Method and device for constructing regional photovoltaic power generation statistical model

Abstract

The present invention provides a method, device, equipment and storage medium for constructing a statistical model of photovoltaic power generation in a region. The method includes: obtaining historical record data of photovoltaic power stations in the target region; processing the historical record data to obtain a per-unit power consumption database set of the photovoltaic power stations; dividing the per-unit power consumption database set of the photovoltaic power stations into several subsets in the form of month dimension and hour dimension, and respectively calculating the sample mean and sample standard deviation of each subset; forming a mean sample matrix based on the sample means, and calculating based on the mean sample matrix to obtain a per-unit power consumption mean matrix of the photovoltaic power stations; forming a standard deviation sample matrix based on the sample standard deviations, and calculating based on the standard deviation sample matrix to obtain a per-unit power consumption standard deviation matrix of the photovoltaic power stations. The present invention can model the statistical law of the time series change of the total power generation power of photovoltaic power stations in a given region, and further can provide reference information for the evaluation of power adequacy in the new energy development stage planning of this region.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid statistics, and particularly to a method, device, equipment and storage medium for constructing a statistical model of regional photovoltaic power generation Background Art

[0002] Due to the new characteristics of wind power and photovoltaic power generation, such as strong uncertainty, high sensitivity to weather condition changes, and strong spatio-temporal coupling, the new power system faces not only the challenge of short-term power balance in terms of power supply reliability, but also the risk of insufficient power adequacy.

[0003] From a long time scale, the power generation of wind farms and photovoltaic power stations changes with seasons. From the perspective of daily operation management, photovoltaic power generation has characteristics such as not generating electricity at night, large output fluctuations on cloudy days, and small output on rainy days, which may lead to insufficient power in some periods of the day. In the analysis of power and electricity balance, underestimating the photovoltaic power generation capacity will lead to overconstruction of conventional power sources. Overestimating the photovoltaic power generation capacity in some periods will reduce the adequacy of power supply.

[0004] Currently, the research on photovoltaic power prediction and probability models mainly focuses on the power prediction of individual photovoltaic power stations. There is less research on the probability model of the total power generation of all photovoltaic power stations in a region. In addition, with the rapid development of centralized and distributed photovoltaics in China, it is difficult to simply generalize the analysis of the power generation characteristics of existing photovoltaic power stations to the overall characteristics of a large number of newly added photovoltaics in the region.

[0005] Therefore, there is an urgent need for a method that can model the statistical law of the temporal variation of the total power generation of photovoltaic power stations in a given region, so as to carry out power adequacy assessment for the new energy development stage of the region. Summary of the Invention

[0006] The present invention aims to provide a method, device, equipment and storage medium for constructing a statistical model of regional photovoltaic power generation to solve the above technical problems, so as to be able to model the statistical law of the temporal variation of the total power generation of photovoltaic power stations in a given region, and further provide reference information for carrying out power adequacy assessment for the new energy development stage of the region.

[0007] To solve the above technical problems, the present invention provides a method for constructing a statistical model of regional photovoltaic power generation, including:

[0008] Obtaining the historical power record data of all photovoltaic power stations in the target region, and determining the time point and installed capacity of each photovoltaic power station expansion;

[0009] Processing the historical power record data based on the determined time points and installed capacities of the photovoltaic power station expansions to obtain the total per-unit power consumption database set of the photovoltaic power stations in the target area; wherein, the total per-unit power consumption database set of the photovoltaic power stations consists of the total per-unit power consumption of the photovoltaic power stations during a number of sequentially arranged preset unit time periods;

[0010] Dividing the total per-unit power consumption database set of the photovoltaic power stations into a number of subsets in the form of month dimension and hour dimension, and respectively calculating the sample mean and sample standard deviation of each subset;

[0011] Taking the sample mean of each subset as an element to form a mean sample matrix, and performing calculations based on a preset fitting curve model and the mean sample matrix to obtain the total per-unit power consumption mean matrix of the photovoltaic power stations in the target area;

[0012] Taking the sample standard deviation of each subset as an element to form a standard deviation sample matrix, and performing calculations based on a preset fitting model and the standard deviation sample matrix to obtain the total per-unit power consumption standard deviation matrix of the photovoltaic power stations in the target area;

[0013] Wherein, the mean sample matrix, the standard deviation sample matrix, the total per-unit power consumption mean matrix of the photovoltaic power stations, and the total per-unit power consumption standard deviation matrix of the photovoltaic power stations are all matrices of 12 rows and 24 columns; the rows and columns of the mean sample matrix are arranged according to the corresponding month dimension and hour dimension.

[0014] Further, the determining the time points and installed capacities of the expansions of each photovoltaic power station includes:

[0015] Using a vector to record the maximum daily power values of the photovoltaic power station in chronological order;

[0016] Dividing the vector into sub-vectors according to a preset length;

[0017] Based on a preset percentage, recording the quantile points of each sub-vector to form corresponding new sub-vectors;

[0018] Calculating the ratio of two adjacent new sub-vectors, and determining the time points corresponding to the two new sub-vectors with a ratio greater than a preset ratio threshold as the time points of the expansion of the photovoltaic power station;

[0019] Calculating the installed capacity of the photovoltaic power station in each time period based on the maximum power of the photovoltaic power station in each time period and a preset maximum utilization coefficient of the photovoltaic capacity.

