{"ID":2898492,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08011","arxiv_id":"2507.08011","title":"Energy Management for Renewable-Colocated Artificial Intelligence Data Centers","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.","short_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. W...","url_abs":"https://arxiv.org/abs/2507.08011","url_pdf":"https://arxiv.org/pdf/2507.08011v2","authors":"[\"Siying Li\",\"Lang Tong\",\"Timothy D. Mount\"]","published":"2025-07-04T18:25:42Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.AI\",\"eess.SY\"]","methods":"[]","has_code":false}