Method and device for evaluating multi-dimensional complementary characteristics of water, wind and light based on improved rattan structure

Abstract

The invention relates to a water-wind-light multi-dimensional complementary characteristic evaluation method and device based on an improved vine structure, and the method comprises the steps: combining a mutual information theory with a vine Copula improved vine structure, and obtaining a vine Copula function model; constructing a multi-dimensional evaluation index capable of measuring the water and electricity regulation and control capability; defining output correlation indexes of the water-wind-light multi-dimensional complementary system, obtaining water-wind-light combined output based on a vine Copula function model, and calculating combined fluctuation change rates and independent fluctuation change rates of different water-wind-light combinations; and selecting the output fluctuation ratio, the power fluctuation stabilization degree and the output smoothness as the overall output stability and fluctuation evaluation indexes of the water-wind-light system to obtain a multi-dimensional complementary characteristic evaluation system of different water-wind-light combinations and the overall level of the system. Therefore, the problems that a traditional evaluation index system is only suitable for evaluating the complementarity between two power generation systems, the fluctuation trend correlation of the whole evaluation period and the slope correlation of adjacent time periods cannot be captured at the same time, and the influence of the fluctuation amplitude is neglected are solved.

Description

Technical Field

[0001] The present application relates to the technical field of optimized operation of new power systems, and in particular to a method and device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure. Background Art

[0002] In the integrated water, wind and solar power system in the river basin, the high proportion of uncertain wind and solar power sources connected to the grid and externally transmitted to the water, wind and solar power system has brought great challenges to the safe and stable operation of the water, wind and solar power system; thanks to the good regulation capability of cascade hydropower and the multi-dimensional complementary characteristics of water, wind and solar power, the complementary operation of wind power, photovoltaic power and hydropower stations has become an important means for large-scale water, wind and solar power bases in the river basin to cope with the uncertainty of new energy and ensure the large-scale consumption of wind and solar power.

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

[0004] However, although the traditional evaluation index system can evaluate the complementarity between energy sources to a certain extent, it is often only applicable to evaluating the complementarity between two power generation systems. It is unable to simultaneously capture the fluctuation trend correlation of the entire evaluation period and the slope correlation of adjacent time periods. At the same time, it largely ignores the impact of fluctuation amplitude, which greatly limits its application effect in power system operation evaluation and needs to be solved urgently. Summary of the invention

[0005] The present application provides a method and device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure, so as to solve the problems that the traditional evaluation index system is often only applicable to evaluating the complementarity between two power generation systems, cannot simultaneously capture the fluctuation trend correlation of the entire evaluation period and the slope correlation of adjacent time periods, and ignores the impact of the fluctuation amplitude.

[0006] The first aspect of the present application provides a method for evaluating the multi-dimensional complementary characteristics of water, wind and light 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, so as to generate a hydropower regulation capability evaluation index according to the hydropower regulation speed, the power level information and the power supply capacity; calculating the joint fluctuation change rate and the independent fluctuation change rate of different combinations of water, wind and light 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 according to the joint fluctuation change rate and the independent fluctuation change rate of different combinations of water, wind and light; determining the output stability index and the volatility index corresponding to the target power generation system based on a preset multi-source coordinated scheduling and energy management strategy, and constructing 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. Optionally, in one embodiment of the present application,

[0007] Optionally, in one embodiment of the present application, the obtaining of the hydropower regulation speed, power level information and power supply capacity of the target power generation system to generate a hydropower regulation capability evaluation index based on the hydropower regulation speed, the power level information and the power supply capacity includes: determining the external ideal regulation power supply capacity of the target power generation system, and calculating the minimum ideal regulation power supply capacity requirement data of the target power generation system based on the external ideal regulation power supply capacity; obtaining the maximum power supply climbing capability information and power supply rated power data of the target power generation system, and calculating the maximum response speed of the target power generation system through the maximum power supply climbing capability information and the power supply rated power data; determining the equivalent ideal regulation power supply power of the target power generation system, and calculating the maximum ideal regulation power supply equivalent level data of the target power generation system based on the equivalent ideal regulation power supply power; and constructing the generated hydropower regulation capability evaluation index based on the minimum ideal regulation power supply capacity requirement data, the maximum response speed and the maximum ideal regulation power supply equivalent level data.

