A stochastic simulation method and system for water-wind-solar scenarios considering spatio-temporal correlation

Abstract

The present invention belongs to the technical field related to power scenario simulation, and discloses a stochastic simulation method and system for water-wind-solar scenarios considering spatio-temporal correlation. The method includes: collecting historical data of three types of water-wind-solar resources; determining the scenario simulation order and fitting the marginal distribution of each variable at any time period; for the first resource, constructing the first conditional distribution of the variable at the t time period of -1 t according to the first conditional distribution; obtaining the simulation value of the variable at the t time period; for any one of the second resource and the third resource, constructing the second conditional distribution of the variable at the t time period of the previous resource and the variable at the t time period of -1 of this any resource; obtaining the simulation value of the variable at the t time period of this any resource according to the second conditional distribution; obtaining the simulation value of the variable at the t time period. The present invention incorporates the correlation characteristics of different resources in space, and can more comprehensively simulate and analyze the multiple uncertainties of water-wind-solar resources.

Description

Technical Field

[0001] The present invention belongs to the technical field related to power scenario simulation, and more specifically, relates to a stochastic simulation method and system for water-wind-solar scenarios considering spatio-temporal correlation. Background Art

[0002] In recent years, the integrated development of water-wind-solar in China has been developing rapidly. The large-scale access of renewable energy sources such as hydropower, wind power, and photovoltaic power has provided broad prospects for the efficient utilization of clean energy and the low-carbonization of the power system. However, in this process, the integrated development of water-wind-solar still faces many challenges. In particular, the volatility of water, wind, and solar resources in time and space and their interactions lead to significant uncertainties in the integrated development. How to effectively evaluate and quantify these uncertainties has become a key link in further promoting the integrated development of water-wind-solar.

[0003] To solve this problem, it is urgent to construct a method system that can scientifically describe multiple uncertainties, and the stochastic simulation technology is one of the effective means to deal with uncertainty analysis. Through stochastic simulation, different scenarios considering the spatio-temporal changes of water, wind, and solar resources can be generated, and then the uncertainty problem can be transformed into a quantifiable research object. However, traditional stochastic simulation methods mostly focus on the time correlation analysis of a single energy source and are difficult to comprehensively depict the complex interrelationships of water, wind, and solar resources in different time scales and spatial regions, and there are limitations in describing the uncertainties of multi-source coupling. Summary of the Invention

[0004] In view of the above defects or improvement requirements of the prior art, the present invention provides a stochastic simulation method and system for water-wind-solar scenarios considering spatio-temporal correlation, which is used to solve the problem that traditional stochastic simulation methods mostly focus on the time correlation analysis of a single energy source and have limitations in describing the uncertainties of multi-source coupling.

[0005] To achieve the above object, according to one aspect of the present invention, a stochastic simulation method for water-wind-solar scenarios considering spatio-temporal correlation is provided, including:

[0006] S1, collecting historical data of water resource runoff, wind resource wind power output, and solar resource photovoltaic power output; calculating the correlation information between any two of the water-wind-solar resources according to the historical data, determining the simulation order of the water-wind-solar scenarios based on the correlation information, and sequentially taking the first resource, the second resource, and the third resource in the order; and according to the historical data, using the kernel density estimation method to fit the marginal distributions of the water-wind-solar resource variables at any time period t ;

[0007] S2, for the first resource, using a two-dimensional Copula function to utilize t the time period variable and tConstruction of the marginal distribution of the -1 time period variable t The time period variable and t The two-dimensional joint distribution of the -1 time period variable to obtain the known t When the -1 time period variable t The first conditional distribution of the time period variable;

[0008] According to the first conditional distribution and t The marginal distribution value of the -1 time period variable, obtain t The marginal distribution value of the time period variable, and then obtain t The simulated value of the time period variable;

[0009] S3. For any one of the second resource and the third resource, use the three-dimensional Copula function to utilize the previous resource t The time period variable, this any one resource t The -1 time period variable and this any one resource t Construct the marginal distribution of the previous resource of the time period variable t The time period variable, this any one resource t The -1 time period variable and this any one resource t The three-dimensional joint distribution of the time period variable to obtain the known previous resource t The time period variable and this any one resource t When the -1 time period variable, this any one resource t The second conditional distribution of the time period variable;

[0010] According to the second conditional distribution, the marginal distribution value of the previous resource t The marginal distribution value of the time period variable and this any one resource t The marginal distribution value of the -1 time period variable, obtain this any one resource t The marginal distribution value of the time period variable, and then obtain this any one resource t The simulated value of the time period variable.

[0011] According to the stochastic simulation method of the water-wind-solar scenario considering spatio-temporal correlation provided by the present invention, the historical data of the water-wind-solar resources in S1 are all M Row N Column matrix Q :

[0012] ;

[0013] In the formula, Q Is the historical data of the resource over the years, M Is the number of years, , N Determined according to the target time scale.

