为万兆瓦级AI数据中心设计新型虚拟电厂控制框架，解决毫秒级功率波动难题。
A Theoretical Framework for Virtual Power Plant Integration with Gigawatt-Scale AI Data Centers: Multi-Timescale Control and Stability Analysis
- 构建四层分时控系统，从100微秒到24小时跨尺度调控。
- 证明传统架构无法稳定应对万兆瓦级数据中心每秒超1000兆瓦的波动。
- 提出新稳定性标准，关键清除时间缩短至83毫秒，支持99.95%服务可用性。
人工智能爆发催生万兆瓦级数据中心，其功率波动在秒级超过500兆瓦，毫秒级变化达热设计功率的50%-75%。本文提出全新理论框架，重构虚拟电厂（VPP）以应对极端动态，采用从100微秒到24小时的四层分时控制架构。针对以变流器为主导、负载脉冲达兆瓦级的系统，提出定制化控制机制与稳定性准则。证明传统VPP架构（响应时间秒至分钟级）在面对万兆瓦级数据中心、上升速率超1000兆瓦/秒的场景下无法保持稳定。本框架引入：（1）与数据中心电力电子直接交互的亚毫秒控制层，主动抑制功率振荡；（2）融合保护系统动态的新稳定性准则，显示万兆瓦级脉冲负载下关键清除时间由150毫秒降至83毫秒；（3）量化工作负载可延迟性，实现30%峰值降低的同时，保障AI服务可用性高于99.95%。本研究建立数学基础，支撑未来将占数据中心用电量50%-70%的AI基础设施稳定接入电网。
原文摘要 · Abstract (English)
The explosive growth of artificial intelligence has created gigawatt-scale data centers that fundamentally challenge power system operation, exhibiting power fluctuations exceeding 500 MW within seconds and millisecond-scale variations of 50-75% of thermal design power. This paper presents a comprehensive theoretical framework that reconceptualizes Virtual Power Plants (VPPs) to accommodate these extreme dynamics through a four-layer hierarchical control architecture operating across timescales from 100 microseconds to 24 hours. We develop control mechanisms and stability criteria specifically tailored to converter-dominated systems with pulsing megawatt-scale loads. We prove that traditional VPP architectures, designed for aggregating distributed resources with response times of seconds to minutes, cannot maintain stability when confronted with AI data center dynamics exhibiting slew rates exceeding 1,000 MW/s at gigawatt scale. Our framework introduces: (1) a sub-millisecond control layer that interfaces with data center power electronics to actively dampen power oscillations; (2) new stability criteria incorporating protection system dynamics, demonstrating that critical clearing times reduce from 150 ms to 83 ms for gigawatt-scale pulsing loads; and (3) quantified flexibility characterization showing that workload deferability enables 30% peak reduction while maintaining AI service availability above 99.95%. This work establishes the mathematical foundations necessary for the stable integration of AI infrastructure that will constitute 50-70% of data center electricity consumption by 2030.
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