Evaluation of Electric Vehicles Hosting Capacity using Artificial Intelligence Techniques

The integration of Electric Vehicles (EVs) into the distribution network is rapidly increasing. This EV integration impacts the voltage profile, causes thermal limits of lines to be exceeded, and leads to transformer overloads. Therefore, it is crucial to determine the Hosting Capacity of EVs (HCEVs) that can be integrated into the network without violating grid parameters. Existing methods for evaluating the HCEVs are static, whereas the grid requires a dynamic model for accurate HC assessment. This paper proposes a novel model consisting of five sub-models that utilize Artificial Intelligence (AI). These sub-models assist in calculating the HC of EVs. The results from Module M5 (the proposed model) are used to predict energy demand for workplaces and evaluate HCEV using AI in Python and MATLAB software. It was observed that Machine Learning (ML) models are highly effective in this process.