An unscented transformation based probabilistic power flow for studies on uncertainty sources in AC/DC grid

• Published in: 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) pp. 221 - 226
• Main Authors: Sui Peng, Junjie Tang, Weizhou Wang, Fuchao Liu, Jingjing Zheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Correlation; correlation matrix transformation method; Gaussian distribution; Input variables; linear correlation; Load flow; probabilistic power flow (PPF); Random variables; renewable energy generation; unscented transformation (UT); VSC-MTDC
• DOI: 10.1109/ICSGSC.2017.8038580

Probabilistic power flow (PPF) approach based on unscented transformation (UT) method, which is particular for a variety of interdependent uncertainty sources associated with AC/DC hybrid grid, is presented herein. The applied traditional UT method is inherent with capability to tackle correlated random variables, however, the accuracy is poor in case of asymmetric distributions. In order to address this issue, the correlation matrix transformation method is adopted accordingly to transform the linear correlated variables following arbitrary distributions into the variables being subject to standard normal distributions with another new correlation matrix, as the preliminary step of the proposed approach. Hence, the sample points can be selected properly by the traditional UT in a uniform way, thus leading to an accuracy improvement. The modified IEEE 14-bus system mainly in terms of AC grid, involving with additional VSC-MTDC (Voltage Source Converter-Multiple Terminal Direct Current), stochastic renewable generations and diverse consumer behaviors, is used to demonstrate the effectiveness of the proposed approach and verify the relevant conclusions.