Method and device for calculating abandoned wind power

Abstract

The application discloses a wind power curtailment calculation method and device, and relates to the technical field of wind power generation. The method comprises the following steps: obtaining target wind speed information corresponding to each sub-power plant; calculating theoretical power generation of each sub-power plant according to a theoretical power generation calculation model corresponding to each sub-power plant and the target wind speed information corresponding to each sub-power plant; obtaining target actual power generation of each sub-power plant, and calculating wind power curtailment of each sub-power plant according to the target actual power generation and the theoretical power generation of each sub-power plant, wherein the target actual power generation of any sub-power plant is actual power generation of the sub-power plant in a target date; and calculating total wind power curtailment of a target wind power plant according to the wind power curtailment of each sub-power plant.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power generation technology, in particular to a wind curtailment calculation method and device. BACKGROUND

[0002] Wind curtailment of a wind power plant refers to the power that the wind power plant can generate but fails to generate due to factors such as transmission channel limitation of a power grid, power grid peak regulation needs, power grid safe and stable operation needs, and power grid equipment maintenance and faults. The calculation accuracy of wind curtailment of a wind power plant directly affects the evaluation of new energy consumption capacity by a power department, and further affects the new energy planning and development in the region where the wind power plant is located. Therefore, how to accurately calculate the wind curtailment of a wind power plant is crucial.

[0003] At present, a plurality of benchmark wind turbines are usually selected in a wind power plant, and the daily average power generation of each benchmark wind turbine is determined without power limiting the plurality of benchmark wind turbines. Then, the theoretical daily power generation of the wind power plant is calculated according to the daily average power generation of each benchmark wind turbine and the total number of wind turbines in the wind power plant. Finally, the wind curtailment of the wind power plant on a certain day is calculated according to the actual power generation and the theoretical power generation of the wind power plant on the certain day. However, there is no particularly rigorous method for selecting benchmark wind turbines at present. Therefore, when the selected benchmark wind turbines are not representative, the calculated theoretical power generation of the wind power plant is not accurate, and thus the calculated wind curtailment of the wind power plant is also not accurate. SUMMARY

[0004] Embodiments of the present application provide a wind curtailment calculation method and device, which mainly aims to accurately calculate the wind curtailment of a target wind power plant.

[0005] To solve the above technical problems, embodiments of the present application provide the following technical solutions:

[0006] In a first aspect, the present application provides a wind curtailment calculation method, which is applied to a wind curtailment calculation application program for calculating the wind curtailment corresponding to a target wind power plant, the target wind power plant comprising a plurality of sub-power plants, and the method comprising:

[0007] obtaining target wind speed information corresponding to each sub-power plant, wherein for any sub-power plant, the target wind speed information corresponding to the sub-power plant comprises a plurality of target wind speed values collected within a target date and a collection time corresponding to each target wind speed value;

[0008] According to the theoretical power generation calculation model corresponding to each sub-power plant and the target wind speed information corresponding to each sub-power plant, the theoretical power generation corresponding to each sub-power plant is calculated, wherein for any sub-power plant, the theoretical power generation calculation model corresponding thereto is obtained by iteratively training a preset extreme learning machine model according to a training sample set corresponding to the sub-power plant, and the training sample set includes multiple training samples, and for any training sample, it includes historical wind speed information and historical actual power generation corresponding to the historical wind speed information;

[0009] The target actual power generation corresponding to each sub-power plant is obtained, and according to the target actual power generation and the theoretical power generation corresponding to each sub-power plant, the curtailment wind power corresponding to each sub-power plant is calculated, wherein for any sub-power plant, the target actual power generation corresponding thereto is the actual power generation of the sub-power plant within a target date;

[0010] The total curtailment wind power corresponding to the target wind power plant is calculated according to the curtailment wind power corresponding to each sub-power plant.

[0011] In a second aspect, the present application also provides a curtailment wind power calculation device, which is applied to a curtailment wind power calculation application program for calculating the curtailment wind power corresponding to a target wind power plant, and the target wind power plant includes multiple sub-power plants, and the device includes:

[0012] An acquisition unit is configured to acquire the target wind speed information corresponding to each sub-power plant, wherein for any sub-power plant, the target wind speed information corresponding thereto includes multiple target wind speed values collected within a target date and a collection time corresponding to each target wind speed value;

[0013] A first calculation unit is configured to calculate the theoretical power generation corresponding to each sub-power plant according to the theoretical power generation calculation model corresponding to each sub-power plant and the target wind speed information corresponding to each sub-power plant, wherein for any sub-power plant, the theoretical power generation calculation model corresponding thereto is obtained by iteratively training a preset extreme learning machine model according to a training sample set corresponding to the sub-power plant, and the training sample set includes multiple training samples, and for any training sample, it includes historical wind speed information and historical actual power generation corresponding to the historical wind speed information;

[0014] A second calculation unit is configured to obtain the target actual power generation corresponding to each sub-power plant, and calculate the curtailment wind power corresponding to each sub-power plant according to the target actual power generation and the theoretical power generation corresponding to each sub-power plant, wherein for any sub-power plant, the target actual power generation corresponding thereto is the actual power generation of the sub-power plant within a target date.

[0015] a third calculating unit, configured to calculate total curtailment wind power of the target wind power plant according to the curtailment wind power of each of the sub power plants.

[0016] In a third aspect, an embodiment of the present application provides a storage medium, which comprises a stored program, wherein the storage medium controls a device where the storage medium is located to execute the curtailment wind power calculation method in the first aspect when the program runs.

[0017] In a fourth aspect, an embodiment of the present application provides a curtailment wind power calculation device, which comprises a storage medium and one or more processors, the storage medium is coupled with the processors, the processors are configured to execute program instructions stored in the storage medium, and the program instructions execute the curtailment wind power calculation method in the first aspect when running.

