A method and apparatus for evaluating the multidimensional complementary characteristics of water, wind and light based on improved vine structures

Abstract

This application relates to a method and apparatus for evaluating the multidimensional complementary characteristics of hydropower, wind power, and solar power based on an improved vine structure. The method includes: obtaining a vine Copula function model by combining mutual information theory with the improved vine Copula structure; constructing a multidimensional evaluation index to measure hydropower regulation capability; defining an output correlation index for the multidimensional complementary hydropower system; obtaining the combined output of hydropower, wind power, and solar power based on the vine Copula function model; calculating the combined fluctuation rate and independent fluctuation rate of different combinations of hydropower, wind power, and solar power; selecting output fluctuation rate, power fluctuation suppression, and output smoothness as evaluation indicators for the overall output stability and volatility of the hydropower system, thus obtaining a multidimensional complementary characteristic evaluation system at the level of different combinations of hydropower, wind power, and solar power, and at the overall system level. This solves the problems that traditional evaluation index systems often only apply to assessing the complementarity between two power generation systems, failing to simultaneously capture the fluctuation trend correlation and slope correlation between adjacent time periods throughout the evaluation period, and neglecting the influence of fluctuation amplitude.

Description

Technical Field

[0001] This application relates to the field of new power system optimization operation technology, and in particular to a method and device for evaluating the multidimensional complementary characteristics of water, wind and solar power based on an improved vine structure. Background Technology

[0002] In integrated hydro-wind-solar systems, the high proportion of uncertain wind and solar power supplied to the grid poses a significant challenge to the safe and stable operation of the hydro-wind-solar system. Thanks to the good regulation capabilities of cascade hydropower and the multi-dimensional complementary characteristics of hydro-wind-solar, the complementary operation of wind power, photovoltaic power and hydropower stations has become an important means for large-scale hydro-wind-solar bases in the basin to cope with the uncertainty of new energy and ensure the large-scale consumption of wind and solar power.

[0003] Effective evaluation of the complementarity of water, wind and solar power is an important prerequisite for integrated scheduling of water, wind and solar power. Mathematically speaking, complementarity is essentially a negative correlation between data sequences. Therefore, most existing technologies directly use or improve traditional evaluation indicators to evaluate the complementarity between energy sources.

[0004] However, while traditional evaluation index systems can assess the complementarity between energy sources to some extent, they are often only applicable to evaluating the complementarity between two power generation systems. They cannot simultaneously capture the correlation of fluctuation trends throughout the entire evaluation period and the slope correlation between adjacent time periods. Furthermore, they largely ignore the impact of fluctuation amplitude, which greatly limits their application in power system operation evaluation and urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method and apparatus for evaluating the multidimensional complementary characteristics of water, wind and solar power based on an improved vine structure. This addresses the problems that traditional evaluation index systems are often only applicable to assessing the complementarity between two power generation systems, failing to simultaneously capture the correlation of fluctuation trends throughout the evaluation period and the slope correlation between adjacent time periods, and ignoring the impact of fluctuation amplitude.

[0006] The first aspect of this application provides a method for evaluating the multidimensional complementary characteristics of water, wind, and solar power based on an improved vine structure, comprising the following steps: constructing a vine Copula function model of a target power generation system based on a preset mutual information theory and a maximum spanning tree algorithm; obtaining the hydropower regulation speed, power level information, and power supply capacity of the target power generation system, and generating a hydropower regulation capability evaluation index based on the hydropower regulation speed, the power level information, and the power supply capacity; calculating the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind, and solar power corresponding to the target power generation system based on the vine Copula function model, and determining the output complementarity index of the target power generation system based on the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind, and solar power; determining the output stability index and fluctuation index corresponding to the target power generation system based on a preset multi-source coordinated scheduling and energy management strategy, and constructing a multidimensional complementary evaluation index corresponding to the target power generation system through the hydropower regulation capability evaluation index, the output complementarity index, the output stability index, and the fluctuation index, so as to obtain the complementary capability evaluation result of the target power generation system based on the multidimensional complementary evaluation index. Optionally, in one embodiment of this application,

[0007] Optionally, in one embodiment of this application, the step of obtaining the hydropower regulation speed, power level information, and power capacity of the target power generation system, and generating a hydropower regulation capability evaluation index based on the hydropower regulation speed, the power level information, and the power capacity, includes: determining the external ideal regulation power capacity of the target power generation system, and calculating the minimum ideal regulation power capacity requirement data of the target power generation system based on the external ideal regulation power capacity; obtaining the maximum power ramp-up capability information and rated power data of the target power generation system, and calculating the maximum response speed of the target power generation system based on the maximum power ramp-up capability information and rated power data; determining the equivalent ideal regulation power of the target power generation system, and calculating the maximum ideal regulation power equivalent level data of the target power generation system based on the equivalent ideal regulation power; and constructing the generated hydropower regulation capability evaluation index based on the minimum ideal regulation power capacity requirement data, the maximum response speed, and the maximum ideal regulation power equivalent level data.

[0008] Optionally, in one embodiment of this application, the step of calculating the joint fluctuation rate and independent fluctuation rate of different combinations of hydropower, wind power, and solar power corresponding to the target power generation system based on the Copula function model, and determining the power output complementarity index of the target power generation system based on the joint fluctuation rate and independent fluctuation rate of different combinations of hydropower, wind power, and solar power, includes: acquiring the hydropower, wind power, and solar power output data of the target power generation system, and normalizing the hydropower, wind power, and solar power output data to generate normalized hydropower, wind power, and solar power output data corresponding to the target power generation system; calculating the wind power installed capacity share, hydropower installed capacity share, and photovoltaic installed capacity share corresponding to the target power generation system respectively, and determining the power output complementarity index of the target power generation system based on the wind power installed capacity... The following methods are used to determine discount coefficients for different combinations of hydropower, wind power, and solar power: First, the combined output of these combinations is calculated using the discount coefficients. Then, the combined fluctuation rate and independent fluctuation rate at time i are calculated based on the combined output. Finally, the complementarity index between wind, hydropower, and solar power outputs at the target time scale is calculated based on the combined fluctuation rate and independent fluctuation rate. And finally, the power output complementarity index of the target power generation system is calculated based on the weighting factor and the complementary index between wind, hydropower, and solar power outputs at the target time scale.