[0020] Further, the processing the historical power record data based on the determined time points and installed capacities of the photovoltaic power station expansions to obtain the total per-unit power consumption database set of the photovoltaic power stations in the target area includes:

[0021] During each preset unit time period, sum up the power generation of all photovoltaic power stations in the target area to obtain the total power generation of the regional photovoltaic power stations, and obtain the sum of the installed capacities of all photovoltaic power stations during this unit time period;

[0022] Take the ratio of the total power generation of the regional photovoltaic power stations to the sum of the installed capacities of all photovoltaic power stations as the total per-unit power of the photovoltaic power stations corresponding to this unit time period;

[0023] Arrange the total per-unit powers of the photovoltaic power stations corresponding to each unit time period in sequence to form the total per-unit power database set of the photovoltaic power stations.

[0024] Further, the step of forming a mean sample matrix with the sample means of each subset as elements and calculating the total per-unit power mean matrix of the photovoltaic power stations in the target area based on a preset fitting curve model and the mean sample matrix includes:

[0025] Form a mean sample matrix with the sample means of each subset as elements;

[0026] Identify the first target elements in the mean sample matrix that are less than or equal to a preset first threshold, and set the elements in the corresponding sequence of the total per-unit power mean matrix of the photovoltaic power stations to zero;

[0027] Determine the starting and ending points of the power curve according to the elements in the total per-unit power mean matrix of the photovoltaic power stations that are set to zero, and assign values to each element between the starting and ending points of the power curve based on the preset fitting curve parameters to obtain the total per-unit power mean matrix of the photovoltaic power stations in the target area.

[0028] Further, the step of forming a standard deviation sample matrix with the sample standard deviations of each subset as elements and calculating the total per-unit power standard deviation matrix of the photovoltaic power stations in the target area based on a preset fitting model and the standard deviation sample matrix includes:

[0029] Form a standard deviation sample matrix with the sample standard deviations of each subset as elements, and form a relative standard deviation sample matrix with the ratios obtained by dividing the elements of the standard deviation sample matrix by the elements of the mean sample matrix one by one;

[0030] Identify the second target elements in the mean sample matrix that are less than or equal to a preset second threshold, and set the elements in the corresponding sequence of the total per-unit power standard deviation matrix of the photovoltaic power stations to zero;

[0031] Identify the third target elements in the mean sample matrix that are greater than the preset second threshold, and form a binary sample set with the elements in the corresponding sequence of the mean sample matrix and the relative standard deviation sample matrix;

[0032] Perform a least squares fit on the binary sample set to obtain a linear regression equation, and assign values to the elements in the corresponding sequence of the total per-unit power consumption standard deviation matrix of the photovoltaic power station based on the linear regression equation and the corresponding elements in the mean sample matrix.

[0033] The present invention also provides a device for constructing a regional photovoltaic power generation statistical model, including:

[0034] A data acquisition module, configured to acquire historical power record data of all photovoltaic power stations in a target area, and determine the time point and installed capacity of each photovoltaic power station expansion;

[0035] A data preprocessing module, configured to process the historical power record data based on the determined time point and installed capacity of the photovoltaic power station expansion to obtain a total per-unit power consumption database set of the photovoltaic power stations in the target area; wherein, the total per-unit power consumption database set of the photovoltaic power stations consists of the total per-unit power consumption of the photovoltaic power stations during a plurality of preset unit time periods arranged in sequence;

[0036] A subset calculation module, configured to divide the total per-unit power consumption database set of the photovoltaic power stations into several subsets in the form of month dimension and hour dimension, and calculate the sample mean and sample standard deviation of each subset respectively;

[0037] A mean matrix establishment module, configured to form a mean sample matrix with the sample means of each subset as elements, and perform calculations based on a preset fitting curve model and the mean sample matrix to obtain a total per-unit power consumption mean matrix of the photovoltaic power stations in the target area;

[0038] A standard deviation matrix establishment module, configured to form a standard deviation sample matrix with the sample standard deviations of each subset as elements, and perform calculations based on a preset fitting model and the standard deviation sample matrix to obtain a total per-unit power consumption standard deviation matrix of the photovoltaic power stations in the target area;

[0039] Wherein, the mean sample matrix, the standard deviation sample matrix, the total per-unit power consumption mean matrix of the photovoltaic power stations, and the total per-unit power consumption standard deviation matrix of the photovoltaic power stations are all matrices of 12 rows and 24 columns; the rows and columns of the mean sample matrix are arranged according to the corresponding month dimension and hour dimension respectively.

