Wide-area distributed photovoltaic power generation efficiency evaluation method

Abstract

The invention provides a wide-area distributed photovoltaic power generation efficiency evaluation method, and relates to the technical field of photovoltaic power generation efficiency evaluation, and the method comprises the steps: obtaining the reanalysis time series data of the solar radiation of an area where a distributed photovoltaic power station is located, calculating a feature vector through employing an empirical orthogonal function decomposition method, and calculating the power generation efficiency of the distributed photovoltaic power station; a meteorological resource representative point set of final site selection is screened out in combination with site selection conditions; collecting the earth surface solar total radiation measurement data of the meteorological resource representative point set, and correcting the reanalysis time sequence data through a singular value decomposition method; matching a solar radiation reanalysis grid for the distributed photovoltaic power station, and evaluating the distributed photovoltaic power generation efficiency through a physical method and a statistical method based on the reanalysis time sequence data after correction processing to obtain an evaluation value. The method has wide applicability, and resource distribution analysis can be realized only by using a public weather reanalysis data set covering the whole world.

Description

Technical Field

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

[0002] Referring to the national standard "Technical Specifications for Performance Evaluation of Photovoltaic Power Stations" (GB / T39854-2021), the power generation efficiency of photovoltaic systems can be characterized by indicators such as system efficiency (i.e., the ratio of the total power output of a photovoltaic power station in a certain period of time to the total radiation absorbed by the inclined surface of the photovoltaic module) or system energy efficiency (i.e., PR efficiency, which is the ratio of the equivalent utilization hours of a photovoltaic power station in a certain period of time to the peak sunshine hours of the inclined surface of the photovoltaic module). 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 of the photovoltaic power generation system. In response to this demand, 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 index calculation formula provided by the relevant technical specifications. For centralized photovoltaic power stations, the installed capacity of the power station is large and the operation and maintenance work is relatively concentrated, so this solution can be widely adopted.

[0003] However, in recent years, wide-area distributed photovoltaic power generation has gradually developed, that is, photovoltaic power generation systems are dispersedly installed near users in a wide geographical area, such as on building roofs, open spaces, etc., to achieve local power generation and electricity consumption. This model helps to optimize the energy structure, improve energy efficiency, and help reduce the impact on the environment. Wide-area distributed photovoltaic power generation will replace centralized photovoltaics and become the mainstream form of photovoltaic power generation in the future. Wide-area distributed photovoltaic power generation has decentralized installation, small single-unit capacity, and decentralized operation and maintenance. Therefore, for a region (cities, districts and counties), which may contain tens of thousands of distributed power generation systems, it is not feasible to install independent meteorological resource monitoring devices in each distributed photovoltaic power generation system to achieve power generation efficiency evaluation in terms of both economy and operation and maintenance.

[0004] The general scheme of the existing distributed photovoltaic power generation system efficiency evaluation method is to select a small number of representative points where the photovoltaic power generation system is relatively concentrated in a region to install meteorological monitoring devices to achieve full coverage of meteorological resource measurement. This scheme can only solve the efficiency evaluation of a few areas where the distributed photovoltaic installation is relatively concentrated and the number of installations is small, ignoring the actual situation of a large number of distributed photovoltaic installations in a wide area and scattered installations. Once distributed photovoltaics develop rapidly and the installation tends to be dispersed, the original meteorological monitoring devices will lose their regional meteorological representativeness due to unreasonable installation locations, and the small number of installations will lose the ability to cover 100% of the entire region. These problems will gradually become prominent.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

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

[0007] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows:

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

[0009] 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;

[0010] 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;

[0011] 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.

[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 characteristic vector using the empirical orthogonal function decomposition method, and selecting the representative point set of meteorological resources for the final site selection in combination with the site selection conditions comprises the following steps:

[0013] 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;

[0014] 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;

[0015] 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;

[0016] 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.

[0017] Preferably, 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 constructing the anomaly value spatiotemporal matrix by calculating the spatial anomaly value includes the following:

[0018] 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;

[0019] 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;

[0020] S113. Construct a spatiotemporal matrix of anomalies based on the spatial anomalies.

[0021] Preferably, the calculation formula for calculating the spatial anomaly value based on the global 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 i-th grid point, Represents the mean of the global radiation reanalysis matrix.

[0024] Preferably, the selecting of a candidate meteorological resource representative point set according to 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 comprises:

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

[0026] 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;

[0027] 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;

[0028] 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;

[0029] 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.

[0030] Preferably, 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:

[0031] ;

[0032] ;

[0033] 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.

[0034] Preferably, the collecting of the total solar radiation measurement data 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 surface solar total radiation measurement data, and construct a total radiation measurement matrix;

[0036] 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;

[0037] 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;

[0038] S24, determining a coupling relationship between a total radiation measurement matrix and a total radiation reanalysis matrix according to two orthogonal linear transformation matrices;

[0039] 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.

[0040] Preferably, the total radiation reanalysis value matrix is ​​corrected by the total radiation measurement value matrix to obtain the calculation formula of the surface incident shortwave radiation correction value:

[0041] ;

[0042] In the formula, 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, C 0 and C 1 represents the undetermined coefficient.

[0043] Preferably, the distributed photovoltaic power station is matched with a 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:

[0044] 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;

[0045] 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;

[0046] S33, correcting the reanalyzed solar radiation data based on the surface incident shortwave radiation correction value to obtain corrected reanalyzed solar radiation data;

[0047] 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.

[0048] Preferably, the calculation formula for calculating the evaluation value of distributed photovoltaic power generation efficiency based on the photovoltaic power generation data and the corrected reanalyzed solar radiation data is:

[0049] ;

[0050] 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.

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

[0052] 1. The present invention has wide applicability and can realize resource distribution analysis by only using a publicly available meteorological reanalysis dataset covering the entire world.

[0053] 2. The present invention is highly economical and only requires the installation of a small number of meteorological resource monitoring devices combined with distributed power generation data to achieve wide-area distributed photovoltaic power generation efficiency evaluation.

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

[0055] 4. The present invention realizes accurate evaluation of regional distributed photovoltaic power generation efficiency through a wide-area distributed photovoltaic power generation efficiency evaluation method, providing more abundant data support for distributed power generation optimization operation and operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

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

[0058] Figure 2 This is a second flow chart of a method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to an embodiment of the present invention;

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

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

[0061] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

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

[0063] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 to Figure 2 As shown, a method for evaluating the efficiency of wide-area distributed photovoltaic power generation 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 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;

[0065] As a preferred implementation, the method of obtaining the reanalysis time series data of solar radiation in the area where the distributed photovoltaic power station is located, calculating the characteristic vector using the empirical orthogonal function decomposition method, and selecting the representative point set of meteorological resources for the final site selection in combination with the site selection conditions includes the following steps:

[0066] 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;

[0067] As a preferred implementation, 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 the following:

[0068] 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;

[0069] 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;

[0070] S113. Construct a spatiotemporal matrix of anomalies based on the spatial anomalies.

[0071] 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;

[0072] 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;

[0073] 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.

[0074] As a preferred implementation, 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, drawing a spatial distribution contour map before drawing, and screening out a meteorological resource representative point set for the final site selection in combination with the site selection conditions includes:

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

[0076] 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;

[0077] 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;

[0078] 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;

[0079] 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.

[0080] 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;

[0081] As a preferred implementation, the acquisition of the total solar radiation measurement data of the meteorological resource representative point set and the correction processing of the reanalysis time series data by the singular value decomposition method include:

[0082] 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;

[0083] 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;

[0084] 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;

[0085] S24, determining a coupling relationship between a total radiation measurement matrix and a total radiation reanalysis matrix according to two orthogonal linear transformation matrices;

[0086] 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.

[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, 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:

[0089] 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;

[0090] 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;

[0091] S33, correcting the reanalyzed solar radiation data based on the surface incident shortwave radiation correction value to obtain corrected reanalyzed solar radiation data;

[0092] 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.

