Effects of Driving Path and Path Shadow on Orderly Parking and Charging Strategies of VIPV-Equipped Electric Vehicles

In the context of coordinated carbon reduction in buildings and transportation, the combination of orderly charging of electric vehicles and building photovoltaic consumption is an important path to achieve carbon peak and carbon neutralization goals. However, most of the existing research focuses on parking and charging scheduling strategies in static scenarios. However, in urban environments, it is difficult for Vehicle-Integrated Photovoltaics (VIPV) to accurately obtain photovoltaic power generation due to building dynamic occlusion, which will affect the orderly charging of EVs and the consumption of building photovoltaic residual power. Therefore, this study proposes a power update model that combines road shadow assessment and VIPV power generation simulation. The model performs sunlight analysis on buildings along the routes using ArcGIS software, A path shadow spatio-temporal data set with an interval of 10 m is constructed in the JAVA environment. Based on the agent’s driving path, the photovoltaic power generation of the vehicle on the path is dynamically simulated, and its initial State of Charge (SOC) is updated. The model is embedded into the MATSim simulation framework to analyze the impact of path shadows on parking decisions, charging strategies, and consumption of PV surplus electricity. The results show that the model effectively improves the matching degree between the charging demand of EV and photovoltaic power generation in time and space, thus improving user satisfaction and reducing the consumption of PV surplus electricitys. This study provides an important method support for the collaborative carbon reduction of building traffic.