[0008] Optionally, in one embodiment of the present application, based on the vine Copula function model, the combined fluctuation change rate and the independent fluctuation change 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 combined fluctuation change rate and the independent fluctuation change rate of different combinations of water, wind and solar power, including: obtaining the 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 the normalized water, wind and solar power output data corresponding to the target power generation system; respectively calculating the wind power installed capacity share, hydropower installed capacity share and photovoltaic installed capacity share corresponding to the target power generation system, and based on the wind power installed capacity The share, the hydropower installed capacity share and the photovoltaic installed capacity share are used to determine the discount coefficients corresponding to the different combinations of water, wind and photovoltaic; the joint output of the different combinations of water, wind and photovoltaic is calculated by the discount coefficient, and the joint fluctuation change rate and the independent fluctuation change rate corresponding to the different combinations of water, wind and photovoltaic at time i are calculated according to the joint output; based on the joint fluctuation change rate and the independent fluctuation change rate, the complementarity index between the wind, water and photovoltaic output on the target time scale is calculated, and the weighting factor corresponding to the complementarity index between the wind, water and photovoltaic output on the target time scale is determined; the output complementarity index of the target power generation system is calculated according to the weighting factor and the complementarity index between the wind, water and photovoltaic output on the target time scale.

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

[0010] The second aspect of the present application provides a device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure, including: a modeling module, which is used to construct 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 indicator generation module, which is used to obtain the hydropower regulation speed, power level information and power supply capacity of the target power generation system, so as to generate a hydropower regulation capability evaluation index according to the hydropower regulation speed, the power level information and the power supply capacity; a second indicator generation module, which is used to calculate the linkage characteristics of different combinations of water, wind and light corresponding to the target power generation system based on the vine Copula function model. The combined fluctuation rate of change and the independent fluctuation rate of change, and the output complementarity index of the target power generation system is determined according to the combined fluctuation rate of change and the independent fluctuation rate of change of different combinations of water, wind and light; a complementary capacity 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 collaborative scheduling and energy management strategy, and construct the multi-dimensional complementary evaluation index corresponding to the target power generation system 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 capacity evaluation result of the target power generation system according to the multi-dimensional complementary evaluation index.

[0011] Optionally, in one embodiment of the present application, the first indicator generation module includes: a first determination unit, used to determine the external ideal control power capacity of the target power generation system, and calculate the minimum ideal control power capacity requirement data of the target power generation system based on the external ideal control power capacity; an acquisition unit, used to acquire the maximum power climbing capability information and power rated power data of the target power generation system, and calculate the maximum response speed of the target power generation system through the maximum power climbing capability information and the power rated power data; a second determination unit, used to determine the equivalent ideal control power power of the target power generation system, so as to calculate the maximum ideal control power equivalent level data of the target power generation system based on the equivalent ideal control power power; a construction unit, used to construct the generated hydropower control capability evaluation index based on the minimum ideal control power capacity requirement data, the maximum response speed and the maximum ideal control power equivalent level data.

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

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

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

[0015] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on the improved vine structure.

[0016] The fifth aspect embodiment of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on the improved vine structure.

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

[0018] The embodiments of the present application can construct a vine Copula function model of the target power generation system based on the 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 a hydropower regulation capability evaluation index based on the hydropower regulation speed, power level information and power supply capacity; based on the vine Copula function model, calculate the joint fluctuation change rate and independent fluctuation change rate of different combinations of water, wind and light corresponding to the target power generation system, and determine the output complementarity index of the target power generation system according to the joint fluctuation change rate and independent fluctuation change rate of different combinations of water, wind and light; based on the preset multi-source collaborative scheduling and energy management strategy, determine the output stability index and volatility index corresponding to the target power generation system, and construct a multidimensional complementary evaluation index corresponding to 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 according to the multidimensional complementary evaluation index, thereby significantly improving the application prospects and value in the power system operation evaluation. This solves the problems that the traditional evaluation index system is often only applicable to evaluating the complementarity between two power generation systems, cannot simultaneously capture the fluctuation trend correlation of the entire evaluation period and the slope correlation of adjacent time periods, and ignores the impact of fluctuation amplitude.

