数据中心负荷的稳态等效电路模型
Steady-State Equivalent Circuit Model for Data Center Loads
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中文总结 AI 辅助
本文提出数据中心稳态等效电路模型，可捕捉异构工作负载分布的电网影响，实验表明该模型能更准确评估线路负载，均匀利用率会高估线路负载变化17%-46%。
中文摘要 AI 辅助
规划人员目前在稳态互联和故障研究中将数据中心表示为聚合的恒PQ或ZIP负荷。这些聚合模型计算简便，但会模糊计算工作负载、服务器利用率与电网侧需求之间的电气关系，忽略IT负荷的内部电力电子转换级，并假设计算集群间的工作负载分布均匀，从而隐藏了与工作点相关的转换器损耗和效率变化。本文提出了一种数据中心的稳态等效电路模型（ECM），该模型明确为IT负荷、电源、冷却及辅助系统构建电路模型；对于电源，等效电路模型明确表示其内部电力电子转换级；对于IT负荷，开发了与利用率相关的服务器功率模型，并将其与考虑损耗的电源ECM相结合。该方法既能捕捉异构工作负载分布对电网侧的影响，又能保持与传统潮流分析的兼容性。我们在大规模输电潮流中评估了该数据中心ECM，采用蒙特卡洛模拟，分别考虑集群利用率异构和均匀两种情况。与固定效率的恒PQ模型相比，ECM预测最紧张线路在约30%的蒙特卡洛样本中超过其热极限；结果还显示，与异构服务器利用率相比，均匀服务器利用率会高估线路负载变化幅度17%-46%，具体数值取决于集群内工作负载的相关性。
英文摘要
Planners currently represent data centers as aggregate constant-PQ or ZIP loads in steady-state interconnection and contingency studies. These aggregate models are computationally convenient. However, they obscure the electrical relationship between computational workloads, server utilization, and grid-side demand. They ignore the internal power-electronic conversion stages of IT loads and assume homogeneous workload distributions across the compute clusters. This hides operating-point-dependent converter losses and efficiency variations. We propose a steady-state equivalent-circuit model (ECM) for data centers, which explicitly builds circuit models for IT loads, power supply units, cooling, and auxiliary systems. For power supply units, the equivalent circuit model explicitly represents internal power-electronic conversion stages. For IT loads, we develop a utilization-dependent server power model, and we combine it with loss-aware ECMs of power supply units. This approach captures the grid-side impact of heterogeneous workload distributions while preserving compatibility with conventional power-flow analysis. We evaluate this data center ECM in large-scale transmission power flows, using Monte Carlo simulations under heterogeneous and homogeneous cluster utilization. In comparison with the fixed-efficiency constant-PQ model, the ECM predicts that the most stressed line exceeds its thermal limit in about 30% of Monte Carlo samples. The results further show that homogeneous server utilization overstates line-loading variability by 17%-46% relative to heterogeneous server utilization, depending on the intra-cluster workload correlation.
