Power grid new energy station power generation method and system considering spatial correlation

Abstract

The invention discloses a power grid new energy station power generation method and system considering spatial correlation. The method comprises the following steps: calculating correlation coefficients of any two different new energy stations based on a power change sequence; calculating the distance between stations by using the correlation coefficient, and carrying out clustering based on the distance to form a plurality of station groups; and for each station group, constructing a correlation coefficient matrix, and generating a new new energy station power change sequence through Cholesky decomposition. According to the invention, through the improved correlation analysis method, correlation characteristics between new energy units are accurately quantified; an optimized clustering algorithm is utilized to classify highly-correlated units into one group, so that the spatial dimension of a sample is remarkably reduced; a random generation method based on an advanced statistical model is adopted to generate a new energy power sequence with real randomness and correlation, so that the randomness of new energy power output is kept, and a correlation structure between units is ensured.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and more specifically, to a method and system for generating power at a new energy station in a power grid taking spatial correlation into consideration. Background Art

[0002] With the rapid transformation of the global energy structure and the vigorous development of new energy technologies, new energy high penetration power grids have gradually become an important development direction for the power industry. However, the power output of new energy stations has significant randomness and volatility, which brings huge challenges to the stable operation and dispatch of the power grid.

[0003] In a power grid with a high proportion of new energy connected, the operating space of conventional units is gradually compressed, and problems such as reduced power grid frequency regulation capability, insufficient reactive power support, aggravated voltage stability problems and increased risk of over-limit are gradually emerging. Especially in the case of large-scale grid connection of new energy, traditional power grid control strategies have been unable to cope with these complex challenges.

[0004] In the prior art, power forecasting and optimization methods are mainly based on time series analysis of historical data. For example, the autoregressive integrated moving average (ARIMA) model is a typical statistical method that is widely used in short-term power forecasting. This model stabilizes the non-stationary time series by performing differential processing on historical power data, and then uses autoregression and moving average to predict future power values. However, the ARIMA model mainly focuses on changes in the time dimension, and does not fully consider the spatial correlation between stations in the power forecast of new energy stations.

[0005] In fact, since new energy sites usually have a strong correlation in geographical location and meteorological conditions, for example, multiple wind farms in the same area may be affected by the same meteorological conditions at the same time, so their power output often shows similar fluctuation trends. The existing technology ignores these spatial correlations, resulting in obvious deficiencies in its prediction accuracy when facing new energy site clusters. In addition, the existing technology is relatively simple in data preprocessing and outlier processing, and may be too sensitive to abnormal data, which in turn affects the reliability of the prediction results.

[0006] In addition, with the rapid growth in the number of renewable energy stations and the scale of data, the computational complexity of existing statistical methods has increased significantly, resulting in excessive consumption of computing resources when processing large-scale renewable energy grid data, affecting the feasibility of real-time applications. At the same time, the existing methods have weak generalization capabilities for the prediction models due to the high volatility of renewable energy power output and insufficient data for newly built stations, making it difficult to accurately respond to complex and variable power changes in renewable energy stations.

[0007] Therefore, in the field of power prediction in power grids with high penetration of renewable energy, there is an urgent need for an intelligent power generation method for renewable energy sites that takes spatial correlation into account. Summary of the invention

[0008] The present invention proposes a method and system for generating power of a new energy station in a power grid taking spatial correlation into consideration, so as to solve the problem of how to efficiently generate power data of a new energy station.

[0009] In order to solve the above problem, according to one aspect of the present invention, a method for generating power in a power grid new energy station considering spatial correlation is provided, the method comprising:

[0010] Calculate the correlation coefficient between any two different renewable energy stations based on the power change sequence;

[0011] Calculating the distance between stations based on the correlation coefficient, and clustering based on the distance to form a plurality of station groups;

[0012] For each station group, a correlation coefficient matrix is ​​constructed, and a new power change sequence of new energy stations is generated through Cholesky decomposition.

[0013] Preferably, the calculation of the correlation coefficient between any two different new energy stations based on the power change sequence includes:

[0014]

[0015] Among them, ρ X,Y is the correlation coefficient between the power change sequences X and Y of the two new energy stations; X t and Y t is the power value at the tth time point; n is the number of time points; and is the mean of the corresponding sequence; w t is the weight coefficient based on the power change rate; σ X and σ Y are the standard deviations of series X and Y, respectively.

[0016] Preferably, the distance between stations is calculated based on the correlation coefficient, comprising:

[0017]

[0018] Among them, d ij is the distance between new energy station i and new energy station j; ρ X,Y Represents the correlation coefficient between the power change sequences X and Y corresponding to the new energy stations i and j.

[0019] Preferably, the method further comprises:

[0020] According to the data scale and expected clustering granularity, set the initial range of the number of clusters K [K min ,K max ];

[0021] For each candidate K value, perform the improved K-Means clustering algorithm to calculate the corresponding global weighted silhouette coefficient And look for The largest K value is taken as the optimal number of clusters K*; if [K min ,K max ] If the optimal value is not found within the range, then according to Expand the search scope based on the changing trends of

[0022] Preferably, the global weighted contour coefficient is determined in the following manner, comprising:

[0023]

[0024] Among them, a i represents the average distance between sample i and its cluster and other samples; b i represents the average distance between sample i and the nearest samples in other clusters; w i represents the weight coefficient of the i-th sample, which is determined by the installed capacity, historical power generation and data quality of the new energy unit; s i represents the weighted silhouette coefficient of the i-th sample, s i >0 indicates that the samples are reasonably clustered, with close clusters and separation between clusters; i =0 means that the sample is located at the boundary of two clusters and the clustering effect is average; s i <0 indicates that the samples may be clustered incorrectly, the distance within the cluster is greater than the distance between clusters, and one new energy site is one sample.

