Data-driven Resilient Grid Operation Under Growing EV Penetration

A smart distribution system with electric vehicles (EVs) is a major option for reducing carbon emissions and for a greener transport system. This paper presents an efficient and resilient framework for a small smart distribution system. The smart system is developed in DIgSILENT PowerFactory, optimized in the Renewable Energy Optimization tool (RE-opt) as well as its resiliency and cost analysis, along with the integration of machine learning (ML) algorithms for the system's fault classification. The developed system achieved lifetime savings of $243,875 despite having a high initial investment. The classification approach successfully classified the system's fault with 99.97% accuracy. This fusion of DIgSILENT simulations, RE-opt, and ML techniques successfully provided a robust analysis. This approach addressed the challenges of renewable and EV integration, optimized the system, and furthermore provided fault security for the system.