Wind storage system pre-adjustment method, system and equipment considering wind resource uncertainty

Abstract

The invention provides a wind storage system pre-adjustment method, system and device considering wind resource uncertainty, and the method comprises the steps: dividing obtained historical wind resource data and historical wind power prediction data, and obtaining a plurality of different wind conditions and prediction wind condition power data; acquiring power generation power data of the target wind turbine generator under the normal power generation working condition of the time sequence corresponding to each wind condition; dividing the generated power data corresponding to each wind condition to obtain a plurality of wind speed buckets, and counting the generated power variance corresponding to each wind speed bucket; calculating the Euclidean distance between the obtained generated power data and the obtained predicted generated power data; establishing a relation matrix between the predicted wind condition and the real wind condition; the obtained relation matrix of the predicted wind condition and the real wind condition is used for adjusting the storage and discharge capacity of energy storage equipment in the wind storage system; according to the method, the energy storage reserve can be reasonably allocated in advance under the condition of power prediction deviation under different wind conditions, so that the energy storage system has the capability of participating in wind power prediction compensation.

Description

Technical Field

[0001] The invention belongs to the field of renewable energy and electricity, and in particular relates to a method, system and equipment for pre-adjusting a wind storage system taking into account the uncertainty of wind resources. Background Art

[0002] In the context of promoting low-carbon energy transformation and responding to climate change, wind power, as a clean and renewable energy, occupies an increasingly important position in the new energy structure. However, one of the challenges of wind power is the instability of its output, mainly due to the variability and unpredictability of wind energy. In order to solve the problem of unstable output, introducing energy storage systems is an effective solution. Energy storage systems can not only stabilize the output of wind farms, but also improve the overall reliability of wind power systems.

[0003] The cooperation between energy storage system and wind farm can more effectively realize functions such as frequency regulation, active dispatch and wind power forecast compensation. Wind power forecast has been used as a standard configuration for wind farm construction. The long-term and short-term wind power forecast data are transmitted to the power grid dispatch system as a reference for the power grid to formulate power generation plans. However, due to the limited prediction scale of wind power forecast, the prediction accuracy is difficult to guarantee, and the prediction effect of wind farms in complex terrain is often worse; while wind power forecast compensation is achieved by coordinating wind farms with energy storage systems, designing energy storage charging and discharging strategies to achieve the matching of wind farm output power with the reported forecast, reducing the deviation between wind farm output and report, and reducing assessment risks. The specific implementation is: when the wind farm output is lower than the forecast curve, the energy storage system supplements the power so that the wind power storage system outputs near the forecast curve; when the wind farm output is higher than the forecast curve, the energy storage system absorbs the power so that the wind power storage system outputs near the forecast curve. Summary of the invention

[0004] The purpose of the present invention is to provide a wind storage system pre-adjustment method, system and equipment that takes into account the uncertainty of wind resources, which solves the impact of the uncertainty of existing wind resources on the pre-adjustment strategy of the energy storage system. Wind resources are a manifestation of a random process. If the uncertainty of the current wind resources and the predicted wind power is not evaluated, the energy storage system may not be able to implement a reasonable charging and discharging strategy to respond to the wind power prediction curve.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] The present invention provides a wind storage system pre-adjustment method considering wind resource uncertainty, comprising the following steps:

[0007] Step 1, obtaining historical wind resource data and historical wind power forecast data;

[0008] Step 2, dividing the obtained historical wind resource data and historical wind power forecast data respectively to obtain a variety of different wind conditions and forecast wind power data;

[0009] Step 3, obtaining the power generation data of the target wind turbine under normal power generation conditions at the time sequence corresponding to each wind condition;

[0010] Step 4: Divide the power generation data corresponding to each wind condition to obtain multiple wind speed buckets, and calculate the power generation variance corresponding to each wind speed bucket;

[0011] Step 5, calculating the Euclidean distance between the obtained power generation data and the obtained predicted power generation data;

[0012] Step 6, establishing a relationship matrix between the predicted wind condition and the actual wind condition based on all the generated power variances and all the obtained Euclidean distances;

[0013] Step 7, using the obtained relationship matrix between the predicted wind conditions and the actual wind conditions to adjust the storage and discharge capacity of the energy storage equipment in the wind storage system.

