Prediction of fast charging load in coupled power-transportation network considering users' bounded rationality

With the rapid development of electric vehicles (EVs) and their charging facilities, the coupling between the power distribution network (PDN) and the transportation network (TN) has been deepening. The increasing demand for fast charging of EVs can impact the stable operation of the coupled power and transportation network (CPTN). Therefore, accurate prediction of EV fast charging loads is crucial. In light of the limitations of traditional charging load prediction models that fail to fully consider traffic flow characteristics and user behavior, this paper proposes an EV fast charging load prediction model in CPTN considering users' bounded rationality. A macro traffic flow model is employed to capture congestion dynamics within the TN, enabling a more realistic forecasting of traffic flow evolution. Additionally, a charging station dynamics model is developed to simulate the charging and queuing behaviors at charging stations, yielding the spatiotemporal distribution of charging loads. The micro user behavior decision model accounts for simultaneous route and departure time choices, reflecting the non-cooperative game behavior among user groups, and quantifies users' bounded rationality as their tolerance for travel costs, leading to a more accurate equilibrium flow. By considering users' bounded rationality, the model significantly reduces the iteration count, enhancing both computational efficiency and solution quality. Simulation results validate the effectiveness of the proposed model and algorithm, underscoring the necessity of accounting for traffic congestion characteristics and users' bounded rationality.