A photovoltaic power station theoretical historical power data processing method, device, equipment, medium and program product

Abstract

This invention relates to the field of photovoltaic power generation prediction technology, and discloses a method, device, equipment, medium, and program product for processing theoretical historical power data of photovoltaic power plants. This invention directly calculates theoretical power from the actual power of sub-arrays, without relying on easily missing meteorological data such as irradiance and temperature, effectively avoiding calculation failures caused by monitoring equipment malfunctions and missing data. Employing characteristic curve cluster matching and whole-field cumulative correction, it can adapt to complex terrain, various types of components, and equipment attenuation differences, significantly improving the accuracy of theoretical power and the representativeness of the entire field. Simultaneously, it avoids the inherent biases of traditional physical models, making the obtained historical theoretical power more closely reflect the actual operating characteristics of the power plant.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation prediction technology, specifically to a method, apparatus, equipment, medium, and program product for processing theoretical historical power data of photovoltaic power plants. Background Technology

[0002] Photovoltaic power generation forecasting is a core technology for grid-connected dispatching of new energy sources. It typically utilizes historical meteorological and power generation data, employing statistical or artificial intelligence algorithms to construct predictive models, which are then input into future weather forecasts to predict power generation. With the expansion of new energy scale, the grid uses AGC (Automatic Generation Control) to curtail power from photovoltaic power plants, resulting in a distinction between actual and theoretical power. Actual power is the measured power transmitted from the power plant, while theoretical power is the maximum achievable power without curtailment. Against this backdrop, power forecasting has become more refined into the prediction of theoretical power, and modeling relies on historical theoretical power data. Accurately obtaining this data has become a significant challenge for the industry.

[0003] However, existing theoretical power calculations generally employ the meteorological resource method. This method calculates theoretical power based on measured data such as irradiance and temperature, along with physical models, and is used for predictive model training. However, this method relies on high-precision meteorological data, which is prone to missing or inaccurate measurements from monitoring equipment. Furthermore, it is susceptible to the effects of dust accumulation and shading, resulting in low calculation accuracy and poor predictive performance. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and program product for processing theoretical historical power data of photovoltaic power plants, in order to solve the problems of existing technologies that rely on high-precision meteorological data, are prone to missing or inaccurate measurements by monitoring equipment, and suffer from the effects of dust accumulation, shading, etc., resulting in low calculation accuracy and poor prediction effects.

[0005] In a first aspect, the present invention provides a method for processing theoretical historical power data of a photovoltaic power plant, the method comprising:

[0006] Based on the actual power of each sub-array of the photovoltaic array, determine the theoretical power of the corresponding sub-array; The theoretical power of all the sub-arrays is accumulated over the entire field, and the accumulated theoretical power value is corrected for deviation to obtain the theoretical power at the grid connection point during the power curtailment period. The theoretical power of the grid connection point during the power curtailment period is spliced ​​with the actual power of the grid connection point during the non-power curtailment period to obtain the total theoretical power of the photovoltaic array at the grid connection point.

[0007] This invention directly calculates theoretical power from the actual power of the sub-array, eliminating the need for easily missing meteorological data such as irradiance and temperature, effectively avoiding calculation failures caused by monitoring equipment malfunctions and missing data. Employing characteristic curve cluster matching and field-wide cumulative correction, it can adapt to complex terrain, various component types, and equipment attenuation differences, significantly improving the accuracy and representativeness of theoretical power across the entire field. Simultaneously, it avoids the inherent biases of traditional physical models, making the obtained historical theoretical power more closely reflect the actual operating characteristics of the power plant.

[0008] In one optional implementation, determining the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array includes: A predetermined number of photovoltaic array components are connected in parallel to form a sub-array; Multiple characteristic curves of the sub-matrix under different irradiation conditions are constructed to obtain the characteristic curve cluster of the sub-matrix; Collect historical power generation voltage data, historical current data, and historical power curtailment period data for each of the aforementioned sub-arrays; Using the historical generation voltage data and historical current data corresponding to the historical power curtailment period data, the actual power operating point of the sub-array is located; In the characteristic curve family of the sub-matrix, match multiple characteristic curves that are closest to the actual power operating point; Based on the most similar characteristic curves, the theoretical power corresponding to the actual power operating point is determined.

[0009] This invention constructs a cluster of characteristic curves for a sub-array, forming a complete power characteristic benchmark based on the array's electrical connections and different irradiance conditions. It eliminates the need for readily available and potentially biased meteorological data such as irradiance and temperature measurements, thus preventing theoretical power calculation failures caused by equipment malfunctions and missing data. By matching the actual power operating point with the characteristic curve cluster, it accurately reconstructs the true theoretical power under power-limited conditions. This invention is adaptable to complex terrain and various component types, effectively eliminating calculation errors caused by dust accumulation, shading, and equipment attenuation, significantly improving the accuracy, overall representativeness, and modeling applicability of theoretical power.

[0010] In one optional implementation, the step of summing up the theoretical power of all the sub-arrays over the entire field and correcting the deviation of the summed theoretical power to obtain the theoretical power at the grid connection point during the power curtailment period includes: Calculate the ratio between the theoretical power of the sub-array and the maximum power of the inverter to obtain the first load factor; Based on the power generation conversion efficiency curve, determine the efficiency of the first inverter corresponding to the first load rate. The theoretical AC power of the sub-array is obtained by multiplying the theoretical power of the sub-array by the efficiency of the first inverter. The theoretical AC power of all the sub-arrays is linearly added together to obtain the total theoretical power accumulation value for the entire field during the power-limited period. The cumulative theoretical power of the entire field corresponding to the power restriction period is input into a preset relational model for deviation correction to obtain the theoretical power of the grid connection point during the power restriction period.

