microsoft/GridSFM_Open

Grid Small Foundation Model (GridSFM)

Model summary

| Field | Value | | --- | --- | | Developer | Microsoft Corporation

Authorized representative: Microsoft Ireland Operations Limited 70 Sir John Rogerson’s Quay, Dublin 2, D02 R296, Ireland | | Description | *GridSFM is a graph neural-network-based model trained on structured representations of power grid systems. The model leverages simulated data to learn relationships between topology, load distribution, and system behavior, enabling generalization across diverse grid configurations. Training involves large-scale synthetic scenario generation combined with supervised learning on simulation outputs.* | | Model architecture | *Heterogeneous graph attention network with Hodge positional encoding, residual blocks, and dual heads for AC-OPF operating-point prediction and feasibility classification* | | Parameters | *1-500M* | | Inputs | Inputs are graph-structured numerical scenarios representing AC-OPF problem instances: heterogeneous node features (buses, generators, loads, voltage limits, dispatch capacities, cost coefficients, demand) and edge features (AC lines, transformers, thermal ratings, angle limits, impedance), together with the topology defining the graph. | | Context length | *N/A* | | Outputs | Graph-structured numerical predictions matching the input topology: per-bus voltage magnitude (V) and angle (θ), per-generator real and reactive power dispatch (Pg, Qg), per-edge active and reactive branch flows (Pij, Qij), and one per-scenario scalar feasibility logit. | | Public data summary (or summaries) | microsoft/GridSFM_US_power_grid · Datasets at Hugging Face | | Training Dates | *May 1, 2026 to May 10, 2026* | | Release date

Release date in the EU (if different) | May 13, 2026 | | License | MIT license | | Model dependencies: | *N/A* | | List and link to any additional related assets | http://github.com/microsoft/gridsfm | | Acceptable use policy | N/A |

1. Model overview

*GridSFM is a graph neural-network-based model trained on structured representations of power grid systems. The model leverages simulated data to learn relationships between topology, load distribution, and system behavior, enabling generalization across diverse grid configurations. Training involves large-scale synthetic scenario generation combined with supervised learning on simulation outputs.*

*Unlike traditional physics-based solvers, GridSFM focuses on data-driven approximations and learning-based representations. This enables faster inference and supports research into scalable, AI-driven transmission grid modeling. However, the model is not intended to replace numerical solvers in production settings and it is for research purposes only.*

1.1 Alignment approach

GridSFM is not a generative language model and does not generate open-ended content. As such, no standard LLM alignment techniques were applied. Instead, risk mitigation focuses on constrained outputs, clear documentation of intended use, and restricting use to research contexts involving simulated data.

2. Usage

2.1 Primary use cases

GridSFM is intended for research and experimentation in AI-driven power system modeling. It can be used for approximating power flow, studying system behavior under different operating conditions, and evaluating learning-based optimization strategies.

The model is particularly useful in scenarios where large-scale simulation is required, enabling faster evaluation of grid configurations and supporting research into resilient and efficient grid operation.

2.2 Out-of-scope use cases

GridSFM is not designed for real-time operational decision-making in power grids. It should not be used for safety-critical applications, infrastructure control, or deployment in production environments without extensive validation.

The model is not intended for use outside the domain of power system modeling and may produce unreliable outputs when applied to other domains or data formats.

2.3 Distribution channels

Model checkpoint can be downloaded from Hugging Face and Foundry

2.4 Input formats

gridSFM expects structured input in JSON format representing a power grid instance. Each sample contains three top-level fields: grid, solution, and metadata.

The grid field defines the input state and includes:

• nodes: entities such as buses, generators, loads, and shunts, represented as feature matrices where rows correspond to entities and columns to attributes

• edges: connectivity information including transmission lines and transformers, with sender/receiver indices and associated physical features

• context: global system information (e.g., baseMVA)

The input captures a full graph representation of a power system, where:

• nodes encode local properties (e.g., voltage limits, generation bounds, demand)

• edges encode physical connections and constraints (e.g., line impedance, thermal limits)

The expected format is consistent across all examples, with structured numerical tensors derived from the JSON representation. Inputs must follow this schema and match the feature ordering used during training.

2.5 Technical requirements and integration guidance

GridSFM is a lightweight model (~15M parameters) and can be run on CPU for inference and small-scale experimentation. For larger datasets or more efficient processing, GPU acceleration is recommended but not required.

The model can be integrated into standard machine learning pipelines using common frameworks (e.g., PyTorch). Inputs must be preprocessed into structured graph representations derived from JSON grid data, and outputs require post-processing to map back to physical quantities such as voltages and power flows.

GridSFM is best suited for offline research workflows, simulation pipelines, and decision-support applications. It is not designed for real-time control or fully autonomous deployment.

2.6 Responsible AI considerations

GridSFM is a research model trained on simulated power system data and is intended for offline analysis and experimentation. As a result, its outputs may not fully capture real-world grid complexity, rare failure modes, or operational constraints. Performance may degrade when applied to out-of-distribution grid configurations or real-world systems.

A key risk is overreliance on model outputs. The model provides approximations based on learned patterns…