Flywheel-storage battery hybrid energy storage power distribution method for stabilizing wind power fluctuation

Abstract

The invention relates to a flywheel-storage battery hybrid energy storage power distribution method and device for stabilizing wind power fluctuation, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of electric power. The method comprises the following steps: carrying out self-adjusting moving average filtering processing on collected wind power data to obtain wind power grid-connected power; obtaining hybrid energy storage power according to the wind power data and the wind power grid-connected power; decomposing the hybrid energy storage power to obtain a plurality of modal components; the multiple modal components are distributed to flywheel energy storage and a storage battery, and the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery are obtained; and according to the charge state of the flywheel energy storage and the charge state of the storage battery, the primary distribution power of the flywheel energy storage and the primary distribution power of the storage battery are updated, and the secondary distribution power of the flywheel energy storage and the secondary distribution power of the storage battery are obtained. By adopting the method, the power distribution efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a flywheel-battery hybrid energy storage power distribution method, device, computer equipment, computer-readable storage medium and computer program product for smoothing wind power fluctuations. Background Art

[0002] With the development of renewable energy technology, wind power generation has been widely used as a form of clean energy. However, the intermittent and volatile nature of wind power generation poses challenges to the stable operation of the power grid. How to efficiently distribute power has become an important research direction.

[0003] Traditional technology usually distributes power by manually setting fixed parameters; however, this method requires a lot of manual processing time, resulting in low efficiency of power distribution. Summary of the invention

[0004] Based on this, it is necessary to provide a flywheel-battery hybrid energy storage power distribution method, device, computer equipment, computer-readable storage medium and computer program product that can improve the efficiency of power distribution and smooth wind power fluctuations in order to address the above technical problems.

[0005] In a first aspect, the present application provides a flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations. The method comprises:

[0006] Performing self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data;

[0007] Obtaining hybrid energy storage power according to the wind power data and the wind power grid-connected power;

[0008] Decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power;

[0009] Distributing the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery;

[0010] According to the charge state of the flywheel energy storage and the charge state of the battery, the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0011] In one embodiment, the method of allocating the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery includes:

[0012] Determining high and low frequency dividing points of the multiple modal components;

[0013] Allocating the modal components with corresponding frequencies higher than the high- and low-frequency dividing points to the flywheel energy storage to obtain the initial distribution power of the flywheel energy storage;

[0014] The modal components corresponding to the frequencies lower than or equal to the high- and low-frequency dividing points are distributed to the battery to obtain the initial distributed power of the battery.

[0015] In one embodiment, determining the high-frequency and low-frequency demarcation points of the multiple modal components includes:

[0016] Performing frequency spectrum generation processing on the multiple modal components to obtain frequency spectrums of the multiple modal components;

[0017] The high- and low-frequency dividing points are determined according to the frequency spectrum.

[0018] In one embodiment, the updating process of the primary distribution power of the flywheel energy storage and the primary distribution power of the battery according to the charge state of the flywheel energy storage and the charge state of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery includes:

[0019] Using a first fuzzy controller, the primary distribution power of the flywheel energy storage is updated according to the charge state of the flywheel energy storage to obtain the secondary distribution power of the flywheel energy storage;

[0020] The second fuzzy controller is used to update the primary distributed power of the battery according to the state of charge of the battery to obtain the secondary distributed power of the battery.

[0021] In one embodiment, the self-adjusting sliding average filtering process is performed on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data, including:

[0022] Set the initial sliding window length;

[0023] According to the initial sliding window length, performing sliding average calculation on the wind power data to obtain the initial wind power grid-connected power corresponding to the wind power data;

[0024] In the case where the initial wind power grid-connected power meets a preset smoothing condition, the initial wind power grid-connected power is used as the wind power grid-connected power.

[0025] In one embodiment, after the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated according to the charge state of the flywheel energy storage and the charge state of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery, the method further includes:

[0026] Controlling the flywheel energy storage to charge and discharge according to the secondary distributed power of the flywheel energy storage;

[0027] The battery is controlled to be charged and discharged according to the secondary distributed power of the battery.

[0028] In a second aspect, the present application also provides a flywheel-battery hybrid energy storage power distribution device for smoothing wind power fluctuations. The device comprises:

[0029] A data processing module, used for performing self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data;

[0030] A power calculation module, used for obtaining the hybrid energy storage power according to the wind power data and the wind power grid-connected power;

[0031] A power decomposition module, used for decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power;

[0032] A component allocation module, used for allocating the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components, so as to obtain the initial allocation power of the flywheel energy storage and the initial allocation power of the storage battery;

[0033] The power update module is used to update the primary distribution power of the flywheel energy storage and the primary distribution power of the battery according to the charge state of the flywheel energy storage and the charge state of the battery, so as to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0034] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0035] Performing self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data;

[0036] Obtaining hybrid energy storage power according to the wind power data and the wind power grid-connected power;

[0037] Decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power;

[0038] Distributing the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery;

[0039] According to the charge state of the flywheel energy storage and the charge state of the battery, the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Performing self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data;

[0042] Obtaining hybrid energy storage power according to the wind power data and the wind power grid-connected power;

[0043] Decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power;

[0044] Distributing the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery;

[0045] According to the charge state of the flywheel energy storage and the charge state of the battery, the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0046] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0047] Performing self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data;

[0048] Obtaining hybrid energy storage power according to the wind power data and the wind power grid-connected power;

[0049] Decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power;

[0050] Distributing the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery;

