Modelling and Analysis of Renewable Energy Integration and Electric Vehicle Impact on Microgrid Performance: The PSO-Based MPPT and ANFIS-Controlled Battery Approach

Abstract

The rapid rise in pollution levels and green house gas emissions has accelerated an adoption of Electrical Vehicles (EVs), which are expected to play the pivotal role in a future energy landscape. As EVs integrate with electrical grids, their impact on voltage profiles and grid load distribution becomes increasingly significant. This study explores an integration of renewable energy sources, EVs into the microgrid and with the emphasis on energy production and consumption optimization. In this analysis, the diesel generator is a microgrid, Photovoltaic (PV) array coupled with the wind farm and the Vehicle-to-Grid system near a load. A paper introduces the model for a PV array that employs Maximum Power Point Tracking (MPPT) through Particle Swarm Optimization (PSO) to maximize an efficiency of a solar energy conversion. Furthermore, it investigates battery control using Adaptive Neuro-Fuzzy Inference System (ANFIS) to manage a storage and distribution of energy from both renewable sources and EVs. A microgrid is designed to cater to an energy demands of various establishments, including hospitals, universities, and EV charging stations. The comprehensive analysis of a micro grid’s performance, with the particular focus on effects of EV integration, is presented using MATLAB/Simulink, revealing an influence of EVs on an overall network stability and energy management.