How can foundation models and neural solvers accelerate steady-state power-system analysis?
In this hands-on tutorial, PhD student Alban Puech will introduce OpenGridFM, an open source framework for developing and deploying neural solvers for power-flow and optimal-power-flow applications.
Participants will use interactive notebooks to build an end-to-end neural power-system workflow, including generating realistic datasets with gridfm-datakit and training and evaluating GENCO with gridfm-graphkit. They will also compare its accuracy and computational performance with classical power-system solvers.
The tutorial is intended for power-system practitioners, researchers, and developers. No prior experience with OpenGridFM is required; basic familiarity with Python and power-system concepts is recommended.
