Modern large data centers running AI training create rapid, repeated swings in power use. Those swings can push voltages in the local electric distribution network outside safe ranges. This paper proposes a simple, local control method that takes advantage of a key fact about training: the total load tends to alternate between two dominant levels — a high-power compute phase and a lower-power communication phase. By aligning voltage targets with those two levels, the method reduces voltage swings while cutting the amount of corrective action needed.

The authors start from how power engineers normally control voltage: inverter devices adjust reactive power (a form of power that helps shape voltage) based on a fixed voltage reference. That standard approach, often called droop control, reacts after a voltage has moved and is tuned for slowly changing loads. The paper models data-center power as switching between two modes and shows that if the controller instead switches its voltage reference between two matching levels, part of the voltage change caused by a load step is absorbed by the reference itself. In the paper’s linearized network model, choosing the reference appropriately can roughly halve the worst post-transition voltage deviation compared with a fixed reference.

Importantly, the proposed controller is decentralized: each inverter updates its reactive power using only its local voltage measurement and a time-varying local reference. The authors derive conditions under which voltages converge and describe how to pick the two reference levels so they align with the data center’s two operating modes. They also develop a local switching rule so the controller changes reference based on measured voltage, not on direct coordination with the data center’s job scheduler. Simulation studies reported in the paper show substantial reductions in both voltage deviations and in the total reactive power changes (control effort) compared with conventional fixed-reference droop control. The approach is compatible with internal data-center measures such as battery smoothing.