[0051] According to the charge state of the flywheel energy storage and the charge state of the battery, the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0052] The flywheel-battery hybrid energy storage power distribution method, device, computer equipment, computer-readable storage medium and computer program product for smoothing wind power fluctuations perform self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data; obtain the hybrid energy storage power based on the wind power data and the wind power grid-connected power; decompose the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power; distribute the multiple modal components to the flywheel energy storage and the battery based on the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the battery; update the initial distribution power of the flywheel energy storage and the initial distribution power of the battery based on the charge state of the flywheel energy storage and the charge state of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery. This solution filters and decomposes wind power data, distributes power based on the characteristics of flywheels and batteries, and updates the energy storage device based on the charge state. This helps to fully utilize the advantages of different energy storage devices, effectively smooth out wind power fluctuations, improve grid stability, and increase the efficiency and accuracy of power distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic flow chart of a flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations in one embodiment;

[0055] Figure 2A schematic diagram of a wind power generation system in one embodiment;

[0056] Figure 3 A schematic diagram of a process of self-adjusting sliding average filtering in one embodiment;

[0057] Figure 4 A schematic diagram of a flow chart of a fuzzy control secondary allocation strategy in one embodiment;

[0058] Figure 5 A structural block diagram of a flywheel-battery hybrid energy storage power distribution device for smoothing wind power fluctuations in one embodiment;

[0059] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0062] In an exemplary embodiment, Figure 1 As shown, a flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations is provided. This embodiment uses the method applied to a terminal as an example for illustration; it can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones, tablet computers, etc.; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the method includes the following steps:

[0063] Step S101 , performing self-adjusting sliding average filtering processing on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data.

[0064] Step S102, obtaining the hybrid energy storage power according to the wind power data and the wind power grid-connected power.

[0065] Step S103, decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power.

[0066] Step S104, allocating the multiple modal components to the flywheel energy storage and the battery according to the frequencies of the multiple modal components, and obtaining the initial distribution power of the flywheel energy storage and the initial distribution power of the battery.

[0067] Step S105, according to the charge state of the flywheel energy storage and the charge state of the battery, the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0068] The wind power data may be power data output by a wind power generation system within a certain period of time. For example, the wind power data may be actual output power values ​​of wind turbine generator sets at each sampling moment.

[0069] Among them, the self-adjusting sliding average filtering process can be a data smoothing method that dynamically adjusts the filter window size. For example, the self-adjusting sliding average filtering process can be a process of continuously adjusting the length of the sliding window until the wind power grid-connected power that meets the preset smoothing requirements is obtained.

[0070] The wind power grid-connected power may be the wind power after smoothing, and is used as the power data for connection to the power grid. For example, the wind power grid-connected power may be the wind power output power that meets the stability requirements of the power grid.

[0071] The hybrid energy storage power may be the power required by the energy storage system for smoothing wind power fluctuations. For example, the hybrid energy storage power may be the difference between wind power data and wind power grid-connected power.

[0072] The modal components may be components of different frequency characteristics obtained after decomposing the hybrid energy storage power. For example, the modal components may be signal components with different center frequencies obtained by a successive variational modal decomposition method.

[0073] Among them, flywheel energy storage can be an energy storage device that uses the rotation of a flywheel to store kinetic energy. For example, flywheel energy storage can be an energy storage device for quickly responding to high-frequency power fluctuations.

[0074] Among them, the battery can be a device that stores and releases electrical energy through chemical reactions. For example, the battery can be an energy storage device for processing low-frequency power fluctuations.

[0075] Among them, the initially distributed power can be the power initially distributed to the flywheel energy storage and the battery according to the frequency characteristics of the modal components. For example, the initially distributed power can be the power obtained after allocating high-frequency modal components to the flywheel energy storage and low-frequency modal components to the battery.

[0076] The state of charge may be a ratio of the amount of electricity currently stored in the energy storage device to its maximum storage capacity. For example, the state of charge may be the current charge level of the flywheel energy storage or the battery.

[0077] Among them, the secondary distributed power can be the final power distribution result obtained by adjusting the primary distributed power after considering the charge state of the energy storage device. For example, the secondary distributed power can be the power obtained by correcting the primary distributed power according to the charge state through a fuzzy controller.

[0078] Optionally, the terminal obtains wind power data and performs self-adjusting sliding average filtering on the wind power data. The terminal first sets the initial sliding window length, and then performs sliding average calculation on the wind power data according to the initial sliding window length to obtain the initial wind power grid-connected power. The terminal determines whether the initial wind power grid-connected power meets the preset smoothing requirements. If not, the sliding window length is increased, and the sliding average calculation and judgment steps are repeated until the wind power grid-connected power that meets the preset smoothing requirements is obtained. The terminal calculates the hybrid energy storage power based on the wind power data and the wind power grid-connected power, and decomposes the hybrid energy storage power to obtain multiple modal components. The terminal allocates high-frequency modal components to flywheel energy storage and low-frequency modal components to batteries based on the frequency characteristics of these modal components, thereby obtaining the initial distribution power of the flywheel energy storage and the initial distribution power of the battery. For example, the terminal can use the successive variational modal decomposition method to decompose the hybrid energy storage power to obtain multiple modal components. The terminal performs Hilbert transform on these modal components, generates a spectrum diagram, and determines the high-low frequency dividing point based on the spectrum diagram. The terminal allocates the modal components above the high-low frequency dividing point to the flywheel energy storage, and allocates the modal components below the high-low frequency dividing point to the battery. The terminal constructs a first fuzzy controller and a second fuzzy controller, which are used to process the power distribution of the flywheel energy storage and the battery, respectively. The terminal uses the first fuzzy controller to adjust the initial distribution power of the flywheel energy storage according to the charge state of the flywheel energy storage, and uses the second fuzzy controller to adjust the initial distribution power of the battery according to the charge state of the battery, and finally obtains the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0079] In the above-mentioned flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations, the collected wind power data is subjected to self-adjusting sliding average filtering to obtain the wind power grid-connected power corresponding to the wind power data; the hybrid energy storage power is obtained based on the wind power data and the wind power grid-connected power; the hybrid energy storage power is decomposed to obtain multiple modal components of the hybrid energy storage power; the multiple modal components are distributed to the flywheel energy storage and the battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the battery; the initial distribution power of the flywheel energy storage and the initial distribution power of the battery are updated according to the charge state of the flywheel energy storage and the charge state of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery. This solution filters and decomposes wind power data, distributes power based on the characteristics of flywheels and batteries, and updates the energy storage device based on the charge state. This helps to fully utilize the advantages of different energy storage devices, effectively smooth out wind power fluctuations, improve grid stability, and increase the efficiency and accuracy of power distribution.

