Agent-Based Modeling for Scale Evolution of Plug-In Electric Vehicles and Charging Demand

Scale evolution pattern recognition of plug-in electric vehicles (PEVs) and charging demand modeling are essential for various involved sectors to promote PEV proliferation and integration into power systems. Considering that the market penetration development of PEVs will drive the evolution of charging demand, an integrated dynamic method based on an agent-based modeling technology is proposed in this paper by combining scale evolution model with charging demand model to jointly detect the possible PEVs evolution patterns and long-term charging demand profiles. Heterogeneous consumers presenting different preferences in making vehicle purchase decisions and the interactions with other consumers via social dynamics are taken into consideration in the scale evolution model. After obtain the scale of PEVs by aggregating individual consumer agents purchase behavior, the driving patterns, charging behavior habits, and charging strategies are systematically incorporated into the charging demand model. Case studies demonstrate the feasibility and effectiveness of the proposed methodology by taking an urban area as an example. Furthermore, the factors that affect the market evolution of PEVs and the charging demand are also simulated and analyzed.