2 citations · 4 across the 5 of their papers we have counts for
5 papers
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
Alban Puech, Matteo Mazzonelli, Celia Cintas +11
We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solve…
Foundation Models for the Electric Power Grid
Hendrik F. Hamann, Thomas Brunschwiler, Blazhe Gjorgiev +24
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets throug…
QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
Sihan Zeng, Youngdae Kim, Yuxuan Ren +1
At the heart of power system operations, alternating current optimal power flow (ACOPF) studies the generation of electric power in the most economical way under network-wide load…
On Solving Unit Commitment with Alternating Current Optimal Power Flow on GPU
Weiqi Zhang, Youngdae Kim, Kibaek Kim
We consider the unit commitment (UC) problem that employs the alternating current optimal power flow (ACOPF) constraints, which is formulated as a mixed-integer nonlinear programmi…
GPU-Accelerated Sequential Quadratic Programming Algorithm for Solving ACOPF
Bowen Li, Michel Schanen, Kibaek Kim
Sequential quadratic programming (SQP) is widely used in solving nonlinear optimization problem, with advantages of warm-starting solutions, as well as finding high-accurate soluti…