Sustainable Grid Integration of AI Data Centers: Carbon-Aware Co-Optimization of Training Workloads and Battery Storage Under Renewable Uncertainty
TL;DR
A carbon-aware co-optimization framework that schedules checkpoint intervals, workload migration, dynamic voltage and frequency scaling, and behind-the-meter storage jointly with unit commitment, pricing system CO2 emissions beyond the market-internalized level, with split-conformal scenario bands and an out-of-sample cost certificate is developed.
Abstract
The electricity demand of artificial intelligence (AI) data centers is growing faster than the grids hosting them can decarbonize, yet carbon-aware computing is rarely audited at the system level: workloads follow scalar-price or carbon signals, whose claimed savings need not survive network-wide dispatch. We develop a carbon-aware co-optimization framework that schedules checkpoint intervals, workload migration, dynamic voltage and frequency scaling, and behind-the-meter storage jointly with unit commitment, pricing system CO2 emissions beyond the market-internalized level, with split-conformal scenario bands and an out-of-sample cost certificate. On IEEE 39/118-bus systems mapped to 2025 French and German measurements, carbon-priced dispatch reduces emissions by 29.5% at a generation-cost premium below 0.01% (the carbon-signal headroom of this mapped system); system-level decomposition attributes more than 99% of this reduction at reference penetration to generation redispatch, an operator-side instrument. AI-load flexibility adds 0.23% of system emissions today, equivalent to about one quarter of the data-center fleet’s own carbon footprint, and 3.3% at gigawatt scale, where it cuts worst-day load shedding by up to 49%. The endogenous checkpoint policy recovers the reliability-optimal interval, whereas following the average carbon-intensity signal can increase system-level emissions. Conformal calibration maintains at-or-above-nominal coverage (97.5%); empirical bands under-cover (86.0%).