[0025] Preferably, for each station group, a correlation coefficient matrix is ​​constructed, and a new power change sequence of new energy stations is generated by Cholesky decomposition, including:

[0026] For each station group, extract the corresponding correlation coefficient submatrix R from the correlation coefficient matrix constructed based on the correlation coefficient;

[0027] Based on the advanced statistical model of Cholesky decomposition, the correlation coefficient matrix is ​​decomposed into R = L·L T ; Where L is a lower triangular matrix over the real number field;

[0028] Generate a random matrix Z=randn(k,m) that conforms to the standard normal distribution, Where k is the length of data to be generated, m is the number of stations in the station group, and randn represents sampling from the standard normal distribution N(0,1);

[0029] The random matrix Z is linearly transformed through the lower triangular matrix L to introduce the specified correlation structure, and S = Z·L is obtained T ;

[0030] Perform inverse normalization on each column of the matrix S to obtain Each column of the generated random data matrix S is used as a new power change sequence of new energy stations that satisfies the correlation coefficient submatrix R;

[0031] in,

[0032]

[0033] Among them, S ij is a correlation-mapped matrix, each column of which is a standardized random sequence with a specified correlation; S ij ′ is the power change value; and Var(X i ) are the mean and variance of the power series of the i-th renewable energy station; n is the length of the time series; A T represents the transpose of A.

[0034] According to another aspect of the present invention, a power generation system for a new energy station in a power grid considering spatial correlation is provided, the system comprising:

[0035] A correlation coefficient calculation unit, used to calculate the correlation coefficient of any two different new energy stations based on the power change sequence;

[0036] A clustering unit, used for calculating the distance between stations based on the correlation coefficient, and performing clustering based on the distance to form a plurality of station groups;

[0037] The data generation unit is used to construct a correlation coefficient matrix for each station group and generate a new power change sequence of new energy stations through Cholesky decomposition.

[0038] Preferably, the correlation coefficient calculation unit calculates the correlation coefficient of any two different new energy stations based on the power change sequence, including:

[0039]

[0040] Among them, ρ X,Y is the correlation coefficient between the power change sequences X and Y of the two new energy stations; X t and Y t is the power value at the tth time point; n is the number of time points; and is the mean of the corresponding sequence; w t is the weight coefficient based on the power change rate; σ X and σ Y are the standard deviations of series X and Y, respectively.

[0041] Preferably, the clustering unit calculates the distance between stations based on the correlation coefficient, comprising:

[0042]

[0043] Among them, d ij is the distance between new energy station i and new energy station j; ρ X,Y Represents the correlation coefficient between the power change sequences X and Y corresponding to the new energy stations i and j.

[0044] Preferably, the clustering unit further comprises:

[0045] According to the data scale and expected clustering granularity, set the initial range of the number of clusters K [K min ,K max ];

[0046] For each candidate K value, perform the improved K-Means clustering algorithm to calculate the corresponding global weighted silhouette coefficient And look for The largest K value is taken as the optimal number of clusters K*; if [K min ,K max ] If the optimal value is not found within the range, then according to Expand the search scope based on the changing trends of

[0047] Preferably, the clustering unit determines the global weighted silhouette coefficient in the following manner, including:

[0048]

[0049] Among them, a i represents the average distance between sample i and its cluster and other samples; b i represents the average distance between sample i and the nearest samples in other clusters; w i represents the weight coefficient of the i-th sample, which is determined by the installed capacity, historical power generation and data quality of the new energy unit; s i represents the weighted silhouette coefficient of the i-th sample, s i >0 indicates that the samples are reasonably clustered, with close clusters and separation between clusters; i =0 means that the sample is located at the boundary of two clusters and the clustering effect is average; s i<0 indicates that the samples may be clustered incorrectly, the distance within the cluster is greater than the distance between clusters, and one new energy site is one sample.

[0050] Preferably, the data generation unit constructs a correlation coefficient matrix for each station group, and generates a new power change sequence of new energy stations through Cholesky decomposition, including:

[0051] For each station group, extract the corresponding correlation coefficient submatrix R from the correlation coefficient matrix constructed based on the correlation coefficient;

[0052] Based on the advanced statistical model of Cholesky decomposition, the correlation coefficient matrix is ​​decomposed into R = L·L T ; Where L is a lower triangular matrix over the real number field;

[0053] Generate a random matrix Z=randn(k,m) that conforms to the standard normal distribution, Where k is the length of data to be generated, m is the number of stations in the station group, and randn represents sampling from the standard normal distribution N(0,1);

[0054] The random matrix Z is linearly transformed through the lower triangular matrix L to introduce the specified correlation structure, and S = Z·L is obtained T ;

[0055] Perform inverse normalization on each column of the matrix S to obtain Each column of the generated random data matrix S is used as a new power change sequence of new energy stations that satisfies the correlation coefficient submatrix R;

[0056] in,

[0057]

[0058] Among them, S ij is a correlation-mapped matrix, each column of which is a standardized random sequence with a specified correlation; S ij ′ is the power change value; and Var(X i ) are the mean and variance of the power series of the i-th renewable energy station; n is the length of the time series; A T represents the transpose of A.

