Studies On The Impacts Of Electric Vehicles Charging To The Power System And Coordinated Charging

With the sustained social and economic development, people’s demand for energy is increasing. Under this background, governments and car companies consider electric vehicle is the important direction for future development of the automobile because its high energy efficiency, less pollution, low noise advantages, and it is one of the important means to achieve energy saving, meanwhile some countries put a lot of manpower, material in the development of electric vehicles and made a series of research results.In this paper, first of all, establishing the model of electric vehicle charging power demand, the survey results of the U. S. Department of transportation on the national household travel survey are used as the driving characteristics of the electric vehicle users, we get the user to start the charging time, day travel mileage, charging time and other key parameters. After setting the parameters of the user charging mode, the battery charging characteristics and other parameters, the charging power demand model of single and multiple electric vehicles are simulated by Monte Carlo method, this model can reflect the charging demand of electric vehicle users in different time.Secondly, the effect of the large number electric vehicle charging on the distribution network is analyzed. Taking the IEEE33 node system as an example, quantitative calculation the voltage offsets and network losses caused by random access of electric vehicle in distribution system. The results show that, when the penetration rate of electric vehicles is larger, the influence of the electric vehicle load on the voltage deviation and the network loss is greater; when the permeability is constant, the influence of the charge load on the voltage offset is related to the location of the access node.At last, an ordered charge control strategy based on TOU price and an ordered charge control strategy based on the regional price are established. The former optimized peak and valley periods first, and the response of the user to the TOU price is analyzed through the change rate of the power consumption which is predicted by the price elasticity matrix. On the grid side, take the standard deviation of the daily load curve as the control objective and the optimization is carried out by genetic algorithm, guide the user to select the valley period; the latter through the regional price to guide the user to select a low load density area for charging, and the method of spatial load forecasting is used to partition a city of Zhejiang.