Research On Optimization Of Time-of-use Electricity Price Scheme For Electric Vehicles Under The Background Of Power Refor

Due to the increasing awareness of environmental energy use,people not only demand the use of wind power,photovoltaic and other low-carbon and environmentally friendly power generation methods in electricity production,but also demand the use of cleaner ways of electricity consumption.Because of its clean,pollution-free,high performance and other advantages,electric vehicles have been widely used and rapid development.However the large increase of electric vehicle ownership has brought serious charging burden to the power grid.The disorderly charging of electric vehicles has intensified the safe and economic operation of the power system.Therefore,it is necessary to conduct orderly charging guidance for electric vehicles to achieve the goal of safe and economic operation.In view of the above situation,from the perspective of EV demand response,this paper guides EV charging load through time-of-use electricity price to achieve the goal of reducing the negative impact on the power system and realizing multi-win for all participants.The main research in this paper is categorised by residential scenario,urban road network scenario and new energy consumption scenario as follows:In the context of residential areas,for the problem that old electric equipment cannot meet the charging demand of electric vehicles,this paper proposes a time-sharing tariff strategy for electric vehicle charging optimization combined with electric vehicle user classification and residential area load reduction model.This paper analyzes the EV state model and constructs a disorderly charging load model.In the analysis of the impact of charging optimization behavior,this paper establishes a time-sharing tariff model by analyzing the least comprehensive tariff sensitivity of EV owners,and considers the impact of residential load-cutting coefficients on EV owners.This paper also uses the weed algorithm to improve the particle swarm algorithm,and the results show that the orderly charging and time-sharing tariff approach using load aggregators can meet the demand of electric vehicles,while using the load reduction model does not significantly affect the satisfaction of electric vehicle owners.In the smart city scenario,a time-sharing tariff model based on user choice is proposed for the problem of spatial and temporal imbalance of EV charging load.The graph theory is used to describe the EV path selection,and the travel chain structure to analyze the possible charging methods of EVs.Finally,the logit discrete choice model is used to build a time-sharing tariff model targeting charging volume fluctuation and load power fluctuation.The experimental results show that the hybrid charging mode is more helpful to balance the charging station load situation,and the time-sharing tariff scheme proposed in this paper can effectively balance the charging volume and load power of the station.In the scenario of new energy consumption,this paper proposes a CVa R-based time-of-use tariff model in view of the shortcomings of tariff studies that do not fully consider the risk appetite of load aggregators and the fluctuating uncertainty of EV loads.Firstly,according to the characteristics of electric vehicles,the charging load curve of multi-day and multi-unit electric vehicles is formed.Then,the probabilistic kmeans clustering algorithm is used to cluster the multiple charging load curves into several typical curves.Finally,under the background of typical wind power,the profit model of load aggregator based on CVa R is established,and the particle swarm optimization algorithm is used to solve the TOU of the model.The main point of this paper is that load aggregators use time-of-use tariffs as a means to direct electric vehicle loads,providing theoretical and methodological support for load aggregators to meet charging demand while pursuing economic benefits.