[0019] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 1 A flow chart of a method for evaluating multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure provided according to an embodiment of the present application;

[0022] Figure 2 A schematic diagram of an R vine structure provided for one embodiment of the present application;

[0023] Figure 3 A schematic diagram of a C vine structure provided for one embodiment of the present application;

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

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

[0026] Figure 6 A schematic diagram of the execution logic of a method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure provided in one embodiment of the present application;

[0027] Figure 7 This is an exemplary diagram of a device for evaluating multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure according to an embodiment of the present application;

[0028] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0029] Among them, 10-a device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure; 100-a modeling module, 200-a first indicator generation module, 300-a second indicator generation module, 400-a complementary capability evaluation module; 801-a memory, 802-a processor, 803-a communication interface. DETAILED DESCRIPTION

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

[0031] The following describes, with reference to the accompanying drawings, a method and device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure according to an embodiment of the present application. In response to the problems mentioned in the above background technology, the present application provides a method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure. In this method, a vine Copula function model of a target power generation system is constructed based on a preset mutual information theory and a maximum spanning tree algorithm; the hydropower regulation speed, power level information and power supply capacity of the target power generation system are obtained to generate a hydropower regulation capability evaluation index based on the hydropower regulation speed, power level information and power supply capacity; based on the vine Copula function model, the joint fluctuation change rate and independent fluctuation change rate of different combinations of water, wind and light 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 change rate and independent fluctuation change rate of different combinations of water, wind and light; 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, and a multi-dimensional complementary evaluation index corresponding to the target power generation system is constructed 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, thereby significantly improving the application prospects and value in the operation evaluation of the power system. This solves the problems that the traditional evaluation index system is often only applicable to evaluating the complementarity between two power generation systems, cannot simultaneously capture the fluctuation trend correlation of the entire evaluation period and the slope correlation of adjacent time periods, and ignores the impact of fluctuation amplitude.

[0032] Specifically, Figure 1 A flow chart of a method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure provided in an embodiment of the present application.

[0033] like Figure 1 As shown, the water-wind-light multi-dimensional complementary characteristics evaluation method based on the improved rattan structure includes the following steps:

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

[0035] The embodiment of the present application can firstly combine the mutual information theory with the vine copula to improve the vine structure.

[0036] Specifically, the embodiments of the present 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 of 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 i,i=1,2,L,n}, P(x) is 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] If H(X) is lower, the amount of information it contains is less and the uncertainty of the variable is less, and vice versa.

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

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

[0044] Among them, f(x,y) and f(y|x) are the joint probability density function and 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:

[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. For example, the rank correlation coefficient is a description of the interaction between variables. In information theory, there is also a similar concept, namely the mutual information in information entropy. Unlike the correlation coefficient, the mutual information quantifies the amount of information that one variable can obtain from another variable by comparing the similarity between the joint distribution f(x, y) of the variables and the marginal distribution product f(x)·f(y).

[0052] For two random variables X, Y, the mutual information I(X,Y) is:

[0053]

[0054] Where 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] It can be understood that mutual information I captures the mutual dependence between variables X and Y. The larger the mutual information, the higher the correlation between the variables and the more common information. I is zero if and only if they are independent of each other, that is, determining one of the variables does not provide any help in obtaining information about the other random variable. The relationship between mutual information, joint entropy and conditional entropy is:

[0056]

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

[0058]

[0059] Among them, f(x,y,z) is the joint density function of X, Y, and Z, f(x,y|z) represents the conditional joint probability density function of X and Y when Z is given, 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:

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

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

[0063]

[0064] Among them, 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 have no correlation.

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

[0066]

[0067] Therefore, the embodiment of the present application can be extended to an n-dimensional data set X=(X1, X2, L, X n ), when other variables are determined, the two random variables X i and X j The conditional mutual information ratio between is:

[0068]

[0069] Among them, X -i,-j Indicates that variable X is removed i and X j The remaining variables after .

[0070] Therefore, the embodiments of the present application can use formulas (11), (12) and (13) to express the degree of influence of the information of the interaction between variables on the information contained in the whole, 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 of establishing the R-vine Copula model based on the mutual information method in the embodiment of the present application are as follows:

[0072] Step 1: Determine the first tree

[0073] In the embodiment of the present application, the information proportion D(X) is calculated by using formula (11) for the original data. i ,X j ), and then use the maximum spanning tree to complete the construction of the first tree; the C vine structure is suitable for the situation where the correlation of multiple variables to a random variable is significantly stronger than that of the other variables; when the correlation between the random variables is relatively average, the D vine structure shows better fitting accuracy; but for high-dimensional variables, the correlation between the variables is more complicated, and the R vine structure is Figure 2 As shown in Figure 2, it often has better universality. For R-vine, the linking method can be freely selected according to the characteristics of the data. Through the connection method of C-vine, the variables are selected in turn, and then the D(X) between the 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 )), it can form Figure 3 The T1 connection shown in the figure is as follows; through the D-vine connection method, each variable is traversed, and then the D(X i ,Xj ) andΣD(X i ,X j ), satisfying the condition S=MAX(ΣD(X i ,X j )), then the following can be formed: Figure 4 The T1 connection is shown, thus completing the connection of the first tree;

