Uncertainty quantification in state estimation using the probabilistic collocation method

This paper proposes a probabilistic collocation method (PCM) to quantify the uncertainties in state estimation. Comparing to classic Monte-Carlo (MC) method, the proposed PCM is based on sparse grid points and uses a smaller number of sparse grid points to quantify the uncertainty. Thus, the proposed PCM can quantify a large number of uncertain power system variables with relatively lower computational cost. The algorithm and procedure are outlined. The proposed PCM is applied to IEEE 14 bus system to quantify the uncertainty of power system state estimation. Comparison is made with MC method. The simulation results shows that the proposed PCM can achieve same accuracy as MC method with smaller ensemble size and thus is computationally more efficient than MC method.