Energy Management for Renewable-Colocated Artificial Intelligence Data Centers

Siying Li, Lang Tong, Timothy D. Mount
7/4/2025
math.OCcs.AIeess.SY

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute

85% match2026

Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

83% match2024

Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning

81% match2026

EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management

81% match2024

DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads

80% match2025

AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey

80% match2024

Cite this paper

@article{li2025energy,
  title  = {Energy Management for Renewable-Colocated Artificial Intelligence Data Centers},
  author = {Siying Li and Lang Tong and Timothy D. Mount},
  year   = {2025},
  eprint = {2507.08011},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.08011v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2507.08011.svg)](https://codeofpaper.com/paper/2507.08011)

Discussion