Neuro-Reachability of Networked Microgrids

TL;DR

This paper introduces Neuro-Reachability, a neural ODE-based method for verifying the dynamic behavior of networked microgrids with uncertainties, combining data-driven modeling and physics integration for reliable analysis.

Contribution

It presents a novel ODE-Net-enabled dynamic modeling approach, a physics-data-integrated microgrid model, and a conformance-based reachability analysis for microgrid verification.

Findings

Effective in microgrid dynamic model discovery

Verifies NMs under multiple uncertainties

Enhances reliability of dynamic verification

Abstract

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.