Learning-Based Augmentation and Adaptation for Grid Sim-to-Real Model Discrepancy
Modern power systems can encounter increased discrepancy between the operators' simulation model and the actual true dynamics of the grid, driven by uncertainties caused by integration of new inverter-based resources (IBRs), large loads, unmodeled dynamics, parameter drifts, etc., to name a few. All of these impact the control room operations, where some critical oscillations may not be captured during the transient studies. To circumvent these issues, we propose a learning-augmented hybrid approach where the operator simulation model is supplemented with artificial intelligence (AI)-learned residual models using the phasor measurement unit (PMU)/ point-on-wave (PoW) based sensed trajectory data. The physics-based operator model provides interpretability and structural consistency, while the learned residual captures discrepancies caused by non-idealities. The learned model employs advanced neural architectures and consists of a backbone encoder and multi-head decoder layers for heterogeneous grid channels. Subsequently, we formulated a continual learning-motivated adaptation framework such that the baseline residual AI model can also be updated when the underlying real grid model changes in future conditions. Extensive numerical simulations are performed on the IEEE 68-bus benchmark model with a diverse set of disturbances, and different state-of-the-art predictive architectures involving recurrent learners, latent neural ODEs, and transformers are explored to demonstrate both residual learning and adaptation capabilities.