Electric vehicle charging station load forecasting method

Abstract

An electric vehicle charging station load forecasting method is divided into a simplified method and a dynamic simulation method. The simplifying method comprises the following steps of counting a vehicle entering a station flow in a typical set time interval in a day by a historical statistics data so that a section curve description formula of an electric vehicle entering the station flow curve is obtained; solving the number of the vehicles entering in the station and being charged in the interval [t-TC, t]; and calculating active power at any time. The dynamic simulation method comprises the following steps of describing charging time by a normal distribution probability density function; performing counting and curve fitting to historical charging time to get a mean value and a standard deviation; and obtaining the whole number of the vehicles being charged and the charging power of the vehicles at every time in a day so that overall charging power of the charging station is calculated. The method is simple in arithmetic, definite in a data interface, fast in operation speed and capable of supporting dynamic interactive simulation of the electric vehicles in a large scale so that time and space distribution of charging load of the electric vehicles can be forecasted and the foundation method can be offered for studying influence on an electric power system by the charging load.

Description

technical field [0001] The invention relates to a method for predicting the load of an electric vehicle charging station. Background technique [0002] Many problems of large-scale charging network have attracted more and more attention, and the prediction of charging load has become an important topic. If a prediction algorithm can be proposed to accurately calculate the load of charging stations, then based on the distribution of charging stations, it is easy to calculate the load of the entire charging network. [0003] Since the load change of a single electric vehicle charging station or charging equipment cluster is affected by various factors such as specific traffic flow, battery charging time, battery initial state of charge (SOC), and the number of charging equipment, the changing law is complex. The shape of the load curve of each charging station is very different, so parameter identification methods such as segmental curve fitting can fit the load curve of a sp...

AI Technical Summary

Problems solved by technology

[0003] Since the load change of a single electric vehicle charging station or charging equipment cluster is affected by various factors such as specific traffic flow, battery charging time, battery initial state of charge (SOC), and the number of charging equipment, the changing law is complex. The shape of the load curve of each charging station is very different, so parameter identification methods such as segmental curve fitting can fit the load curve of a specific substation under specific circumstances, but the curve may change greatly after the conditions change , resulting in serious deviations from the description

[0004] At present, there is no literature that proposes a clear prediction method to calculate the load of charging stations.

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