A Wide-Area Distributed Photovoltaic Power Generation Efficiency Evaluation Method

Abstract

The present invention provides a method for evaluating the efficiency of wide-area distributed photovoltaic power generation, which relates to the technical field of photovoltaic power generation efficiency evaluation, and includes: obtaining the reanalysis time-series data of solar radiation in the area where the distributed photovoltaic power station is located, calculating the eigenvectors by using the empirical orthogonal function decomposition method, and screening out the set of meteorological resource representative points for the final site selection in combination with the site selection conditions; collecting the measured data of the total solar radiation on the surface of the set of meteorological resource representative points, and performing correction processing on the reanalysis time-series data by using the singular value decomposition method; matching the solar radiation reanalysis grid for the distributed photovoltaic power station, and evaluating the efficiency of distributed photovoltaic power generation by using the physical method and the statistical method based on the corrected reanalysis time-series data to obtain the evaluation value. The present invention has wide applicability, and only needs to use the publicly available global meteorological reanalysis data set to realize the resource distribution analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation efficiency evaluation. Specifically, it particularly relates to a method for evaluating the efficiency of wide-area distributed photovoltaic power generation. Background Art

[0002] Referring to the national standard "Technical Specification for Performance Evaluation of Photovoltaic Power Stations" (GB / T 39854-2021), the power generation efficiency of a photovoltaic system can be characterized by indicators such as system efficiency (i.e., the ratio of the total power generation output of a photovoltaic power station during a certain period to the total solar radiation absorbed by the inclined surface of the photovoltaic modules) or system energy efficiency (i.e., PR efficiency, which is the ratio of the equivalent utilization hours of a photovoltaic power station during a certain period to the peak sunshine hours of the inclined surface of the photovoltaic modules). The calculation of both system efficiency and system energy efficiency indicators depends on the accurate measurement of core meteorological elements such as local solar radiation and temperature in the photovoltaic power generation system. To meet this requirement, the existing solution is to install high-precision meteorological resource monitoring devices in the area where the photovoltaic power generation system is located, measure the values of meteorological elements such as solar radiation and temperature in real time, and then calculate and evaluate the power generation efficiency in real time according to the efficiency indicator calculation formula provided by relevant technical specifications. For centralized photovoltaic power stations, with large installed capacity and relatively concentrated operation and maintenance work, this solution can be widely adopted.

[0003] However, in recent years, wide-area distributed photovoltaic power generation has gradually developed. That is, within a wide geographical area, photovoltaic power generation systems are installed dispersedly near users, such as on building rooftops, open spaces, etc., to achieve on-site power generation and consumption. This model helps to optimize the energy structure, improve energy utilization efficiency, and reduce the impact on the environment. Wide-area distributed photovoltaic power generation will replace centralized photovoltaics as the future mainstream form of photovoltaic power generation utilization. Wide-area distributed photovoltaic power generation is installed dispersedly, with small individual capacity and decentralized operation and maintenance. Therefore, for a region (city, district, or county), there may be thousands of distributed power generation systems. Implementing a solution to install independent meteorological resource monitoring devices in each distributed photovoltaic power generation system to evaluate the power generation efficiency is economically and operationally infeasible.

[0004] The general solution of the existing methods for evaluating the efficiency of distributed photovoltaic power generation systems is to install meteorological monitoring devices at a small number of representative points where the installed photovoltaic systems are relatively concentrated in a region to achieve full coverage of meteorological resource measurement. This solution can only solve the efficiency evaluation of areas with relatively concentrated installation of a small number of distributed photovoltaics, ignoring the actual situation of a large number and dispersed installation of wide-area distributed photovoltaics. Once distributed photovoltaics develop rapidly and the installation tends to be more dispersed, problems such as the loss of regional meteorological representativeness due to the unreasonable installation location of the original meteorological monitoring devices and the loss of the ability to achieve 100% full coverage of the entire region due to the small number of installations will gradually become prominent.

[0005] In view of the problems in the related art, no effective solution has been proposed yet. Summary of the Invention

[0006] In view of this, the present invention provides a method for evaluating the power generation efficiency of a wide-area distributed photovoltaic power generation to solve the above-mentioned problems.

[0007] To solve the above problems, the specific technical solution adopted by the present invention is as follows:

[0008] A method for evaluating the power generation efficiency of a wide-area distributed photovoltaic power generation, including:

[0009] S1. Obtain the reanalysis time series data of solar radiation in the area where the distributed photovoltaic power station is located, calculate the eigenvectors by using the empirical orthogonal function decomposition method, and screen out the set of representative points of meteorological resources for the final site selection in combination with the site selection conditions;

[0010] S2. Collect the measured data of the total solar radiation on the ground of the set of representative points of meteorological resources, and correct the reanalysis time series data by using the singular value decomposition method;

[0011] S3. Match the solar radiation reanalysis grid for the distributed photovoltaic power station, and evaluate the power generation efficiency of the distributed photovoltaic power generation based on the corrected reanalysis time series data by using the physical method and the statistical method to obtain the evaluation value.