[0059] The present invention provides a method and system for generating power of a new energy station in a power grid considering spatial correlation, including: calculating the correlation coefficient of any two different new energy stations based on a power change sequence; calculating the distance between stations based on the correlation coefficient, and clustering based on the distance to form multiple station groups; for each station group, constructing a correlation coefficient matrix, and generating a new power change sequence of new energy stations through Cholesky decomposition. The present invention accurately quantifies the correlation characteristics between new energy units through an improved correlation analysis method; uses an optimized clustering algorithm to group highly correlated units together, significantly reducing the sample space dimension; uses a random generation method based on an advanced statistical model to generate a new energy power sequence with true randomness and correlation, which not only maintains the randomness of the new energy power output, but also ensures the correlation structure between units, and has high efficiency and applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0061] Figure 1 It is a flowchart of a method 100 for generating power in a power grid new energy station considering spatial correlation according to an embodiment of the present invention;

[0062] Figure 2 is an example diagram of a distribution histogram of all correlation coefficients according to an embodiment of the present invention;

[0063] Figure 3 is an example diagram of a distribution histogram of maximum correlation coefficients of all stations according to an embodiment of the present invention;

[0064] Figure 4 4 is a schematic structural diagram of a power generation system 400 for a power grid new energy station taking spatial correlation into consideration according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0066] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0067] The present invention focuses on optimizing the intelligent generation of power at new energy sites through spatial correlation analysis, further improving the efficiency and stability of the power grid. The core of this method is to use spatial correlation analysis to deeply explore the interdependence of power generation between new energy sites, thereby achieving high-precision prediction and control of power fluctuations. Through refined statistical analysis, the intrinsic connection between different new energy sites can be revealed, providing a solid data foundation for the effective integration and utilization of new energy. In particular, the method of the present invention processes the power data of wind farms and photovoltaic power stations separately to ensure the accuracy and reliability of the analysis.

[0068] Figure 1 FIG. 1 is a flow chart of a method 100 for generating power of a power grid new energy station considering spatial correlation according to an embodiment of the present invention. Figure 1 As shown, the power generation method of a new energy station in a power grid considering spatial correlation provided by an embodiment of the present invention accurately quantifies the correlation characteristics between new energy units through an improved correlation analysis method; uses an optimized clustering algorithm to group highly correlated units together, significantly reducing the sample space dimension; and adopts a random generation method based on an advanced statistical model to generate a new energy power sequence with true randomness and correlation, which not only maintains the randomness of the new energy power output, but also ensures the correlation structure between units, and has high efficiency and applicability. The power generation method 100 of a new energy station in a power grid considering spatial correlation provided by an embodiment of the present invention starts from step 101. In step 101, the correlation coefficient of any two different new energy stations is calculated based on the power change sequence.

[0069] Preferably, the calculation of the correlation coefficient between any two different new energy stations based on the power change sequence includes:

[0070]

[0071]

[0072] Among them, ρ X,Y is the correlation coefficient between the power change sequences X and Y of the two new energy stations; X t and Y t is the power value at the tth time point; n is the number of time points; and is the mean of the corresponding sequence; w tis the weight coefficient based on the power change rate; σ X and σ Y are the standard deviations of series X and Y, respectively.

[0073] In the present invention, spatial correlation analysis is the core link, which aims to deeply explore the power output correlation between different new energy stations and provide a solid foundation for subsequent data generation and model optimization. Traditional statistical analysis methods usually use the standard Pearson correlation coefficient to measure the degree of linear correlation between two variables. However, when processing power data of new energy stations, the standard Pearson correlation coefficient may not be able to fully capture the complex characteristics of the data, especially when faced with nonlinear relationships and outliers. Therefore, in the present invention, spatial correlation analysis is performed in the following way, as follows:

[0074]

[0075] In the formula, X and Y represent the power change sequences of two different renewable energy stations, X t and Y t is the power value at the tth time point; n is the number of time points; and is the mean of the corresponding sequence; w t is a weight coefficient based on the power change rate, which is used to reduce the impact of outliers on the correlation calculation and is defined as follows:

[0076]

[0077] Among them, σ X and σ Y are the standard deviations of sequences X and Y, respectively. This weight coefficient utilizes the idea of ​​Gaussian kernel function. When the power change rate is large (possibly abnormal fluctuation), the weight will be reduced, thereby reducing its impact on the correlation calculation.

[0078] In step 102, the distances between stations are calculated based on the correlation coefficients, and clustering is performed based on the distances to form a plurality of station groups.

[0079] Preferably, the distance between stations is calculated based on the correlation coefficient, comprising:

[0080]

[0081] Among them, d ij is the distance between new energy station i and new energy station j; ρ X,Y Represents the correlation coefficient between the power change sequences X and Y corresponding to the new energy stations i and j.

