# Adapting Datacenter Capacity for Greener Datacenters and Grid

arXiv: https://arxiv.org/abs/2301.03148 · HTML: https://mlsanity.com/paper/2301.03148/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2023.

## Built on by (most cited first, 6 of 6)

- [Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems](https://mlsanity.com/paper/2407.04014/) (2024)
- [Beyond Efficiency: Scaling AI Sustainably](https://mlsanity.com/paper/2406.05303/) (2024)
- [Input-Dependent Power Usage in GPUs](https://mlsanity.com/paper/2409.18324/) (2024)
- [A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers](https://mlsanity.com/paper/2605.04074/) (2026)
- [Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand](https://mlsanity.com/paper/2606.00811/) (2026)
- [Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning](https://mlsanity.com/paper/2606.30316/) (2026)

## Abstract (arXiv)

Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%--5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%--7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%--12.6%, 1.56x--1.26x better than the best local, online approach's results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management.