[0018] By means of the above technical solutions, the technical solutions provided by the present application have at least the following advantages:

[0019] The present application provides a curtailment wind power calculation method and device, and the present application can obtain target wind speed information corresponding to each sub power plant included in a target wind power plant by a curtailment wind power calculation application program, calculate theoretical power generation of each sub power plant according to a theoretical power generation calculation model corresponding to each sub power plant and the target wind speed information corresponding to each sub power plant by the curtailment wind power calculation application program, obtain target actual power generation of each sub power plant, calculate curtailment wind power of each sub power plant according to the target actual power generation and the theoretical power generation corresponding to each sub power plant, and calculate total curtailment wind power of the target wind power plant according to the curtailment wind power of each of the sub power plants. In the present application, the theoretical power generation calculation model corresponding to any sub power plant is trained according to multiple sets of historical wind speed information-historical actual power generation corresponding to the sub power plant, so the theoretical power generation calculation model corresponding to the sub power plant can accurately calculate the theoretical power generation of the sub power plant, thereby accurately calculating the curtailment wind power of each sub power plant, i.e., accurately calculating the total curtailment wind power of the target wind power plant.

[0020] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which a number of embodiments of the present application are shown by way of example, and wherein like reference numerals refer to like elements throughout. In the drawings:

[0022] Figure 1 A flow chart of a method for calculating curtailed wind power provided by an embodiment of the present application is shown;

[0023] Figure 2 A flow chart of another method for calculating curtailed wind power provided by an embodiment of the present application is shown;

[0024] Figure 3 A block diagram of a device for calculating curtailed wind power provided by an embodiment of the present application is shown;

[0025] Figure 4 A block diagram of another device for calculating curtailed wind power provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] Exemplary embodiments of the present application will be described herein below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is to be understood that the present application can be embodied in various forms without being limited by the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

[0027] In addition, the terms "first", "second", and similar terms used herein are not intended to denote any order, quantity, or importance, but are used to distinguish different parts.

[0028] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present application should be understood as their common meanings to those skilled in the art to which the present application pertains.

[0029] Currently, a plurality of benchmark wind turbines are usually selected in a wind power plant in advance, and the daily average power generation of each benchmark wind turbine is determined without limiting the power supply to the plurality of benchmark wind turbines. Then, the theoretical daily power generation of the wind power plant is calculated according to the daily average power generation of each benchmark wind turbine and the total number of wind turbines in the wind power plant. Finally, the curtailed wind power of the wind power plant on a certain day is calculated according to the actual power generation and the theoretical power generation of the wind power plant on the certain day. However, there is currently no particularly rigorous method for selecting benchmark wind turbines. Therefore, when the selected benchmark wind turbines are not representative, the calculated theoretical power generation of the wind power plant is inaccurate, and thus the calculated curtailed wind power of the wind power plant is also inaccurate.

[0030] Therefore, in order to ensure accurate calculation of the wind power plant of the target wind power plant, the application provides a wind power plant of the target wind power plant, which includes a plurality of sub power plants, such as Figure 1 As shown, the method comprises:

[0031] 101, obtaining the target wind speed information corresponding to each sub power plant.

[0032] For any one sub power plant, the target wind speed information corresponding to the target date includes a plurality of target wind speed values collected in the target date and the collection time corresponding to each target wind speed value.

[0033] In the embodiments of the application, the execution subject in each step is the wind power plant of the target wind power plant, which can be but not limited to a computer, a server, a tablet computer and the like.

[0034] When the staff of the target wind power plant expects to calculate the wind power plant of the target wind power plant corresponding to the target date, the staff can issue corresponding instructions to the wind power plant of the target wind power plant, and the wind power plant of the target wind power plant will obtain the target wind speed information corresponding to each sub power plant included in the target wind power plant after receiving the instructions.

[0035] 102, according to the theoretical power generation calculation model corresponding to each sub power plant and the target wind speed information corresponding to each sub power plant, calculating the theoretical power generation corresponding to each sub power plant.

[0036] For any one sub power plant, the corresponding theoretical power generation calculation model is obtained by iteratively training the preset extreme learning machine model according to the training sample set corresponding to the sub power plant, and the training sample set includes a plurality of training samples. For any one training sample, it includes historical wind speed information and historical actual power generation corresponding to the historical wind speed information, wherein the historical wind speed information includes a plurality of historical wind speed values collected in a certain historical date and the collection time corresponding to each historical wind speed value, and the historical actual power generation is the actual power generation of the target wind power plant in the historical date, and the historical date is a certain day before the target date, and the sub power plant is not limited in the historical date.

[0037] After obtaining the target wind speed information corresponding to each sub-power plant included in the target wind power plant, the abandoned wind power calculation application program can calculate the theoretical power generation corresponding to each sub-power plant according to the theoretical power generation calculation model corresponding to each sub-power plant and the target wind speed information corresponding to each sub-power plant. Since, for any sub-power plant, the theoretical power generation calculation model corresponding to the sub-power plant is trained according to a plurality of sets of historical wind speed information-historical actual power generation corresponding to the sub-power plant, the theoretical power generation calculation model corresponding to the sub-power plant can accurately calculate the theoretical power generation corresponding to the sub-power plant.

[0038] 103. Obtain the target actual power generation corresponding to each sub-power plant, and calculate the abandoned wind power corresponding to each sub-power plant according to the target actual power generation corresponding to each sub-power plant and the theoretical power generation corresponding to each sub-power plant.

[0039] For any sub-power plant, the target actual power generation corresponding to the sub-power plant is the actual power generation of the sub-power plant in the target date, and the abandoned wind power corresponding to the sub-power plant is the abandoned wind power of the sub-power plant in the target date.

[0040] After calculating the theoretical power generation corresponding to each sub-power plant, the abandoned wind power calculation application program can obtain the target actual power generation corresponding to each sub-power plant, and calculate the abandoned wind power corresponding to each sub-power plant according to the target actual power generation corresponding to each sub-power plant and the theoretical power generation corresponding to each sub-power plant, that is, for any sub-power plant, the theoretical power generation corresponding to the sub-power plant is subtracted from the target actual power generation corresponding to the sub-power plant to obtain the abandoned wind power corresponding to the sub-power plant.