[0080] In an exemplary embodiment, multiple modal components are allocated to flywheel energy storage and batteries according to the frequencies of multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the battery, which specifically includes the following contents: determining the high- and low-frequency dividing points of multiple modal components; allocating the corresponding modal components with frequencies higher than the high- and low-frequency dividing points to the flywheel energy storage to obtain the initial distribution power of the flywheel energy storage; allocating the corresponding modal components with frequencies lower than or equal to the high- and low-frequency dividing points to the battery to obtain the initial distribution power of the battery.

[0081] The high- and low-frequency dividing point may be a frequency threshold for distinguishing high-frequency modal components from low-frequency modal components. For example, the high- and low-frequency dividing point may be a specific frequency value determined by analyzing the frequency spectrum of multiple modal components.

[0082] Among them, the initial distribution power of the flywheel energy storage can be the power value initially distributed to the flywheel energy storage calculated based on the high-frequency modal components. For example, the initial distribution power of the flywheel energy storage can be the power obtained by adding all modal components with frequencies higher than the high- and low-frequency dividing points.

[0083] Among them, the initial distributed power of the battery can be the power value initially distributed to the battery calculated based on the low-frequency modal components. For example, the initial distributed power of the battery can be the power obtained by adding all modal components with frequencies lower than or equal to the high and low frequency dividing points.

[0084] Among them, the modal components can be signal components with different frequency characteristics obtained by decomposing the hybrid energy storage power. For example, the modal components can be sub-signals with different center frequencies extracted from the hybrid energy storage power using the successive variational modal decomposition method.

[0085] The frequency may be an oscillation characteristic of a modal component, and is used to characterize the degree of rapid change of the modal component. For example, the frequency may be a center frequency or a main frequency component obtained by performing a Hilbert transform on the modal component.

[0086] Optionally, the terminal performs frequency analysis on multiple modal components to determine the high- and low-frequency dividing point. The terminal first performs a Hilbert transform on each modal component to generate a corresponding spectrum diagram. Then, the terminal analyzes these spectrum diagrams to identify the characteristics and changing trends of the frequency distribution. Based on these analysis results, the terminal determines a suitable frequency value as the high- and low-frequency dividing point to distinguish high-frequency modal components from low-frequency modal components. The terminal allocates modal components with frequencies higher than the high- and low-frequency dividing point to the flywheel energy storage, and allocates modal components with frequencies lower than or equal to the high- and low-frequency dividing point to the battery.

[0087] The technical solution provided in this embodiment is conducive to making full use of the characteristics of flywheel energy storage's rapid response and battery's large capacity by determining the high- and low-frequency demarcation points and allocating modal components accordingly. This allocation method allows high-frequency fluctuations to be processed by flywheel energy storage and low-frequency fluctuations to be processed by batteries, thereby improving the overall efficiency of the hybrid energy storage system, more effectively smoothing wind power fluctuations, and improving grid stability.

[0088] In an exemplary embodiment, determining the high- and low-frequency demarcation points of multiple modal components specifically includes the following contents: performing spectrum generation processing on the multiple modal components to obtain spectrum graphs of the multiple modal components; and determining the high- and low-frequency demarcation points based on the spectrum graphs.

[0089] The spectrum diagram generation process may be a mathematical transformation process of converting a time domain signal into a frequency domain representation. For example, the spectrum diagram generation process may be an operation of performing a Hilbert transform or a fast Fourier transform on multiple modal components.

[0090] The spectrum diagram may be a graph representing energy distribution of a signal at different frequencies. For example, the spectrum diagram may be a two-dimensional graph in which the horizontal axis represents frequency and the vertical axis represents amplitude or power.

[0091] Optionally, the terminal performs Hilbert transform on multiple modal components respectively to generate a spectrum diagram for each modal component. The terminal superimposes the spectrum diagrams of all modal components to obtain a comprehensive spectrum diagram. The terminal analyzes the energy distribution characteristics in the comprehensive spectrum diagram to identify the frequency intervals with concentrated energy and the frequency intervals with lower energy. The terminal determines a suitable frequency value as the high-low frequency dividing point based on the changing trend of the energy distribution. This high-low frequency dividing point divides the spectrum diagram into two parts, one corresponding to the high-frequency modal component and the other corresponding to the low-frequency modal component.