[0011] This invention matches inverter efficiency with load rate to accurately convert the theoretical power of the sub-array into the theoretical power on the AC side, closely aligning with the actual conversion characteristics of the inverter and improving the consistency and reliability of power calculations. Through linear accumulation of the entire sub-array, a complete theoretical power accumulation value for the entire field during power curtailment periods is formed, taking into account complex sub-sites and various equipment operating conditions. Further deviation correction using a pre-set relational model effectively offsets systematic errors caused by transformer losses, line losses, component attenuation, and equipment differences, making the output grid-connected theoretical power more closely resemble the actual unrestricted output state of the power station.

[0012] In one optional implementation, before inputting the accumulated theoretical power value of the entire grid corresponding to the power restriction period into a preset relational model for deviation correction to obtain the grid connection point theoretical power during the power restriction period, the method further includes: Obtain the power generation capacity of all sub-arrays and the actual power at the grid connection point during non-power curtailment periods; Calculate the ratio between the generated power and the maximum power of the inverter to obtain the second load factor; Based on the power generation conversion efficiency curve, determine the second inverter efficiency corresponding to the second load rate; The theoretical AC power of the sub-array during non-power-limited periods is obtained by multiplying the power generation of the sub-array by the efficiency of the second inverter. The theoretical AC power of all the sub-arrays during the non-power-limited period is linearly added together to obtain the cumulative value of the total theoretical power of the field during the non-power-limited period. The theoretical power accumulation value of the entire field corresponding to the non-power curtailment period is correlated with the actual power of the grid connection point to obtain the theoretical power dataset for the non-power curtailment period.

[0013] This invention constructs a standard dataset based on real-world operating data during non-power-curtailment periods. It unifies power calculations by matching load rate with inverter efficiency, ensuring that power conversion accurately reflects the actual operating characteristics of the equipment. The theoretical power on the AC side of all sub-arrays is linearly accumulated to form the total theoretical power accumulation value for the entire site during non-power-curtailment periods. This value is then accurately correlated with the actual power at the grid connection point, comprehensively reflecting the overall deviation characteristics of the site's line losses, transformer losses, and component attenuation. The resulting dataset is authentic, reliable, and highly representative.

[0014] In one optional implementation, the step of inputting the accumulated theoretical power value of the entire grid corresponding to the power curtailment period into a preset relational model for deviation correction to obtain the grid connection point theoretical power during the power curtailment period includes: Using the theoretical power dataset from the non-power-restricted periods, a training set and a test set are constructed; The training set is input into a preset relational model for training to obtain a new relational model; The new relation model is tested using the test set to obtain the final relation model. The cumulative theoretical power of the entire field corresponding to the power restriction period is input into the final relational model for deviation correction to obtain the theoretical power of the grid connection point during the power restriction period.

[0015] This invention utilizes a standard dataset from non-power-curtailment periods to construct training and testing sets, training and validating a pre-defined relational model to form a final relational model that accurately reflects the comprehensive loss characteristics of a power plant. This model effectively adapts to nonlinear deviations such as component attenuation, equipment differences, and line losses. By correcting deviations in the cumulative theoretical power value during power curtailment periods using this model, systematic errors can be eliminated, significantly improving the authenticity and accuracy of the theoretical power at the grid connection point.

[0016] In an optional implementation, the method further includes: Determine whether each sub-array of the photovoltaic array has been changed; If so, obtain the actual power of all sub-arrays after the change, and jump to execute the step of determining the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array.

[0017] This invention achieves adaptive updates to the theoretical power calculation process by real-time judgment of changes in the sub-array status. When changes occur in the sub-array, such as component replacement or array addition / reduction, a recalculation process can be automatically triggered. This ensures that the characteristic curve cluster, power conversion, and deviation correction stages all use the latest site parameters and operating data, effectively adapting to changes in scenarios such as equipment attenuation and structural modifications, and improving the real-time performance, accuracy, and long-term applicability of the theoretical power calculation results.

[0018] Secondly, the present invention provides a device for processing theoretical historical power data of a photovoltaic power plant, the device comprising: The preprocessing module is used to determine the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array. The correction module is used to accumulate the theoretical power of all the sub-arrays over the entire field and correct the deviation of the accumulated theoretical power value to obtain the theoretical power of the grid connection point during the power curtailment period. The splicing module is used to splice the theoretical power of the grid connection point during the power curtailment period with the actual power of the grid connection point during the non-power curtailment period to obtain the overall theoretical power of the photovoltaic array at the grid connection point.

