  A neural ordinary differential equations network (ODE-Net)-enabled
reachability method (Neuro-Reachability) is devised for the dynamic
verification of networked microgrids (NMs) with unidentified subsystems and
heterogeneous uncertainties. Three new contributions are presented: 1) An
ODENet-enabled dynamic model discovery approach is devised to construct the
data-driven state-space model which preserves the nonlinear and differential
structure of the NMs system; 2) A physics-data-integrated (PDI) NMs model is
established, which empowers various NM analytics; and 3) A
conformance-empowered reachability analysis is developed to enhance the
reliability of the PDI-driven dynamic verification. Extensive case studies
demonstrate the efficacy of the ODE-Net-enabled method in microgrid dynamic
model discovery, and the effectiveness of the Neuro-Reachability approach in
verifying the NMs dynamics under multiple uncertainties and various operational
scenarios.

Systems and Control;Artificial Intelligence;Systems and Control