At global scale, data-center electricity demand is growing faster than the grids that supply it, while system operators increasingly require large flexible loads that can adjust power within seconds to absorb variable wind and solar generation. For multi-megawatt AI/HPC facilities, the key unresolved question is practical and measurable: how quickly can the software stack translate a grid request into a real change in GPU power at the facility meter, where commitments are settled? We answer this on real hardware with GridPilot, a three-tier predictive controller operating across milliseconds, seconds, and hours, augmented by a deterministic safety-island bypass for fast response. On a three-GPU NVIDIA V100 testbed, GridPilot achieves a measured end-to-end trigger-to-target response of 97.2 ms, which is 6.9x faster than the 700 ms requirement of Nordic Fast Frequency Reserve. We further incorporate an instantaneous Power Usage Effectiveness (PUE) correction so dispatched commitments remain robust at meter level rather than only at IT load level. In replay experiments across six representative European grids (from Sweden to Poland), the PUE-aware controller closes 2.5-5.8 percentage points of cooling-overhead drag. GridPilot is released as open source and serves as a proof of concept that MW-scale AI/HPC demand can be engineered as controllable, grid-responsive flexibility by design.

翻译：在全球范围内，数据中心的电力需求增速已超过为其供电的电网发展速度，而系统运营商越来越需要能够快速调整功率（数秒内）以吸纳风能与太阳能间歇性发电的大型柔性负载。对于百万瓦级AI/HPC设施而言，尚未解决的关键问题兼具实践性与可测量性：从软件栈将电网请求转化为设施电表处GPU功率的实际变化（该处为电力承诺结算点）究竟需要多快？我们在真实硬件上通过GridPilot解答了这一问题——这是一款跨毫秒、秒、小时三个时间尺度的三级预测控制器，并辅以确定性安全岛旁路机制实现快速响应。在三GPU NVIDIA V100测试平台上，GridPilot实现了97.2毫秒的端到端触发至目标响应速度，较北欧快速频率储备（FFR）700毫秒要求快6.9倍。我们进一步引入瞬时电能使用效率（PUE）修正，使调度承诺在电表层面（而非仅IT负载层面）保持稳健。在涵盖瑞典至波兰六个典型欧洲电网的回放实验中，PUE感知控制器将冷却系统损耗拖尾效应缩小了2.5-5.8个百分点。GridPilot已作为开源项目发布，其证明了百万瓦级AI/HPC负载可被设计为可控的、具备电网响应灵活性的系统。