This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This project aims to strengthen dependability and robustness of the electric power grid by improving the capability to aggregated power system state estimation (PSSE) methods to monitor and assess the health of a power grid. The electric power grid is a cyber-physical systems, essential for modern daily life that is always available on-demand nearly anywhere at any time. The grid is arguably the largest global engineered structure. The goals of this project are to (1) understand vulnerabilities intrinsic to traditional PSSE methods, and (2) improve the dependability and robustness of PSSE algorithms to potentially disruptive conditions. This project extends recently developed power system optimization techniques to enable better situation awareness of the operations of the overall power system. The project will work with the Arkansas State University?s outreach program ?P-20 Educational Innovation Center? to share this research and encourage careers in STEM fields.

This project extends convex relaxation-based techniques for large-scale cyber-physical power system in order to improve the monitoring, analysis, and controllability of these systems. The project presents an efficient convex relaxation-based approach to the PSSE problem. The project leverages semidefinite programming relaxation and extreme point approaches for large-scale alternating current (AC) power systems to provide tighter bounds on flow analyses than traditional PSSE. This project also presents an analysis showing the infeasibility of transitioning between certain operating regions in the power system that can be used to provide safety and security guarantees before initiating state transitions. This approach can identify specific types of sparse false data, that might arise from corrupted sensors by nature or design, and go undetected by current power systems' bad data detection algorithms. The proposed tighter relaxation schemes will be broadly applicable to a wide variety of nonconvex and nonlinear optimization problems in large flow-system models and in complex optimization problems.