Abstract
In view of the influence of large-scale electric vehicle access to the distribution network on spatial load prediction, this paper proposes a spatial load prediction method for urban distribution network considering the spatial and temporal distribution of electric vehicle charging load. Firstly, electric vehicles are classified according to charging mode and travel characteristics of various types of vehicles. Secondly, the probability distribution function is fitted to the travel rules of electric vehicles according to the travel survey and statistical data of residents. Then, the model of electric vehicle travel chain is constructed, and the charging load in different regions and different times is calculated by Monte Carlo method. Finally, based on the actual data of a certain area, the predicted spatial load values of different functional communities in one day are obtained, which can provide reference for future urban distribution network planning.
Highlights
Spatial load prediction, known as district load forecasting, was first proposed and perfected by H.L.Willis of the United States in the 1980s[1]
The main methods used in spatial load prediction are trend method, multivariable method, load density index method and land use simulation method
Literature[3,4,5] adopted land use simulation method to realize spatial load prediction; reference[6,7,8] adopted load density index method to realize spatial load prediction, but these methods did not consider the impact of large-scale EVs access on spatial load forecasting results
Summary
The load of EV will be affected by many factors, so its size and spatial distribution characteristics are determined by these factors, such as the performance of EVs, users' habits, charging environment, use conditions and so on. In order to predict the spatial and temporal distribution of EV charging load, the following factors need to be considered: the type of EVs, the starting time of EV journey, the mileage of EVs, the parking time, the charging mode
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