Dynamic pricing strategy for electric vehicle charging stations to distribute the congestion and maximize the revenue

Abstract

Electric vehicle (EV) charging station (CS) congestion is highly dependent on the EV owner’s behavior and their selected CS as charging choice. A fixed pricing strategy causes some CSs to be congested with EVs waiting in line to charge, while there are some other CSs with available electric plugs, which adversely affects both charging station operator (CSO) revenue and EV users’ welfare. To solve this problem, this paper presents a dynamic pricing strategy aimed at conducting EVs from congested CSs to the uncongested ones through controlling the charging prices of CSs at different times. The problem is formulated as a scenario-based stochastic optimization with the objective of maximizing overall revenue of the CSO. Moreover, an attraction function model is developed to quantify the charging choice of the EV owners by considering the effective parameters of CSs in EV charging choice decisions. QGIS software is used in this work to formulate a realistic modeling for CS locations and EV routes to calculate the distances from EVs to CSs. Three scenarios are designed to evaluate the performance of the proposed framework and to compare the results with the fixed pricing approach. The results indicate that the proposed dynamic pricing strategy mitigates the congestion of CSs while facilitating an increased number of charged EVs up to 48% as well as increasing the overall revenue of the CSO.

Keywords

• Charging station pricing
• Dynamic pricing strategy
• Electric vehicles charging
• Scenario-based stochastic optimization