Research On Charge And Discharge Control Discharge Control Strategy Of Electric Vehicles In V2G Mode

The severe environmental pollution and the reduction of fossil energy are accelerating the development of new energy vehicles.As a green travel tool subsidized by policies,the penetration rate of new energy vehicles is getting higher and higher,and their charging behavior is very strong in time and space.Uncertainty,so many electric vehicles(EVs)without charging also bring serious challenges to the traditional distribution network,such as increased load levels,decreased power quality and increased network losses.V2G(Vehicles-to-Grid)technology is an integrated application that combines power electronics,communication and metering technologies to achieve bidirectional transmission of electrical energy.In the V2G mode,the charging behavior of electric vehicles is analyzed,the EVs load distribution is optimized through electricity price incentives,and the negative effects of EVs load are mitigated.At the same time,the EVs energy storage advantages can balance the insufficient output of distributed power sources,and promote the better integration of smart vehicles into the future in the grid.In this context,relevant research work has been done on the EVs charging strategy,as follows:(1)Taking the private electric vehicle as the research object,the traditional vehicle driving data is used to replace the electric vehicle driving law.The Monte Carlo simulation method is used to obtain the charging power distribution by probability sampling simulation.The results are analyzed without any control measures.The peak load period coincides with the daily peak load of residents,which is not conducive to the stable operation of the distribution network and causes severe load fluctuations.(2)Proposed an orderly charging strategy based on demand response for peak-to-valley time-sharing electricity price,using the fuzzy principle to divide the peak-to-valley period,analyzing the demand price elasticity matrix,and expounding the relationship between demand and price,which can be predicted by the demand price elasticity coefficient.After the change of electricity price,the change of load demand in each period of response,the minimum standard deviation of load is taken as the charging control target of electric vehicle,and the optimal result is obtained by genetic algorithm.Simulation studies show that the charging control method proposed in this chapter can significantly reduce the peak-to-valley difference and make the power supply of the power grid more continuous and reliable.(3)The micro-grid optimization scheduling problem is studied,the mathematical principles of each distributed generation unit are analyzed,and multi-target operation targets are proposed.Different charging scenarios are set according to whether the electric vehicle is discharged,and NSGA-II is used to solve the optimization result,and according to the selection principle.The Pareto frontier solution selects the global optimal solution.Comparing the optimization results to the electric vehicle discharge can reduce the micro-grid energy storage construction cost,and can better optimize the distributed power output,alleviate the shortage of power during the peak power consumption period,and reduce the system.The load fluctuates,and at the same time,the owner can gain revenue through discharge,and realize the overall optimized operation of the V2G system.