Abstract: The power grid is going through significant changes with the introduction of renewable energy sources and incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. At the same time, these need to resemble the actual power system model and dynamics. In this talk, I describe two frameworks -- (i) for constructing synthetic power distribution networks for a given geographic region which closely resembles the actual physical counterpart, and (ii) for performing cascading failure analysis in the power grid when subjected to a severe disturbance, such that it resembles the actual power grid events as closely as possible. For the first framework, I use openly available information about interdependent road and building infrastructures and incorporate engineering and economic constraints to construct the distribution networks. The networks synthesized by this framework represent realistic power distribution systems that can be used by network scientists to analyze complex events in power grids. The second framework for cascading failure analysis uses a realistic representation of the underlying power grid, including the topology, the control and protection components and a dynamic stability analysis that goes beyond traditional work consisting of structural and linear flow analysis. The proposed framework can be used to assess vulnerability of the power grid to any disturbance like a physical attack, cyber-attack or any severe weather event. Particularly, I consider the case of a targeted physical attack on the power grid of Washington DC. The results show that realistic representations and analysis can lead to fundamentally new insights that are not possible by using simplified models.


Rounak Meyur is a Data Scientist with the Data Science and Machine Intelligence group at Pacific Northwest National Laboratory (PNNL). His research interests lie at the intersection of optimization, control, and learning applied to complex networked systems. In particular, his expertise lies in the area of mathematical programming, convex optimization, optimal control, heuristics and approximation algorithms. He applies these techniques to solve problems that arise in enhancing cyber-physical security, creating synthetic networked infrastructure, decarbonization of energy infrastructure systems, and improving resiliency of infrastructure systems.

He was a Research Assistant at the Biocomplexity Institute & Initiative (BII) of University of Virginia (UVA) between 2020 and 2022. He was involved in developing frameworks for the realistic modeling and analysis of power grids. Rounak holds a PhD in Electrical & Computer Engineering from University of Virginia, MS in Electrical Engineering from Virginia Tech, and a BTech in Electrical & Electronics Engineering from National Institute of Technology (NIT) Tiruchirappalli, India.

Host: Lucius Lichte, Biocomplexity Institute, University of Virginia

Organizers: Professors Mona Zebarjadi and Cong Shen, Department of Electrical & Computer Engineering