considers high-dimension dependence of wind powers, is proposed. Firstly, the regular vine copulas, which use bivariate copulas as building blocks, are used to construct the primary high dimensional dependence. Then, truncation technology is adopted to reduce the computation burden and the memory consumption caused by the rapidly increased parameters number of input variables. Meanwhile, the nonparametric kernel estimation is used to estimate the wind speed marginal distributions and the bandwidth of kernel function is obtained by the direct plug-in method. Further, QMC method is integrated into the probabilistic power flow computation for obtaining the sampled data of input variables. By the numerical simulation experiments on the modified IEEE 118-bus power system, the superiority of the proposed probabilistic load flow computation method is verified.