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：人工智能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、储能运行与碳排放随时间动态交互。本文研究面向地理分布式且具备微电网产消者能力的人工智能数据中心的碳感知计算—电力联合调度问题。我们提出一种混合整数线性规划（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、调度本地发电与电池储能，并管理双向电网交互。该模型捕捉了新兴人工智能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。在合成但具有实际意义的实例上的实验表明，所提出的联合MILP相较于仅计算调度和仅能量调度基线显著提升了总运营效益，同时降低了排放。结果进一步表明，推理路由灵活性是价值的主要来源，电池储能提供了有用的时间灵活性，而本地发电资源丰富的场景尤为有利。该框架为可持续且电网交互的人工智能数据中心提供了一种可处理的优化抽象方法。