Dynamic Load Model for Data Centers with Pattern-Consistent Calibration

Siyu Lu and co-authors have posted a paper to arXiv presenting a new dynamic load model for data centers with pattern-consistent calibration (submitted 8 Feb 2026).

• Main announcement: The paper proposes a physics-based parameterized load model for large electronic loads (LELs) combined with pattern-consistent calibration using temporal contrastive learning (TCL); the model is calibrated locally at facilities (privacy-preserving: only calibrated parameters are shared with utilities) and validated on operational datasets MIT Supercloud, ASU Sol, Blue Waters, and ASHRAE and integrated into the ANDES platform for grid tests.
• Background and evaluation details: The calibrated model was integrated into ANDES and evaluated on transmission test systems IEEE 39-bus, NPCC 140-bus, and WECC 179-bus; authors report that interactions among LELs alter post-disturbance recovery, producing compound disconnection-reconnection dynamics and delayed stabilization. The paper is available on arXiv (DOI via DataCite pending).