[0093] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are further described below from the perspective of architecture and principle, as follows:

[0094] Step 1: Obtain gridded solar radiation reanalysis time series data for more than 5 years in the longitude and latitude range of the area where the distributed photovoltaic power station group to be evaluated is located, calculate the spatial anomaly values ​​of the solar radiation data at each longitude and latitude grid point at each time point, and construct the anomaly space-time matrix; then calculate the covariance matrix of the anomaly space-time matrix, use the empirical orthogonal function decomposition method (EOF) to decompose the anomaly space-time matrix into a spatial distribution matrix and a time coefficient matrix, and calculate the eigenvalues ​​and corresponding eigenvectors of the covariance matrix. The eigenvectors are the EOF modes, and the spatial distribution contour maps of the first n modes are drawn respectively; then the center position of each contour surface is selected as the candidate meteorological resource representative point set; finally, the meteorological resource representative point set for the final site selection is selected in combination with local geographical, communication, and maintenance conditions.

[0095] Step 1.1: According to the longitude and latitude range (London) of the area where the distributed photovoltaic power station group to be evaluated is located, lower ,Lon upper ,Lat lower ,Lat upper ), obtain more than 5 years of gridded solar shortwave downward total radiation reanalysis time series data DSR n×q×w (Time,lon,lat), whose dimensions are n×q×w, n represents the number of time points in the collection period, q represents the number of grid points in the longitude direction, and w represents the number of grid points in the latitude direction. n×q×w The spatial latitude and longitude grid point data is expanded into a one-dimensional vector and then converted 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 point (m=q×w), and n represents the time point. ij Represents the total radiation value at the jth time point at the ith grid point.

[0098] The total radiation anomaly value is calculated according to the calculation formula of the spatial anomaly value, and the anomaly space-time matrix X is constructed. jp .

[0099] Among them, the calculation formula for calculating the spatial anomaly value based on the global 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 i-th grid point, Represents the mean of the global radiation reanalysis matrix.

[0102] Step 1.2, calculate X jp The covariance matrix S = X jp X jp T , S represents an m×m real symmetric matrix. The superscript T represents the matrix transpose. According to the properties of linear algebra about real symmetric matrices, the covariance matrix S can be decomposed by eigenvalue to obtain:

[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 the eigenvector of V represents an EOF spatial mode, so V is also called X jp Spatial distribution matrix.

[0105] The empirical orthogonal function decomposition method (EOF) can be used to convert the anomaly value spatiotemporal matrix X jp Decomposed into spatial distribution matrix and time coefficient matrix, the formula is as follows:

[0106] X jp =VP;

[0107] Where P = V T X jp , called X jp The time coefficient matrix of .

[0108] Sort the eigenvalues ​​Λ from large to small, such as Figure 3 to Figure 4 As shown (in Figure 3 In the figure, the horizontal axis represents longitude and the vertical axis represents latitude. Figure 3 The area in the area is approximately 86°-110° east longitude and 30°-45° north latitude. Figure 4 In the figure, the horizontal axis represents the year, which is 1980-2010, and the vertical axis represents the time coefficient corresponding to each year of the n-th mode generated by EOF analysis. Its physical meaning is the statistical value of the relative change degree of regional solar radiation over time generated by EOF analysis. Positive values ​​indicate an increase, and negative values ​​indicate a decrease). The spatial distribution contour map and time coefficient distribution map of the first n EOF modes corresponding to the first n eigenvectors are drawn respectively;

[0109] Step 1.3: In the isosurface maps of the first n EOF modes, search and determine the coordinates of each center point in the isosurface map to construct the candidate meteorological resource representative point coordinate set G n(Lon, Lat). In practice, the first three or four modes of the contour surface map are usually selected to search for the coordinates of the center point of the contour surface. Considering the distance between the center point coordinates and the fluctuation of the contour surface value over time, the candidate representative points are screened and the coordinate set G of the candidate representative points is finally determined. CT (Lon, Lat), wherein the representative point screening method steps 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] 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 respectively represent the upper and lower bounds of the longitude and latitude coordinates of the area where the distributed photovoltaic power station is located.