[0082] Preferably, the method further comprises:

[0083] According to the data scale and expected clustering granularity, set the initial range of the number of clusters K [K min ,K max ];

[0084] For each candidate K value, perform the improved K-Means clustering algorithm to calculate the corresponding global weighted silhouette coefficient And look for The largest K value is taken as the optimal number of clusters K*; if [K min ,K max ] If the optimal value is not found within the range, then according to Expand the search scope based on the changing trends of

[0085] Preferably, the global weighted contour coefficient is determined in the following manner, comprising:

[0086]

[0087] Among them, a i represents the average distance between sample i and its cluster and other samples; b i represents the average distance between sample i and the nearest samples in other clusters; w i represents the weight coefficient of the i-th sample, which is determined by the installed capacity, historical power generation and data quality of the new energy unit; s i represents the weighted silhouette coefficient of the i-th sample, s i >0 indicates that the samples are reasonably clustered, with close clusters and separation between clusters; i =0 means that the sample is located at the boundary of two clusters and the clustering effect is average; s i <0 indicates that the samples may be clustered incorrectly, the distance within the cluster is greater than the distance between clusters, and one new energy site is one sample.

[0088] In the present invention, in order to effectively group the new energy stations, the K-means algorithm is adopted, and innovative optimization is performed based on the characteristics of the power data of the new energy stations. The traditional K-means algorithm usually uses the Euclidean distance as the similarity metric, but this method has limitations when dealing with negative values ​​or zero values ​​in the power data of new energy stations that violate the non-negativity principle of the distance metric, and cannot fully reflect the spatial correlation between stations and the similarity of power output.

[0089] In order to solve this problem, the present invention proposes a distance measurement method based on correlation coefficient, which directly uses the correlation between stations for distance calculation. Specifically, the following distance conversion formula is designed:

[0090]

[0091] Among them, d ij represents the distance between station i and station j, ρ X,Y is the correlation coefficient between the power change sequences X and Y of the two new energy stations. The correlation coefficient is converted into a distance value through the nonlinear mapping of the exponential function. ij When it is close to 1 (i.e. highly correlated), the corresponding distance d ij Close to 0; when ρ ij When it is close to 0 or negative (i.e. weak correlation or negative correlation), the distance d ij This mapping method can more sensitively capture the subtle differences in correlation between stations and improve the resolution of clustering. At the same time, it ensures that the distance d ij It is always non-negative, satisfying the basic requirement of distance metric.

[0092] For the K-Means clustering algorithm, one of the key parameters is the number of clusters K, and its selection directly affects the effectiveness of the clustering results and the accuracy of subsequent analysis. In the clustering of power data of new energy sites, the traditional fixed K value method may not be able to fully capture the complex structure of the data, resulting in poor clustering results. To this end, this application proposes an adaptive K value selection method that combines the silhouette coefficient and data characteristics to accurately determine the optimal number of clusters.

[0093] The traditional silhouette coefficient is defined as follows:

[0094]

[0095] In the formula, a is the average distance between the sample and other samples in the same cluster, reflecting the compactness within the cluster; b is the average distance between the sample and the samples in the nearest cluster, reflecting the separation between clusters.

[0096] However, in the power data of new energy stations, the correlation between stations may be affected by multiple factors such as geographical location and meteorological conditions, resulting in uneven data distribution. To this end, the present invention improves the silhouette coefficient by adding a weight factor w i To reflect the importance or representativeness of each site.

[0097]

[0098] Weight factor w i It can be determined based on indicators such as the installed capacity of the site, historical power generation or data quality. The global weighted contour coefficient is obtained by averaging the weighted contour coefficients of all samples.

[0099]

[0100] Among them, ai represents the average distance between sample i and its cluster and other samples (intra-cluster distance); b i represents the average distance between sample i and the samples in the nearest other clusters (the nearest inter-cluster distance); w i represents the weight coefficient of the i-th sample, reflecting the importance or representativeness of the sample, which is determined by the installed capacity, historical power generation and data quality of the new energy unit in this patent; s i represents the weighted silhouette coefficient of the i-th sample, s i >0 indicates that the samples are reasonably clustered, with close clusters and separation between clusters; i =0 means that the sample is located at the boundary of two clusters and the clustering effect is average; s i <0 indicates that the samples may be clustered incorrectly, and the distance within the cluster is greater than the distance between clusters.

[0101] In order to find the optimal K value, the present invention designs an adaptive search algorithm: First, the initial range of K value [K min ,K max ], for each candidate K value, execute the improved K-Means clustering algorithm to calculate the corresponding global weighted silhouette coefficient And look for The largest K value is taken as the optimal number of clusters K*. min ,K max ] No obvious optimal value is found in the range, so According to the changing trend, expand the search scope appropriately.

[0102] In step 103, for each station group, a correlation coefficient matrix is ​​constructed, and a new power change sequence of new energy stations is generated through Cholesky decomposition.

