Inference as Flexibility: Ramp Management for Transmission-Connected AI Data Centres

Abstract

The rapid growth of large AI data centres introduces new operational challenges for power systems, including rapid ramping, oscillatory load behavior, voltage fluctuations, and supply-demand balancing impacts. For example, the Alberta Electric System Operator (AESO) has identified transmission-connected data centres (TCDCs) as large non-conforming loads that may need to limit their point-of-connection ramp rates. Existing mitigation approaches mainly rely on exogenous electrical resources, such as battery energy storage systems (BESS). This paper presents a proof-of-concept demonstration of a complementary software-defined mitigation layer: using flexible large language model (LLM) inference serving as endogenous TCDC flexibility to partially offset AI training power ramps. We consider a 150 MW TCDC with training, inference, and base-load components. A measured LLaMA-2-70B fine-tuning power profile is scaled to represent an aggregate training block, while measured LLaMA-3.1-70B inference power traces are used to model batch-size-dependent inference flexibility. Three strategies are compared: BESS-only mitigation, batch-size-only control, and coordinated batch-size plus BESS control. Simulation results show that the hybrid strategy reduces BESS discharge energy by 71% and peak discharge power by 51%, while maintaining near-complete compliance with a 10 MW/min ramp limit.