Fast Estimation of Total Transfer Capability Considering both Load and Source Uncertainties

Abstract

With the development of smart grid and new energy technologies, both load and source uncertainties need be considered in the calculation of total transfer capability (TTC) of power system transmission. Based on deep learning technology and the improved multi-point estimation method, a fast TTC estimation method considering uncertainties of load power, demand response and wind power generation is proposed. The uncertainties are represented by probability distribution of prediction error and Nataf transformation is introduced to deal with the non-normal probability distributions and their correlations. The stacked denoising autoencoder is employed to estimate TTCs of operating scenarios generated by Nataf transform and the improved multi-point estimation method is used to obtain their probability. Simulation results demonstrate that the fast estimation method is able to consider both load and source uncertainties effectively and calculate TTC fast and accurately.