[0009] Optionally, in one embodiment of this application, determining the output stability index and volatility index corresponding to the target power generation system includes: calculating the power output volatility index, the average volatility effect index within the target time window, the power fluctuation smoothness, and the output smoothness corresponding to the target power generation system; determining the volatility index through the power output volatility index, the average volatility effect index within the target time window, and the power fluctuation smoothness, and establishing the output stability index using the output smoothness.

[0010] A second aspect of this application provides a device for evaluating the multidimensional complementary characteristics of water, wind, and solar power based on an improved vine structure, comprising: a modeling module for constructing a vine Copula function model of a target power generation system based on a preset mutual information theory and a maximum spanning tree algorithm; a first index generation module for acquiring information on the hydropower regulation speed, power level, and power supply capacity of the target power generation system, and generating hydropower regulation capability evaluation indicators based on the hydropower regulation speed, the power level, and the power supply capacity; and a second index generation module for calculating the combined effects of different combinations of water, wind, and solar power corresponding to the target power generation system based on the vine Copula function model. The system combines the combined and independent fluctuation rates of the hydropower, wind, and solar power systems, and determines the output complementarity index of the target power generation system based on the combined and independent fluctuation rates of different combinations of hydropower, wind, and solar power. A complementarity capability evaluation module is used to determine the output stability and fluctuation indicators of the target power generation system based on a preset multi-source coordinated scheduling and energy management strategy. It then constructs a multi-dimensional complementarity evaluation index for the target power generation system using the hydropower regulation capability evaluation index, the output complementarity index, the output stability index, and the fluctuation index, and obtains the complementarity capability evaluation result of the target power generation system based on the multi-dimensional complementarity evaluation index.

[0011] Optionally, in one embodiment of this application, the first index generation module includes: a first determining unit, configured to determine the external ideal controllable power capacity of the target power generation system and calculate the minimum ideal controllable power capacity requirement data of the target power generation system based on the external ideal controllable power capacity; an acquiring unit, configured to acquire the maximum power ramp-up capability information and rated power data of the target power generation system, and calculate the maximum response speed of the target power generation system based on the maximum power ramp-up capability information and rated power data; a second determining unit, configured to determine the equivalent ideal controllable power of the target power generation system, and calculate the maximum ideal controllable power equivalent level data of the target power generation system based on the equivalent ideal controllable power; and a construction unit, configured to construct the generated hydropower control capability evaluation index based on the minimum ideal controllable power capacity requirement data, the maximum response speed, and the maximum ideal controllable power equivalent level data.

[0012] Optionally, in one embodiment of this application, the second index generation module includes: a normalization unit, used to acquire the hydropower, wind power, and solar power output data of the target power generation system, and normalize the hydropower, wind power, and solar power output data to generate normalized hydropower, wind power, and solar power output data corresponding to the target power generation system; a first calculation unit, used to calculate the wind power installed capacity share, hydropower installed capacity share, and photovoltaic installed capacity share corresponding to the target power generation system respectively, and determine the discount coefficients corresponding to different combinations of hydropower, wind power, and solar power based on the wind power installed capacity share, the hydropower installed capacity share, and the photovoltaic installed capacity share; and a second calculation unit. The system is used to calculate the combined output of different combinations of water, wind, and solar power using the discount factor, and to calculate the combined fluctuation rate and the independent fluctuation rate corresponding to the different combinations of water, wind, and solar power at time i based on the combined output; the third calculation unit is used to calculate the complementarity index between wind, water, and solar power outputs on the target time scale based on the combined fluctuation rate and the independent fluctuation rate, and to determine the weighting factor corresponding to the complementarity index between wind, water, and solar power outputs on the target time scale; the fourth calculation unit is used to calculate the output complementarity index of the target power generation system based on the weighting factor and the complementarity index between wind, water, and solar power outputs on the target time scale.

[0013] Optionally, in one embodiment of this application, the complementary capability evaluation module includes: a fifth calculation unit, used to calculate the power output volatility index, the average volatility effect index within the target time window, the power fluctuation smoothness, and the output smoothness corresponding to the target power generation system; and an establishment unit, used to determine the volatility index through the power output volatility index, the average volatility effect index within the target time window, and the power fluctuation smoothness, and to establish the output stability index using the output smoothness.

[0014] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure as described in the above embodiments.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure.

[0016] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure.

[0017] Therefore, the embodiments of this application have the following beneficial effects:

[0018] The embodiments of this application can construct a Copula function model of the target power generation system based on a preset mutual information theory and maximum spanning tree algorithm; obtain the hydropower regulation speed, power level information and power supply capacity of the target power generation system, and generate hydropower regulation capability evaluation indicators based on the hydropower regulation speed, power level information and power supply capacity; calculate the joint fluctuation rate and independent fluctuation rate of different combinations of hydropower, wind power and solar power corresponding to the target power generation system based on the Copula function model, and determine the output complementarity index of the target power generation system based on the joint fluctuation rate and independent fluctuation rate of different combinations of hydropower, wind power and solar power; determine the output stability index and fluctuation index of the target power generation system based on a preset multi-source coordinated scheduling and energy management strategy, and construct a multi-dimensional complementary evaluation index of the target power generation system through the hydropower regulation capability evaluation index, output complementarity index, output stability index and fluctuation index, so as to obtain the complementary capability evaluation result of the target power generation system based on the multi-dimensional complementary evaluation index, thereby significantly improving the application prospects and value in power system operation evaluation. This solves the problems that traditional evaluation index systems are often only applicable to assessing the complementarity between two power generation systems, and cannot simultaneously capture the correlation of fluctuation trends throughout the entire evaluation period and the slope correlation between adjacent time periods, while ignoring the impact of fluctuation amplitude.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is a flowchart illustrating a method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, according to an embodiment of this application.

[0022] Figure 2 A schematic diagram of an R-vine structure is provided for one embodiment of this application;

[0023] Figure 3 A schematic diagram of a C-vine structure is provided for one embodiment of this application;

[0024] Figure 4 A schematic diagram of a D-vine structure is provided for one embodiment of this application;

[0025] Figure 5 A schematic diagram of a multi-dimensional complementary evaluation index system for water, wind and solar energy is provided as an embodiment of this application;

[0026] Figure 6 A schematic diagram of the execution logic of an evaluation method for the multidimensional complementary characteristics of water, wind and light based on an improved vine structure, provided as an embodiment of this application;

[0027] Figure 7 This is an example diagram of a device for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, according to an embodiment of this application.

