Research On Coordinated Optimal Scheduling Strategy Of Electric Vehicle Transform And Wind Farm

Society is progressing,mankind is developing,the world economy is growing by leaps and bounds,and the accompanying global energy problems are intensifying.The exhaustion of traditional fossil energy sources and the country's high requirements for environmental quality have led to rapid development of new energy sources such as electric vehicles and wind power.However,due to the uncertainty of power generation due to the natural changes in wind power,the real-time changes in load and the increasing number of electric vehicles randomly connected to the grid for charging,all bring great challenges to the smooth operation of the power system.In this thesis,the research on the problem of increasingly large-scale gridconnected electric vehicles and wind power generation to participate in power system optimization scheduling is first studied.First,the research is carried out on the load of electric vehicles,from which to find influencing factors,the relationship between electric vehicle charging and discharging and the grid for further analysis.The thesis considers the various types of vehicles produced by the electric vehicle industry and the overall number,as well as the energy sources installed on electric vehicles,and of course the different ways of charging stations set by the grid and the habits of each vehicle owner.On this basis build the corresponding load model and use the traditional Monte Carlo simulation calculation method to solve the model.The results of the calculation example show that the charging of different amounts and types of electric vehicles will cause great interference to the stable operation of the power system.The vehicle usage habits of the vehicle owners indirectly lead to a sharp increase in the peak load of the power grid in a certain period of time,which has a very great impact on the system obvious.Secondly,according to the car habits of car owners,this thesis divides the electricity price by time according to the output of wind power.With the goal of suppressing load fluctuations,reducing car owners' charging costs and improving wind power utilization,combined with the load peak and valley difference index,the impact of electric vehicle load Factors and wind power are used as constraints.The factors include energy source capacity and energy flow balance,and a corresponding coordinated and optimized scheduling model is constructed.The improved adaptive genetic algorithm is used to solve the model.The results show that the time-sharing electricity price system can more effectively reduce the grid load peak-to-valley difference and the charging cost of electric vehicle users and effectively increase the wind power utilization.Finally,with the rapid development of wind power generation in China,this thesis takes the equivalent daily load mean square deviation and the charging cost of electric vehicle users under the constraints of battery capacity,electric vehicle charging and discharging power and 24 hours,etc.To achieve the goal,establish a model of coordinated optimization scheduling strategy that maximizes wind power utilization and electric vehicle transduction.This strategy achieves the goal of "low peak charge and peak discharge" through reasonable control of the charging time of electric vehicles to smooth the load Curve,and at the same time enhance the reliability of the power supply system to maximize the use of wind power.The model uses an adaptive nonlinear genetic algorithm to solve the problem.The simulation results show that the proposed strategy has a very significant effect on improving the reliability of the power system.It also shows that the development of the electric vehicle industry chain has a huge boost for the promotion of maximum wind power utilization.