Parallel-in-Time Neural Simulation of Networked Microgrids With Low-Fidelity Physics Guidance

A Low-fidelity Physics-guided, High-fidelity Neural Simulation (LPHN-Sim) is devised for parallel-in-time dynamic simulation of networked microgrids (NMGs). Three contributions are presented: 1) We design the LPHN-Sim architecture, where a Mori-Zwanzig-based physics solver guides the simulation and a high-fidelity neural network corrects the error; 2) We develop a parallel-in-time reformulation and implementation of LPHNSim to circumvent the step-by-step process and enable further acceleration; 3) We train LPHN-Sim via a lightweight physicsinformed loss to incorporate dynamic derivatives, which enhances the trainability while maintaining moderate computational cost. Notable features of the method are its ability to generate dynamic trajectories at multiple time points simultaneously and to accurately capture multi-time-scale dynamics. We validate LPHN-Sim in an inverter-dominated NMG under diverse disturbances (e.g., uncertainties, load changes, faults, and cyber interruptions) and demonstrate its accuracy, efficiency, and superiority over purely physics-based or data-driven simulations.