[0028] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0029] Among them, 10-Evaluation device for multi-dimensional complementary characteristics of water, wind and light based on improved vine structure; 100-Modeling module, 200-First index generation module, 300-Second index generation module, 400-Complementary capability evaluation module; 801-Memory, 802-Processor, 803-Communication interface. Detailed Implementation

[0030] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0031] The following describes, with reference to the accompanying drawings, a method and apparatus for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, according to embodiments of this application. To address the problems mentioned in the background, this application provides a method for evaluating the multidimensional complementary characteristics of hydropower, wind power, and solar power based on an improved vine structure. This method constructs a vine Copula function model of the target power generation system based on a pre-defined mutual information theory and a maximum spanning tree algorithm. It acquires information on the hydropower regulation speed, power level, and power supply capacity of the target power generation system to generate hydropower regulation capability evaluation indicators. Based on the vine Copula function model, it calculates the joint and independent fluctuation rates of different combinations of hydropower, wind power, and solar power corresponding to the target power generation system, and determines the output complementarity index of the target power generation system based on these rates. Based on a pre-defined multi-source coordinated scheduling and energy management strategy, it determines the output stability and volatility indicators of the target power generation system. Finally, it constructs a multidimensional complementary evaluation index for the target power generation system using the hydropower regulation capability evaluation index, output complementarity index, output stability index, and volatility index. This multidimensional complementary evaluation index yields the evaluation results of the target power generation system's complementary capabilities, significantly enhancing its application prospects and value in power system operation evaluation. This solves the problems that traditional evaluation index systems are often only applicable to assessing the complementarity between two power generation systems, and cannot simultaneously capture the correlation of fluctuation trends throughout the entire evaluation period and the slope correlation between adjacent time periods, while ignoring the impact of fluctuation amplitude.

[0032] Specifically, Figure 1 A flowchart illustrating a method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, provided in an embodiment of this application.

[0033] like Figure 1 As shown, the evaluation method for the multidimensional complementary characteristics of water, wind, and light based on the improved vine structure includes the following steps:

[0034] In step S101, a Copula function model of the target power generation system is constructed based on the preset mutual information theory and maximum spanning tree algorithm.

[0035] The embodiments of this application can first combine mutual information theory with the vine Copula to improve the vine structure.

[0036] Specifically, embodiments of this application may first introduce the concepts of mutual information and joint entropy. Those skilled in the art should understand that in physics, information entropy is used to describe the degree of disorder in a system; in statistics, information entropy is used to measure the uncertainty of a random variable.

[0037] For a univariate discrete random variable X = {x iLet {i = 1, 2, ..., n} be the probability density function of X, then the information entropy H(X) is:

[0038]

[0039] If X is a continuous random variable, and f(x) is the probability density function of X, then the information entropy H(X) is:

[0040] H(X)=-∫f(x)log(f(x))dx(2)

[0041] The lower H(X), the less information it contains and the less uncertainty the variable has, and vice versa.

[0042] When variable X is determined, the degree of uncertainty of variable Y can be measured by the conditional entropy H(Y|X), which is defined as follows:

[0043] H(Y|X)=-∫∫f(x,y)log(f(y|x))dxdy(3)

[0044] Where f(x,y) and f(y|x) are the joint probability density function and the conditional probability density function, respectively.

[0045] For two variables X and Y, their joint entropy is defined as:

[0046] H(X,Y)=-∫∫f(x,y)log(f(x,y))dxdy(4)

[0047] For variables X, Y, and Z, when variable Z is determined, the conditional joint entropy of variables X and Y is defined as:

[0048] H(X,Y|Z)=-∫∫∫∫f(x,y,z)log(f(x,y|z))dxdydz(5)

[0049] The relationship between information entropy, conditional entropy, and joint entropy is as follows:

[0050] H(X,Y)=H(X)+H(Y|X)=H(Y)+H(X|Y)(6)

[0051] In statistics, the dependence between two variables is called the correlation coefficient, such as the rank correlation coefficient, which describes the interaction between variables. A similar concept exists in information theory: mutual information within information entropy. Unlike the correlation coefficient, mutual information quantifies the amount of information a variable can obtain from another variable by comparing the similarity between the joint distribution f(x,y) and the product of their marginal distributions f(x)·f(y).

[0052] The mutual information I(X,Y) between two random variables X and Y is:

[0053]

[0054] Here, f(x,y) is the joint probability density function of two random variables, and f(x) and f(y) are the marginal probability density functions of variables X and Y, respectively.

[0055] Understandably, mutual information I captures the interdependence between variables X and Y. The greater the mutual information, the higher the correlation between the variables and the more shared information they contain. I is zero if and only if they are mutually independent; that is, determining one variable does not help in obtaining information about the other random variable. The relationship between mutual information, joint entropy, and conditional entropy is as follows:

[0056]

[0057] Conditional mutual information I(X,Y|Z) represents the relevant information between variables X and Y when variable Z is determined, and can be expressed as:

[0058]

[0059] Where f(x,y,z) is the joint probability density function of X, Y, and Z, f(x,y|z) represents the conditional joint probability density function of X and Y given Z, f(x|z) and f(y|z) are the corresponding conditional probability density functions, and f(x), f(y), and f(z) are the marginal probability density functions of variables X, Y, and Z.

[0060] The relationship between conditional mutual information and conditional joint entropy is as follows:

[0061] I(X,Y|Z)=H(X|Z)+H(Y|Z)-H(X,Y|Z)(10)

[0062] From equations (8) and (10), it can be seen that the mutual information between variables is a part of the joint entropy of variables, and the conditional mutual information between variables is a part of the conditional joint entropy of variables. Therefore, the proportion of mutual information between two random variables X and Y can be defined by equations (6) and (7), as shown in the following equation:

[0063]

[0064] Where D(X,Y)∈[0,1], when D(X,Y)=0, it means that the mutual information between variables X and Y is zero, and the two variables are not correlated.

[0065] The proportion of conditional mutual information between two random variables X and Y when variable Z is determined is defined by equations (5) and (9), as shown in the following equation:

[0066]

[0067] Therefore, the embodiments of this application can be extended to n-dimensional datasets X = (X1, X2, L, X n In the case of ), given that other variables are determined, two random variables X i and X j The proportion of conditional mutual information between them is:

[0068]

[0069] Among them, X -i,-j This means that variable X has been removed. i and X j The remaining variables after that.

[0070] Therefore, the embodiments of this application can use equations (11), (12) and (13) to represent the degree of influence of the information of interaction between variables on the overall information, and use the maximum spanning tree algorithm to construct a vine structure for multidimensional variables to generate a vine Copula function model.

