The rapid growth of data centers has made large electronic load (LEL) modeling increasingly important for power system analysis. Such loads are characterized by fast workload-driven variability and protection-driven disconnection and reconnection behavior that are not captured by conventional load models. Existing data center load modeling includes physics-based approaches, which provide interpretable structure for grid simulation, and data-driven approaches, which capture empirical workload variability from data. However, physics-based models are typically uncalibrated to facility-level operation, while trajectory alignment in data-driven methods often leads to overfitting and unrealistic dynamic behavior. To resolve these limitations, we design the framework to leverage both physics-based structure and data-driven adaptability. The physics-based structure is parameterized to enable data-driven pattern-consistent calibration from real operational data, supporting facility-level grid planning. We further show that trajectory-level alignment is limited for inherently stochastic data center loads. Therefore, we design the calibration to align temporal and statistical patterns using temporal contrastive learning (TCL). This calibration is performed locally at the facility, and only calibrated parameters are shared with utilities, preserving data privacy. The proposed load model is calibrated by real-world operational load data from the MIT Supercloud, ASU Sol, Blue Waters, and ASHRAE datasets. Then it is integrated into the ANDES platform and evaluated on the IEEE 39-bus, NPCC 140-bus, and WECC 179-bus systems. We find that interactions among LELs can fundamentally alter post-disturbance recovery behavior, producing compound disconnection-reconnection dynamics and delayed stabilization that are not captured by uncalibrated load models.

翻译：数据中心的快速增长使得大型电子负荷建模在电力系统分析中日益重要。此类负荷具有传统负荷模型无法捕捉的快速工作负载驱动变化特性以及保护驱动的断开与重连行为。现有数据中心负荷建模方法包括基于物理原理的方法（为电网仿真提供可解释的结构）和数据驱动方法（从数据中捕捉经验性工作负载变化）。然而，基于物理的模型通常未在设施运行层面进行校准，而数据驱动方法中的轨迹对齐常导致过拟合和非真实的动态行为。为克服这些局限，我们设计了融合物理结构与数据驱动适应性的框架。该物理结构经过参数化设计，支持基于真实运行数据进行数据驱动的模式一致性校准，从而服务于设施级电网规划。我们进一步证明，对于本质随机性的数据中心负荷，轨迹级对齐存在局限性。因此，我们设计了使用时序对比学习的校准方法，以实现时序模式与统计模式的对齐。该校准在本地设施执行，仅将校准后的参数共享给公用事业公司，从而保护数据隐私。所提出的负荷模型采用来自MIT Supercloud、ASU Sol、Blue Waters及ASHRAE数据集的真实运行负荷数据进行校准，随后集成至ANDES平台，并在IEEE 39节点、NPCC 140节点及WECC 179节点系统中进行评估。研究发现，大型电子负荷间的相互作用会从根本上改变故障后恢复行为，产生复合型断开-重连动态及延迟稳定现象，这些是未经校准的负荷模型无法捕捉的。