Energy storage frequency modulation instruction prediction method and system based on radius method

Abstract

The invention discloses an energy storage frequency modulation instruction prediction method and system based on a radius method, and belongs to the technical field of power system scheduling and optimization, and the method comprises the steps: evaluating a prediction difficulty value of a frequency modulation instruction sequence through the radius method; an ANFIS prediction network structure is improved according to the prediction difficulty value; and predicting a value of a frequency modulation instruction through the improved ANFIS network and outputting a result. The frequency modulation instruction prediction method solves the problems that an existing frequency modulation instruction prediction method lacks non-linear prediction difficulty evaluation of original data, personalized model adjustment cannot be carried out for different sequence complexity, response time is delayed, and prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching and optimization, and in particular to a method and system for predicting energy storage frequency regulation instructions based on a radius method. Background Art

[0002] With the rapid development of renewable energy and energy storage technology, frequency regulation command prediction technology has gradually become an important part of smart grid management. The traditional frequency regulation method of hybrid energy storage (ultracapacitor + lithium battery) to assist thermal power units mainly relies on the instantaneous regulation capability of thermal power units. However, with the increase in the proportion of new energy, the frequency regulation demand has become more complex. To meet this challenge, researchers have proposed a variety of auxiliary energy storage methods, including hybrid energy storage systems (such as a combination of ultracapacitors and lithium batteries) to respond to grid frequency fluctuations more efficiently. This technology can not only provide fast energy response, but also maximize the economic benefits of power plants. In recent years, data-driven prediction models such as artificial neural networks (ANN) and adaptive neural fuzzy inference systems (ANFIS) have been increasingly widely used in frequency regulation prediction, laying the foundation for the integration of various energy forms in the future.

[0003] Although the existing technology has made some progress in frequency modulation instruction prediction, there are still many shortcomings. First, traditional prediction methods often lack the evaluation of the prediction difficulty in the original data, which makes it difficult to grasp the response difference caused by time delay or signal transmission delay. Secondly, the original sequence of frequency modulation instructions usually has highly nonlinear and difficult to regularize characteristics, which makes it impossible for some unified parameter adjustment prediction models to obtain ideal prediction results. More importantly, traditional methods fail to achieve personalized customization for the complexity of specific frequency modulation sequences, resulting in different prediction effects and unable to adapt to frequency modulation requirements of different complexities. These problems not only affect the real-time response capability of energy storage equipment, but also further weaken the economic benefits of power plants. Therefore, there is an urgent need for a method to evaluate the prediction difficulty and optimize the prediction model structure accordingly, so as to improve the prediction accuracy and response speed of frequency modulation instructions, thereby ensuring the stability and economy of the power grid. Summary of the invention

[0004] In order to solve the above technical problems, a method for predicting energy storage frequency modulation instructions based on the radius method is proposed, including evaluating the prediction difficulty value of the frequency modulation instruction sequence through the radius method; improving the ANFIS prediction network structure according to the prediction difficulty value; predicting the value of the frequency modulation instruction through the improved ANFIS network and outputting the result; the prediction difficulty value includes the prediction difficulty evaluation, which is expressed as: , Wherein, D represents the prediction difficulty value, K represents the number of circle centers that satisfy two similarities at the same time, and N represents the number of times the frequency modulation instructions are collected.

[0005] As a preferred solution of the method for predicting energy storage frequency modulation instructions based on the radius method described in the present invention, wherein: the frequency modulation instruction sequence includes: assuming that the frequency modulation instruction is , expressed as: , in, Indicates the value of the frequency modulation instruction. , Respectively represent different acquisition times; the sampling interval of the frequency modulation instruction is 1 second, the median of the rectangular coordinate system The coordinate value of .

[0006] As a preferred solution of the method for predicting energy storage frequency modulation instructions based on the radius method described in the present invention, wherein: the prediction difficulty value of the frequency modulation instruction sequence evaluated by the radius method includes determining the center position and radius length of two adjacent points in the frequency modulation instruction sequence in the rectangular coordinate system; assuming that the two adjacent points are and , the coordinates of the two points in the rectangular coordinate system are and , connect A and B, and make a perpendicular bisector of line segment AB, then the centers of two adjacent points are on the straight line Up, straight line It is expressed as: , Let the centers of two adjacent points be , Always on the center line Move up and down, and arrive The distance is the radius to form a circle. By dynamically adjusting the center position of the circle, line segment AB divides the circle into two parts, S1 and S2. In the process of forming the circle, the area of ​​S1 and S2 is when S1 / S2 < 0.03, then the radius length is , the center of the circle is .