[0019] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power plant theoretical historical power data processing method described in the first aspect or any corresponding embodiment thereof.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the photovoltaic power plant theoretical historical power data processing method described in the first aspect or any corresponding embodiment thereof.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the photovoltaic power plant theoretical historical power data processing method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the first type of photovoltaic power plant theoretical historical power data processing method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the photovoltaic power plant theoretical historical power data processing method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the electrical connections of a photovoltaic power station according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the construction of the PV curve of the sub-matrix according to an embodiment of the present invention; Figure 5 This is a schematic diagram of PV curves under different irradiance conditions for the sub-array according to an embodiment of the present invention. Figure 6 This is a schematic diagram illustrating the conversion between actual power and theoretical power during power rationing periods, according to an embodiment of the present invention. Figure 7This is a schematic diagram illustrating the conversion of actual power to theoretical power based on the PV curve of a sub-array according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the inverter efficiency curve according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the theoretical power calculation process for the entire grid connection point according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the theoretical power calculation and update process according to an embodiment of the present invention; Figure 11 This is a structural block diagram of a photovoltaic power plant theoretical historical power data processing device according to an embodiment of the present invention; Figure 12 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] This invention provides a method for processing theoretical historical power data of photovoltaic power plants. The theoretical power is directly calculated from the actual power of the sub-arrays, eliminating the need for easily missing meteorological data such as irradiance and temperature, effectively avoiding calculation failures caused by monitoring equipment malfunctions and missing data. Employing characteristic curve cluster matching and whole-field cumulative correction, it can adapt to complex terrain, various types of components, and equipment attenuation differences, significantly improving the accuracy and representativeness of theoretical power across the entire field. Simultaneously, it avoids the inherent biases of traditional physical models, achieving a more accurate historical theoretical power that reflects the actual operating characteristics of the power plant.

[0028] According to an embodiment of the present invention, a method for processing theoretical historical power data of a photovoltaic power plant is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for processing theoretical historical power data of photovoltaic power plants. Figure 1 This is a flowchart of a method for processing theoretical historical power data of a photovoltaic power plant according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Determine the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array.

[0030] It should be noted that a photovoltaic array refers to a photovoltaic power generation unit composed of multiple photovoltaic modules connected in series or in parallel.

[0031] A subarray refers to the smallest photovoltaic power generation calculation unit, which consists of several components connected in series and parallel and equipped with an independent inverter.

[0032] Actual power refers to the measured output power of the photovoltaic sub-array under both power curtailment and non-power curtailment conditions.

[0033] Theoretical power refers to the maximum power output that a photovoltaic subarray can achieve under conditions of no power curtailment and no output constraints.

[0034] In this embodiment of the invention, a preset number of components are connected in series and parallel to form a sub-array according to the electrical topology of the photovoltaic array. A cluster of characteristic curves for the sub-array is constructed based on different irradiation conditions. Historical power generation voltage, current and power curtailment period data of the sub-array are collected. The actual power operating point is located based on the voltage and current during the power curtailment period. Multiple characteristic curves that are closest to the actual power operating point are matched in the cluster of characteristic curves. The theoretical power corresponding to the actual power operating point is then calculated based on the matched characteristic curves.

[0035] Step S102: The theoretical power of all sub-arrays is accumulated over the entire field, and the accumulated theoretical power value is corrected for deviation to obtain the theoretical power of the grid connection point during the power curtailment period.

[0036] It should be noted that the total power of the entire field refers to the sum of the theoretical power of all sub-arrays of the power station according to the electrical topology.

[0037] The total theoretical power summation refers to the sum of the theoretical power on the AC side of all sub-arrays.

[0038] Power curtailment periods refer to the time periods during which the power grid restricts the output of photovoltaic power plants through AGC (Automatic Generation Control) instructions.

[0039] The theoretical power at the grid connection point refers to the maximum power that a power plant can transmit when there is no power curtailment or dispatch.

[0040] In this embodiment of the invention, the load rate is calculated based on the theoretical power of each sub-array and the maximum power of the inverter. The corresponding inverter efficiency is matched and converted into the theoretical power on the AC side. The theoretical power on the AC side of all sub-arrays is linearly added to obtain the cumulative value of the total theoretical power during the power curtailment period. Then, the cumulative value is corrected for deviation using a relational model pre-trained based on data from non-power curtailment periods. Finally, the theoretical power at the grid connection point that can truly reflect the unrestricted power output state of the power station during the power curtailment period is output.

[0041] Step S103: The theoretical power of the grid connection point during the power curtailment period is spliced ​​with the actual power of the grid connection point during the non-power curtailment period to obtain the total theoretical power of the photovoltaic array at the grid connection point.

[0042] It should be noted that the actual power of the grid connection point during non-power curtailment periods refers to the measured power of the grid connection point when there is no power curtailment, which is directly equivalent to the theoretical power during that period.

[0043] The theoretical power at the grid connection point refers to the complete theoretical power time-series data of the power plant at the grid connection point under unrestricted conditions throughout the entire time period.

[0044] In this embodiment of the invention, the theoretical power of the grid-connected points during the power-restricted period after deviation correction is aligned with the actual power of the grid-connected points during the non-power-restricted period, which is not subject to scheduling power restrictions and can be directly used as theoretical power, according to a unified time axis. This results in a complete, continuous, accurate and reliable theoretical power of the entire grid-connected point, covering the entire operating period.

[0045] This embodiment provides a method for processing theoretical historical power data of photovoltaic power plants. Figure 2 This is a flowchart of a method for processing theoretical historical power data of a photovoltaic power plant according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Determine the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array.

[0046] In some optional implementations, step S201 above includes: Step S2011: Connect a preset number of photovoltaic array components in parallel to form a sub-array.

[0047] It should be noted that a module string refers to a basic unit of DC power generation in which multiple photovoltaic modules are connected in series to achieve voltage superposition.

