Distributed photovoltaic abandoned and limited power statistical method and device, electronic equipment and medium

Abstract

The application provides a distributed photovoltaic abandoned and limited power statistical method and device, electronic equipment and a storage medium, and belongs to the technical field of power statistics. The method comprises the following steps: determining a plurality of sample distributed templates in a region; calculating the estimated limited power of the plurality of sample distributed templates; correcting the estimated limited power based on a correction coefficient to obtain the distributed photovoltaic abandoned and limited power; dividing the region to be counted into partitions, determining the estimated limited power based on the sample distributed templates, correcting the estimated limited power, and then obtaining the distributed photovoltaic abandoned and limited power, so that the accurate statistics of the photovoltaic abandoned and limited power is realized, and the accuracy of the photovoltaic abandoned and limited power statistics is improved.

Description

Technical Field

[0001] This application relates to the field of power statistics technology, specifically to a method, device, electronic equipment, and storage medium for statistical analysis of distributed photovoltaic curtailment power. Background Technology

[0002] In recent years, with the continuous increase in the installed capacity of new energy power plants, my country's energy structure has undergone significant changes, gradually shifting from the hydropower and thermal power models that once dominated to new energy models such as photovoltaic and wind power.

[0003] Currently, the photometric method and the prototype method are commonly used to calculate the curtailment of distributed photovoltaic (PV) power. The photometric method, which calculates the theoretical power generation after grid disconnection by multiplying factors such as solar radiation, total panel area, photoelectric conversion efficiency, and panel degradation rate, is heavily influenced by weather factors such as panel cleanliness and dust storms, making it overly reliant on the accuracy of meteorological measurements. The prototype method uses prototypes to calculate the overall theoretical power generation of distributed PV. This method is less affected by weather factors, but it relies heavily on the selection of prototypes. Given the wide distribution and varying power generation efficiencies of distributed PV systems, the formula contains a certain amount of error. The difficulty in selecting prototypes lies mainly in the need for a large number and wide distribution, and the fact that factors affecting power generation efficiency, such as the degree of pollution and panel degradation rate, change over time, leading to a gradual increase in estimation error. Summary of the Invention

[0004] In view of this, this application provides a method, device, electronic device and storage medium for calculating the curtailment power of distributed photovoltaic power, which solves the technical problem of low accuracy in the calculation of curtailment power of distributed photovoltaic power in the prior art.

[0005] According to one aspect of this application, a method for calculating the curtailment of distributed photovoltaic power is provided, comprising:

[0006] Determine a distributed template of multiple samples within the region;

[0007] Calculate the estimated power limit for multiple sample distributed templates;

[0008] The estimated curtailment power is corrected based on the correction factor to obtain the distributed photovoltaic curtailment power.

[0009] In one possible embodiment, before correcting the estimated curtailment power based on the correction factor to generate the distributed photovoltaic curtailment power, the method further includes:

[0010] The final model parameters are determined based on the regression correction algorithm and historical power generation efficiency.

[0011] The correction coefficient is determined based on the final model parameters and historical power generation efficiency.

[0012] In one possible embodiment, determining the final model parameters based on the regression correction algorithm and historical power generation efficiency includes:

[0013] The standard regression algorithm model is trained based on the historical power generation efficiency data to obtain initial model parameters and calculate the corresponding loss function.

[0014] Based on the loss function, the initial model parameters are updated using gradient descent.

[0015] If the convergence condition is met, the update will stop, and the corresponding model parameters will be saved as the final model parameters.

[0016] In one possible embodiment, before correcting the estimated curtailment power based on the correction factor to generate the distributed photovoltaic curtailment power, the method further includes:

[0017] Compare the estimated power rationing with the historical actual power rationing;

[0018] If the estimated curtailment amount is not equal to the historical actual curtailment amount, then the following steps are performed: the estimated curtailment amount is corrected based on the correction coefficient to generate the distributed photovoltaic curtailment amount.

[0019] In one possible embodiment, the distributed template of multiple samples within the defined region includes:

[0020] The area to be statistically evaluated for abandoned electricity is divided into multiple regions;

[0021] Multiple distributed templates are randomly selected from each of the regions, and the selected distributed templates are marked as the sample distributed templates.

[0022] In one possible embodiment, calculating the estimated power rationing of the plurality of sample distributed templates includes: calculating the estimated power rationing of each sample distributed template based on the current power generation and capacity of each sample distributed template.