[0014] Preferably, after step 1 and before step 2, the obtained historical wind resource data and historical wind power forecast data are cleaned to remove abnormal values, thereby obtaining cleaned historical wind resource data and historical wind power forecast data.

[0015] Preferably, the cleaned historical wind resource data is divided to obtain a variety of different wind conditions. The specific method is:

[0016] The cleaned historical wind resource data are divided according to macro-meteorological conditions to obtain m types of historical wind resource sub-data;

[0017] Each category of historical wind resource sub-data is divided according to atmospheric stability to obtain n categories of historical wind resource sub-data;

[0018] The time series wind data in each type of historical wind resource sub-data is abstracted into a wind spectrum to obtain a variety of different wind conditions.

[0019] Preferably, the cleaned historical wind power prediction data is divided to obtain a variety of different predicted wind power data. The specific method is:

[0020] The cleaned historical wind power forecast data are divided according to macro-meteorological conditions to obtain m types of historical wind power forecast sub-data;

[0021] Each type of historical wind power forecast sub-data is divided according to the atmospheric stability to obtain n types of historical wind power forecast sub-data;

[0022] The time series wind data in each type of historical wind power forecast sub-data is abstracted into a wind spectrum to obtain a variety of different predicted wind power data.

[0023] A wind storage system pre-adjustment system considering wind resource uncertainty, comprising:

[0024] A data acquisition unit, used to acquire historical wind resource data and historical wind power forecast data;

[0025] A data division unit is used to divide the obtained historical wind resource data and historical wind power forecast data respectively to obtain a variety of different wind conditions and forecast wind power data;

[0026] A power generation data acquisition unit, used to acquire power generation data of a target wind turbine generator set under normal power generation conditions at a time sequence corresponding to each wind condition;

[0027] A power generation variance calculation unit is used to divide the power generation data corresponding to each wind condition to obtain multiple wind speed buckets, and to calculate the power generation variance corresponding to each wind speed bucket;

[0028] A Euclidean distance calculation unit, used for calculating the Euclidean distance between the obtained power generation data and the obtained predicted power generation data;

[0029] A relationship matrix building unit, used for building a relationship matrix between the predicted wind condition and the actual wind condition according to all the obtained power generation variances and all the obtained Euclidean distances;

[0030] The adjustment unit is used to adjust the storage and discharge capacity of the energy storage equipment in the wind storage system by using the relationship matrix between the predicted wind conditions and the actual wind conditions.

[0031] Preferably, it also includes a data cleaning unit for cleaning the obtained historical wind resource data and historical wind power forecast data to remove abnormal values ​​and obtain cleaned historical wind resource data and historical wind power forecast data.

[0032] Preferably, the data partitioning unit includes a first data partitioning sub-unit, and the first data partitioning sub-unit includes:

[0033] The first submodule of data division is used to divide the cleaned historical wind resource data according to macro-meteorological conditions to obtain m types of historical wind resource sub-data;

[0034] The second submodule of data division is used to divide each category of historical wind resource sub-data obtained according to atmospheric stability to obtain n categories of historical wind resource sub-data;

[0035] The third data partitioning submodule is used to abstract the time series wind data in each type of historical wind resource sub-data into a wind spectrum to obtain a variety of different wind conditions.

[0036] Preferably, the data partitioning unit includes a second data partitioning subunit, and the second data partitioning subunit includes:

[0037] The first data division submodule is used to divide the cleaned historical wind power forecast data according to macro-meteorological conditions to obtain m types of historical wind power forecast sub-data;

[0038] The second submodule of data division is used to divide each type of historical wind power prediction sub-data obtained according to atmospheric stability to obtain n types of historical wind power prediction sub-data;

[0039] The third submodule of data division is used to abstract the time series wind data in each type of historical wind power prediction sub-data into a wind spectrum to obtain a variety of different predicted wind power data.