[0071] The specific steps for establishing the R-vine Copula model based on the mutual information method in this embodiment are as follows:

[0072] Step 1: Determine the first tree

[0073] In this embodiment of the application, the information proportion D(X) of the original data is calculated using formula (11). i ,X j Then, the first tree is constructed using the maximum spanning tree. The C-vine structure is suitable when the correlation between multiple variables and a certain random variable is significantly stronger than that between the other variables. When the correlations between the random variables are relatively average, the D-vine structure shows better fitting accuracy. However, for high-dimensional variables, the correlations between variables are more complex, and the R-vine structure... Figure 2 As shown, it often has better universality. For R-links, the linking method can be freely chosen according to the characteristics of the data; through the C-linking method, variables are selected in sequence, and then the D(X) relationship between that variable and other variables is calculated. i ,X j ), calculate ΣD(X) i ,X j ), satisfying the condition S=MAX(ΣD(X) i ,X j )) can then form such Figure 3 The T1 connection is shown; by using the D-vine connection method, traverse each variable, and then calculate the D(X) relationship between that variable and other variables. i ,Xj The sum of ΣD(X) i ,X j ), satisfying the condition S=MAX(ΣD(X) i ,X j )) can form such Figure 4 The T1 connection shown completes the connection of the first tree.

[0074] Step 2: Determine the second tree

[0075] Based on the first tree, the proportion of conditional mutual information between the two variables is calculated using equation (12), and then the connection status of the second tree is obtained using the maximum spanning tree.

[0076] Step 3: Repeat step 2 until the last tree is identified, thus completing the vine structure.

[0077] Step 4: Data Conversion

[0078] The original data of each variable are fitted with a distribution, and then the data are mapped to the range [0,1] through a marginal distribution function;

[0079] Step 5: For each edge of the first tree, select the optimal binary Copula function from various binary Copula functions such as Gaussian Copula, t Copula, and Frank Copula. Using edge distribution data, estimate the parameters of each binary Copula function in the tree structure through maximum likelihood estimation. In this embodiment, the observed value of the second tree T2 is calculated using the following formula:

[0080]

[0081] Where v represents the N-1 dimensional vector excluding vector x, v j v represents one of the components. -j To remove the (N-2)-dimensional components of the j-th component, It is a binary Copula function;

[0082] Step 6: Use the observations from Step 5 to perform bivariate Copula function fitting and parameter estimation on the second tree T2;

[0083] Step 7: Repeat step 6 to complete the bivariate Copula function fitting and parameter estimation for all trees, as follows: Figure 4 As shown.

[0084] Therefore, this embodiment of the application divides the vine Copula structure into two parts—the determination of the vine structure and the selection and estimation of Copulas—using an improved method based on mutual information. After determining the last tree, the selection and parameter estimation of pairwise Copula functions begin from the first tree. Thus, compared to traditional vine structure models, the tree structure of the R-vine Copula model in this embodiment is unaffected by the marginal distribution function and the previously specified Copulas, greatly improving the robustness of the model.

[0085] In step S102, the hydropower regulation speed, power level information and power supply capacity of the target power generation system are obtained, so as to generate hydropower regulation capability evaluation index based on the hydropower regulation speed, power level information and power supply capacity.

[0086] Furthermore, embodiments of this application also need to consider hydropower regulation speed, power level, and power supply capacity in order to construct a multi-dimensional evaluation index that can measure hydropower regulation capability.

[0087] Optionally, in one embodiment of this application, obtaining the hydropower regulation speed, power level information, and power capacity of the target power generation system to generate a hydropower regulation capability evaluation index based on the hydropower regulation speed, power level information, and power capacity includes: determining the external ideal regulation power capacity of the target power generation system and calculating the minimum ideal regulation power capacity demand data of the target power generation system based on the external ideal regulation power capacity; obtaining the maximum power ramp-up capability information and rated power data of the target power generation system and calculating the maximum response speed of the target power generation system based on the maximum power ramp-up capability information and rated power data; determining the equivalent ideal regulation power of the target power generation system and calculating the maximum ideal regulation power equivalent level data of the target power generation system based on the equivalent ideal regulation power; and constructing and generating a hydropower regulation capability evaluation index based on the minimum ideal regulation power capacity demand data, the maximum response speed, and the maximum ideal regulation power equivalent level data.

[0088] It is understandable that the factors restricting the regulation capabilities of controllable power sources such as hydropower mainly include regulation speed, power level, and power volume. Hydropower has a fast regulation speed, high power level, and limited power volume. Hydropower-wind-solar hybridization fully mobilizes multiple flexible power sources and leverages their respective regulation advantages to maximize the absorption and support of new energy-load mismatch power.

[0089] Therefore, this application proposes the concept of an "ideal regulated power source", which has the characteristics of fast regulation speed, a certain level of bidirectional power capability and unlimited power. The multi-energy complementarity effect is evaluated with the similarity between the water and electricity complementarity and the ideal power source. The closer the comprehensive regulation characteristics are to the ideal regulated power source, the stronger the regulation capability, and the better the multi-source complementarity is considered.

[0090] It should be noted that the embodiments of this application take into account the limitations of hydropower in terms of regulation speed, power level, and power volume, and take the similarity of ideal regulation power source as the core. The analysis and evaluation of the complementary regulation capability of hydropower is carried out from three aspects: minimum ideal regulation power source capacity requirement, maximum response speed, and maximum ideal regulation power source equivalent level, as described in detail below:

[0091] (1) Minimum ideal regulated power supply capacity requirement

[0092] For hydro-wind-solar systems, sufficient additional hydropower peak-shaving capacity can achieve fully flexible load regulation. In practical implementation, the additional hydropower peak-shaving capacity can be considered as an ideal regulating power source. Therefore, the evaluation of the complementary regulation capability of hydro-wind-solar systems can be reduced to focusing on the required capacity of the ideal regulating power source. Thus, this application proposes a "minimum ideal regulating power source capacity requirement" as shown in the following formula:

[0093]

[0094] Among them, P req This refers to the required capacity of the ideal external regulating power supply; this indicator also refers to the auxiliary regulating capacity required per unit load level.

[0095] (2) Maximum response speed

[0096] Regarding the adjustment speed, this application embodiment uses the ratio of the power supply's maximum ramp-up capability to the total power capacity over a certain time scale as an indicator to measure the complementary regulation capability, as shown in the following formula.

