Research On Energy Distribution And Charging Optimization Of EVs In Vehicle Energy Network Based On V2G

The Vehicle Energy Network based on V2G(Vehicle-to-Grid)technologies is a new type of energy network that integrates electric vehicles,power grids,cloud computing and edge computing.Electric vehicles(EVs)are used as energy carriers to participate in energy scheduling.The central server performs intelligent charging and discharging managements on both the grid and EVs to optimize energy utilization,load balancing,revenue costs of grid,mitigate grid overload and congestion caused by the rapid growth of number of EVs.At present,due to the limited batteries of EVs and the grid,the uncoordinated travel of a large number of electric vehicles will cause traffic jams and high travel costs.Unbalanced load in different charging stations result in low utilization of renewable energy.Besides,due to the limited batteries and power fluctuations of renewable energy power plants,a large amount of renewable energy has to be discarded.Existing related works about scheduling strategies of EVs less consider charging stations with removable batteries and less pay attention to the actual limitations of EVs when they participating in energy transmission.In this paper,in terms of optimizing the transmission efficiency of renewable energy and the revenue of EVs,several practical limitations are considered and studied in the vehicle energy network: First,we propose a joint optimization of storage of grid and route scheduling of EVs which combined with LSTM to improve the transmission efficiency of renewable energy,reduce abandoned rate of renewable energy.Secondly,Second,we consider the practical limitations of charging stations and EVs and optimize the overall revenue of EVs participating in the scheduling of renewable energy to improve the utilization of renewable energy in charging stations.The main contributions of this paper are as follows:(1)Taking EVs and batteries of charging stations as the specific scheduling objects,we constructed the Time-Expanded Network which considered the time variability of traffic flow and the limited but schedulable batteries of charging station.The goal of improving the transmission rate of renewable energy is modeled as large-scale-integer programming problem which is going to be solved by maximum-flow algorithm and the integer programming algorithm respectively.Then,the method we proposed is extended to be used in the scenario where the traffic network information is incomplete.Finally,a simulation was conducted on the public traffic data set(PeMS)which is obtained by California Department of Transportation(Caltrans).(2)Take EVs as scheduling objects,we decompose the behavior of EVs in charging stations into three types,namely “charging”,” selling” and “waiting”.A BehaviorDecomposition based Time-Expanded Network was constructed,which consider the travel time tolerance of EVs,limited charging space and power supply of charging stations.The improvement of the overall revenue of EVs is described as a nonlinear quadratic optimization problem.Then,the problem is converted into integer programming based on the collaborative flow model.Finally,the proposed algorithm was tested on the map around Santa Clara,California,which is also obtained from public traffic data set(PeMS).