Resiliency-Guided Grid-Forming Converter Control of Distributed Solar-Powered Electric Vehicles

This paper proposes an intelligent control framework designed for distributed solar-powered electric vehicles (DSPEVs) operating within weak microgrids (MGs) that are experiencing a substantial influx of distributed converter-interfaced resources. Main contributions include developing and analyzing a new DSPEV model, which incorporates distributed electric vehicles and a solar-powered charging station with a battery. In islanding mode, DSPEV converters are modelled as grid-forming (GFM) converters, which enhances MG stability while also charging/discharging distributed electric vehicles. The resiliency-guided physics-informed neural network (named RPiNN) is applied for GFM converters, which considers the dynamics of low-inertia MGs and incorporates the physics laws governing DSPEV and MG behaviors. Furthermore, a novel resiliency-guided control framework that coordinates DSPEVs with GFM converters and the RPiNN is proposed. This framework addresses unexpected islanding scenarios and ensures both grid synchronization and re-synchronization, while maintaining acceptable voltage and frequency profiles for various MG operations. The proposed framework is validated through simulation results in islanding and grid-connected scenarios within a weak MG. Results shown that the proposed RPiNN effectively stabilizes islanding scenarios by reducing voltage and frequency fluctuations, demonstrating resilience and robustness in various operating conditions, and facilitating smooth grid synchronization.