[0048] In the embodiments of the present invention, see Figure 3 As shown, the general electrical connection diagram of a photovoltaic power station can be represented as follows: m Individual components are chained together to form a component string. n (i.e., a preset number) of components are then connected in parallel to form a sub-array. A sub-array converts DC to AC via an inverter, and then... k The individual sub-arrays are combined into one array, and then transmitted via array transformers and overall field step-up transformers, and finally merged and sent out.

[0049] Step S2012: Construct multiple characteristic curves of the sub-matrix under different irradiation conditions to obtain the characteristic curve cluster of the sub-matrix.

[0050] It should be noted that the characteristic curve is specifically the PV curve.

[0051] A characteristic curve cluster refers to a set of curves formed by integrating multiple characteristic curves corresponding to sub-matrixes under different irradiation conditions according to their operating conditions.

[0052] In an embodiment of the present invention, m When several components are chained together to form a component string, it will be like this: Figure 4 As shown, considering the characteristics of voltage superposition and constant current in series connection, the PV curve of the module string is generated based on the original module PV curve. n When components are connected in series and parallel to form a sub-matrix, it will be as follows: Figure 4 As shown, considering the characteristics of constant voltage and superimposed current in parallel connection, the PV curve of the sub-matrix is ​​formed based on the PV curve of the component string.

[0053] Because the original module will have multiple PV curves under different irradiation conditions depending on the variation in irradiance, forming a PV curve cluster. Similarly, according to the above method, the sub-array will also have PV curve clusters under different conditions, such as... Figure 5 As shown.

[0054] Step S2013: Collect historical power generation voltage data, historical current data, and historical power curtailment period data for each sub-array.

[0055] It should be noted that historical power generation voltage data refers to the time-series record data of DC power generation voltage output by the subarray during its past operation.

[0056] Historical current data refers to the time-series record of DC power generation current output by the subarray during its past operation.

[0057] Historical power curtailment period data refers to historical data that records the specific time periods during which the power grid dispatching terminal issues power curtailment instructions to photovoltaic power plants through the AGC system, along with the corresponding markers.

[0058] In this embodiment of the invention, historical DC power generation voltage and historical DC power generation current time-series data during the operation of each sub-array are synchronously collected from the SCADA system and power prediction system of the photovoltaic power station, and historical power curtailment period marker data are also collected to ensure that the collected data corresponds one-to-one with the sub-array and is synchronized in time.

[0059] Step S2014: Using historical power generation voltage data and historical current data corresponding to historical power curtailment periods, locate the actual power operating point of the sub-array.

[0060] It should be noted that the actual power operating point refers to the operating coordinate point formed by the actual power generation and the corresponding power generation voltage of the subarray in each time sequence during the power curtailment period.

[0061] In this embodiment of the invention, historical power generation voltage and current data of the sub-array within the time period corresponding to historical power curtailment periods are selected. The actual power generation of the sub-array within that time period is calculated by combining these two data points. The actual power generation and corresponding power generation voltage for each time series are used as a set of coordinates. Specifically, as follows... Figure 6 As shown, this represents the actual power of the sub-array during the power rationing period. (The corresponding sub-array generating voltage is denoted as) ), to the theory of corresponding submatrices (The corresponding voltage is denoted as) The conversion of ). For example, Figure 7 As shown, the actual power operating point of the positioning sub-array ( , (Data).

[0062] Step S2015: Match multiple characteristic curves in the characteristic curve cluster of the sub-matrix that are closest to the actual power operating point.

[0063] In this embodiment of the invention, the voltage parameter of the actual power operating point of the sub-array is extracted, and compared with the constructed sub-array characteristic curve cluster, multiple characteristic curves that correspond to the voltage parameter and are closest to the irradiation condition are selected to ensure that the matching characteristic curves can accurately fit the operating conditions of the actual power operating point.

[0064] Specifically, such as Figure 7 As shown, based on the actual power operating point of the sub-array ( , The data, along with the sub-matrix PV curve cluster, are used to determine the two closest PV curves and the corresponding actual power operating points on the curves. , )and( , ).

[0065] Step S2016: Based on the most similar characteristic curves, determine the theoretical power corresponding to the actual power operating point.

[0066] In this embodiment of the invention, using the most similar characteristic curves that have been matched as a reference, the maximum power point parameters corresponding to each curve are extracted. Combined with the voltage coordinates of the actual power operating point, a linear interpolation method is used to calculate the theoretical power corresponding to the actual power operating point under unlimited output conditions. Specifically, based on two most similar PV curves and the corresponding actual power operating points on the curves (…),… , )and( , ).in, , .

[0067] Considering the role of the maximum power point tracking controller (MPPT) of the photovoltaic sub-array, and the fact that the data acquisition frequency is on the order of minutes, which is much greater than the control transient transition time of the MPPT, the maximum power generation point on the same PV curve can be considered as the theoretical power point.

[0068] Therefore, in ( , )and( , Find the corresponding ( ) on the PV curve of the sub-matrix where they are located. , )and( , ) work points, among which, , .

[0069] Considering the linear proportional relationship between irradiance and power of photovoltaic modules and sub-arrays, It can be calculated using the following formula: This allows us to obtain the actual power operating point ( ). , The theoretical power operating point corresponding to ) , ).

[0070] Since the theoretical power calculation method is based on the linear proportional relationship between irradiance and power, not only under different irradiance conditions, but also when some photovoltaic cells break, resulting in current characteristic degradation and PV curve decline, the conversion of actual power to theoretical power can still be achieved through this proportional relationship.