[0023] In one possible embodiment, the step of correcting the estimated curtailment power based on a correction coefficient to obtain the distributed photovoltaic curtailment power includes: determining the sum of the estimated curtailment power and the correction coefficient as the distributed photovoltaic curtailment power.

[0024] As another aspect of this application, a distributed photovoltaic curtailment power counting device is provided, comprising:

[0025] The determination module is used to determine the distributed template of multiple samples within a region;

[0026] The calculation module is used to calculate the estimated power limit of multiple sample distributed templates;

[0027] The correction module is used to correct the estimated curtailment power based on the correction coefficient to obtain the distributed photovoltaic curtailment power.

[0028] As a third aspect of this application, an electronic device is provided, characterized in that the electronic device comprises:

[0029] Processor; and

[0030] Memory used to store processor-executable information;

[0031] The processor is used to execute the distributed photovoltaic curtailment power statistics method described above.

[0032] As a fourth aspect of this application, a computer-readable storage medium is provided, on which a distributed photovoltaic curtailment power statistics program is stored, wherein the distributed photovoltaic curtailment power statistics program is executed by a processor to implement the steps of the distributed photovoltaic curtailment power statistics method as described above.

[0033] Compared with existing technologies, this application provides a method, apparatus, electronic device, and storage medium for statistical analysis of distributed photovoltaic (PV) curtailment power. The method includes: determining multiple sample distributed modules within a region; calculating the estimated curtailment power for the multiple sample distributed modules; correcting the estimated curtailment power based on a correction coefficient to obtain the distributed PV curtailment power; partitioning the region to be analyzed, determining the estimated curtailment power based on the sample distributed modules, and correcting the estimated curtailment power to obtain the distributed PV curtailment power. This achieves accurate statistical analysis of PV curtailment power and improves the accuracy of PV curtailment power statistics. Attached Figure Description

[0034] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0035] Figure 1 The diagram shown is a flowchart illustrating a distributed photovoltaic curtailment power statistics method provided in an embodiment of this application.

[0036] Figure 2 The diagram shown is a flowchart illustrating a distributed photovoltaic curtailment power statistics method according to another embodiment of this application.

[0037] Figure 3 The diagram shown is a schematic representation of the distributed photovoltaic curtailment power statistics device provided in an embodiment of this application.

[0038] Figure 4 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application.

[0039] Specific implementation methods

[0040] In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, top, bottom, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the figures). If the specific posture changes, the directional indication will also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0041] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0043] Centralized photovoltaic (PV) power plants typically use a method of calculating capacity using PV sample units. Referring to the method for calculating curtailment power in centralized PV power plants, distributed PV can be divided into different regions, and sample units can be selected according to each region (county). After curtailment, the total curtailment power is estimated using the sample units in each region, and then the off-grid distributed PV capacity is statistically analyzed to calculate the curtailment power. This minimizes the error caused by differences in solar radiation. The advantages of this method are high practicality, simple calculation, and ease of operation. However, due to the wide distribution of distributed PV and significant differences in power generation efficiency, the formula contains a certain amount of error. Further research is needed to utilize artificial intelligence and machine learning methods, using historical data to approximate the actual power output with the estimated power output.

[0044] Distributed photovoltaic (PV) power generation specifically refers to PV facilities built near user sites, operating with user-side self-consumption and surplus electricity fed into the grid, and characterized by balancing and regulating the distribution system. Compared to centralized PV, distributed PV has advantages such as local power generation, local grid connection, local conversion, and local use. It can not only efficiently convert solar energy into clean electrical energy, but also effectively solve the problems of power loss during voltage boosting and long-distance transmission. With the increasing proportion of renewable energy, grid peak-shaving resources are becoming increasingly scarce, and sometimes centralized PV power plants and wind farms alone are insufficient to meet grid peak-shaving demands. Distributed PV's participation in grid peak-shaving is gradually becoming an unavoidable issue. However, distributed PV has a wider distribution range than centralized PV, and its control involves numerous manufacturers and users, affecting the interests of multiple parties, making peak-shaving management difficult. Based on this, this application proposes a method for calculating the amount of curtailed power after peak shaving, in order to calculate the amount of curtailed solar power in the grid and the user losses.

