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An adversarially robust approach to security-constrained optimal power flow
Neeraj Vijay Bedmutha · Priya Donti · J. Zico Kolter

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in Machine Learning for Engineering Modeling, Simulation and Design »

Security-constrained optimal power flow (SCOPF) is a critical problem for the operation of power systems, aiming to schedule power generation in a way that is robust to potential equipment failures. However, many SCOPF approaches require constructing large optimization problems that explicitly account for each of these potential system failures, thus suffering from issues of computational complexity that limit their use in practice. In this paper, we propose an approach to solving SCOPF inspired by adversarially robust training in neural networks. In particular, we frame SCOPF as a bi-level optimization problem -- viewing power generation settings as parameters associated with a neural network defender, and equipment failures as (adversarial) attacks -- and solve this problem via gradient-based techniques. We describe the results of initial experiments on a 30-bus test system.

Author Information

Neeraj Vijay Bedmutha (Carnegie Mellon University)
Priya Donti (Carnegie Mellon)
J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

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