Energy Management for Renewable-Colocated Artificial Intelligence Data Centers

• URL: http://arxiv.org/abs/2507.08011v1
• Date: Fri, 04 Jul 2025 18:25:42 GMT
• Title: Energy Management for Renewable-Colocated Artificial Intelligence Data Centers
• Authors: Siying Li, Lang Tong, Timothy D. Mount,
• Abstract summary: We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocated renewable generation.<n>Under a profit-maximizing framework, the EMS co-optimizes AI workload scheduling, on-site renewable utilization, and electricity market participation.
• Score: 2.9562742331218725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocated renewable generation. Under a profit-maximizing 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 profit gains from renewable and AI data center colocations.

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