Charging-Discharging Load And Scheduling Strategy Research Of Electric Vehicles

With oil resource's being depleted and people's growing attention to the environmental problems, governments and automobile manufacturers are now focusing on promoting the development of low carbon economy. Therefore, electric vehicles, as a new means of transportation, will become the leading direction of the future automobile industry's development. The research object is electric vehicles,the charging load of electric vehicles is obtained by Monte Carlo simulation contained Latin hypercube sampling based on analysis of the behavior characteristics of electric vehicles, and the impacts on the grid are analyzed under different charging modes. Then, the optimal scheduling strategy model of electric vehicles charging-battery swapping station is established, and the optimal solution of the objective function is obtained by the particle swarm optimization algorithm. The main work of the paper is as follows:(1) The research status of V2 G technology at home and abroad is introduced, and the problem of electric vehicles to grid is analyzed according to the development of electric vehicles and the status of electric vehicles charging facilities. The different types of electric vehicles' daily mileage, charging-discharging time and charging-discharging modes are analyzed in this paper. Then the mathematical models of behavior characteristics are established.(2) The electric vehicles charging load are calculated by using Latin hypercube sampling contained Monte Carlo simulation instead of the traditional Monte Carlo simulation. Then the expected value and variance with the simple Monte Carlo simulation results are compared; Vehicles arrangement and different charging modes of impacts on power load characteristics are analyzed in detail under different charging modes of electric vehicles. The scheme of unified management of electric vehicles charging and discharging is presented; Due to discrete combinatorial optimization scheduling problem of electric vehicles charging station, the control variables for charging and discharging power of charging-battery swapping station are proposed, the different objective functions of scheduling strategy mathematical model are built.(3) The charging load of electric vehicles is calculated through the experiments which are implemented on Matlab and simulink software. The effectiveness and feasibility of the proposed approach are verified by comparing the two methods; In order to solve the above problems, a self-adaptive particle swarm optimization algorithm is proposed. It analyzes improvement effect on the load curve and economic operation of the charging-battery swapping station and validity and feasibility of the proposed method are verified.