| Driven by the support of social development and national industrial policies,China’s electric vehicle industry and the number of vehicles has increased rapidly,the trend of replacing traditional vehicles is obvious.However,as a new type of power load,electric vehicles have certain randomness in the sequence and space distribution of access.Before the corresponding scheduling optimization,large-scale electric vehicles will randomly connect to the power grid.The overlapping load leads to the occurrence of "peaks and peaks",which has a great test of the smooth operation of the power grid.This dissertation base on the large-scale electric vehicles random connect to the power grid.The effects of the load generated after accessing the power grid and its V2G(VEHICLE To GRID,twoway interaction)behavior optimization strategy of the power grid.The main content is as follows:(1)Based on the statistical results of NATIONAL HOUSEHOLD Travel Survey(NHTS),establish an electric vehicle charging model.Under the premise of considering the charging access time of electric vehicles and the charging scale,analyze the user’s charging behavior characteristics,and use the Monte Carlo Method to simulate the charging load and access time of electric vehicles at different sizes,and will get it and get it.The load results are overlay in the load of the original power grid,and the total load curve of the grid is obtained.The simulation results show that electric vehicle accessing power grid charging has increased the peak load of the grid,expanding the difference between the load peak valley,and the increase in the number of access.(2)Discuss the impact of time-sharing electricity prices on the load of the power grid.Considering the application of time-sharing electricity prices to guide users to change the charging behavior and alleviate the load pressure.Combined with V2 G technology to reduce the peak difference rate and reduce the electricity cost of electric vehicle users,optimize the charging and discharge power of each vehicle within an hour,and establish an orderly charging discharge model of electric vehicles.On the basis of time-sharing electricity prices,further reduce the operating cost of the overall grid and improve the stability and economy of the power grid operation.(3)Aiming at the characteristics of multi-purpose,multi-constraints,and multidimensional levels of the charging model,the gray wolf optimization algorithm was selected.Secondly,for the limitations and actual optimization needs of the gray wolf optimization algorithm in the model,the target form and adaptation formula of the gray wolf optimization algorithm is adjusted,and the problem of multi-target optimization is transformed into a single target optimization problem,which improves the efficiency of solution;introduce the penalty function to improve the convergence and accuracy of the gray wolf optimization algorithm,so that the optimization iteration of the algorithm is compatible with the V2 G process.(4)Use the improved gray wolf optimization algorithm to simulate the large-scale electric vehicle accessing the power grid for V2 G behavior,and verify that under the IEEE33 node,1,000 electric vehicles at 20%,40%,and 60%of users participate in V2 G regulation Can the following meet the requirements of inhibiting the fluctuation of the load of the power grid and reducing user charging costs.The simulation results show that using the improved gray wolf optimization algorithm to optimize the large-scale electric vehicle V2 G optimization can inhibit the fluctuation of the load of the power grid.At the same time,the time-sharing electricity price also reduces the user’s charging costs.Effective effects of enterprises and users.Figure [32] table [10] reference [81]... |
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