[0012] Preferably, the step of obtaining the reanalysis time series data of solar radiation in the area where the distributed photovoltaic power station is located, calculating the eigenvectors by using the empirical orthogonal function decomposition method, and screening out the set of representative points of meteorological resources for the final site selection includes the following steps:

[0013] S11. Obtain the reanalysis time series data of the gridded solar radiation in the longitude and latitude range of the area where the distributed photovoltaic power station is located, and construct a spatio-temporal matrix of anomaly values by calculating the spatial anomaly values;

[0014] S12. Calculate the covariance matrix of the spatio-temporal matrix of anomaly values, and decompose the spatio-temporal matrix of anomaly values into a spatial distribution matrix and a time coefficient matrix by using the empirical orthogonal function decomposition method;

[0015] S13. Calculate the eigenvalues of the covariance matrix and the eigenvectors corresponding to the eigenvalues according to the spatial distribution matrix and the time coefficient matrix;

[0016] S14. According to the eigenvalues of the covariance matrix and the eigenvectors corresponding to the eigenvalues, draw the isosurface map of the first spatial distribution to select the candidate set of representative points of meteorological resources, and screen out the set of representative points of meteorological resources for the final site selection in combination with the site selection conditions.

[0017] Preferably, obtaining the reanalysis time series data of the gridded solar radiation in the longitude and latitude range of the area where the distributed photovoltaic power station is located, and constructing the anomaly value spatio-temporal matrix by calculating the spatial anomaly value includes the following:

[0018] S111. Expand the spatial longitude and latitude grid point data of the reanalysis time series data of the gridded solar radiation into a one-dimensional vector to obtain a two-dimensional matrix form;

[0019] S112. Construct the total radiation reanalysis matrix according to the two-dimensional matrix form, and calculate the spatial anomaly value according to the total radiation reanalysis matrix;

[0020] S113. Construct the anomaly value spatio-temporal matrix according to the spatial anomaly value.

[0021] Preferably, the calculation formula for calculating the spatial anomaly value according to the total radiation reanalysis matrix is:

[0022] ;

[0023] In the formula, represents the spatial anomaly value, x ij represents the total radiation value at the jth time point on the ith grid point, represents the mean value of the total radiation reanalysis matrix.

[0024] Preferably, according to the eigenvalues of the covariance matrix and the eigenvectors corresponding to the eigenvalues, drawing the spatial distribution isosurface map of the front space to select the candidate meteorological resource representative point set, and screening out the final selected meteorological resource representative point set in combination with the site selection conditions includes:

[0025] S141. Sort the eigenvalues of the covariance matrix from large to small, and select a preset number of eigenvectors from the sorting results;

[0026] S142. Draw the spatial distribution isosurface map and the time coefficient vector distribution map of the spatial modes corresponding to a preset number of eigenvectors respectively;

[0027] S143. Search and determine the coordinates of the central points in the isosurface map in the spatial distribution isosurface map, and construct a candidate meteorological resource representative point coordinate set according to the coordinates of each central point;

[0028] S144. According to the time coefficient vector distribution map and the candidate meteorological resource representative point coordinate set, calculate the relative distance between the coordinates of two representative points in different spatial modes and the Pearson correlation coefficient of the time coefficient vectors of the eigenvectors corresponding to the coordinates of two representative points in different spatial modes respectively;

[0029] S145. Initially screen the candidate meteorological resource representative point coordinate set according to the relative distance and Pearson correlation coefficient, and screen out the final selected meteorological resource representative point set from the initial screening results in combination with the site selection conditions.

[0030] Preferably, the formulas for respectively calculating the relative distance between the coordinates of two representative points in different spatial modes and the Pearson correlation coefficient of the time coefficient vectors of the eigenvectors corresponding to the coordinates of two representative points in different spatial modes are:

[0031] ;

[0032] ;

[0033] In the formula, D ij represents the relative distance between the coordinates of two representative points in the i-th spatial mode and the j-th spatial mode, Lon i , Lat i and Lon j , Lat j respectively represent the longitudes and latitudes of the representative points in the i-th spatial mode and the j-th spatial mode, and i < j, Lon upper , Lat upper and Lon lower , Lat lower respectively represent the upper and lower bounds of the longitude and latitude coordinates of the area where the distributed photovoltaic power station is located, R ij represents the Pearson correlation coefficient of the time coefficient vectors in the i-th spatial mode and the j-th spatial mode, p ik represents the k-th element of the time coefficient vector corresponding to the i-th spatial mode, p jk represents the k-th element of the time coefficient vector corresponding to the j-th spatial mode, represents the element mean of the time coefficient vector corresponding to the i-th spatial mode, represents the element mean of the time coefficient vector corresponding to the j-th spatial mode, and n represents the number of elements of the time coefficient vector corresponding to the spatial mode.

[0034] Preferably, the collection of the measured data of the total solar radiation on the ground of the meteorological resource representative point set and the correction processing of the reanalysis time series data by the singular value decomposition method include:

[0035] S21. Equip meteorological resource monitoring stations according to the meteorological resource representative point set, collect the measured data of the total solar radiation on the ground, and construct a total radiation measurement matrix;

[0036] S22. Obtain the reanalysis data of the total incident short-wave radiation on the ground within the same longitude and latitude range and the same time period, and construct a total radiation reanalysis matrix;

[0037] S23. Calculate the cross-covariance matrix of the total radiation measurement matrix and the total radiation reanalysis matrix. Based on the singular value decomposition method, determine two orthogonal linear transformation matrices that can maximize the covariance between the total radiation measurement matrix and the total radiation reanalysis matrix through orthogonal linear transformation;

[0038] S24. Determine the coupling relationship between the total radiation measurement matrix and the total radiation reanalysis matrix according to the two orthogonal linear transformation matrices;

[0039] S25. According to the coupling relationship between the total radiation measurement matrix and the total radiation reanalysis matrix, correct the total radiation reanalysis value matrix through the total radiation measurement value matrix to obtain the corrected value of the incident short-wave radiation on the ground.

