Energy Management for Renewable-Colocated Artificial Intelligence Data Centers
This work addresses energy efficiency and cost savings for AI data center operators, though it appears incremental as it applies existing optimization frameworks to a specific colocation scenario.
The paper tackles the problem of minimizing electricity costs for AI data centers with colocated renewable generation by developing an energy management system that co-optimizes workload scheduling, renewable utilization, and market participation, resulting in significant cost reductions as demonstrated with real-world data.
We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocated renewable generation. Under a cost-minimizing framework, the EMS of renewable-colocated data center (RCDC) co-optimizes AI workload scheduling, on-site renewable utilization, and electricity market participation. Within both wholesale and retail market participation models, the economic benefit of the RCDC operation is maximized. Empirical evaluations using real-world traces of electricity prices, data center power consumption, and renewable generation demonstrate significant electricity cost reduction from renewable and AI data center colocations.