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

[0114] (2) Calculation of C del and C ref The Pearson correlation coefficient of the time coefficient vector of the corresponding i-th spatial mode and j-th spatial mode is:

[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 kth element of the time coefficient vector corresponding to the i-th spatial mode, p jkrepresents 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.

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

[0118] Step 1.4, combined with site selection conditions (local geography, communication, and maintenance conditions), CT The representative point set G of meteorological resources for the final site selection is selected from (Lon, Lat) T (Lon,Lat), as shown in Table 1.

[0119] Table 1 Examples of representative meteorological resource point sets area Center longitude Center Latitude Region 1 120.12 31.86 Area 2 119.96 31.83 Area 3 120.10 31.68 Region 4 120.11 31.56

[0120] In practice, when selecting the final site for a representative meteorological resource point, an on-site survey is required to prevent the resource monitoring devices installed at the representative point from being blocked, reflected, scattered, and interfered with by buildings, vegetation, mountains, etc. The communication conditions should also be investigated on site, with maintainability and reliability as the priority principles.

[0121] Step 2: Install meteorological resource monitoring devices at the longitude and latitude of each meteorological resource representative point to collect multi-month surface total solar radiation measurement data; analyze and calculate the mapping relationship between the solar radiation measurement data of 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.

[0122] Step 2.1, based on G T (Lon, Lat) Install meteorological resource monitoring stations, collect multi-month surface solar total radiation measurement data, 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 x=1,2,…,Ns, and the number of time points in the collection period is t. The expression of the total radiation measurement matrix is:

[0123] ;

[0124] Obtain the surface incident shortwave total radiation reanalysis data for the same longitude and latitude 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:

[0125] ;

[0126] In practice, the time scales of the reanalysis matrix and the measurement matrix are strictly consistent. The number of grid points contained in the reanalysis matrix is ​​required to be greater than the number of meteorological resource monitoring stations contained in the study area.

[0127] Step 2.2, the co-variance matrix of the two matrices S and Z is C SZ =<SZ T >, the superscript T indicates matrix transposition, and the symbol < > indicates averaging. Based on the singular value decomposition method (SVD), orthogonal linear transformation can be applied to find two orthogonal linear transformation matrices L and R after changing the left and right fields respectively, so that there is a maximized covariance between the two fields, that is:

[0128] ;

[0129] According to the theory of linear algebra, L and R that satisfy the above conditions can be uniquely solved.

[0130] , ;

[0131] By determining L and R, the coupling relationship between S and Z can be uniquely determined. The kth column vector l of L and R k and r k are respectively called the kth left and right singular vectors, i.e. the kth modal space type. 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.

[0132] Step 2.3, L and R uniquely determined by the SVD method are orthogonal matrices, so the matrix transformation can be S=AL, Z=BR.

[0133] The current N-mode cumulative covariance contribution can explain the covariance C SZ Most of the time (usually the cumulative covariance contribution rate needs to be >= 90%), we can set a k and b k The two columns of time coefficient vectors satisfy a linear relationship, namely:

[0134] ;

[0135] In the formula, a k represents the k-th mode left singular vector l k The corresponding time coefficient vector b k represents the k-th mode right singular vector r k The corresponding time coefficient vector. C 0k and C 0k represents the undetermined coefficient.

[0136] Then we have:

[0137] ;

[0138] In the formula, 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, C 0 and C 1 represents the undetermined coefficient.

[0139] The above calculation formula can be used to correct the total radiation reanalysis value matrix Z through the total radiation measurement value matrix S, and output the surface incident shortwave radiation correction value .

[0140] In practice, the undetermined coefficients can be obtained by inputting historical data for multiple months and using the linear fitting method. In order to track the changes in the coupling relationship between S and Z over time, this step needs to be performed on a monthly basis, and the undetermined coefficients are constantly updated with new measurement data and reanalysis data.

[0141] Step 3, according to the longitude and latitude information of distributed photovoltaic power generation and the installation status of meteorological resource monitoring stations, match the corresponding solar radiation grid for each distributed power station to be evaluated; input the distributed photovoltaic power generation data and the corrected reanalyzed solar radiation data of the matched grid, analyze and calculate according to the physical and statistical methods, and output the distributed photovoltaic power generation efficiency evaluation value.

