Coordination And Optimization Control Strategy Based On Electric Vehicles In Distribution Network

In recent years,the problem of environmental pollution and energy shortage is more and more serious.With the development of vehicle battery technology and power electronics technology,more and more people begin to regard electric vehicles as an effective way to solve the problems of energy and environment.Electric vehicle is not only a mobile energy storage load,but also has two properties of power and load.However,everything has two sides.As the number of electric vehicles has increased exponentially in recent years,the proportion of electric vehicles in all motor vehicles is also increasing.If such a large scale electric vehicle is charged at randomly,it will make the heavily loaded distribution network worse.Therefore,how to use V2G(Vehicle to Grid)technology to rationally optimize the charge and discharge of large scale electric vehicles in order,as well as make the electric car owners participating in V2 G service obtains economic benefits is the key of the research.As the scale trend of electric vehicles is becoming more and more significant,a large number of electric vehicles charge uncontrolled and randomly will bring great uncertainty to the safe and stable operation of the power grid system,and the participation of electric vehicles in the V2 G auxiliary service is restricted by two different interests of the power grid side and the user side.The power grid side mainly reduce the peak valley difference and increase the load rate,the user side focuses on the maximum economic profit on the premise of reducing the number of battery charge and discharge,so it is unable to obtain the V2 G auxiliary service strategy to meet the requirements of all aspects.And in the local distribution network,electric vehicles how to help to reduce the volatility of distributed power supply and meet the target of the grid side and the user side,also become a hot spot.In this paper,firstly,based on Monte Carlo simulation algorithm,the charging load of different kinds of electric vehicles in different years is predicted,which lays the foundation for the study of charge and discharge control strategy of electric vehicles.Then,the V2 G control model of the grid side and the user side is established,and two different control models are solved by particle swarm optimization algorithm.Finally,in a regional distribution network containing a variety of distributed power sources,a multi target auxiliary service optimization model is established,and the particle swarm optimization algorithm is used to solve the model.The effectiveness of the proposed electric vehicle to participate in the auxiliary service control strategy is verified.The main work of this article is as follows:(1)The size of electric vehicle charging load in Chongqing area is forecasted.First,the main influencing factors of electric vehicle charging load are summarized,and the calculation model of electric vehicle charging power in different time and space is set up according to the driving characteristics and charging rules of different kinds of vehicles.Then the Monte Carlo sampling algorithm is used to predict the charge load of the future different functional areas in Chongqing,and the influence of the charge load on the original load curve of the power grid is calculated and analyzed,which lays a foundation for the following research work.(2)An orderly charging and discharging control model of electric vehicle is set up on the grid side and the user side.In the control strategy of the power grid side,this paper mainly takes the average variance of the load curve of the power grid as the objective function,and takes into account the available time and energy availability of the vehicle.Finally,the particle swarm optimization algorithm is used to solve the model.In the control strategy of the user side,this paper mainly takes the maximum economic benefit obtained by the owner of the V2 G electric vehicle as the objective function,so that it can maximize the participation of the owner and minimize the peak and valley difference of the power grid load on this basis,and also consider many constraints of the electric vehicle.Considering the time-sharing price of power grid,particle swarm optimization algorithm is applied to solve the model.The simulation results show that the V2 G control strategy on the grid side can effectively cut the peak and fill the valley of the power grid load.The control strategy of the user side can bring economic benefits to the owners and reduce the peak and valley difference of the power grid load.(3)A multi-objective auxiliary service optimization model considering grid side and user side in active distribution network is established.First of all,a typical regional distribution network model is built,which contains a variety of distributed power sources.Then,when the optimal control model is established,the different interests of the power grid side and the user side are taken into consideration,and the maximum income of the users participating in the V2 G auxiliary service is maximization while the peak and valley difference of the power grid is minimization.Finally,the particle swarm optimization(PSO)algorithm is used to optimize the model under the constraints of a variety of distributed power supply,available time,available capacity and charge and discharge power.The results show that the participation of electric vehicles in V2 G auxiliary service can not only increase the load rate of the distribution network significantly,bring economic benefits for the owners of electric vehicles,but also effectively stabilize the fluctuation of the distributed power supply and improve the stability of the system.