[0092] The technical solution provided in this embodiment is conducive to accurately identifying the frequency characteristics and energy distribution of the signal by generating and processing the spectrum diagram of multiple modal components and analyzing them. This can more accurately determine the high- and low-frequency dividing points, which is conducive to optimizing the power distribution of flywheel energy storage and batteries, improving the overall efficiency of the hybrid energy storage system, and more effectively smoothing wind power fluctuations.

[0093] In an exemplary embodiment, the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated according to the state of charge of the flywheel energy storage and the state of charge of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery, which specifically includes the following contents: using a first fuzzy controller to update the primary distribution power of the flywheel energy storage according to the state of charge of the flywheel energy storage to obtain the secondary distribution power of the flywheel energy storage; using a second fuzzy controller to update the primary distribution power of the battery according to the state of charge of the battery to obtain the secondary distribution power of the battery.

[0094] The first fuzzy controller may be an intelligent control unit for processing flywheel energy storage power distribution. For example, the first fuzzy controller may be a fuzzy logic system for making decisions based on the charge state of the flywheel energy storage and the initial distribution power.

[0095] The second fuzzy controller may be an intelligent control unit for processing battery power distribution. For example, the second fuzzy controller may be a fuzzy logic system for making decisions based on the battery state of charge and the initial power distribution.

[0096] The secondary distributed power may be the final distributed power after fuzzy control adjustment. For example, the secondary distributed power may be a power value optimized and adjusted after taking into account the charge state of the energy storage device.

[0097] The updating process may be a process of adjusting the initially allocated power. For example, the updating process may be an operation of increasing or decreasing the power value according to a fuzzy control rule.

[0098] Optionally, the terminal uses a first fuzzy controller to process the power distribution of the flywheel energy storage. The terminal first obtains the current state of charge and the initial distribution power of the flywheel energy storage, and inputs these two parameters into the first fuzzy controller. The first fuzzy controller fuzzifies the input parameters according to a preset fuzzy rule set, then performs fuzzy reasoning, and finally obtains a power correction coefficient through defuzzification. The terminal multiplies this correction coefficient by the initial distribution power of the flywheel energy storage to obtain the secondary distribution power of the flywheel energy storage. The terminal also uses a similar process for the power distribution of the battery, and uses the second fuzzy controller for processing.

[0099] The technical solution provided in this embodiment uses a fuzzy controller to update the initial power distribution according to the charge state of the energy storage device, which is conducive to dynamically adjusting the power distribution and avoiding excessive charging and discharging of the energy storage device. This can better balance the use of the energy storage device, thereby helping to extend the service life of the energy storage system and improve the stability of the overall system.

[0100] In an exemplary embodiment, the collected wind power data is subjected to a self-adjusting sliding average filtering process to obtain the wind power grid-connected power corresponding to the wind power data, specifically including the following contents: setting an initial sliding window length; performing a sliding average calculation on the wind power data according to the initial sliding window length to obtain the initial wind power grid-connected power corresponding to the wind power data; and when the initial wind power grid-connected power meets a preset smoothing condition, using the initial wind power grid-connected power as the wind power grid-connected power.

[0101] The initial sliding window length may be the number of initial data points used when performing sliding average calculations. For example, the initial sliding window length may be the number of sampling points within 1 minute or 10 consecutive data points.

[0102] Among them, the sliding average calculation can be a method for smoothing time series data. For example, the sliding average calculation can be to calculate the average value of all data points in a sliding window and replace the value of the center point of the window with this average value.

[0103] The initial wind power grid-connected power may be wind power data obtained after an initial sliding average calculation. For example, the initial wind power grid-connected power may be a power curve processed by a sliding average of an initial sliding window length.

[0104] The preset smoothing condition may be a standard for evaluating the smoothness of wind power grid-connected power. For example, the preset smoothing condition may be that the power change rate does not exceed a certain threshold or the power fluctuation amplitude is within a certain range.

[0105] Optionally, the terminal sets an initial sliding window length, for example, 60 data points. The terminal performs a sliding average calculation on the wind power data based on this initial sliding window length. The terminal moves the sliding window on the wind power data, one data point at a time, and calculates the average value of all data points in the window. The terminal replaces the original value of the center point of the window with the calculated average value to obtain the initial wind power grid-connected power. The terminal checks whether the initial wind power grid-connected power meets preset smoothing conditions, such as whether the power change rate is less than a certain threshold. If the conditions are met, the terminal uses this initial wind power grid-connected power as the final wind power grid-connected power. If the conditions are not met, the terminal will increase the sliding window length and repeat the above process until the conditions are met.

[0106] The technical solution provided in this embodiment is beneficial to dynamically smoothing wind power fluctuations through self-adjusting sliding average filtering processing, and can automatically adjust the sliding window length according to the actual power data characteristics, which is beneficial to minimizing the filtering delay while ensuring the smoothing effect, improving the accuracy of determining the wind power grid-connected power, and better meeting the grid stability requirements.

[0107] In an exemplary embodiment, after the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated according to the charge state of the flywheel energy storage and the charge state of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery, the following contents are also included: according to the secondary distribution power of the flywheel energy storage, the flywheel energy storage is controlled to be charged and discharged; according to the secondary distribution power of the battery, the battery is controlled to be charged and discharged.

[0108] Among them, controlling the flywheel energy storage to charge and discharge may be to adjust the operating state of the flywheel energy storage according to the allocated power. For example, controlling the flywheel energy storage to charge and discharge may be to achieve energy storage or release by adjusting the flywheel speed.