[0040] A processing device comprises at least a processor and a memory, wherein a computer program is stored in the memory, and when the processor runs the computer program, the computer program is executed to implement the steps of the method.

[0041] A computer storage medium stores computer readable instructions thereon, wherein the computer readable instructions can be executed by a processor to implement the steps of the method.

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

[0043] The present invention provides a wind storage system pre-adjustment method that takes into account the uncertainty of wind resources. Quantifying the uncertainty of wind resources can enable the wind power storage system to more effectively perform wind power forecasting and compensation. Combining meteorology and statistics, historical wind resources are classified into several wind conditions, and the power performance of different wind conditions and actual wind farms is analyzed. The deviation of power performance under different wind conditions is combined with historical wind power forecast data to obtain the error of wind power forecast under different wind conditions, which is used as the basis for the pre-charge and pre-discharge adjustment strategy of the energy storage system. The purpose is to reasonably pre-allocate energy storage reserves under the power forecast deviation of different wind conditions, so that the energy storage system is capable of participating in wind power forecasting and compensation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0045] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0046] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0047] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0048] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0049] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0050] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0051] Example 1

[0052] This embodiment provides a wind storage system pre-adjustment method that takes into account the uncertainty of wind resources. According to historical wind resource data, historical wind power prediction data and wind turbine output power data, the wind resource uncertainty corresponding to different wind conditions is abstracted, and the uncertainty of the current wind resource is simulated in combination with the historical prediction deviation. This is used as the basis for the pre-charge and pre-discharge adjustment strategy of the energy storage system to meet the wind power prediction compensation response capability.

[0053] like Figure 1 As shown, the method specifically includes the following steps:

[0054] Step 1, obtaining historical wind resource data and historical wind power forecast data;

[0055] Step 2: Clean the obtained historical wind resource data and historical wind power forecast data to remove abnormal values, and obtain cleaned historical wind resource data and historical wind power forecast data;

[0056] Step 3, dividing the cleaned historical wind resource data to obtain a variety of different wind conditions;

[0057] Step 4, counting the power generation data of the target wind turbine generator set under normal power generation conditions at the time sequence corresponding to each wind condition;

[0058] Step 5: divide the power generation data corresponding to each wind condition to obtain multiple wind speed buckets; calculate the power generation variance corresponding to each wind speed bucket, and use the obtained power generation variance as the uncertainty of measuring the power performance of historical wind conditions;

[0059] Step 6, dividing the cleaned historical wind power prediction data to obtain a variety of different predicted wind power data;

[0060] Step 7, counting the predicted power generation data of the target wind turbine under normal power generation conditions corresponding to the time series of each predicted wind condition power data;

[0061] Step 8, calculating the Euclidean distance between the power generation data obtained in step 4 and the predicted power generation data obtained in step 7 as a reference for the uncertainty of wind power prediction;

[0062] Step 10, establishing a relationship matrix between the predicted wind conditions and the actual wind conditions based on all the generated power variances obtained in step 5 and all the Euclidean distances obtained in step 8;

[0063] Step 11, using the obtained relationship matrix between the predicted wind conditions and the actual wind conditions to adjust the storage and discharge capacity of the energy storage equipment in the wind storage system.

[0064] Example 2

[0065] Based on Example 1, this embodiment provides a wind storage system pre-adjustment method considering the uncertainty of wind resources. In step 3, the cleaned historical wind resource data is divided to obtain a variety of different wind conditions. The specific method is:

[0066] The cleaned historical wind resource data are divided according to macro-meteorological conditions to obtain m types of historical wind resource sub-data;

[0067] Each category of historical wind resource sub-data is divided according to atmospheric stability to obtain n categories of historical wind resource sub-data;

[0068] The time series wind data in each type of historical wind resource sub-data is abstracted into a wind spectrum to obtain a variety of different wind conditions.