[0142] Step 3.1, according to the longitude and latitude information of distributed photovoltaic power generation and the installation location of the meteorological resource monitoring station, match the corresponding solar radiation reanalysis grid for each distributed power station to be evaluated. In practice, according to the resolution of the reanalysis data grid, it is usually required that the center of the assigned grid is no more than 10 kilometers away from the distributed power station; for distributed photovoltaic power stations no more than 5 kilometers away from the meteorological resource monitoring station, the measurement data of the meteorological resource monitoring station is used as the data source for power generation efficiency evaluation.

[0143] Step 3.2: Input distributed photovoltaic power generation data and reanalyzed solar radiation correction data of the matching grid , based on physical and statistical analysis and calculation, the distributed photovoltaic power generation efficiency evaluation value is output.

[0144] In practice, the PR efficiency, i.e. the overall power generation efficiency of a photovoltaic power station, is usually used to evaluate the efficiency of a photovoltaic power generation system. The calculation formula is:

[0145] ;

[0146] 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.

[0147] Among them, Y f =E AC / P 0 , called the actual power generation hours, in hours; E AC Indicates the actual power generation of the photovoltaic system, in kWh; P 0 Indicates the system installed capacity.

[0148] Y r =S A / G 0 , called theoretical power generation hours, in hours; S A Indicates the cumulative radiation per unit surface of the array in kWh / m 2 ; G 0 Indicates the standard irradiance of 1000W / m under STC conditions (Standard Test Conditions) 2 .

[0149] Among them, E AC It can be collected through the electricity meter at the photovoltaic grid point, S A Generally, the reanalyzed cumulative radiation is used after correction in step 2. For distributed photovoltaic power stations that are no more than 5 kilometers away from the meteorological resource monitoring station, the cumulative radiation measured by the meteorological resource station is used.

[0150] In summary, with the aid of the above-mentioned technical scheme of the present invention, the present invention has wide applicability, and only needs to use the public meteorological reanalysis data set covering the whole world to realize resource distribution analysis. The present invention has high economy, and only needs to install a small number of meteorological resource monitoring devices in combination with distributed power generation data to realize wide-area distributed photovoltaic power generation efficiency evaluation. The present invention has high accuracy, and the global meteorological reanalysis data is corrected by the measured meteorological resource data improved by the meteorological resource monitoring device. On the basis of ensuring full coverage of meteorological resource distribution data in the whole region, the monitoring accuracy of resource data is improved, thereby improving the accuracy of power generation efficiency evaluation. The present invention realizes accurate evaluation of regional distributed photovoltaic power generation efficiency through the wide-area distributed photovoltaic power generation efficiency evaluation method, and provides richer data support for distributed power generation optimization operation and operation and maintenance inspection.

[0151] It should be understood by those skilled in the art that the 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 aspects. 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, optical storage, etc.) containing computer-usable program code.

[0152] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection 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. 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.

2. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 1, characterized in that: The method of obtaining the reanalysis time series data of solar radiation in the area where the distributed photovoltaic power station is located, calculating the characteristic vector by using the empirical orthogonal function decomposition method, and selecting the meteorological resource representative point set for the final site selection in combination with the site selection conditions includes the following steps: 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 is screened out in combination with the site selection conditions.

3. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 2, characterized in that: The gridded solar radiation reanalysis time series data of the longitude and latitude range of the distributed photovoltaic power station area is obtained, and the spatial anomaly value is calculated to construct the anomaly value spatiotemporal matrix. the following: 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.

4. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 3, 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.

5. 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.

6. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 5, 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.

7. A method for evaluating the efficiency of wide-area distributed photovoltaic power generation according to claim 1, characterized in that: The collecting of the total solar radiation measurement data of the meteorological resource representative point set and the correction processing of the reanalysis time series data by the singular value decomposition method include: 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.

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 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, 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.

9. A method for evaluating wide-area distributed photovoltaic power generation efficiency 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.

10. A method for evaluating wide-area distributed photovoltaic power generation efficiency according to claim 9, 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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