[0040] The present invention also provides a terminal device, including a processor and a memory storing a computer program, and when the processor executes the computer program, it implements the method for constructing a regional photovoltaic power generation statistical model according to any one of the above.

[0041] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for constructing a statistical model of photovoltaic power generation in a region described in any one of the above is implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention provides a method, apparatus, device and storage medium for constructing a statistical model of photovoltaic power generation in a region. The method includes: obtaining historical power record data of all photovoltaic power stations in a target region; processing the historical power record data to obtain a total per-unit power consumption database set of photovoltaic power stations; dividing the total per-unit power consumption database set of photovoltaic power stations into several subsets in the form of month dimension and hour dimension, and calculating the sample mean and sample standard deviation of each subset respectively; forming a mean sample matrix with the sample mean of each subset as an element, and calculating based on the mean sample matrix to obtain the total per-unit power consumption mean matrix of photovoltaic power stations in the target region; forming a standard deviation sample matrix with the sample standard deviation of each subset as an element, and calculating based on the standard deviation sample matrix to obtain the total per-unit power consumption standard deviation matrix of photovoltaic power stations in the target region. The present invention can model the statistical law of the temporal variation of the total power generation power of photovoltaic power stations in a given region, and thus can provide reference information for evaluating the power adequacy in the new energy development stage planning in this region. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic flowchart of the method for constructing a statistical model of photovoltaic power generation in a region provided by the present invention;

[0045] Figure 2 is a schematic flowchart of the EAM and SIG matrix modeling provided by the present invention;

[0046] Figure 3 is a schematic diagram of the maximum output curve of each month of a certain photovoltaic power station from 2019 to 2021 provided by the present invention;

[0047] Figure 4 is a schematic structural diagram of the apparatus for constructing a statistical model of photovoltaic power generation in a region provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0049] Please refer to Figure 1, embodiments of the present invention provide a method for constructing a statistical model of photovoltaic power generation in a region, which may include the steps:

[0050] S1. Obtain the historical power record data of all photovoltaic power stations in the target region, and determine the time point and installed capacity of each photovoltaic power station expansion;

[0051] S2. Process the historical power record data based on the determined time point and installed capacity of the photovoltaic power station expansion to obtain the total per-unit power generation database set of the photovoltaic power stations in the target region; wherein, the total per-unit power generation database set of the photovoltaic power stations is composed of the total per-unit power generation of the photovoltaic power stations during a number of preset unit time periods arranged in sequence;

[0052] S3. Divide the total per-unit power generation database set of the photovoltaic power stations into several subsets in the form of month dimension and hour dimension, and calculate the sample mean and sample standard deviation of each subset respectively;

[0053] S4. Use the sample mean of each subset as an element to form a mean sample matrix, and perform calculations based on a preset fitting curve model and the mean sample matrix to obtain the total per-unit power generation mean matrix of the photovoltaic power stations in the target region;

[0054] S5. Use the sample standard deviation of each subset as an element to form a standard deviation sample matrix, and perform calculations based on a preset fitting model and the standard deviation sample matrix to obtain the total per-unit power generation standard deviation matrix of the photovoltaic power stations in the target region;

[0055] Wherein, the mean sample matrix, the standard deviation sample matrix, the total per-unit power generation mean matrix of the photovoltaic power stations, and the total per-unit power generation standard deviation matrix of the photovoltaic power stations are all matrices with 12 rows and 24 columns; the rows and columns of the mean sample matrix are arranged according to the corresponding month dimension and hour dimension.

[0056] In the embodiments of the present invention, further, the determining the time point and installed capacity of each photovoltaic power station expansion includes:

[0057] Use a vector to record the maximum daily power value of the photovoltaic power station in chronological order;

[0058] Divide the vector into sub-vectors according to a preset length;

[0059] Record the quantile points of each sub-vector based on a preset percentage to form corresponding new sub-vectors;

[0060] Calculate the ratio of two adjacent new sub-vectors, and determine the points corresponding to the two new sub-vectors with a ratio greater than a preset ratio threshold as the time point of the photovoltaic power station expansion;

[0061] The installed capacity of the PV power station for each time period is calculated based on the maximum power of the PV power station in each time period and a preset maximum utilization coefficient of the PV capacity.

[0062] In an embodiment of the present invention, further, the historical power record data is processed based on the determined time point and installed capacity of the PV power station expansion to obtain the total per-unit power database set of the PV power stations in the target area, including:

[0063] During each preset unit time period, the power generation amounts of all PV power stations in the target area are summed to obtain the total power generation of the regional PV power stations, and the sum of the installed capacities of all PV power stations during this unit time period is obtained;

[0064] The ratio of the total power generation of the regional PV power stations to the sum of the installed capacities of all PV power stations is used as the total per-unit power of the PV power stations corresponding to this unit time period;

[0065] The total per-unit power of the PV power stations corresponding to each unit time period is arranged in sequence to form the total per-unit power database set of the PV power stations.

