Dynamic Resilience Assessment of Power Systems With Data Center Load Events Using Physics-Informed Neural Networks

Abstract

Large data center loads introduce new resilience challenges to power systems because their disconnection and staged reconnection can induce fast voltage and frequency dynamics that are not captured by static service-status or energy-based metrics. This paper proposes a utility-side, physics-informed resilience assessment framework that evaluates these events using only grid-side dynamic models and observable post-disturbance trajectories, without requiring detailed internal data center models. An unsupervised differential algebraic equation-physics informed neural network (DAE-PINN) based on an implicit backward Euler residual is developed to jointly predict dynamic and algebraic states, enabling repeated post-disturbance trajectory evaluation while enforcing network algebraic consistency. Normalized multi-phase resilience metrics are then used to quantify disturbance, degraded-state, and restoration-period impacts and to screen data center reconnection timing and load-ramping strategies under security constraints. Case studies on a modified IEEE 33-bus feeder show that the proposed DAE-PINN accurately tracks numerical DAE solutions and substantially reduces computation time in repeated restoration screening. The proposed metrics distinguish the effects of disturbance size, data center location, and reconnection strategy, revealing the trade-off between restoration speed and transient resilience loss.