[0040] Preferably, the formula for correcting the total radiation reanalysis value matrix through the total radiation measurement value matrix to obtain the corrected value of the incident short-wave radiation on the ground is:

[0041] ;

[0042] In the formula, represents the corrected value of the incident short-wave radiation on the ground at time t, R and L represent two different orthogonal linear transformation matrices, A represents the time coefficient matrix of the total radiation measurement value matrix, T represents matrix transpose, and S t represents the total radiation measurement value at time t, and C0 and C1 represent undetermined coefficients.

[0043] Preferably, match the solar radiation reanalysis grid for the distributed photovoltaic power station, and based on the corrected reanalysis time series data, evaluate the distributed photovoltaic power generation efficiency through physical methods and statistical methods. The obtained evaluation values include:

[0044] S31. According to the latitude and longitude information of the distributed photovoltaic power generation and the location of the meteorological resource monitoring station, match the corresponding solar radiation reanalysis grid for the distributed photovoltaic power station;

[0045] S32. Obtain the photovoltaic power generation data of the distributed photovoltaic power station and the reanalysis solar radiation data of the corresponding solar radiation reanalysis grid matched by the distributed photovoltaic power station;

[0046] S33. Correct the reanalysis solar radiation data based on the corrected value of the incident short-wave radiation on the ground to obtain the corrected reanalysis solar radiation data;

[0047] S34. Calculate the evaluation value of the distributed photovoltaic power generation efficiency according to the photovoltaic power generation data and the corrected reanalysis solar radiation data.

[0048] Preferably, the formula for calculating the evaluation value of the distributed photovoltaic power generation efficiency according to the photovoltaic power generation data and the corrected reanalysis solar radiation data is:

[0049] ;

[0050] Wherein, PR represents the evaluation value of the distributed photovoltaic power generation efficiency, Y f represents the actual power generation hours, Y r represents the theoretical power generation hours.

[0051] The beneficial effects of the present invention are as follows:

[0052] 1. The present invention has wide applicability. Only by using the publicly available global meteorological reanalysis dataset can the resource distribution analysis be realized.

[0053] 2. The present invention has high economy. Only by installing a small number of meteorological resource monitoring devices in combination with distributed power generation data can the evaluation of the wide-area distributed photovoltaic power generation efficiency be realized.

[0054] 3. The present invention has high precision. By correcting the global meteorological reanalysis data with the measured meteorological resource data improved by the meteorological resource monitoring device, on the basis of ensuring the full coverage of the meteorological resource distribution data in the whole region, the monitoring precision of the resource data is improved, thereby improving the precision of the power generation efficiency evaluation.

[0055] 4. The present invention realizes the accurate evaluation of the regional distributed photovoltaic power generation efficiency through the wide-area distributed photovoltaic power generation efficiency evaluation method, providing richer data support for the optimized operation, operation and maintenance of distributed power generation. Description of the Drawings

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings. In the drawings:

[0057] Figure 1 is one of the flowcharts of a wide-area distributed photovoltaic power generation efficiency evaluation method according to an embodiment of the present invention;

[0058] Figure 2 is the second flowchart of a wide-area distributed photovoltaic power generation efficiency evaluation method according to an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of the isosurface of the n-th mode spatial distribution in the EOF decomposition in a wide-area distributed photovoltaic power generation efficiency evaluation method according to an embodiment of the present invention;

[0060] Figure 4It is a schematic diagram of the distribution of the time coefficients of the nth mode in the EOF decomposition in a wide-area distributed photovoltaic power generation efficiency evaluation method according to an embodiment of the present invention. Detailed implementation mode

[0061] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0062] According to an embodiment of the present invention, a wide-area distributed photovoltaic power generation efficiency evaluation method is provided.

[0063] Now, the present invention will be further described in conjunction with the accompanying drawings and specific implementation modes. As Figures 1 to 2 shown, the wide-area distributed photovoltaic power generation efficiency evaluation method according to an embodiment of the present invention includes:

[0064] S1. Obtain the reanalysis time series data of solar radiation in the area where the distributed photovoltaic power station is located, calculate the eigenvectors using the empirical orthogonal function decomposition method, and screen out the set of representative points of meteorological resources for the final site selection in combination with the site selection conditions;

[0065] As a preferred implementation mode, the step of obtaining the reanalysis time series data of solar radiation in the area where the distributed photovoltaic power station is located, calculating the eigenvectors using the empirical orthogonal function decomposition method, and screening out the set of representative points of meteorological resources for the final site selection includes the following steps:

[0066] S11. Obtain the reanalysis time series data of gridded solar radiation in the longitude and latitude range of the area where the distributed photovoltaic power station is located, and construct a spatio-temporal matrix of anomaly values by calculating the spatial anomaly values;

[0067] As a preferred implementation mode, the step of obtaining the reanalysis time series data of gridded solar radiation in the longitude and latitude range of the area where the distributed photovoltaic power station is located, and constructing a spatio-temporal matrix of anomaly values by calculating the spatial anomaly values includes the following:

[0068] S111. Expand the spatial longitude and latitude grid point data of the reanalysis time series data of gridded solar radiation into a one-dimensional vector to obtain a two-dimensional matrix form;

[0069] S112. Construct a total radiation reanalysis matrix according to the two-dimensional matrix form, and calculate the spatial anomaly values according to the total radiation reanalysis matrix;

[0070] S113. Construct a spatio-temporal matrix of anomaly values according to the spatial anomaly values.

[0071] S12. Calculate the covariance matrix of the spatio-temporal matrix of anomaly values, and decompose the spatio-temporal matrix of anomaly values into a spatial distribution matrix and a time coefficient matrix by using the empirical orthogonal function decomposition method;

[0072] S13. Calculate the eigenvalues of the covariance matrix and the eigenvectors corresponding to the eigenvalues according to the spatial distribution matrix and the time coefficient matrix;

[0073] S14. According to the eigenvalues of the covariance matrix and the eigenvectors corresponding to the eigenvalues, draw the spatial distribution isosurface map of the first few, select the candidate meteorological resource representative point set, and screen out the finally selected meteorological resource representative point set in combination with the site selection conditions.

[0074] As a preferred implementation manner, the step of drawing the spatial distribution isosurface map of the first few according to the eigenvalues of the covariance matrix and the eigenvectors corresponding to the eigenvalues, selecting the candidate meteorological resource representative point set, and screening out the finally selected meteorological resource representative point set in combination with the site selection conditions includes:

[0075] S141. Sort the eigenvalues of the covariance matrix from large to small, and select a preset number of eigenvectors from the sorting results;

[0076] S142. Draw the spatial distribution isosurface map of the spatial mode corresponding to the preset number of eigenvectors and the time coefficient vector distribution map respectively;

[0077] S143. Search and determine the coordinates of the center points in the isosurface map in the spatial distribution isosurface map, and construct a candidate meteorological resource representative point coordinate set according to the coordinates of the center points;

[0078] S144. According to the time coefficient vector distribution map and the candidate meteorological resource representative point coordinate set, calculate the relative distance between the coordinates of two representative points in different spatial modes and the Pearson correlation coefficient of the time coefficient vectors of the eigenvectors corresponding to the coordinates of two representative points in different spatial modes respectively;

[0079] S145. Preliminarily screen the candidate meteorological resource representative point coordinate set according to the relative distance and the Pearson correlation coefficient, and screen out the finally selected meteorological resource representative point set from the preliminary screening results in combination with the site selection conditions.

[0080] S2. Collect the measured data of the total solar radiation on the ground of the meteorological resource representative point set, and correct the reanalysis time series data by using the singular value decomposition method;

[0081] As a preferred implementation manner, the step of collecting the measured data of the total solar radiation on the ground of the meteorological resource representative point set and correcting the reanalysis time series data by using the singular value decomposition method includes:

[0082] S21. Equip meteorological resource monitoring stations according to the meteorological resource representative point set, collect the measured data of the total solar radiation on the ground surface, and construct a total radiation measurement matrix;

[0083] S22. Obtain the reanalysis data of the total incident shortwave radiation on the ground surface within the same longitude and latitude range and the same time period, and construct a total radiation reanalysis matrix;

[0084] S23. Calculate the cross-covariance matrix of the total radiation measurement matrix and the total radiation reanalysis matrix. Based on the singular value decomposition method, determine two orthogonal linear transformation matrices that can satisfy the maximized covariance between the total radiation measurement matrix and the total radiation reanalysis matrix through orthogonal linear transformation;

[0085] S24. Determine the coupling relationship between the total radiation measurement matrix and the total radiation reanalysis matrix according to the two orthogonal linear transformation matrices;

[0086] S25. According to the coupling relationship between the total radiation measurement matrix and the total radiation reanalysis matrix, correct the total radiation reanalysis value matrix through the total radiation measurement value matrix to obtain the corrected value of the incident shortwave radiation on the ground surface.

[0087] S3. Match the solar radiation reanalysis grid for the distributed photovoltaic power station, and evaluate the distributed photovoltaic power generation efficiency through physical and statistical methods based on the corrected reanalysis time series data to obtain an evaluation value.

[0088] As a preferred implementation manner, the step of matching the solar radiation reanalysis grid for the distributed photovoltaic power station and evaluating the distributed photovoltaic power generation efficiency through physical and statistical methods based on the corrected reanalysis time series data to obtain an evaluation value includes:

[0089] S31. Match the corresponding solar radiation reanalysis grid for the distributed photovoltaic power station according to the longitude and latitude information of the distributed photovoltaic power generation and the location of the meteorological resource monitoring station;

[0090] S32. Obtain the photovoltaic power generation data of the distributed photovoltaic power station and the reanalysis solar radiation data of the corresponding solar radiation reanalysis grid matched by the distributed photovoltaic power station;

[0091] S33. Correct the reanalysis solar radiation data based on the corrected value of the incident shortwave radiation on the ground surface to obtain the corrected reanalysis solar radiation data;

[0092] S34. Calculate the evaluation value of the distributed photovoltaic power generation efficiency according to the photovoltaic power generation data and the corrected reanalysis solar radiation data.

[0093] For the convenience of understanding the above technical solutions of the present invention, the above technical solutions of the present invention are further described from the perspectives of architecture and principle as follows:

[0094] Step 1: Obtain the gridded solar radiation reanalysis time series data for more than 5 years within the longitude and latitude range of the distributed photovoltaic power station group to be evaluated. Calculate the spatial anomaly values of the solar radiation data at each longitude and latitude grid point at each time point, and construct an anomaly value spatio-temporal matrix. Then calculate the covariance matrix of the anomaly value spatio-temporal matrix, and use the empirical orthogonal function decomposition method (EOF) to decompose the anomaly value spatio-temporal matrix into a spatial distribution matrix and a time coefficient matrix. Calculate the eigenvalues and corresponding eigenvectors of the covariance matrix. The eigenvectors are the EOF modes. Draw the spatial distribution isosurface maps of the first n modes respectively. Then select the central positions of each isosurface as the candidate meteorological resource representative point set. Finally, combined with the local geography, communication, and maintenance conditions, screen out the final selected meteorological resource representative point set from them.

[0095] Step 1.1: According to the longitude and latitude range (Lon lower , Lon upper , Lat lower , Lat upper ) of the distributed photovoltaic power station group to be evaluated, obtain the gridded solar shortwave downward total radiation reanalysis time series data DSR n×q×w (Time, lon, lat), whose dimension is n×q×w, where n represents the number of time points in the acquisition time period, q represents the number of grid points in the longitude direction, and w represents the number of grid points in the latitude direction. Expand the spatial longitude and latitude grid point data of DSR n×q×w into a one-dimensional vector, and thus convert it into a two-dimensional matrix form to construct the total radiation reanalysis matrix X(m, n). The expression of the two-dimensional matrix is:

[0096] ;

[0097] In the formula, m represents the spatial grid points (m = q×w), and n represents the time points. x ij represents the total radiation value at the jth time point on the ith grid point.

[0098] Calculate the total radiation anomaly value according to the calculation formula of the spatial anomaly value, and construct the anomaly value spatio-temporal matrix X jp .

[0099] Among them, the calculation formula for calculating the spatial anomaly value according to the total radiation reanalysis matrix is:

[0100] ;

[0101] In the formula, represents the spatial anomaly value, x ij represents the total radiation value at the jth time point on the ith grid point, represents the mean value of the total radiation reanalysis matrix.

[0102] Step 1.2, calculate X jp The covariance matrix S of jp X jp T is S = X

[0103] S = VΛV T ;

[0104] where Λ represents a diagonal matrix, the elements on the diagonal are the eigenvalues of the covariance matrix S, V is the eigenvector matrix of the covariance matrix S, and each column of eigenvectors of V represents an EOF spatial mode. Therefore, V is also called the jp spatial distribution matrix of X

[0105] Using the empirical orthogonal function decomposition method (EOF), the anomaly value spatio-temporal matrix X jp can be decomposed into a spatial distribution matrix and a time coefficient matrix, and the formula is as follows:

[0106] X jp = VP;

[0107] where P = V T X jp is called the time coefficient matrix of X jp

[0108] Sort the eigenvalues Λ from largest to smallest, as shown in Figures 3 to 4 (in Figure 3 , the abscissa represents longitude and the ordinate represents latitude, Figure 3 the regional range in Figure 4 is approximately 86° - 110° E longitude and 30° - 45° N latitude, and in

[0109] Step 1.3, in the isosurface maps of the first n EOF modes, search and determine the coordinates of the center points in the isosurface maps to construct a candidate meteorological resource representative point coordinate set G n ​(Lon, Lat). In practice, the isosurface center point coordinates are usually searched for the isosurface diagrams of the first 3 or the first 4 modes respectively. Considering the distance between the center point coordinates and the fluctuation of the isosurface value with the time coefficient, the candidate representative points are screened, and finally the coordinate set G of the candidate representative points is determined. CT (Lon, Lat), where the method steps for screening representative points include:

[0110] (1) Calculate the relative distance between the coordinates of two representative points in different modes. The relative distance calculation formula is:

[0111] ;

[0112] In the formula, D ij represents the relative distance between the coordinates of two representative points in the i-th spatial mode and the j-th spatial mode. Lon i , Lat i and Lon j , Lat j represent the longitude and latitude of the representative points in the i-th spatial mode and the j-th spatial mode respectively, and i < j. Lon upper , Lat upper and Lon lower , Lat lower represent the upper and lower bounds of the longitude and latitude coordinates of the area where the distributed photovoltaic power station is located respectively.

[0113] When D ij < 0.1, the representative point C j , Lat j ) in the j-th spatial mode with coordinates (Lon del (Lon j , Lat j ) is marked as a point to be screened out, and the representative point C ref (Lon i , Lat i ) in the corresponding i-th spatial mode is called the reference point.

[0114] (2) Calculate the Pearson correlation coefficient of the time coefficient vectors of the i-th spatial mode and the j-th spatial mode corresponding to C del and C ref :

[0115] ;

[0116] In the formula, R ij represents the Pearson correlation coefficient of the time coefficient vectors of the i-th spatial mode and the j-th spatial mode, p ik represents the k-th element of the time coefficient vector corresponding to the i-th spatial mode, p jkdenotes the k-th element of the time coefficient vector corresponding to the j-th spatial mode, denotes the mean value of the elements of the time coefficient vector corresponding to the i-th spatial mode, denotes the mean value of the elements of the time coefficient vector corresponding to the j-th spatial mode, and n denotes the number of elements of the time coefficient vector corresponding to the spatial mode.

