Neuro-Reachability of Networked Microgrids
This work addresses reliability verification for networked microgrids, a domain-specific problem, with incremental improvements through integration of physics and data.
The paper tackled the dynamic verification of networked microgrids with unidentified subsystems and uncertainties by developing a Neuro-Reachability method using neural ODE networks, achieving effective verification under multiple uncertainties and operational scenarios as demonstrated in case studies.
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.