13 papers
Conditions for Quantum Advantage in AC Power Flow
Parikshit Pareek, Abhijith Jayakumar, Carleton Coffrin +1
This paper aims to contextualize the requirements for Quantum Computing (QC) algorithms to achieve a quantum advantage in solving the alternating current power flow (ACPF) problem,…
Discrete distributions are learnable from metastable samples
Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra +1
Physically motivated stochastic dynamics are widely used to sample from high-dimensional distributions. However, such samplers often get trapped in metastable states, approximately…
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…
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…
Potential Applications of Quantum Computing at Los Alamos National Laboratory
Andreas Bärtschi, Francesco Caravelli, Carleton Coffrin +16
The emergence of quantum computing technology over the last decade indicates the potential for a transformational impact in the study of quantum mechanical systems. It is natural t…
Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
Parikshit Pareek, Sidhant Misra, Deepjyoti Deka
The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability…