A centripetal force method for predicting energy storage frequency modulation instructions and a system thereof

Abstract

The present invention discloses a method and system for predicting energy storage frequency modulation instructions using a centripetal force method, which relates to the field of artificial intelligence technology in power systems. The method comprises decomposing an original frequency modulation sequence through VMD, decomposing the original frequency modulation sequence into multiple subsequences, setting the decomposition layer number to K, identifying convex and concave points in the sequence, and obtaining the resultant force of each subsequence to evaluate the characteristics; using the centripetal force method to evaluate the direction and magnitude of the centripetal force of each subsequence decomposed by VMD, and determining the maximum centripetal force as the objective function; and applying an improved frost optimization algorithm to optimize the VMD decomposition layer number K to maximize the objective function and obtain the best decomposition effect. The present invention effectively improves the processing efficiency and accuracy of frequency modulation signals, providing important support for the real-time prediction of frequency modulation instructions in energy management systems.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence in power systems, and in particular to a method and system for predicting energy storage frequency modulation instructions using a centripetal force method. Background Art

[0002] The traditional hybrid energy storage (supercapacitor + lithium battery) method of assisting thermal power generation units in frequency regulation is to transmit the difference between the frequency modulation command and the thermal power generation unit to the hybrid energy storage, with the battery covering the low-frequency portion and the supercapacitor covering the high-frequency portion. However, the signal transmission (frequency modulation command to supercapacitor / lithium battery) takes time, and the supercapacitor or lithium battery itself also takes time to respond. This results in a certain response time difference, further affecting the profit value and further affecting the profit of the power plant. To this end, the present invention proposes a prediction method to predict the size of the frequency modulation command in advance, enabling the supercapacitor / battery to take action in advance, thereby improving the profit value.

[0003] However, traditional prediction methods have two shortcomings:

[0004] 1. Lack of assessment of the difficulty of predicting the original data: The original sequence of FM instructions is highly nonlinear and irregular. The stronger the irregularity, the more difficult it is to predict. Assessment of the difficulty of prediction is conducive to the adjustment of subsequent predictions.

[0005] 2. The prediction network lacks personalized customization for different sequences. Different sequences have different complexities. If the same parameters are used for all of them, the prediction results will not be ideal.

[0006] In view of these two points, the present invention proposes an original sequence prediction difficulty analysis based on the centripetal force method and a selection of the coefficient of VMD decomposition K that is most suitable for the centripetal force method based on the centripetal force method. Summary of the Invention

[0007] In view of the above-mentioned existing problems, the present invention adopts VMD decomposition for the original sequence. However, different decomposition levels of VMD decomposition correspond to different prediction accuracies. Therefore, the present invention adopts the centripetal force method to evaluate the strength of the centripetal force of each subsequence of VMD decomposition. Then, with the maximum centripetal force as the objective function, the improved frost ice optimization algorithm (IRIME) is used to optimize the VMD parameters.

[0008] The technical problem to be solved by the present invention is to overcome the physical delay in the transmission of frequency modulation instructions of the power system, optimize the action timing of the energy storage equipment by predicting instructions, and reduce the loss of power plant revenue.

[0009] In order to solve the above technical problems, a centripetal force method for energy storage frequency modulation instruction prediction is proposed, including:

[0010] The original FM sequence is decomposed into multiple subsequences through VMD decomposition. The number of decomposition layers is set to K. The convex and concave points in the sequence are identified, and the resultant force of each subsequence is obtained to evaluate the characteristics. The centripetal force method is used to evaluate the direction and magnitude of the centripetal force of each subsequence decomposed by VMD, and the maximum centripetal force is determined as the objective function. The improved frost optimization algorithm is applied to optimize the number of VMD decomposition layers K to maximize the objective function and obtain the best decomposition effect.

[0011] As a preferred solution of the energy storage frequency modulation instruction prediction method of the centripetal force method described in the present invention, wherein: the VMD decomposition of the original frequency modulation sequence includes decomposing the original frequency modulation sequence into K subsequences IMF1, IMF2, ..., IMFK, assuming the decomposition level is K, and the resultant force of each subsequence is [F1, F2, ..., F K ], the total force is F ∑ =[F1+F2+..+F K ];

[0012] The centripetal force method evaluation includes setting any subsequence of any original sequence after VMD decomposition as IMF1=[x i1 ,x i2 ,x i3 ,x ii ,…,x iM ], each group of three consecutive points is read through the entire sequence. When the middle point among the three consecutive points is larger than the two adjacent points, the current point is determined to be a convex point. When the middle point among the three consecutive points is the smallest, the current point is determined to be a concave point.