[0007] As a preferred solution of the method for predicting energy storage frequency modulation instructions based on the radius method described in the present invention, wherein: the prediction difficulty value of the frequency modulation instruction sequence evaluated by the radius method also includes obtaining the frequency modulation instruction sequence by the radius method The radius sequence of two adjacent points in and the center sequence ; Perform similarity analysis on the radius and center of all adjacent points in the frequency modulation instruction sequence to find out the points with similar center coordinates and similar radius; the points with similar center coordinates include calculating the distance between two points in the center sequence, and setting the farthest distance as , set a center The distance from the center of other circles is less than / 2, then As the center of the circle, / Draw a circle with radius / The center of the circle with the radius is defined as the center of the circle with similar coordinates. Let the number be Radius similarity includes setting the radius sequence The radius of the circle with the largest radius is , the minimum radius is , the average value is , if the radius Satisfies the similarity interval, then the radius in the similarity interval satisfies the radius similarity, let the number be Similar intervals are expressed as: , If a circle center satisfies both center similarity and radius similarity, then the number of similarities that are satisfied at the same time is denoted as .

[0008] As a preferred solution of the method for predicting energy storage frequency modulation instructions based on the radius method described in the present invention, the prediction difficulty value also includes that the larger the value of D, the greater the difficulty of prediction.

[0009] As a preferred solution of the method for predicting energy storage frequency modulation instructions based on the radius method described in the present invention, wherein: the improving the ANFIS prediction network structure according to the prediction difficulty value includes adjusting the membership function of the ANFIS network based on the prediction difficulty value D and ; Membership function according to prediction difficulty Improved adjustment, the improved membership function , expressed as: , in, , represent the width and center of the Gaussian function respectively, Indicates that according to the prediction difficulty membership function Adjust the improved value, expressed as: , According to the prediction difficulty, the membership function Improve and adjust the membership function , expressed as: , in, Indicates that according to the prediction difficulty membership function Adjust the improved value, expressed as: , and The maximum value is 1 and the minimum value is 0.

[0010] As a preferred solution of a method for predicting energy storage frequency modulation instructions based on the radius method described in the present invention, wherein: the predicting the value of the frequency modulation instruction and outputting the result includes inputting the frequency modulation instruction sequence into the improved ANFIS network for prediction, the improved ANFIS network dynamically adjusts the network structure according to the prediction difficulty value D, and outputs the prediction result; the prediction result is used as the final value for the energy storage system to act in advance.

[0011] Another object of the present invention is to provide a system for predicting energy storage frequency modulation instructions based on the radius method. The present invention solves the problems of the existing frequency modulation instruction prediction methods, such as the lack of prediction difficulty assessment for the nonlinearity of the original data, the inability to perform personalized model adjustment for different sequence complexities, and the delay in response time, so as to improve the prediction accuracy. The original frequency modulation sequence is carefully analyzed to evaluate its prediction difficulty, and the structure of the ANFIS network is dynamically adjusted according to the evaluation results, so as to achieve efficient prediction of the frequency modulation instructions and dynamic adjustment of the prediction network structure. While reducing the response time of the prediction network and improving the prediction accuracy, the frequency modulation response speed and economic benefits of the power plant are also improved.

[0012] As a preferred solution of the system for predicting energy storage frequency modulation instructions based on the radius method described in the present invention, it is characterized by including a frequency modulation evaluation module, a difficulty network module, and a prediction output module; the frequency modulation evaluation module is used to evaluate the prediction difficulty value of the frequency modulation instruction sequence through the radius method; the difficulty network module is used to improve the ANFIS prediction network structure according to the prediction difficulty value; the prediction output module is used to predict the value of the frequency modulation instruction through the improved ANFIS network and output the result.

[0013] 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 a method for predicting energy storage frequency modulation instructions based on a radius method are implemented.

[0014] 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 based on a radius method are implemented.

