A Bilevel Dynamic Pricing Methodology for Electric Vehicle Charging Stations Considering the Drivers’ Charging Willingness

Abstract

The increasing penetration of electric vehicles (EVs) presents both challenges and opportunities for integrated transportation and power systems. This paper addresses the pricing issues of distribution networks and charging stations (CSs) simultaneously, proposing a bilevel noncooperative pricing methodology that considers traffic flow, power flow, and renewable energy integration. Key stakeholders—including distribution networks, CSs, and EVs—are thoroughly analyzed, with EV charging behavior modeled through a combination of charging probability, pricing, detour distance, and charging level. The upper-level model focuses on optimal economic scheduling and calculates locational marginal prices using a power flow trace method. Meanwhile, the lower-level model represents CS price adjustments as a noncooperative game, solved via a greedy algorithm. To validate this pricing methodology, an integrated traffic and power distribution network testbed based on the Dublin area was established. Results demonstrate that the proposed dynamic price of the game (DPG) significantly enhances the EV charging market environment compared to traditional time-of-use tariffs or flat rates. Notably, the DPG improves the profitability and service ratio of CSs located near wind farms, with daily profits for these stations increasing by an average of 17.55% and 17.03% compared to the other pricing mechanisms. Furthermore, the average daily utilization rate of these CSs rose by 7.08% and 6.42%. In terms of promoting renewable energy use and alleviating traffic congestion, the DPG also outperforms the other pricing strategies by effectively adjusting charging prices to influence EV drivers’ charging behavior. This dynamic pricing strategy is poised to be widely applicable in future integrated transportation and power systems with high levels of renewable energy penetration.