CN-121981576-A - Evaluation method and medium for electric automobile charging behavior influence factors and electronic equipment

CN121981576ACN 121981576 ACN121981576 ACN 121981576ACN-121981576-A

Abstract

The application provides an evaluation method, medium and electronic equipment for electric vehicle charging behavior influence factors, which can acquire charging pile data of a plurality of charging function areas, generate a model according to the charging pile data and preset travel return time, determine corresponding values of subjective factors and objective factors in the electric vehicle charging behavior data, identify factor influence degree of the electric vehicle charging behavior data through a DEMAEL algorithm, determine key factors of the electric vehicle charging behavior according to factor influence degree identification results through an ISM algorithm, and accordingly identify key driving factors and result factors influencing the electric vehicle charging behavior, support for optimizing electric vehicle charging schedule, relieving power grid pressure and improving power grid stability, and provide decision support for charging facility planning.

Inventors

ZHENG YURONG
CHEN SONG
YUAN MINGHAN
JI KUNHUA
GE WEIDONG
YAO YIN

Assignees

国家电网有限公司华东分部

Dates

Publication Date: 20260505
Application Date: 20260128

Claims (10)

1. The method for evaluating the influence factors of the charging behavior of the electric automobile is characterized by comprising the following steps of: acquiring charging pile data of a plurality of charging function areas; Determining a corresponding value of a first objective factor in electric vehicle charging behavior data according to the charging pile data and a preset travel return time generation model, wherein the travel return time generation model is used for determining relevant time of electric vehicle charging behavior according to the change of the number of electric vehicles in an electric vehicle storage pool corresponding to a front time period and a rear time period, and the first objective factor comprises arrival time and residence time; Determining a corresponding value of subjective factors in charging behavior data of the electric automobile, wherein the subjective factors comprise electric quantity anxiety, electricity price sensitivity and charging willingness; Determining a corresponding value of a second objective factor in the electric vehicle charging behavior data according to the charging pile data, wherein the second objective factor comprises a functional area, charging power, initial SOC, holiday marks, the number of charging piles, load clustering and electricity price; Carrying out factor influence degree identification on the electric vehicle charging behavior data through a DEMATEL algorithm; And determining key factors of the charging behavior of the electric automobile through an ISM algorithm according to the factor influence degree identification result.
2. The method of claim 1, wherein the constructing of the trip return time generation model comprises: Determining the number of vehicles which stay corresponding to each preset time period according to the charging pile data; adding a new arrival vehicle with the number being the difference of the number of the vehicles in the front and rear time periods into a vehicle pool under the condition that the number of the vehicles in the rear time period is larger than the number of the vehicles in the front time period, and determining the rear time period as the arrival time of the new arrival vehicle; and removing the earliest arriving vehicle with the number of the vehicles in the front and rear time periods from the vehicle pool when the number of the vehicles in the rear time period is smaller than the number of the vehicles in the front time period, and determining the rear time period as the travel time of the earliest arriving vehicle.
3. The method of claim 1, wherein determining the corresponding value of the subjective factor in the electric vehicle charging behavior data comprises: Obtaining expert interval scores corresponding to subjective factors in charging behavior data of the electric automobile; And carrying out numerical conversion on the expert interval scores through a GREY algorithm, and determining the corresponding values of the subjective factors.
4. A method according to claim 3, wherein numerically converting the expert interval score by a GREY algorithm to determine the corresponding value of the subjective factor comprises: Converting the numerical value in the expert scoring matrix into a gray scale interval according to the expert interval score, and determining a fuzzy matrix, wherein the fuzzy matrix comprises an upper limit matrix and a lower limit matrix corresponding to the expert score; Calculating an average value of the upper limit matrix and the lower limit matrix; And whitening the fuzzy matrix according to the average value of the upper limit matrix and the lower limit matrix to obtain a clear value matrix corresponding to the subjective factors.
5. The method of claim 4, further comprising, prior to factor influence recognition of the electric vehicle charging behavior data by a DEMATEL algorithm: normalizing the matrix of the electric vehicle charging behavior data to obtain a normative influence matrix; And carrying out squaring and adding on the canonical influence matrix to obtain a comprehensive influence matrix.
6. The method of claim 5, wherein the factor influence recognition of the electric vehicle charging behavior data by a DEMATEL algorithm comprises: And solving influence degree, influenced degree, central degree and cause degree of each factor in the electric vehicle charging behavior data according to the comprehensive influence matrix.
7. The method of claim 6, wherein determining key factors of the electric vehicle charging behavior by ISM algorithm based on factor influence on the recognition result comprises: calculating a relation matrix according to the comprehensive influence matrix; calculating a square matrix according to the relation matrix; Calculating an reachable matrix according to the squaring matrix; carrying out condensation and edge shrinking on the reachable matrix to obtain a condensation reachable matrix and a condensation skeleton matrix; Adding points to the condensed point skeleton matrix to obtain a general skeleton matrix; substituting the influence degree corresponding to the comprehensive influence matrix into the general skeleton matrix to obtain an electric vehicle charging behavior hierarchical relation matrix.
8. The method of claim 7, wherein calculating a relationship matrix from the composite influence matrix comprises: calculating corresponding mean values and standard deviations according to the comprehensive influence matrix; calculating an intercept according to the mean value and the standard deviation; And calculating a relation matrix according to the intercept and the comprehensive influence matrix.
9. A computer readable medium having stored thereon computer readable instructions executable by a processor to implement the method of any of claims 1 to 8.
10. An electronic device comprising a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 8.