[0041] 104. Calculate the total abandoned wind power corresponding to the target wind power plant according to the abandoned wind power corresponding to each sub-power plant.

[0042] After calculating the abandoned wind power corresponding to each sub-power plant, the abandoned wind power calculation application program can calculate the total abandoned wind power corresponding to the target wind power plant according to the abandoned wind power corresponding to each sub-power plant, that is, the abandoned wind power corresponding to a plurality of sub-power plants is summed to obtain the total abandoned wind power corresponding to the target wind power plant; wherein the total abandoned wind power corresponding to the target wind power plant is the abandoned wind power of the target wind power plant in the target date.

[0043] The embodiment of the present application provides a wind curtailment calculation method, and the embodiment of the present application can obtain the target wind speed information corresponding to each sub-power plant included in the target wind power plant by the wind curtailment calculation application, calculate the theoretical power generation of each sub-power plant according to the theoretical power generation calculation model corresponding to each sub-power plant and the target wind speed information corresponding to each sub-power plant by the wind curtailment calculation application, obtain the target actual power generation of each sub-power plant, calculate the wind curtailment of each sub-power plant according to the target actual power generation and the theoretical power generation of each sub-power plant, and calculate the total wind curtailment of the target wind power plant according to the wind curtailment of each sub-power plant. In the embodiment of the present application, for any sub-power plant, the theoretical power generation calculation model corresponding to the sub-power plant is trained according to the historical wind speed information-historical actual power generation corresponding to the sub-power plant, so that the theoretical power generation of the sub-power plant can be accurately calculated based on the theoretical power generation calculation model corresponding to the sub-power plant, thereby enabling the wind curtailment of each sub-power plant to be accurately calculated, that is, the total wind curtailment of the target wind power plant to be accurately calculated.

[0044] In order to describe in more detail, the embodiment of the present application provides another wind curtailment calculation method, as shown in the following Figure 2 The method comprises the following steps:

[0045] 201, training the theoretical power generation calculation model corresponding to each sub-power plant.

[0046] In order to ensure that the wind curtailment calculation application can accurately calculate the theoretical power generation of each sub-power plant, the wind curtailment calculation application needs to pre-train the theoretical power generation calculation model corresponding to each sub-power plant. The following will describe in detail how the wind curtailment calculation application trains the theoretical power generation calculation model corresponding to each sub-power plant.

[0047] For any sub-power plant, the specific process of training the theoretical power generation calculation model corresponding to the sub-power plant is as follows: first, obtain the training sample set corresponding to the sub-power plant; second, iteratively train the preset extreme learning machine model based on the training sample set, wherein the preset extreme learning machine model comprises an input layer, a hidden layer and an output layer, and the hidden layer comprises a plurality of nodes; wherein after each round of training, it is judged whether the preset stop condition is reached; if the preset stop condition is reached, the preset extreme learning machine model obtained after the current round of training is determined as the theoretical power generation calculation model corresponding to the sub-power plant; if the preset stop condition is not reached, the weight value of the output layer, the weight value and the bias value of each node are optimized and adjusted, and the preset extreme learning machine model after the optimization and adjustment is entered into the next round of training.

[0048] The preset stop condition is any one of the following: a current cumulative iteration training duration (i.e., a duration of iteration training of the preset extreme learning machine model based on the training sample set at the current moment) reaches a preset duration threshold, a current cumulative iteration training round number (i.e., a round number of iteration training of the preset extreme learning machine model based on the training sample set at the current moment) reaches a preset round threshold, an accuracy corresponding to the preset extreme learning machine model reaches a requirement (for example, after inputting a plurality of historical wind speed values contained in any one training sample and a collection time corresponding to each historical wind speed value into the preset extreme learning machine model, a least square difference value between a theoretical power generation output by the preset extreme learning machine model and a historical actual power generation contained in the training sample is less than a preset threshold, and it is determined that the accuracy corresponding to the preset extreme learning machine model reaches the requirement), and the like.

[0049] 202. Obtain target wind speed information corresponding to each sub-power plant.

[0050] For step 202, obtaining target wind speed information corresponding to each sub-power plant, reference can be made to the description of the corresponding part, and details will not be repeated here. Figure 1 For step 202, obtaining target wind speed information corresponding to each sub-power plant, reference can be made to the description of the corresponding part, and details will not be repeated here.

[0051] 203. Calculate theoretical power generation corresponding to each sub-power plant according to a theoretical power generation calculation model corresponding to each sub-power plant and target wind speed information corresponding to each sub-power plant.

[0052] After obtaining target wind speed information corresponding to each sub-power plant contained in the target wind power plant, the wind curtailment calculation application program can calculate theoretical power generation corresponding to each sub-power plant according to a theoretical power generation calculation model corresponding to each sub-power plant and target wind speed information corresponding to each sub-power plant. Details of how the wind curtailment calculation application program calculates theoretical power generation corresponding to each sub-power plant according to a theoretical power generation calculation model corresponding to each sub-power plant and target wind speed information corresponding to each sub-power plant will be described below.

[0053] First, it needs to be pointed out that for any one sub-power plant, when the sub-power plant is power limited, the actual power generation corresponding to the sub-power plant increases with the increase of wind speed when the wind speed is between 0 and a certain specific wind speed value, and the actual power generation corresponding to the sub-power plant decreases with the increase of wind speed after the specific wind speed value, wherein the specific wind speed value is the turning wind speed value corresponding to the sub-power plant, which can be determined according to a plurality of historical wind speed values corresponding to the sub-power plant and real-time power generation corresponding to each historical wind speed value, and the present embodiment does not limit how to determine the turning wind speed value corresponding to the sub-power plant.

