Research On Optimization Strategy Of Orderly Charging Management Of Electric Vehicle

The advantages of energy saving and emission reduction,intelligence and driving quality make the development of Electric Vehicle(EV)promising.The state’s attention to it and the proportion of related investment also aggravate the rapid development of electric vehicles.Its convenience and lower cost has also become the preferred choice of more and more people when traveling.But then comes the problem of EV charging.More EV charging will have an impact on the power grid,but expanding the infrastructure is not immediately effective and costly,so charging management for EV is a quick and cost effective way.It is of great significance to study the optimization strategy of orderly charging management for electric vehicles.Firstly,the role and participation of user demand response and tariffs in the charging market are introduced to analyze user charging characteristics.Different orderly charging management modes are described.Based on the disordered charging model of electric vehicles,it is found that the enthusiasm of electric vehicle users to implement charging management strategies is affected by user demand,time and cost.On this basis,an orderly charging management model based on time-of-use tariff is established and optimized using algorithmic simulation.The tariff can guide electric vehicle users to make conscious charging choices and reduce the impact on the grid.Secondly,an orderly charging management strategy for electric vehicle path selection is proposed.Based on the user’s urgent psychology of charging and considering the joint influence of EV driving time,queuing time and charging time,the user’s sensitivity to time is studied,and a three-way orderly charging management model of user,traffic road and charging station is established.The Dijkstra algorithm is used to optimize the solution and determine the proximity charging mode with the shortest total charging time as the goal,verifying that the user’s charging time selection can effectively regulate the grid fluctuation and avoid the load burden within the region due to the random charging of electric vehicle users.Finally,based on the above model,load aggregators(LA)are introduced to participate in charge management,and the impact of users’ responsiveness to electricity price and time on the grid is studied.A master-slave game-based charging management model is developed,in which the load aggregator in the upper layer and the EV user in the lower layer are the subjects of the game,with the objective of maximizing revenue in the upper layer and minimizing charging cost in the lower layer,and a linearization process is performed to convert the nonlinear problem into a mixed-integer linear programming problem for solving the algorithm.Under the influence of time-sharing tariff and real-time tariff,the real-time pricing strategy and maximum revenue of the load aggregator for the intra-regional game charging model are studied,as well as the impact of charging choices of different types of quantity of EV users under this mechanism on the pricing of the load aggregator and the charging cost of users,and the role of the capacity size of energy storage equipment on the revenue of the load aggregator is discussed.The proposed model can smooth the load fluctuation of the grid as a whole and the validity of is verified.