标题： 将人工智能数据中心转变为电网互动资产：亚利桑那州凤凰城现场演示结果 显示英文标题

标题： Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona

摘要： 人工智能（AI）正在推动电力需求的指数级增长，威胁电网可靠性，提高社区为新能源基础设施支付的价格，并因数据中心等待连接到受限电网而阻碍AI创新。 本文展示了与主要企业合作伙伴合作的首个现场演示，一种仅软件的方法——Emerald Conductor——将AI数据中心转变为灵活的电网资源，可以在不进行大规模基础设施建设的情况下高效且立即利用现有电力系统。 该试验在亚利桑那州凤凰城一家商业超大规模云数据中心内运行的256-GPU集群上进行，该集群运行代表性的AI工作负载，在电网高峰事件期间，三小时内实现了集群用电量减少25%，同时保持了AI服务质量（QoS）保证。 通过基于实时电网信号协调AI工作负载，无需硬件修改或储能设备，该平台重新定义了数据中心作为电网互动资产的角色，从而增强电网可靠性，促进可负担性，并加速AI的发展。 显示英文摘要

摘要： 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.