[0069] Example 3

[0070] Based on Example 1, this embodiment provides a wind storage system pre-adjustment method considering the uncertainty of wind resources. In step 6, the cleaned historical wind power forecast data is divided to obtain a variety of different predicted wind power data. The specific method is:

[0071] The cleaned historical wind power forecast data are divided according to macro-meteorological conditions to obtain m types of historical wind power forecast sub-data;

[0072] Each type of historical wind power forecast sub-data is divided according to the atmospheric stability to obtain n types of historical wind power forecast sub-data;

[0073] The time series wind data in each type of historical wind power forecast sub-data is abstracted into a wind spectrum to obtain a variety of different predicted wind power data.

[0074] Example 4

[0075] In order to better illustrate the implementation of the present invention, specific embodiments are given below to illustrate the specific application mode and effect of the present invention.

[0076] Assume that there is a wind farm located in a mountainous terrain area, which is significantly affected by seasonal wind speed changes, especially in winter and summer. The wind farm is equipped with a wind power energy storage system based on the characteristics of meteorological changes. The specific implementation steps are as follows:

[0077] Data collection and preprocessing: First, historical wind resource data and wind power forecast data are collected and preprocessed, including data cleaning, removal of outliers, etc., to ensure the accuracy and reliability of the data.

[0078] Wind resource division and wind power prediction error statistics: The historical wind resource data is abstracted into different wind conditions, and the normal power generation of wind turbines under each wind condition is counted. The variance is calculated based on the wind resource data and the actual power generation as a measure of the uncertainty of the power performance under historical wind conditions. At the same time, the historical wind power prediction data is processed in the same way to obtain the relationship matrix between the predicted wind conditions and the actual wind conditions.

[0079] Simulate the uncertainty of current wind resources and pre-charge and discharge adjustment strategies: Based on historical data and relationship matrix, simulate the uncertainty of current wind resources, and combine the relationship matrix between predicted wind conditions and actual wind conditions to establish the relationship matrix between different predicted wind conditions and actual wind conditions and the uncertainty of wind conditions themselves. Based on the current wind power forecast data, estimate the amount of electricity that the energy storage system needs to store in advance, and adjust the pre-charge and discharge strategy of the energy storage system.

[0080] Real-time monitoring and adjustment: Integrate advanced meteorological monitoring and forecasting technologies to monitor and analyze changes in key meteorological parameters such as wind speed, wind direction, temperature, and humidity in real time, and continuously adjust the pre-charge and discharge strategies of the energy storage system to ensure that the system can respond to changes in wind power forecasts in a timely manner, thereby maximizing the stability and reliability of the wind power system.

[0081] Performance evaluation and optimization: Statistics and analysis of data during the implementation process, optimization and adjustment of strategies to continuously improve the operating efficiency and stability of the system.

[0082] Example 5

[0083] This embodiment provides a wind storage system pre-adjustment system considering wind resource uncertainty, including:

[0084] A data acquisition unit, used to acquire historical wind resource data and historical wind power forecast data;

[0085] A data division unit is used to divide the obtained historical wind resource data and historical wind power forecast data respectively to obtain a variety of different wind conditions and forecast wind power data;

[0086] A power generation data acquisition unit, used to acquire power generation data of a target wind turbine generator set under normal power generation conditions at a time sequence corresponding to each wind condition;

[0087] A power generation variance calculation unit is used to divide the power generation data corresponding to each wind condition to obtain multiple wind speed buckets, and to calculate the power generation variance corresponding to each wind speed bucket;

[0088] A Euclidean distance calculation unit, used for calculating the Euclidean distance between the obtained power generation data and the obtained predicted power generation data;

[0089] A relationship matrix building unit, used for building a relationship matrix between the predicted wind condition and the actual wind condition according to all the obtained power generation variances and all the obtained Euclidean distances;

[0090] The adjustment unit is used to adjust the storage and discharge capacity of the energy storage equipment in the wind storage system by using the relationship matrix between the predicted wind conditions and the actual wind conditions.