[0109] Controlling the battery to charge and discharge may be to adjust the operating state of the battery according to the allocated power. For example, controlling the battery to charge and discharge may be to store or release energy by adjusting the charge and discharge current.

[0110] Optionally, the terminal generates a control instruction according to the secondary distribution power of the flywheel energy storage, and sends the instruction to the flywheel energy storage control system. The terminal adjusts the speed of the flywheel energy storage through the control instruction to realize the charging and discharging process of the flywheel energy storage. At the same time, the terminal generates a corresponding control instruction according to the secondary distribution power of the battery, and sends the instruction to the battery management system. The terminal adjusts the charging and discharging current of the battery through the control instruction to realize the charging and discharging process of the battery. The terminal continuously monitors the actual output power of the flywheel energy storage and the battery, and compares it with the secondary distribution power, and makes real-time adjustments when necessary to ensure that the energy storage system works as expected.

[0111] The technical solution provided in this embodiment controls the charge and discharge of flywheel energy storage and batteries according to the secondary distributed power, which is conducive to accurately implementing the power distribution strategy and making full use of the characteristics of each energy storage device. This can dynamically balance the power demand of the system and the state of the energy storage device, thereby facilitating the improvement of the stability and efficiency of the entire wind storage power generation system and better smoothing out wind power fluctuations.

[0112] The following is an application example to illustrate the flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations provided in this application. This application example uses the method applied to a terminal as an example.

[0113] The growth of energy demand and the concern about environmental impact have promoted the extensive development and application of new energy technologies. However, new energy sources such as wind and solar power are volatile and intermittent, uncertain and uncontrollable, and face huge challenges in large-scale application in power grids. The foundation for the large-scale development and application of renewable energy shows good development prospects, but it is constrained by the characteristics of renewable energy power generation. The existing power operation mechanism is difficult to adapt to the needs of large-scale development of renewable energy, mainly manifested in the fact that the power system dominated by traditional energy cannot meet the requirements of grid-connected operation of volatile renewable energy. On the other hand, the installed capacity of renewable energy has increased rapidly year by year, but the utilization efficiency is not high, and its sustainable utilization potential has not been fully tapped.

[0114] In order to alleviate the impact of the volatility and intermittency of new energy on the power grid, energy storage technology has attracted much attention. Hybrid energy storage technology is an energy storage system that combines multiple energy storage technologies to achieve complementary advantages and synergy of multiple energy storage technologies. The hybrid energy storage system has the advantages of strong flexibility, fast response speed, and high energy storage efficiency. It can effectively alleviate the volatility and intermittency of wind power generation and improve the reliability and dispatchability of wind power. By combining energy storage devices with different characteristics, the functionality and economy of wind farm operation can be improved. Energy storage devices can be divided into energy type and power type according to their output characteristics: energy type energy storage devices are represented by batteries and hydrogen energy storage, which have a slower response speed but a larger energy density, and are suitable for processing low-frequency fluctuations with a high energy ratio; power type energy storage devices are represented by supercapacitors and flywheel energy storage, which have the characteristics of fast response, but have a lower energy density, and are suitable for dealing with high-frequency fluctuations with a low energy ratio.

[0115] Energy storage power allocation strategies include low-pass filtering algorithms, empirical mode decomposition (EMD), and wavelet packet decomposition. Since the filtering time constant of the low-pass filtering algorithm is fixed, its cutoff characteristics are poor, and the delay in the filtering process will lead to unreasonable power allocation. During the decomposition process, EMD will produce mutual influence and aliasing between intrinsic mode functions (IMFs), resulting in inaccurate and unreliable decomposition results. In contrast, variational mode decomposition (VMD) has better noise suppression and decomposition accuracy, can more effectively handle noise and modal aliasing in signals, and has a solid mathematical theoretical foundation and good robustness. However, the empirical selection of the number of modes K and the penalty factor α of VMD is subjective, which can easily lead to problems of over-decomposition or under-decomposition.

[0116] The Kalman filter assumes that the system is linear, and has poor processing effects on nonlinear systems, and its performance is highly dependent on the accuracy of the system model, including the state transition model and the observation model. If the model is inaccurate, the filtering effect will be significantly reduced. The Kalman filter is sensitive to the selection of the initial state and the initial covariance matrix. Inappropriate initial values ​​may cause the filter to converge more slowly or produce deviations. In a dynamic environment, the Kalman filter may not be able to quickly adapt to changes in system characteristics, and the model parameters need to be constantly adjusted, which may be challenging in real-time applications. For wind power systems with large fluctuations, there is a certain error in using this method to obtain the smoothing signal.

[0117] EMD may cause modal aliasing when decomposing signals, that is, there is mutual influence or aliasing between different intrinsic mode functions (IMFs). This will lead to inaccurate decomposition results and difficulty in extracting useful features. Since the decomposition process of IMFs relies on local features, the reliability and stability of IMFs may be affected by noise and signal characteristics, resulting in inconsistency in decomposition results. The number of IMFs included in the results of EMD is not predetermined, but automatically generated. This requires further selection and verification in subsequent processing and analysis, which increases the complexity. Although EMD is a time domain method based on local features, the calculation process can become very time-consuming and complicated when dealing with long time series and high-dimensional data.