[0014] According to the method for stochastic simulation of water-wind-solar scenarios considering spatio-temporal correlation provided by the present invention, in S1, the correlation information between any two of the water-wind-solar resources is calculated based on historical data, and the simulation order of the water-wind-solar scenarios is determined according to the correlation information, specifically including:

[0015] Calculate the mutual information between any two of the water-wind-solar resources based on historical data, and obtain the mutual information value between any two of the water-wind-solar resources;

[0016] Calculate the sum of the mutual information values between any one resource and other resources, obtain the comprehensive mutual information index of any one resource, and determine the sequence of simulation of the three resource scenarios according to the comprehensive mutual information index.

[0017] According to the method for stochastic simulation of water-wind-solar scenarios considering spatio-temporal correlation provided by the present invention, in S2, for the first resource, given t -1 time period variable t The first conditional distribution of the time period variable Specifically:

[0018] ;

[0019] In the formula: is the first resource t -1 time period variable; is the first resource t time period variable; C is the selected Copula function type; 、 respectively represent the marginal distributions of the first resource t -1 time period variable and t time period variable.

[0020] According to the method for stochastic simulation of water-wind-solar scenarios considering spatio-temporal correlation provided by the present invention, in S2, according to the first conditional distribution and t the marginal distribution value of the -1 time period variable, obtain t the marginal distribution value of the time period variable, and further obtain t the simulation value of the time period variable, specifically including:

[0021] Obtain a random number between 0 and 1 as the first conditional distribution value, and according to the first conditional distribution value, the first conditional distribution and t the marginal distribution value of the -1 time period variable, obtain t the marginal distribution value of the time period variable;

[0022] According to t the marginal distribution value of the time period variable and t the marginal distribution of the time period variable, obtain t the simulation value of the time period variable;

[0023] Among them, when obtaining the first simulated value of the first resource, t the marginal distribution value of the -1 period variable is taken as a random number, or calculated and obtained according to the average value of the variable information corresponding to the maximum period in the historical data of the first resource and the marginal distribution of the maximum period;

[0024] After that, the marginal distribution value is calculated using the previous simulated value and the marginal distribution of the corresponding period as t the marginal distribution value of the -1 period variable to obtain the simulated value of the current period.

[0025] According to the method for random simulation of water, wind and light scenarios considering spatio-temporal correlation provided by the present invention, obtaining the second conditional distribution in S3 specifically includes:

[0026] Using the two-dimensional Copula function to utilize the t period variable of the previous resource and the t marginal distribution of the -1 period variable of any one of these resources to construct the two-dimensional joint distribution of the t period variable of the previous resource and the t -1 period variable of any one of these resources, and obtaining the third conditional distribution of the t -1 period variable of any one of these resources when the t period variable of the previous resource is known;

[0027] Using the two-dimensional Copula function to utilize the t period variable of the previous resource and the t marginal distribution of the period variable of any one of these resources to construct the two-dimensional joint distribution of the t period variable of the previous resource and the t period variable of any one of these resources, and obtaining the fourth conditional distribution of the t period variable of any one of these resources when the t period variable of the previous resource is known;

[0028] Substitute the third conditional distribution and the fourth conditional distribution into the formula of the second conditional distribution, and convert the second conditional distribution into a function about the third conditional distribution and the fourth conditional distribution.

[0029] According to the method for random simulation of water, wind and light scenarios considering spatio-temporal correlation provided by the present invention, for any one of the second resource and the third resource in S3, when the t period variable of the previous resource and the t -1 period variable of any one of these resources are known, the t second conditional distribution of the period variable of any one of these resources

[0030] ;

[0031] ;

[0032] ;

[0033] Where: For any resource in the second resource or the third resource t Period variables; For any resource in the second resource or the third resource t -1 period variable; For the previous resource t Period variables; Indicates the previous resource t marginal distribution of period variables; , Respectively represents any resource in the second resource and the third resource t -1 period variable and t Marginal distribution of a time period variable: f is the probability density function of the variable, c is the probability density function of the Copula function; Indicates known u 2 conditional copula function.

[0034] According to the random simulation method of water, scenery and light scenes considering time and space correlation provided by the present invention, in S3, according to the second condition distribution, the previous resource t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated values ​​of the time period variables include:

[0035] According to the previous resource t The marginal distribution value of the time period variable, any resource t -1 The marginal distribution value of the time period variable and the third conditional distribution obtain the third conditional distribution value;

[0036] Obtain a random number between 0 and 1 as a second conditional distribution value, and obtain a fourth conditional distribution value according to the second conditional distribution value, the second conditional distribution, and the third conditional distribution value;

[0037] According to the previous resource t The marginal distribution value of the time period variable, the fourth conditional distribution and the fourth conditional distribution value, obtain the any resource t Marginal distribution values ​​of the time period variable;

[0038] According to any resource tThe marginal distribution values and marginal distributions of time period variables, and obtaining any one of the resources t The simulated values of time period variables.