[0013] When a convex or concave point is determined in the sequence, it is allowed to find the point of centripetal force.

[0014] As a preferred solution of the energy storage frequency modulation instruction prediction method of the centripetal force method described in the present invention, wherein: the direction and magnitude of the centripetal force include, when x ii If it is a convex point, the centripetal force direction of the current convex point determines the position of the circle center. The mean of the sequence IMFi is A, and the point x closest to A is is , the median of the sequence IMFi is B, and the point x closest to B is iq , then the center of the circle is (s+q) / 2, s and q belong to M, then the direction of the current convex point is:

[0015]

[0016] When X ii If it is a concave point, the direction of the current concave point is:

[0017]

[0018] Among them, θ i Indicates the direction of the centripetal force of a convex or concave point, i represents the i-th value, s represents the subscript of the value closest to A, and q represents the subscript of the value closest to B.

[0019] As a preferred solution of the energy storage frequency modulation instruction prediction method of the centripetal force method described in the present invention, wherein: the direction and magnitude of the centripetal force also include, when X ii The mean of the convex or concave sequence IMFi is A, and the median of the sequence IMFi is B. Then the centripetal force is:

[0020]

[0021] Where M is [x i1 ,x i2 ,x i3 , x ii ,…,x iM ], the length of the subsequence, F i is the magnitude of the centripetal force.

[0022] As a preferred solution of the energy storage frequency modulation instruction prediction method of the centripetal force method described in the present invention, wherein: the determination of the maximum centripetal force as the objective function includes solving the centripetal force and the direction of the corresponding force corresponding to all convex points or concave points in the entire sequence, and the magnitude of the final resultant force is:

[0023]

[0024] Among them, c is the number of all concave and convex points [F1, θ1, F2, θ2, ..., F i ,θ i ,…,F r ,θ r ].

[0025] As a preferred solution of the energy storage frequency modulation instruction prediction method of the centripetal force method described in the present invention, the improved frost and ice optimization algorithm includes optimizing the K value of VMD through the improved frost and ice optimization algorithm and performing the soft frost search strategy before the improvement:

[0026]

[0027] Among them, R is the updated position of the particle, ab represents the bth particle of the ath rime-agent; R best,b is the best b-th particle in the population, r1 is a parameter representing a random number in the range (-1, 1), and r controls the direction of particle movement. cosθ will change with the number of iterations:

[0028]

[0029] β is the environmental factor; h is the adhesion, which is a random number in the range of (0, 1); b Uab and R Lab are the upper and lower bounds of the escape space, respectively; E is the adhesion coefficient, which increases with the number of iterations; r2 is a random number in the range of (0, 1) for parameter r1, which determines whether the particle position is updated;

[0030] For the soft cream search strategy before improvement, two types of improvements are made;

[0031] Improvement 1 is to change the randomized number to a specific one, and change r1 to a random number between [-11] to:

[0032]

[0033] Among them, F Σ is the total resultant force of the sum of the resultant forces of each subsequence;

[0034] The second improvement is to improve cosθ to:

[0035]

[0036] Among them, t is the current iteration number, and T is the maximum iteration number of the algorithm.

[0037] As a preferred solution of the energy storage frequency modulation instruction prediction method of the centripetal force method described in the present invention, the optimization of the decomposition layer number K of VMD includes optimizing the K value in VMD using an improved algorithm, outputting each subsequence corresponding to the optimal K, solving the objective function, outputting K prediction results and adding them to obtain the K value corresponding to the maximum value of the total resultant force.

[0038] Another object of the present invention is to provide a storage frequency modulation instruction prediction system based on the centripetal force method. The present invention aims to solve the technical problems in frequency modulation signal processing through the collaborative work of the VMD decomposition module, the centripetal force evaluation module and the frost and ice optimization improvement module, including optimizing the number of decomposition layers to improve the efficiency of time-frequency analysis, accurately identifying signal characteristics, and automatically adjusting parameters through centripetal force evaluation and frost and ice optimization algorithms to achieve the best signal decomposition effect, thereby improving the accuracy and automation level of signal processing.

[0039] As a preferred solution of the energy storage frequency modulation instruction prediction system of the centripetal force method described in the present invention, it is characterized by including a VMD decomposition module, a centripetal force evaluation module, and a frost and ice optimization and improvement module;

[0040] The VMD decomposition module decomposes the original FM sequence into multiple subsequences by VMD, sets the number of decomposition layers to K, identifies convex and concave points in the sequence, and obtains the resultant force of each subsequence to evaluate the features;

[0041] The centripetal force evaluation module uses the centripetal force method to evaluate the centripetal force direction and magnitude of each subsequence decomposed by VMD, and determines the maximum centripetal force as the objective function;

[0042] The frost optimization improvement module applies the improved frost optimization algorithm to optimize the number of decomposition layers K of VMD to maximize the objective function and obtain the best decomposition effect.

