Probabilistic Power Flow Analysis based on the Adaptive Polynomial Chaos-ANOVA Method

While the conventional generalized polynomial chaos method exhibits excellent computational efficiency and accuracy in the probabilistic power flow calculations applied to medium-size power systems, it suffers from the curse of dimensionality when applied to large-scale systems and is limited to standard probability distributions assumed for the input variables. In this paper, we propose to adaptively reduce the dimension and thereby, to significantly speed up the calculations of the generalized polynomial chaos method via the ANOVA decomposition. We also propose to make use of the Steltijes' procedure so that any probability distribution may be assumed for the input random variables. Simulation results carry out on the IEEE 118-bus system provided with correlated power generation outputs from renewable energy resources show that our proposed method outperforms the conventional generalized polynomial chaos technique and the Monte Carlo method.