Research On Optimization Of Urban Electric Vehicle Charg-Ing Scheduling Strategy

As an important means to cope with energy crisis and environmental pollution,electric vehicles have become the focus of research at home and abroad by virtue of their high energy efficiency and low emissions,and have been gradually promoted in countries around the world.However,disordered charging of large-scale electric vehicles poses a threat to the safe operation of the power grid.Therefore,studying the optimal scheduling strategy for electric vehicle charging is of great significance for the coordinated development of electric vehicles and power grids.Firstly,the US Department of Transportation's statistics on ordinary household cars is used to study the charging power demand of electric vehicles,fully considering the influenc-ing factors such as user charging mode and battery charging characteristics,and analyzing parameters such as user charging time,daily driving mileage and charging duration,The charging power requirements of single and multiple electric vehicles were simulated by Mon-te Carlo method.The results show that there are obvious peak-to-valley characteristics for the electric vehicles under disordered charging conditions.Secondly,in order to reduce the ad-verse effects of load peak-to-valley difference on the grid,an ordered charging scheduling strategy based on peak-to-valley time-sharing electricity price is proposed.According to the fluctuation characteristics of typical daily load curve,the whole day is divided into peak-to-valley time by the strategy,and the response behavior of users under the charging scheduling strategy is analyzed.The results show that the grid load peak-to-valley difference after implementing the scheduling strategy is lower than that under disordered charging con-ditions.Finally,in order to better play the role of scheduling strategy,in this paper,the opti-mization of peak-to-valley time is continued to study,so the optimization model of ordered charging scheduling strategy is established,and the best effect of smoothing the charging load fluctuation of electric vehicle is taken as the objective function.Based on genetic algorithm and simulated annealing algorithm,an improved genetic annealing algorithm is proposed to optimize the model.Compared with the traditional algorithm,a more reasonable peak-to-valley period is obtained,which effectively reduces the peak-to-valley difference.The simulation shows that the implementation of the ordered charging scheduling strat-egy is useful for reducing the peak-to-valley difference for the grid load.At the same time,the proposed genetic annealing algorithm has better convergence effect in the model optimization.In this paper,the experimental results provide a theoretical basis for the development of an orderly charging scheduling strategy for the grid.