Novel frequency modulation method and system for supercapacitor coupled lithium battery, electronic device and readable storage medium

Abstract

The present application relates to the technical field of power grid frequency modulation, and in particular provides a novel frequency modulation method and system for a supercapacitor coupled lithium battery. The method comprises: determining a hybrid energy storage response demand on the basis of a received frequency modulation instruction; decomposing the hybrid energy storage response demand by means of an improved VMD algorithm to obtain a target number of modal components, wherein the improved VMD algorithm is obtained by optimizing parameters of a traditional VMD algorithm using a comprehensive rating index, and the comprehensive rating index is obtained on the basis of various indexes; dividing the target number of modal components to obtain a high-frequency component and a low-frequency component; and controlling a supercapacitor to respond according to the high-frequency component, and controlling a lithium battery to respond according to the low-frequency component. By utilizing the method of the present application, the power distribution result of hybrid energy storage devices can be optimized.

Description

A novel frequency modulation method, system, electronic device and readable storage medium for supercapacitor-coupled lithium battery

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to a Chinese patent application filed with the Patent Office of China on February 1, 2024, with application number 202410140890.X and invention name “A novel frequency modulation method and system for supercapacitor coupled lithium battery”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the technical field of power grid frequency modulation, and in particular to a novel frequency modulation method, system, electronic device and readable storage medium for supercapacitor-coupled lithium batteries. Background Art

[0004] For thermal power units, long-term frequency regulation leads to increased coal consumption, reduced reliability, and shortened operating life. Furthermore, high-quality, efficient frequency-regulating power sources are scarce, and coal-fired thermal power units remain the primary source of frequency regulation. Coupled with the demand for large-scale renewable energy grid integration, environmental pressures restricting unit regulation capabilities, and the "heat-based electricity" problem for heating units, the demand for power frequency regulation is further increasing. However, the performance of the auxiliary frequency regulation energy storage equipment currently used in thermal power plants cannot meet the efficiency and reliability requirements of the units themselves, and the power distribution of hybrid energy storage devices is less than ideal, affecting the receipt of grid subsidies and resulting in low economic benefits.

[0005] Summary of the Invention

[0006] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the first purpose of this application is to propose a novel frequency modulation method for supercapacitor-coupled lithium batteries to optimize the power distribution results of a hybrid energy storage device.

[0008] The second purpose of this application is to propose a new frequency modulation system for supercapacitor-coupled lithium batteries.

[0009] The third objective of this application is to provide an electronic device.

[0010] The fourth object of this application is to provide a computer-readable storage medium.

[0011] To achieve the above objectives, the first embodiment of the present application proposes a novel frequency modulation method for supercapacitor-coupled lithium batteries. The hybrid energy storage device configured in a thermal power plant includes a lithium battery and a supercapacitor. The frequency modulation method includes the following steps:

[0012] determining a hybrid energy storage response requirement based on the received frequency modulation command;

[0013] Decomposing the hybrid energy storage response demand by an improved VMD algorithm to obtain a target number of modal components, wherein the improved VMD algorithm is obtained by optimizing parameters of a traditional VMD algorithm using a comprehensive rating index, wherein the comprehensive rating index is obtained based on multiple indicators;

[0014] Dividing the modal components based on the target number to obtain high-frequency components and low-frequency components;

[0015] The supercapacitor is controlled to respond according to the high frequency component and the lithium battery is controlled to respond according to the low frequency component.

[0016] In the method of the first aspect of the present application, the multiple indicators include energy retention, sample entropy and improved cosine similarity.

[0017] In the method of the first aspect of the present application, the comprehensive rating index satisfies: Where Z is the comprehensive rating index, K is the number of modal components, and E rs is the energy retention, sampEn is the sample entropy function, IMF i is the i-th modal component, q represents the embedding dimension; r represents the similarity tolerance, cos * (IMF i ) represents improved cosine similarity.

