Carbon-Aware Compute--Power Scheduling for AI Data Centers with Microgrid Prosumer Operations

2026-05-05

AI Summary

This work addresses the deep coupling between AI data centers and power systems, highlighting the urgent need for joint optimization of computational and electrical scheduling to reduce carbon emissions. The paper proposes the first unified framework that simultaneously schedules rigid training workloads and elastic inference requests while co-optimizing local generation, energy storage, and bidirectional grid interactions within a microgrid. The approach achieves carbon-aware integrated compute-energy scheduling under constraints on latency, workload continuity, and carbon budgets. Formulated as a mixed-integer linear program, the model jointly optimizes task scheduling, load routing, storage dispatch, and grid interaction strategies. Experiments demonstrate that the proposed method significantly improves operational efficiency and reduces emissions compared to baselines that optimize only computation or energy in isolation, with inference routing flexibility and energy storage playing pivotal roles—particularly when abundant local renewable generation is available.

Abstract

AI data centers are increasingly becoming tightly coupled compute--energy systems, where workload placement, cooling demand, electricity procurement, storage operation, and carbon emissions interact over time. This paper studies carbon-aware compute--power scheduling for geographically distributed AI data centers with microgrid prosumer capabilities. We propose a mixed-integer linear programming (MILP) framework that jointly schedules rigid training jobs, routes elastic inference workloads, dispatches local generation and battery storage, and manages bidirectional grid interaction under latency, continuity, power-balance, and carbon-budget constraints. The model captures two key features of emerging AI infrastructure: heterogeneous workload flexibility and site-level energy prosumer operation. Experiments on synthetic yet practically motivated instances show that the proposed joint MILP substantially improves total operational benefit over compute-only and energy-only baselines while reducing emissions. The results further indicate that inference-routing flexibility is a major source of value, battery storage provides useful temporal flexibility, and local-generation-rich settings are particularly favorable. The framework provides a tractable optimization abstraction for sustainable and grid-interactive AI data centers.

Problem

• carbon-aware scheduling
• AI data centers
• microgrid prosumer
• compute-power integration
• emissions reduction

Innovation

• carbon-aware scheduling
• AI data centers
• microgrid prosumer
• mixed-integer linear programming
• workload flexibility