[0097]

[0098] Wherein, ΔP i P represents the maximum ramp-up capability of the power supply. i,N This is the rated power of the power supply; this indicator reflects the proportion of ramp-up capacity in the total power capacity.

[0099] (3) Equivalent level of the maximum ideal regulated power supply

[0100] In this embodiment, the maximum output that the system can achieve and sustain for a certain period of time can be taken as the ideal equivalent level of the regulated power supply of the system, as shown in the following formula:

[0101]

[0102] Among them, P eq The equivalent ideal controllable power supply is used. This indicator reflects the equivalent level of the ideal controllable power supply in a multi-source system. If a more concise representation of the complementary controllability of hydropower is required, only the "maximum ideal controllable power supply equivalent level" can be selected.

[0103] Therefore, the embodiments of this application construct a multi-dimensional evaluation index that can measure the hydropower regulation capability by means of hydropower regulation speed, power level and power supply capacity, thereby effectively ensuring the reliability of the evaluation of the multi-dimensional complementary characteristics of water, wind and solar power.

[0104] In step S103, based on the Copula function model, the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power corresponding to the target power generation system are calculated, and the output complementarity index of the target power generation system is determined according to the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power.

[0105] Furthermore, embodiments of this application also require obtaining the combined power output of water, wind, and solar based on the Copula function model, calculating the combined fluctuation rate and independent fluctuation rate of the three combinations of water-wind, water-solar, and wind-solar, in order to determine the complementarity index of water, wind, and solar power output, and considering the weights under multiple time scales, thereby realizing the calculation of the optimal water, wind, and solar installed capacity ratio.

[0106] Optionally, in one embodiment of this application, based on the Copula function model, the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind, and solar power corresponding to the target power generation system are calculated, and the power output complementarity index of the target power generation system is determined according to the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind, and solar power. This includes: acquiring water, wind, and solar power output data of the target power generation system, and normalizing the water, wind, and solar power output data to generate normalized water, wind, and solar power output data corresponding to the target power generation system; calculating the wind power installed capacity share, hydropower installed capacity share, and photovoltaic power share corresponding to the target power generation system, respectively. Based on the installed capacity share of wind power, hydropower, and photovoltaic power, the combined output of different combinations of hydropower, wind power, and photovoltaic power is calculated. The combined fluctuation rate and independent fluctuation rate of different combinations of hydropower, wind power, and photovoltaic power are calculated at time i based on the combined output. Based on the combined fluctuation rate and independent fluctuation rate, the complementarity index between wind, hydro, and photovoltaic power outputs at the target time scale is calculated, and the weighting factor corresponding to the complementarity index at the target time scale is determined. Based on the weighting factor and the complementarity index between wind, hydro, and photovoltaic power outputs at the target time scale, the output complementarity index of the target power generation system is calculated.

[0107] It should be noted that after selecting the optimal Copula function to simulate the joint probability distribution of hydropower, wind power, and photovoltaic power output in a hydro-wind-solar hybrid power generation system, the embodiments of this application can calculate the complementarity index of the hydro-wind-solar combination.

[0108] Specifically, in the embodiments of this application, the specific calculation process of the complementarity index is as follows:

[0109] 1. To improve the calculation speed, this embodiment first obtains water-wind-solar power output data and performs linear normalization on the maximum and minimum values, the mathematical expression of which is as follows:

[0110]

[0111] Where P′(i) represents the output of water-wind-light at time i, P max and P min They represent the maximum and minimum values ​​of water, wind and solar power output, respectively, and P(i) represents the normalized value of water, wind and solar power output (i.e., normalized water, wind and solar power output data);

[0112] 2. The combined power output at time i for the three combinations of hydropower-photovoltaic, wind power-photovoltaic, and wind power-hydropower is calculated as follows:

[0113]

[0114] Among them, P w (i), P h (i), P s (i) represent the normalized theoretical output values ​​of wind power, hydropower, and photovoltaic power at time i, respectively; P wh (i), P hs (i), P ws (i) represents the combined output of the three combinations of water-wind, water-solar, and wind-solar at time i; α, β, and γ are the installed capacity ratios of wind power, hydropower, and photovoltaic, respectively.

[0115] Subsequently, the embodiments of this application can calculate the joint volatility change rate and the independent volatility change rate at time i→j. When j>i, the formula is as follows:

[0116]

[0117] Among them, R w (i,j), R h (i,j), R s (i,j) represent the power output fluctuation rates of wind power, hydropower, and photovoltaic, respectively;

[0118]

[0119] Where, ΔR h,s (i,j), ΔR w,s (i,j), ΔR w,h (i,j) represent the fluctuation rate of the combined output of water-solar, wind-solar, and wind-water combinations, respectively;

[0120] 3. In this embodiment, the comprehensive index C between wind, water, and solar power output is calculated for the corresponding time scale T. The larger the value of C, the better the complementarity between the power sources in the system. The formula is as follows:

[0121]

[0122] For a given total installed capacity of hydropower, wind power, and photovoltaic power, the embodiments of this application can introduce corresponding weights to assign corresponding weights to the complementarity indicators at different time scales, thereby realizing a multi-time-scale evaluation of hydropower-wind power-solar complementarity. The formula is as follows:

[0123] C = p year C year +p month C month +p day C day +p hour C hour (twenty three)

[0124] Among them, C year C month C day C hour These represent comprehensive indicators on the time scales of year, month, day, and hour, respectively; p year p month p day p hour These represent the corresponding weights. The magnitude of this weighting factor depends on the importance of the time scale in complementarity and varies depending on the application.

[0125] Therefore, this application embodiment defines the output correlation index of the water-wind-solar multidimensional complementary system and obtains the joint output of water-wind-solar based on the Copula function model, so as to calculate the joint fluctuation rate and independent fluctuation rate of different combinations of water-wind-solar, thereby further improving the evaluation system of water-wind-solar multidimensional complementary characteristics.

[0126] In step S104, based on the preset multi-source coordinated scheduling and energy management strategy, the output stability index and volatility index corresponding to the target power generation system are determined. Then, a multi-dimensional complementary evaluation index corresponding to the target power generation system is constructed through the hydropower regulation capacity evaluation index, output complementarity index, output stability index and volatility index, so as to obtain the complementary capability evaluation result of the target power generation system according to the multi-dimensional complementary evaluation index.