[0066] In an embodiment of the present invention, further, a mean sample matrix is formed with the sample means of each subset as elements, and calculations are performed based on a preset fitting curve model and the mean sample matrix to obtain the total per-unit power mean matrix of the PV power stations in the target area, including:

[0067] A mean sample matrix is formed with the sample means of each subset as elements;

[0068] The first target elements in the mean sample matrix that are less than or equal to a preset first threshold are identified, and the elements in the corresponding sequence of the total per-unit power mean matrix of the PV power stations are set to zero;

[0069] Based on the elements in the total per-unit power mean matrix of the PV power stations that are set to zero, the starting and ending points of the power curve are determined, and each element between the starting and ending points of the power curve is assigned a value based on preset fitting curve parameters to obtain the total per-unit power mean matrix of the PV power stations in the target area.

[0070] In an embodiment of the present invention, further, a standard deviation sample matrix is formed with the sample standard deviations of each subset as elements, and calculations are performed based on a preset fitting model and the standard deviation sample matrix to obtain the total per-unit power standard deviation matrix of the PV power stations in the target area, including:

[0071] Taking the sample standard deviation of each subset as elements to form a standard deviation sample matrix, and using the ratio obtained by dividing the elements of the standard deviation sample matrix by the elements of the mean sample matrix one by one as elements to form a relative standard deviation sample matrix;

[0072] Identifying the second target elements in the mean sample matrix that are less than or equal to a preset second threshold, and setting the elements in the corresponding sequence of the total per-unit power standard deviation matrix of the photovoltaic power station to zero;

[0073] Identifying the third target elements in the mean sample matrix that are greater than the preset second threshold, and forming a binary sample set with the elements in the corresponding sequence of the mean sample matrix and the relative standard deviation sample matrix;

[0074] Performing least squares fitting on the binary sample set to obtain a linear regression equation, and assigning values to the elements in the corresponding sequence of the total per-unit power standard deviation matrix of the photovoltaic power station based on the linear regression equation and the corresponding elements in the mean sample matrix.

[0075] It should be noted that the embodiment of the present invention is directed to the power and energy balance analysis of a planned power grid containing a high proportion of new energy power generation, and involves the time series probability modeling of the total photovoltaic power generation in a regional power grid. Specifically, by using the power generation records of existing photovoltaic power stations in the region, a model of the mean and variance of the total power generation of photovoltaic power stations in different months and different hours of the whole year in the region is established, which can be used to evaluate the power generation capacity and power supply adequacy of the power grid in the power grid planning and operation stages of regions with a high proportion of photovoltaic power stations.

[0076] Based on the above scheme, for better understanding of the method for constructing a statistical model of regional photovoltaic power generation provided by the embodiment of the present invention, the following is a detailed description:

[0077] First, the definitions of the mean matrix and the standard deviation matrix of the total per-unit power of regional photovoltaic power are described:

[0078] Denote the per-unit power generation of a single photovoltaic power station m, m = 1, …, M at time t as:

[0079] PU m (t) = P m (t) / P N,m , t ∈ [T t , T t+1 , ΔT = T t+1 - T t

[0080] Wherein, P m (t) is the nominal value of the power of photovoltaic power station m, P N,mLet \(P_m\) be the installed capacity of the photovoltaic power station \(m\), and \(\Delta T\) can be taken from 30 minutes to 60 minutes. This power can be further decomposed into three randomly varying components:

[0081]

[0082] In the formula, the random variable In \([T t ,T t+1 , it is a constant, representing the mean of the per-unit power generation of the photovoltaic power station in a region, which mainly reflects the change of weather conditions in a region and is strongly correlated with seasons.

[0083] \(\delta m (t)\) reflects the deviation of the average power generation of the photovoltaic power station \(m\) relative to the mean value caused by the difference in the specific location of the photovoltaic power station. Obviously, \(\sum m \delta m (t)\) has a mean value of zero. Denote its variance as \(\sigma_1^2(t)\).

[0084] \(\varepsilon m (t)\) is a random variable with a mean value of zero and a variance of \(\sigma_1\), which reflects the short-term random fluctuation of the power generation of the photovoltaic power station \(m\), usually with a period of 1 - 10 minutes.

[0085] The short-term random fluctuation in the power generation of a single photovoltaic power station comes from the movement of clouds. Although the movement of clouds is random, for a region, when the power of a photovoltaic panel decreases due to the movement of clouds above, the photovoltaic power of another rooftop may increase due to the clouds drifting away. Generally, the more balanced the distribution of rooftop photovoltaics in a region, the smaller the short-term volatility of the sum of all photovoltaic power generation. It can be considered that the variance of \(\sum m \varepsilon m (t)\) approaches zero.

