Probabilistic power flow computation using nested point estimate method

The probabilistic power flow (PPF) computation involves quantifying and propagating uncertainty over hundreds of variables. Zhao's point estimate method (PEM) has been widely used for PPF computation, and the computational burden increases linearly with respect to the number of PPF inputs. In this paper, Zhao's PEM is reformulated as the Kronecker product of Gauss-Hermite quadrature and an identity matrix. Following this formulation, a new PEM termed Nested PEM is developed based on Hadamard matrix, which can reduce the computational burden nearly by half relative to Zhao's PEM. In order to consider the dependence among PPF inputs, the extended generalized lambda distribution (EGLD) is employed to model marginal distributions of correlated PPF inputs, and the t-copula is introduced to map PPF problem to the independent standard normal space. Finally, case studies are conducted on a modified IEEE 118-bus system to illustrate the proposed PEM.