[0018] In the method of the first aspect of the present application, the parameters of the traditional VMD algorithm include a penalty factor and the number of decomposition layers. The improved VMD algorithm obtained by optimizing the parameters of the traditional VMD algorithm using the comprehensive rating index includes: setting a first range and a first step length of the penalty factor, a second range and a second step length of the decomposition layer; confirming the initial value of the penalty factor, the final value of the penalty factor, the initial value of the decomposition layer and the final value of the decomposition layer based on the first range and the second range respectively; setting the decomposition layer to the initial value of the decomposition layer, updating the penalty factor according to the first step length from the initial value of the penalty factor to obtain a first comprehensive rating index set corresponding to different penalty factors, and when the penalty factor is updated to the final value of the penalty factor, updating the decomposition layer according to the second step length to obtain a second comprehensive rating index set corresponding to different decomposition layers, wherein the penalty factor corresponding to the maximum comprehensive rating index of the first comprehensive rating index set and the second comprehensive rating index set is the optimized value of the penalty factor, and the corresponding number of decomposition layers is the optimized value of the decomposition layer.

[0019] In the method of the first aspect of the present application, the improved VMD algorithm obtained by optimizing the parameters of the traditional VMD algorithm using a comprehensive rating index also includes: obtaining a third range and a third step size based on the optimized value of the penalty factor, and confirming the upper limit of the optimized value of the penalty factor based on the third range; setting the number of decomposition layers to the optimized value of the decomposition layers, updating the penalty factor according to the third step size from the upper limit of the optimized value of the penalty factor to obtain the frequency center of each modal component after decomposition corresponding to different penalty factors, and if the amplitude and frequency values ​​of the modal component meet the requirements, the optimal value of the penalty factor is obtained.

[0020] In the method of the first aspect of the present application, the target number is the optimized value of the decomposition layer number, and the modal component division based on the target number obtains high-frequency components and low-frequency components, including: rounding half of the target number to obtain the filter order; performing high- and low-frequency reconstruction of the modal components of the target number based on the filter order, wherein the sum of the modal components less than or equal to the filter order is the high-frequency component, and the sum of the modal components greater than the filter order is the low-frequency component.

[0021] To achieve the above objectives, the second embodiment of the present application proposes a novel supercapacitor-coupled lithium battery frequency modulation system. The hybrid energy storage device configured in the thermal power plant includes a lithium battery and a supercapacitor. The frequency modulation system includes:

[0022] a determination module, configured to determine a hybrid energy storage response requirement based on a received frequency modulation instruction;

[0023] a decomposition module for decomposing the hybrid energy storage response demand to obtain a target number of modal components using an improved VMD algorithm, wherein the improved VMD algorithm is obtained by optimizing parameters of a traditional VMD algorithm using a comprehensive rating index, wherein the comprehensive rating index is obtained based on multiple indicators;

[0024] A division module, configured to divide the modal components of the target number into high-frequency components and low-frequency components;

[0025] A control module is used to control the supercapacitor to respond according to the high-frequency component and to control the lithium battery to respond according to the low-frequency component.

[0026] In the system of the second aspect of the present application, in the decomposition module, the multiple indicators include energy retention, sample entropy and improved cosine similarity.

[0027] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the first aspect of the present application.

[0028] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method proposed in the first aspect of the present application.

[0029] The present application provides a novel frequency modulation method, system, electronic device and storage medium for supercapacitor-coupled lithium batteries. The hybrid energy storage device configured in the thermal power plant includes a lithium battery and a supercapacitor. The frequency modulation method includes the following steps: determining the hybrid energy storage response demand based on the received frequency modulation instruction; decomposing the hybrid energy storage response demand by an improved VMD algorithm to obtain a target number of modal components, wherein the parameters of the traditional VMD algorithm are optimized by using a comprehensive rating index to obtain an improved VMD algorithm, and the comprehensive rating index is obtained based on multiple indicators; high-frequency components and low-frequency components are obtained based on the target number of modal components; the supercapacitor is controlled to respond according to the high-frequency component and the lithium battery is controlled to respond according to the low-frequency component. In this case, a comprehensive rating index is obtained based on multiple indicators, and the parameters of the traditional VMD algorithm are optimized by using the comprehensive rating index to obtain an improved VMD algorithm. The improved VMD algorithm is then used to decompose the hybrid energy storage response demand to obtain high-frequency components and low-frequency components, avoiding the need to give parameters based on experience as in the existing VMD algorithm, thereby being able to more accurately obtain the number of modal components, and then better divide the high-frequency components and low-frequency components, thereby optimizing the power distribution results of the hybrid energy storage device.

