Enhancing Voltage Control in Smart Microgrids for EV Reactive Power Management using Waterwheel Plant Algorithm

The development of sustainable energy systems depends on electric vehicle (EV) integration within modern smart microgrids (MGs). Due to intermittent EV charging and changing power demands the achievement of stable voltage levels and reactive power management becomes extremely challenging. To address these issues, this study proposes the Waterwheel Plant Algorithm (WPA), an adaptive optimization approach inspired by the dynamic balance of traditional waterwheels, for enhancing voltage control in EV-integrated smart MGs. The main goal of this research involves reducing Total Harmonic Distortion (THD) and Mean Absolute Error (MAE) as well as Root Mean Square Error (RMSE) through optimal reactive power compensation methods. The WPA actively adjusts its control parameters through changing grid conditions which leads to enhanced voltage control as well as lower power losses and stronger MG resilience. The WPA implementation on MATLAB serves as the base to evaluate its performance against techniques Improved Firefly Algorithm (IFA), Multiagent Reinforcement Learning (MRL), and differential evolution (DE) under different test conditions. The proposed WPA system generates results showing 1.1% THD and 0.019 MAE and 0.042 RMSE which surpass previously used methods in distribution system practices. The WPA demonstrates superior ability at harmonic suppression and superior power quality maintenance along with effective voltage stability in interconnected smart MGs that absorb numerous EVs.