[0118] The cutoff frequency of low-pass filtering algorithms is usually fixed, which means that the filter cannot adapt to changes in the signal spectrum. The ability to separate different frequency components is limited, and it may not be able to effectively handle non-steady-state or spectrally changing signals. The delay introduced by the low-pass filter during the filtering process may cause phase distortion of the signal, especially in real-time applications. This delay may affect the responsiveness and real-time performance of the system. When designing a low-pass filter, it is necessary to select the appropriate filter order and cutoff frequency. The design of the filter can be complex and requires a trade-off between balancing the performance of the filter and computing resources.

[0119] This application example proposes to connect flywheel-battery hybrid energy storage to the wind farm to smooth out wind power fluctuations. The wind power is filtered by the self-adjusting sliding average filter method to obtain the wind power grid-connected power that meets the requirements, and the smooth wind power fluctuation power required by the energy storage system is calculated. In terms of energy storage power distribution, it is proposed to use a relatively stable successive variational mode decomposition (SVMD) algorithm to decompose and reconstruct the hybrid energy storage power signal to complete the primary distribution. In order to maintain the charge state of each energy storage device within a reasonable range and avoid excessive charging and discharging, fuzzy control is used to perform secondary corrections on the power command.

[0120] The specific steps of this application example are as follows:

[0121] Step 1: Establish a wind storage power generation system based on flywheel-battery.

[0122] System reference for wind power generation Figure 2 , which consists of three parts: flywheel-battery hybrid energy storage system, wind power generation system and power grid, including wind power generation system, hybrid energy storage system, battery, flywheel, DC / AC, energy management system, power grid, signal, power, etc.

[0123] Figure 2 middle is the total power output of the fan at time t, is the total power of battery charging and discharging at time t, is the total power of the flywheel energy storage charging and discharging at time t, is the total power input or output of the hybrid energy storage system at time t, is the total power connected to the grid at time t. According to the power conservation law, there is the following equivalent relationship:

[0124] (1)

[0125] (2)

[0126] Step 2: Self-adjusting sliding average filter to obtain grid-connected power.

[0127] The main steps are as follows: First, select a time window of fixed length, and calculate the average of all values ​​in this window. Then, replace the value at the center of the window with this average. Next, move the window forward by one unit and repeat the above steps for the values ​​in the new window. This process is iterated until all data points are processed. The calculation formula of this method is:

[0128] (3)

[0129] Where: is the actual power of the fan at the mth sampling point, is the grid-connected power at the mth sampling point, and L is the selected window length.

[0130] According to formula (3), the larger the window L value, the smoother the grid-connected power output, but the requirement for energy storage capacity will increase. If the L value is too small, it will not meet the grid-connected requirements. Therefore, it is necessary to self-adjust the window length L. The flowchart of the self-adjusting sliding average filter can be referenced in Figure 3 , including: start, read wind power data P w , let the window length L=1, determine whether the grid-connected power after sliding average filtering meets the requirements, if not, then L=L+1, if so, then output the grid-connected power P g , calculate the total energy storage power P hess ,Finish.

[0131] Step 3: Decomposition of hybrid energy storage power signal.

[0132] The hybrid energy storage power signal is obtained according to formula (1), and then the hybrid energy storage power signal is decomposed using the successive variational mode decomposition (SVMD) method, which has the following steps:

[0133] (1) The input hybrid energy storage power signal Divided into the modal components currently being extracted and margin signal Two parts, namely:

[0134] (4)

[0135] In the formula Contains unhandled signals and extracted modal components, then:

[0136] (5)

[0137] When first extracted When , the second term on the right side of the above formula is 0.

[0138] (2) The bandwidth of all modal components at the center frequency must be compact, which can be minimized using the following constraint:

[0139] (6)

[0140] Where: is the impulse response; is the convolution operator; is the eigenmode component The center frequency of

[0141] (3) Separation of the modal components being extracted When the margin signal The spectral energy distribution of is minimized to avoid overlap. After filtering, the minimization constraint is applied, that is,

[0142] (7)

[0143] (8)

[0144] Where: For filter The time domain impulse response of .

[0145] (4) Only in and Under these two constraints, the modal components currently extracted may be modal components that have been extracted before and cannot be distinguished. To solve this problem, you can use The processing method adds a filter bank and establishes the minimization constraint, that is,

[0146] (9)

[0147] (10)

[0148] Where: For filter The time domain impulse response of .

[0149] (5) To ensure that the hybrid energy storage input signal can be reconstructed, additional constraints are added, namely:

[0150] (11)

[0151] According to the above constraints and the principle of objective additivity, the entire constrained variational problem is expressed as follows:

[0152] (12)

[0153] Where: is the regularization coefficient. It is worth noting that The value should change linearly to avoid the problem of modal component overlap caused by too large a value and the problem of convergence error caused by too small a value.

[0154] (6) The constrained minimization problem is transformed by introducing the Lagrangian operator and the quadratic penalty function to obtain the unconstrained augmented Lagrangian problem, which is:

[0155] (13)

[0156] (7) Convert formula (13) to the frequency domain and solve it according to Parseval's theorem:

[0157] (14)

[0158] (8) Using the alternating direction multiplier method to iteratively solve, we have:

[0159] (15)

[0160] (16)

[0161] (17)

[0162] Where n is the number of iterations and τ is the iteration step size.

[0163] When the reconstruction error is less than a certain threshold, the input hybrid energy storage power signal is decomposed into multiple modal components with different characteristics through the above steps.

[0164] Step 4: Primary distribution of hybrid energy storage power.

[0165] The decomposed modal components have different frequencies, forming a series of IMFs with frequencies from low to high. The spectrum is then generated through Hilbert transform, and the high-low frequency boundary is observed. The frequency above the boundary is the high-frequency component, which is allocated to the flywheel energy storage; the frequency below the boundary is the low-frequency component, which is allocated to the battery. The reconstructed hybrid energy storage power is:

[0166] (18)

[0167] (19)

[0168] Where: m is the dividing point between high and low frequencies.