[0030] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0032] FIG1 is a schematic diagram of the connection between a thermal power plant and a power grid provided in an embodiment of the present application;

[0033] FIG2 is a flow chart of a novel frequency modulation method for supercapacitor-coupled lithium batteries provided in an embodiment of the present application;

[0034] FIG3 is a schematic diagram of a specific flow chart of a parameter improvement process of a VMD algorithm provided in an embodiment of the present application;

[0035] FIG4 is a graph of the frequency modulation instructions provided in an embodiment of the present application;

[0036] FIG5 is a block diagram of a novel supercapacitor-coupled lithium battery frequency modulation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0038] The following describes a novel frequency modulation method and system for a supercapacitor-coupled lithium battery according to an embodiment of the present application with reference to the accompanying drawings.

[0039] At present, for thermal power units, long-term frequency regulation will lead to increased coal consumption, reduced reliability, and shortened operating life of the units. On the other hand, high-quality and efficient frequency regulation power sources are scarce, and coal-fired thermal power units are still the main frequency regulation power sources. In addition, the demand for large-scale new energy grid connection, environmental protection pressure restricts the regulation capacity of the units, and the problem of "heat-to-electricity" of heating units has further increased the demand for power frequency regulation. However, the performance of the auxiliary frequency regulation energy storage equipment of the current thermal power plants cannot meet the efficiency and reliability requirements of the units themselves, and the power distribution of the hybrid energy storage device is not ideal, which affects the acquisition of grid subsidy income and makes the economic benefits low. Based on this, the embodiment of the present application provides a new frequency regulation method of supercapacitor-coupled lithium battery to optimize the power distribution results of the hybrid energy storage device.

[0040] In this application, the hybrid energy storage device configured in the thermal power plant includes lithium batteries and supercapacitors, and the hybrid energy storage device assists the thermal power units in participating in frequency regulation.

[0041] Figure 1 is a schematic diagram of the connection between a thermal power plant and a power grid provided in an embodiment of the present application. As shown in Figure 1, the thermal power unit G is connected to the power grid via a busbar, and the lithium battery and supercapacitor are connected to the power grid via a first converter and a second converter respectively. When the power grid issues a frequency modulation instruction, the frequency modulation instruction carries a load response demand P. T After receiving the frequency modulation instruction, the thermal power plant G is based on the thermal power plant load P G The rest is responded by the hybrid energy storage device, that is, the hybrid energy storage device responds with P J (P T -P G =P J ) of the energy storage output, where the lithium battery responds with battery power P L In response, the supercapacitor generates a supercapacitor power P C The new power system frequency regulation method of the present application can more accurately determine the battery power P in the hybrid energy storage device. L and excess power P C The value of .

[0042] FIG2 is a flow chart of a novel supercapacitor coupled lithium battery frequency modulation method provided in an embodiment of the present application. As shown in FIG2 , the novel supercapacitor coupled lithium battery frequency modulation method includes the following steps:

[0043] Step S101: Determine a hybrid energy storage response requirement based on a received frequency modulation instruction.

[0044] Specifically, in step S101, since the frequency modulation instruction carries the load response requirement P T , so the load response demand P can be obtained based on the received frequency modulation instruction T , obtain the load P of the thermal power unit after the thermal power plant receives the frequency regulation instruction G , calculate the load response demand P T and thermal power unit load P G The difference between the two is the hybrid energy storage response demand P J (also called energy storage output), namely P T -P G =P J .

[0045] In step S102 , the hybrid energy storage response demand is decomposed by an improved VMD algorithm to obtain a target number of modal components, wherein the improved VMD algorithm is obtained by optimizing the parameters of the traditional VMD algorithm using a comprehensive rating index, and the comprehensive rating index is obtained based on multiple indicators.

[0046] Specifically, in step S102 , a traditional variational mode decomposition (VMD) algorithm is first introduced.

[0047] It is easy to understand that the traditional VMD algorithm is a completely non-recursive modal variation method that decomposes the original signal f(t) into multiple modal components u with certain sparse properties. k , modal component u k The bandwidth calculation formula is (1):

[0048] Where u k is the kth modal component (also represented by the symbol u k (t) represents), k is 1, 2, ..., K, K is the number of decomposition layers; ω k is the frequency center of the kth modal component, is the partial derivative of the function with respect to time t, δ(t) is the Dirac distribution, j is the imaginary unit, and * is the convolution operation. Introducing the penalty factor α and the Lagrange operator λ, we can obtain Equation (2) based on Equation (1):

[0049] Using the Alternating Direction Method of Multipliers (ADMM) and Fourier transform, after n+1 cycles we get:

[0050] In the formula is the Wiener filter of the kth modal component in the n+1th cycle of the algorithm, is the frequency center of the kth modal component in the n+1th cycle of the algorithm, is the frequency domain representation of the original signal f(t), is the Fourier transform, ω is the frequency, is the Wiener filter of the kth modal component in the first cycle of the algorithm, is the Wiener filter of the i-th modal component in the first cycle of the algorithm.