[0015] Beneficial effects of the present invention: The method for predicting energy storage frequency modulation instructions based on the radius method provided by the present invention can quantitatively analyze the difficulty of frequency modulation instructions through prediction difficulty assessment based on the radius method, can effectively identify the complexity of frequency modulation instruction sequences, and reduce prediction errors caused by data nonlinearity and complexity, thereby improving the accuracy of prediction results; the network structure of ANFIS is adjusted by the prediction difficulty value, so that the prediction model can be flexibly customized according to frequency modulation instructions of different complexities, ensuring that the model can adapt to variable frequency modulation instructions, thereby further improving the prediction effect; by predicting the value of frequency modulation instructions in advance through the ANFIS prediction network, the energy storage system can be activated in advance, reducing the time delay caused by signal transmission and equipment response, and the introduction of advance can improve the response speed of energy storage equipment, optimize the frequency modulation performance of power plants, and thus improve economic benefits; secondly, due to the improvement of prediction accuracy and response speed, the dependence and cost of standby power generation equipment can be reduced, the frequency modulation effect can be increased, and the frequency modulation cost can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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 creative work.

[0017] Figure 1 An overall flow chart of a method for predicting energy storage frequency modulation instructions based on the radius method is provided for one embodiment of the present invention.

[0018] Figure 2 A schematic diagram of a radius length determination method for a method for predicting energy storage frequency modulation instructions based on a radius method provided by an embodiment of the present invention.

[0019] Figure 3 A system solution module diagram of a system for predicting energy storage frequency modulation instructions based on the radius method provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] Example 1, reference Figure 1-Figure 2, which is the first embodiment of the present invention, provides a method for predicting energy storage frequency modulation instructions based on the radius method, comprising: S1: Evaluate the prediction difficulty value of the FM instruction sequence by the radius method.

[0022] Furthermore, the frequency modulation instruction sequence includes, assuming that the frequency modulation instruction is , expressed as: , in, Indicates the value of the frequency modulation instruction. , Respectively represent different acquisition times; the sampling interval of the frequency modulation instruction is 1 second, the median of the rectangular coordinate system The coordinate value of .

[0023] It should be noted that the prediction difficulty value of the frequency modulation instruction sequence evaluated by the radius method includes determining the center position and radius length of two adjacent points in the frequency modulation instruction sequence in the rectangular coordinate system; assuming that the two adjacent points are and , the coordinates of the two points in the rectangular coordinate system are and , connect A and B, and make a perpendicular bisector of line segment AB, then the centers of two adjacent points are on the straight line Up, straight line It is expressed as: , like Figure 2 As shown, let the center of two adjacent points be , Always on the center line Move up and down, and arrive The distance is the radius to form a circle. By dynamically adjusting the center position of the circle, line segment AB divides the circle into two parts, S1 and S2. In the process of forming the circle, the area of ​​S1 and S2 is when S1 / S2 < 0.03, then the radius length is , the center of the circle is .

[0024] It should also be noted that evaluating the prediction difficulty value of the frequency modulation instruction sequence by the radius method also includes obtaining the frequency modulation instruction sequence by the radius method. The radius sequence of two adjacent points in and the center sequence ; Perform similarity analysis on the radius and center of all adjacent points in the frequency modulation instruction sequence, and find out the points with similar center coordinates and similar radius; the points with similar center coordinates include calculating the distance between two points in the center sequence, and setting the farthest distance to , set a center The distance from the center of other circles is less than / 2, then As the center of the circle, / Draw a circle with radius / The center of the circle with the radius is defined as the center of the circle with similar coordinates. Let the number be Radius similarity includes setting the radius sequence The radius of the circle with the largest radius is , the minimum radius is , the average value is , if the radius Satisfies the similarity interval, then the radius in the similarity interval satisfies the radius similarity, let the number be Similar intervals are expressed as: , If a circle center satisfies both center similarity and radius similarity, then the number of similarities that are satisfied at the same time is denoted as .

[0025] It should also be noted that the prediction difficulty value also includes the prediction difficulty evaluation, which is expressed as: , Among them, D represents the prediction difficulty value; the larger the value of D is, the greater the difficulty of prediction is.