[0071] like Figure 6As shown, the above data processing is performed on each power generation operating point during the power curtailment period to obtain the theoretical power operating point corresponding to all actual power operating points during the power curtailment period, forming the theoretical power during the power curtailment period, and constructing the sub-matrix theoretical power curve.

[0072] Step S202: The theoretical power of all sub-arrays is accumulated over the entire field, and the accumulated theoretical power value is corrected for deviation to obtain the theoretical power of the grid connection point during the power curtailment period.

[0073] In some optional implementations, step S202 above includes: Step S2021: Calculate the ratio between the theoretical power of the sub-array and the maximum power of the inverter to obtain the first load factor.

[0074] It should be noted that the maximum power of the inverter refers to the rated maximum output power of the inverter connected to the sub-array, which is the maximum output limit set during the inverter design.

[0075] The first load factor refers to the ratio of the theoretical power of the subarray to the maximum power of the corresponding inverter during the power curtailment period.

[0076] In embodiments of the present invention, such as Figure 8 As shown, the power conversion efficiency curve of the sub-array inverter is obtained. This is achieved through the theoretical power output on the DC side. and inverter maximum power The load factor λ is derived from the ratio of the load factor to the load ratio λ, where the formula for calculating the load factor λ is: .

[0077] Step S2022: Based on the power generation conversion efficiency curve, determine the efficiency of the first inverter corresponding to the first load rate.

[0078] It should be noted that the power conversion efficiency curve refers to a pre-plotted curve that reflects the efficiency relationship between the inverter and the conversion of DC power to AC power under different load rates.

[0079] The first inverter efficiency refers to the actual conversion efficiency of the inverter when converting the theoretical power of the DC side of the subarray into the power of the AC side during the power curtailment period, corresponding to the first load rate.

[0080] In this embodiment of the invention, the inverter efficiency η corresponding to the load rate λ is found by using the power generation conversion efficiency curve.

[0081] Step S2023: The theoretical AC power of the sub-array is obtained by multiplying the theoretical power of the sub-array by the efficiency of the first inverter.

[0082] It should be noted that the theoretical power on the AC side refers to the maximum theoretical power output on the AC side after the theoretical power on the DC side of the subarray is converted by the inverter.

[0083] In this embodiment of the invention, the obtained theoretical DC-side power of the sub-array is... Based on the obtained inverter efficiency η, the theoretical AC power of the sub-array inverter is calculated as follows: .

[0084] Step S2024: Linearly add the theoretical AC power of all sub-arrays to obtain the cumulative theoretical power value of the entire field during the power-limited period.

[0085] In this embodiment of the invention, the theoretical AC power of each sub-array that has been converted is directly linearly summed according to the electrical topology of the photovoltaic power station, without introducing additional complex correction coefficients, to ensure that the accumulation process conforms to the actual power summation logic of the power station, and finally obtains the sum of the theoretical AC power of all sub-arrays of the photovoltaic array during the power curtailment period, that is, the accumulated value of the total theoretical power of the entire field corresponding to the power curtailment period.

[0086] Specifically, the theoretical power curve of the sub-array constructed in step S2016 is transformed to finally obtain the theoretical power curve of the AC side of the sub-array inverter, that is, the theoretical power curve of the AC side in the case of a single sub-array. Figure 3 As shown, in k Each sub-matrix forms a square matrix. p In the case where the entire field consists of several square formations, it is necessary to calculate the total number of square formations. Each subarray undergoes all processes from S2011 to S2024 to obtain the theoretical power of all subarrays, which are then linearly added together to obtain the cumulative theoretical power of the entire field during the power-limited period.

[0087] Specifically, prior to step S2025, the method further includes: Step a1: Obtain the power generation of all sub-arrays and the actual power at the grid connection point during non-power curtailment periods.

[0088] It should be noted that the power generation of the sub-array refers to the actual DC power output of the sub-array during non-power curtailment periods.

[0089] The actual power at the grid connection point refers to the measured AC power transmitted by the photovoltaic power station at the connection point between the photovoltaic power station and the power grid.

[0090] In this embodiment of the invention, by acquiring the power generation and curtailment period information, only the portion outside the curtailment period is extracted to obtain the power generation of all sub-arrays during the non-curtailment period. The actual grid connection point power during the non-curtailment period is then extracted to obtain the actual grid connection point power of all sub-arrays during the non-curtailment period. Since it is not affected by curtailment, the actual grid connection point power during this period is also recorded as the theoretical grid connection point power.

[0091] Step a2: Calculate the ratio between the generated power and the inverter's maximum power to obtain the second load factor.

[0092] It should be noted that the second load factor refers to the ratio of the power generation of the subarray to the maximum power of the corresponding inverter during non-power curtailment periods.

[0093] In this embodiment of the invention, the theoretical cumulative power value of the entire field during non-power-curtailment periods is constructed by summing the DC power generation of all sub-array inverters during non-power-curtailment periods and performing the inverter conversion efficiency calculation in step S202, thus obtaining the power generation conversion efficiency curve of the sub-array inverters. This is achieved by combining the power generation and the inverter's maximum power. The load factor λ is derived from the ratio.

[0094] Step a3: Based on the power generation conversion efficiency curve, determine the second inverter efficiency corresponding to the second load rate.

[0095] It should be noted that the second inverter efficiency refers to the actual conversion efficiency of the inverter when converting the measured DC power generated by the sub-array into AC power, corresponding to the second load rate, during non-power curtailment periods.

