Converter-Based Loads And The Future Of Grid Reliability

Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I’m a Transmission and Distribution Project Manager with Dominion Energy, focused on grid infrastructure, interconnections, and system reliability. Most of my recent work has focused on integrating large loads and inverter-based resources, where the gap between planning assumptions and real-world performance becomes especially evident. I spend a lot of time bridging planning, engineering, and field execution, so my perspective is grounded in how these systems actually behave once they’re energized, not just how they model on paper.

Q2. With AI data center load forecasts doubling in key hubs like Northern Virginia (reaching 4.2 GW), which utilities are successfully shifting from traditional Inductive Load planning to managing the high-frequency "converter-based" transient responses of GPU clusters?

The shift we’re seeing isn’t just about scale, it’s about behavior. Traditional inductive load assumptions don’t hold with GPU-driven data centers. These are fast, converter-based loads that introduce voltage sensitivity and harmonic interactions you won’t fully capture in steady-state studies.

Utilities that are getting ahead of this are tightening requirements around dynamic modeling and not accepting black-box assumptions. In PJM, there’s a noticeable push…