[0045] Figure 1 The diagram shown is a flowchart illustrating a method for calculating the curtailment of distributed photovoltaic power according to an embodiment of this application. Figure 1 As shown, the statistical method for distributed photovoltaic curtailment includes:

[0046] Step S101: Determine multiple sample distributed templates within the region;

[0047] The area to be statistically analyzed for surplus electricity is pre-divided into multiple regions. The surplus electricity in each region is calculated first, and then the surplus electricity in each region is summed to determine the surplus electricity in all regions. Generally, the more regions are divided, the less the impact of differences in sunlight caused by different geographical locations can be reduced.

[0048] When determining the curtailment capacity for each region, several distributed templates are first randomly selected from the distributed templates within the region, and these selected distributed templates are marked as sample distributed templates. Understandably, the more sample distributed templates there are, the higher the accuracy of the corresponding results.

[0049] Step S102: Calculate the estimated power limit for multiple sample distributed templates;

[0050] Specifically, the estimated power rationing for each of the sample distributed modules is calculated based on the current power generation and capacity of each sample distributed module.

[0051] The current power generation is expressed as The estimated power rationing is expressed as The distributed template capacity is represented as ,but

[0052]

[0053] in, This represents a distributed template for samples. This indicates the number of distributed sample templates within the region.

[0054] The total installed capacity of distributed photovoltaic power is expressed as... The amount of distributed photovoltaic curtailment is expressed as The total power of distributed photovoltaic power is expressed as ,but

[0055]

[0056] Thus, based on the total power of distributed photovoltaic systems and the current power generation, if an accurate estimate of the curtailment capacity is obtained... The amount of electricity that can be abandoned can then be determined.

[0057] Step S103: Correct the estimated curtailment power based on the correction coefficient to obtain the distributed photovoltaic curtailment power.

[0058] The correction factor is expressed as Then the curtailment of distributed photovoltaic power can be expressed as:

[0059]

[0060] Because the power generation efficiency varies with the cleanliness and lifespan of the photovoltaic panels, These parameters may also change. In this embodiment, the correction coefficient is determined based on historical power generation efficiency. Specifically, the final model parameters are determined based on the regression correction algorithm and historical power generation efficiency; and the correction coefficient is determined based on the final model parameters and historical power generation efficiency.

[0061] Regression is a supervised algorithm used in machine learning to build an algorithmic model (function) to determine the mapping relationship between attributes and labels. During the learning and training process, model parameters are obtained to achieve the best fit between the function and the parameters. In a regression algorithm, the final result of the function is a continuous data value, and the input value is a numerical vector. The standard formula for a regression algorithm is as follows:

[0062]

[0063] in, X represents a tag, and X represents an attribute.

[0064] In this embodiment, a model is established and the model parameters are determined based on a regression algorithm model. Specifically, the standard regression algorithm model is trained based on the historical power generation efficiency data to obtain initial model parameters and calculate the corresponding loss function.

[0065] Based on the loss function, the initial model parameters are updated using gradient descent.

[0066] If the convergence condition is met, the update will stop, and the corresponding model parameters will be saved as the final model parameters.

[0067] Let the power generation efficiency of region j for the first n days be expressed as follows: The initial model parameters are expressed as Then the correction factor The initial model is:

[0068]

[0069] Its loss function for:

[0070]

[0071] Use gradient descent to obtain the minimum loss function. When the minimum loss function is obtained After the model converges, the corresponding model parameters are: to ,Will to These parameters are determined as the final model parameters, and correction coefficients are determined based on these final model parameters. :

[0072]

[0073] In this way, by using historical power generation efficiency for model training and taking into account historical photovoltaic curves, training data can be easily obtained, and accurate and practical correction coefficients can be obtained.

[0074] Understandably, in other embodiments, parameters such as meteorological conditions and geographical location can be used as correction factors to obtain a more accurate correction coefficient.

[0075] Furthermore, the sum of the estimated curtailment power and the correction coefficient is determined as the distributed photovoltaic curtailment power, i.e., the distributed photovoltaic curtailment power. :

[0076]

[0077] Based on estimated power rationing and correction factor The amount of curtailment of distributed photovoltaic power can then be determined.

[0078] Based on the above scheme, this embodiment divides the area to be counted into zones, determines the estimated curtailment power based on the sample distributed template, and corrects the estimated curtailment power to obtain the distributed photovoltaic curtailment power. This achieves accurate statistics on photovoltaic curtailment power and improves the accuracy of photovoltaic curtailment power statistics.

