Flexibility of Data Centers in Load-Concentrated Grids : A Comprehensive Impact Analysis

Abstract

The rapid expansion of artificial intelligence (AI) workloads is driving a sharp increase in electricity demand from data centers, particularly in the Seoul metropolitan area, where demand is highly concentrated and transmission capacity is structurally constrained. Under Korea’s carbon-neutrality targets, this trend exacerbates transmission congestion, renewable curtailment, and system costs. This study quantitatively evaluates how data-center siting and flexible operation strategies affect a load-concentrated power system using annual unit commitment and economic dispatch (UC/ED) simulations in PLEXOS for 2030 and 2038. Measured data-center load data are analyzed to extract seasonal load patterns and feasible demand response (DR) participation capacity, which are then scaled using projected contracted capacity to reconstruct system-level load profiles. Five scenarios are examined: a baseline aligned with the 11th Basic Plan for Long-term Electricity Supply and Demand, rapid data-center demand growth, regional siting redistribution, reliability-based DR via load shaving, and an upper-bound DR potential case. The results show that unmitigated data-center expansion leads to severe metropolitan interconnection congestion by 2038, with increased reliance on thermal generation, higher congestion-related costs, and frequent price spikes. While siting redistribution provides partial relief, flexible operation through DR delivers substantially larger system benefits. Reliability-based DR is most effective under moderate system stress by targeting critical peak periods, whereas large-scale DR deployment becomes essential under high-stress conditions. Overall, the findings indicate that combining spatial siting diversification with appropriately designed data-center flexibility strategies offers a more robust and cost-effective approach than any single measure alone, providing quantitative insights for transmission planning and demand-side policy design.