[0074] Step 2: Determine the second tree

[0075] Based on the first tree, the conditional mutual information ratio of the two variables is calculated using formula (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 determined, thus completing the vine structure;

[0077] Step 4: Data conversion

[0078] Perform distribution fitting on the original data of each variable, and then map the data to [0,1] through the marginal distribution function;

[0079] Step 5: For each edge of the first tree, select the corresponding optimal binary Copula function from multiple binary Copula functions such as Gaussian Copula, t Copula, Frank Copula, etc., and use the marginal distribution data to perform parameter estimation on each binary Copula function in the tree structure through maximum likelihood estimation. The embodiment of the present application calculates the observation value of the second tree T2 by the following formula:

[0080]

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

[0082] Step 6: Use the observed values ​​in step 5 to perform binary Copula function fitting and parameter estimation on the second tree T2;

[0083] Step 7: Repeat step 6 to complete the binary Copula function fitting and parameter estimation of all trees, such as Figure 4 shown.

[0084] Therefore, the embodiment of the present application divides the vine Copula structure into two parts: determination of the vine structure and selection and estimation of the Copula through an improved method based on mutual information, so that after the last tree is determined, the selection and parameter estimation of paired Copula functions are performed starting from the first tree. Therefore, compared with the traditional vine structure model, the tree structure of the R vine Copula model of the embodiment of the present application will not be affected by the marginal distribution function and the previously specified Copula, which greatly improves 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 to generate a hydropower regulation capability evaluation index according to the hydropower regulation speed, power level information and power supply capacity.

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

[0087] Optionally, in one embodiment of the present application, the hydropower regulation speed, power level information and power supply capacity of the target power generation system are obtained to generate a hydropower regulation capability evaluation index based on the hydropower regulation speed, power level information and power supply capacity, including: determining the external ideal regulation power supply capacity of the target power generation system, and calculating the minimum ideal regulation power supply capacity demand data of the target power generation system based on the external ideal regulation power supply capacity; obtaining the maximum power supply climbing capability information and power supply rated power data of the target power generation system, and calculating the maximum response speed of the target power generation system through the maximum power supply climbing capability information and power supply rated power data; determining the equivalent ideal regulation power supply power of the target power generation system, and calculating the maximum ideal regulation power supply equivalent level data of the target power generation system based on the equivalent ideal regulation power supply power; constructing and generating a hydropower regulation capability evaluation index based on the minimum ideal regulation power supply capacity demand data, the maximum response speed and the maximum ideal regulation power supply equivalent level data.

[0088] It is understandable that the factors that restrict the regulation capacity 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. The complementarity of water, wind and solar power is to fully mobilize a variety of flexible power sources and give full play to their respective regulation advantages to maximize the absorption and support of new energy-load mismatched power.

[0089] Therefore, the embodiment of the present application proposes the concept of "ideal regulated power supply", which has the characteristics of fast regulation speed, a certain level of bidirectional power capability and unlimited power, etc.; the multi-energy complementarity effect is evaluated based on the similarity between hydropower complementation and the ideal power supply. The closer the comprehensive regulation characteristics are to the ideal regulated power supply, the stronger the regulation capability is, and the better the multi-source complementarity is considered to be.

[0090] It should be noted that the embodiment of the present application takes into account the limitations of hydropower in terms of regulation speed, power level and power, and takes the ideal regulated power source similarity as the core, and analyzes and evaluates the hydropower complementary regulation capability from three aspects: the minimum ideal regulated power source capacity requirement, the maximum response speed and the maximum ideal regulated power source equivalent level, as described below:

[0091] (1) Minimum ideal control power capacity requirement

[0092] For the hydropower, wind and solar power system, if it is equipped with sufficient hydropower peak-shaving capacity, fully flexible load regulation can be achieved. In the actual implementation process, the additional hydropower peak-shaving capacity can be regarded as an ideal regulating power source. The evaluation of the complementary regulation capability of the hydropower, wind and solar power system can be evolved into focusing on the capacity of the required ideal regulating power source. Therefore, the embodiment of the present application can propose a "minimum ideal regulating power source capacity requirement" indicator, as shown in the following formula:

[0093]

[0094] Among them, P req It is the capacity of the required external ideal regulated power source; this indicator also refers to the auxiliary regulation capacity required under the unit load level.