[0103] Preferably, for each station group, a correlation coefficient matrix is ​​constructed, and a new power change sequence of new energy stations is generated by Cholesky decomposition, including:

[0104] For each station group, extract the corresponding correlation coefficient submatrix R from the correlation coefficient matrix constructed based on the correlation coefficient;

[0105] Based on the advanced statistical model of Cholesky decomposition, the correlation coefficient matrix is ​​decomposed into R = L·L T ; Where L is a lower triangular matrix over the real number field;

[0106] Generate a random matrix Z=randn(k,m) that conforms to the standard normal distribution, Where k is the length of data to be generated, m is the number of stations in the station group, and randn represents sampling from the standard normal distribution N(0,1);

[0107] The random matrix Z is linearly transformed through the lower triangular matrix L to introduce the specified correlation structure, and S = Z·L is obtained T ;

[0108] Perform inverse normalization on each column of the matrix S to obtain Each column of the generated random data matrix S is used as a new power change sequence of new energy stations that satisfies the correlation coefficient submatrix R;

[0109] in,

[0110]

[0111] Among them, S ij is a correlation-mapped matrix, each column of which is a standardized random sequence with a specified correlation; S ij ′ is the power change value; and Var(X i ) are the mean and variance of the power series of the i-th renewable energy station; n is the length of the time series; A T represents the transpose of A.

[0112] The random generation process of the present invention is based on an advanced statistical model, and is intended to keep the correlation structure between each subsequence consistent with the correlation coefficient matrix R of the actual observed data in the generated sample data. To this end, the present invention systematically optimizes the extraction and construction of the correlation coefficient submatrix and the generation process of random data.

[0113] First, for each station group, the corresponding correlation coefficient submatrix R is extracted from the global correlation coefficient matrix. The submatrix R is a symmetric matrix, and its element value range is usually between 0.5 and 1.0, reflecting the strong correlation between the stations in the station group. This provides a reliable statistical basis for subsequent data generation. In order to construct a matrix A that can accurately reflect the correlation between stations in the group, the original data is standardized, and the form of the constructed matrix A is as follows:

[0114]

[0115] Among them, d ij represents the power change value of the i-th station at the j-th time point, and Var(X i ) are the mean and variance of the power sequence of the ith station, m is the number of stations in the group, and n is the length of the time series.

[0116] Through the above standardization process, each column of the matrix A is standard normalized data with a mean of zero and a variance of one. Next, verify the properties of the correlation coefficient submatrix R constructed from the matrix A. Specifically, the matrix R can be expressed as:

[0117]

[0118] To ensure the accuracy and stability of R, it is proved that R is a semi-positive definite matrix. When the column vectors of matrix A are linearly independent (that is, the columns of A are full rank), R is a positive definite matrix. The proof process is as follows: For any non-zero vector Yes T Rb=b T A T Ab=(Ab) T ·Ab=||Ab|| 2 ≥0. Therefore, R is a semi-positive definite matrix. In practical applications, the data of matrix A comes from real measurement data and usually satisfies n>>m. Therefore, the possibility of linear correlation of column vectors of A is extremely low. It can be considered that the columns of A are full rank, so R is a positive definite matrix. After confirming that R is a positive definite matrix, we use Cholesky decomposition technology to decompose it into R = L·L T . Where L is a lower triangular matrix over the real number field. The existence and uniqueness of the Cholesky decomposition ensure the stability and efficiency of the decomposition process. This decomposition provides a solid mathematical foundation for subsequent random data generation. Next, in order to generate a random data sequence with a specified correlation structure, the following steps are taken:

[0119] Generate a random matrix that follows a standard normal distribution Where k is the length of data to be generated (i.e., the number of samples), m is the number of stations in the station group, Z = randn(k,m), and randn represents sampling from the standard normal distribution N(0,1).

[0120] The random matrix Z is linearly transformed through the lower triangular matrix L to introduce the specified correlation structure: S = Z·L T , after this linear transformation, the column vectors of matrix S have a correlation structure consistent with R.

[0121] To restore the actual scale of the data, we denormalize each column of the matrix S using the previously calculated mean and standard deviation Var(X i ):

[0122]

[0123] Through the above steps, it can be obtained that each column of the generated random data matrix S is a station power change sequence that satisfies the preset correlation coefficient matrix R.

[0124] The present invention focuses on the spatial correlation between new energy stations, and adopts an improved Pearson correlation coefficient, a distance conversion formula based on the correlation coefficient, and an optimized K-means clustering algorithm to effectively capture the mutual influence and dependency between new energy stations. In addition, by using advanced statistical models and algorithms, it is possible to accurately generate new energy power data based on the correlation between stations, providing an efficient and reliable method for intelligent power generation for new power grids. This technology not only improves the accuracy of new energy power prediction, but also provides strong data support for new energy integration and power grid optimization, showing obvious advancement and practical value. Through the method of the present invention, the effective reduction of the original feature space dimension is achieved, the difficulty of spatial sampling and sample preparation is simplified, and the efficiency of sequence generation is significantly improved.

[0125] In order to verify the effectiveness and advancement of the method, the present invention conducted a statistical analysis of the power data of new energy stations based on the online data of a provincial power grid with a high proportion of new energy penetration. The data set contains the operating data of 69 new energy stations (including wind farms and photovoltaic power stations), totaling 8457 time points.