[0127] Furthermore, embodiments of this application also require, based on multi-source coordinated scheduling and energy management strategies, the selection of power output volatility, average volatility effect, power output volatility mitigation, and system output smoothness as evaluation indicators to measure the output stability and volatility of the hydro-wind-solar multi-energy complementary system; subsequently, as... Figure 5 As shown, embodiments of this application can also obtain a multi-dimensional complementary evaluation index system for water, wind and solar power based on output correlation index, hydropower regulation capacity index, output stability index, and output fluctuation index, so as to obtain the evaluation results of the complementary capabilities of the water, wind and solar multi-energy complementary system using the multi-dimensional complementary evaluation index system for water, wind and solar power.

[0128] Optionally, in one embodiment of this application, determining the output stability index and volatility index corresponding to the target power generation system includes: calculating the power output volatility index, the average volatility effect index within the target time window, the power fluctuation smoothness, and the output smoothness corresponding to the target power generation system; determining the volatility index through the power output volatility index, the average volatility effect index within the target time window, and the power fluctuation smoothness, and establishing the output stability index using the output smoothness.

[0129] Specifically, embodiments of this application can utilize the power output volatility index to describe the complementary effect of the integrated hydro-wind-solar system (i.e., the target power generation system) on mitigating power output volatility and its complementary capability with quantized signals. This index can be represented by set B:

[0130]

[0131] B={β i |β i =Σγ m,i |,m∈M,i=1~N}(25)

[0132] The average fluctuation effect of the water-wind-solar system within the time window is expressed as:

[0133]

[0134] The power fluctuation suppression of a hydro-wind-solar system is expressed as follows:

[0135]

[0136] Where, σ A σ represents the root mean square deviation of the output power of the largest natural resource-constrained power source in the hydro-wind-solar system; B The mean square deviation of the total output power of the system after adding other power sources.

[0137] It should be noted that, in the embodiments of this application, the hydro-wind-solar system simultaneously includes both resource-constrained power sources and adjustable power sources. Through comprehensive complementarity, the power output of the hydro-wind-solar system can be made more continuous and stable. Specifically, the power output smoothness index of the hydro-wind-solar system is used for evaluation, as shown in the following formula:

[0138]

[0139] in,

[0140]

[0141] P i =P load,i -P nature,i -P control,i (30)

[0142]

[0143] Among them, P load,i P represents the load at time i. nature,i P represents the output of a resource-constrained power source at time i. control,i This represents the output of the adjustable power supply at time i.

[0144] Therefore, this application embodiment selects output fluctuation rate, power fluctuation suppression degree, and output smoothness as evaluation indicators of the overall output stability and fluctuation of the hydro-wind-solar system, thereby obtaining a multi-dimensional complementary characteristic evaluation system for different power supply combinations of hydro-wind-solar systems and the overall system level.

[0145] The execution logic of the water, wind, and light multidimensional complementary characteristic evaluation method based on the improved vine structure of this application is explained below with reference to the accompanying drawings.

[0146] Figure 6 This is a schematic diagram illustrating the execution logic of the multi-dimensional complementary characteristic evaluation method for water, wind, and light based on an improved vine structure proposed in this application. Figure 6 As shown, the execution process of the water-wind-solar multidimensional complementary characteristic evaluation method based on the improved vine structure of this application is as follows:

[0147] S601: First, the concepts of mutual information and joint entropy are introduced. Mutual information is used to represent the degree of influence of the information of interaction between variables on the overall information. The maximum spanning tree algorithm is used to construct a vine structure for multidimensional variables to generate a vine Copula function model.

[0148] S602: First, the concept of "ideal control power source" is proposed. Taking the similarity of ideal control power source as the core, based on the flexibility of hydropower operation, the minimum ideal control power source capacity requirement, the maximum response speed, and the maximum ideal control power source equivalent level are selected to measure the hydropower control capability.

[0149] S603: Based on the Copula function model, the combined output of water, wind and solar power is obtained. The combined fluctuation rate and independent fluctuation rate of the three combinations of water-wind, water-solar and wind-solar are calculated. The complementarity index of water, wind and solar power output is defined, and the weights under multiple time scales are considered to calculate the complementarity index under multiple time scales.

[0150] S604: Based on the multi-source collaborative scheduling and energy management strategy, the power output volatility, average volatility effect, power fluctuation smoothness, and system output smoothness are selected as evaluation indicators to measure the output stability and volatility of the hydro-wind-solar multi-energy complementary system.

[0151] S605: Based on the output correlation index, hydropower regulation capacity index, output stability index, and output fluctuation index, a multi-dimensional complementary evaluation index system for water, wind and solar power is obtained, and the complementary capability evaluation results of the water, wind and solar multi-energy complementary system are obtained by using the multi-dimensional complementary evaluation index system for water, wind and solar power.

[0152] The method for evaluating the multidimensional complementary characteristics of water, wind, and solar power based on an improved vine structure proposed in this application constructs a vine Copula function model of the target power generation system based on a preset mutual information theory and maximum spanning tree algorithm. It acquires information on the hydropower regulation speed, power level, and power supply capacity of the target power generation system to generate hydropower regulation capability evaluation indicators. Based on the vine Copula function model, it calculates the joint and independent fluctuation rates of different combinations of water, wind, and solar power corresponding to the target power generation system, and determines the output complementarity index of the target power generation system based on these rates. Based on a preset multi-source coordinated scheduling and energy management strategy, it determines the output stability and volatility indicators of the target power generation system. Finally, it constructs a multidimensional complementary evaluation index for the target power generation system using the hydropower regulation capability evaluation index, output complementarity index, output stability index, and volatility index. This multidimensional complementary evaluation index yields the evaluation results of the target power generation system's complementary capabilities, significantly enhancing its application prospects and value in power system operation evaluation.

[0153] Secondly, with reference to the accompanying drawings, the device for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, according to an embodiment of this application, is described.

[0154] Figure 7 This is a block diagram of an evaluation device for the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, according to an embodiment of this application.

[0155] like Figure 7 As shown, the water-wind-solar multidimensional complementary characteristic evaluation device 10 based on the improved vine structure includes: a modeling module 100, a first index generation module 200, a second index generation module 300, and a complementary capability evaluation module 400.

[0156] Among them, the modeling module 100 is used to construct the Copula function model of the target power generation system based on the preset mutual information theory and the maximum spanning tree algorithm.

[0157] The first indicator generation module 200 is used to obtain information on the hydropower regulation speed, power level, and power capacity of the target power generation system, so as to generate hydropower regulation capability evaluation indicators based on the hydropower regulation speed, power level, and power capacity.