[0117] When R ij < 0.9, mark C del as a point to be screened out and delete it from the candidate representative point coordinate set.

[0118] Step 1.4, in combination with the site selection conditions (local geography, communication, and maintenance conditions), screen out the meteorological resource representative point set G CT (Lon, Lat) from G T (Lon, Lat), as shown in Table 1.

[0119] Table 1 Example of Meteorological Resource Representative Point Set

[0120] Region Central longitude Central latitude Region 1 120.12 31.86 Region 2 119.96 31.83 Region 3 120.10 31.68 Region 4 120.11 31.56

[0121] In practice, when conducting the final site selection of meteorological resource representative points, on-site surveys need to be carried out to avoid interference such as shielding, reflection, and scattering caused by buildings, vegetation, mountains, etc. to the resource monitoring devices installed at the representative points. The communication conditions should also be investigated on-site, with the principle of maintainability and reliability as the priority.

[0122] Step 2, install meteorological resource monitoring devices at the latitudes and longitudes of each meteorological resource representative point, collect multi-month measured data of the total solar radiation on the ground surface; analyze and calculate the mapping relationship between the measured solar radiation data at the representative point and the reanalysis solar radiation data of the grid corresponding to the representative point; finally, use the mapping relationship to correct the reanalysis data of other grids in the region.

[0123] Step 2.1, based on G T (Lon, Lat), install meteorological resource monitoring stations, collect multi-month measured data of the total solar radiation on the ground surface, and construct the total radiation measurement matrix S(x, t) according to the method in Step 1.1. The number of meteorological resource monitoring stations is x = 1, 2,..., Ns, and the number of time points in the collection time period is t. The expression of the total radiation measurement matrix is:

[0124] ;

[0125] Obtain the reanalysis data of the total incident short-wave radiation on the ground surface in the same latitude and longitude range and the same time period, and construct the total radiation reanalysis matrix Z(y, t) according to the method in Step 1.1. The number of reanalysis grid points is y = 1, 2,..., Nz. The expression of the total radiation reanalysis matrix is:

[0126] ;

[0127] In practice, it is required that the time scales of the reanalysis matrix and the measurement matrix be strictly unified. It is required that the number of grid points contained in the reanalysis matrix be greater than the number of meteorological resource monitoring stations contained in the study area.

[0128] Step 2.2, the cross-covariance matrix of matrices S and Z is C SZ = <SZ T >, where the superscript T represents the matrix transpose, and the symbol <> represents taking the average. Based on the singular value decomposition method (SVD), an orthogonal linear transformation can be applied so that after separately transforming the left and right fields, two orthogonal linear transformation matrices L and R can be found, such that the covariance between the two fields is maximized, that is:

[0129] ;

[0130] According to the theory of linear algebra, L and R that satisfy the above conditions can be uniquely solved.

[0131] , ;

[0132] By determining L and R, the coupling relationship between S and Z can be uniquely determined. The k-th column vectors l k and r k of L and R are respectively called the k-th left and right singular vectors, that is, the k-th modal spatial pattern. Let A = L T S, B = R T Z, where A is called the time coefficient matrix of S, and B is called the time coefficient matrix of Z.

[0133] Step 2.3, since L and R uniquely determined by the SVD method are orthogonal matrices, the matrix transformation can be S = AL, Z = BR.

[0134] The cumulative covariance contribution of the current N modes can explain the covariance C SZ For most cases (usually the cumulative covariance contribution rate >= 90%), it can be assumed that the two column time coefficient vectors a k and b k satisfy a linear relationship, that is:

[0135] ;

[0136] In the formula, a k represents the time coefficient vector corresponding to the k-th modal left singular vector l k , and b k represents the time coefficient vector corresponding to the k-th modal right singular vector r k . C 0k and C 0kDenote undetermined coefficients.

[0137] Then we have:

[0138] ;

[0139] In the formula, denotes the corrected value of the incident short-wave radiation on the earth's surface at time t, R and L denote two different orthogonal linear transformation matrices, A denotes the time coefficient matrix of the total radiation measurement value matrix, T denotes the matrix transpose, and S t denotes the total radiation measurement value at time t, and C0 and C1 denote undetermined coefficients.

[0140] Through the above calculation formula, it is possible to correct the total radiation reanalysis value matrix Z through the total radiation measurement value matrix S and output the corrected value of the incident short-wave radiation on the earth's surface .

[0141] In practice, the undetermined coefficients can be obtained by inputting historical data for multiple months and using the method of linear fitting. To track the change of the coupling relationship between S and Z over time, this step needs to be executed monthly in a rolling manner, constantly refreshing the undetermined coefficients with new measurement data and reanalysis data.

[0142] Step 3: According to the latitude and longitude information of distributed photovoltaic power generation and the installation of meteorological resource monitoring stations, match the corresponding solar radiation grid for each distributed power generation station to be evaluated; input the distributed photovoltaic power generation data and the corrected reanalysis solar radiation data of the matched grid, and analyze and calculate according to the physical method and statistical method to output the evaluation value of the distributed photovoltaic power generation efficiency.