[0126] First, using the improved Pearson correlation coefficient, we conducted a detailed spatial correlation analysis of the power data of 69 new energy sites (including wind farms and photovoltaic power stations). We constructed a 69×69 correlation coefficient matrix. Excluding the part with a correlation coefficient of 1.0 on the diagonal, the number of remaining correlation coefficients is 4692. This method introduces weight coefficients through prior knowledge, reduces the impact of outliers and data noise on correlation calculation, and improves the accuracy and robustness of the analysis. Among the total 4692 site pairs, there are 3018 positively correlated site pairs, accounting for 64.32%. Strong positive correlation (|ρ X,Y >0.5|) There are 1446 station pairs, accounting for 30.82%. All stations have strong positive correlations with at least one other station. These results fully verify the effectiveness of our improved correlation analysis method. Strong positive correlations mainly appear between stations with close geographical locations, which is highly consistent with the similarity of meteorological conditions in local areas, reflecting the accurate capture of spatial correlation by the method. The distribution of all correlation coefficient results is shown in the figure. Figure 2 As shown in Figure 2, since this analysis analyzes the data of wind farms and photovoltaic power stations together, the proportion of weak negative correlation is relatively high. Further statistics on each new energy station show that all stations have at least one other station with strong correlation. The distribution of the maximum correlation coefficient of all wind farms is shown in Figure 2. Figure 3 shown.

[0127] In view of the particularity of power data of new energy stations, the K-Means clustering algorithm based on the distance metric optimization of the correlation coefficient proposed above is used. In order to determine the optimal number of clusters K, the improved silhouette coefficient method is adopted. This method introduces a weight factor, comprehensively considers the intra-cluster compactness and inter-cluster separation, and improves the accuracy of clustering evaluation. After trying the K value range between 5 and 20, it was finally determined that the optimal number of clusters was K = 8, and the corresponding improved silhouette coefficient was 0.4154.

[0128] The clustering results show that the 69 stations are effectively divided into 8 groups, of which the internal minimum correlation coefficient of 6 groups is greater than 0.5, and the stations within the group are highly correlated. The first group contains 16 photovoltaic power stations, and the other 5 groups are wind farms, which verifies the fact that the correlation between wind farms and photovoltaic power stations is low. Through clustering, the original 69-dimensional input data is reduced to 8 dimensions, of which 49 stations are classified into 6 highly correlated groups, as shown in Table 1. Reducing the input dimension from 49 dimensions to 6 dimensions significantly reduces the complexity of data processing. This optimized clustering method not only improves the accuracy of the clustering results, but also effectively reduces the dimension of the feature space and simplifies the subsequent sample generation process.

[0129] Table 1 List of power grid station groups

[0130] serial number Number of stations Average correlation coefficient Minimum correlation coefficient 1 16 0.90 0.79 2 15 0.79 0.61 3 6 0.87 0.79 4 5 0.74 0.64 5 4 0.80 0.61 6 3 0.90 0.87

[0131] In the process of random data generation, an advanced statistical model based on Cholesky decomposition is adopted. By performing Cholesky decomposition on the correlation coefficient matrix R, we successfully generated a new energy power sequence with the same correlation structure as the original data. Taking the 4th group (containing 5 wind farms) as an example: a data sequence with a length of 8457 was generated, which is consistent with the length of the original online data. Comparing the correlation coefficient matrix of the generated data with the original data, it is found that the corresponding element deviation of the two is usually below 0.01, and the maximum deviation is only 0.0105. Table 2 and Table 3 Correlation coefficient matrix of the 4th group in the original online data and the generated data.

[0132] Table 2 Correlation coefficient matrix of group 4 in the original online data

[0133] Station 1 Station 2 Station 3 Station 4 Station 5 Station 1 1.0000 0.6645 0.8200 0.7588 0.6484 Station 2 0.6645 1.0000 0.7225 0.6389 0.6956 Station 3 0.8200 0.7225 1.0000 0.8586 0.7686 Station 4 0.7588 0.6389 0.8586 1.0000 0.7754 Station 5 0.6484 0.6956 0.7686 0.7754 1.0000

[0134] Table 3 Correlation coefficient matrix of group 4 in generated data

[0135]

[0136]

[0137] By comparing the generated data with the original data, it can be found that the correlation coefficient matrix of the generated data is highly consistent with the original data, with minimal deviation, indicating that the method is effective in maintaining the correlation between stations. The generated power series is highly consistent with the original data in terms of volatility and trend, reflecting the accuracy of the method in capturing the power characteristics of new energy. The improvement in computing efficiency is due to clustering dimensionality reduction and optimized random generation algorithms. The time cost of data processing and generation is greatly reduced, meeting the needs of large-scale data processing and meeting the requirements of online data processing.

[0138] Figure 4 FIG. 4 is a schematic diagram of a power generation system 400 for a power grid new energy station considering spatial correlation according to an embodiment of the present invention. Figure 4 As shown, a power generation system 400 for a new energy station in a power grid taking into account spatial correlation provided by an embodiment of the present invention includes: a correlation coefficient calculation unit 401 , a clustering unit 402 and a data generation unit 403 .

[0139] Preferably, the correlation coefficient calculation unit 401 is used to calculate the correlation coefficient of any two different new energy stations based on the power change sequence.

[0140] Preferably, the correlation coefficient calculation unit 401 calculates the correlation coefficient of any two different new energy stations based on the power change sequence, including:

[0141]

[0142] Among them, ρ X,Y is the correlation coefficient between the power change sequences X and Y of the two new energy stations; X t and Y t is the power value at the tth time point; n is the number of time points; and is the mean of the corresponding sequence; w t is the weight coefficient based on the power change rate; σ X and σ Y are the standard deviations of series X and Y, respectively.

[0143] Preferably, the clustering unit 402 is used to calculate the distance between stations based on the correlation coefficient, and perform clustering based on the distance to form a plurality of station groups.

