# Energy Management for Renewable-Colocated Artificial Intelligence Data Centers

arXiv: https://arxiv.org/abs/2507.08011 · HTML: https://mlsanity.com/paper/2507.08011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Towards a Systematic Survey for Carbon Neutral Data Centers](https://mlsanity.com/paper/2110.09284/) (2021)
- [Power Modeling for Effective Datacenter Planning and Compute Management](https://mlsanity.com/paper/2103.13308/) (2021)
- [Renewable-Colocated Green Hydrogen Production: Optimal Scheduling and Profitability](https://mlsanity.com/paper/2504.18368/) (2025)

## Abstract (arXiv)

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.