Eigenvector dimension-reduction method for reliability analysis of power systems under the time-dependent load uncertainty

ABSTRACT

Power systems have become central pillars of modern society. Various uncertainties in power systems, however, make reliability analysis difficult. To date, the worst-case scenario was usually employed to tackle uncertainties in the system, which could make the overall system design prohibitively expensive especially when more and more uncertainty sources would be considered in modern power grids. This paper proposes reliability analysis of power systems under the time-dependent load uncertainty using an effective uncertainty quantification (UQ) method, i.e., the eigenvector dimension reduction (EDR) method. As such, reliability can be explicitly calculated in the probability manner for meeting the performance constraints without significantly overdesigning the system. Compared to the Monte Carlo simulation (MCS), the EDR method is much more efficient with satisfactory accuracy. Two case studies, including a 12-bus and a modified IEEE 118-bus power system, are used to demonstrate the feasibility of the proposed work.

KEYWORDS:

• Power system
• reliability analysis
• uncertainty quantification
• eigenvector dimension reduction
• load uncertainty

Acknowledgments

The research was supported by National Science Foundation, by DARPA – Young Faculty Award, and by Rutgers New Faculty Startup funding.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Defense Advanced Research Projects Agency [Young Faculty Award]; National Science Foundation [1507198].

Notes on contributors

Zhetao Chen

Zhetao Chen is a Ph.D. student in the Department of Industrial and Systems Engineering at the Rutgers University – New Brunswick. He received his B.S. degree in Quality Engineering and M.S. degree in Systems Engineering at the China Jiliang University in 2014 and 2017, respectively. His research interests include reliability and resilience analysis of the electric power system, and design for microgrids under uncertainty.

Zhimin Xi

Zhimin Xi is an Assistant Professor in the Department of Industrial and Systems Engineering at the Rutgers University – New Brunswick. He received his B.S. and M.S. degree in Mechanical Engineering at the University of Science and Technology Beijing in 2001 and 2004, respectively. He obtained his Ph.D. in Reliability Engineering at the University of Maryland – College Park in 2010. His research interests include reliability and safety for energy storage systems, design for reliable energy systems, prognostics and health management for engineering systems, model validation under uncertainty, and system reliability analysis. He is the recipient of 2019 Rutgers A. Walter Tyson Assistant Professorship Award and 2016 DARPA - Young Faculty Award. He is the winners of multiple (including twice Top 10) Best Paper Awards from ASME – Design Automation Conference in 2008, 2011, 2013, and 2015 respectively. He is a member of IISE, ASME, and IEEE.