[0039] According to the method for stochastic simulation of water-wind-solar scenarios considering spatio-temporal correlation provided by the present invention, obtaining any one of the resources in S3 t The simulated values of time period variables further include:

[0040] The previous resource t The marginal distribution values of time period variables are calculated based on the previous resource t The simulated values and marginal distributions of time period variables;

[0041] When obtaining the first simulated value of any one of the resources, t The marginal distribution value of the -1 time period variable takes a random number, or is calculated based on the average value of the variable information corresponding to the maximum time period in the historical data of any one of the resources and the marginal distribution of the maximum time period;

[0042] After that, the marginal distribution value is calculated using the previous simulated value and the marginal distribution of the corresponding time period as t The marginal distribution value of the -1 time period variable, and the simulated value of the current time period is obtained.

[0043] According to another aspect of the present invention, there is provided a system for stochastic simulation of water-wind-solar scenarios considering spatio-temporal correlation. The system includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it executes the method for stochastic simulation of water-wind-solar scenarios considering spatio-temporal correlation described in any one of the above.

[0044] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the method and system for stochastic simulation of water-wind-solar scenarios considering spatio-temporal correlation provided by the present invention:

[0045] 1. When performing scenario simulation, a three-dimensional Copula model is used to combine the time series characteristics and spatial correlation of water, wind, and solar resources. While considering the time series characteristics of water, wind, and solar resources, it can further incorporate the spatial correlation characteristics between different resources, which is beneficial to comprehensively depict the complex mutual relationships of water, wind, and solar resources at different time scales and spatial regions, thereby more comprehensively simulating and analyzing the multiple uncertainties of water-wind-solar resources; this method provides a solid theoretical basis and technical support for realizing the optimal scheduling and efficient coordination of water-wind-solar multi-energy complementarity;

[0046] 2. Compared with the traditional method that is limited to the time correlation analysis of a single resource, this method describes the spatial correlation characteristics between different resources through a three-dimensional joint distribution, realizes the dimensionality reduction processing of the complex interrelationships among multi-source data, greatly improves the ability to characterize multiple uncertainties, and thus provides more comprehensive and accurate model support for the scheduling optimization of water-wind-solar multi-energy complementarity;

[0047] 3. The involved three-dimensional Copula function converts the second conditional distribution into a function of the third and fourth conditional distributions through the concept of conditional distribution, that is, reduces the three-dimensional conditional distribution to multiple two-dimensional Copula functions, effectively avoiding the difficulties of constructing high-dimensional models and estimating high-dimensional model parameters, and effectively describing the spatio-temporal correlation of water-wind-solar resources in a relatively simple way. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flowchart of the water-wind-solar scenario stochastic simulation method considering spatio-temporal correlation provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] Please refer to Figure 1 , an embodiment of the present invention provides a water-wind-solar scenario stochastic simulation method considering spatio-temporal correlation, and the water-wind-solar scenario stochastic simulation method includes:

[0051] S1. Collect historical data of water resource runoff, wind resource wind power output, and light resource photovoltaic power output; calculate the correlation information between any two of the water-wind-solar resources according to the historical data, determine the order of water-wind-solar scenario simulation based on the correlation information, and sequentially be the first resource, the second resource, and the third resource in sequence; and according to the historical data, use the kernel density estimation method to fit the marginal distributions of the water-wind-solar resource variables at any time period t ;

[0052] S2. For the first resource, use the two-dimensional Copula function to utilize t time period variable and t -1 time period variable marginal distributions to construct t time period variable and t -1 time period variable two-dimensional joint distribution, and obtain the first conditional distribution of the t -1 time period variable when the t time period variable is known;

[0053] Based on the first conditional distribution and t the marginal distribution value of the variable at -1 period, obtain t the marginal distribution value of the variable at the period, and further obtain t the simulated value of the variable at the period;

[0054] S3. For any one of the second resource and the third resource, use the three-dimensional Copula function to utilize the variable at the previous period of the previous resource t the variable at the period, and this any one resource t the variable at -1 period of this any one resource and the variable at the period of this any one resource t to construct the three-dimensional joint distribution of the variable at the previous period of the previous resource t the variable at the period, and this any one resource t the variable at -1 period of this any one resource and the variable at the period of this any one resource t to obtain the second conditional distribution of the variable at the period of this any one resource when the variable at the previous period of the previous resource and the variable at -1 period of this any one resource are known; t Based on the second conditional distribution, the marginal distribution value of the variable at the previous period of the previous resource, and the marginal distribution value of the variable at -1 period of this any one resource, obtain t the marginal distribution value of the variable at the period of this any one resource, and further obtain t the simulated value of the variable at the period of this any one resource.

[0055] Specifically, the historical data of the water-wind-solar resources in S1, that is, the historical measured water-wind-solar data, are all t a matrix of t rows and t columns: t In the formula,

[0056] is the historical data of the resource over the years, M is the number of years, N Q :

[0057]

[0058] Q where M is the historical data of the resource over the years, is the number of years, N N N is determined according to the target time scale; if it is daily scale data, N is 365; if it is dekadal scale data, N is 36; if it is monthly scale data, is 12. The kernel density estimation method is used to fit the marginal distribution of each variable of the water-wind-solar at each period, that is, each variable obtains

[0059] marginal distributions.

