Opening the black box of data center electricity consumption forecasting: An application of system dynamics modeling to Northern Virginia

Policymakers in data center-intensive regions face surging electricity demand driven by artificial intelligence (AI), especially Generative AI (GenAI), which can create tensions between data center expansion and regional grid capacity. Institutional data center electricity consumption forecasts from JLARC, EPRI, and the IEA project continued demand growth but do not model the feedback dynamics — time delays, regulatory responses, rebound effects — that determine whether grid adequacy thresholds are breached or maintained. This article develops a system dynamics model that makes these feedback structures explicit, disaggregating GenAI and non-GenAI data center demand and coupling them with three NERC-aligned electricity supply trajectories. We apply the model to Northern Virginia, the world’s largest data center market, where data centers already consume approximately 25 % of regional electricity. Under four different baseline scenarios, the anticipated reserve margin (ARM) falls below the reference margin level (RML) of 17.7 % within a 3–5 year window, with the abundance-realistic scenario breaching this threshold by 2028–2029 and data center share of total electricity consumption reaching 36–40 % by 2031. When ARM-triggered regulatory feedback is introduced, the model shows that grid adequacy can be restored, but only after a four-year overshoot during which the ARM remains below the RML — a delay driven by the asymmetry between fast demand adjustment (0–2 years) and slow supply expansion (3–10 years). These findings indicate that static forecasts systematically underestimate the timing challenge. The model is designed to be portable to other DC-intensive regions by substituting locally valid parameter values.