[0086] Therefore, the mean value of the total power generation \(A(T t ,T t+1 ) of all photovoltaic power stations in a region during the period \([T t \) is:

[0087]

[0088] The mean value \(E_A * (i,j)\) of the total per-unit power generation of photovoltaics in the region is defined as:

[0089]

[0090] where \(T t \in\) the \(i\)-th month of a year & the \(j\)-th hour of a day

[0091] The standard deviation \(\sigma\) of the total per-unit power generation of photovoltaics in the regionA* (i, j) is defined as:

[0092]

[0093] T t ∈ the i-th month of a year & the j-th hour of a day

[0094] The variance of A(T t ) is jointly determined by the distribution of the random variable and the distribution of ∑ m,t δ m (t). Among them, the former depends on the uncertainty of the regional weather conditions during this period and is strongly correlated with months and seasons. The latter depends on the spatial distribution of the photovoltaic power plants in the region.

[0095] Definition: The dimension of the total per-unit power quantity mean matrix EAM of the regional photovoltaic power plants is 12 * 24, and the element in its i-th row and j-th column is EA * (i, j).

[0096] Definition: The dimension of the total per-unit power quantity standard deviation matrix SIG of the regional photovoltaic power plants is 12 * 24, and the element in its i-th row and j-th column is σ A* (i, j).

[0097] II. The following explains the data-driven modeling process of the EAM and SIG matrices:

[0098] For a specified region, through the historical power records of existing photovoltaic power plants, the EAM matrix (total per-unit power quantity mean matrix of the regional photovoltaic power plants) and the SIG matrix (total per-unit power quantity standard deviation matrix of the regional photovoltaic power plants) can be identified and generated. The modeling process is as Figure 2 shown.

[0099] 1. Data preprocessing:

[0100] Collect the historical power records of existing photovoltaic power plants in the specified region, and determine the time points and installed capacities of the expansion of each photovoltaic power plant. Specifically:

[0101] (1) Use the vector X to record the maximum daily power values of the photovoltaic power plants in chronological order.

[0102] (2) Divide the vector into sub-vectors of a fixed length T, for example, T = 30 days (the length of a month).

[0103] (3) Record the 80th percentile points of each sub-vector to form a new vector Y.

[0104] (4) Calculate the ratio r(k) = y(k + 1) / y(k), and regard the points where r(k) ≥ 1.5 as the time points of the expansion of the photovoltaic power plant, that is, the starting points of the new installed capacities.

[0105] (5) Obtain the installed capacity of the PV power station in each time period = the maximum power of the PV during this period / γ. Here, γ is the maximum utilization coefficient of the PV capacity, generally taking values from 1.0 to 1.1, and the typical value is 1.0.

[0106] Sum up the power generation of all PV power stations in the region during the time period [T t , T t+1 to obtain the total power generation A(T t ) of the regional PV power stations. Denote the sum of the installed capacities of all PV power stations in the region during the time period [T t , T t+1 as ∑ m P N,m . Calculate according to the following formula to obtain the total per-unit power database set of the regional PV power stations:

[0107]

[0108] 2. Establishment of the EAM matrix:

[0109] Divide the total per-unit power database set of the regional PV power stations by 12 months * 24 hours to obtain 288 subsets Ψ i,j . Calculate the sample mean of each subset Ψ i,j , denoted as the (i, j)-th element of the mean sample matrix S EA . S EA is a 12-row and 24-column matrix.

[0110] Perform the following operations row by row according to S EA to obtain the corresponding rows of the EAM matrix. Take the i-th row of S EA as an example, the operation process is as follows:

[0111] (1) When S EA (i, j) ≤ 0.01, EAM(i, j) = 0. Among the j values that satisfy EAM(i, j) = 0, the largest j in the morning is denoted as h sr ; the smallest j in the afternoon is denoted as h ss .

[0112] (2) Denote the j corresponding to the maximum value of S EA (i, j) as h i,p .

[0113] (3) Use the following formula as the fitting curve to perform least squares fitting on the i-th row sample of S EA to obtain the parameter e i,p .

[0114]

[0115] (4) Based on the above fitting curve model, substitute the values of \(i\) and \(j\) to obtain the element \(EA_{ij}\) in the \(i\)-th row and \(j\)-th column of the EAM matrix. * (i, j).

[0116] 3. Establishment of the SIG matrix:

[0117] Calculate the sample standard deviation of each subset \(\Psi\) i,j and denote it as the \((i, j)\)-th element of the standard deviation sample matrix \(S\) SIG . \(S\) SIG is a 12-row and 24-column matrix. Then obtain the relative standard deviation sample matrix \(S'\), that is: R \[S'(i,j)=\frac{S(i,j)}{S(i,j)}\]

[0118] S R (i, j) = S SIG (i, j) / S EA (i, j)

[0119] Perform the following operations row by row according to \(S'\) R to obtain the corresponding row of the SIG matrix. The operation process is as follows:

[0120] (1) If \(S'\) EA (i, j) ≤ 0.05, then the corresponding \(SIG(i, j)=0\). If \(S'\) EA (i, j)>0.05, execute the following steps (2) to (4).