[0059] In some specific embodiments, in S1, the correlation information between any two of the water, wind, and light resources is calculated based on historical data, and the sequence of water-wind-light scenario simulation is determined according to the correlation information. Specifically, it includes:

[0060] Calculate the mutual information between any two of the water, wind, and light resources according to historical data, use the mutual information to determine the correlation information between any two of the water, wind, and light resources, and obtain the mutual information value between any two of the water, wind, and light resources;

[0061] Calculate the sum of the mutual information values between any one resource and the other resources, obtain the comprehensive mutual information index of any one resource, and determine the sequence of simulation of the three resource scenarios according to the comprehensive mutual information index.

[0062] The mutual information value between any two of the water, wind, and light resources is specifically:

[0063]

[0064] In the formula, is a random variable X and Y the mutual information value between (such as water and wind, wind and light, light and water), x and y are the observed values of the random variables X and Y which represent specific data points in a random experiment or dataset. is x and y the joint probability distribution of, are respectively x and y the marginal probability distributions of.

[0065] The comprehensive mutual information index of a resource, which represents the overall correlation between a certain resource and the other two resources; the sequence of scenario generation is determined according to the magnitude of the comprehensive mutual information index value of each resource, and the larger the comprehensive mutual information index, the higher the priority. The formula for the comprehensive mutual information index is as follows:

[0066]

[0067] In the formula, is the comprehensive mutual information index of the random variable X .

[0068] In some other specific embodiments, in S1, the correlation information between any two of the water, wind, and light resources is calculated based on historical data, and the sequence of water-wind-light scenario simulation is determined according to the correlation information. Specifically, it includes: calculating the correlation degree between any two of the water, wind, and light resources using the Kendall correlation coefficient, and obtaining their respective correlation coefficients;

[0069] Taking a certain resource as the leading factor, adding the absolute values of the correlation coefficients between it and the other two resources to obtain the comprehensive correlation index of this resource; according to the magnitudes of the comprehensive correlation indices of each resource, determining the random simulation order of water, wind, and light resources, where the larger the index, the higher the priority. This embodiment proposes multiple methods for determining the simulation order of water-wind-light scenarios, which can be flexibly selected according to the actual situation and are not specifically limited.

[0070] Specifically, for the first resource in S2, it is known that t When the variable at time -1 t The first conditional distribution of the variable at time Specifically, it is:

[0071]

[0072] In the formula: Is the first resource t The variable at time -1; Is the first resource t The variable at time; C Is the selected Copula function type; 、 Respectively represent the first resource t The marginal distribution of the variable at time -1 and t The marginal distribution of the variable at time.

[0073] Furthermore, in S2, according to the first conditional distribution and t The marginal distribution value of the variable at time -1, obtaining t The marginal distribution value of the variable at time, and then obtaining t The simulation value of the variable at time, specifically including:

[0074] Obtaining a random number between 0 and 1 as the first conditional distribution value, and according to the first conditional distribution value, the first conditional distribution, and t The marginal distribution value of the variable at time -1, obtaining t The marginal distribution value of the variable at time;

[0075] According to t The marginal distribution value of the variable at time and t The marginal distribution of the variable at time, obtaining t The simulation value of the variable at time;

[0076] Among them, when obtaining the first simulation value of the first resource, t The marginal distribution value of the variable at time -1 takes a random number, or is calculated and obtained according to the average value of the variable information corresponding to the maximum time period in the historical data of the first resource and the marginal distribution of the maximum time period;

[0077] After that, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding time period as the t marginal distribution value of the variable at the -1 time period, and the simulation value of the current time period is obtained.

[0078] In some specific embodiments, obtaining the second conditional distribution in S3 specifically includes:

[0079] Using the two-dimensional Copula function, the previous resource t time period variable and the marginal distribution of the variable at the -1 time period of any one of the resources t are used to construct the two-dimensional joint distribution of the previous resource t time period variable and the variable at the -1 time period of any one of the resources t to obtain the third conditional distribution of the variable at the -1 time period of any one of the resources when the previous resource t time period variable is known; t

[0080] Using the two-dimensional Copula function, the previous resource time period variable and the marginal distribution of the variable at the -1 time period of any one of the resources t are used to construct the two-dimensional joint distribution of the previous resource t time period variable and the variable at the -1 time period of any one of the resources t to obtain the fourth conditional distribution of the variable at the -1 time period of any one of the resources when the previous resource t time period variable is known; t t The third conditional distribution and the fourth conditional distribution are substituted into the formula of the second conditional distribution, and the second conditional distribution is converted into a function of the third conditional distribution and the fourth conditional distribution.

[0081]

[0082] Specifically, for any one of the second resource and the third resource in S3, when the previous resource t time period variable and the variable at the -1 time period of any one of the resources t are known, the second conditional distribution of the variable at the -1 time period of any one of the resources t is specifically:

[0083] ;

[0084] ;

[0085] ;

[0086] In the formula: is the t time period variable of any one of the second resource and the third resource; is the t ​​​-1 time period variable; is the t time period variable of the previous resource; represents the t marginal distribution of the time period variable of the previous resource; and respectively represent the t -1 time period variable and t the marginal distribution of the time period variable of any one of the second resource and the third resource: f is the probability density function of the variable, c is the probability density function of the Copula function; and represent the known u conditional Copula function of 2.

