Electric vehicle charging load prediction method considering space-time distribution

Abstract

The invention discloses an electric vehicle charging load prediction method considering space-time distribution. The electric vehicle charging load prediction method considering space-time distribution includes the following steps: 1) according to the trip occurrence quantity and the trip attraction of an existing traffic zone, utilizing a growth factor method to calculate the trip occurrence quantity and the trip attraction of a future traffic zone; 2) utilizing a traffic distribution prediction model to calculate traffic volume distribution among each traffic zone in the future; 3) through ashortest path method, covering all the roads, of the road network, with the OD data among all the traffic zones, and calculating the traffic flow on all the trip paths in the road network, and the traffic flow values of all the roads; and 4) inputting the original data of an electric vehicle, based on the traffic flow of each road, performing analog simulation on the trip path of the electric vehicle, and calculating the charging load of the electric vehicle during the charging period to generate a charging load curve of each charging station in the set time quantum in the future so as to provide data support for planning and operating of the charging stations so as to provide basis for space load prediction of the power distribution network.

Description

technical field [0001] The invention relates to a method for predicting electric vehicle charging loads considering time and space distribution, and belongs to the technical field of power grids. Background technique [0002] As a low-carbon and clean means of transportation, electric vehicles have received more and more attention. At present, the industrial development of electric vehicles is mainly restricted by factors such as price, cruising range, and construction of charging facilities. At the same time, the charging load of electric vehicles has uncertain characteristics such as randomness, intermittency, and volatility in time and space, which brings new problems to power grid planning, safe operation, and optimal scheduling. [0003] The existing load forecasting technology usually only focuses on the time-varying characteristics of electric vehicle charging load when analyzing its impact on distribution network load. However, its spatial distribution is rarely co...

AI Technical Summary

Problems solved by technology

At the same time, the charging load of electric vehicles has uncertain characteristics such as randomness, intermittency, and volatility in time and space, which brings new problems to power grid planning, safe operation, and optimal scheduling.

[0003] Existing load forecasting technology usually only focuses on the time-varying characteristics of electric vehicle charging load when analyzing its impact on distribution network load

However, its spatial distribution is rarely considered, and most forecasting methods do not fully consider the influencing factor of traffic flow.

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