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.

翻译：AI数据中心正日益成为计算-能源紧耦合系统，其中工作负载部署、冷却需求、电力采购、储能运行及碳排放随时间相互影响。本文研究了具备微电网产消者能力的地理分布式AI数据中心碳感知计算-电力调度问题。我们提出一个混合整数线性规划（MILP）框架，该框架在延迟、连续性、功率平衡和碳排放预算约束下，联合调度刚性训练任务、路由弹性推理工作负载、调配本地发电与电池储能，并管理双向电网交互。该模型捕捉了新兴AI基础设施的两个关键特征：异构工作负载灵活性与站点级能源产消者运行。在合成但具有实践激励性的实例上的实验表明，所提出的联合MILP相比纯计算和纯能源基线方法，在降低排放的同时显著提升了总运营收益。结果进一步表明，推理路由灵活性是主要价值来源，电池储能提供了有效的时间灵活性，而本地发电充裕场景尤为有利。该框架为可持续且与电网交互的AI数据中心提供了可求解的优化抽象。