Leveraging Deep Reinforcement Learning within Optimal Renewable Energy Strategies for Sustainable AI Data Centers

AI computing's rapid expansion is steeply increasing data center electricity use, intensifying sustainability concerns. We develop the first framework that couples deep reinforcement learning (DRL) control with cost-effective optimization to boost efficiency and enable economically viable renewable integration in AI data centers. Using seven public, real-world AI workloads and up-to-date open-source grid and renewable-cost data sets, we assess energy, water, and carbon performance at ten globally representative sites, benchmarking against an ASHRAE standard-aligned baseline controller. DRL attains near-optimal free-cooling operation, delivering over 6% energy reduction and over 8% water savings. Sustaining higher server utilization could further cut auxiliary cooling by up to 60% per unit of server energy when wet-bulb temperatures exceed the free-cooling thresholds. We also evaluate price- and carbon-oriented demand response potentials combined with battery storage. The integrated strategy yields concurrent cost and emission reductions, lowering the total cost of a 50% emission cut by 9-28% and placing abatement costs at $107-$500 per ton for on-site renewable adoption across selected locations. These results show that intelligent controls, paired with renewable strategies, can deliver scalable, cost-effective decarbonization of AI infrastructure consistent with global efficiency and net-zero goals.