[0095] (2) Maximum response speed

[0096] In terms of regulation speed, the embodiment of the present application uses the ratio of the maximum climbing capacity of the power supply under a certain time scale to the total power capacity as an indicator to measure the complementary regulation capability, as shown in the following formula.

[0097]

[0098] Where ΔP i is the maximum climbing capacity of the power supply, P i,N is the rated power of the power supply; this indicator reflects the proportion of climbing capacity in the total power capacity.

[0099] (3) Maximum ideal control power equivalent level

[0100] In the embodiment of the present application, the maximum output that the system can achieve and last for a certain period of time can be used as the ideal control power equivalent level of the system, as shown in the following formula:

[0101]

[0102] Among them, P eq It is the equivalent ideal regulated power source power; this indicator reflects the equivalent level of ideal regulated power source of multi-source system. If you need to express the complementary regulation capability of hydropower more concisely, you can only select “maximum ideal regulated power source equivalent level”.

[0103] Therefore, the embodiments of the present application construct a multi-dimensional evaluation index that can measure the hydropower regulation capability through 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 vine Copula function model, the combined fluctuation change rate and independent fluctuation change 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 combined fluctuation change rate and independent fluctuation change rate of different combinations of water, wind and solar power.

[0105] Furthermore, the embodiments of the present application also need to obtain the combined output of water, wind and solar power based on the vine Copula function model, calculate the combined fluctuation change rate and independent fluctuation change rate of the three combinations of water-wind, water-solar and wind-solar, so as to determine the complementarity index of water, wind and solar power output, and consider the weights under multiple time scales, so as to realize the calculation of the optimal water, wind and solar power installed capacity ratio.

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

[0107] It should be noted that after selecting the optimal Copula function to simulate the joint probability distribution of hydraulic, wind and photovoltaic outputs in the hydro-wind-solar complementary power generation system, the embodiment of the present application can calculate the complementarity index of the hydro-wind-solar combination.

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

[0109] 1. In order to improve the calculation rate, the embodiment of the present application can first obtain the water-wind-solar output data, and linearly normalize the maximum and minimum values. The mathematical expression is as follows:

[0110]

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

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

[0113]

[0114] Among them, P w (i), P h (i), P s (i) represents the normalized theoretical output value 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 hydropower-wind, hydropower-solar and wind-solar at time i; α, β and γ are the installed capacity ratios of wind power, hydropower and photovoltaic power respectively.

[0115] Afterwards, the embodiment of the present application can calculate the combined fluctuation change rate and the independent fluctuation 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 output fluctuation rate of wind power, hydropower and photovoltaic power respectively;

[0118]

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

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

[0121]

[0122] For a given total installed capacity of hydropower, wind power and photovoltaic power, the embodiment of the present application may introduce corresponding weights to assign corresponding weights to the complementarity indicators of different time scales, thereby realizing the water-wind-photovoltaic complementarity evaluation considering multiple time scales, and 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 Respectively represent comprehensive indicators on the annual, monthly, daily, and hourly time scales; p year , p month , p day , p hour They represent the corresponding weights respectively. The size of this weighting factor depends on the importance of the time scale in complementarity, which varies with the usage scenario.

[0125] Therefore, the embodiment of the present application defines the output correlation index of the multi-dimensional complementary system of water, wind and solar power, and obtains the combined output of water, wind and solar power based on the vine Copula function model to calculate the combined fluctuation change rate and independent fluctuation change rate of different combinations of water, wind and solar power, thereby further improving the evaluation system of the multi-dimensional complementary characteristics of water, wind and solar power.

[0126] In step S104, 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, and the multidimensional complementary evaluation index corresponding to the target power generation system is constructed 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 capacity evaluation result of the target power generation system according to the multidimensional complementary evaluation index.

