Harmonic Assessment And Prediction Of Resident Loads Including Electric Vehicles

With the improvement of the quality of life of Chinese residents,annual household electricity consumption increase steadily.In addition to the large-scale development of private electric vehicles in China,if large-scale electric vehicles are charged during the peak period of power grid,the "peak to peak load"phenomenon will be caused and the stable operation of power grid will be affected.Electric vehicle and residential load belong to non-linear load,and have high harmonic distortion rate.The power grid will be affected by the harmonic components and the quality of power supply will be reduced.Therefore,the harmonic model of residential load including EV is studied and users are guided to adopt approprnate charging strategies in order to improve the system power quality,smooth the system load,reduce network loss,improve the operation economy of distribution network,and reduce the impact of large-scale EV disorderly charging on the system.improving the power quality of the system.In this paper,a harmonic coupled dominant component electrical model and a behavioral model reflecting the stochastic operation state of load of various residential loads including electric vehicles are established based on the measured data.A harmonic load evaluation platform is established,by which the harmonic situation of single household,multiple households and regional load can be predicted.The main contents of this paper are as follows:(1)In order to determine the real-time variation of power and harmonics of each load,the status of harmonic distortion in distribution network is analysed based on measured data.The parameters of harmonic coupling dominant component model for each residential load are calculated.According to the prior distribution of each voltage,a harmonic coupled dominant component model is established,which can consider the influence of distribution network voltage variation during power consumption and the effect of SOC of Electric Vehicles on power consumption data.(2)Resident load behavior model is divided into switch probability models of typical household appliances,multi-state appliances and electric vehicles,according to the difference of parameters of different types of load random models.The stochastic model of typical household appliances is determined by opening time and using time.The stochastic model of multi-state electrical appliances is determined by the opening time and operation state at the opening time,the state transition time and the state after the transition.The charging time of EV is determined by the initial charging time and the charging time,among which the charging time is determined by the battery capacity,charging power,the daily driving distance of EV,the remaining charging state of battery and other factors.The correction coefficient and influence factor are introduced by considering all factors affecting residential electricity consumption in China,including the characteristics of members(effective number,age,sex)and residential area,real-time electricity price and economic level,season and weather.A behavioral model of electricity consumption suitable for the characteristics of civil load is established.(3)The power forecasting and harmonic evaluation platform of residential load is established by combining the electrical and behavioral models of each load.The idea of bottom-up hierarchical modeling is adopted by using Markov Chain Monte Carlo method.The accuracy and applicability of the platform are verified from two aspects:electrical model and behavioral model.The power consumption and harmonics of single household,multi-household and regional loads are predicted through this platform.The aggregation and dispersion effects between loads are analyzed.The impact of electric vehicle access on residential load is assessed.The charging time of electric vehicles is optimized,aiming at the minimum load fluctuation of distribution network and the minimum electricity cost of users,respectively.Demand response strategy is implemented through three control measures:time-sharing price guidance,interruptible load control and direct load control.The validity of the model is verified.