Sandia National Labs Academic Programs Collaboration Report

Plugging into electric power systems research Sandia-UT Austin collaborators are working to increase the resilience of U.S. energy systems via two Grid Resilient Energy research projects including a three-year project titled “Critical Node Identification, Vulnerability Modeling, and Topology Optimization for the Electric Grid” under the Sandia Resilient Energy Systems (RES) Grand Challenge, and an energy storage project titled “Improving Grid Resilience with Optimal Restoration Utilizing Energy Storage” funded by the DOE Office of Electricity (OE). Brian Pierre, manager of Sandia’s Electric Power Systems Research Department, is teaming with Sandia’s Bryan Arguello (PI for the RES project) and Manuel Garcia (PI for the OE project) and UT Austin Professors Surya Santoso and Erhan Kutanoglu and their three PhD students, Brent Austgen, Joshua Yip, and Baris Bilir on the research. “Professors Santoso and Kutanoglu have been excellent partners with deep knowledge in the areas of power systems and optimization,” said Pierre, “and the PhD students on this project have shown great growth and work ethic.” For RES, the team is working to identify bulk system electric grid nodes that are critical to resilience, their levels of vulnerability to regional threats, and how to optimally invest, prepare, and recover from catastrophic disasters. The OE energy storage project focuses on large-scale energy storage optimization models for investment decisions, preemptive action decisions, and restoration decisions to improve electric grid resilience from major disturbances. Other areas of focus include the use of large-scale energy storage to assist with black start and how to best utilize mobile energy storage for grid recovery. For the OE funded project, Sandia and UT Austin are continuing to work toward improved identification of electric grid critical nodes and their vulnerability levels to regional threats using new interdiction modeling with alternating current power flow approximations verified against an innovative dynamic

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Academic Programs