[0169] Step 5: Secondary distribution of hybrid energy storage power.

[0170] In order to effectively control the SOC variation range of energy storage, fuzzy control is used to perform secondary distribution of hybrid energy storage power. The fuzzy control secondary distribution strategy is referenced Figure 4 .

[0171] Figure 4 middle For flywheel energy storage SOC, The power distributed by the flywheel energy storage after fuzzy control, is the battery SOC, The power allocated to the battery after fuzzy control, P fes (t) can be expressed as With P fes The difference between (t).

[0172] During the operation of fuzzy controller 1, when the discharge power is large and Smaller, or the charging power is larger and When it is larger, the flywheel energy storage power is adjusted and the adjusted part is transferred to the battery. Similarly, fuzzy controller 2 also follows this rule. The calculation formula for the secondary distribution is:

[0173] (20)

[0174] (twenty one)

[0175] Where: , These are power correction examples of fuzzy controller 1 and fuzzy controller 2 respectively.

[0176] Among them, (1) Empirical Mode Decomposition (EMD): English full name: Empirical Mode Decomposition. It is a time-frequency analysis method, mainly used to process nonlinear and non-stationary signals. (2) Intrinsic Mode Function (IMF): English full name: Intrinsic Mode Function. It is an intrinsic component extracted from the original signal. Each IMF represents a basic oscillation mode in the signal. (3) Variational Mode Decomposition (VMD): English full name: Variational Mode Decomposition. It is an advanced signal decomposition method. (4) Battery State of Charge (SOC): English full name: State of Charge. It refers to the ratio of the current amount of electricity stored in the battery to its maximum storage capacity. (5) Successive Variational Mode Decomposition (EMD): English full name: Spectral Variational Mode Decomposition. It is an improved method based on VMD, combining the characteristics of the frequency domain and the idea of ​​variational optimization to further improve the decomposition effect. (6) Parseval's theorem is an important result in Fourier analysis, which shows that the energy of a signal in the time domain is equal to its energy in the frequency domain. In other words, the total power of a signal in the time domain and the frequency domain is the same. (7) Hilbert Transform is an important tool in signal processing and mathematical analysis, used to convert real-valued signals into complex-valued signals, and then analyze the instantaneous characteristics, frequency characteristics, and phase information of the signal.

[0177] The technical solution provided in this application example is as follows: (1) For wind power systems with large fluctuations, a self-adjusting sliding average filtering method is used. This algorithm is easy to understand and implement, can highlight the long-term trend and periodic characteristics of the data, and is helpful for data analysis. The window size of the sliding average is self-adjusted to adapt to different data characteristics and analysis requirements. When processing real-time data, no additional time lag is introduced, which makes it advantageous in real-time control applications. In addition, the sliding average algorithm can also improve the stability and reliability of the signal, making subsequent processing and analysis more accurate. (2) The SVMD method is used to decompose the hybrid energy storage power signal. SVMD is a single variable self-adjusting signal decomposition method based on variational mode decomposition (VMD). VMD has obvious advantages over other signal decomposition methods. It transforms the frequency mixture variation problem into a quasi-orthogonal analysis problem through Wiener filtering and Hilbert transform. A nonlinear signal is decomposed into multiple intrinsic mode functions (IMFs) through VMD. Different IMFs have different center frequencies and special sparse characteristics, which makes VMD widely used in signal decomposition and feature extraction. However, when VMD is used for signal decomposition, if the number of IMFs K is not set appropriately, the decomposition effect will be greatly affected. In addition, improper setting of the quadratic penalty factor α will also significantly reduce the decomposition effect. Similar to VMD, SVMD regards the modal components as signals with the minimum compact spectrum and constructs a variational problem with the same theoretical logic. The difference is that in order to achieve the successive extraction of modal components, new constraint criteria are added, and the number of IMFs does not need to be determined in advance. The complexity of the algorithm is greatly reduced, and the robustness of the initial center frequency is enhanced. (3) Kalman filtering relies on the precise mathematical model of the system and is suitable for processing linear systems. It can provide high-precision state estimation and noise suppression, but it has strong dependence on the model and high computational complexity. Fuzzy control does not require a precise mathematical model. It is based on empirical rules and is suitable for processing nonlinear and large uncertain systems. It is relatively simple to design and implement and has good robustness.

[0178] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0179] Based on the same inventive concept, the embodiment of the present application also provides a flywheel-battery hybrid energy storage power distribution device for smoothing wind power fluctuations for implementing the flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the flywheel-battery hybrid energy storage power distribution device for smoothing wind power fluctuations provided below can refer to the limitations of the flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations above, and will not be repeated here.