[0054] For any one sub-power plant, according to the theoretical power generation of the sub-power plant corresponding to the calculation model and the target wind speed information corresponding to the sub-power plant, the specific process for calculating the theoretical power generation corresponding to the sub-power plant is:

[0055] Firstly, the limit power flag corresponding to the sub-power plant is obtained in the management system of the target wind power plant, wherein the limit power flag corresponding to the sub-power plant is used to indicate whether the sub-power plant is limited in the target date;

[0056] If it is determined according to the limit power flag corresponding to the sub-power plant that the sub-power plant is limited in the target date, then the multiple target wind speed values are divided into multiple first groups and multiple second groups according to the turning wind speed value corresponding to the sub-power plant and the collection time corresponding to each target wind speed value; secondly, the multiple target wind speed values contained in each first group are input into the theoretical power generation calculation model corresponding to the sub-power plant, so that the theoretical power generation corresponding to each first group is output by the theoretical power generation calculation model corresponding to the sub-power plant, that is, the multiple target wind speed values contained in the first first group are input into the theoretical power generation calculation model corresponding to the sub-power plant, so that the theoretical power generation corresponding to the first first group is output by the theoretical power generation calculation model corresponding to the sub-power plant, and the multiple target wind speed values contained in the second first group are input into the theoretical power generation calculation model corresponding to the sub-power plant, so that the theoretical power generation corresponding to the second first group is output by the theoretical power generation calculation model corresponding to the sub-power plant; thirdly, the daily average power generation of the virtual benchmark wind turbine corresponding to the sub-power plant is obtained, and the theoretical power generation corresponding to each second group is calculated according to the collection time corresponding to each target wind speed value contained in each second group, the daily average power generation corresponding to the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub-power plant; finally, the theoretical power generation corresponding to the sub-power plant is calculated according to the theoretical power generation corresponding to each first group and the theoretical power generation corresponding to each second group, that is, the theoretical power generation corresponding to the multiple first groups and the theoretical power generation corresponding to the multiple second groups are summed up to obtain the theoretical power generation corresponding to the sub-power plant;

[0057] If it is determined according to the limit power flag corresponding to the sub-power plant that the sub-power plant is not limited in the target date, then the multiple target wind speed values and the collection time corresponding to each target wind speed value are input into the theoretical power generation calculation model corresponding to the sub-power plant, so that the theoretical power generation corresponding to the sub-power plant is output by the theoretical power generation calculation model corresponding to the sub-power plant.

[0058] The specific process of dividing the plurality of target wind speed values into a plurality of first groups and a plurality of second groups according to the turning wind speed value corresponding to the sub power plant and the collection time corresponding to each target wind speed value is as follows: a plurality of wind speed values with adjacent collection times and each less than or equal to the turning wind speed value are divided into the same group to obtain a plurality of first groups; and a plurality of wind speed values with adjacent collection times and each greater than the turning wind speed value are divided into the same group to obtain a plurality of second groups.

[0059] The specific process of calculating the theoretical power generation corresponding to each second group according to the collection time corresponding to each target wind speed value included in each second group, the daily average power generation corresponding to the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub power plant is as follows: for any second group, the starting collection time and the ending collection time corresponding to the second group are first determined according to the collection time corresponding to each target wind speed value included in the second group, that is, the smallest collection time among the collection times corresponding to the plurality of target wind speed values is determined as the starting collection time corresponding to the second group, and the largest collection time among the collection times corresponding to the plurality of target wind speed values is determined as the ending collection time corresponding to the second group; secondly, the collection duration corresponding to the second group is determined according to the starting collection time and the ending collection time corresponding to the second group, for example, the ending collection time corresponding to the second group is subtracted from the starting collection time corresponding to the second group, and then the collection time interval between any two adjacent collected target wind speed values is added to obtain the collection duration corresponding to the second group, wherein the unit of the collection duration corresponding to the second group is hour; finally, the collection duration corresponding to the second group, the daily average power generation corresponding to the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub power plant are substituted into a preset formula to calculate the theoretical power generation corresponding to the second group, wherein the preset formula is specifically as follows:

[0060] The theoretical power generation corresponding to the second group = the collection duration corresponding to the second group * the daily average power generation corresponding to the virtual benchmark wind turbine * the theoretical power generation corresponding to the second group / 24.

[0061] The specific process of pre-calculating the daily average power generation of the virtual benchmark wind turbine corresponding to the sub power plant is as follows: first, the specified date actual power generation corresponding to the sub power plant and the total number of wind turbines corresponding to the sub power plant are obtained, wherein the specified date actual power generation corresponding to the sub power plant is the actual power generation of the sub power plant within a specified date, and the specified date is a day before the target date, and the sub power plant is not subjected to power limiting within the specified date; secondly, the specified date actual power generation corresponding to the sub power plant is divided by the total number of wind turbines corresponding to the sub power plant to obtain the daily average power generation of the virtual benchmark wind turbine corresponding to the sub power plant.

[0062] 204、acquire the target actual power generation corresponding to each sub power plant, and calculate the abandoned wind power corresponding to each sub power plant according to the target actual power generation corresponding to each sub power plant and the theoretical power generation.

[0063] Wherein, about step 204, acquire the target actual power generation corresponding to each sub power plant, and calculate the abandoned wind power corresponding to each sub power plant according to the target actual power generation corresponding to each sub power plant and the theoretical power generation, can refer to Figure 1 The description of the corresponding part, this application embodiment will not be repeated here.

[0064] 205、calculate the total abandoned wind power corresponding to the target wind power plant according to the abandoned wind power corresponding to each sub power plant.

[0065] Wherein, about step 205, calculate the total abandoned wind power corresponding to the target wind power plant according to the abandoned wind power corresponding to each sub power plant, can refer to Figure 1 The description of the corresponding part, this application embodiment will not be repeated here.

