Research On The Impacts Of Large-scale Electric Vehicles Charging Load And Coordinated Charging

With the increasing shortage of fossil energy and the increasing energy demand of people,the development of electric vehicles has been highly valued by governments and automobile enterprises.Electric vehicles are also favored by users because of their clean characteristics,high energy efficiency and low noise.As an important development direction of the future automotive industry.After the number of regional electric vehicles is scaled,disordered charging will definitely cause some certain impact on the distribution network.Therefore,the analysis of the impact of disordered charging and study an efficient optimized scheduling method to achieve the economic and safe operation of the grid.The main research work done in the paper is as follows:(1)The main charging methods of electric vehicles and the charging characteristics of conventional electric vehicle power batteries are analyzed.According to the National Household Travel Survey(NHTS)to simulate the pattern of private car travel as a basis for date,the law of private car travel is used as a basis Data,extract basic data such as the mileage of electric vehicle users on the day,the starting time of charging and the length of charging demand,and finally use the Monte Carlo method to simulate and calculate the load curve for large-scale electric vehicle charging.(2)According to the time and space distribution characteristics of large-scale electric vehicle disordered charging,the IEEE33 node power distribution system is used as an example to calculate the impact of random access of large-scale electric vehicles on the IEEE33 node power distribution system under different penetration rates.Valley difference,network loss,node voltage offset and other indicators.The experimental simulations shows that when the penetration rate of electric vehicle is certain,the disordered charging load affects different nodes of the distribution system differently,and the peak-valley difference is significantly increased,and the network loss is increased.When the penetration level increases,the disordered charging load has a significantly increase in the peak-to-valley difference,voltage offset and network loss of the distribution system.(3)An orderly charging control strategy for electric vehicles considering the regional differences is proposed.The control layer mainly includes the central control area and the regional control area.The central control area obtains the basic load situation and charging demand information of a certain area.The main goal of minimizing the peak-valley difference of the total load of the distribution network is to calculate the best charging guidance load curve suitable for the optimization goal of the region.Finally giving the charging instructions to user.The teaching strategy and nonlinear inertia weight are used to improve the Whale Algorithm,which effectively improves the accuracy and convergence speed of Whale Algorithm.The improved Whale Algorithm is used to solve the distribution network and regional requirements,and the superiority and efficiency of the improved Whale algorithm are verified through simulation.The optimization results show that the strategy can effectively solve the regional difference problem.(4)For the study of the Micro-Grid area containing electric vehicle charging,two control strategies are proposed,with the minimum operating cost of the Micro-Grid and the minimum comprehensive load variance of the grid as the optimization target,to study the situation of electric vehicles participating in optimal scheduling and not participating,the charge and discharge of the energy storage system are optimized and dispatched at the same time.The piecewise mutation strategy is used to improve the Whale Algorithm and the improved Whale Algorithm is used to solve it.The simulation results show that the operating cost and the comprehensive load variance of the Micro-Grid are effectively reduced after optimizing the scheduling of electric vehicle charging.