@article{lia-liu-lej-zhu-hua-lib-zha-20-aa-powflow,
  author = {Xiaobing Liao and Kaipei Liu and Jian Le and Shu Zhu and Qing Huai and Ben Li and Yantian Zhang},
  title = {Extended Affine Arithmetic-Based Global Sensitivity Analysis for Power Flow with Uncertainties},
  journal = {International Journal of Electrical Power {\&} Energy Systems},
  volume = {115},
  pages = {105440},
  year = 2020,
  doi = {10.1016/j.ijepes.2019.105440},
  abstract = {In order to quantitatively assess the impacts of input uncertainties on power flow solutions, a novel analytical variance-based global sensitivity analysis method using extended affine arithmetic was proposed in this paper. With input uncertain variables described as intervals, the power flow output models based on extended affine arithmetic were originally derived. These models are normally expressed by second-order interval response surface model, and thus the total variance of the models can be calculated analytically. Finally, we proposed a novel framework of variance decomposition based global sensitivity analysis method to clarify the components of total variance contributions. The tests on the IEEE14-bus, IEEE300--bus, 2383wp system demonstrate that extended affine arithmetic-based global sensitivity analysis method can acquire maximum relative error of total sensitivity indices is less than 4.55{\%} and max relative error of main sensitivity indices is less than 9.1{\%} when comparing to Monte Carlo simulation method, which is able to greatly improve the evaluation efficiency relative to Monte Carlo simulation method.}
}