[0121] (2) For all samples that satisfy \(S'\) EA (i, j)>0.05, form the following binary sample set: \(SS: \{S(i, j)=S\) R (i, j), E(i, j)=S EA (i, j)\}

[0122] (3) Using \(E\) as the abscissa and \(S\) as the ordinate, perform least squares fitting on the binary sample set \(SS\) to obtain the linear regression equation: \(S = k\times E + b\) and the coefficients \(k\) and \(b\).

[0123] (4) Substitute the values of \(i\) and \(j\) into the following formula to obtain the element in the \(i\)-th row and \(j\)-th column of the SIG matrix:

[0124] \[SIG(i,j)=(k\times S\] EA (i, j)+b)\times S EA (i, j)

[0125] III. The following lists specific calculation examples for illustration:

[0126] (1) Data preprocessing:

[0127] For a certain area in the south, historical output data of 16 typical photovoltaic power stations from 2019 to 2021 were collected, and the data sampling interval was 15 minutes.

[0128] The maximum monthly output of one of the photovoltaic power stations from 2019 to 2021 is as Figure 3 shown. In the figure, 1 - 36 represent the months from 2019 to 2021 arranged in sequence.

[0129] Using the method in point (1) above, it was identified that the photovoltaic power station was expanded in December 2020, and its installed capacity from January 2019 to November 2020 was estimated to be 35 MW, and the installed capacity from December 2020 to December 2021 was 90 MW.

[0130] Similar processing was performed on all 16 photovoltaic power stations, and the installed capacity of each sample photovoltaic power station in each time period could be obtained. Further, the total per - unit power database set of the regional photovoltaic power stations was obtained.

[0131] (2) Establishment of the EAM matrix:

[0132] Taking September as an example (i = 9), the process of establishing the EAM matrix is described.

[0133] According to the method in point (2) above, first, it was identified that h sr = 6, h ss = 18, h i,p = 12.

[0134] Subsequently, through curve fitting, e 9,p = 0.589 was obtained, and the fitting curve model is:

[0135]

[0136] Substituting i = 9, j = 0:24 into the above formula, the 9th row of the EAM matrix is:

[0137] [0, 0, 0, 0, 0, 0, 0.152, 0.295, 0.416, 0.510, 0.569, 0.589, 0.569, 0.510, 0.416, 0.295, 0.152,

[0138] 0, 0, 0, 0, 0, 0, 0].

[0139] (3) Establishment of the SIG matrix:

[0140] Still taking September as an example, the process of establishing the SIG matrix is described. According to the method in point (3) above, the fitting coefficients in the relationship expression between the relative standard deviation and the mean obtained by fitting are: k = - 0.289, b = 0.427. Thus, the data of the 9th row of the SIG matrix can be obtained as:

[0141] [0,0,0,0,0,0,0.058,0.101,0.128,0.143,0.149,0.151,0.149,0.143,0.128,0.101,0.058

[0142] 0,0,0,0,0,0,0]。

[0143] It should be noted that the features of the embodiments of the present invention are as follows:

[0144] 1. A modeling method is defined to describe the statistical laws of the intra-day and seasonal variations of the total generating power of regional photovoltaic power stations by using two characteristic matrices, namely, the mean matrix EAM of the total per-unit power of regional photovoltaic power stations and the standard deviation matrix SIG of the total per-unit power of regional photovoltaic power stations.

[0145] 2. A data preprocessing method for the power sequences of existing photovoltaic power stations is proposed. In particular, a method for identifying the change in the installed capacity of photovoltaic power stations based on the generating power sequence is proposed.

[0146] 3. A statistical modeling method is proposed to identify and establish the proposed mean matrix of the total per-unit power of regional photovoltaic power and the variance matrix of the total per-unit power based on the power records of existing photovoltaic power stations.

[0147] The model established in the embodiments of the present invention can be applied to the analysis and prediction of the time distribution and variation range of the monthly grid photovoltaic power generation after a large increase in the total number of photovoltaic power stations in the planned target year. It can provide model support for the analysis of the power adequacy of the grid under the background of large-scale photovoltaic power generation grid connection.

[0148] It should be noted that for the above method or process embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of the present invention are not limited by the described action sequence, because according to the embodiments of the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily essential for the embodiments of the present invention.

[0149] Please refer to Figure 4 , the embodiments of the present invention also provide a device for constructing a statistical model of regional photovoltaic power generation, including:

[0150] A data acquisition module 1, configured to acquire the historical power record data of all photovoltaic power stations in the target area, and determine the expansion time point and installed capacity of each photovoltaic power station;

[0151] A data preprocessing module 2, configured to process the historical power record data based on the determined time point and installed capacity of the photovoltaic power station expansion, so as to obtain a total per-unit power consumption database set of the photovoltaic power stations in the target area; wherein, the total per-unit power consumption database set of the photovoltaic power stations consists of the total per-unit power consumption of the photovoltaic power stations during a plurality of preset unit time periods arranged in sequence;

[0152] A subset calculation module 3, configured to divide the total per-unit power consumption database set of the photovoltaic power stations into a plurality of subsets in the form of month dimension and hour dimension, and calculate the sample mean and sample standard deviation of each subset respectively;