[0066] Further, as the implementation of the method shown in the above Figure 1 and Figure 2 The application further provides another embodiment of a wind power abandoned amount calculation device, which is applied to a wind power abandoned amount calculation application program, and is used for calculating the abandoned wind power corresponding to a target wind power plant, wherein the target wind power plant comprises a plurality of sub power plants. The device embodiment corresponds to the foregoing method embodiment, and for the convenience of reading, the details in the foregoing method embodiment will not be described one by one. However, it should be clear that the device in the embodiment can correspondingly implement all the contents in the foregoing method embodiment. The device is applied to accurately calculate the abandoned wind power of the target wind power plant, and specifically as shown in the following, the device comprises: Figure 3

[0067] The acquisition unit 31 is used for acquiring the target wind speed information corresponding to each sub power plant, wherein for any one sub power plant, the target wind speed information corresponding to the sub power plant comprises a plurality of target wind speed values collected in a target date and the collection time corresponding to each target wind speed value.

[0068] ​The first calculation unit 32 is configured to calculate the theoretical power generation of each sub-power plant according to the theoretical power generation calculation model corresponding to each sub-power plant and the target wind speed information of each sub-power plant obtained by the obtaining unit 31. For any sub-power plant, the theoretical power generation calculation model corresponding to the sub-power plant is obtained by iteratively training a preset extreme learning machine model according to a training sample set corresponding to the sub-power plant. The training sample set includes a plurality of training samples. For any training sample, the training sample includes historical wind speed information and historical actual power generation corresponding to the historical wind speed information.

[0069] The second calculation unit 33 is configured to obtain the target actual power generation of each sub-power plant, and calculate the curtailed wind power of each sub-power plant according to the target actual power generation and the theoretical power generation of each sub-power plant. For any sub-power plant, the target actual power generation of the sub-power plant is the actual power generation of the sub-power plant in the target date.

[0070] The third calculation unit 34 is configured to calculate the total curtailed wind power of the target wind power plant according to the curtailed wind power of each sub-power plant calculated by the second calculation unit 33.

[0071] Further, as shown in Figure 4 The device further comprises:

[0072] The training unit 35 is configured to obtain a training sample set corresponding to each sub-power plant before the obtaining unit 31 obtains the target wind speed information of each sub-power plant. The training unit 35 is further configured to iteratively train a preset extreme learning machine model based on the training sample set. The preset extreme learning machine model includes an input layer, a hidden layer and an output layer. The hidden layer includes a plurality of nodes. After each round of training, it is determined whether a preset stop condition is reached. If the preset stop condition is reached, the preset extreme learning machine model obtained after the current round of training is determined as the theoretical power generation calculation model corresponding to the sub-power plant. If the preset stop condition is not reached, the weight value of the output layer, the weight value and the bias value of each node are optimized and adjusted, and the next round of training is entered based on the preset extreme learning machine model after the optimization and adjustment.

[0073] Further, as shown in Figure 4As shown in the first computing unit 32, specifically for obtaining the sub-power plant corresponding to the power limit flag; if the target date is determined according to the sub-power plant corresponding to the power limit flag The sub-power plant is power limited, then according to the sub-power plant corresponding to the turning wind speed value and the acquisition time corresponding to each target wind speed value, the plurality of target wind speed values are divided into a plurality of first groups and a plurality of second groups; respectively, the plurality of target wind speed values contained in each first group are input into the theoretical power generation calculation model corresponding to the sub-power plant, so that the theoretical power generation calculation model outputs the theoretical power generation corresponding to each first group; obtain the daily average power generation of the virtual benchmark wind turbine corresponding to the sub-power plant, and according to the acquisition time corresponding to each target wind speed value contained in each second group, the daily average power generation corresponding to the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub-power plant, the theoretical power generation corresponding to each second group is calculated; according to the theoretical power generation corresponding to each first group and the theoretical power generation corresponding to each second group, the theoretical power generation corresponding to the sub-power plant is calculated; if the target date is determined according to the sub-power plant corresponding to the power limit flag The sub-power plant is not power limited, then the plurality of target wind speed values and the acquisition time corresponding to each target wind speed value are input into the theoretical power generation calculation model corresponding to the sub-power plant, so that the theoretical power generation calculation model outputs the theoretical power generation corresponding to the sub-power plant.

[0074] Further, as shown in the first computing unit 32, specifically for dividing the plurality of wind speed values adjacent to the acquisition time and less than or equal to the turning wind speed value into the same group to obtain a plurality of first groups; the plurality of wind speed values adjacent to the acquisition time and greater than the turning wind speed value are divided into the same group to obtain a plurality of second groups. Figure 4

[0075] Further, as shown in the first computing unit 32, specifically for determining the starting acquisition time and the ending acquisition time corresponding to the second group according to the acquisition time corresponding to each target wind speed value contained in the second group; according to the starting acquisition time and the ending acquisition time corresponding to the second group, the acquisition duration corresponding to the second group is determined; the acquisition duration corresponding to the second group, the daily average power generation corresponding to the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub-power plant are substituted into the preset formula to calculate the theoretical power generation corresponding to the second group. Figure 4

[0076] Further, as shown in the first computing unit 32, specifically for dividing the plurality of wind speed values adjacent to the acquisition time and less than or equal to the turning wind speed value into the same group to obtain a plurality of first groups; the plurality of wind speed values adjacent to the acquisition time and greater than the turning wind speed value are divided into the same group to obtain a plurality of second groups. Figure 4 ​​As shown, the first calculation unit 32 is specifically configured to acquire actual power generation of the sub-power plant on the specified date and total number of wind turbines of the sub-power plant; and divide the actual power generation of the sub-power plant on the specified date by the total number of wind turbines of the sub-power plant to obtain daily average power generation of the virtual benchmark wind turbine of the sub-power plant.

[0077] Further, as shown in the figure, Figure 4 As shown, the preset stopping condition is any one of the following: the current cumulative iteration training duration reaches a preset duration threshold, the current cumulative iteration training round number reaches a preset round number threshold, and the accuracy of the preset extreme learning machine model reaches a requirement.