[0144] Preferably, the clustering unit 402 calculates the distance between stations based on the correlation coefficient, including:

[0145]

[0146] Among them, d ijis the distance between new energy station i and new energy station j; ρ X,Y Represents the correlation coefficient between the power change sequences X and Y corresponding to the new energy stations i and j.

[0147] Preferably, the clustering unit 402 further includes:

[0148] According to the data scale and expected clustering granularity, set the initial range of the number of clusters K [K min ,K max ];

[0149] For each candidate K value, perform the improved K-Means clustering algorithm to calculate the corresponding global weighted silhouette coefficient And look for The largest K value is taken as the optimal number of clusters K*; if [K min ,K max ] If the optimal value is not found within the range, then according to Expand the search scope based on the changing trends of

[0150] Preferably, the clustering unit 402 determines the global weighted silhouette coefficient in the following manner, including:

[0151]

[0152] Among them, a i represents the average distance between sample i and its cluster and other samples; b i represents the average distance between sample i and the nearest samples in other clusters; w i represents the weight coefficient of the i-th sample, which is determined by the installed capacity, historical power generation and data quality of the new energy unit; s i represents the weighted silhouette coefficient of the i-th sample, s i >0 indicates that the samples are reasonably clustered, with close clusters and separation between clusters; i =0 means that the sample is located at the boundary of two clusters and the clustering effect is average; s i <0 indicates that the samples may be clustered incorrectly, the distance within the cluster is greater than the distance between clusters, and one new energy site is one sample.

[0153] Preferably, the data generating unit 403 is used to construct a correlation coefficient matrix for each station group, and generate a new new energy station power change sequence through Cholesky decomposition.

[0154] Preferably, the data generating unit 403 constructs a correlation coefficient matrix for each station group, and generates a new power change sequence of new energy stations through Cholesky decomposition, including:

[0155] For each station group, extract the corresponding correlation coefficient submatrix R from the correlation coefficient matrix constructed based on the correlation coefficient;

[0156] Based on the advanced statistical model of Cholesky decomposition, the correlation coefficient matrix is ​​decomposed into R = L·L T ; Where L is a lower triangular matrix over the real number field;

[0157] Generate a random matrix Z=randn(k,m) that conforms to the standard normal distribution, Where k is the length of data to be generated, m is the number of stations in the station group, and randn represents sampling from the standard normal distribution N(0,1);

[0158] The random matrix Z is linearly transformed through the lower triangular matrix L to introduce the specified correlation structure, and S = Z·L is obtained T ;

[0159] Perform inverse normalization on each column of the matrix S to obtain Each column of the generated random data matrix S is used as a new power change sequence of new energy stations that satisfies the correlation coefficient submatrix R;

[0160] in,

[0161]

[0162] Among them, S ij is a correlation-mapped matrix, each column of which is a standardized random sequence with a specified correlation; S ij ′ is the power change value; and Var(X i ) are the mean and variance of the power series of the i-th renewable energy station; n is the length of the time series; A T represents the transpose of A.

[0163] The power generation system 400 of a power grid renewable energy station considering spatial correlation according to an embodiment of the present invention corresponds to the power generation method 100 of a power grid renewable energy station considering spatial correlation according to another embodiment of the present invention, which will not be described in detail herein.

[0164] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0165] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a / said / the [means, components, etc.]" are to be openly interpreted as at least one instance of said means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

[0166] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0168] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0169] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for generating power at a new energy station in a power grid considering spatial correlation, characterized in that: The method comprises: Calculate the correlation coefficient between any two different renewable energy stations based on the power change sequence; Calculating the distance between stations based on the correlation coefficient, and clustering based on the distance to form a plurality of station groups; For each station group, a correlation coefficient matrix is ​​constructed, and a new power change sequence of new energy stations is generated through Cholesky decomposition.

2. The method according to claim 1, characterized in that The calculation of the correlation coefficient between any two different new energy stations based on the power change sequence includes: Among them, ρ X,Y is the correlation coefficient between the power change sequences X and Y of the two new energy stations; X t and Y t is the power value at the tth time point; n is the number of time points; and is the mean of the corresponding sequence; w t is the weight coefficient based on the power change rate; σ X and σ Y are the standard deviations of series X and Y, respectively.

3. The method according to claim 1, characterized in that The distance between stations is calculated based on the correlation coefficient, including: Among them, d ij is the distance between new energy station i and new energy station j; ρ X,Y Represents the correlation coefficient between the power change sequences X and Y corresponding to the new energy stations i and j.

4. The method according to claim 1, characterized in that: The method further comprises: According to the data scale and expected clustering granularity, set the initial range of the number of clusters K [K min ,K max ]; For each candidate K value, perform the improved K-Means clustering algorithm to calculate the corresponding global weighted silhouette coefficient And look for The largest K value is taken as the optimal number of clusters K*; if [K min ,K max ] If the optimal value is not found within the range, then according to Expand the search scope based on the changing trends of 5. The method according to claim 4, characterized in that The global weighted silhouette coefficient is determined using the following method, including: Among them, a i represents the average distance between sample i and its cluster and other samples; b i represents the average distance between sample i and the nearest samples in other clusters; w i represents the weight coefficient of the i-th sample, which is determined by the installed capacity, historical power generation and data quality of the new energy unit; s i represents the weighted silhouette coefficient of the i-th sample, s i >0 indicates that the samples are reasonably clustered, with close clusters and separation between clusters; i =0 means that the sample is located at the boundary of two clusters and the clustering effect is average; s i <0 indicates that the samples may be clustered incorrectly, the distance within the cluster is greater than the distance between clusters, and one new energy site is one sample.