[0127] Furthermore, the embodiments of the present application also need to select power output fluctuation rate, average fluctuation effect, power power fluctuation suppression degree, and system output smoothness as evaluation indicators for measuring the output stability and volatility of the water-wind-solar multi-energy complementary system based on multi-source coordinated scheduling and energy management strategies; then, Figure 5 As shown, the embodiments of the present application can also obtain a water-wind-solar multi-dimensional complementary evaluation index system based on the output correlation index, the hydropower regulation capability index, the output stability index, and the output volatility index, so as to obtain the complementary capability evaluation result of the water-wind-solar multi-energy complementary system by utilizing the water-wind-solar multi-dimensional complementary evaluation index system.

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

[0129] Specifically, the embodiment of the present application can use the output fluctuation rate index to describe the comprehensive complementarity of the water-wind-solar system (i.e., the target power generation system) on the smoothing effect of the water-wind-solar output fluctuation and the complementary ability with the quantitative signal, and the 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 volatility effect of the water-wind-solar system in the time window is expressed as:

[0133]

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

[0135]

[0136] Among them, σ A represents the mean square error of the output power of the largest natural resource-constrained power source in the hydro-wind-solar system; σ B is the mean square error of the total output power of the system after adding other power supplies.

[0137] It should be noted that in the embodiment of the present application, the water-wind-solar system includes both a natural resource-constrained power source and an adjustable power source. Through comprehensive complementarity, the output of the water-wind-solar system can be made more continuous and stable. Specifically, the water-wind-solar system output smoothness index 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 represents the load at time i, P nature,i represents the output of natural resource-constrained power sources at time i, P control,i Represents the output of the adjustable power supply at time i.

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

[0145] The following describes the execution logic of the water-wind-light multi-dimensional complementary characteristics evaluation method based on the improved vine structure of the present application in combination with the accompanying drawings.

[0146] Figure 6 This is a schematic diagram of the execution logic of the water-wind-light multi-dimensional complementary characteristics evaluation method based on the improved rattan structure of this application. Figure 6 As shown, the execution process of the water-wind-light multi-dimensional complementary characteristics evaluation method based on the improved rattan structure of the present application is as follows:

[0147] S601: First, the concepts of mutual information and joint entropy are introduced, and the mutual information is used to express the influence of the information of the interaction between variables on the information contained in the whole. 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 ideal control power source similarity as the core, based on the flexibility of hydropower operation, the minimum ideal control power source capacity requirement, maximum response speed, and maximum ideal control power source equivalent level are selected to measure the hydropower control capability;

[0149] S603: Based on the rattan Copula function model, the combined output of water, wind and solar power is obtained, the combined fluctuation change rate and independent fluctuation change 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 coordinated dispatch and energy management strategy, the power output fluctuation rate, average fluctuation effect, power supply power fluctuation suppression degree, 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: A multi-dimensional complementary evaluation index system of water, wind and solar power is obtained according to the output correlation index, the hydropower regulation capability index, the output stability index and the output volatility index, so as to obtain the complementary capability evaluation result of the water, wind and solar power multi-energy complementary system by using the multi-dimensional complementary evaluation index system of water, wind and solar power.

[0152] According to the method for evaluating the multi-dimensional complementary characteristics of water, wind and solar power based on the improved vine structure proposed in the embodiment of the present application, a vine Copula function model of the target power generation system is constructed based on the preset mutual information theory and maximum spanning tree algorithm; the hydropower regulation speed, power level information and power supply capacity of the target power generation system are obtained to generate a hydropower regulation capability evaluation index according to the hydropower regulation speed, power level information and power supply capacity; based on the vine Copula function model, the joint fluctuation change rate and independent fluctuation change 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 change rate and independent fluctuation change rate of different combinations of water, wind and solar power; 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, and the multi-dimensional complementary evaluation index corresponding to the target power generation system is constructed 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, thereby significantly improving the application prospect and value in the power system operation evaluation.

[0153] Secondly, the water-wind-light multi-dimensional complementary characteristics evaluation device based on the improved vine structure proposed in accordance with the embodiment of the present application is described with reference to the accompanying drawings.

[0154] Figure 7 It is a block diagram of a device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure according to an embodiment of the present application.

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

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

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

[0158] The second indicator generation module 300 is used to calculate the combined fluctuation change rate and independent fluctuation change 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 determine the output complementarity index of the target power generation system according to the combined fluctuation change rate and independent fluctuation change 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 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.

[0160] Optionally, in one embodiment of the present application, the first indicator generating module 200 includes: a first determining unit, an acquiring unit, a second determining unit and a constructing unit.