[0078] The embodiments of the present application provide a wind curtailment power calculation method and device. After the wind curtailment power calculation application program acquires target wind speed information corresponding to each sub-power plant included in a target wind power plant, the wind curtailment power calculation application program calculates theoretical power generation of each sub-power plant according to a theoretical power generation calculation model corresponding to each sub-power plant and the target wind speed information corresponding to each sub-power plant, acquires target actual power generation corresponding to each sub-power plant, calculates wind curtailment power corresponding to each sub-power plant according to the target actual power generation corresponding to each sub-power plant and the theoretical power generation corresponding to each sub-power plant, and calculates total wind curtailment power corresponding to the target wind power plant according to the wind curtailment power corresponding to each sub-power plant. In the embodiments of the present application, for any sub-power plant, the theoretical power generation calculation model corresponding to the sub-power plant is trained according to multiple sets of historical wind speed information-historical actual power generation corresponding to the sub-power plant, so that the theoretical power generation calculation model corresponding to the sub-power plant can accurately calculate the theoretical power generation corresponding to the sub-power plant, thereby accurately calculating the wind curtailment power corresponding to each sub-power plant, i.e., accurately calculating the total wind curtailment power corresponding to the target wind power plant.

[0079] The embodiments of the present application provide a storage medium including a stored program, wherein the program controls a device where the storage medium is located to execute the wind curtailment power calculation method when the program runs.

[0080] The storage medium can include a non-permanent memory in a computer readable medium, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

[0081] The embodiment of the present application further provides a device for calculating abandoned wind power, the device comprising a storage medium and one or more processors, the storage medium being coupled with the processors, and the processors being configured to execute program instructions stored in the storage medium; the program instructions perform the method for calculating abandoned wind power.

[0082] The embodiment of the present application provides a device, the device comprising a processor, a memory, and a program stored in the memory and executable on the processor, and the processor performs the following steps when executing the program:

[0083] obtaining target wind speed information corresponding to each of the sub power plants, wherein for any one of the sub power plants, the target wind speed information corresponding to the sub power plant comprises a plurality of target wind speed values collected in a target date and a collection time corresponding to each of the target wind speed values;

[0084] calculating theoretical power generation corresponding to each of the sub power plants according to a theoretical power generation calculation model corresponding to each of the sub power plants and the target wind speed information corresponding to each of the sub power plants, wherein for any one of the sub power plants, the theoretical power generation calculation model corresponding to the sub power plant is obtained by iteratively training a preset extreme learning machine model according to a training sample set corresponding to the sub power plant, and the training sample set comprises a plurality of training samples, and for any one of the training samples, the training sample comprises historical wind speed information and historical actual power generation corresponding to the historical wind speed information;

[0085] obtaining target actual power generation corresponding to each of the sub power plants, and calculating abandoned wind power corresponding to each of the sub power plants according to the target actual power generation corresponding to each of the sub power plants and the theoretical power generation corresponding to each of the sub power plants, wherein for any one of the sub power plants, the target actual power generation corresponding to the sub power plant is actual power generation of the sub power plant in the target date;

[0086] calculating total abandoned wind power corresponding to the target wind power plant according to the abandoned wind power corresponding to each of the sub power plants.

[0087] Further, before the obtaining of the target wind speed information corresponding to each of the sub power plants, the method further comprises:

[0088] obtaining a training sample set corresponding to the sub power plants;

[0089] iteratively training a preset extreme learning machine model based on the training sample set, wherein the preset extreme learning machine model comprises an input layer, a hidden layer and an output layer, the hidden layer comprises a plurality of nodes; wherein,

[0090] after each round of training, determining whether a preset stop condition is reached;

[0091] If the condition is met, the preset extreme learning machine model obtained after the current training is determined as the theoretical power generation calculation model corresponding to the sub power plant.

[0092] If the condition is not met, the weight values corresponding to the output layer, the weight values and bias values corresponding to each node are optimized and adjusted, and the preset extreme learning machine model after optimization and adjustment enters the next round of training.

[0093] Further, the theoretical power generation corresponding to each sub power plant is calculated according to the theoretical power generation calculation model corresponding to each sub power plant and the target wind speed information corresponding to each sub power plant, comprising:

[0094] Obtaining the power limiting flag corresponding to the sub power plant;

[0095] If it is determined according to the power limiting flag corresponding to the sub power plant that the sub power plant is limited on the target date, a plurality of target wind speed values and the collection time corresponding to each target wind speed value are input into the theoretical power generation calculation model corresponding to the sub power plant, so that the theoretical power generation calculation model outputs the theoretical power generation corresponding to each first group; the daily average power generation of the virtual benchmark wind turbine corresponding to the sub power plant is obtained, and the theoretical power generation corresponding to each second group is calculated according to the collection time corresponding to each target wind speed value included in each second group, the daily average power generation corresponding to the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub power plant; the theoretical power generation corresponding to the sub power plant is calculated according to the theoretical power generation corresponding to each first group and the theoretical power generation corresponding to each second group.

[0096] If it is determined according to the power limiting flag corresponding to the sub power plant that the sub power plant is not limited on the target date, a plurality of target wind speed values and the collection time corresponding to each target wind speed value are input into the theoretical power generation calculation model corresponding to the sub power plant, so that the theoretical power generation calculation model outputs the theoretical power generation corresponding to the sub power plant.

[0097] Further, the plurality of target wind speed values are divided into a plurality of first groups and a plurality of second groups according to the turning wind speed value corresponding to the sub power plant and the collection time corresponding to each target wind speed value, comprising:

[0098] The plurality of wind speed values adjacent in collection time and less than or equal to the turning wind speed value are divided into the same group to obtain a plurality of first groups.

[0099] The wind speed values adjacent in time and greater than the turning wind speed value are divided into the same group to obtain a plurality of the second groups.

