Research On V2G Control Method Based On Load Aggregation

With the development of social industrialization,the problem of global environmental pollution and resource shortage has become increasingly prominent.As an emerging vehicle with non-pollution and low energy consumption,electric vehicle has developed rapidly,and its sales volume has shown a leap growth in recent years.However,with the wide use of electric vehicle,the randomness,indirectness of electric vehicle unordered charging brings many burden to the stable operation of the power grid,such as increasing peak valley difference,voltage deviation,harmonic pollution and damaging the service life of other equipment.Therefore,it is particularly important to optimize the management and control of charging process of electric vehicle to reduce the impact of charging load.This research mainly focuses on private electric vehicle,analyzes their charging behavior characteristics,and puts forward reasonable charging and discharging scheduling methods.The main works in this thesis as follows:First of all,the factors affecting EV charging behavior are analyzed to study the impact of EV charging load on the power grid.The influence of user’s usage characteristics,charging modes,battery characteristics and scale of electric vehicles on charging load is analyzed,and the Monte Carlo method is used to establish the calculation model of disordered charging load of electric vehicles.The example proves that large-scale electric vehicle disorderly charging will add a large amount of charging load in the peak period and increase the peak valley difference rate,and with the expansion of disordered charging scale,the peak-valley drop will become larger.Secondly,load aggregator,as energy regulation mechanism,participates in energy regulation of the electric power market on behalf of electric vehicles.In order to reduce the impact of large-scale charging load on the power grid,load aggregator establishes a linear programming orderly charging scheduling optimization model.In this optimization model,constraints such as the optimization cycle length of load aggregator,user charging demand information and charging power are considered comprehensively,and linear solution is obtained by using LP＿SOLVE.The example shows that the orderly charging scheduling of electric vehicles can not only reduce the total charging cost of load aggregator,but also shift the charging load to achieve the adjustment of peak load clipping and valley filling.Finally,in order to deepen the energy interaction between electric vehicles and the power grid,load aggregator uses V2 G technology to implement energy interaction between electric vehicle and the power grid,so as to reduce the power supply pressure of the power grid in the peak period.In order to fully consider the economic benefits of all parties in the process of energy interaction,a charging and discharging optimization scheduling model is established base on the minimization of battery aging loss cost,network loss cost and user charge cost.The optimization calculation is carried out by using convex optimization interior point method.The example shows that the optimal charging and discharging scheduling of electric vehicles through load aggregators can reduce the charging cost of each electric vehicle,reduce the loss of distribution network,and relieve the power supply pressure of the power grid in the peak period.