Charging behavior analysis method based on Copula function, electronic equipment and medium

Abstract

The invention relates to a Copula function-based charging behavior analysis method, electronic equipment and a medium, and the method comprises the steps: determining two to-be-researched parameters of a charging behavior; respectively inputting the observation data of the parameters to be researched into a plurality of Copula functions, and calculating an AIC criterion function value corresponding to each Copula function; and taking the Copula function with the minimum AIC criterion function value as a final Copula function, and calculating the correlation of the to-be-researched parameters by adopting the final Copula function. According to the method, the probability distribution and the correlation of each parameter of different types of charging behaviors can be calculated through the Copula function.

Description

Technical Field

[0001] The present invention relates to the technical field of charging behavior analysis, and in particular to a charging behavior analysis method based on a Copula function, an electronic device and a medium. Background Art

[0002] The application field of electric vehicles is gradually being introduced into the power system due to its advantages of energy saving and environmental protection. The energy of electric vehicles mainly comes from the power grid, and its large-scale development cannot be separated from the support of the power system. However, the disordered charging behavior of electric vehicles is random and simultaneous in time and space. The random charging of a large number of electric vehicles may have an impact on the load, operation and planning of the distribution network. For example, the large-scale access of electric vehicles to the grid, the unreasonable distribution of electric vehicle charging equipment types, and their insufficient endurance; electric vehicle users charge disorderly during the peak load period of the power grid, resulting in insufficient power grid capacity, etc. At the same time, electric vehicles can also be used as mobile energy storage, and have broad application prospects in peak load shaving, providing power system auxiliary services, and coordinated consumption of new energy.

[0003] The charging behavior characteristics of electric vehicles (charging start time, connection time, charging power) have a certain impact on the configuration of charging power level. Therefore, it is necessary to develop a charging behavior analysis method to analyze different types of charging behaviors. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a charging behavior analysis method, electronic device and medium based on Copula function, which can calculate the probability distribution and correlation of various parameters of different types of charging behaviors through Copula function.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide a charging behavior analysis method based on Copula function, comprising the following steps:

[0006] Determine the two parameters to be studied for charging behavior;

[0007] Inputting the observed data of the parameters to be studied into multiple Copula functions respectively, and calculating the AIC criterion function value corresponding to each Copula function;

[0008] The Copula function with the smallest AIC criterion function value is used as the final Copula function, and the final Copula function is used to calculate the correlation between the parameters to be studied.

[0009] The two parameters to be studied are the charging start time and the charging duration, or the charging duration and the charging power.

[0010] The Copula function includes a two-dimensional Gaussian Copula function, a two-dimensional t Copula function, a two-dimensional Gumbel Copula function, and a two-dimensional Clayton Copula function.

[0011] The distribution function of the two-dimensional Gaussian Copula function is: C(u,v;θ)=Φ θ (Φ -1 (u),Φ -1 (v)), the density function of the two-dimensional Gaussian Copula function is: Where C(u,v;θ) is the distribution function of the two-dimensional Gaussian Copula function, u and v represent two parameters to be studied, θ is the correlation coefficient representing the correlation between the two parameters to be studied, and Φ θ represents the two-dimensional standard normal distribution function with correlation coefficient θ; Φ -1 represents the inverse function of the one-dimensional standard normal distribution function; c(u,v;θ) is the density function of the two-dimensional Gaussian Copula function, ζ1=Φ -1 (u) and ζ2=Φ -1 (v) is a standard normal distribution variable.

[0012] The distribution function of the two-dimensional t Copula function is: The density function of the two-dimensional tCopula function is: Where C(u,v;θ,k) is the distribution function of the two-dimensional tCopula function, u and v represent two parameters to be studied, θ is the correlation coefficient representing the correlation between the two parameters to be studied, and k is the degree of freedom of the t distribution; T θ,k represents the two-dimensional T distribution function with correlation coefficient θ and degrees of freedom k; represents the inverse function of the one-dimensional T distribution function with k degrees of freedom; c(u,v;θ,k) is the density function of the two-dimensional t Copula function, t θ,k represents the two-dimensional t distribution function with correlation coefficient θ and degrees of freedom k, t k One-dimensional t-distribution function with k degrees of freedom.

[0013] The two-dimensional Gumbel Copula function is: Among them, C(u,v) is the two-dimensional Gumbel Copula function, u and v represent two parameters to be studied, τ represents the rank correlation coefficient.

[0014] The distribution function of the two-dimensional Clayton Copula function is: The density function of the two-dimensional Clayton Copula function is: Among them, C′(u,v) is the distribution function of the two-dimensional Clayton Copula function, c′(u,v) is the density function of the two-dimensional Clayton Copula function, u and v represent two parameters to be studied respectively, τ represents the rank correlation coefficient.

[0015] The AIC criterion function is: Among them, AIC k is the AIC criterion function value, k is the number of parameters in the Copula function, N is the number of samples, c is the density function of the Copula function to be selected, and are the values ​​of the probability distribution function of the first parameter variable x and the second parameter variable y at the i-th observation data point, respectively. k is the correlation coefficient in the Copula function of k variables.