[0026] It should also be noted that the center of the circle moves downward along the perpendicular bisector, and a circle is drawn with the distance from the center of the circle to the point as the radius, until the area S1 / S2 of the two parts S1 and S2 divided by the line segment AB in the process of forming the circle is less than 0.03, and the radius length is determined. This process ensures that the shape of the circle matches the distribution characteristics of the data points by dynamically adjusting the radius, thereby reflecting the local fluctuation of the frequency modulation instruction sequence; by determining the radius length and the position of the center of the circle, a quantitative standard is provided for the subsequent evaluation of the prediction difficulty of the frequency modulation instruction sequence. The smaller the radius length, the more drastic the fluctuation between the two adjacent points and the greater the prediction difficulty; conversely, the larger the radius length, the more gradual the change of the data points and the lower the prediction difficulty, which achieves an accurate characterization of the local characteristics of the frequency modulation instruction sequence and lays the foundation for the quantitative evaluation of the prediction difficulty.

[0027] S2: Improve the ANFIS prediction network structure according to the prediction difficulty value.

[0028] Furthermore, improving the ANFIS prediction network structure according to the prediction difficulty value includes adjusting the membership function of the ANFIS network based on the prediction difficulty value D and ; Membership function according to prediction difficulty Improve and adjust the membership function , expressed as: , in, , represent the width and center of the Gaussian function respectively, Indicates that according to the prediction difficulty membership function Adjust the improved value, expressed as: , According to the prediction difficulty, the membership function Improve and adjust the membership function , expressed as: , in, Indicates that according to the prediction difficulty membership function Adjust the improved value, expressed as: , and The maximum value is 1 and the minimum value is 0.

[0029] It should be noted that the main fuzzy inference system FIS is composed of five functional modules, including a rule base, a database, a decision unit, a fuzzy interface and a defuzzification result, in which the initial member function and the rules are defined according to the actual situation; the adaptive network is a multi-layer feedforward network, which is composed of square nodes and circular nodes. The nodes are connected together by directional links. The direction nodes have parameters, while the center nodes do not. The adaptive neural fuzzy inference system ANFIS is composed of FIS and an adaptive network. The performance of FIS can be improved by adjusting the fuzzy inference rules and membership functions. ANFIS functionally combines the interpretability characteristics of FIS and the learning ability of the adaptive network. ANFIS consists of two parts, the first part is the antecedent and the second part is the conclusion, which are interconnected in the form of a network through rules.

[0030] Taking 3 inputs and 9 rules as an example, the ANFIS model structure is divided into 5 layers, each layer contains several nodes. The first layer is the fuzzification layer, which performs a fuzzification process representing the membership function for each input change, mapping the value of the input variable to the membership degree of the fuzzy set. and As input, select Gaussian function as membership function, and The maximum value is 1 and the minimum value is 0. The improvement of the ANFIS prediction model according to the prediction difficulty is located in the fuzzification layer; the second layer is the rule layer, which obtains the incentive strength of each rule and the fuzzy set of the output variable by logically running the membership of the input variable; the third layer is the normalization layer, which normalizes the incentive strength of each rule obtained in the previous layer to obtain its standardized weight, and the number of standardized weights is the same as the number of fuzzy rules; the fourth layer is the reasoning layer, which executes the conclusion part of the fuzzy rules and infers the output value from the rules; the fifth layer is the output layer, which performs weighted average on the output value of each rule to obtain the exact output result.

[0031] It should also be noted that the membership function of the ANFIS network is dynamically adjusted according to the prediction difficulty value D, so that the network can better adapt to frequency modulation instruction sequences of different complexities; through the dynamic adjustment of the membership function and fuzzy rules, the improved ANFIS network can more efficiently process nonlinear frequency modulation instruction sequences and reduce prediction errors; the improved ANFIS network can more accurately predict the value of the frequency modulation instruction by combining the interpretability of the fuzzy reasoning system and the learning ability of the adaptive network; by dynamically adjusting the network structure and optimizing the prediction process, the improved ANFIS network can generate prediction results faster, thereby reducing the response time of the energy storage system.

[0032] S3: Predict the value of the frequency modulation instruction through the improved ANFIS network and output the result.

[0033] Furthermore, predicting the value of the frequency modulation instruction and outputting the result includes inputting the frequency modulation instruction sequence into the improved ANFIS network for prediction, the improved ANFIS network dynamically adjusts the network structure according to the prediction difficulty value D, and outputs the prediction result; the prediction result is used as the final value for the energy storage system to act in advance.