[0087] Furthermore, in S3, according to the second conditional distribution, the marginal distribution value of the time period variable of the previous resource t and the marginal distribution value of the -1 time period variable of any one of the second resource and the third resource, obtain the marginal distribution value of the time period variable of any one of the second resource and the third resource t and further obtain the simulated value of the time period variable of any one of the second resource and the third resource t Specifically including: t

[0088] Obtain the third conditional distribution value according to the marginal distribution value of the time period variable of the previous resource t and the marginal distribution value of the -1 time period variable of any one of the second resource and the third resource and the third conditional distribution t

[0089] Obtain a random number between 0 and 1 as the second conditional distribution value, and obtain the fourth conditional distribution value according to the second conditional distribution value, the second conditional distribution and the third conditional distribution value;

[0090] t t Obtain the marginal distribution value of the time period variable of any one of the second resource and the third resource according to the marginal distribution value of the time period variable of the previous resource t the fourth conditional distribution and the fourth conditional distribution value; t

[0091] Obtain the simulated value of the time period variable of any one of the second resource and the third resource according to the marginal distribution value of the time period variable of any one of the second resource and the third resource and the marginal distribution. t t

[0092] t

[0093] Furthermore, in S3, obtaining the simulated value of the time period variable of any one of the second resource and the third resource further includes: t

[0093] t The marginal distribution value of the time period variable of the previous resource is based on the previous resource t time period variable marginal distribution value according to the previous resourcet Obtain the simulated values of the time period variables and calculate the marginal distributions;

[0094] When obtaining the first simulated value of any one of these resources, t the marginal distribution value of the -1 time period variable is taken as a random number, or calculated based on the average value of the variable information corresponding to the maximum time period in the historical data of any one of these resources and the marginal distribution of the maximum time period;

[0095] After that, calculate the marginal distribution value using the previous simulated value and the marginal distribution of the corresponding time period as t the marginal distribution value of the -1 time period variable, and obtain the simulated value of the current time period.

[0096] In some specific embodiments, aiming at the defects of the prior art, this embodiment provides a stochastic simulation method of water-wind-solar based on a three-dimensional Copula model according to the present invention, aiming to solve problems such as existing stochastic simulation methods only considering the simulation of a single resource or the simulation of a single correlation. A stochastic simulation method of water-wind-solar based on a three-dimensional Copula model provided by this embodiment includes the following steps:

[0097] Step 1, collect the historical data of three resources: water (runoff h ), wind (wind power output w ), and light (photovoltaic power output p ), calculate the correlation degree between water-wind-solar pairwise, and determine the order of water-wind-solar stochastic simulation according to the calculation results (assuming w > p > h). The variables of the three resources of water-wind-solar at t the -1 time period and t the time period are respectively h t-1 and h t , w t-1 and w t , p t-1 and p t .

[0098] Step 2, according to the historical measured water-wind-solar data, use the kernel density estimation method to fit the marginal distribution of each variable of water-wind-solar at each time period.

[0099] Step 3, according to the marginal distribution of each variable at each time period described in Step 2, use the two-dimensional Copula function to construct w the two-dimensional joint distribution of w t and w t-1 , and obtain the known w t-1 whenw t Conditional distribution; Obtain a random number between 0 and 1 ε 1. According to ε 1. w t-1 Marginal distribution value of w t-1 When w t Calculate the known w t-1 When w t Marginal distribution value of w t Marginal distribution value of w t Obtain the w t Simulation value of w t As the new w t-1 Until enough w Variable data is simulated;

[0100] Step 4. According to the marginal distributions of the w Variable and the p Variable at each time period, use the two-dimensional Copula function to construct the w t And p t-1 Two-dimensional joint distribution, w t And p t Two-dimensional joint distribution, and respectively obtain the conditional distribution of the known w t When p t-1 And the conditional distribution of the known w t When p t Accordingly, use the three-dimensional Copula function to construct the w t , p t-1 And p t Three-dimensional joint distribution, and obtain the conditional distribution of the known w t And p t-1 When p t According to w t And pt-1 The marginal distribution value and the known w t When p t-1 The conditional distribution of w t When p t-1 The conditional distribution value; Obtain a random number between 0 and 1 ε 2. According to ε 2. The known w t When p t-1 The conditional distribution value and the known w t And p t-1 When p t The conditional distribution of w t When p t The conditional distribution value; According to w t The marginal distribution value of w t When p t The conditional distribution and the known w t When p t The conditional distribution value of p t The marginal distribution value of p t The marginal distribution value of p t Obtain the p t The simulated value; Loop this operation, and use the calculated p t As the new p t-1 Until enough p Variable data;

