AI-Powered Dynamic Load Management in Adaptive Microgrids for Sustainable EV Charging

The growing adoption of electric vehicles (EVs) has highlighted the need for efficient and sustainable EV charging solutions. The proposed study presents an AI-powered dynamic load management system for adaptive microgrids to optimize EV charging by managing energy flows from photovoltaic (PV) systems, battery storage, and the grid. The system uses Deep Q-Networks (DQN), a reinforcement learning technique, to make real-time charging, discharging, and energy storage decisions. The model utilizes data from synthetic and real-world sources, processed through MATLAB files, including variables such as EV load profiles, PV energy generation, and grid pricing. By prioritizing renewable energy sources, minimizing grid dependency, and ensuring optimal energy distribution, the system helps to reduce operational costs and enhance sustainability. This research addresses challenges in current EV charging systems, such as computational complexity and scalability, by leveraging AI to improve decision-making. The system is designed to respond to real-time inputs, including energy prices, demand forecasts, and weather data, enabling it to adapt to changing conditions and effectively balance energy supply and demand. Additionally, the proposed framework improves battery health by managing charging cycles to prevent overcharging and maximize battery life. Integrating AI into microgrid systems provides a scalable, cost-effective, and ecofriendly solution for sustainable EV charging, reducing grid dependency and promoting renewable energy use.