Research On Pricing Strategy Of Electric Vehicle Charging Service Fee Based On Demand Response

It is one of the important methods to improve the economic and stable operation of electric power system to construct the guided flexible load resources of electric vehicles and tap the potential of demand side response fully.Based on the analysis of the charging behavior rule and decision-making behavior mechanism of electric vehicle users,this thesis proposes a time of use charging service fee formulation strategy under demand response.Guiding users to charge in an orderly way through price signals can not only reduce users’ charging costs,but also improve the operation efficiency of power grid assets and the income of charging station,meanwhile,promoting the good interactions among "electric vehicle-power grid-charging station".The specific research contents are as follows:(1)Based on the charging records of charging stations,the boundary-corrected adaptive bandwidth Gaussian kernel density function is used to build the probability distribution model of charging start moment,charging end moment and state of charge(SOC)at charging start moment,and the goodness of model’s fit is tested.Using Monte Carlo method to simulate the daily load curve of electric vehicle under the disordered charging behavior.(2)Based on the annual load of a region,select the typical daily load in winter and summer as the basis of peak-valley periods division,using the fuzzy membership function method,aggregation method and fuzzy C-means clustering algorithm to divide the peak-valley periods of typical daily load in winter and summer respectively and verify the rationality of the division methods.(3)Users are classified based on their charging preferences,and comprehensive satisfaction models of different types of users are built.The cumulative prospect theory(CPT)is introduced to characterize the user’s value perception at different charging moments,and the selection probability model of charging moment is built based on the principle of utility maximization.Built the economic models of peak shaving demand response by analyzing the cost and benefit composition of power grid operators,users and charging station operators.Built the multi-objective function models with the objective of minimizing the fluctuation of load,minimizing the charging cost of users and maximizing the net income of charging station,using the improved multi-objective particle swarm optimization and sequential quadratic programming to solve the model.(4)Compared the charging service fee schemes under different base loads and different proportions of users’ types,the effectiveness and rationality of the charging service fee pricing strategy this thesis proposed are verified through economic analysis.Based on a certain charging service fee scheme,the cost and benefit of power grid,users and charging station are compared under the change of proportions of users’ types,and obtained the minimum compensation price that ensure the minimum net income of the charging station is greater than the original net income under any proportion of users’ types.By using the net income of the charging station as an intermediate variable,analyzed the relationship between the compensation price and the peak shaving response rate when users do not participate in the distribution of compensation income.