OpenGridFM

GridFMs have the potential to cope with the increasing complexity and uncertainties stemming from the energy transition by providing a significant speed-up in computation. A key benefit of FMs is their ability for stakeholders to fine-tune a pre-trained model for specific applications, using their own proprietary data in a scalable and economical way.

This makes OpenGridFM an ideal technology platform to collaborate across the energy sector, academia and AI specialists to harness emerging AI capabilities for a variety of applications.

Initially, the project will focus on establishing a large collection of solved AC power flow for different grid topologies, parameters, and different load conditions.

In the second phase, suitable architectures will be evaluated by pre-training performance analysis, and by adjusting models, training strategies, and loss functions. OpenGridFM-v0 will be pre-trained to reconstruct masked power flow data as illustrated below.

Fig 2. Masked power flow data

This first model will be tested and fine-tuned with data located behind utility firewall for downstream applications closely related to power flow like complementing numerical power flow solvers, contingency analysis and optimal power flow.

In the long-term, we envision expanding and addressing more grid applications by including more diverse data modalities to pre-train a single GridFM or by combining different GridFMs.

Contributors

• Hydro-Québec
• IBM

Resources

• Artwork