Communication Delay Robust Control of BESS for AI Training Load Smoothing

AI training loads can exhibit rapid power fluctuations because their power demand differs significantly between computational and communication phases, creating challenging ramp rates at the data center point of common coupling (PCC). Integrating battery energy storage systems (BESS) in data centers is a promising mitigation option. This paper proposes a hybrid BESS control strategy that combines droop-based grid-forming (GFM) control with instantaneous load current-based compensation to suppress high frequency load fluctuations. The GFM control loop regulates the long-term power exchange of the BESS, while the load-following control provides fast compensation for short-term AI workload fluctuations. Communication delay between the load current measurements and the BESS controller is explicitly modeled, and its impact on smoothing performance is analyzed. To mitigate delay-induced degradation, a predictor-based compensation method is incorporated into the BESS control structure. High-fidelity electromagnetic transient simulations are conducted to validate the proposed approach. Results demonstrate effective smoothing of AI training load fluctuations across different grid strength conditions and under time varying communication delays.