[0180] In an exemplary embodiment, Figure 5 As shown, a flywheel-battery hybrid energy storage power distribution device for smoothing wind power fluctuations is provided. The flywheel-battery hybrid energy storage power distribution device 500 for smoothing wind power fluctuations may include:

[0181] The data processing module 501 is used to perform self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data;

[0182] The power calculation module 502 is used to obtain the hybrid energy storage power according to the wind power data and the wind power grid-connected power;

[0183] A power decomposition module 503 is used to decompose the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power;

[0184] A component allocation module 504 is used to allocate multiple modal components to the flywheel energy storage and the battery according to the frequencies of the multiple modal components, so as to obtain the initial allocation power of the flywheel energy storage and the initial allocation power of the battery;

[0185] The power update module 505 is used to update the primary distribution power of the flywheel energy storage and the primary distribution power of the battery according to the charge state of the flywheel energy storage and the charge state of the battery, so as to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

[0186] In an exemplary embodiment, the component allocation module 504 is also used to determine the high- and low-frequency dividing points of multiple modal components; allocate the corresponding modal components with frequencies higher than the high- and low-frequency dividing points to the flywheel energy storage to obtain the initial distribution power of the flywheel energy storage; allocate the corresponding modal components with frequencies lower than or equal to the high- and low-frequency dividing points to the battery to obtain the initial distribution power of the battery.

[0187] In an exemplary embodiment, the component allocation module 504 is further configured to perform spectrum generation processing on the multiple modal components to obtain spectrum graphs of the multiple modal components; and determine the high- and low-frequency dividing point according to the spectrum graph.

[0188] In an exemplary embodiment, the power update module 505 is also used to use a first fuzzy controller to update the initial distribution power of the flywheel energy storage according to the charge state of the flywheel energy storage to obtain the secondary distribution power of the flywheel energy storage; and use a second fuzzy controller to update the initial distribution power of the battery according to the charge state of the battery to obtain the secondary distribution power of the battery.

[0189] In an exemplary embodiment, the data processing module 501 is also used to set the initial sliding window length; according to the initial sliding window length, the wind power data is subjected to a sliding average calculation to obtain the initial wind power grid-connected power corresponding to the wind power data; when the initial wind power grid-connected power meets the preset smoothing conditions, the initial wind power grid-connected power is used as the wind power grid-connected power.

[0190] In an exemplary embodiment, the device 500 further includes: an energy storage control module for controlling the charge and discharge of the flywheel energy storage according to the secondary distribution power of the flywheel energy storage; and controlling the charge and discharge of the battery according to the secondary distribution power of the battery.

[0191] Each module in the flywheel-battery hybrid energy storage power distribution device for smoothing wind power fluctuations can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0192] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0193] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0194] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0195] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0196] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0197] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0198] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0199] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A flywheel-battery hybrid energy storage power distribution method for smoothing wind power fluctuations, characterized in that: The method comprises: Performing self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data; Obtaining hybrid energy storage power according to the wind power data and the wind power grid-connected power; Decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power; Distributing the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery; According to the charge state of the flywheel energy storage and the charge state of the battery, the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

2. The method according to claim 1, characterized in that The method of allocating the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components to obtain the initial distribution power of the flywheel energy storage and the initial distribution power of the storage battery comprises: Determining high and low frequency dividing points of the multiple modal components; Allocating the modal components with corresponding frequencies higher than the high- and low-frequency dividing points to the flywheel energy storage to obtain the initial distribution power of the flywheel energy storage; The modal components corresponding to the frequencies lower than or equal to the high- and low-frequency dividing points are distributed to the battery to obtain the initial distributed power of the battery.

3. The method according to claim 2, characterized in that Determining the high-frequency and low-frequency dividing points of the multiple modal components includes: Performing frequency spectrum generation processing on the multiple modal components to obtain frequency spectrums of the multiple modal components; The high- and low-frequency dividing points are determined according to the frequency spectrum.

4. The method according to claim 1, characterized in that: The updating process of the primary distribution power of the flywheel energy storage and the primary distribution power of the battery according to the charge state of the flywheel energy storage and the charge state of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery comprises: Using a first fuzzy controller, the primary distribution power of the flywheel energy storage is updated according to the charge state of the flywheel energy storage to obtain the secondary distribution power of the flywheel energy storage; The second fuzzy controller is used to update the primary distributed power of the battery according to the state of charge of the battery to obtain the secondary distributed power of the battery.

5. The method according to claim 1, characterized in that The self-adjusting sliding average filtering process is performed on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data, including: Set the initial sliding window length; According to the initial sliding window length, performing sliding average calculation on the wind power data to obtain the initial wind power grid-connected power corresponding to the wind power data; In the case where the initial wind power grid-connected power meets a preset smoothing condition, the initial wind power grid-connected power is used as the wind power grid-connected power.

6. The method according to claim 1, characterized in that After the primary distribution power of the flywheel energy storage and the primary distribution power of the battery are updated according to the charge state of the flywheel energy storage and the charge state of the battery to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery, the method further includes: Controlling the flywheel energy storage to charge and discharge according to the secondary distributed power of the flywheel energy storage; The battery is controlled to be charged and discharged according to the secondary distributed power of the battery.

7. A flywheel-battery hybrid energy storage power distribution device for smoothing wind power fluctuations, characterized in that: The device comprises: A data processing module, used for performing self-adjusting sliding average filtering on the collected wind power data to obtain the wind power grid-connected power corresponding to the wind power data; A power calculation module, used for obtaining the hybrid energy storage power according to the wind power data and the wind power grid-connected power; A power decomposition module, used for decomposing the hybrid energy storage power to obtain multiple modal components of the hybrid energy storage power; A component allocation module, used for allocating the multiple modal components to the flywheel energy storage and the storage battery according to the frequencies of the multiple modal components, so as to obtain the initial allocation power of the flywheel energy storage and the initial allocation power of the storage battery; The power update module is used to update the primary distribution power of the flywheel energy storage and the primary distribution power of the battery according to the charge state of the flywheel energy storage and the charge state of the battery, so as to obtain the secondary distribution power of the flywheel energy storage and the secondary distribution power of the battery.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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