[0101] Step Five. According to the p Variable and h The marginal distribution of each time period of the variable, use the two-dimensional Copula function to construct p t And h t-1 The two-dimensional joint distribution of p t And h tThe two-dimensional joint distribution, respectively obtain the known p t when h t-1 conditional distribution and the known p t when h t conditional distribution; Based on this, use the three-dimensional Copula function to construct p t , h t-1 and h t three-dimensional joint distribution, obtain the known p t and h t-1 when h t conditional distribution; According to p t and h t-1 marginal distribution values and the known p t when h t-1 conditional distribution to calculate the known p t when h t-1 conditional distribution value; Obtain a random number between 0 and 1 ε 3, according to ε 3, the known p t when h t-1 conditional distribution value and the known p t and h t-1 when h t conditional distribution to calculate the known p t when h t conditional distribution value; According to p t marginal distribution value, the known h t when h t conditional distribution and the known p t when h t conditional distribution value to calculate h t marginal distribution value, according to h t marginal distribution value andh t Obtaining the marginal distribution h t simulation values; Repeat this operation, and use the calculated h t as the new h t-1 , until sufficient h variable data is simulated;

[0102] Furthermore, Step 3 includes:

[0103] 31: The two-dimensional Copula function can connect the joint distribution of two variables with their respective marginal distribution functions. The two-dimensional joint distribution expression of the two variables constructed by the two-dimensional Copula function is:

[0104]

[0105] In the formula: is the selected Copula function type; is the parameter of the Copula function, which is determined by the Kendall correlation coefficient and the partial correlation coefficient; , are the constructed variables X , variable Y 's marginal distribution; is the variable X and variable Y 's joint distribution.

[0106] 32: Use the two-dimensional Copula function to construct w t and w t-1 's two-dimensional joint distribution, and the expression is:

[0107]

[0108] In the formula: represents w t and w t-1 's two-dimensional joint distribution, , respectively represent w variable t 's marginal distribution at the -1 period and t 's marginal distribution at the period.

[0109] 33: Given w t-1 when w t 's conditional distribution , the expression is:

[0110]

[0111] In the formula: represents the known u conditional Copula function of 1.

[0112] When considering the number of years of the required simulated data m later, the expression is:

[0113]

[0114] In the formula: is the simulated value of the m th year and the t th time period, is the simulated value of the m th year and the t -1th time period, are respectively the m th year w variable t -1th time period and the t marginal distribution values of the time period.

[0115] 34: Obtain a random number between 0 and 1 ε 1, let ε 1 be equal to and calculate the when the known ; when m = 1 and t = 1, use random number generation or according to the w variable information in the historical data when t = N take the average value and then calculate and obtain according to the corresponding marginal distribution;

[0116] 35: Perform inverse transformation sampling on to obtain the simulated value , the expression is:

[0117]

[0118] 36: Take the calculated as the new ; when t = N , m = m + 1, that is, enter the simulation of the next year; when the simulation of the new year t = 1, ; repeat the above steps 34 - 36 until mEqual to the target number of years.

[0119] Furthermore, Step 4 includes:

[0120] 41: Construct a w t and p t-1 two-dimensional joint distribution, w t and p t two-dimensional joint distribution, and the expression is:

[0121]

[0122] In the formula: represents w t and p t-1 two-dimensional joint distribution, represents w t and p t two-dimensional joint distribution, , respectively represent p the variable t -1 period and t the marginal distribution of the period.

[0123] 42: Given w t when p t-1 the conditional distribution and given w t when p t the conditional distribution and the expression is:

[0124]

[0125] In the formula: represents the conditional Copula function given u 2.

[0126] 43: The three-dimensional Copula function can connect the joint distribution of three variables with their respective marginal distribution functions. The expression for constructing the three-dimensional joint distribution of three variables through the three-dimensional Copula function is:

[0127]

[0128] In the formula: is the constructed variableZ marginal distribution; is a variable X , Y and Z joint distribution.

[0129] 44: According to w t and p t-1 two-dimensional joint distribution, w t and p t two-dimensional joint distribution to construct w t , p t-1 and p t three-dimensional joint distribution, to obtain the conditional distribution of known w t and p t-1 when p t The steps are as follows:

[0130]

[0131] In the formula: Let , , respectively representing the conditional distributions of known w t when p t-1 and p t ;

[0132] ;

[0133] In the formula: is the probability density function of the variable, is the probability density function of the Copula function.

[0134] Therefore can be written as:

[0135] ;

[0136] where , ;

[0137] Therefore can be written as:

[0138] ;

[0139] When considering the number of years of the required simulated datam After that, the expression is:

[0140] ;

[0141] In the formula: are respectively the m year w variable t time period and p variable t -1 time period and t the marginal distribution values of the time period. are respectively the m known w t at time p t-1 and p t conditional distribution values.

[0142] 45: Calculate the conditional distribution value of the known w t and p t-1 according to the marginal distribution values of w t at time p t-1 and the known w t at time p t-1 that is, calculate .

