5 papers
PACR: Parameter-Optimized AC Power Flow Restoration for AC Feasible DCOPF Dispatch
Michael A. Boateng, Russell Bent, Sidhant Misra +3
The DC optimal power flow is widely used in power system operations because of its computational efficiency and scalability. However, DC dispatches are not guaranteed to satisfy th…
E-Globe: Scalable -Global Verification of Neural Networks via Tight Upper Bounds and Pattern-Aware Branching
Wenting Li, Saif R. Kazi, Russell Bent +2
Neural networks achieve strong empirical performance, but robustness concerns still hinder deployment in safety-critical applications. Formal verification provides robustness guara…
Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies
Miao Li, Michael Klamkin, Pascal Van Hentenryck +2
This paper studies optimization proxies, machine learning (ML) models trained to efficiently predict optimal solutions for AC Optimal Power Flow (ACOPF) problems. While promising,…
Towards AC Feasibility of DCOPF Dispatch
Michael A. Boateng, Russell Bent, Sidhant Misra +3
DC Optimal Power Flow (DCOPF) is widely utilized in power system operations due to its simplicity and computational efficiency. However, its lossless, reactive power-agnostic model…
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
Mohamad Fares El Hajj Chehade, Wenting Li, Brian W. Bell +3
The robustness of neural networks is crucial in safety-critical applications, where identifying a reliable input space is essential for effective model selection, robustness evalua…