[0079] Furthermore, referring to Figure 2 , Figure 2 The diagram shown is a flowchart illustrating a distributed photovoltaic curtailment power statistics method according to another embodiment of this application.

[0080] like Figure 2 As shown, the procedure before step S103 also includes:

[0081] Step S1031: Compare the estimated power ration with the historical actual power ration;

[0082] If the estimated curtailment amount is not equal to the historical actual curtailment amount, then the following steps are performed: the estimated curtailment amount is corrected based on the correction coefficient to generate the distributed photovoltaic curtailment amount.

[0083] If the estimated power rationing is equal to the historical actual power rationing, the correction factor is obtained directly.

[0084] The historical actual power rationing can be the actual power generation of any previous day, such as the power generation of the previous 20 days. Alternatively, it can be the average of the historical actual power rationing over several days.

[0085] In this way, the estimated power rationing will be compared with the actual historical power rationing, and corrections will be made when the two are inconsistent, so as to improve the accuracy of power rationing statistics.

[0086] Further, refer to Figure 3 , Figure 3 The diagram shown is a schematic representation of the distributed photovoltaic curtailment power statistics device provided in an embodiment of this application. Figure 3 As shown, the distributed photovoltaic curtailment power statistics device 1 includes:

[0087] Module 11 is used to determine multiple sample distributed templates within a region;

[0088] Calculation module 12 is used to calculate the estimated power limit of multiple sample distributed templates;

[0089] The correction module 13 is used to correct the estimated curtailment power based on the correction coefficient to obtain the distributed photovoltaic curtailment power.

[0090] Below, for reference Figure 4 This describes an electronic device according to embodiments of the present application. Figure 4 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application.

[0091] like Figure 4 As shown, the electronic device 600 includes one or more processors 601 and memory 602.

[0092] The processor 601 may be a central processing unit (CPU) or other form of processing unit with data processing and / or information execution capabilities, and may control other components in the electronic device 600 to perform desired functions.

[0093] The operations performed by the processor 601 during runtime include:

[0094] Determine a distributed template of multiple samples within the region;

[0095] Calculate the estimated power limit for multiple sample distributed templates;

[0096] The estimated curtailment power is corrected based on the correction factor to obtain the distributed photovoltaic curtailment power.

[0097] The processor 601 also performs the following operations during operation:

[0098] The final model parameters are determined based on the regression correction algorithm and historical power generation efficiency.

[0099] The correction coefficient is determined based on the final model parameters and historical power generation efficiency.

[0100] The processor 601 also performs the following operations during operation:

[0101] The standard regression algorithm model is trained based on the historical power generation efficiency data to obtain initial model parameters and calculate the corresponding loss function.

[0102] Based on the loss function, the initial model parameters are updated using gradient descent.

[0103] If the convergence condition is met, the update will stop, and the corresponding model parameters will be saved as the final model parameters.

[0104] The processor 601 also performs the following operations during operation:

[0105] Compare the estimated power rationing with the historical actual power rationing;

[0106] If the estimated curtailment amount is not equal to the historical actual curtailment amount, then the following steps are performed: the estimated curtailment amount is corrected based on the correction coefficient to generate the distributed photovoltaic curtailment amount.

[0107] The processor 601 also performs the following operations during operation:

[0108] The area to be statistically evaluated for abandoned electricity is divided into multiple regions;

[0109] Multiple distributed templates are randomly selected from each of the regions, and the selected distributed templates are marked as the sample distributed templates.

[0110] The processor 601 also performs the following operations during operation:

[0111] The estimated power rationing for each sample distributed module is calculated based on its current power generation and capacity.

[0112] The processor 601 also performs the following operations during operation:

[0113] The sum of the estimated curtailment power and the correction factor is determined as the distributed photovoltaic curtailment power.

[0114] The memory 602 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program information may be stored on the computer-readable storage medium, and the processor 601 may run the program information to implement the distributed photovoltaic curtailment power statistics method of the various embodiments of this application described above, or other desired functions.

[0115] In one example, the electronic device 600 may also include an input device 603 and an output device 604, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0116] The input device 603 may include, for example, a keyboard, a mouse, etc.