[0143] 46: Obtain a random number between 0 and 1 ε 2, according to ε 2, the known w t at time p t-1 conditional distribution values and the known w t and p t-1 at time p t calculate the conditional distribution value of the known w t at time p t that is, according to , , let ε 2 and be equal, calculate ; when m = 1 and t = 1, use random number generation or according to p historical datat = N The variable information at

[0144] 47: According to w t the marginal distribution value of w t at p t the conditional distribution of w t at p t and the known p t the conditional distribution value of , calculate , that is, calculate p t the marginal distribution value of

[0145] 48: Perform inverse transformation sampling on to obtain the simulated value , and the expression is:

[0146] ;

[0147] 49: Take the calculated as the new ; When t = N , m = m + 1, that is, enter the simulation of the next year; The simulation of the new year t = 1, ; Repeat 46 - 49 until m is equal to the target number of years.

[0148] 410: The known w t described in 41 - 49 are all the simulated values in Step 3 .

[0149] Furthermore, Step 5 is similar to Step 4: Replace the known w t , p t-1 with p t replaced by p t , h t-1 with h t replaced, and perform according to the steps described in Step 4 hSimulation of variables, the known p t are all the simulation values in the fourth step .

[0150] Furthermore, this embodiment provides a stochastic simulation system for water-wind-solar scenarios considering spatio-temporal correlation. The system includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it executes the stochastic simulation method for water-wind-solar scenarios considering spatio-temporal correlation described in any one of the above.

[0151] Taking the daily-scale historical hydrological data of Xiluodu Hydropower Station and the daily-scale historical wind speed and photovoltaic intensity in the centralized area of the planned installed capacity of wind power and photovoltaic power near Xiluodu Hydropower Station as the implementation object, the historical wind speed and photovoltaic intensity are converted into wind power output and photovoltaic power output according to the planned installed capacity. The sequence lengths of runoff, wind power output, and photovoltaic power output are all from 1940 to 2021. The sequences are randomly simulated using the present invention, and simulation sequences with a scale of 1000 years are generated for each. The embodiment will use statistical indicators: mean, standard deviation, skewness coefficient, kurtosis coefficient; time correlation indicator (Pearson correlation coefficient); space correlation indicator (average absolute error of Kendall correlation coefficient) to measure the time correlation of the simulation sequences and the space correlation between them.

[0152] Tables 1 - 3 respectively give the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) between the statistical characteristics and correlation coefficients of the daily-scale water, wind, and light sequences simulated by the method of the present invention and the two-dimensional Copula method and the statistical characteristics and correlation coefficients of the measured daily-scale water-wind-solar sequences. Table 4 gives the space correlation indicator (average absolute error of Kendall correlation coefficient) between the daily-scale wind-solar, water-wind, and water-solar sequences simulated by the method of the present invention and the two-dimensional Copula method. Among them, the smaller the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE), the higher the statistical characteristics and correlation degree between the simulation sequence and the measured sequence; the smaller the space correlation indicator (average absolute error of Kendall correlation coefficient), the more accurately the simulation sequence can describe the space correlation characteristics between various resources; the bold numbers in Tables 1 - 4 indicate that the values of this method are optimal; ;

[0153] ;

[0154] ;

[0155] ;

[0156] In the comparison method, the two-dimensional Copula method only considers the spatial correlation between resources. As can be seen from Tables 1 - 3, the simulation effect of the present invention is better than that of the comparison method in terms of statistical characteristics and temporal correlation. As can be seen from Table 4, the simulation effect of the present invention is better than that of the comparison method in terms of the spatial correlation between the wind-solar, water-wind, and water-solar sequences.

[0157] It is easy for those skilled in the art to understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A random simulation method for water-landscape scenes considering temporal and spatial correlation, characterized in that: include: S1, collect historical data of water resource runoff, wind resource wind power output and light resource photovoltaic output; calculate the relevant information between water, wind and light resources according to the historical data, and determine the order of water, wind and light scene simulation according to the relevant information, which is the first resource, the second resource and the third resource in order; and use the kernel density estimation method to fit the variables of water, wind and light resources in any period according to the historical data. t The marginal distribution of S2, for the first resource, using the two-dimensional Copula function t Time period variables and t -1 period marginal distribution construction t Time period variables and t -1 Two-dimensional joint distribution of period variables, obtain the known t -1 period variable t The first conditional distribution of the period variable; According to the first conditional distribution and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated value of the period variable; S3, for any resource of the second resource and the third resource, utilize the previous resource through the three-dimensional Copula function t Time period variable, any resource t -1 period variable and any resource t The marginal distribution of the period variable builds the previous resource t Time period variable, any resource t -1 period variable and any resource t Three-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is associated with any resource t -1 period variable when any resource t The second conditional distribution of the period variable; According to the second condition distribution, the previous resource t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated value of the period variable; The acquisition of the second conditional distribution in S3 specifically includes: Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t -1 period variable marginal distribution builds previous resource t The time period variable is associated with any resource t -2D joint distribution of variables in period 1, obtaining known previous resources t The time period variable is any resource t -1 period variable's third conditional distribution; Utilize the previous resource through 2D Copula function t The time period variable is associated with any resource t The marginal distribution of the period variable builds the previous resource t The time period variable is associated with any resource t Two-dimensional joint distribution of time period variables, obtaining known previous resources t The time period variable is any resource t The fourth conditional distribution of the period variable; The third conditional distribution and the fourth conditional distribution are substituted into the formula of the second conditional distribution to convert the second conditional distribution into a function of the third conditional distribution and the fourth conditional distribution.