[0161] Among them, the first determination unit is used to determine the external ideal control power capacity of the target power generation system, and calculate the minimum ideal control power capacity demand data of the target power generation system according to the external ideal control power capacity.

[0162] The acquisition unit is used to acquire the maximum power supply climbing capability information and the power supply rated power data of the target power generation system, and calculate the maximum response speed of the target power generation system through the maximum power supply climbing capability information and the power supply rated power data.

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

[0164] A construction unit is used to construct and generate a hydropower regulation capacity evaluation index based on minimum ideal regulation power supply capacity demand data, maximum response speed and maximum ideal regulation power supply equivalent level data.

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

[0166] Among them, the normalization unit is used to obtain the water, wind and solar power output data of the target power generation system, and normalize 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.

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

[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 calculate the combined fluctuation change rate and independent fluctuation change rate corresponding to the different combinations of water, wind and solar power at time i according to the combined output.

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

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

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

[0173] A unit is established, which is 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 supply power fluctuation smoothness, and to establish the output stability index using the output smoothness.

[0174] It should be noted that the above explanation of the embodiment of the method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on the improved vine structure is also applicable to the device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on the improved vine structure of this embodiment, and will not be repeated here.

[0175] The device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure proposed in the embodiment of the present application includes 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 indicator generation module for obtaining the hydropower regulation speed, power level information and power supply capacity of the target power generation system, so as to generate a hydropower regulation capability evaluation index according to the hydropower regulation speed, power level information and power supply capacity; a second indicator generation module for calculating the combined fluctuation change rate and independent fluctuation rate of different combinations of water, wind and light corresponding to the target power generation system based on the vine Copula function model. The complementary capacity 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 dispatch and energy management strategy, and construct the multi-dimensional complementary evaluation index corresponding to the target power generation system through the hydropower regulation capacity evaluation index, output complementarity index, output stability index and volatility index, so as to obtain the complementary capacity evaluation result of the target power generation system according to the multi-dimensional complementary evaluation index, thereby significantly improving the application prospect and value in the power system operation evaluation.

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

[0177] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0178] When the processor 802 executes the program, the water-wind-light multi-dimensional complementary characteristics evaluation method based on the improved vine structure provided in the above embodiment is implemented.

[0179] Furthermore, the electronic device further comprises:

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

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

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

[0183] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

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

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

[0186] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on the improved vine structure.

[0187] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on the improved vine structure.

[0188] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

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

[0190] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0191] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0192] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0193] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0194] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0195] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on improved vine structure, characterized in that: The following steps are involved: Based on the preset mutual information theory and maximum spanning tree algorithm, the rattan Copula function model of the target power generation system is constructed; Acquiring a hydropower regulation speed, power level information, and power supply capacity of the target power generation system, so as to generate a hydropower regulation capability evaluation index according to the hydropower regulation speed, the power level information, and the power supply capacity; Based on the vine Copula function model, the combined fluctuation change rate and the independent fluctuation change 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 combined fluctuation change rate and the independent fluctuation change 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, and the multidimensional complementary evaluation index corresponding to the target power generation system is constructed 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 multidimensional complementary evaluation index.

2. The method according to claim 1, characterized in that The obtaining of the hydropower regulation speed, power level information and power supply capacity of the target power generation system to generate a hydropower regulation capability evaluation index according to the hydropower regulation speed, the power level information and the power supply capacity includes: Determining the external ideal regulated power capacity of the target power generation system, and calculating the minimum ideal regulated power capacity requirement data of the target power generation system according to the external ideal regulated power capacity; Acquire the maximum climbing capability information of the power source and the rated power data of the power source of the target power generation system, and calculate the maximum response speed of the target power generation system according to the maximum climbing capability information of the power source and the rated power data of the power source; Determine the equivalent ideal regulated power source power of the target power generation system, so as to calculate the maximum ideal regulated power source equivalent level data of the target power generation system according to the equivalent ideal regulated power source power; Based on the minimum ideal control power supply capacity demand data, the maximum response speed and the maximum ideal control power supply equivalent level data, the generated hydropower control capability evaluation index is constructed.