[0117] The output device 604 can output various information to the outside. The output device 604 may include, for example, a display, a communication network, and remote output devices connected thereto.

[0118] Of course, for the sake of simplicity, Figure 4 Only some of the components of the electronic device 600 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 600 may include any other suitable components depending on the specific application.

[0119] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program information that, when run by a processor, causes the processor to perform the steps in the distributed photovoltaic curtailment power statistics method according to various embodiments of this application as described in this specification.

[0120] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0121] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program information thereon, which, when run by a processor, causes the processor to execute the steps in the distributed photovoltaic curtailment power statistics method according to various embodiments of this application.

[0122] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0123] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0124] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0125] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0126] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0127] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Any modifications or equivalent substitutions made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for statistical analysis of distributed photovoltaic curtailment power, characterized in that, include: Determine a distributed template of multiple samples within the region; Calculate the estimated power limit for multiple sample distributed templates; The estimated curtailment power is corrected based on the correction factor to obtain the distributed photovoltaic curtailment power. Before correcting the estimated curtailment power based on the correction factor to generate the distributed photovoltaic curtailment power, the following steps are also included: The final model parameters are determined based on the regression correction algorithm and historical power generation efficiency. The correction coefficient is determined based on the final model parameters and historical power generation efficiency. The determination of the final model parameters based on the regression correction algorithm and historical power generation efficiency includes: The standard regression algorithm model is trained based on the historical power generation efficiency data to obtain initial model parameters and calculate the corresponding loss function. Based on the loss function, the initial model parameters are updated using gradient descent. If the convergence condition is met, the update will stop and the corresponding model parameters will be saved as the final model parameters. The formula for calculating the estimated power rationing of multiple sample distributed templates is as follows: In the formula, The estimated power limit for multiple distributed sample templates. This represents the current power generation capacity. Let i be the distributed template capacity, i be the sample distributed template, and n be the number of sample distributed templates in the region.

2. The method according to claim 1, characterized in that, Before correcting the estimated curtailment power based on the correction factor to generate the distributed photovoltaic curtailment power, the method further includes: Compare the estimated power rationing with the historical actual power rationing; If the estimated curtailment amount is not equal to the historical actual curtailment amount, then the following steps are performed: the estimated curtailment amount is corrected based on the correction coefficient to generate the distributed photovoltaic curtailment amount.

3. The method according to claim 1, characterized in that, The multiple sample distributed templates within the defined area include: The area to be statistically evaluated for abandoned electricity is divided into multiple regions; Multiple distributed templates are randomly selected from each of the regions, and the selected distributed templates are marked as the sample distributed templates.

4. The method according to claim 1, characterized in that, The calculation of the estimated power limit for multiple sample distributed templates includes: The estimated power rationing for each sample distributed module is calculated based on its current power generation and capacity.

5. The method according to claim 1, characterized in that, The step of correcting the estimated curtailment power based on a correction factor to obtain the distributed photovoltaic curtailment power includes: The sum of the estimated curtailment power and the correction factor is determined as the distributed photovoltaic curtailment power.

6. A distributed photovoltaic curtailment power statistics device, characterized in that, include: The determination module is used to determine the distributed template of multiple samples within a region; The calculation module is used to calculate the estimated power limitation for multiple sample distributed templates. The formula for calculating the estimated power limitation for multiple sample distributed templates is as follows: In the formula, The estimated power limit for multiple distributed sample templates. This represents the current power generation capacity. Let i be the distributed template capacity, i be the sample distributed template, and n be the number of sample distributed templates in the region. The correction module is used to train the standard regression algorithm model based on historical power generation efficiency data, obtain initial model parameters, and calculate the corresponding loss function; based on the loss function, the initial model parameters are updated by gradient descent. If the convergence condition is met, the update stops and the corresponding model parameters are saved as the final model parameters; the correction coefficient is determined based on the final model parameters and the historical power generation efficiency; the estimated curtailment power is corrected based on the correction coefficient to obtain the distributed photovoltaic curtailment power.

7. An electronic device, characterized in that, The electronic device includes: Processor; and Memory used to store processor-executable information; The processor is used to execute the distributed photovoltaic curtailment power statistics method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a distributed photovoltaic curtailment power statistics program, which, when run by a processor, implements the steps of the distributed photovoltaic curtailment power statistics method as described in any one of claims 1-5.

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