Green Energy‐Based Active Grid Optimization Using Deep Learning for EV Charging Infrastructure

Sustainable transportation has been revolutionized by integrating active grid technology with artificial intelligence. An active grid enables the integration of renewable energy sources, including solar, wind, and hydroelectric energy into an EV charging network. The network can effectively prioritize charging activities by dynamically adapting to fluctuations in renewable energy availability and grid demand. Deep-learning-based active grid optimization has abundant potential for electric vehicle (EV) charging infrastructure by maximizing the utilization of green energy resources and minimizing carbon emissions. Deep learning algorithms are trained using extensive datasets that include the energy output, grid demand, weather patterns, and past charging behavior. It renders accurate predictions in real time by forecasting energy generation and consumption patterns with precision. Furthermore, bidirectional power flow technologies in active grid systems enable EV batteries to serve as grid storage resources, enhancing grid flexibility and resilience through vehicle-to-grid (V2G) functionalities. Grid-interactive charging algorithms optimize charging schedules based on real-time data, ensuring environmentally friendly charging operations while minimizing grid impact. In this chapter, two DC microgrids with renewable energy sources (solar and wind) and an EV battery-charging system are designed. A logic-based control algorithm was developed for the dynamic optimization of the active grid for EV charging. A deep-learning-based dynamic model was adapted to predict renewable energy generation, grid demand, and charging patterns to optimize the active grid. Overall, a significant step towards a sustainable transportation ecosystem, where green EV charging infrastructure actively contributes to reducing carbon emissions and promoting renewable energy adoption.