[0051] The parameters of the traditional VMD algorithm include a penalty factor, the number of decomposition levels, and other parameters. For the traditional VMD algorithm, the number of decomposition levels (i.e., the number of modal components) K and the penalty factor α have the greatest impact on the decomposition results. Therefore, in step S102, the parameters of the traditional VMD algorithm, i.e., the number of decomposition levels K and the penalty factor α, are optimized using a comprehensive rating index to obtain an improved VMD algorithm, so that more accurate decomposition results can be obtained when the improved VMD algorithm is subsequently used to decompose the hybrid energy storage response demand.

[0052] In step S102, a comprehensive rating index is obtained based on multiple indicators, including energy retention, sample entropy, and improved cosine similarity.

[0053] Energy retention meets:

[0054] Where u(i) is the original signal f(t) (i.e., the hybrid energy storage response demand), and E is the signal energy value. When u(i) takes the i-th IMF, the energy value E of the i-th IMF can be obtained: i , N represents the signal length, Ers represents the energy retention, Es is the energy value of the original signal, the higher the energy retention Ers, the smaller the loss in the decomposition process and the better the degree of decomposition.

[0055] Sample entropy is a measurement parameter that quantitatively describes whether the data is regular. It can well reflect the frequency characteristics of each IMF. The smaller the entropy value, the more obvious the IMF frequency characteristics and the weaker the modal aliasing phenomenon.

[0056] The sample entropy of the original signal satisfies: sampEn(f(t),q,r)=lnB q (r)-lnBq+1 (r)

[0057] Where sampEn is the sample entropy function; q represents the embedding dimension; r represents the similarity tolerance; and B represents the probability that two vectors match q or q+1 real numbers under the similarity tolerance r.

[0058] Traditional cosine similarity can characterize the similarity between each component and the original signal. The closer the cosine similarity value is to 1, the closer the component is to the original signal. Because the original FM signal and the subsequence are highly uncorrelated, if the traditional calculation formula is used, the calculated values ​​between different subsequences are relatively close, making the impact on the comprehensive evaluation index less obvious. Therefore, this application proposes an improved cosine similarity to "amplify" the results calculated after different subsequences. The improved cosine similarity satisfies:

[0059] Where cos * (IMF i ) represents the improved cosine similarity of the i-th modal component, N represents the signal length, Xn0 represents the n0-th value in the i-th subsequence, and Y n0 Represents the corresponding n0-th value in the original sequence.

[0060] Comprehensive rating indicators meet: Where Z is the comprehensive rating index, K is the number of modal components, and E rs is the energy retention, sampEn is the sample entropy function of the i-th modal component, IMF i is the i-th modal component, q represents the embedding dimension; r represents the similarity tolerance, cos * (IMF i ) represents improved cosine similarity.

[0061] In step S102, the parameters of the traditional VMD algorithm are optimized using the comprehensive rating index to obtain an improved VMD algorithm, including: setting a first range and a first step length of the penalty factor, a second range and a second step length of the decomposition layer number; confirming the initial value of the penalty factor, the final value of the penalty factor, the initial value of the decomposition layer number and the final value of the decomposition layer number based on the first range and the second range respectively; setting the decomposition layer number to the initial value of the decomposition layer number, updating the penalty factor according to the first step length from the initial value of the penalty factor to obtain a first comprehensive rating index set corresponding to different penalty factors, and when the penalty factor is updated to the final value of the penalty factor, updating the decomposition layer number according to the second step length to obtain a second comprehensive rating index set corresponding to different decomposition layer numbers, wherein the penalty factor corresponding to the maximum comprehensive rating index of the first comprehensive rating index set and the second comprehensive rating index set is the penalty factor optimization value α', and the corresponding decomposition layer number is the decomposition layer optimization value K'.

