Application of non-intrusive polynomial chaos expansion in probabilistic power flow with truncated random variables

In this paper, the authors apply a surrogate model-based method for probabilistic power flow (PPF) in the power system subject to truncated random variables. Due to a growing number of uncertainty sources are being brought into the modern power system, the traditional deterministic power flow analysis lacks its ability to recognize the realistic states of power systems, and thus turns to PPF for help. However, the PPF analysis is still facing several challenges: the computational effort required by the traditional simulation method is prohibitively expensive; and the modeling of uncertainty sources needs the improvement on both distribution type selection and parameter evaluation. The novelty of this work lies in taking advantage of both general polynomial chaos (gPC) expansion and ordinary least squares (OLS) to deal with PPF in presence of the truncated random variables. The performance of the proposed method is verified on the IEEE 30-Bus test system, considering uncertain factors brought by active power at load buses. In different test scenarios, the proposed method shows sound performances at the cost of less computational effort, compared to the traditional approach