[0034] It should be noted that the improved ANFIS network can make adaptive adjustments according to the prediction difficulty value of the frequency modulation instruction sequence, which enhances the flexibility and applicability of the model; by improving the prediction accuracy and reducing the response time, the improved ANFIS network reduces the dependence on backup power generation equipment, thereby reducing the frequency modulation cost and improving the economic benefits of the power plant; the improved ANFIS network can better capture the local fluctuation characteristics of the frequency modulation instruction sequence and optimize the frequency modulation response speed and stability of the power plant.

[0035] Example 2, reference Figure 3 , which is the second embodiment of the present invention, provides a method for predicting energy storage frequency modulation instructions based on the radius method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0036] First, obtain the frequency modulation instructions from a power plant, frequency modulation sequence 1 and frequency modulation sequence 2. The acquisition time of frequency modulation sequence 1 is from 0:00 to 15:00 on the 1st of a certain month, and a data point is collected every 1 second; the acquisition time of frequency modulation sequence 2 is from 0:00 to 15:00 on the 2nd of a certain month, and a data point is collected every 1 second.

[0037] Secondly, the experiment adopts the prediction method of the present invention and the prediction method of ANFIS to predict the FM sequence. The present invention evaluates the prediction difficulty value of the FM instruction sequence through the radius method, adjusts and improves the ANFIS network structure according to the difficulty value obtained by the evaluation, predicts the value of the FM instruction based on the improved ANFIS prediction model and outputs the result, records the experimental data and calculates the prediction error according to the evaluation index, and compares and analyzes it with the prediction method of ANFIS. The prediction error experimental data is shown in Table 1 below.

[0038] Table 1 Prediction error experimental data , Table 2 below shows four evaluation indicators.

[0039] Table 2 Four evaluation indicators , Among them, MAE represents the mean absolute prediction error, SSE represents the within-group sum of squares, RMSE represents the root mean square prediction error, and MAPE represents the mean absolute error.

[0040] It can be seen from the experimental data that through the prediction difficulty assessment based on the radius method and the improved ANFIS prediction model, the experimental data show that the accuracy of the prediction results is significantly improved. Compared with the unimproved ANFIS method, the improved ANFIS network can more accurately capture the complexity and nonlinear characteristics of the frequency modulation instruction sequence, thereby reducing the prediction error. Specifically, the prediction results have a higher fit with the measured values ​​and a lower error rate.

[0041] By dynamically adjusting the ANFIS network structure based on the prediction difficulty value, the model can adapt to FM instruction sequences of different complexities. Experiments show that the improved model is more stable, reliable and adaptable when processing high volatility and nonlinear FM instructions.

[0042] Embodiment 3 is the third embodiment of the present invention, which is different from the first two embodiments in that: 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, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0043] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0044] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0045] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0046] Example 4, reference Figure 3 , which is the fourth embodiment of the present invention, provides a system for predicting energy storage frequency modulation instructions based on the radius method, including a frequency modulation evaluation module, a difficulty network module, and a prediction output module.

[0047] The FM evaluation module is used to evaluate the prediction difficulty value of the FM instruction sequence through the radius method; the difficulty network module is used to improve the ANFIS prediction network structure according to the prediction difficulty value; the prediction output module is used to predict the value of the FM instruction through the improved ANFIS network and output the result.

[0048] 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 the 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 based on the radius method, characterized in that: include, The prediction difficulty value of the frequency modulation instruction sequence is evaluated by the radius method; Improve the ANFIS prediction network structure according to the prediction difficulty value; The improved ANFIS network is used to predict the value of the frequency modulation instruction and output the result; The prediction difficulty value includes the prediction difficulty evaluation, expressed as: ， Wherein, D represents the prediction difficulty value, K represents the number of circle centers that satisfy two similarities at the same time, and N represents the number of times the frequency modulation instructions are collected.

2. The method for predicting energy storage frequency modulation instructions based on the radius method as claimed in claim 1, characterized in that: The frequency modulation instruction sequence includes: assuming the frequency modulation instruction is , expressed as: ， in, Indicates the value of the frequency modulation instruction. , They represent different acquisition times respectively; The sampling interval of the frequency modulation instruction is 1 second, and the median value of the rectangular coordinate system is The coordinate value of .