[0100] Further, the theoretical power generation corresponding to each of the second groups is calculated according to the time of acquisition corresponding to each of the target wind speed values contained in each of the second groups, the daily average power generation corresponding to the virtual benchmark wind turbine, and the total number of wind turbines corresponding to the sub power plant.

[0101] The starting acquisition time and the ending acquisition time corresponding to the second group are determined according to the time of acquisition corresponding to each of the target wind speed values contained in the second group.

[0102] The acquisition duration corresponding to the second group is determined according to the starting acquisition time and the ending acquisition time corresponding to the second group.

[0103] The acquisition duration corresponding to the second group, the daily average power generation corresponding to the virtual benchmark wind turbine, and the total number of wind turbines corresponding to the sub power plant are substituted into a preset formula to calculate the theoretical power generation corresponding to the second group.

[0104] Further, the method further comprises:

[0105] The actual power generation on a specified date corresponding to the sub power plant and the total number of wind turbines corresponding to the sub power plant are obtained.

[0106] The actual power generation on the specified date corresponding to the sub power plant is divided by the total number of wind turbines corresponding to the sub power plant to obtain the daily average power generation of the virtual benchmark wind turbine corresponding to the sub power plant.

[0107] Further, the preset stopping condition is any one of the following: the current cumulative iteration training duration reaches a preset duration threshold, the current cumulative iteration training round number reaches a preset round number threshold, and the accuracy of the preset extreme learning machine model reaches a requirement.

[0108] The application further provides a computer program product, which is suitable for executing program codes for initializing the following method steps when executed on a data processing device: obtaining target wind speed information corresponding to each of the sub-power plants, wherein for any one of the sub-power plants, the target wind speed information corresponding to the sub-power plant comprises a plurality of target wind speed values collected within a target date and a collection time corresponding to each of the target wind speed values; calculating theoretical power generation of each of the sub-power plants according to a theoretical power generation calculation model corresponding to each of the sub-power plants and the target wind speed information corresponding to each of the sub-power plants, wherein for any one of the sub-power plants, the theoretical power generation calculation model corresponding to the sub-power plant is obtained by iteratively training a preset extreme learning machine model according to a training sample set corresponding to the sub-power plant, and the training sample set comprises a plurality of training samples, and for any one of the training samples, the training sample comprises historical wind speed information and historical actual power generation corresponding to the historical wind speed information; obtaining target actual power generation of each of the sub-power plants, and calculating curtailed wind power of each of the sub-power plants according to the target actual power generation and the theoretical power generation of each of the sub-power plants, wherein for any one of the sub-power plants, the target actual power generation corresponding to the sub-power plant is actual power generation of the sub-power plant within the target date; and calculating total curtailed wind power of the target wind power plant according to the curtailed wind power of each of the sub-power plants.

[0109] Those skilled in the art will understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0111] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0113] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0114] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, or a combination of non-volatile memories in different forms. The memory is an example of computer readable storage media.

[0115] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0116] It is also to be noted that the terms "comprising", "including", and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0117] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0118] The embodiments of the present application are only illustrative and are not intended to limit the present application. Various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for calculating abandoned wind power, characterized in that, The method is applied to a wind curtailment calculation application program for calculating wind curtailment of a target wind power plant, the target wind power plant comprising a plurality of sub-plants, and the method comprises: obtaining target wind speed information corresponding to each of the sub-plants, wherein for any one of the sub-plants, the target wind speed information corresponding to the sub-plant comprises a plurality of target wind speed values collected within a target date and a collection time corresponding to each of the target wind speed values; calculating theoretical power generation of each of the sub-plants according to a theoretical power generation calculation model corresponding to each of the sub-plants and the target wind speed information corresponding to each of the sub-plants, wherein for any one of the sub-plants, the theoretical power generation calculation model corresponding to the sub-plant is obtained by iteratively training a preset extreme learning machine model according to a training sample set corresponding to the sub-plant, the training sample set comprising a plurality of training samples, and for any one of the training samples, the training sample comprises historical wind speed information and historical actual power generation corresponding to the historical wind speed information; obtaining target actual power generation corresponding to each of the sub-plants, and calculating wind curtailment corresponding to each of the sub-plants according to the target actual power generation corresponding to each of the sub-plants and theoretical power generation corresponding to each of the sub-plants, wherein for any one of the sub-plants, the target actual power generation corresponding to the sub-plant is actual power generation of the sub-plant within the target date; calculating total wind curtailment corresponding to the target wind power plant according to the wind curtailment corresponding to each of the sub-plants; the calculating of the theoretical power generation corresponding to each of the sub-plants according to the theoretical power generation calculation model corresponding to each of the sub-plants and the target wind speed information corresponding to each of the sub-plants comprises: obtaining a power curtailment flag corresponding to the sub-plant; if it is determined according to the power curtailment flag corresponding to the sub-plant that the sub-plant is subjected to power curtailment on the target date, dividing a plurality of the target wind speed values into a plurality of first groups and a plurality of second groups according to a turning point wind speed value corresponding to the sub-plant and the collection time corresponding to each of the target wind speed values; inputting a plurality of target wind speed values included in each of the first groups into the theoretical power generation calculation model corresponding to the sub-plant, so that the theoretical power generation calculation model outputs theoretical power generation corresponding to each of the first groups; obtaining daily average power generation of a virtual benchmark wind turbine corresponding to the sub-plant, and calculating theoretical power generation corresponding to each of the second groups according to the collection time corresponding to each of the target wind speed values included in each of the second groups, the daily average power generation corresponding to the virtual benchmark wind turbine, and a total number of wind turbines corresponding to the sub-plant; and calculating the theoretical power generation corresponding to the sub-plant according to the theoretical power generation corresponding to each of the first groups and the theoretical power generation corresponding to each of the second groups. If it is determined according to the power limiting flag of the sub power plant that the sub power plant is not power limited on the target date, a plurality of target wind speed values and the collection time corresponding to each target wind speed value are input into the theoretical power generation calculation model corresponding to the sub power plant, so that the theoretical power generation calculation model outputs the theoretical power generation corresponding to the sub power plant.