[0016] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned charging behavior analysis method based on the Copula function when executing the computer program.

[0017] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the charging behavior analysis method based on the Copula function are implemented.

[0018] Beneficial Effects

[0019] Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention adopts a variety of typical Copula functions to model the relationship between time and energy parameters of different types of charging behaviors, and the probability distribution and correlation of various parameters of different types of charging behaviors can be calculated in the above-mentioned manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart showing the steps of a charging behavior analysis method based on a Copula function according to a first embodiment of the present invention.

[0021] Figure 2 A flow chart showing the selection of Copula function parameters and forms for the relationship between different variables in the first embodiment.

[0022] Figure 3A schematic diagram showing the Copula functions between different variables in the first embodiment is shown. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0024] The first embodiment of the present invention relates to a charging behavior analysis method based on Copula function, such as Figure 1 As shown, the following steps are included:

[0025] Step 101, determining two parameters to be studied of the charging behavior. The two parameters to be studied in this step are the charging start time and the charging duration, or the charging duration and the charging power.

[0026] Step 102, inputting the observation data of the parameters to be studied into a plurality of Copula functions respectively, and calculating the AIC criterion function value corresponding to each Copula function.

[0027] The Copula function in this step includes a two-dimensional Gaussian Copula function, a two-dimensional t Copula function, a two-dimensional Gumbel Copula function, and a two-dimensional Clayton Copula function. Figure 2 As shown, in this embodiment, four typical Copula functions are used to model the relationship between time and energy parameters of different types of charging behaviors. The parameters of some Copula functions can be directly obtained by the correlation coefficient between variables, while some need to be calculated by methods such as maximum likelihood estimation.

[0028] In the maximum likelihood estimation method based on nonparametric kernel density, the marginal distribution function is replaced by the distribution function obtained by nonparametric kernel density estimation of each variable:

[0029]

[0030] The value of the log-likelihood function can be expressed as:

[0031]

[0032] In the formula, ω is the parameter to be estimated, c is the Copula probability density function to be selected, The probability distribution functions of variables x and y are the values ​​of the i-th observation data point, respectively. Then the estimated value of ω is:

[0033] The distribution function of the two-dimensional Gaussian Copula function in this embodiment is: C(u,v;θ)=Φ θ (Φ -1 (u),Φ -1 (v)), the density function of the two-dimensional Gaussian Copula function is: Where C(u,v;θ) is the distribution function of the two-dimensional Gaussian Copula function, u and v represent two parameters to be studied, θ is the correlation coefficient representing the correlation between the two parameters to be studied, and Φ θ represents the two-dimensional standard normal distribution function with correlation coefficient θ; Φ -1 represents the inverse function of the one-dimensional standard normal distribution function; c(u,v;θ) is the density function of the two-dimensional Gaussian Copula function, ζ1=Φ -1 (u) and ζ2=Φ -1 (v) is a standard normal distribution variable. The two-dimensional Gaussian Copula function has a symmetrical structure and is insensitive to the change of the variable in the tail. It is suitable for describing the relationship between two-dimensional random variables with symmetrical tail characteristics and asymptotically independent tails.

[0034] The distribution function of the two-dimensional t Copula function in this embodiment is: The density function of the two-dimensional t Copula function is: Where C(u,v;θ,k) is the distribution function of the two-dimensional t Copula function, u and v represent two parameters to be studied, θ is the correlation coefficient representing the correlation between the two parameters to be studied, and k is the degree of freedom of the t distribution; T θ,k represents the two-dimensional T distribution function with correlation coefficient θ and degrees of freedom k; represents the inverse function of the one-dimensional T distribution function with k degrees of freedom; c(u,v;θ,k) is the density function of the two-dimensional t Copula function, t θ,k represents the two-dimensional t distribution function with correlation coefficient θ and degrees of freedom k, t k One-dimensional t distribution function with k degrees of freedom. The two-dimensional t Copula function has a symmetrical structure and a thick tail, so it is suitable for describing the relationship between two-dimensional random variables with symmetrical tail characteristics and correlated tails.

[0035] The two-dimensional Gumbel Copula function in this embodiment is: Among them, C(u,v) is the two-dimensional Gumbel Copula function, u and v represent two parameters to be studied, τ represents the rank correlation coefficient. The structure of the two-dimensional Gumbel Copula function is asymmetric and has a thick upper tail, which is suitable for describing the relationship between variables with strong upper tail correlation and asymptotically independent lower tail.

[0036] The distribution function of the two-dimensional Clayton Copula function in this embodiment is: The density function of the two-dimensional Clayton Copula function is: Among them, C′(u,v) is the distribution function of the two-dimensional Clayton Copula function, c′(u,v) is the density function of the two-dimensional Clayton Copula function, u and v represent two parameters to be studied respectively, τ represents the rank correlation coefficient. The structure of the two-dimensional Clayton Copula function is asymmetric and has a thick lower tail. It is suitable for describing the relationship between variables with strong correlation in the lower tail and asymptotically independent upper tail.

