Maximizing Intraday V2G Feasible Capacity of EVs: A Cross-Disciplinary Approach With Traffic Flow and Prospect Theory

The participation of electric vehicles (EVs) in grid interaction depends on accurately assessing dispatchable capacity. To address the existing gap in quantifying and evaluating EV users’ willingness to engage in grid interaction, this paper proposes a novel daily vehicle-to-grid (V2G) feasible capacity modeling method based on cumulative prospect theory (CPT) and a semi-dynamic traffic flow (SDTF) model. First, a user travel decision utility (TDU) model is established based on the time and state of charge (SoC) value functions of EV users. Next, the interactions between travel decisions, traffic networks, and SoC are analyzed, leading to the development of a TDU guiding model and an improved SDTF equilibrium model. Finally, real-time charging power control is analyzed to explore the potential of EVs in V2G participation, and a maximum V2G capacity optimization model is proposed. Simulation results show that in a 12-node network with 1,000 EVs, the proposed method increases average intra-day V2G capacity from 164.53 to 177.71 kWh, an 8.01% improvement, while enhancing user travel experience by 5.65%. In a 20-node network with 10,000 EVs, capacity rises from 1,851.1 to 1,907.4 kWh, a 3.04% improvement, with user travel experience increasing by 7.48%. Additionally, we found that this strategy also improves traffic equilibrium.