[0062] In step S102, the parameters of the traditional VMD algorithm are optimized using the comprehensive rating index to obtain an improved VMD algorithm, which also includes: obtaining a third range and a third step size based on the optimized value of the penalty factor, and confirming the upper limit of the optimized value of the penalty factor based on the third range; setting the number of decomposition layers to the optimized value of the decomposition layers, and updating the penalty factor according to the third step size from the upper limit of the optimized value of the penalty factor to obtain the frequency center of each modal component after decomposition corresponding to different penalty factors. If the amplitude and frequency values ​​of the modal component meet the requirements, the optimal value of the penalty factor is obtained.

[0063] Taking the first range of the penalty factor α as [100, 3000] and the first step length as 100, the second range of the decomposition level K as [2, 30], the second step length as 1, the third range of the penalty factor α as [α'-10, α'+10], and the third step length as 1 as an example, Figure 3 is a specific flow chart of the parameter improvement process of the VMD algorithm provided in an embodiment of the present application.

[0064] As shown in Figure 3, the parameter improvement process of the VMD algorithm includes:

[0065] The original signal (i.e., the hybrid energy storage response demand) is input. The initial value of the penalty factor α is set to 100 based on the lower limit of the first range, and the initial value of the decomposition level K is set to 2 based on the lower limit of the second range. The original signal is subjected to variational modal decomposition (VMD). The comprehensive rating index Z obtained under the current K and α is calculated. The penalty factor α is updated with a first step length of 100 until α = 3000 to obtain the first comprehensive rating index set corresponding to different penalty factors when the decomposition level K is 2. When α = 3000, K is updated with a second step length of 1 (K = K + 1). The iteration is repeated until K = 30 and then ends to obtain the second comprehensive rating index set corresponding to different decomposition levels K when α = 3000. From the first and second comprehensive rating index sets, the optimized decomposition level value K' and the optimized penalty factor value α' corresponding to the maximum comprehensive rating index Z value are output. At this point, both values ​​are optimal. The parameters of the variational modal decomposition are preliminarily determined.

[0066] Under the condition of the optimized value K' of the decomposition layer and the optimized value α' of the penalty factor, the upper limit α' of the optimized value of the penalty factor is set as the initial value of the penalty factor fine-tuning. The original signal is decomposed under the optimized value K' of the decomposition layer and the initial value α'-10 of the penalty factor fine-tuning to obtain the center frequency of each modal component (there are many ways to obtain the modal center frequency, such as MATLAB implementation). Then, the value of α' is continuously iterated from [α'-10, α'+10] with a step size of 1 to observe the change of the center frequency. When the center frequencies of adjacent modes are far away from each other (that is, the frequencies of each modal component after decomposition are close to each other), the original signal is decomposed under the optimized value K' of the decomposition layer and the initial value α'-10. The center frequency of each modal component is obtained. The modal data is exported and the amplitude-frequency graph is drawn. The frequency-amplitude graph includes the amplitude-frequency of each mode. The center frequency refers to the amplitude corresponding to -10%-+10% of the middle value of each modal frequency range. Each time α' is changed, a graph is drawn. The maximum value of the sum of the differences in the frequency values ​​corresponding to the maximum amplitude values ​​of each two modal components within the iteration range) is obtained. The optimized value of the penalty factor α' at this time is the optimal value of the penalty factor α". When α' is increased to α'+10, the penalty factor for the optimal center frequency of each mode is output (the optimal value of the penalty factor α"). The optimized value of the decomposition layer K' and the optimal value of the penalty factor α" are the final number of decomposition layers and penalty factors used to improve the traditional VMD algorithm.

[0067] In step S102, the optimized value of the number of decomposition layers and the optimal value of the penalty factor are used as the values ​​of the number of decomposition layers and the penalty factor in the traditional VMD algorithm, thereby obtaining an improved VMD algorithm. The hybrid energy storage response demand is then decomposed using the improved VMD algorithm to obtain a target number of modal components (IMFs). The target number is the optimized value of the number of decomposition layers.

[0068] Step S103 : dividing the modal components based on the target number to obtain high-frequency components and low-frequency components.

[0069] According to the characteristics of lithium batteries and supercapacitors in smoothing power fluctuations, in step S103, high-frequency components and low-frequency components are obtained based on the target number of modal components, including: rounding half of the target number to obtain the filter order; reconstructing the high and low frequencies of the target number of modal components based on the filter order, wherein the sum of the modal components less than or equal to the filter order is the high-frequency component, and the sum of the modal components greater than the filter order is the low-frequency component.