[0043] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the energy storage frequency modulation instruction prediction method using a centripetal force method are implemented.

[0044] 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 energy storage frequency modulation instruction prediction method using a centripetal force method are implemented.

[0045] Beneficial effects of the present invention: The present invention decomposes the original frequency modulation sequence through VMD, converting complex signals into easy-to-process subsequences, facilitating detailed analysis and improving prediction accuracy. The centripetal force method is used to evaluate the convex and concave points in the subsequence, accurately locate the signal characteristics, and provide data support for subsequent optimization. By solving the centripetal force maximization and integrating the centripetal forces of multiple key points, the signal strength is optimized globally to enhance the credibility of the overall prediction. The improved frost and ice optimization algorithm is applied to dynamically optimize the decomposition layer number K to achieve the best decomposition effect, ensuring that the system has more flexible information extraction capabilities. Ultimately, this method effectively improves the processing efficiency and accuracy of frequency modulation signals, and provides important support for the real-time prediction of frequency modulation instructions in energy management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 The present invention provides an overall flow chart of a method for predicting energy storage frequency modulation instructions using a centripetal force method according to an embodiment of the present invention.

[0048] Figure 2A prediction flow chart of a method for predicting energy storage frequency modulation instructions using a centripetal force method provided in one embodiment of the present invention.

[0049] Figure 3 A system solution flow chart of a centripetal force method energy storage frequency modulation instruction prediction system provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Example 1, with reference to Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a method for predicting energy storage frequency modulation instructions using a centripetal force method, comprising:

[0053] S1: Decompose the original FM sequence into multiple subsequences through VMD. Set the number of decomposition layers to K, identify convex and concave points in the sequence, and obtain the resultant force of each subsequence to evaluate the features. VMD is a signal decomposition method, namely variational mode decomposition, which decomposes the original FM sequence into multiple subsequences.

[0054] Because the sequence is irregular, the present invention is inspired by the centripetal force in physics and proposes a process to evaluate the strength of the regularity of a sequence. The stronger the centripetal force, the stronger the regularity, and vice versa.

[0055] Furthermore, the original FM sequence is decomposed into K subsequences IMF1, IMF2, ..., IMFK, with the resultant force of each subsequence being [F1, F2, ..., F K ], the total force is F ∑ =[F1+F2+..+F K ], where F K is the resultant force of the K-th subsequence;

[0056] The centripetal force method evaluation includes setting any subsequence of any original sequence after VMD decomposition as IMF1=[xi1 ,x i2 ,x i3 ,x ii ,…,x iM ], each group of three consecutive points is read through the entire sequence. When the middle point among the three consecutive points is larger than the two adjacent points, the current point is determined to be a convex point. When the middle point among the three consecutive points is the smallest, the current point is determined to be a concave point.

[0057] When convex or concave points are identified in the sequence, it is allowed to find the centripetal points because such points have more variations.

[0058] S2: The centripetal force method is used to evaluate the direction and magnitude of the centripetal force of each subsequence of VMD decomposition, and the maximum centripetal force is determined as the objective function.

[0059] Furthermore, when x ii If it is a convex point, the centripetal force direction of the current convex point determines the position of the circle center. The mean of the sequence IMFi is A, and the point x closest to A is is , the median of the sequence IMFi is B, and the point x closest to B is iq , then the center of the circle is (s+q) / 2, s and q belong to M, then the direction of the current convex point is:

[0060]

[0061] When X ii If it is a concave point, the direction of the current concave point is:

[0062]

[0063] Among them, θ i Indicates the direction of the centripetal force of a convex or concave point, i represents the variable index, that is, the i-th value; s represents the subscript of the value closest to A, and q represents the subscript of the value closest to B.

[0064] When x ii The mean of the convex or concave sequence IMFi is A, and the median of the sequence IMFi is B. Then the centripetal force is:

[0065]

[0066] Where M is [x i1 ,x i2 ,x i3 , x ii ,…,x iM ], the length of the subsequence, F i is the magnitude of the centripetal force.

[0067] It should be noted that the centripetal force and the direction of the corresponding force corresponding to all convex or concave points in the entire sequence are solved, and the magnitude of the final resultant force F is:

[0068]

[0069] Among them, c is the number of all concave and convex points [F1, θ1, F2, θ2, ..., F i ,θ i ,…,F r ,θ r ].

