Energy Management for Renewable-Colocated Artificial Intelligence Data Centers

2025-07-04

AI Summary

This paper addresses profit optimization for AI data centers co-located with renewable energy generation. Method: We propose a joint optimization framework integrating computational workload scheduling and energy management, jointly determining AI task allocation, on-site renewable energy utilization, and bilateral participation in wholesale and retail electricity markets—all under a profit-maximization objective. An empirical model is built using real-world time-series data on electricity prices, equipment power consumption, and renewable generation profiles; a mixed-integer optimization algorithm enables coordinated, multi-timescale decision-making. Contribution/Results: Compared to baseline strategies, our approach significantly improves the data center’s overall profitability. Results demonstrate that deep compute–energy co-optimization is critical to unlocking the economic potential of co-located systems, providing a practical, intelligent energy management paradigm for green AI infrastructure.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocated renewable generation. Under a profit-maximizing 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 profit gains from renewable and AI data center colocations.

Problem

• Optimize AI workload and renewable energy use
• Maximize profit in electricity market participation
• Improve economic benefits of colocated data centers

Innovation

• EMS optimizes AI workload and renewable energy
• Co-optimizes market participation for profit maximization
• Empirical evaluations show significant profit gains