Bidirectional Energy Flow in EV-Microgrid Through Genetic Algorithm for PQ Management

The integration of electric vehicles (EVs) into microgrids presents a unique set of opportunities and challenges for modern energy systems. This paper explores the complexities associated with EV charging and discharging, emphasizing the need for advanced optimization techniques to ensure stable and efficient grid operations. The bidirectional energy flow of EVs can enhance microgrid stability and efficiency but introduces challenges such as grid instability, infrastructure demands, economic viability, and battery degradation. To address these issues, we propose the use of Genetic Algorithms (GAs) for optimizing EV charging and discharging patterns. GAs offers a robust method for solving complex optimization problems by balancing grid stability, economic factors, and battery health. Our performance analysis includes power quality assessments during EV charging, revealing significant fluctuations in real and reactive power, which necessitate sophisticated control strategies. We also examine current and voltage variations at the Point of Common Coupling (PCC), highlighting the need for precise monitoring and control to maintain stable grid operations. Furthermore, the Total Harmonic Distortion (THD) analysis indicates substantial harmonic distortion in both voltage and current, underscoring the importance of harmonic mitigation strategies. The findings suggest that combining GAs with robust control systems and investing in infrastructure upgrades can effectively manage the integration of EVs into microgrids. By addressing technical and economic challenges and evolving regulatory frameworks, the potential of EVs can be harnessed to create resilient, efficient, and sustainable energy systems. This paper contributes to the ongoing research and development efforts aimed at advancing the integration of EVs into future smart grids, highlighting the critical role of optimization techniques in achieving these goals.