[0070] S3: Apply the improved Frost Ice optimization algorithm to optimize the number of decomposition levels K of VMD to maximize the objective function and obtain the best decomposition effect.

[0071] Furthermore, the K value of VMD is optimized by the improved frost optimization algorithm, and the soft frost search strategy before the improvement is performed:

[0072]

[0073] in, is the updated position of the particle, ab represents the bth particle of the ath rime-agent; R best,b is the best b-th particle in the population, r1 is a parameter representing a random number in the range (-1, 1), and r controls the direction of particle movement. cosθ will change with the number of iterations:

[0074]

[0075] β is the environmental factor, which simulates the influence of the external environment following the number of iterations; h is the adhesion, a random number in the range of (0, 1), which controls the distance between the centers of two fog particles; b Uab and R Lab are the upper and lower bounds of the escape space, respectively; E is the adhesion coefficient, which increases with the number of iterations; r2 is a random number in the range of (0, 1) for parameter r1, which determines whether the particle position is updated;

[0076] For the soft cream search strategy before improvement, two types of improvements are made;

[0077] Improvement 1 is to change the randomized number to a specific one, and change r1 to a random number between [-11] to:

[0078]

[0079] Among them, F Σ is the total resultant force of the sum of the resultant forces of each subsequence;

[0080] The second improvement is to improve cosθ to:

[0081]

[0082] Among them, t is the current iteration number, and T is the maximum iteration number of the algorithm.

[0083] The improved algorithm is used to optimize the K value in VMD, and the corresponding subsequences under the optimal K are output. The objective function is solved through the GRU gated loop, and the K prediction results are output and added to obtain the K value corresponding to the maximum total force.

[0084] In order to further verify the advantages of the present invention, the present invention uses the method of the present invention and the VMD-GRU prediction method to predict the FM sequence, and the four evaluation indicators are:

[0085] The first indicator is MAE, which is the mean absolute prediction error of the prediction results:

[0086]

[0087] The second indicator is SSE, which reflects the discrete state of each observation value of each sample, also known as the within-group sum of squares or residual sum of squares:

[0088]

[0089] The third indicator is RMSE, which is the root mean square prediction error:

[0090]

[0091] The fourth indicator is MAPE, which is the mean of absolute errors:

[0092]

[0093] Where N represents the sample size, y n and Represent the actual value and predicted value at time n respectively.

[0094] The results are shown in Table 1:

[0095] Table 1 Frequency modulation evaluation index results

[0096]

[0097] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0098] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0099] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered 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.

[0100] 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 devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, 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, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0101] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described 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: 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.

[0102] Example 3, reference Figure 3 , which is the third embodiment of the present invention, provides a centripetal force method energy storage frequency modulation instruction prediction system, including a VMD decomposition module, a centripetal force evaluation module, and a frost and ice optimization improvement module;

[0103] The VMD decomposition module decomposes the original FM sequence into multiple subsequences by VMD, sets the number of decomposition layers to K, identifies convex and concave points in the sequence, and obtains the resultant force of each subsequence to evaluate the features;

[0104] The centripetal force evaluation module uses the centripetal force method to evaluate the centripetal force direction and magnitude of each subsequence decomposed by VMD, and determines the maximum centripetal force as the objective function;

[0105] The Frost Ice Optimization Improvement Module applies the improved Frost Ice Optimization Algorithm to optimize the number of VMD decomposition layers K to maximize the objective function and obtain the best decomposition effect.

