Study On Control Strategy For Electric Vehicle Ordered Charging Based On Improved Genetic Algorithm

The application of electric vehicles(EVs) reduces the consumption of fossil fuel, electricity costs and the emissions of greenhouse gas, thus it has received the widespread attention. Large-scale electric vehicle randomly access grid may further increase the difference between peak and valley of the grid and load fluctuations, even more surpass the power supply capability of distribution network and bear ability, affect the stable operation of the power grid, at the same time it can make the high charging cost, thus hinder the development of electric vehicles industrialization. So by studying the optimization scheduling method of electric vehicle charging, to reduce the negative impact of the load of power grid, it is of great significance.This paper has conducted the thorough research around the strategy of the electric car ordered charging responded by real-time electricity price, the main research work is as follows:Firstly, this paper expounds the background of this research and significance of this article. To establish the probability distribution model of electric vehicle start charging time and Initial SOC, then deduced the calculation method of electric vehicle charging duration, and adapt the Monte Carlo to simulate the condition of disordered charging and the effect on the grid.Secondly, this paper puts forward an improved genetic algorithm, the orthogonal crossover operator is used in population initialization and crossover operation to make the individual distribution be more homogeneous, solve the problem of standard genetic algorithm about slow operation speed, difficult to get the local optimal solution. Through experiment simulation the standard genetic algorithm and improved genetic algorithm are compared, verified the aspects of convergence performance, such as the number of iterations, computing time, convergence rate and so on, The experimental results show that: the improved genetic algorithm could do better at global optimization, solved the problem of “premature convergence” and computation speed has a lot of improvement at the same time.Thirdly, this paper sets up a vehicle charging model, this model achieved the purpose of order scheduling through real-time electricity price, the model used minimum charging fees and difference between peak and valley as objective function, constraint conditions are load fluctuation, power limitation, and adopted an improved genetic algorithm for optimization. Through the simulation results can be concluded that compared to the disordered charging, ordered charging has a big progress in smoothing power grid load fluctuation, reducing the grid peak valley and reducing charging fees; at the same time, compared the improved genetic algorithm with standard genetic algorithm, the improved genetic algorithm has further optimization in speeding up the convergence speed, reducing the difference between peak and valley, steadying load fluctuation and reducing charging fees.The simulation results prove that the charging model and the solving algorithm used in this article are reliable and efficient,realize the optimal charging strategy, reduce the harmful effects of large-scale electric vehicles connected to the electricity grid.