6. The method according to claim 1, characterized in that For each station group, a correlation coefficient matrix is ​​constructed, and a new power change sequence of new energy stations is generated through Cholesky decomposition, including: For each station group, extract the corresponding correlation coefficient submatrix R from the correlation coefficient matrix constructed based on the correlation coefficient; Based on the advanced statistical model of Cholesky decomposition, the correlation coefficient matrix is ​​decomposed into R = L·L T ; Where L is a lower triangular matrix over the real number field; Generate a random matrix Z=randn(k,m) that conforms to the standard normal distribution, Where k is the length of data to be generated, m is the number of stations in the station group, and randn represents sampling from the standard normal distribution N(0,1); The random matrix Z is linearly transformed through the lower triangular matrix L to introduce the specified correlation structure, and S = Z·L is obtained T ; Perform inverse normalization on each column of the matrix S to obtain Each column of the generated random data matrix S is used as a new power change sequence of new energy stations that satisfies the correlation coefficient submatrix R; in, Among them, S ij is a correlation-mapped matrix, each column of which is a standardized random sequence with a specified correlation; S ij ′ is the power change value; and Var(X i ) are the mean and variance of the power series of the i-th renewable energy station; n is the length of the time series; A T represents the transpose of A.

7. A power generation system for a new energy station in a power grid considering spatial correlation, characterized in that: The system comprises: A correlation coefficient calculation unit, used to calculate the correlation coefficient of any two different new energy stations based on the power change sequence; A clustering unit, used for calculating the distance between stations based on the correlation coefficient, and performing clustering based on the distance to form a plurality of station groups; The data generation unit is used to construct a correlation coefficient matrix for each station group and generate a new power change sequence of new energy stations through Cholesky decomposition.

8. The system according to claim 7, characterized in that The correlation coefficient calculation unit calculates the correlation coefficient of any two different new energy stations based on the power change sequence, including: Among them, ρ X,Y is the correlation coefficient between the power change sequences X and Y of the two new energy stations; X t and Y t is the power value at the tth time point; n is the number of time points; and is the mean of the corresponding sequence; w t is the weight coefficient based on the power change rate; σ X and σ Y are the standard deviations of series X and Y, respectively.

9. The system according to claim 7, characterized in that The clustering unit calculates the distance between stations based on the correlation coefficient, including: Among them, d ij is the distance between new energy station i and new energy station j; ρ X,Y Represents the correlation coefficient between the power change sequences X and Y corresponding to the new energy stations i and j.

10. The system according to claim 7, characterized in that The clustering unit further includes: According to the data scale and expected clustering granularity, set the initial range of the number of clusters K [K min ,K max ]; For each candidate K value, perform the improved K-Means clustering algorithm to calculate the corresponding global weighted silhouette coefficient And look for The largest K value is taken as the optimal number of clusters K*; if [K min ,K max ] If the optimal value is not found within the range, then according to Expand the search scope based on the changing trends of 11. The system according to claim 10, characterized in that The clustering unit determines the global weighted silhouette coefficient in the following manner, including: Among them, a i represents the average distance between sample i and its cluster and other samples; b i represents the average distance between sample i and the nearest samples in other clusters; w i represents the weight coefficient of the i-th sample, which is determined by the installed capacity, historical power generation and data quality of the new energy unit; s i represents the weighted silhouette coefficient of the i-th sample, s i >0 indicates that the samples are reasonably clustered, with close clusters and separation between clusters; i =0 means that the sample is located at the boundary of two clusters and the clustering effect is average; s i <0 indicates that the samples may be clustered incorrectly, the distance within the cluster is greater than the distance between clusters, and one new energy site is one sample.

12. The system according to claim 7, characterized in that The data generation unit constructs a correlation coefficient matrix for each station group, and generates a new power change sequence of new energy stations through Cholesky decomposition, including: For each station group, extract the corresponding correlation coefficient submatrix R from the correlation coefficient matrix constructed based on the correlation coefficient; Based on the advanced statistical model of Cholesky decomposition, the correlation coefficient matrix is ​​decomposed into R = L·L T ; Where L is a lower triangular matrix over the real number field; Generate a random matrix Z=randn(k,m) that conforms to the standard normal distribution, Where k is the length of data to be generated, m is the number of stations in the station group, and randn represents sampling from the standard normal distribution N(0,1); The random matrix Z is linearly transformed through the lower triangular matrix L to introduce the specified correlation structure, and S = Z·L is obtained T ; Perform inverse normalization on each column of the matrix S to obtain Each column of the generated random data matrix S is used as a new power change sequence of new energy stations that satisfies the correlation coefficient submatrix R; in, Among them, S ij is a correlation-mapped matrix, each column of which is a standardized random sequence with a specified correlation; S ij ′ is the power change value; and Var(X i ) are the mean and variance of the power series of the i-th renewable energy station; n is the length of the time series; A T represents the transpose of A.

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