[0158] The second index generation module 300 is used to calculate the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power corresponding to the target power generation system based on the Copula function model, and to determine the output complementarity index of the target power generation system based on the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power.

[0159] The complementary capability evaluation module 400 is used to determine the output stability index and volatility index of the target power generation system based on the preset multi-source coordinated scheduling and energy management strategy. It constructs a multi-dimensional complementary evaluation index of the target power generation system through the hydropower regulation capability evaluation index, output complementarity index, output stability index and volatility index, so as to obtain the complementary capability evaluation result of the target power generation system based on the multi-dimensional complementary evaluation index.

[0160] Optionally, in one embodiment of this application, the first indicator generation module 200 includes: a first determining unit, an acquisition unit, a second determining unit, and a construction unit.

[0161] The first determining unit is used to determine the external ideal controllable power supply capacity of the target power generation system and calculate the minimum ideal controllable power supply capacity requirement data of the target power generation system based on the external ideal controllable power supply capacity.

[0162] The acquisition unit is used to acquire the maximum power ramp-up capability information and rated power data of the target power generation system, and to calculate the maximum response speed of the target power generation system using the maximum power ramp-up capability information and rated power data.

[0163] The second determining unit is used to determine the equivalent ideal regulated power of the target power generation system, so as to calculate the maximum ideal regulated power equivalent level data of the target power generation system based on the equivalent ideal regulated power.

[0164] The construction unit is used to construct and generate hydropower regulation capability evaluation indicators based on minimum ideal control power capacity demand data, maximum response speed and maximum ideal control power equivalent level data.

[0165] Optionally, in one embodiment of this application, the second indicator generation module 300 includes: a normalization unit, a first calculation unit, a second calculation unit, a third calculation unit, and a fourth calculation unit.

[0166] The normalization unit is used to acquire the hydropower, wind power and solar power output data of the target power generation system, and to normalize the hydropower, wind power and solar power output data to generate the normalized hydropower, wind power and solar power output data corresponding to the target power generation system.

[0167] The first calculation unit is used to calculate the wind power installed capacity share, hydropower installed capacity share, and photovoltaic installed capacity share of the target power generation system, respectively.

[0168] The second calculation unit is used to calculate the combined output of different combinations of water, wind and solar power based on the installed capacity share, and to calculate the combined fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power at time i based on the combined output.

[0169] The third calculation unit is used to calculate the complementarity index between wind, water and solar power output on the target time scale based on the joint fluctuation rate and the independent fluctuation rate, and to determine the weighting factor corresponding to the complementarity index between wind, water and solar power output on the target time scale.

[0170] The fourth calculation unit is used to calculate the output complementarity index of the target power generation system based on the weighting factor and the complementarity index between wind, water and solar power output on the target time scale.

[0171] Optionally, in one embodiment of this application, the complementary capability evaluation module 400 includes: a fifth calculation unit and an establishment unit.

[0172] The fifth calculation unit is used to calculate the power output volatility index, the average volatility effect index within the target time window, the power fluctuation smoothness, and the output smoothness of the target power generation system.

[0173] A unit is established to determine the volatility index by means of the power output volatility index, the average volatility effect index within the target time window, and the power fluctuation smoothness index, and to establish the power output stability index by means of the power output smoothness.

[0174] It should be noted that the foregoing explanation of the embodiment of the evaluation method for the multidimensional complementary characteristics of water, wind and light based on the improved vine structure also applies to the evaluation device for the multidimensional complementary characteristics of water, wind and light based on the improved vine structure in this embodiment, and will not be repeated here.

[0175] The water-wind-solar multidimensional complementary characteristic evaluation device based on the improved vine structure proposed in this application includes a modeling module for constructing a vine Copula function model of the target power generation system based on a preset mutual information theory and maximum spanning tree algorithm; a first index generation module for acquiring the hydropower regulation speed, power level information, and power supply capacity of the target power generation system, and generating hydropower regulation capability evaluation indicators based on the hydropower regulation speed, power level information, and power supply capacity; and a second index generation module for calculating the joint fluctuation rate and independent fluctuation of different combinations of water, wind, and solar power corresponding to the target power generation system based on the vine Copula function model. The system determines the output complementarity index of the target power generation system based on the combined and independent fluctuation rates of different combinations of hydropower, wind power, and solar power. The complementarity capability evaluation module, based on preset multi-source coordinated dispatch and energy management strategies, determines the output stability and fluctuation indicators corresponding to the target power generation system. It then constructs a multi-dimensional complementary evaluation index for the target power generation system using hydropower regulation capability evaluation indicators, output complementarity indicators, output stability indicators, and fluctuation indicators. This allows for the acquisition of the complementary capability evaluation results of the target power generation system based on these multi-dimensional complementary evaluation indicators, thereby significantly enhancing its application prospects and value in power system operation evaluation.

[0176] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0177] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0178] When the processor 802 executes the program, it implements the evaluation method for the multidimensional complementary characteristics of water, wind and light based on the improved vine structure provided in the above embodiments.

[0179] Furthermore, electronic devices also include:

[0180] Communication interface 803 is used for communication between memory 801 and processor 802.

[0181] The memory 801 is used to store computer programs that can run on the processor 802.

[0182] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0183] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0184] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0185] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0186] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure.

[0187] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure.