[0096] In this embodiment of the invention, the inverter efficiency η corresponding to the load rate λ is determined by the power generation conversion efficiency curve.

[0097] Step a4: The theoretical AC power of the sub-array during non-power-limited periods is obtained by multiplying the power generation of the sub-array by the efficiency of the second inverter.

[0098] It should be noted that the theoretical AC power during non-power-curtailment periods refers to the theoretical AC power output after the measured DC power generation of the subarray is converted by the inverter during non-power-curtailment periods.

[0099] In this embodiment of the invention, based on the obtained power generation (measured DC power) of the sub-array during the non-power-curtailment period and the determined efficiency of the second inverter, the theoretical AC power of the sub-array after conversion by the inverter during the non-power-curtailment period is obtained by multiplying the two.

[0100] Step a5: Linearly sum the theoretical AC power of all sub-arrays during the non-power-limited period to obtain the cumulative theoretical power value of the entire field during the non-power-limited period.

[0101] It should be noted that the total theoretical power accumulation value for the entire field during non-curtailment periods refers to the total theoretical power of the entire field obtained by linearly summing the AC side theoretical power of all sub-arrays of the photovoltaic array during non-curtailment periods.

[0102] In this embodiment of the invention, the theoretical power of the AC side of all sub-arrays during the non-power-limited period is accumulated to obtain the total theoretical power accumulation value of the entire field during the non-power-limited period.

[0103] Step a6: Associate the total theoretical power accumulation value of the entire field corresponding to the non-power curtailment period with the actual power at the grid connection point to obtain the theoretical power dataset for the non-power curtailment period.

[0104] It should be noted that the theoretical power dataset during non-power curtailment periods refers to a dataset composed of pairs of time-aligned cumulative theoretical power values ​​for the entire field during non-power curtailment periods and actual power at the grid connection point.

[0105] In this embodiment of the invention, the cumulative value of the total theoretical power at the same time point is correlated with the actual power at the grid connection point to obtain the total theoretical power-grid connection point theoretical power dataset during the non-power curtailment period, which is the theoretical power dataset during the non-power curtailment period.

[0106] Step S2025: Input the cumulative value of the total theoretical power of the entire field corresponding to the power restriction period into the preset relational model for deviation correction, and obtain the theoretical power of the grid connection point during the power restriction period.

[0107] Specifically, step S2025 above includes: Step b1: Use the theoretical power dataset during non-power-curtailment periods to construct training and testing sets.

[0108] It should be noted that the training set refers to most of the data extracted from the theoretical power dataset during non-power-restricted periods, which includes multiple sets of time-series pairs of the total theoretical power accumulation value and the actual power at the grid connection point.

[0109] The test set refers to a small portion of data extracted from the theoretical power dataset during non-power-curtailment periods. It has the same data structure as the training set and is used to verify the accuracy of the corrected relational model after training.

[0110] In this embodiment of the invention, the theoretical power dataset during non-power-restricted periods is randomly divided into two parts according to a preset ratio (e.g., 7:3). Most of the time-series paired power data are divided into a training set for subsequent training of the relational model required for bias correction. The remaining small part of the data is divided into a test set to verify the correction accuracy of the trained model.

[0111] Step b2: Input the training set into the preset relational model for training to obtain a new relational model.

[0112] It should be noted that the preset relationship model refers to the initial model set in advance to explore the deviation relationship between the theoretical power accumulation value of the whole field and the actual power of the grid connection point. It is a relationship model between the theoretical power accumulation value and the theoretical power of the grid connection point established by artificial algorithms such as XGBoost.

[0113] The new relational model refers to a relational model that has been trained on a training set, with optimized parameters and accuracy meeting preset requirements.

[0114] In this embodiment of the invention, artificial intelligence algorithms such as XGBoost are used to establish a relationship model between the accumulated theoretical power and the theoretical power at the grid connection point, using a dataset of total theoretical power and grid connection point theoretical power during non-power-curtailment periods as training and testing data. This model reflects factors such as array transformers, total power-scale step-up transformers, and substation line losses.

[0115] Step b3: Use the test set to input the new relational model for testing, and obtain the final relational model.

[0116] It should be noted that the final relational model refers to a model that, after verification with the test set, has a prediction error controlled within a preset accuracy threshold and can stably fit the relationship between the theoretical cumulative power value of the entire field and the actual power deviation at the grid connection point.

[0117] In this embodiment of the invention, the total theoretical power accumulation value of the test set is input as an input parameter into the new relational model. The model outputs the corresponding predicted grid-connected power. The predicted value is compared with the actual grid-connected power in the test set, and the model prediction error is calculated. If the error does not exceed the preset accuracy threshold, the new relational model is confirmed to meet the deviation correction requirements and is used as the final relational model.

[0118] Step b4: Input the cumulative theoretical power value of the entire field corresponding to the power curtailment period into the final relational model for deviation correction to obtain the theoretical power of the grid connection point during the power curtailment period.

[0119] It should be noted that deviation correction refers to using the final relational model, combined with the power deviation patterns mined during non-power curtailment periods, to compensate and adjust the cumulative theoretical power value for the entire power field during power curtailment periods.

[0120] The theoretical power of the grid connection point during power curtailment periods refers to the maximum theoretical power that the power station's grid connection point can output under unconstrained conditions, obtained after final relational model deviation correction during the power curtailment period.

