The integration of electric vehicles (EVs) in the power grid has attracted considerable attention due to its potential benefits, such as demand response and power quality improvement. However, the intermittent and unpredictable nature of EVs’ charging and discharging behavior can cause significant challenges to the grid’s stability and power quality. This research study explores the use of a droop-ANN model to improve power quality in vehicle-to-grid (V2G) systems. The proposed approach integrates an artificial neural network (ANN) into the droop control technique to accurately predict the voltage and frequency of the charger. Through simulations, the model’s effectiveness in reducing power fluctuations and enhancing power quality was validated. The results indicate that the droop-ANN model significantly improves power quality across various battery state of charge (SoC) and charging/discharging scenarios. The findings highlight the potential of the droop-ANN model to enhance stability and reliability in V2G systems. Further research is needed to validate the model in real-world applications and explore its full potential. Overall, the droop-ANN model offers a promising solution for improving power quality in V2G systems.

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test.

Voltage Source Converter based Grid Integrated Performance of Hybrid Renewable Energy Sources