Research And Implementation Of Charging Station Service Pricing Mechanism For Smart City With Hierarchical Game

In recent years,with the popularization of fast charging technology,the electric vehicle(EV)industry has developed rapidly,which has promoted the widespread deployment of electric vehicle charging stations(CSs)in smart cities.In the EV charging market,the pricing of charging station services can have a large impact on the market’s revenue.For a CS manager,it is essential to coordinate the CSs’ pricing to increase the market’s overall revenue in the citywide environment.Therefore,it is of great practical significance to study the charging station services’ pricing mechanism in smart cities.The EV charging market’s revenue depends on CSs’ pricing and EVs’ charging decisions,and there exist interactions among them,which can be summarized as the charging station-charging station(CS-CS)game,the charging station-electric vehicle(CS-EV)game,and the electric vehicle-electric vehicle(EV-EV)game.As these three complex games should be considered at the same time when studying the charging market’s revenue optimization,there is a huge challenge.Existing related work still has some deficiencies in the study of charging station service pricing mechanisms: in terms of modeling,most of the existing work focuses on the game between CSs or EVs,neglecting the three complex games existing among them.Therefore,the constructed model is relatively simple;In terms of solution,most of the existing works use traditional iterative algorithms to solve CSs’ pricing strategy,and rarely prove the optimality or local optimality of the algorithm.To study the complex games and difficult pricing optimization problem in the EV charging market,this thesis models the EV-charging and CS-pricing problems as a hierarchical Stackelberg game and design a segmentation-based pricing algorithm to solve the model.Additionally,the validity of the model and the optimality of the algorithm are also verified through theoretical analysis and experiments.The main contributions are as follows:(1)This thesis proposes a hierarchical model for charging station service pricing.This thesis simultaneously considers three complex games among EVs and CSs.Specifically,this thesis defines EVs’ charging cost optimization and the charging market’s revenue optimization.Subsequently,this thesis analyzes the convergence of the problems and apply a smooth algorithm to solve the problem.The validity of the model and algorithm is verified by experiments.(2)This thesis proposes a Segmentation-Based Pricing with Iterative Optimization algorithm(SPITER).Specifically,this thesis defines a series of non-convex sub-problems for the charging market’s revenue optimization,and then design an optimal algorithm to solve the sub-problems based on the idea of segmentation,and this thesis also proves the algorithm’s optimality.Subsequently,by iteratively solving each sub-problem,the local optimal pricing of each CS can be obtained.The experimental results show that the algorithm can improve the charging market’s revenue.(3)This thesis designs and implements an urban CS service pricing simulation system.The offline part of the system is responsible for managing the data and solving the model,and the online part of the system is responsible for the display of the pricing of different CS services in the smart city.Finally,the dynamic of pricing is verified through system dynamic simulation testing.To sum up,the model and pricing mechanism designed in this paper can effectively improve the EV charging market’s revenue.The results of this paper can help CS managers make better pricing decisions and increase revenues,which can be further applied to the pricing of other urban-sharing facilities.