2. The random simulation method of water-landscape scene considering time-space correlation according to claim 1 is characterized in that: The historical data of water and wind resources in S1 are M OK N Matrix of columns Q : ； In the formula, Q For many years of historical data of resources, M is the number of years, , N Determined according to the target time scale.

3. The random simulation method of water-landscape scene considering time-space correlation according to claim 1, characterized in that: In S1, the relevant information between water, wind and light resources is calculated based on historical data, and the order of water, wind and light scene simulation is determined based on the relevant information, specifically including: Calculate the mutual information between the water, wind and light resources according to historical data, and obtain the mutual information value between the water, wind and light resources; Calculate the sum of the mutual information values ​​between any resource and other resources, obtain the comprehensive mutual information index of any resource, and determine the order of simulation of the three resource scenarios based on the comprehensive mutual information index.

4. The random simulation method of water-landscape scene considering time-space correlation according to claim 1, characterized in that: In S2, for the first resource, it is known that t -1 period variable t The first conditional distribution of the period variable Specifically: ； Where: The first resource t -1 period variable; The first resource t Period variables; C is the selected Copula function type; , Respectively represent the first resource t -1 period variable and t Marginal distribution of the period variable.

5. The random simulation method for water-landscape scene considering time-space correlation according to any one of claims 1 to 4, characterized in that: S2 is distributed according to the first condition and t -1 period variable marginal distribution value, get t The marginal distribution value of the time period variable is obtained t The simulated values ​​of the time period variables include: Get a random number between 0 and 1 as the first conditional distribution value, and t -1 period variable marginal distribution value, get t Marginal distribution values ​​of the time period variable; according to t The marginal distribution values ​​of the time period variables and t Marginal distribution of time period variables, obtain t The simulated value of the period variable; When obtaining the first simulation value of the first resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it according to the average value of the variable information corresponding to the maximum time period in the historical data of the first resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t -1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

6. The random simulation method of water-landscape scene considering time-space correlation according to claim 1, characterized in that: S3 For any resource in the second resource and the third resource, the previous resource is known t The time period variable is associated with any resource t -1 period variable when any resource t Second conditional distribution of the period variable Specifically: ； ； ； Where: For any resource in the second resource or the third resource t Period variables; For any resource in the second resource or the third resource t -1 period variable; For the previous resource t Period variables; Indicates the previous resource t marginal distribution of period variables; , Respectively represents any resource in the second resource and the third resource t -1 period variable and t Marginal distribution of a time period variable: f is the probability density function of the variable, c is the probability density function of the Copula function; Indicates known u 2 conditional copula function.

7. The random simulation method of water-landscape scene considering time-space correlation according to claim 1, characterized in that: S3 distributes the previous resource according to the second condition t The marginal distribution value of the time period variable and any resource t -1 marginal distribution value of the time period variable, obtain any resource t The marginal distribution value of the time period variable is obtained, and then any resource is obtained. t The simulated values ​​of the time period variables include: According to the previous resource t The marginal distribution value of the time period variable, any resource t -1 The marginal distribution value of the time period variable and the third conditional distribution obtain the third conditional distribution value; Obtain a random number between 0 and 1 as a second conditional distribution value, and obtain a fourth conditional distribution value according to the second conditional distribution value, the second conditional distribution, and the third conditional distribution value; According to the previous resource t The marginal distribution value of the time period variable, the fourth conditional distribution and the fourth conditional distribution value, obtain the any resource t Marginal distribution values ​​of the time period variable; According to any resource t The marginal distribution value and marginal distribution of the time period variable, obtain any resource t The simulated value of the period variable.

8. The method for stochastic simulation of water, scenery and light scenes considering temporal and spatial correlation according to claim 7, characterized in that: Get any resource in S3 t The simulated values ​​of the time period variables also include: Previous resource t The marginal distribution value of the time period variable is based on the previous resource t The simulated values ​​of the time period variables and the marginal distribution calculations are obtained; When obtaining the first simulation value of any resource, t -1 The marginal distribution value of the time period variable is obtained by taking a random number, or by calculating and obtaining it based on the average value of the variable information corresponding to the maximum time period in the historical data of any resource and the marginal distribution of the maximum time period; Afterwards, the marginal distribution value is calculated using the previous simulation value and the marginal distribution of the corresponding period as t -1 The marginal distribution value of the period variable is used to obtain the simulation value of the current period.

9. A random simulation system for water, scenery and light scenes considering temporal and spatial correlation, characterized in that: The system includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the random simulation method for water, landscape and light scenes considering time and space correlation described in any one of claims 1 to 8 is executed.

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