2. The method of claim 1, wherein, Before the target wind speed information corresponding to each sub power plant is obtained, the method further comprises: obtaining a training sample set corresponding to the sub power plant; iteratively training a preset extreme learning machine model based on the training sample set, wherein the preset extreme learning machine model comprises an input layer, a hidden layer and an output layer, and the hidden layer comprises a plurality of nodes; wherein, after each round of training, it is determined whether a preset stop condition is reached; if the preset stop condition is reached, the preset extreme learning machine model obtained after the current round of training is determined as the theoretical power generation calculation model corresponding to the sub power plant; if the preset stop condition is not reached, the weight values and bias values corresponding to the output layer and each node are optimized and adjusted, and the preset extreme learning machine model after the optimization and adjustment is used for the next round of training.

3. The method of claim 1, wherein, The plurality of target wind speed values are divided into a plurality of first groups and a plurality of second groups according to the turning wind speed value corresponding to the sub power plant and the collection time corresponding to each target wind speed value, comprising: a plurality of wind speed values with adjacent collection times and each less than or equal to the turning wind speed value are divided into the same group to obtain a plurality of first groups; a plurality of wind speed values with adjacent collection times and each greater than the turning wind speed value are divided into the same group to obtain a plurality of second groups.

4. The method of claim 1, wherein, The theoretical power generation corresponding to each second group is calculated according to the collection time corresponding to each target wind speed value included in the second group, the daily average power generation of the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub power plant, comprising: determining the starting collection time and the ending collection time corresponding to the second group according to the collection time corresponding to each target wind speed value included in the second group; determining the collection duration corresponding to the second group according to the starting collection time and the ending collection time corresponding to the second group; substituting the collection duration corresponding to the second group, the daily average power generation of the virtual benchmark wind turbine and the total number of wind turbines corresponding to the sub power plant into a preset formula to calculate the theoretical power generation corresponding to the second group.

5. The method of claim 1, wherein, The method further comprises: obtaining the actual power generation on a specified date corresponding to the sub power plant and the total number of wind turbines corresponding to the sub power plant; dividing the actual power generation on the specified date corresponding to the sub power plant by the total number of wind turbines corresponding to the sub power plant to obtain the daily average power generation of the virtual benchmark wind turbine corresponding to the sub power plant.

6. The method of claim 2, wherein, The preset stop condition is any one of the following: the current cumulative iteration training duration reaches a preset duration threshold, the current cumulative iteration training round number reaches a preset round number threshold, and the accuracy of the preset extreme learning machine model reaches a requirement. 7.A device for calculating abandoned wind power, characterized in that, The device is applied to a wind curtailment calculation application program for calculating wind curtailment of a target wind power plant, the target wind power plant comprising a plurality of sub-plants, and the device comprises: an acquisition unit configured to acquire target wind speed information corresponding to each of the sub-plants, wherein for any one of the sub-plants, the target wind speed information corresponding to the sub-plant comprises a plurality of target wind speed values collected within a target date and a collection time corresponding to each of the target wind speed values; a first calculation unit configured to calculate theoretical power generation of each of the sub-plants according to a theoretical power generation calculation model corresponding to each of the sub-plants and the target wind speed information corresponding to each of the sub-plants, wherein for any one of the sub-plants, the theoretical power generation calculation model corresponding to the sub-plant is obtained by iteratively training a preset extreme learning machine model according to a training sample set corresponding to the sub-plant, the training sample set comprising a plurality of training samples, and for any one of the training samples, the training sample comprises historical wind speed information and historical actual power generation corresponding to the historical wind speed information; a second calculation unit configured to acquire target actual power generation of each of the sub-plants and calculate wind curtailment of each of the sub-plants according to the target actual power generation and the theoretical power generation of each of the sub-plants, wherein for any one of the sub-plants, the target actual power generation corresponding to the sub-plant is actual power generation of the sub-plant within the target date; a third calculation unit configured to calculate total wind curtailment of the target wind power plant according to the wind curtailment of each of the sub-plants. The first calculation unit is specifically configured to acquire a power curtailment flag corresponding to the sub power plant; if it is determined according to the power curtailment flag corresponding to the sub power plant that the sub power plant is curtailed on a target date, then according to a turning wind speed value corresponding to the sub power plant and a collection time corresponding to each target wind speed value, a plurality of target wind speed values are divided into a plurality of first groups and a plurality of second groups; a plurality of target wind speed values contained in each first group are input into a theoretical power generation calculation model corresponding to the sub power plant, so that the theoretical power generation calculation model outputs a theoretical power generation corresponding to each first group; a daily average power generation of a virtual benchmark wind turbine corresponding to the sub power plant is acquired, and according to a collection time corresponding to each target wind speed value contained in each second group, a daily average power generation corresponding to the virtual benchmark wind turbine and a total number of wind turbines corresponding to the sub power plant, a theoretical power generation corresponding to each second group is calculated; a theoretical power generation corresponding to the sub power plant is calculated according to a theoretical power generation corresponding to each first group and a theoretical power generation corresponding to each second group; if it is determined according to the power curtailment flag corresponding to the sub power plant that the sub power plant is not curtailed on the target date, then a plurality of target wind speed values and a collection time corresponding to each target wind speed value are input into a theoretical power generation calculation model corresponding to the sub power plant, so that the theoretical power generation calculation model outputs a theoretical power generation corresponding to the sub power plant.

8. A storage medium, characterized by The storage medium includes a stored program, wherein the program controls a device in which the storage medium is located to execute the wind power curtailment calculation method in any one of claims 1 to 6 when the program is running. 9.A device for calculating abandoned wind power, characterized in that, The device includes a storage medium and one or more processors, the storage medium is coupled with the processor, and the processor is configured to execute program instructions stored in the storage medium; the program instructions execute the wind power curtailment calculation method in any one of claims 1 to 6 when running.

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