Electric vehicle charging load prediction method based on dynamic energy consumption and user psychology

Abstract

The invention relates to an electric vehicle charging load prediction method based on dynamic energy consumption and user psychology. The method comprises the following steps: setting the number of electric vehicles in a to-be-predicted area and an initial SOC; constructing an electric vehicle travel model and an actual power consumption model; according to the travel model, obtaining the optimal strategy of each electric vehicle through a Markov dynamic path decision model, and simulating the travel process of each electric vehicle according to the corresponding optimal strategy; in the simulation process, for each travel road node of each electric vehicle, the power consumption of the next travel process of the electric vehicle and the current SOC are calculated in advance according to the actual power consumption model and the initial SOC, and the charging demand of the electric vehicle at the current travel road node is determined. And accumulating the charging demands of all the electric vehicles at each travel road node, and obtaining the space-time distribution of the charging demands of the electric vehicles in the to-be-predicted area. Compared with the prior art, the method has the advantages of high accuracy, high reliability and the like.

Description

technical field [0001] The invention relates to the field of power systems, in particular to a method for predicting electric vehicle charging load based on dynamic energy consumption and user psychology. Background technique [0002] Since electric vehicles have the advantages of low carbon and environmental protection, the development and transformation of the automobile industry is an important way to achieve energy conservation and emission reduction. Promoting the development of the new energy automobile industry is a strategic measure to cope with climate change and promote green development. The access of large-scale electric vehicles will affect the reliability of the distribution network, and a refined electric vehicle charging load model can ensure the accuracy of reliability assessment. As a special transferable load and energy storage device, electric vehicle is the carrier connecting road network traffic and urban distribution network. Its own mobility and rando...

AI Technical Summary

Problems solved by technology

[0004] 1. In the prediction model, it is customary to regard the battery capacity as a fixed parameter and ignore the change of EV energy consumption with the environment, which brings errors to the charging load prediction results;

[0005] 2. There is a lack of detailed research on the subjective wishes of users. Users will be affected by the real-time road conditions and user psychology during the actual travel process, and the route selection will not completely follow the established shortest route.

[0006] 3. Aiming at the user's subjective willingness to charge, the influence of the user's psychology is weakened, and the user's own economic level, consumption ability and other user attributes are not considered, and quantitative analysis is lacking.

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