A look-ahead probabilistic contingency analysis framework incorporating smart sampling techniques

Abstract

This paper describes a framework of incorporating smart sampling techniques in a probabilistic look-ahead contingency analysis application. The predictive probabilistic contingency analysis helps describe the impact of uncertainties caused by variable generation and load on potential violations of transmission limits. The objectives of smart sampling techniques are to represent structure and statistical characteristics of different sources of uncertainty in the power system (e.g., load, wind, and solar generation) efficiently and accurately, and to significantly reduce the data set size and the computational time needed for multiple look-ahead contingency analyses. Case studies on the Alstom test system are presented to demonstrate the performance of the framework. The efficiency of the smart sampling techniques is also discussed.