Sustainable Grid Integration of AI Data Centers: Carbon-Aware Co-Optimization of Training Workloads and Battery Storage Under Renewable Uncertainty

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%).