Uncertainty quantification in dynamic simulations of large-scale power system models using the high-order probabilistic collocation method on sparse grids

Authors

Guang Lin, Pacific Northwest National Laboratory
Marcelo Elizondo, Pacific Northwest National Laboratory
Shuai Lu, Pacific Northwest National Laboratory
Xiaoliang Wan, Louisiana State University

Document Type

Article

Publication Date

1-1-2014

Abstract

This paper employs a probabilistic collocation method (PCM) to quantify the uncertainties in dynamic simulations of power systems. The approach was tested on a single machine infinite bus system and the over 15,000 -bus Western Electricity Coordinating Council (WECC) system in western North America. Compared to the classic Monte Carlo (MC) method, the PCM applies the Smolyak algorithm to reduce the number of simulations that have to be performed. Therefore, the computational cost can be greatly reduced using PCM. A comparison was made with the MC method on a single machine as well as the WECC system. The simulation results show that by using PCM only a small number of sparse grid points need to be sampled even when dealing with systems with a relatively large number of uncertain parameters.

Publication Source (Journal or Book title)

International Journal for Uncertainty Quantification

First Page

185

Last Page

204

Recommended Citation

Lin, G., Elizondo, M., Lu, S., & Wan, X. (2014). Uncertainty quantification in dynamic simulations of large-scale power system models using the high-order probabilistic collocation method on sparse grids. International Journal for Uncertainty Quantification, 4 (3), 185-204. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2013003479