| With the implementation of environmental protection policies such as energy conservation and emission reduction by the Chinese government,electric vehicles(EVs)have developed rapidly in metropolis with their advantages of high efficiency,energy saving and eco-friendly.For vehicles with a much longer driving time than parking time,the fast charging station(FCS)is an important way to supplement the electric energy.However,due to the limited land resources of the city,the FCSs in some areas cannot fully meet the charging requirements of large-scale EVs during peak charging hours.Reasonable charging guidance can play the potential response of EVs,solve the contradiction between the excessive charging demand in partial areas and the limited CS facilities,release the fast charging resources in the central of the city,and improve the CS operation efficiency while reducing the waiting time of EV owners,so as to realize the spatial transfer of load.In terms of charging navigation,most of the researches have been conducted on the charging guidance of a single vehicle,and it is difficult to take into account the charging effect between large-scale EVs.In the stationvehicle collaborative scheduling,the time-of-use price strategy is adopted to alleviate the peak of charging,but there is less research on the load transfer between CSs.In order to solve this problem,this dissertation takes the urban road network as the research scene,draws on the relevant theories in traffic simulation,considers the multivehicle charging interaction,and proposes the dynamic response strategy of EVs and urban FCSs.The main contribution of this paper could be summarized as:(1)A dynamic road traffic simulation method for urban road network considering vehicle routing is proposed.Combined link transmission-graph model,a dynamic travel time model is established to simulate the driving characteristics of EVs in the road network.On this basis,considering the impact of travel time on traffic flow distribution,a dynamic path selection model of vehicle is proposed to analyze the impact of realtime road conditions on vehicle owner path decision.(2)The basic functions of the EV fast charge guidance system are briefly described.Considering charging distance,time and economy,a charging navigation model for an EV is established.Based on the evolutionary game theory,this paper analyzes the influence of multi-vehicle interaction on charging,and proposes a time-order evolution decision method for EV charging selection.The influence of the charging navigation method proposed in this dissertation on user path and charging selection is analyzed in urban road network case.The case results show that the proposed method can improve the travel efficiency of EV owners,and the charging time of EV owners is significantly reduced,meeting the needs of multi-vehicle charging requirement.(3)A station-vehicle cooperative scheduling method considering dynamic response is proposed.The rolling optimization method is used to optimize the queuing time of the charging station,and the particle swarm optimization algorithm is used to solve the optimization model.The dynamic demand response capability of EV owners is analyzed.On this basis,the station-vehicle interaction strategy based on stackelberg game is proposed.The case results are analyzed from the queuing time of the CS and the interaction of the station-vehicle,the results show that the congestion in the station is effectively controlled after the optimization of the queuing time of the CS.At the same time,the charging service fee incentive method has a significant impact on the charging choice of EV owners.In the process of pricing,the potential response of EV owners is fully explored,and peak-cut of the load in space is realized. |
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