[0121] In this embodiment of the invention, the cumulative theoretical power value of the entire field during the power curtailment period is used as the input data of the final relational model, and the theoretical power data output of the grid connection point during the power curtailment period can be obtained.

[0122] Step S203: The theoretical power of the grid connection point during the power curtailment period is spliced ​​with the actual power of the grid connection point during the non-power curtailment period to obtain the total theoretical power of the photovoltaic array at the grid connection point.

[0123] In this embodiment of the invention, the theoretical grid-connected power during the power-restricted period and the theoretical grid-connected power during the non-power-restricted period obtained in step a1 are spliced ​​together to finally obtain the total theoretical grid-connected power covering all periods. The entire process is as follows: Figure 9 As shown.

[0124] Step S204: Determine whether each subarray of the photovoltaic array has been changed.

[0125] In this embodiment of the invention, the addition or reduction of arrays within the photovoltaic power station, component replacement, and updates to the dataset to reflect the latest characteristics of the station's components are taken into account, such as... Figure 10 As shown, it can determine in real time whether each subarray of the photovoltaic array has been changed or whether the dataset has been updated.

[0126] Step S205: If yes, obtain the actual power of all sub-arrays after the change, and jump to execute the step of determining the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array.

[0127] In embodiments of the present invention, such as Figure 10 As shown, if there are additions or subtractions to the photovoltaic power station arrays, or if components are replaced, the actual power of all sub-arrays after the changes are re-acquired, and the process jumps to step S201. If the dataset has been updated, the dataset needs to be updated and the model corrected.

[0128] This embodiment also provides a photovoltaic power plant theoretical historical power data processing device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0129] This embodiment provides a device for processing theoretical historical power data of a photovoltaic power station, such as... Figure 11 As shown, it includes: The preprocessing module 301 is used to determine the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array. The correction module 302 is used to accumulate the theoretical power of all sub-arrays over the entire field and correct the deviation of the accumulated theoretical power value to obtain the theoretical power at the grid connection point during the power curtailment period. The splicing module 303 is used to splice the theoretical power of the grid connection point during the power curtailment period with the actual power of the grid connection point during the non-power curtailment period to obtain the total theoretical power of the photovoltaic array at the grid connection point.

[0130] In some alternative implementations, the preprocessing module 301 includes: Parallel unit, used to connect a preset number of photovoltaic array components in parallel to form a sub-array; The building unit is used to construct multiple characteristic curves of the sub-matrix under different irradiation conditions, thereby obtaining the characteristic curve cluster of the sub-matrix. The data acquisition unit is used to collect historical power generation voltage data, historical current data, and historical power curtailment period data for each sub-array. The positioning unit is used to locate the actual power operating point of the sub-array by using historical power generation voltage data and historical current data corresponding to historical power curtailment period data. The matching unit is used to match multiple feature curves in the feature curve family of the submatrix that are closest to the actual power operating point. The preprocessing unit is used to determine the theoretical power corresponding to the actual power operating point based on multiple closest characteristic curves.

[0131] In some alternative implementations, the correction module 302 includes: The calculation unit is used to calculate the ratio between the theoretical power of the sub-array and the maximum power of the inverter to obtain the first load factor; The efficiency calculation unit is used to determine the efficiency of the first inverter corresponding to the first load rate based on the power generation conversion efficiency curve. The multiplication unit is used to obtain the theoretical AC power of the sub-array by multiplying the theoretical power of the sub-array by the efficiency of the first inverter. The summation unit is used to linearly add the theoretical AC power of all sub-arrays to obtain the cumulative theoretical power value of the entire field during the power restriction period. The correction unit is used to input the cumulative value of the total theoretical power of the entire field corresponding to the power restriction period into the preset relational model for deviation correction, so as to obtain the theoretical power of the grid connection point during the power restriction period.

[0132] In some alternative implementations, prior to the correction unit, the following is also included: The acquisition sub-unit is used to acquire the power generation of all sub-arrays and the actual power at the grid connection point during non-power curtailment periods; The calculation subunit is used to calculate the ratio between the generated power and the inverter's maximum power to obtain the second load factor; The efficiency calculation subunit is used to determine the second inverter efficiency corresponding to the second load rate based on the power generation conversion efficiency curve. The multiplication sub-unit is used to obtain the theoretical AC power of the sub-array during non-power-limited periods by taking the multiplication between the power generation of the sub-array and the efficiency of the second inverter. The summing sub-unit is used to linearly add up the theoretical AC power of all sub-arrays during the non-power-limited period to obtain the cumulative value of the total theoretical power of the field during the non-power-limited period. The correlation subunit is used to correlate the total theoretical power accumulation value of the entire field with the actual power at the grid connection point during non-power curtailment periods to obtain the theoretical power dataset for non-power curtailment periods.

[0133] In some alternative implementations, the following are included: Construct sub-units to build training and test sets using theoretical power datasets from non-power-curtailed periods; The training subunit is used to train a new relational model by inputting the training set into the preset relational model. The test subunit is used to test the new relational model using a test set as input, and to obtain the final relational model. The correction sub-unit is used to input the cumulative theoretical power value of the entire field corresponding to the power curtailment period into the final relational model for deviation correction, so as to obtain the theoretical power of the grid connection point during the power curtailment period.

