让AI数据中心响应电网信号，3小时降电25%却不影响性能。
Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona
- 通过软件调度AI任务，根据电网实时信号动态调节算力。
- 在凤凰城测试中，峰值时段功率降低25%，持续3小时。
- 无需改硬件或储能，适合想参与电网调节的云服务商。
人工智能正推动电力需求指数级增长，威胁电网可靠性，推高社区能源基建成本，并因电网接入受限而制约AI发展。本文首次在凤凰城一家商业超大规模云数据中心开展实地验证，与主要企业伙伴合作，展示纯软件方案Emerald Conductor的可行性。该系统在运行256张GPU的集群上，针对典型AI工作负载，在不修改硬件或依赖储能的情况下，基于实时电网信号调度算力，在电网高峰时段实现连续3小时25%的功耗下降，同时保障AI服务质量（QoS）不变。此平台将数据中心转变为可灵活互动的电网资源，有助于提升电网韧性、降低用电成本并加速AI创新。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is fueling exponential electricity demand growth, threatening grid reliability, raising prices for communities paying for new energy infrastructure, and stunting AI innovation as data centers wait for interconnection to constrained grids. This paper presents the first field demonstration, in collaboration with major corporate partners, of a software-only approach--Emerald Conductor--that transforms AI data centers into flexible grid resources that can efficiently and immediately harness existing power systems without massive infrastructure buildout. Conducted at a 256-GPU cluster running representative AI workloads within a commercial, hyperscale cloud data center in Phoenix, Arizona, the trial achieved a 25% reduction in cluster power usage for three hours during peak grid events while maintaining AI quality of service (QoS) guarantees. By orchestrating AI workloads based on real-time grid signals without hardware modifications or energy storage, this platform reimagines data centers as grid-interactive assets that enhance grid reliability, advance affordability, and accelerate AI's development.
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