EMT Modeling Data Gap; Lessons from the ORNL Workshop

For small and medium-sized electric cooperatives, EMT studies are no longer a distant research activity - they are becoming essential to grid reliability and compliance. But as Halley highlighted, the data ecosystem supporting EMT work remains fragmented and cumbersome. [ORNL EMT Workshop 2025]

EMT modeling simulates fast electrical phenomena such as switching transients, faults, and interactions between inverter-based resources (IBRs) and the grid. Unlike traditional steady-state or dynamic studies, EMT models operate on sub-millisecond time steps, demanding precise data from numerous systems.

In Halley’s presentation, he outlined the multi-layered data architecture involved:

Each of these domains often resides in its own database, maintained by different teams. Integrating them into an EMT-ready dataset is a challenge even for large utilities—and nearly impossible for smaller ones without automation.

In many utilities, legacy tools and manual workflows dominate the modeling process. Common practices include:

– Using Excel spreadsheets to track ratings and impedance data.

– Managing multiple disconnected databases for power flow, dynamics, protection, and short-circuit models.

– Relying on manual coordination between planning engineers, vendors, and consultants.

While these methods may have sufficed for N-1 steady-state studies, they crumble under EMT’s data demands. Every inconsistency - an outdated impedance, a mismatched transformer ID, a missing line terminal - introduces potential model instability. The result is an expensive and error-prone process that often delays project schedules and increases consultant dependency.

For small and mid-size cooperatives, which operate with lean staff and limited budgets, these inefficiencies are especially costly. They lead to redundant data entry, uncertainty in model accuracy, and a reliance on external contractors for tasks that should be routine.

The core problem is data fragmentation - and solving it requires a single, structured source of truth that spans all equipment and modeling domains.

This is where the FPS Grid Information Management (GIM) platform offers a practical path forward. GIM consolidates equipment, facility, and model data into one cohesive environment designed for modern planning workflows. Its goal is not to replace existing power system tools, but to connect them through a consistent data layer.

The ORNL workshop highlighted an important shift: EMT modeling is no longer just an R&D topic - it’s becoming a mainstream planning necessity. As inverter-based resources proliferate, utilities must model control-system interactions and fault behaviors that were once abstract academic concerns.

Halley’s presentation brought these realities into focus: the technical challenge is not just in the simulation itself but in assembling reliable, validated data. Without that foundation, even the most sophisticated EMT software is limited in value.

The takeaway is clear - utilities need to rethink how data flows across their organizations. Preparing for EMT studies means not just having the right models, but having trusted, automated, and reusable data pipelines.

For smaller utilities, the transition to advanced modeling doesn’t require a complete overhaul. It starts with recognizing that:

As one cooperative engineer put it during a recent planning roundtable, “It’s not the simulation software that slows us down - it’s everything we have to do before we can even open it.” That sentiment captures the heart of the EMT data problem.

The growing need for EMT studies underscores a broader truth: the future grid demands better data management. Utilities that modernize now - integrating their facility, rating, and modeling datasets - will not only simplify compliance but also prepare for more complex reliability analyses. Modernizing data workflows isn’t just about EMT modeling - it’s about building a foundation that supports every aspect of system planning and compliance.