[0070] Step S104 , controlling the supercapacitor to respond according to the high frequency component and controlling the lithium battery to respond according to the low frequency component.

[0071] Considering that in the hybrid energy storage system, the supercapacitor is a power-type energy storage device with low energy density but high power density and many cycles, which is suitable for compensating the high-frequency fluctuation component of the difference between the grid dispatching power and the unit power; while the battery is an energy-type energy storage device with low power density but high energy density, which is suitable for compensating the low-frequency component of the difference between the grid dispatching power and the unit power. Therefore, in step S104, the high-frequency component is smoothed by the supercapacitor, that is, the supercapacitor power P C Equal to the high frequency component, the low frequency component is suppressed by the lithium battery, that is, the battery power P L =Equal to the low-frequency component, and then control the supercapacitor to respond according to the high-frequency component and control the lithium battery to respond according to the low-frequency component, and then combine the thermal power unit load P G This can achieve accurate response to frequency modulation instructions.

[0072] In order to verify the effect of the method of the present application, an experimental verification was carried out.

[0073] FIG4 is a graph of the frequency modulation instruction provided in the embodiment of the present application. During verification, a simulation analysis is performed using the frequency modulation instruction signal of a certain area power grid as shown in FIG4 , wherein the duration of the signal (i.e., the frequency modulation instruction) is 80 minutes as shown in FIG4 . The sampling interval is set to 1 minute. The load response demand P corresponding to the frequency modulation instruction T Around ±0.4 per unit (Pu).

[0074] In order to further verify the advantages of the method of the present application, the present application respectively uses the algorithm of the present application (iterative calculation can obtain the optimal parameter combination [K, α] = [7, 1850]) and VMD ([K, α] = [6, 2500]) selected according to empirical parameters (i.e., empirically given VMD) and WOA (whale optimization algorithm) - VMD ([K, α] = [7, 800]) which uses an intelligent algorithm to optimize the K and α values ​​to decompose the frequency modulation instructions. The degree of aliasing of adjacent modes is shown in Table 1 below.

[0075] Table 1 Decomposition results of VMD algorithm using the method of this application and random parameter selection

[0076] As can be seen from Table 1, the frequency distinction characteristics of different IMFs of the algorithm proposed in this application are the most obvious. Therefore, the parameter-optimized VMD can better complete the reasonable distribution of power by distinguishing high and low frequency components compared to VMD.

[0077] In order to implement the above embodiment, the present application also proposes a novel frequency modulation system of a supercapacitor coupled with a lithium battery, wherein the hybrid energy storage device configured in the thermal power plant includes a lithium battery and a supercapacitor.

[0078] FIG5 is a block diagram of a novel supercapacitor-coupled lithium battery frequency modulation system provided in an embodiment of the present application.

[0079] As shown in FIG5 , the novel supercapacitor-coupled lithium battery frequency modulation system includes a determination module 11, a decomposition module 12, a division module 13, and a control module 14, wherein:

[0080] A determination module 11 is configured to determine a hybrid energy storage response requirement based on a received frequency modulation instruction;

[0081] a decomposition module 12 for decomposing the hybrid energy storage response demand to obtain a target number of modal components using an improved VMD algorithm, wherein the improved VMD algorithm is obtained by optimizing the parameters of the traditional VMD algorithm using a comprehensive rating index, and the comprehensive rating index is obtained based on multiple indicators;

[0082] A division module 13 is configured to divide the modal components based on the target number to obtain high-frequency components and low-frequency components;

[0083] The control module 14 is used to control the supercapacitor to respond according to the high-frequency component and to control the lithium battery to respond according to the low-frequency component.

[0084] Optionally, in a possible implementation of the embodiment of the present application, in the decomposition module 12, multiple indicators include energy retention, sample entropy and improved cosine similarity.

[0085] Optionally, in a possible implementation of the embodiment of the present application, in the decomposition module 12, the comprehensive rating index satisfies: Where Z is the comprehensive rating index, K is the number of modal components, and E rs is the energy retention, sampEn is the sample entropy function, IMF i is the i-th modal component, q represents the embedding dimension; r represents the similarity tolerance, cos * (IMF i ) represents improved cosine similarity.

