AI数据中心可动态响应电网需求，实现灵活用电。
Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
- 通过电网信号与任务调度联动，实现对GPU集群的精细控电。
- 130kW集群实测支持快速降载、持续削峰和低碳运行。
- 适合关注电网协同与绿色算力的科研与工程团队。
人工智能基础设施的快速发展正推动数据中心用电量空前增长。传统电力规划将大型计算设施视为刚性高峰负荷，导致昂贵的基建升级和漫长的电网接入周期。近期研究显示，通过软件化工作负载调度，AI集群可在用电高峰期间降低能耗。本文探讨了现代基于GPU的AI数据中心作为电网互动资产的潜力，提出集成电网信号、任务调度与功耗遥测的架构，实现细粒度集群功率控制。在130 kW GPU集群的真实部署中，实验验证了多种灵活性：快速负载削减、持续功率削减以及碳感知运行，同时保障关键任务的服务水平。进一步展示了跨地理分布集群的性能感知负载迁移，使工作负载可向电网压力较低区域转移。这些能力使AI基础设施从静态耗电者转变为支持电网可靠性、加速接入并提升计算可持续性的柔性资源。
原文摘要 · Abstract (English)
The rapid expansion of artificial intelligence (AI) infrastructure is driving unprecedented growth in electricity demand from data centers. Traditional power-system planning treats large computing facilities as inflexible peak loads, leading to costly infrastructure upgrades and long delays in grid interconnection. Recent work has shown that AI clusters can reduce electricity consumption during peak demand through software-based workload orchestration. This article explores how modern GPU-based AI data centers can operate as grid-interactive assets that respond dynamically to power system conditions. We describe an architecture integrating grid signals, workload scheduling, and power telemetry for fine-grained cluster power control. Experimental results from a real-world deployment on a 130 kW GPU cluster demonstrate multiple forms of flexibility, including rapid load reduction, sustained curtailment, and carbon-aware operation while preserving service levels for priority jobs. We further demonstrate performance-aware load shifting across geographically distributed clusters, enabling workloads to migrate toward regions with lower grid stress. Together, these capabilities transform AI infrastructure from static electricity consumers into flexible resources that support grid reliability, accelerate interconnection, and improve computing sustainability.
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