From lab to grid: Modeling and Validation of AI-Driven Computational Load

Challenge

AI-driven load introduces layered risks that current interconnection processes do not consistently capture. At the facility level: harmonic disturbances, UPS degradation, flicker, and elevated cooling load. At the grid level: voltage flicker, frequency variation, reduced oscillatory damping, and sub-synchronous interactions with generators and inverter-based resources. A forthcoming NERC Level 3 Alert and new registration criteria targeted for late 2026 will formalize performance requirements that data centers and utilities must begin preparing for now.

Approach

Deploy an integrated end-to-end modeling and verification workflow across three sequential stages:

1. Data Center Modeling
   1. Develop control-focused models
   2. Simulate AI load dynamics, grid interactions, and disturbances
2. Lab Testing
   1. Perform physical HIL lab testing
   2. Verify protection, control, and transient performance
3. Model Validation
   1. Calibrate models using HIL results
   2. Develop validation reports
   3. Reduce commissioning risk

Accurate modeling requires detailed characterization across six data domains, shown on the right.

Results

• Developed control-focused models and simulated AI load dynamics, grid interactions, and disturbance scenarios.
• Performed HIL lab testing to verify protection, control, and transient performance prior to commissioning.
• Calibrated models against test results and produced validation reports.
• Established a repeatable end-to-end workflow supporting defensible interconnection review and NERC compliance readiness.