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صفحه اصلی / icemd2024

A Data-Driven Optimization Framework for Sustainable Electric Vehicle Charging with Grid Stability and Renewable Energy Integration

نویسندگان :

Bagher Khademhamedani¹ Masoud Izadi² Nahid Izadi³

1- دانشگاه صنعتی همدان 2- دانشگاه صنعتی همدان 3- دانشگاه صنعتی همدان

کلمات کلیدی :

Electric vehicles،Grid optimization،Load balancing،Adaptive scheduling

چکیده :

The rapid adoption of electric vehicles (EVs) offers significant environmental benefits but poses challenges for power grids, including increased demand, instability, and renewable energy integration. This paper introduces a data-driven optimization framework to dynamically manage EV charging while maximizing renewable energy utilization and ensuring grid stability. The framework employs real-time data, machine learning-based predictions, and advanced optimization algorithms to balance grid loads and align charging schedules with renewable energy availability. Simulations show a 25% reduction in peak demand, 85% renewable energy utilization, and an 18% reduction in electricity costs compared to uncontrolled charging. Additionally, the framework enhances voltage stability, ensuring reliable grid performance. This study provides actionable insights for advancing electrified transportation and fostering a sustainable energy ecosystem by addressing real-time adaptability, scalability, and renewable energy alignment. The proposed framework supports policymakers and energy providers in achieving resilient and efficient EV infrastructure.