Home /Research /Energy Management for Renewable-Colocated Artificial Intelligence Data Centers
OTHER

Energy Management for Renewable-Colocated Artificial Intelligence Data Centers

Siying Li, Lang Tong, Timothy D. Mount

Year
2025
Access
Open access

Abstract

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.

Keywords

math.OCcs.AIeess.SY

Related papers

Browse all OTHER papers