[0091] It also includes a data cleaning unit for cleaning the obtained historical wind resource data and historical wind power forecast data to remove abnormal values ​​and obtain cleaned historical wind resource data and historical wind power forecast data.

[0092] The data partitioning unit includes a first data partitioning sub-unit, and the first data partitioning sub-unit includes:

[0093] The first submodule of data division is used to divide the cleaned historical wind resource data according to macro-meteorological conditions to obtain m types of historical wind resource sub-data;

[0094] The second submodule of data division is used to divide each category of historical wind resource sub-data obtained according to atmospheric stability to obtain n categories of historical wind resource sub-data;

[0095] The third data partitioning submodule is used to abstract the time series wind data in each type of historical wind resource sub-data into a wind spectrum to obtain a variety of different wind conditions.

[0096] The data partitioning unit includes a second data partitioning sub-unit, and the second data partitioning sub-unit includes:

[0097] The first data division submodule is used to divide the cleaned historical wind power forecast data according to macro-meteorological conditions to obtain m types of historical wind power forecast sub-data;

[0098] The second submodule of data division is used to divide each type of historical wind power prediction sub-data obtained according to atmospheric stability to obtain n types of historical wind power prediction sub-data;

[0099] The third submodule of data division is used to abstract the time series wind data in each type of historical wind power prediction sub-data into a wind spectrum to obtain a variety of different predicted wind power data.

[0100] Example 6

[0101] This embodiment provides a processing device corresponding to a wind storage system pre-adjustment method considering the uncertainty of wind resources provided in this embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0102] The processing device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The memory stores a computer program that can be run on the processor, and when the processor runs the computer program, a wind storage system pre-adjustment method considering wind resource uncertainty provided in this embodiment 1 is executed.

[0103] In some embodiments, the memory may be a high-speed random access memory (RAM: Ran nd om Access

[0104] Memory), and may also include non-volatile memory, such as at least one disk storage.

[0105] In other embodiments, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors of various types, which are not limited herein.

[0106] Example 7

[0107] A method for pre-adjusting a wind-storage system taking into account the uncertainty of wind resources in Example 1 can be specifically implemented as a computer program product, which may include a computer-readable storage medium carrying computer-readable program instructions for executing the method for pre-adjusting a wind-storage system taking into account the uncertainty of wind resources described in Example 1.

[0108] Computer readable storage media may be tangible devices that hold and store instructions for use by instruction execution devices.

[0109] The computer-readable storage medium may be, for example but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing.

[0110] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A wind storage system pre-adjustment method considering wind resource uncertainty, characterized in that: The following steps are involved: Step 1, obtaining historical wind resource data and historical wind power forecast data; Step 2, dividing the obtained historical wind resource data and historical wind power forecast data respectively to obtain a variety of different wind conditions and forecast wind power data; Step 3, obtaining the power generation data of the target wind turbine under normal power generation conditions at the time sequence corresponding to each wind condition; Step 4: Divide the power generation data corresponding to each wind condition to obtain multiple wind speed buckets, and calculate the power generation variance corresponding to each wind speed bucket; Step 5, calculating the Euclidean distance between the obtained power generation data and the obtained predicted power generation data; Step 6, establishing a relationship matrix between the predicted wind condition and the actual wind condition based on all the generated power variances and all the obtained Euclidean distances; Step 7, using the obtained relationship matrix between the predicted wind conditions and the actual wind conditions to adjust the storage and discharge capacity of the energy storage equipment in the wind storage system.

2. A wind storage system pre-adjustment method considering wind resource uncertainty according to claim 1, characterized in that: After step 1 and before step 2, the obtained historical wind resource data and historical wind power forecast data are cleaned to remove abnormal values, thereby obtaining cleaned historical wind resource data and historical wind power forecast data.