[0153] A mean matrix establishment module 4, configured to form a mean sample matrix with the sample means of each subset as elements, and perform calculations based on a preset fitting curve model and the mean sample matrix to obtain a total per-unit power consumption mean matrix of the photovoltaic power stations in the target area;

[0154] A standard deviation matrix establishment module 5, configured to form a standard deviation sample matrix with the sample standard deviations of each subset as elements, and perform calculations based on a preset fitting model and the standard deviation sample matrix to obtain a total per-unit power consumption standard deviation matrix of the photovoltaic power stations in the target area;

[0155] Wherein, the mean sample matrix, the standard deviation sample matrix, the total per-unit power consumption mean matrix of the photovoltaic power stations, and the total per-unit power consumption standard deviation matrix of the photovoltaic power stations are all matrices with 12 rows and 24 columns; the rows and columns of the mean sample matrix are arranged according to the corresponding month dimension and hour dimension respectively.

[0156] It can be understood that the above device item embodiments correspond to the method item embodiments of the present invention. An apparatus for constructing a regional photovoltaic power generation statistical model provided by the embodiments of the present invention can implement the method for constructing a regional photovoltaic power generation statistical model provided by any one of the method item embodiments of the present invention.

[0157] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for constructing a regional photovoltaic power generation statistical model described in any one of the above.

[0158] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0159] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0160] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0161] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device and connects various parts of the entire terminal device through various interfaces and lines.

[0162] The memory can be used to store the computer program. By running or executing the computer program stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the terminal device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0163] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0164] The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for constructing a statistical model of photovoltaic power generation in a region, characterized in that Including: Obtain the historical power record data of all photovoltaic power stations in the target area, and determine the time point and installed capacity of each photovoltaic power station expansion; Process the historical power record data based on the determined time point and installed capacity of the photovoltaic power station expansion to obtain the total per-unit power database set of the photovoltaic power stations in the target area; wherein, the total per-unit power database set of the photovoltaic power stations consists of the total per-unit power of the photovoltaic power stations during a number of preset unit time periods arranged in sequence; Divide the total per-unit power database set of the photovoltaic power stations into several subsets in the form of month dimension and hour dimension, and calculate the sample mean and sample standard deviation of each subset respectively; Use the sample mean of each subset as an element to form a mean sample matrix, and calculate based on a preset fitting curve model and the mean sample matrix to obtain the total per-unit power mean matrix of the photovoltaic power stations in the target area; Use the sample standard deviation of each subset as an element to form a standard deviation sample matrix, and calculate based on a preset fitting model and the standard deviation sample matrix to obtain the total per-unit power standard deviation matrix of the photovoltaic power stations in the target area; Among them, the mean sample matrix, the standard deviation sample matrix, the total per-unit power mean matrix of the photovoltaic power stations, and the total per-unit power standard deviation matrix of the photovoltaic power stations are all matrices with 12 rows and 24 columns; the rows and columns of the mean sample matrix are arranged according to the corresponding month dimension and hour dimension.

2. The method for constructing a regional photovoltaic power generation volume statistical model according to claim 1, wherein The determining the time point and installed capacity of each photovoltaic power station expansion includes: Use a vector to record the maximum daily power value of the photovoltaic power station in chronological order; Divide the vector into sub-vectors according to a preset length; Record the quantile points of each sub-vector based on a preset percentage to form corresponding new sub-vectors; Calculate the ratio of two adjacent new sub-vectors, and determine the points corresponding to the two new sub-vectors with a ratio greater than a preset ratio threshold as the time point of the photovoltaic power station expansion; Calculate the installed capacity of the photovoltaic power station in each time period based on the maximum power of the photovoltaic power station in each time period and a preset maximum utilization coefficient of the photovoltaic capacity.

3. The method for constructing a regional photovoltaic power generation statistical model according to claim 1, wherein The processing the historical power record data based on the determined time point and installed capacity of the photovoltaic power station expansion to obtain the total per-unit power database set of the photovoltaic power stations in the target area includes: During each preset unit time period, sum up the power generation of all photovoltaic power stations in the target area to obtain the total power generation of the regional photovoltaic power stations, and obtain the sum of the installed capacities of all photovoltaic power stations during this unit time period; Use the ratio of the total power generation of the regional photovoltaic power stations to the sum of the installed capacities of all photovoltaic power stations as the total per-unit power of the photovoltaic power stations corresponding to this unit time period; Arrange the total per-unit power of the photovoltaic power stations corresponding to each unit time period in sequence to form the total per-unit power database set of the photovoltaic power stations.

