Scalable Intelligent Energy Allocation for Renewable Vehicle Charging Networks

Abstract

The rapid adoption of electric vehicles (EVs) combined with increasing integration of renewable energy sources has created significant challenges in managing large-scale vehicle charging networks. The intermittent nature of renewable energy, coupled with dynamic charging demand, requires intelligent, scalable, and adaptive energy allocation mechanisms. This study proposes a Scalable Intelligent Energy Allocation Framework for Renewable Vehicle Charging Networks (SIEA-RVCN). The framework leverages artificial intelligence-based prediction models and optimization techniques to dynamically allocate renewable energy across distributed EV charging stations. The system aims to maximize energy utilization, minimize grid overload, and ensure fair distribution among charging nodes. The proposed model is evaluated using simulated EV charging datasets with renewable energy variability. Performance is measured in terms of energy efficiency, load balancing accuracy, charging delay, and system scalability. Experimental results demonstrate that the proposed approach significantly improves energy distribution efficiency compared to traditional scheduling and heuristic-based methods.