Fine-Grained Simulation-Based Charging Network Planning with Increasing EVs Range Capability

The widespread adoption of electric vehicles (EVs) has led to a substantial increase in charging demand, posing significant challenges to existing charging infrastructure planning. Current approaches often fail to account for the temporal and spatial variability of charging demand, focusing on broad, macro-level traffic flow models. Furthermore, the practical implications of vehicle-to-grid (V2G) functionality have been largely overlooked in most studies. To address these limitations, this study proposes a fine-grained simulation-based EV charging network planning. A fine-grained simulation platform incorporating V2G functionality was developed, leveraging real-world road network data from Nanjing for case analysis. This study systematically analyzed the influence of key factors such as battery capacity and charging rate on capacity planning of charging station with the increase of driving range of EVs. In addition, we also study the effect of dynamic pricing mechanism on the spatial and temporal distribution of charging load. Case studies conducted under various EV growth scenarios demonstrate that the proposed method significantly reduces user charging time costs while enhancing the overall efficiency of charging network. This research provides a theoretical foundation for the rational planning of EV charging infrastructure and offers actionable insights for policymakers aiming to design sustainable and resilient charging systems.