A Novel Hybrid Approach for Power Quality Improvement in a Vehicle-to-Grid Setup Using Droop-ANN Model

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.