3. A wind storage system pre-adjustment method considering wind resource uncertainty according to claim 2, characterized in that: The cleaned historical wind resource data is divided to obtain a variety of different wind conditions. The specific method is: The cleaned historical wind resource data are divided according to macro-meteorological conditions to obtain m types of historical wind resource sub-data; Each category of historical wind resource sub-data is divided according to atmospheric stability to obtain n categories of historical wind resource sub-data; The time series wind data in each type of historical wind resource sub-data is abstracted into a wind spectrum to obtain a variety of different wind conditions.

4. A wind storage system pre-adjustment method considering wind resource uncertainty according to claim 2, characterized in that: The cleaned historical wind power forecast data is divided to obtain a variety of different wind power forecast data. The specific method is: The cleaned historical wind power forecast data are divided according to macro-meteorological conditions to obtain m types of historical wind power forecast sub-data; Each type of historical wind power forecast sub-data is divided according to the atmospheric stability to obtain n types of historical wind power forecast sub-data; The time series wind data in each type of historical wind power forecast sub-data is abstracted into a wind spectrum to obtain a variety of different predicted wind power data.

5. A wind storage system pre-adjustment system considering the uncertainty of wind resources, characterized in that: include: A data acquisition unit, used to acquire historical wind resource data and historical wind power forecast data; A data division unit is used to divide the obtained historical wind resource data and historical wind power forecast data respectively to obtain a variety of different wind conditions and forecast wind power data; A power generation data acquisition unit, used to acquire power generation data of a target wind turbine generator set under normal power generation conditions at a time sequence corresponding to each wind condition; A power generation variance calculation unit is used to divide the power generation data corresponding to each wind condition to obtain multiple wind speed buckets, and to calculate the power generation variance corresponding to each wind speed bucket; A Euclidean distance calculation unit, used for calculating the Euclidean distance between the obtained power generation data and the obtained predicted power generation data; A relationship matrix building unit, used for building a relationship matrix between the predicted wind condition and the actual wind condition according to all the obtained power generation variances and all the obtained Euclidean distances; The adjustment unit is used to adjust the storage and discharge capacity of the energy storage equipment in the wind storage system by using the relationship matrix between the predicted wind conditions and the actual wind conditions.

6. A wind storage system pre-adjustment system considering wind resource uncertainty according to claim 5, characterized in that: It also includes a data cleaning unit for cleaning the obtained historical wind resource data and historical wind power forecast data to remove abnormal values ​​and obtain cleaned historical wind resource data and historical wind power forecast data.

7. A wind storage system pre-adjustment system considering wind resource uncertainty according to claim 6, characterized in that: The data partitioning unit includes a first data partitioning sub-unit, and the first data partitioning sub-unit includes: The first submodule of data division is used to divide the cleaned historical wind resource data according to macro-meteorological conditions to obtain m types of historical wind resource sub-data; The second submodule of data division is used to divide each category of historical wind resource sub-data obtained according to atmospheric stability to obtain n categories of historical wind resource sub-data; The third data partitioning submodule is used to abstract the time series wind data in each type of historical wind resource sub-data into a wind spectrum to obtain a variety of different wind conditions.

8. A wind storage system pre-adjustment system considering wind resource uncertainty according to claim 6, characterized in that: The data partitioning unit includes a second data partitioning sub-unit, and the second data partitioning sub-unit includes: The first data division submodule is used to divide the cleaned historical wind power forecast data according to macro-meteorological conditions to obtain m types of historical wind power forecast sub-data; The second submodule of data division is used to divide each type of historical wind power prediction sub-data obtained according to atmospheric stability to obtain n types of historical wind power prediction sub-data; The third submodule of data division is used to abstract the time series wind data in each type of historical wind power prediction sub-data into a wind spectrum to obtain a variety of different predicted wind power data.

9. A processing device, comprising at least a processor and a memory, wherein a computer program is stored in the memory, wherein: When the processor runs the computer program, the computer program is executed to implement the steps of the method according to any one of claims 1 to 5.

10. A computer storage medium, characterized in that: Computer readable instructions are stored thereon, and the computer readable instructions can be executed by a processor to implement the steps of the method according to any one of claims 1 to 5.

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