[0134] In some alternative embodiments, the device further includes: The judgment unit is used to determine whether each sub-array of the photovoltaic array has been changed; The jump unit is used to obtain the actual power of all sub-arrays after the change if the condition is met, and then jump to execute the step of determining the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array.

[0135] The photovoltaic power plant theoretical historical power data processing device provided in this embodiment of the invention can execute the photovoltaic power plant theoretical historical power data processing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0136] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0137] The following is a detailed reference. Figure 12This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. Processor 401, ROM RAM 402 and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0138] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 12 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0139] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the photovoltaic power plant theoretical historical power data processing method of the embodiments of the present invention.

[0140] Figure 12 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0141] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the photovoltaic power plant theoretical historical power data processing method shown in the above embodiments is implemented.

[0142] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0143] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for processing theoretical historical power data of photovoltaic power plants, characterized in that, The method includes: Based on the actual power of each sub-array of the photovoltaic array, determine the theoretical power of the corresponding sub-array; The theoretical power of all the sub-arrays is accumulated over the entire field, and the accumulated theoretical power value is corrected for deviation to obtain the theoretical power at the grid connection point during the power curtailment period. The theoretical power of the grid connection point during the power curtailment period is spliced ​​with the actual power of the grid connection point during the non-power curtailment period to obtain the total theoretical power of the photovoltaic array at the grid connection point.

2. The method according to claim 1, characterized in that, The step of determining the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array includes: A predetermined number of photovoltaic array components are connected in parallel to form a sub-array; Multiple characteristic curves of the sub-matrix under different irradiation conditions are constructed to obtain the characteristic curve cluster of the sub-matrix; Collect historical power generation voltage data, historical current data, and historical power curtailment period data for each of the aforementioned sub-arrays; Using the historical generation voltage data and historical current data corresponding to the historical power curtailment period data, the actual power operating point of the sub-array is located; In the characteristic curve family of the sub-matrix, match multiple characteristic curves that are closest to the actual power operating point; Based on the most similar characteristic curves, the theoretical power corresponding to the actual power operating point is determined.

3. The method according to claim 1, characterized in that, The process of summing up the theoretical power of all the sub-arrays over the entire field and correcting for deviations in the summed theoretical power to obtain the theoretical power at the grid connection point during the power curtailment period includes: Calculate the ratio between the theoretical power of the sub-array and the maximum power of the inverter to obtain the first load factor; Based on the power generation conversion efficiency curve, determine the efficiency of the first inverter corresponding to the first load rate. The theoretical AC power of the sub-array is obtained by multiplying the theoretical power of the sub-array by the efficiency of the first inverter. The theoretical AC power of all the sub-arrays is linearly added together to obtain the total theoretical power accumulation value for the entire field during the power-limited period. The cumulative theoretical power of the entire field corresponding to the power restriction period is input into a preset relational model for deviation correction to obtain the theoretical power of the grid connection point during the power restriction period.

4. The method according to claim 3, characterized in that, Before inputting the accumulated theoretical power value of the entire field corresponding to the power restriction period into a preset relational model for deviation correction to obtain the theoretical power of the grid connection point during the power restriction period, the method further includes: Obtain the power generation capacity of all sub-arrays and the actual power at the grid connection point during non-power curtailment periods; Calculate the ratio between the generated power and the maximum power of the inverter to obtain the second load factor; Based on the power generation conversion efficiency curve, determine the second inverter efficiency corresponding to the second load rate; The theoretical AC power of the sub-array during non-power-limited periods is obtained by multiplying the power generation of the sub-array by the efficiency of the second inverter. The theoretical AC power of all the sub-arrays during the non-power-limited period is linearly added together to obtain the cumulative value of the total theoretical power of the field during the non-power-limited period. The theoretical power accumulation value of the entire field corresponding to the non-power curtailment period is correlated with the actual power of the grid connection point to obtain the theoretical power dataset for the non-power curtailment period.

5. The method according to claim 4, characterized in that, The step of inputting the cumulative theoretical power value of the entire grid corresponding to the power restriction period into a preset relational model for deviation correction to obtain the grid connection point theoretical power during the power restriction period includes: Using the theoretical power dataset from the non-power-restricted periods, a training set and a test set are constructed; The training set is input into a preset relational model for training to obtain a new relational model; The new relation model is tested using the test set to obtain the final relation model. The cumulative theoretical power of the entire field corresponding to the power restriction period is input into the final relational model for deviation correction to obtain the theoretical power of the grid connection point during the power restriction period.

6. The method according to claim 1, characterized in that, The method further includes: Determine whether each sub-array of the photovoltaic array has been changed; If so, obtain the actual power of all sub-arrays after the change, and jump to execute the step of determining the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array.

7. A device for processing theoretical historical power data of a photovoltaic power station, characterized in that, The device includes: The preprocessing module is used to determine the theoretical power of the corresponding sub-array based on the actual power of each sub-array of the photovoltaic array. The correction module is used to accumulate the theoretical power of all the sub-arrays over the entire field and correct the deviation of the accumulated theoretical power value to obtain the theoretical power of the grid connection point during the power curtailment period. The splicing module is used to splice the theoretical power of the grid connection point during the power curtailment period with the actual power of the grid connection point during the non-power curtailment period to obtain the overall theoretical power of the photovoltaic array at the grid connection point.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power plant theoretical historical power data processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the photovoltaic power plant theoretical historical power data processing method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the photovoltaic power plant theoretical historical power data processing method according to any one of claims 1 to 6.

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