Research On The Spatial-Temporal Distribution Prediction Of Charging Load And Orderly Charging Strategy

With the ever deepening of the industrialization process,countries’ consumption of fossil fuels such as oil and gas continues to increase,resulting in a serious energy crisis.In addition,the massive emissions of greenhouse gases such as carbon dioxide have exacerbated the process of global warming.In order to deal with the shortage of resources and environmental deterioration,countries began to vigorously develop electric vehicles,China has introduced a series of policies to promote the development and progress of electric vehicle industry.However,large-scale electric vehicle disorderly charging has caused a huge impact on the power grid,affecting the safe and stable operation of the power grid.At the same time,photovoltaic power generation systems and energy storage equipment have been promoted and popularized as related technologies continue to mature.The optical storage charging station has become a diversified application form that supports and coordinates photovoltaic power generation,energy storage configuration,and charging scheduling.Taking electric vehicles as the research object,this thesis studies the spatiotemporal distribution of charging load and the orderly charging strategy of optical storage charging station.Aiming at the temporal and spatial distribution of charging load,this paper constructs a prediction framework under the "vehicle road network" mode.The prediction framework is based on a road traffic model considering the characteristics of the road network.Firstly,the Origin-Destination Matrix(OD matrix)is used to simulate the transfer characteristics of electric vehicles,and the Dijkstra algorithm is used to plan the shortest travel path of electric vehicles.Secondly,a charging model for a single electric vehicle is established,and the charging elements of the electric vehicle are extracted using the Monte Carlo method.Finally,taking a certain urban area as an example,different charging triggering conditions are set for different types of vehicles,and the charging duration of different types of vehicles is calculated,thereby simulating the temporal and spatial distribution of charging loads in different functional areas of the urban area.Aiming at the research of orderly charging strategy of charging station,this thesis proposes the orderly charging arrangement of optical storage charging station considering multiple factors.Firstly,the optical storage and charging station system is constructed,and the photovoltaic output of a typical day is calculated according to the variation of the light intensity of a typical day and the photoelectric conversion relationship.Secondly,according to the private car travel data from National Household Travel Survey,combined with the unit energy consumption model considering temperature and traffic conditions,the charging demand of private cars in residential areas was simulated.Then,according to the changes of peak-valley electricity price and photovoltaic power output,the energy storage scheduling strategy of charging station was proposed,and the minimum peak-valley difference of distribution network load and the minimum electricity purchase cost of charging station were taken as the optimization objectives.The orderly charging model of electric vehicles was established,and the grey Wolf algorithm was used to optimize the solution.Finally,the simulation results show that the scheduling strategy proposed in this paper can effectively reduce the impact brought by large-scale electric vehicles entering the network.At the same time,it can promote the absorption of photovoltaic and improve the economy of the operation of charging stations.