3. The method according to claim 2, characterized in that The method of calculating the combined fluctuation change rate and the independent fluctuation change 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 according to the combined fluctuation change rate and the independent fluctuation change rate of different combinations of water, wind and solar power, includes: Acquire the water, wind and solar power output data of the target power generation system, and normalize 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; Respectively calculating the wind power installed capacity share, hydropower installed capacity share and photovoltaic installed capacity share corresponding to the target power generation system, and determining the discount coefficients corresponding to different combinations of water, wind and photovoltaic based on the wind power installed capacity share, the hydropower installed capacity share and the photovoltaic installed capacity share; Calculate the combined output of the different combinations of water, wind and solar power by using the discount coefficient, and calculate the combined fluctuation change rate and the independent fluctuation change rate corresponding to the different combinations of water, wind and solar power at time i according to the combined output; Based on the combined fluctuation change rate and the independent fluctuation change rate, calculating the complementarity index between wind, water and solar power outputs on the target time scale, and determining the weighting factor corresponding to the complementarity index between wind, water and solar power outputs on the target time scale; The output complementarity index of the target power generation system is calculated according to the weighting factor and the complementarity index between wind, water and light output on the target time scale.

4. The method according to claim 1, characterized in that: The step of determining the output stability index and the volatility index corresponding to the target power generation system includes: Calculate the power output fluctuation rate index, the average volatility effect index within the target time window, the power fluctuation suppression degree 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 supply power fluctuation smoothness, and the output stability index is established using the output smoothness.

5. A device for evaluating the multi-dimensional complementary characteristics of water, wind and light based on an improved vine structure, characterized in that: include: Modeling module, used to construct the rattan Copula function model of the target power generation system based on the preset mutual information theory and maximum spanning tree algorithm; A first indicator generating module is used to obtain the hydropower regulation speed, power level information and power supply capacity of the target power generation system, so as to generate a hydropower regulation capability evaluation indicator according to the hydropower regulation speed, the power level information and the power supply capacity; The second indicator generation module is used to calculate the combined fluctuation change rate and the independent fluctuation change 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 determine the output complementarity index of the target power generation system according to the combined fluctuation change rate and the independent fluctuation change rate of different combinations of water, wind and solar power; A complementary capability evaluation module is used to determine the output stability index and volatility index corresponding to the target power generation system based on a 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 a complementary capability evaluation result of the target power generation system according to the multi-dimensional complementary evaluation index.

6. The device according to claim 5, characterized in that The first indicator generation module includes: A first determining unit is used to determine the external ideal regulated power capacity of the target power generation system, and calculate the minimum ideal regulated power capacity requirement data of the target power generation system according to the external ideal regulated power capacity; an acquisition unit, configured to acquire the maximum climbing capability information of the power source and the rated power data of the power source of the target power generation system, and calculate the maximum response speed of the target power generation system according to the maximum climbing capability information of the power source and the rated power data of the power source; A second determining unit is used to determine the equivalent ideal regulated power supply power of the target power generation system, so as to calculate the maximum ideal regulated power supply equivalent level data of the target power generation system according to the equivalent ideal regulated power supply power; A construction unit is used to construct the generated hydropower regulation capability evaluation index based on the minimum ideal regulation power supply capacity demand data, the maximum response speed and the maximum ideal regulation power supply equivalent level data.

7. The device according to claim 6, characterized in that The second indicator generating module comprises: A normalization unit, used for acquiring the water, wind and solar power output data of the target power generation system, and performing normalization processing on 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; A first calculation unit is used to respectively calculate the wind power installed capacity share, the hydropower installed capacity share and the photovoltaic installed capacity share corresponding to the target power generation system, and determine the discount coefficients corresponding to different combinations of water, wind and photovoltaic based on the wind power installed capacity share, the hydropower installed capacity share and the photovoltaic installed capacity share; A second calculation unit is used to calculate the combined output of the different combinations of water, wind and solar power by using the discount coefficient, and calculate the combined fluctuation change rate and the independent fluctuation change rate corresponding to the different combinations of water, wind and solar power at time i according to the combined output; A third calculation unit is used to calculate the complementarity index between wind, water and solar power outputs on a target time scale based on the joint fluctuation change rate and the independent fluctuation change rate, and determine a 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 according to the weighting factor and the complementarity index between wind, water and light output on the target time scale.

8. An electronic device, characterized in that: include: 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 multi-dimensional complementary characteristics of water, wind, and light based on the improved vine structure as described in any one of claims 1 to 4.

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

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for evaluating the multi-dimensional complementary characteristics of water, wind and light based on the improved vine structure as described in any one of claims 1 to 4.

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