Research On Optimal Charging And Discharging Scheduling Strategy For Electric Vehicle In Battery-swap Station

As one of the maior consumers of fossil fuels,traditional fuel vehicles are one of the main causes of global warming,air pollution and energy depletion.How to reduce environmental damage and ecological environmental protection has become an important issue for all countries.Compared with conventional cars,electric cars as a kind of zero emission vehicles,there are many traditional cars do not have the advantage in terms of green environmental protection,is an effective means to solve the problem of environmental pollution,has the potential of sustainable development,but the electric vehicle power supply and disorderly behavior will be stable operation of power grid has brought a series of new problems.Electric vehicle charging station as an important place for electric vehicle power supply,power supply in the required time,the battery has distributed cluster scheduling,vehicle charging does not have the advantage,therefore the optimal dispatching mode of electric vehicle charging station is studied,designed to reduce the electric vehicle power supply adverse effects the behavior of the power grid.First of all,from the day before the scheduling level,give full consideration to the electric vehicle users demand uncertainty,the risk assessment in the field of VAR,CVAR introduced the theory of scheduling model,the risk for describing user demand fluctuations,a day before the risk dispatching model under different confidence beta user needs VAR,based on CVAR value.The particle swarm algorithm,the MATLAB simulation results show that the load "peak" in addition to the cost minimization scheduling scheduling strategy,and shows the different confidence level under the influence of selection on beta,plant scheduling user demand risk values,changes in cost and power grid scheduling the load curve superposition.Secondly,from the aspect of real-time scheduling,based on the day before the scheduling model and the prediction error before the date of real-time scheduling model.In the days before the scheduling model,the user needs to get data for the power plant general prediction methods formulated before the schedule;in the real-time scheduling model for power plants,for correcting the subsequent prediction error prediction according to actual demand data in each period,rolling scheduling power adiustment based on follow-up period before the date of dispatch plan on in response to user demand fluctuation trend.The particle swarm algorithm in the MATLAB simulation results show that this scheduling strategy weakens the importance of forecasting the accurate data,which inhibit the volatility of theexchange regulation of the needs of users,to achieve real-time dynamic scheduling of the station battery,and both the user benefits and optimal operation for power grid and income.