Research And Simulation Of Charging Station Real-time Pricing Strategy In V2G

With the growing air pollution and global warming,electric vehicles become the focus of research for new energy vehicles.However,the irregularity of the charging electric vehicles easily leads to the unbalance of power grid and increases power grid management more difficult.In V2G,considering importing the discharging electric vehicles to reduce the overall impact of power gird at the peak hours.How to guide the charging and discharging vehicles to select a reasonable charging station,and how to meet the power grid,charging station and electric vehicles have become the focus of the research.Currently there have been some research about the coordination these roles.But existing research focus mainly on direct scheduling and lack of economic method,such as the pricing,especially the lack of systematic study in V2G scenarios of price setting.To that end,in order to adapt to the future development of a number of possibilities,this paper presents three charging station real-time charging and discharging pricing strategy under three scenarios:1.Charging station load balancing-oriented real time pricing strategy.This pricing strategy aims at large charging stations like gas stations.The stragegy influence customers to select the more favorable charging station for charging and discharging and improve the efficiency and reduce the blocking possibility of charging stations.With the Stackelberg game theory,a game model between users and stations established.Finally,the pricing was computed while solving the game problems.2.Power grid load balancing-oriented real time pricing strategy.This pricing policy aims at small charging station similar with vehicle park.The pricing strategy influence the power of charging and discharging electricity and increase the efficiency of grid and reduce peak-valley rate of charging station.With the Stackelberg game theory,a game model between users and stations established.Finally,the pricing was computed while solving the game problems by backward induction.3.Real-time pricing strategy based on user behavior forecart.This pricing policy aimed at the scenario without specific charging will of each user.The strategy influnce the charge/discharge time of overall user to achieve the purpose of reducing the peak to valley rate of charging station grid demand load.The problems were solved by genetic algorithms and iteration using price elasticity of demand.Simulation result of three methods in the corresponding scene show that those real time pricing strategies can effectively balance the load of charing station and power grid,reduce the peak to valley/average rate and improve the overall social benefits.