[0143] Step 3.1: According to the latitude and longitude information of distributed photovoltaic power generation and the installation location of meteorological resource monitoring stations, match the corresponding solar radiation reanalysis grid for each distributed power generation station to be evaluated. In practice, according to the resolution of the reanalysis data grid, it is usually required that the center of the allocated grid is no more than 10 kilometers away from the distributed power generation station; for distributed photovoltaic power stations within 5 kilometers of the location of the meteorological resource monitoring station, the measurement data of the meteorological resource monitoring station is used as the data source for evaluating the power generation efficiency.

[0144] Step 3.2: Input the distributed photovoltaic power generation data and the corrected reanalysis solar radiation data of the matched grid , and analyze and calculate according to the physical method and statistical method to output the evaluation value of the distributed photovoltaic power generation efficiency.

[0145] In practice, the PR efficiency, that is, the overall power generation efficiency of the photovoltaic power station, is usually used to evaluate the efficiency of the photovoltaic power generation system. The calculation formula is:

[0146] ;

[0147] Wherein, PR represents the evaluation value of the distributed photovoltaic power generation efficiency, and Y f represents the actual power generation hours, and Y r represents the theoretical power generation hours.

[0148] Among them, Y f =E AC / P0, which is called the actual power generation hours, with the unit of hours; E AC represents the actual power generation of the photovoltaic system, with the unit of kWh; P0 represents the installed capacity of the system.

[0149] Y r =S A / G0, which is called the theoretical power generation hours, with the unit of hours; S A represents the cumulative irradiance per unit surface of the array, with the unit of kWh / m 2 ; G0 represents the standard irradiance of 1000 W / m under the STC conditions (Standard Test Conditions) 2 .

[0150] Among them, E AC can be collected through the electric energy meter at the photovoltaic grid connection point, and S A generally adopts the reanalysis cumulative radiation amount corrected through Step 2. For distributed photovoltaic power stations within 5 kilometers from the location of the meteorological resource monitoring station, the cumulative radiation amount measured by the meteorological resource station is adopted.

[0151] To sum up, by means of the above technical solutions of the present invention, the present invention has wide applicability. Only by using the publicly available global meteorological reanalysis data set can the resource distribution analysis be realized. The present invention has high economy. Only by installing a small number of meteorological resource monitoring devices in combination with distributed power generation data can the evaluation of the wide-area distributed photovoltaic power generation efficiency be realized. The present invention has high precision. By correcting the global meteorological reanalysis data with the measured meteorological resource data improved by the meteorological resource monitoring devices, on the basis of ensuring the full coverage of the meteorological resource distribution data in the whole region, the monitoring precision of the resource data is improved, thereby improving the precision of the power generation efficiency evaluation. The present invention realizes the accurate evaluation of the regional distributed photovoltaic power generation efficiency through the wide-area distributed photovoltaic power generation efficiency evaluation method, providing richer data support for the optimized operation, maintenance and repair of distributed power generation.

[0152] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0153] The specific embodiments described above have further elaborated on the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation, characterized in that: include: S1. Obtain the reanalysis time series data of solar radiation in the area where the distributed photovoltaic power station is located, calculate the characteristic vector using the empirical orthogonal function decomposition method, and select the representative point set of meteorological resources for the final site selection based on the site selection conditions; S2, collect the surface solar radiation measurement data of the representative point set of meteorological resources, and correct the reanalysis time series data by singular value decomposition method; S3, matching the solar radiation reanalysis grid for the distributed photovoltaic power station, and evaluating the distributed photovoltaic power generation efficiency by physical and statistical methods based on the corrected reanalysis time series data to obtain an evaluation value; The S1 includes: S11, obtaining the gridded solar radiation reanalysis time series data in the longitude and latitude range of the area where the distributed photovoltaic power station is located, and constructing the anomaly value spatiotemporal matrix by calculating the spatial anomaly value; S12, calculating the covariance matrix of the anomaly value space-time matrix, and decomposing the anomaly value space-time matrix into a spatial distribution matrix and a time coefficient matrix using an empirical orthogonal function decomposition method; S13, calculating the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to the eigenvalues ​​according to the spatial distribution matrix and the time coefficient matrix; S14, according to the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to the eigenvalues, a front spatial distribution contour map is drawn to select a candidate meteorological resource representative point set, and the meteorological resource representative point set for the final site selection is screened out in combination with the site selection conditions; The S2 includes: S21. Equip meteorological resource monitoring stations according to the meteorological resource representative point set, collect surface solar total radiation measurement data, and construct a total radiation measurement matrix; S22, obtaining the surface incident shortwave total radiation reanalysis data within the same longitude and latitude range and the same time period, and constructing a total radiation reanalysis matrix; S23, calculating the co-cross variance matrix of the global radiation measurement matrix and the global radiation reanalysis matrix, and determining two orthogonal linear transformation matrices that can satisfy the maximum covariance between the global radiation measurement matrix and the global radiation reanalysis matrix through orthogonal linear transformation based on the singular value decomposition method; S24, determining a coupling relationship between a total radiation measurement matrix and a total radiation reanalysis matrix according to two orthogonal linear transformation matrices; S25. According to the coupling relationship between the global radiation measurement matrix and the global radiation reanalysis matrix, the global radiation reanalysis value matrix is ​​corrected by the global radiation measurement value matrix to obtain the surface incident shortwave radiation correction value.

2. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 1, characterized in that: The obtaining of the gridded solar radiation reanalysis time series data in the longitude and latitude range of the area where the distributed photovoltaic power station is located, and the calculation of the spatial anomaly value to construct the anomaly value spatiotemporal matrix includes: S111, expanding the spatial latitude and longitude grid point data of the gridded solar radiation reanalysis time series data into a one-dimensional vector to obtain a two-dimensional matrix form; S112, constructing a global radiation reanalysis matrix according to a two-dimensional matrix form, and calculating a spatial anomaly value according to the global radiation reanalysis matrix; S113. Construct a spatiotemporal matrix of anomalies based on the spatial anomalies.

3. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 2, characterized in that: The calculation formula for calculating the spatial anomaly value based on the global radiation reanalysis matrix is: In the formula, represents the spatial anomaly value, x ij represents the total radiation value at the jth time point on the i-th grid point, Represents the mean of the global radiation reanalysis matrix.

4. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 2, characterized in that: The method of selecting a candidate meteorological resource representative point set based on the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to the eigenvalues, and selecting a meteorological resource representative point set for the final site selection in combination with the site selection conditions includes: S141, sorting the eigenvalues ​​of the covariance matrix from large to small, and selecting a preset number of eigenvectors from the sorting results; S142, respectively draw a spatial distribution contour surface diagram and a time coefficient vector distribution diagram of the spatial mode corresponding to a preset number of eigenvectors; S143, searching and determining the coordinates of each center point in the spatial distribution isosurface map, and constructing a candidate meteorological resource representative point coordinate set according to the coordinates of each center point; S144, according to the time coefficient vector distribution map and the candidate meteorological resource representative point coordinate set, respectively calculating the relative distance between the coordinates of two representative points in different spatial modes and the Pearson correlation coefficient of the time coefficient vector of the characteristic vector corresponding to the coordinates of the two representative points in different spatial modes; S145. Preliminary screening is performed on the candidate meteorological resource representative point coordinate set based on relative distance and Pearson correlation coefficient, and the meteorological resource representative point set for final site selection is screened out from the preliminary screening results in combination with the site selection conditions.

5. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 4, characterized in that: The formula for respectively calculating the relative distance between the coordinates of two representative points in different spatial modes and the Pearson correlation coefficient of the time coefficient vector of the characteristic vector corresponding to the coordinates of the two representative points in different spatial modes is: Where D ij Indicates the relative distance between the coordinates of two representative points in the i-th spatial mode and the j-th spatial mode, Lon i , Lat i and Lon j ,Lat j Respectively represent the latitude and longitude of the representative points of the i-th spatial mode and the j-th spatial mode, and i<j, Lon upper , Lat upper and Lon lower ,Lat lower They represent the upper and lower bounds of the longitude and latitude coordinates of the distributed photovoltaic power station area, R ij represents the Pearson correlation coefficient of the time coefficient vectors of the i-th spatial mode and the j-th spatial mode, p ik represents the kth element of the time coefficient vector corresponding to the i-th spatial mode, p jk represents the kth element of the time coefficient vector corresponding to the jth spatial mode, represents the element mean of the time coefficient vector corresponding to the i-th spatial mode, represents the element mean of the time coefficient vector corresponding to the j-th spatial mode, and n represents the number of elements of the time coefficient vector corresponding to the spatial mode.

6. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 5, characterized in that: The calculation formula for obtaining the surface incident shortwave radiation correction value by correcting the total radiation reanalysis value matrix through the total radiation measurement value matrix is: In the formula, Z′ t represents the incident shortwave radiation correction value of the surface at time t, R and L represent two different orthogonal linear transformation matrices, A represents the time coefficient matrix of the total radiation measurement value matrix, T represents the matrix transpose, S t represents the total radiation measurement value at time t, and C0 and C1 represent the unknown coefficients.

7. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 1, characterized in that: The distributed photovoltaic power station is matched with the solar radiation reanalysis grid, and based on the corrected reanalysis time series data, the distributed photovoltaic power generation efficiency is evaluated by physical and statistical methods, and the evaluation values ​​obtained include: S31, matching the corresponding solar radiation reanalysis grid for the distributed photovoltaic power station according to the longitude and latitude information of the distributed photovoltaic power generation and the location of the meteorological resource monitoring station; S32, obtaining photovoltaic power generation data of the distributed photovoltaic power station and reanalyzed solar radiation data of the solar radiation reanalysis grid corresponding to the distributed photovoltaic power station; S33, correcting the reanalyzed solar radiation data based on the surface incident shortwave radiation correction value to obtain corrected reanalyzed solar radiation data; S34. Calculate the evaluation value of distributed photovoltaic power generation efficiency based on the photovoltaic power generation data and the corrected reanalyzed solar radiation data.

8. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 7, characterized in that: The calculation formula for calculating the evaluation value of distributed photovoltaic power generation efficiency based on photovoltaic power generation data and the corrected reanalyzed solar radiation data is: In the formula, PR represents the evaluation value of distributed photovoltaic power generation efficiency, Y f Indicates the actual power generation hours, Y r Indicates theoretical power generation hours.

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