GridSFM: A new, small foundation model for the electric grid

GridSFM: A new, small foundation model for the electric grid - Microsoft Research

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Microsoft releases a lightweight foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings in grid analysis.

At a glance

Microsoft introduces GridSFM, a small foundation model that approximates AC optimal power flow in milliseconds, unlocking decisions that can directly impact up to $20B/year in congestion losses and 3.4 TWh of renewable curtailment.

Beyond estimating generator dispatch and costs, GridSFM produces full AC system states, giving operators direct visibility into congestion, stability, and overall system health.

It provides a foundation for the community to build advanced power grid simulators and planning tools without recreating data or models from scratch.

Microsoft introduces GridSFM , a small foundation model for solving AC optimal power flow (AC-OPF) problems in transmission power grids. This follows our earlier release of a U.S.-based open transmission-topology dataset that powers GridSFM.

Power grids face increasing strain from surging demand, the need to integrate renewable energy sources, transportation electrification, and extreme weather events. Across all these challenges, the core question is the same: what are the optimal operating points that keep the grid functioning under each new condition?

Answering this requires solving AC optimal power flow (AC‑OPF), a complex, non-convex optimization problem that computes the cheapest generator dispatch (how much each generator produces) that meets demands while respecting power flow physics, voltage limits, thermal constraints, and stability requirements, and underpins core power system operations including reliability, real-time dispatch, market clearing, and contingency analysis. These decisions directly govern outcomes at the scale of up $20 billion per year in congestion costs (opens in new tab) and multi‑terawatt‑hour renewable curtailment (opens in new tab) (lost renewable energy due to congestion), making both economic efficiency and grid reliability highly sensitive to how well these operating points are found. However, AC‑OPF is computationally expensive: power utility scale grid can take up to hours solve, forcing a trade-off between solving a small number of carefully selected scenarios or relying on approximations that ignore critical physics, which can misestimate power flows and binding constraints and lead to suboptimal dispatch and degraded reliability under stressed conditions.

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To address this limitation, we introduce GridSFM, a single neural network that approximates AC‑OPF in milliseconds across grids ranging from 500 to 80,000 buses . It takes standard AC‑OPF inputs (grid topology, generator and load specifications, transmission line constraints) and produces an operating point and a feasibility verdict (whether the system satisfies all physical and operational constraints). By removing the compute bottleneck, GridSFM makes it possible to evaluate orders of magnitude more scenarios in real time, enabling more informed decisions and shifting grid operations from reactive response to proactive optimization.

In this initial release we offer two tiers:

GridSFM-Open for research-scale grids up to 4,000 buses.

GridSFM-Premier for production-scale systems up to 80,000 buses.

The model is built as a block-structured discrete neural operator (Figure 1), representing each grid as a directed graph, with buses (connection points in the grid) and generators as vertices, and transmission and AC lines as edges. It is trained using both solver supervision , where reference solutions are generated using the AC-OPF solver ( IPOPT in PowerModels.jl (opens in new tab) ), and physics-based constraints that penalize violations of fundamental physical laws such as Kirchhoff’s voltage and current laws, as well as operating constraints like thermal limits. This enables the model to learn from both feasible and infeasible regimes. Most learning-based AC-OPF surrogates train one model per grid on a narrow distribution (opens in new tab) . GridSFM takes the opposite approach: in this release a single model trained across 150+ base grid topologies (network structures) and roughly half a million scenarios spanning varying load profiles, multi-element outages, line-rating derates, voltage-bound tightening, and different generator cost coefficients, so the model is forced to generalize rather than memorize. Across the 54-grid mix test scenarios for GridSFM-Open, our model…