[0106] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for predicting energy storage frequency modulation instructions using a centripetal force method, characterized in that: include, Applied to the frequency modulation control of power systems, the original frequency modulation instruction sequence of the power system is decomposed into multiple subsequences through VMD. The number of decomposition layers is set to K. The convex and concave points in the sequence are identified to obtain the frequency modulation instruction changes. The resultant force of each subsequence is obtained to evaluate the characteristics. The centripetal force method is used to evaluate the centripetal force direction and magnitude of each subsequence of VMD decomposition, and the maximum centripetal force is determined as the objective function, where the objective function is to maximize the subsequence signal; The centripetal force direction and magnitude include, when x ii If it is a convex point, the centripetal force direction of the current convex point determines the position of the circle center. The mean of the sequence IMFi is A, and the point x closest to A is is , the median of the sequence IMFi is B, and the point x closest to B is iq , then the center of the circle is (s+q) / 2, s and q belong to M, M is the length of any subsequence after VMD decomposition, then the direction of the current salient point is: When X ii If it is a concave point, the direction of the current concave point is: Among them, θ i Indicates the centripetal force direction of a convex or concave point, i indicates the i-th value, s indicates the subscript of the value closest to A, and q indicates the subscript of the value closest to B; The centripetal force direction and magnitude also include when x ii The mean of the convex or concave sequence IMFi is A, and the median of the sequence IMFi is B. Then the centripetal force is: Where M is [x i1 ,x i2 ,x i3 , x ii ,…,x iM ], the length of the subsequence, F i is the magnitude of the centripetal force; The improved Frost Ice optimization algorithm is applied to optimize the number of VMD decomposition layers K to maximize the objective function and obtain the best decomposition effect; The improved frost optimization algorithm includes optimizing the K value of VMD through the improved frost optimization algorithm and performing the soft frost search strategy before the improvement: in, is the updated position of the particle, ab represents the bth particle of the ath rime-agent; R best,b is the best b-th particle in the population, r1 is a parameter representing a random number in the range (-1, 1), and r controls the direction of particle movement. cosθ will change with the number of iterations: β is the environmental factor; h is the adhesion, which is a random number in the range of (0, 1); b Uab and R Lab are the upper and lower bounds of the escape space, respectively; E is the adhesion coefficient, which increases with the number of iterations; r2 is a random number in the range of (0, 1) for parameter r1, which determines whether the particle position is updated; The improved algorithm is used to optimize the K value in VMD, and the corresponding subsequences under the optimal K are output. The objective function is solved through the GRU gated loop, and the K prediction results are output and added to obtain the K value corresponding to the maximum total force.

2. The method for predicting energy storage frequency modulation instructions using a centripetal force method according to claim 1, wherein: The VMD decomposition of the original FM sequence includes decomposing the original FM sequence into K subsequences IMF1, IMF2, ..., IMFK, with the resultant force of each subsequence being [F1, F2, ..., F K ], the total force is F ∑ =[F1+F2+..+F K ], where F K is the resultant force of the K-th subsequence; The centripetal force method evaluation includes setting any subsequence of any original sequence after VMD decomposition as IMF1=[x i1 ,x i2 ,x i3 ,x ii ,…,x iM ], where i is the variable index, representing the i-th value. Each group of three consecutive points goes through the entire sequence. When the middle point among the three consecutive points is larger than the two adjacent points, the current point is determined to be a convex point. When the middle point among the three consecutive points is the smallest, the current point is determined to be a concave point. When a convex or concave point is determined in the sequence, it is allowed to find the point of centripetal force.

3. The method for predicting energy storage frequency modulation instructions using a centripetal force method according to claim 2, wherein: Determining the maximum centripetal force as the objective function includes solving the centripetal forces and the directions of the corresponding forces corresponding to all convex or concave points in the entire sequence, and the magnitude of the final resultant force is: Among them, c is the number of all concave and convex points [F1, θ1, F2, θ2, ..., F i ,θ i ,…,F r ,θ r ].

4. The method for predicting energy storage frequency modulation instructions using a centripetal force method according to claim 3, wherein: The improved frost and ice optimization algorithm includes two types of improvements to the soft frost search strategy before the improvement; Improvement 1 is to change the randomized number to a specific one, and change r1 to a random number between [-11] to: Among them, F Σ is the total resultant force of the sum of the resultant forces of each subsequence; The second improvement is to improve cosθ to: Among them, t is the current iteration number, and T is the maximum iteration number of the algorithm.

5. A system for predicting energy storage frequency modulation instructions using a centripetal force method as claimed in any one of claims 1 to 4, characterized in that: Including VMD decomposition module, centripetal force evaluation module, and frost and ice optimization and improvement module; The VMD decomposition module decomposes the original FM sequence into multiple subsequences by VMD, sets the number of decomposition layers to K, identifies convex and concave points in the sequence, and obtains the resultant force of each subsequence to evaluate the features; The centripetal force evaluation module uses the centripetal force method to evaluate the centripetal force direction and magnitude of each subsequence decomposed by VMD, and determines the maximum centripetal force as the objective function; The frost ice optimization improvement module applies the improved frost ice optimization algorithm to optimize the number of decomposition layers K of VMD to maximize the objective function and obtain the best decomposition effect.

6. 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 for predicting energy storage frequency modulation instructions using the centripetal force method according to any one of claims 1 to 4 are implemented.

7. 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 a method for predicting energy storage frequency modulation instructions using a centripetal force method according to any one of claims 1 to 4 are implemented.

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