Energy Management Strategies for Electric Vehicle Charging in Microgrids: A Case Study of Optimization Techniques

The integration of Electric Vehicles (EVs) into microgrids presents both significant opportunities and complex challenges in energy management. As the adoption of EVs increases, efficient charging strategies become essential for maintaining grid stability, reducing energy costs, and maximizing the utilization of renewable energy sources. This review explores various optimization techniques applied to energy management in EV charging within microgrids, including deterministic approaches, stochastic programming, Model Predictive Control (MPC), game theory, machine learning, and heuristic/metaheuristic methods. Each technique is evaluated based on its strengths, weaknesses, and applicability to different system requirements, such as real-time responsiveness, adaptability to uncertainties, and scalability. Moreover, the paper identifies emerging trends and key research areas, such as hybrid optimization frameworks, decentralized energy markets, Vehicle-to-Grid (V2G) technology, and the integration of explainable AI for enhanced decision-making transparency. Additionally, challenges related to cybersecurity, resilience to system faults, and the integration of large-scale EV infrastructure are discussed. The paper concludes by highlighting the need for multi-objective optimization approaches that balance cost efficiency, user satisfaction, and grid reliability. With rapid advancements in EV technology and microgrid systems, research must focus on developing scalable and secure energy management solutions. While AI-driven methods show strong potential, real-world adoption faces challenges such as high costs, technical complexity, and integration issues. Practical applications highlight feasibility, but broader implementation demands further refinement.