GridSFM is an open-source framework for AC Optimal Power Flow (AC-OPF) optimization. It is designed to determine the cost-minimizing generator dispatch while satisfying all physical and operational constraints of a power grid. The project includes a data pipeline that transforms grid topologies into solved scenarios using Hugging Face loaders, as well as a neural surrogate model capable of performing fast AC-OPF inference on those scenarios.
Key features and components:
- Neural surrogate models for rapid AC-OPF inference
- A Python-based data pipeline to convert topologies into .pyg.json scenarios
- A browser-based viewer for inspecting network topology and OPF results
- A Julia-driven pipeline that processes raw topologies through controlled perturbations such as load scaling, generator outages, and voltage squeezing
Key features and components:
- Neural surrogate models for rapid AC-OPF inference
- A Python-based data pipeline to convert topologies into .pyg.json scenarios
- A browser-based viewer for inspecting network topology and OPF results
- A Julia-driven pipeline that processes raw topologies through controlled perturbations such as load scaling, generator outages, and voltage squeezing
