Energy Management for Renewable-Colocated Artificial Intelligence Data Centers

TL;DR

This paper presents an energy management system for AI data centers colocated with renewable energy sources, optimizing workload scheduling and market participation to reduce electricity costs effectively.

Contribution

It introduces a co-optimization framework for AI workload scheduling and renewable energy utilization in data centers, maximizing economic benefits in wholesale and retail markets.

Findings

Significant reduction in electricity costs demonstrated

Effective co-optimization of workload and renewable energy use

Empirical validation with real-world data

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.