Description

Evaluation method and medium for electric automobile charging behavior influence factors and electronic equipment Technical Field The invention relates to the technical field of smart grids, in particular to an evaluation method of electric vehicle charging behavior influence factors, a medium and electronic equipment. Background At present, an electric automobile is used as a core carrier for clean energy transformation, and a power grid charging load caused by large-scale popularization of the electric automobile presents strong space-time randomness and remarkable power fluctuation, so that peak-valley difference of the power distribution network load is further aggravated, line overload risk is induced, and safety and stability of a power grid are seriously threatened. In order to study the related problems of the electric vehicle charging behavior on the power grid, the entering and leaving related data of the electric vehicle in the charging area are required, however, the data are accumulated in the actual environment to a small extent, and the requirement of the study is difficult to meet. The existing scheme can generate relevant data through a simulation method, however, the traditional simulation method such as Monte Carlo simulation, parameter fitting and the like generates relevant travel return time data which are difficult to meet the requirement of multi-scene adaptability, easily deviate from the actual distribution rule and the like. In addition, the existing scheme tends to focus on a single factor, and lacks consideration of nonlinear coupling relation among multiple influencing factors, so that causal relation and hierarchical structure among the multiple influencing factors are difficult to analyze, and driving factors playing a leading role cannot be identified. Therefore, a technical scheme capable of simulating the real charging behavior of the electric vehicle and quantitatively analyzing the coupling relation of various influencing factors to determine key influencing factors is needed, so that the accurate planning and dynamic regulation and control of the charging load are supported, and the system analyzes a multidimensional driving mechanism influencing the charging behavior of the electric vehicle. Disclosure of Invention The application aims to provide an evaluation method, medium and electronic equipment for electric vehicle charging behavior influence factors, which are used for solving the problems that in the prior art, real electric vehicle charging behavior data are difficult to obtain and quantitative evaluation on various influence factors influencing electric vehicle charging behavior is lacking. To achieve the above object, some embodiments of the present application provide a method for evaluating an influence factor of charging behavior of an electric vehicle, the method comprising: acquiring charging pile data of a plurality of charging function areas; Determining a corresponding value of a first objective factor in electric vehicle charging behavior data according to charging pile data and a preset travel return time generation model, wherein the travel return time generation model is used for determining relevant time of electric vehicle charging behavior according to the change of the number of electric vehicles in an electric vehicle storage pool corresponding to a front time period and a rear time period, and the first objective factor comprises arrival time and residence time; Determining corresponding values of subjective factors in charging behavior data of the electric automobile, wherein the subjective factors comprise electric quantity anxiety, electric price sensitivity and charging willingness; Determining a corresponding value of a second objective factor in the charging behavior data of the electric automobile according to the charging pile data, wherein the second objective factor comprises a functional area, charging power, initial SOC, holiday marks, the number of charging piles, load clustering and electricity price; carrying out factor influence degree identification on the charging behavior data of the electric automobile through a DEMATEL algorithm; And determining key factors of the charging behavior of the electric automobile through an ISM algorithm according to the factor influence degree identification result. Further, the construction of the travel return time generation model comprises the following steps: determining the number of vehicles which stay corresponding to each preset time period according to the charging pile data; Adding new arrival vehicles with the number of the vehicles in the front and rear time periods as the difference value of the numbers of the vehicles in the front and rear time periods into a vehicle pool under the condition that the number of the vehicles in the rear time period is larger than the number of the vehicles in the front time period, and determining the time period as the arrival time of the new a