3. The method for predicting energy storage frequency modulation instructions based on the radius method as claimed in claim 2, characterized in that: The method of evaluating the prediction difficulty value of the frequency modulation instruction sequence by the radius method includes determining the center position and radius length of two adjacent points in the frequency modulation instruction sequence in a rectangular coordinate system; Assume that two adjacent points are and , the coordinates of the two points in the rectangular coordinate system are and , connect A and B, and make a perpendicular bisector of line segment AB, then the centers of two adjacent points are on the straight line Up, straight line It is expressed as: ， Let the centers of two adjacent points be , Always on the center line Move up and down, and arrive The distance is the radius to form a circle. By dynamically adjusting the center position of the circle, line segment AB divides the circle into two parts, S1 and S2. In the process of forming the circle, the area of ​​S1 and S2 is when S1 / S2 < 0.03, then the radius length is , the center of the circle is .

4. The method for predicting energy storage frequency modulation instructions based on the radius method as claimed in claim 3, characterized in that: The method of evaluating the prediction difficulty value of the frequency modulation instruction sequence by the radius method also includes obtaining the frequency modulation instruction sequence by the radius method. The radius sequence of two adjacent points in and the center sequence ; Perform similarity analysis on the radius and center of all adjacent points in the frequency modulation instruction sequence to find out the points with similar center coordinates and similar radius; The points with similar center coordinates include calculating the distance between two points in the circle center sequence, assuming that the farthest distance is , set a center The distance from the center of other circles is less than / 2, then As the center of the circle, / Draw a circle with radius / The center of the circle with the radius is defined as the center of the circle with similar coordinates. Let the number be indivual; Radius similarity includes setting the radius sequence The radius of the circle with the largest radius is , the minimum radius is , the average value is , if the radius Satisfies the similarity interval, then the radius in the similarity interval satisfies the radius similarity, let the number be indivual; Similarity interval, expressed as: ， If a circle center satisfies both center similarity and radius similarity, then the number of similarities that are satisfied at the same time is denoted as .

5. The method for predicting energy storage frequency modulation instructions based on the radius method as claimed in claim 4, characterized in that: The prediction difficulty value also includes that the larger the value of D is, the greater the difficulty of prediction is.

6. The method for predicting energy storage frequency modulation instructions based on the radius method as claimed in claim 4, characterized in that: The improving the ANFIS prediction network structure according to the prediction difficulty value includes adjusting the membership function of the ANFIS network based on the prediction difficulty value D. and ; According to the prediction difficulty, the membership function Improve and adjust the membership function , expressed as: ， in, , represent the width and center of the Gaussian function respectively, Indicates that according to the prediction difficulty membership function Adjust the improved value, expressed as: ， According to the prediction difficulty, the membership function Improve and adjust the membership function , expressed as: ， in, Indicates that according to the prediction difficulty membership function Adjust the improved value, expressed as: ， and The maximum value is 1 and the minimum value is 0.

7. The method for predicting energy storage frequency modulation instructions based on the radius method as claimed in claim 4, characterized in that: The predicting the value of the frequency modulation instruction and outputting the result comprises inputting the frequency modulation instruction sequence into the improved ANFIS network for prediction, the improved ANFIS network dynamically adjusting the network structure according to the prediction difficulty value D, and outputting the prediction result; The prediction result is used as the final value for early action of the energy storage system.

8. A system for predicting energy storage frequency modulation instructions based on the radius method, using the method for predicting energy storage frequency modulation instructions based on the radius method as claimed in any one of claims 1 to 7, characterized in that: include: FM evaluation module, difficulty network module, prediction output module; The frequency modulation evaluation module is used to evaluate the prediction difficulty value of the frequency modulation instruction sequence by using the radius method; The difficulty network module is used to improve the ANFIS prediction network structure according to the prediction difficulty value; The prediction output module is used to predict the value of the frequency modulation instruction through the improved ANFIS network and output the result.

9. 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 a method for predicting energy storage frequency modulation instructions based on the radius method according to any one of claims 1 to 7 are implemented.

10. 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 based on the radius method according to any one of claims 1 to 7 are implemented.

Images