[0188] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0189] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0190] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0191] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0192] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0193] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0194] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0195] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, characterized in that, Includes the following steps: Based on the pre-defined mutual information theory and maximum spanning tree algorithm, a Copula function model of the target power generation system is constructed. The hydropower regulation speed, power level information, and power capacity of the target power generation system are obtained, and hydropower regulation capability evaluation index is generated based on the hydropower regulation speed, the power level information, and the power capacity. Based on the aforementioned Copula function model, the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power corresponding to the target power generation system are calculated, and the output complementarity index of the target power generation system is determined based on the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power. Based on the preset multi-source collaborative scheduling and energy management strategy, the output stability index and volatility index corresponding to the target power generation system are determined. Then, a multi-dimensional complementary evaluation index corresponding to the target power generation system is constructed through the hydropower regulation capacity evaluation index, the output complementarity index, the output stability index and the volatility index, so as to obtain the complementary capability evaluation result of the target power generation system according to the multi-dimensional complementary evaluation index. The step of acquiring the hydropower regulation speed, power level information, and power supply capacity of the target power generation system, and generating hydropower regulation capability evaluation indicators based on the hydropower regulation speed, the power level information, and the power supply capacity, includes: Determine the external ideal controllable power supply capacity of the target power generation system, and calculate the minimum ideal controllable power supply capacity requirement data of the target power generation system based on the external ideal controllable power supply capacity; Obtain the maximum power ramp-up capability information and rated power data of the target power generation system, and calculate the maximum response speed of the target power generation system using the maximum power ramp-up capability information and rated power data; Determine the equivalent ideal regulated power of the target power generation system, and calculate the maximum ideal regulated power equivalent level data of the target power generation system based on the equivalent ideal regulated power. Based on the minimum ideal control power capacity requirement data, the maximum response speed and the equivalent level data of the maximum ideal control power, the evaluation index of the generated hydropower control capability is constructed. Based on the Copula function model, the joint and independent fluctuation rates of different combinations of water, wind, and solar power for the target power generation system are calculated. The output complementarity index of the target power generation system is then determined based on these joint and independent fluctuation rates, including: Acquire the hydropower, wind power, and solar power output data of the target power generation system, and normalize the hydropower, wind power, and solar power output data to generate normalized hydropower, wind power, and solar power output data corresponding to the target power generation system. Calculate the wind power installed capacity share, hydropower installed capacity share, and photovoltaic installed capacity share corresponding to the target power generation system respectively, and determine the discount coefficients corresponding to different combinations of hydropower, wind power, and photovoltaic based on the wind power installed capacity share, the hydropower installed capacity share, and the photovoltaic installed capacity share; The combined output of different combinations of water, wind, and solar power is calculated using the discount factor, and the combined output is then used to calculate... i The combined fluctuation rate and the independent fluctuation rate corresponding to different combinations of water, wind and light at different times; Based on the joint fluctuation rate and the independent fluctuation rate, the complementarity index between wind, water and solar power output on the target time scale is calculated, and the weighting factor corresponding to the complementarity index between wind, water and solar power output on the target time scale is determined. The power output complementarity index of the target power generation system is calculated based on the weighting factor and the complementarity index between wind, water and solar power output on the target time scale. The determination of the output stability index and fluctuation index corresponding to the target power generation system includes: Calculate the power output volatility index, the average volatility effect index within the target time window, the power fluctuation smoothness, and the output smoothness corresponding to the target power generation system; The volatility index is determined by the power output volatility index, the average volatility effect index within the target time window, and the power fluctuation smoothness, and the power output stability index is established by the power output smoothness. The mathematical expression for the output smoothness index is: in, in, express i The load of time, express i Output of natural resource-constrained power sources at any given time. express i The output of a power source that can be adjusted at any time.

2. A device for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure, characterized in that, include: The modeling module is used to construct the Copula function model of the target power generation system based on the preset mutual information theory and the maximum spanning tree algorithm. The first indicator generation module is used to obtain the hydropower regulation speed, power level information and power capacity of the target power generation system, so as to generate hydropower regulation capability evaluation indicators based on the hydropower regulation speed, the power level information and the power capacity. The second index generation module is used to calculate the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power corresponding to the target power generation system based on the Copula function model, and to determine the output complementarity index of the target power generation system based on the joint fluctuation rate and independent fluctuation rate of different combinations of water, wind and solar power. The complementary capability evaluation module is used to determine the output stability index and volatility index corresponding to the target power generation system based on the preset multi-source coordinated scheduling and energy management strategy, and to construct a multi-dimensional complementary evaluation index corresponding to the target power generation system through the hydropower regulation capability evaluation index, the output complementarity index, the output stability index and the volatility index, so as to obtain the complementary capability evaluation result of the target power generation system according to the multi-dimensional complementary evaluation index. The first indicator generation module includes: The first determining unit is used to determine the external ideal controllable power supply capacity of the target power generation system, and calculate the minimum ideal controllable power supply capacity requirement data of the target power generation system based on the external ideal controllable power supply capacity. The acquisition unit is used to acquire the maximum power ramp-up capability information and rated power data of the target power generation system, and to calculate the maximum response speed of the target power generation system using the maximum power ramp-up capability information and rated power data. The second determining unit is used to determine the equivalent ideal regulated power of the target power generation system, so as to calculate the maximum ideal regulated power equivalent level data of the target power generation system based on the equivalent ideal regulated power. A construction unit is used to construct the hydropower regulation capability evaluation index based on the minimum ideal control power capacity demand data, the maximum response speed, and the maximum ideal control power equivalent level data. The second indicator generation module includes: The normalization unit is used to acquire the hydropower, wind power and solar power output data of the target power generation system, and to normalize the hydropower, wind power and solar power output data to generate normalized hydropower, wind power and solar power output data corresponding to the target power generation system. The first calculation unit is used to calculate the wind power installed capacity share, hydropower installed capacity share and photovoltaic installed capacity share corresponding to the target power generation system respectively, and to determine the discount coefficients corresponding to different combinations of hydropower, wind power and photovoltaic based on the wind power installed capacity share, the hydropower installed capacity share and the photovoltaic installed capacity share; The second calculation unit is used to calculate the combined output of different combinations of water, wind, and solar power using the discount factor, and to calculate the combined output based on the combined output. i The combined fluctuation rate and the independent fluctuation rate corresponding to different combinations of water, wind and light at different times; The third calculation unit is used to calculate the complementarity index between wind, water and solar power output on the target time scale based on the joint fluctuation change rate and the independent fluctuation change rate, and to determine the weighting factor corresponding to the complementarity index between wind, water and solar power output on the target time scale. The fourth calculation unit is used to calculate the power output complementarity index of the target power generation system based on the weighting factor and the power output complementarity index between wind, water and solar power on the target time scale. The complementary capability evaluation module includes: The fifth calculation unit is used to calculate the power output volatility index, the average volatility effect index within the target time window, the power fluctuation smoothness, and the output smoothness of the target power generation system. A unit is established to determine the volatility index through the power output volatility index, the average volatility effect index within the target time window, and the power fluctuation smoothness, and to establish the power output stability index using the power output smoothness. The mathematical expression for the output smoothness index is: in, in, express i The load of time, express i Output of natural resource-constrained power sources at any given time. express i The output of a power source that can be adjusted at any time.

3. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for evaluating the multidimensional complementary characteristics of water, wind, and light based on an improved vine structure as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for evaluating the multidimensional complementary characteristics of water, wind, and light based on the improved vine structure as described in claim 1.

5. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the method for evaluating the multidimensional complementary characteristics of water, wind, and light based on the improved vine structure as described in claim 1.

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