Title: Inverse Optimal Power Flow: Assessing the Vulnerability of Power Grid Data
Speaker: Priya L. Donti
In deregulated electricity markets, gaining knowledge of critical information such as grid structure, generator bidding cost curves, and nodal power demands could pose risks to market efficiency and cybersecurity. It is thus in the best interest of grid operators to protect critical information from the general public, so as to ensure fair, efficient, and safe market operation. At the same time, system operators such as PJM and governmental agencies such as the EPA regularly publish information about public market quantities such as energy prices and generator power outputs, which could potentially expose private data.
We seek to investigate the question of whether and to what extent privately-held market information is potentially exposed by published market information, given our knowledge that private and public parameters are related via an optimization problem called AC optimal power flow (ACOPF). Specifically, we formulate an algorithm called “inverse optimal power flow” (Inverse OPF) that uses gradient descent-based methods implemented within a neural network to learn unknown market and grid parameters. The eventual goal is to quantify the potential risks of having this information exposed.
The talk will run about 30 minutes, with 30 minutes of discussion following the talk. I welcome any feedback about the research methods as they currently stand, as well as advice regarding quantification of the real-world effects of our findings.
Priya Donti is a third-year Ph.D. student in the Computer Science Department and the Department of Engineering & Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her research is at the intersection of deep learning and energy policy, exploring topics such as marginal emissions prediction, grid data vulnerability, and end-to-end task-based approaches for coordinating