[0037] For two variables to be studied, after determining the parameters for modeling their correlation using different Copula functions, the AIC (Akaike Information) criterion based on the likelihood function is used to select the Copula model that best describes the correlation between the two variables. When the marginal distribution function of a random variable is expressed by a nonparametric kernel density estimate, the AIC criterion function is:

[0038]

[0039] Among them, AIC k is the AIC criterion function value, k is the number of parameters in the Copula function, N is the number of samples, please explain, θ k is the correlation coefficient in the Copula function of k variables.

[0040] Step 103, the Copula function that minimizes the AIC criterion function value is used as the final Copula function to calculate the correlation between the parameters to be studied.

[0041] The correlation between the start time, duration and charging capacity of intra-day and inter-day charging behaviors was analyzed, and the results were as follows: Figure 3 After completing the modeling of the electric vehicle charging behavior characteristics, the charging behaviors of different types of electric vehicles can be simulated according to the probability distribution of each parameter and the correlation between the parameters.

[0042] A second embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the charging behavior analysis method based on the Copula function of the first embodiment when executing the computer program.

[0043] The third embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the charging behavior analysis method based on the Copula function of the first embodiment are implemented.

[0044] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0045] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0048] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A charging behavior analysis method based on Copula function, characterized in that: The following steps are involved: Determine the two parameters to be studied for charging behavior; Inputting the observed data of the parameters to be studied into multiple Copula functions respectively, and calculating the AIC criterion function value corresponding to each Copula function; The Copula function with the smallest AIC criterion function value is used as the final Copula function, and the final Copula function is used to calculate the correlation between the parameters to be studied.

2. The charging behavior analysis method based on Copula function according to claim 1, characterized in that: The two parameters to be studied are the charging start time and the charging duration, or the charging duration and the charging power.

3. The charging behavior analysis method based on Copula function according to claim 1, characterized in that: The Copula function includes a two-dimensional Gaussian Copula function, a two-dimensional t Copula function, a two-dimensional Gumbel Copula function, and a two-dimensional Clayton Copula function.

4. The charging behavior analysis method based on Copula function according to claim 3 is characterized in that: The distribution function of the two-dimensional Gaussian Copula function is: C(u,v;θ)=Φ θ (Φ -1 (u),Φ -1 (v)), the density function of the two-dimensional Gaussian Copula function is: Where C(u,v;θ) is the distribution function of the two-dimensional Gaussian Copula function, u and v represent two parameters to be studied, θ is the correlation coefficient representing the correlation between the two parameters to be studied, and Φ θ represents the two-dimensional standard normal distribution function with correlation coefficient θ; Φ -1 represents the inverse function of the one-dimensional standard normal distribution function; c(u,v;θ) is the density function of the two-dimensional Gaussian Copula function, ζ1=Φ -1 (u) and ζ2=Φ -1 (v) is a standard normal distribution variable.

5. The charging behavior analysis method based on Copula function according to claim 3 is characterized in that: The distribution function of the two-dimensional t Copula function is: The density function of the two-dimensional t Copula function is: Where C(u,v;θ,k) is the distribution function of the two-dimensional t Copula function, u and v represent two parameters to be studied, θ is the correlation coefficient representing the correlation between the two parameters to be studied, and k is the degree of freedom of the t distribution; T θ,k represents the two-dimensional T distribution function with correlation coefficient θ and degrees of freedom k; represents the inverse function of the one-dimensional T distribution function with k degrees of freedom; c(u,v;θ,k) is the density function of the two-dimensional t Copula function, t θ,k represents the two-dimensional t distribution function with correlation coefficient θ and degrees of freedom k, t k One-dimensional t-distribution function with k degrees of freedom.

6. The charging behavior analysis method based on Copula function according to claim 3, characterized in that: The two-dimensional Gumbel Copula function is: Among them, C(u,v) is a two-dimensional GumbelCopula function, u and v represent two parameters to be studied, τ represents the rank correlation coefficient.

7. The charging behavior analysis method based on Copula function according to claim 3, characterized in that: The two-dimensional The distribution function of the Clayton Copula function is: The two-dimensional Clayton The density function of the Copula function is: Among them, C′(u,v) is the distribution function of the two-dimensional Clayton Copula function, c′(u,v) is the density function of the two-dimensional Clayton Copula function, u and v represent two parameters to be studied respectively, τ represents the rank correlation coefficient.

8. The charging behavior analysis method based on Copula function according to claim 1, characterized in that: The AIC criterion function is: Among them, AIC k is the AIC criterion function value, k is the number of parameters in the Copula function, N is the number of samples, c is the density function of the Copula function to be selected, and are the values ​​of the probability distribution function of the first parameter variable x and the second parameter variable y at the i-th observation data point, respectively. k is the correlation coefficient in the Copula function of k variables.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the charging behavior analysis method based on the Copula function as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the charging behavior analysis method based on the Copula function as claimed in any one of claims 1 to 8 are implemented.

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