[0086] Optionally, in a possible implementation of an embodiment of the present application, in the decomposition module 12, the parameters of the traditional VMD algorithm include a penalty factor and a decomposition layer number, and the parameters of the traditional VMD algorithm are optimized using a comprehensive rating index to obtain an improved VMD algorithm, including: setting a first range and a first step length of the penalty factor, a second range and a second step length of the decomposition layer number; confirming the initial value of the penalty factor, the final value of the penalty factor, the initial value of the decomposition layer number, and the final value of the decomposition layer number based on the first range and the second range respectively; setting the decomposition layer number to be the initial value of the decomposition layer number, updating the penalty factor according to the first step length from the initial value of the penalty factor to obtain a first comprehensive rating index set corresponding to different penalty factors, and when the penalty factor is updated to the final value of the penalty factor, updating the decomposition layer number according to the second step length to obtain a second comprehensive rating index set corresponding to different decomposition layer numbers, wherein the penalty factor corresponding to the maximum comprehensive rating index of the first comprehensive rating index set and the second comprehensive rating index set is the optimized value of the penalty factor, and the corresponding number of decomposition layers is the optimized value of the decomposition layer number.

[0087] Optionally, in a possible implementation of an embodiment of the present application, in the decomposition module 12, the parameters of the traditional VMD algorithm include a penalty factor and the number of decomposition layers, and the improved VMD algorithm is obtained by optimizing the parameters of the traditional VMD algorithm using a comprehensive rating index, further comprising: obtaining a third range and a third step size based on the optimized value of the penalty factor, and confirming the upper limit of the optimized value of the penalty factor based on the third range; setting the number of decomposition layers to the optimized value of the decomposition layers, and updating the penalty factor according to the third step size from the upper limit of the optimized value of the penalty factor to obtain the frequency center of each modal component after decomposition corresponding to different penalty factors; if the amplitude and frequency values ​​of the modal component meet the requirements, the optimal value of the penalty factor is obtained.

[0088] Optionally, in a possible implementation of an embodiment of the present application, the division module 13 is specifically used to round half of the target number to obtain a filter order; based on the filter order, the modal components of the target number are reconstructed in high and low frequencies, wherein the sum of the modal components less than or equal to the filter order is a high-frequency component, and the sum of the modal components greater than the filter order is a low-frequency component.

[0089] It should be noted that the above explanation of the embodiment of the frequency modulation method of the novel supercapacitor coupled lithium battery is also applicable to the frequency modulation system of the novel supercapacitor coupled lithium battery of this embodiment, and will not be repeated here.

[0090] In an embodiment of the present application, a hybrid energy storage device configured in a thermal power plant includes a lithium battery and a supercapacitor, and a frequency modulation method includes the following steps: determining a hybrid energy storage response demand based on a received frequency modulation instruction; decomposing the hybrid energy storage response demand by an improved VMD algorithm to obtain a target number of modal components, wherein the parameters of the traditional VMD algorithm are optimized using a comprehensive rating index to obtain an improved VMD algorithm, and the comprehensive rating index is obtained based on multiple indicators; high-frequency components and low-frequency components are obtained based on the target number of modal components; the supercapacitor is controlled to respond according to the high-frequency component and the lithium battery is controlled to respond according to the low-frequency component. In this case, a comprehensive rating index is obtained based on multiple indicators, and the parameters of the traditional VMD algorithm are optimized using the comprehensive rating index to obtain an improved VMD algorithm, and then the hybrid energy storage response demand is decomposed by the improved VMD algorithm to obtain high-frequency components and low-frequency components, thereby avoiding the need to give parameters based on experience as in the existing VMD algorithm, thereby being able to more accurately obtain the number of modal components, and then better divide the high-frequency components and low-frequency components, thereby optimizing the power distribution results of the hybrid energy storage device. The parameters of the VMD algorithm in the method of the present application have a theoretical basis, which reduces the possibility of modal aliasing of the decomposition results, thereby reducing the impact on the power distribution results, has low economic cost, short running time, and good economy.

[0091] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0092] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided in the above embodiments.

[0093] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0094] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0095] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0096] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of optional embodiments of the present application includes additional implementations in which functions may be performed in a sequence other than as shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, as should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0097] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0098] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0100] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0101] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A novel frequency modulation method for supercapacitor coupled lithium battery, characterized in that: The hybrid energy storage device configured in the thermal power plant includes lithium batteries and supercapacitors. The frequency modulation method includes the following steps: determining a hybrid energy storage response requirement based on the received frequency modulation command; Decomposing the hybrid energy storage response demand by an improved VMD algorithm to obtain a target number of modal components, wherein the improved VMD algorithm is obtained by optimizing parameters of a traditional VMD algorithm using a comprehensive rating index, wherein the comprehensive rating index is obtained based on multiple indicators; Dividing the modal components based on the target number to obtain high-frequency components and low-frequency components; The supercapacitor is controlled to respond according to the high frequency component and the lithium battery is controlled to respond according to the low frequency component.

