A hybrid approach for transmission grid resilience assessment using reliability metrics and power system local network topology

ABSTRACT

Due to increasing threats on power systems from various extreme events such as adverse weather and cyber/physical attacks, research on power grid resilience is recently gaining a substantial traction. In this study, we evaluate the transmission grid resilience using the local topological summaries derived under a framework of topological data analysis (TDA) and more conventional power system reliability metrics. The dynamics of persistent topological features after an extreme event are examined to evaluate the impact on the underlying network structure. In addition, a framework based on an optimal power flow model is developed to investigate power system reliability metrics under extreme events. The developed methods are applied to a synthetic power system that is built on the footprint of the Texas power system. By comparing the TDA summaries with the power system reliability metrics, our findings show that local topological summaries can successfully reflect changes in the grid resilience.

KEYWORDS:

• Grid resilience
• power system
• graph theory
• topological data analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Gel is partially supported by NSF DMS [1736368]; NSF ECCS [1824716]; and NSF IIS 1633331.

Notes on contributors

Binghui Li

Binghui Li received his Ph.D. in Civil Engineering from North Carolina State University in 2018. He is currently working as a postdoctoral research associate at the University of Texas at Dallas. His research interests are in the fields of energy system analysis, stochastic programming, and electricity markets.

Dorcas Ofori-Boateng

Dorcas Ofori-Boateng is presently a Ph.D candidate in Statistics, under the supervision of Professor Yulia R. Gel, at the University of Texas at Dallas. Her research interests are associated with statistical foundations of data science, topological data analysis and anomaly detection in application to power systems, fMRI data and other dynamic complex network structures.

Yulia R. Gel

Yulia R. Gel is Professor in the Department of Mathematical Science at the University of Texas at Dallas. Her research interests include statistical foundation of Data Science, inference for random graphs and complex networks, time series analysis and predictive analytics. She holds a Ph.D in Mathematics, followed by a postdoctoral position in Statistics at the University of Washington.  Prior to joining UT Dallas, she was a tenured faculty member at the University of Waterloo, Canada. She held visiting positions at Johns Hopkins University, University of California, Berkeley, and the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK. She is Fellow of the American Statistical Association.

Jie Zhang

Jie Zhang received the B.S. and M.S. degrees in mechanical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2006 and 2008, respectively, and the Ph.D. degree in mechanical engineering from Rensselaer Polytechnic Institute, Troy, NY, USA, in 2012. He is currently an Assistant Professor with the Department of Mechanical Engineering, University of Texas at Dallas, Dallas, TX, USA. His research interests include multidisciplinary design optimization, complex engineered systems, big data analytics, wind and solar forecasting, renewable integration, and energy systems modeling and simulation.