English

Energy Management for Renewable-Colocated Artificial Intelligence Data Centers

Optimization and Control 2025-09-25 v2 Artificial Intelligence Systems and Control Systems and Control

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

Cite

@article{arxiv.2507.08011,
  title  = {Energy Management for Renewable-Colocated Artificial Intelligence Data Centers},
  author = {Siying Li and Lang Tong and Timothy D. Mount},
  journal= {arXiv preprint arXiv:2507.08011},
  year   = {2025}
}
R2 v1 2026-07-01T03:55:16.873Z