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Add blog post about microsoft/GridSFM (#206)
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_posts/2026-06-28-msft-research.md

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layout: post
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title: "JuMP powers foundational models for the electric grid"
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date: 2026-06-28
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categories: [open-energy-modeling]
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author: "Oscar Dowson"
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---
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Microsoft Research [recently published GridSFM](https://www.microsoft.com/en-us/research/blog/gridsfm-a-new-small-foundation-model-for-the-electric-grid/),
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a small foundation model for the electric grid. We were pleased to notice that
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the training pipeline is powered by JuMP and [PowerModels.jl](https://lanl-ansi.github.io/PowerModels.jl/stable/).
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To generate the training data for GridSFM, the Microsoft team solved hundreds of
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thousands of AC optimal power flow (AC-OPF) problems across 150+ real grid
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topologies. They did this using [PowerModels.jl](https://lanl-ansi.github.io/PowerModels.jl/stable/),
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a Julia package for power network optimization built on JuMP, with Ipopt as the
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underlying solver. Their code is open source on GitHub at [microsoft/GridSFM](https://github.com/microsoft/GridSFM/tree/1ca775fd436d7ce013a1c0ab946e61ac7ef59ad6/power_grid/US/topology_solver_pipeline).
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A related project is the [GridFM DataKit](https://github.com/gridfm/gridfm-datakit),
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which similarly uses JuMP and PowerModels.jl to generate training data for grid
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foundation models. The team behind it have [published an arXiv paper](https://arxiv.org/pdf/2512.14658)
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describing their work. An interesting aspect of GridFM DataKit is that the
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front-end is a Python package, but it calls Julia and PowerModels via
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[juliacall](https://juliapy.github.io/PythonCall.jl/stable/juliacall/).
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It's great to see JuMP and the broader Julia ecosystem being used at this scale.
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You can learn more about foundation models of the electric grid by watching the
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recordings of the [5th Workshop on Foundation Models of the Electric Grid](https://gridfm.org/harvard/).
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If your work uses JuMP, we'd love to hear about it. The best way to tell us is
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by [opening a GitHub issue](https://github.com/jump-dev/jump-dev.github.io/issues).

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