4. The method for constructing a regional photovoltaic power generation statistical model according to claim 1, wherein, The using the sample mean of each subset as an element to form a mean sample matrix, and calculating based on a preset fitting curve model and the mean sample matrix to obtain the total per-unit power mean matrix of the photovoltaic power stations in the target area includes: Use the sample mean of each subset as an element to form a mean sample matrix; Identify the first target elements in the mean sample matrix that are less than or equal to a preset first threshold, and set the elements in the corresponding sequences of the total per-unit power consumption mean matrix of the photovoltaic power station to zero; Determine the start and end points of the power consumption curve based on the elements in the total per-unit power consumption mean matrix of the photovoltaic power station that are set to zero, and assign values to each element between the start and end points of the power consumption curve based on preset fitting curve parameters to obtain the total per-unit power consumption mean matrix of the photovoltaic power station in the target area.

5. The method for constructing a regional photovoltaic power generation statistical model according to claim 1, wherein The forming a standard deviation sample matrix with the sample standard deviations of each subset as elements and calculating based on a preset fitting model and the standard deviation sample matrix to obtain the total per-unit power consumption standard deviation matrix of the photovoltaic power station in the target area includes: Form a standard deviation sample matrix with the sample standard deviations of each subset as elements, and form a relative standard deviation sample matrix with the ratios obtained by dividing the elements of the standard deviation sample matrix by the elements of the mean sample matrix one by one as elements; Identify the second target elements in the mean sample matrix that are less than or equal to a preset second threshold, and set the elements in the corresponding sequences of the total per-unit power consumption standard deviation matrix of the photovoltaic power station to zero; Identify the third target elements in the mean sample matrix that are greater than the preset second threshold, and form a binary sample set with the elements in the corresponding sequences of the mean sample matrix and the relative standard deviation sample matrix; Perform least squares fitting on the binary sample set to obtain a linear regression equation, and assign values to the elements in the corresponding sequences of the total per-unit power consumption standard deviation matrix of the photovoltaic power station based on the linear regression equation and the corresponding elements in the mean sample matrix.

6. A device for constructing a statistical model of photovoltaic power generation in a region, characterized in that, Includes: A data acquisition module for acquiring the historical power record data of all photovoltaic power stations in the target area and determining the time points and installed capacities of the expansion of each photovoltaic power station; A data preprocessing module for processing the historical power record data based on the determined time points and installed capacities of the expansion of the photovoltaic power stations to obtain the total per-unit power consumption database set of the photovoltaic power stations in the target area; wherein, the total per-unit power consumption database set of the photovoltaic power stations consists of the total per-unit power consumption of the photovoltaic power stations during a number of sequentially arranged preset unit time periods; A subset calculation module for dividing the total per-unit power consumption database set of the photovoltaic power stations into several subsets in the form of month dimension and hour dimension, and respectively calculating the sample mean and sample standard deviation of each subset; A mean matrix establishment module for forming a mean sample matrix with the sample means of each subset as elements and calculating based on a preset fitting curve model and the mean sample matrix to obtain the total per-unit power consumption mean matrix of the photovoltaic power station in the target area; A standard deviation matrix establishment module for forming a standard deviation sample matrix with the sample standard deviations of each subset as elements and calculating based on a preset fitting model and the standard deviation sample matrix to obtain the total per-unit power consumption standard deviation matrix of the photovoltaic power station in the target area; Among them, the mean sample matrix, the standard deviation sample matrix, the total per-unit power quantity mean matrix of the photovoltaic power station, and the total per-unit power quantity standard deviation matrix of the photovoltaic power station are all matrices with 12 rows and 24 columns; the rows and columns of the mean sample matrix are arranged according to the corresponding month dimension and hour dimension respectively.

7. The device for constructing a regional photovoltaic power generation amount statistical model according to claim 6, wherein The data acquisition module is specifically configured to: Record the maximum daily power value of the photovoltaic power station in chronological order using a vector; Divide the vector into sub-vectors according to a preset length; Record the quantile points of each sub-vector based on a preset percentage to form corresponding new sub-vectors; Calculate the ratio of two adjacent new sub-vectors, and determine the time points for the expansion of the photovoltaic power station as the points corresponding to the two new sub-vectors whose ratio is greater than the preset ratio threshold; Calculate the installed capacity of the photovoltaic power station in each time period based on the maximum power of the photovoltaic power station in each time period and the preset maximum utilization coefficient of the photovoltaic capacity.

8. The device for constructing a regional photovoltaic power generation amount statistical model according to claim 6, characterized in that, The data preprocessing module is specifically configured to: During each preset unit time period, sum up the power generation of all photovoltaic power stations in the target area to obtain the total power generation of the regional photovoltaic power stations, and obtain the sum of the installed capacities of all photovoltaic power stations during this unit time period; Take the ratio of the total power generation of the regional photovoltaic power stations to the sum of the installed capacities of all photovoltaic power stations as the total per-unit power quantity of the photovoltaic power stations corresponding to this unit time period; Arrange the total per-unit power quantities of the photovoltaic power stations corresponding to each unit time period in sequence to form the total per-unit power quantity database set of the photovoltaic power stations.

9. A terminal device, comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the method for constructing a regional photovoltaic power generation statistical model according to any one of claims 1 to 5.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for constructing a regional photovoltaic power generation statistical model according to any one of claims 1 to 5.

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