2. The frequency modulation method of the novel supercapacitor coupled lithium battery according to claim 1, characterized in that: The multiple indicators include energy retention, sample entropy and improved cosine similarity.

3. The frequency modulation method of the novel supercapacitor coupled lithium battery according to claim 2, characterized in that: The comprehensive rating indicators meet the following requirements: Where Z is the comprehensive rating index, K is the number of modal components, and E rs is the energy retention, sampEn is the sample entropy function, IMF i is the i-th modal component, q represents the embedding dimension; r represents the similarity tolerance, cos * (IMF i ) represents improved cosine similarity.

4. The frequency modulation method of the novel supercapacitor coupled lithium battery according to claim 1, characterized in that: The parameters of the traditional VMD algorithm include a penalty factor and the number of decomposition levels. The improved VMD algorithm obtained by optimizing the parameters of the traditional VMD algorithm using the comprehensive rating index includes: Setting a first range and a first step length of the penalty factor, a second range and a second step length of the number of decomposition levels; and determining an initial value of the penalty factor, a final value of the penalty factor, an initial value of the number of decomposition levels, and a final value of the number of decomposition levels based on the first range and the second range, respectively; Let the number of decomposition layers be the initial value of the decomposition layer, and update the penalty factor according to the first step length from the initial value of the penalty factor to obtain The first comprehensive rating indicator set corresponding to different penalty factors, when the penalty factor is updated to the final value of the penalty factor, the decomposition level is updated according to the second step size to obtain the second comprehensive rating indicator set corresponding to different decomposition levels, where the penalty factor corresponding to the maximum comprehensive rating indicator of the first comprehensive rating indicator set and the second comprehensive rating indicator set is the optimized value of the penalty factor, and the corresponding decomposition level is the optimized value of the decomposition level.

5. The novel frequency modulation method of supercapacitor coupled lithium battery according to claim 4, characterized in that: The improved VMD algorithm obtained by optimizing the parameters of the traditional VMD algorithm using the comprehensive rating index further includes: Obtaining a third range and a third step size based on the optimized value of the penalty factor, and determining an upper limit of the optimized value of the penalty factor based on the third range; Let the number of decomposition layers be the optimized value of the number of decomposition layers, and update the penalty factor according to the third step length from the upper limit of the penalty factor optimization value to obtain the frequency center of each modal component after decomposition corresponding to different penalty factors. If the amplitude and frequency values of the modal component meet the requirements, the optimal value of the penalty factor is obtained.

6. The novel frequency modulation method of supercapacitor coupled lithium battery according to claim 4, characterized in that: The target number is the optimized value of the decomposition layer number, and the modal component division based on the target number to obtain high-frequency components and low-frequency components includes: Rounding off half of the target number to obtain a filter order; High and low frequency reconstruction is performed on the target number of modal components based on the filter order, wherein the sum of the modal components less than or equal to the filter order is the high frequency component, and the sum of the modal components greater than the filter order is the low frequency component.

7. A new supercapacitor coupled lithium battery frequency modulation system, characterized in that: The hybrid energy storage device configured in the thermal power plant includes lithium batteries and supercapacitors, and the frequency regulation system includes: a determination module, configured to determine a hybrid energy storage response requirement based on a received frequency modulation instruction; a decomposition module for decomposing the hybrid energy storage response demand to obtain a target number of modal components using an improved VMD algorithm, wherein the improved VMD algorithm is obtained by optimizing parameters of a traditional VMD algorithm using a comprehensive rating index, wherein the comprehensive rating index is obtained based on multiple indicators; A division module, configured to divide the modal components of the target number into high-frequency components and low-frequency components; A control module is used to control the supercapacitor to respond according to the high-frequency component and to control the lithium battery to respond according to the low-frequency component.

8. The novel supercapacitor coupled lithium battery frequency modulation system according to claim 7, characterized in that: In the decomposition module, the multiple indicators include energy retention, sample entropy and improved cosine similarity.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

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