Lithium battery energy storage frequency modulation instruction prediction method and related product

Abstract

The invention discloses a lithium battery energy storage frequency modulation instruction prediction method and a related product. Belongs to the technical field of lithium battery energy storage. Firstly, a historical frequency modulation instruction sequence of the lithium battery energy storage system is obtained, and then the historical data is input into a pre-trained prediction model to obtain a final frequency modulation instruction prediction sequence. In the training process of the prediction model, historical frequency modulation instructions are collected to serve as a training set, and the training set is input into the prediction model for training until model training is completed. The technical problems that in the prior art, frequency modulation instruction prediction is complex, prediction precision is not high, and the effect is not ideal are solved, the prediction precision and stability of the frequency modulation instruction are remarkably improved, the system response time is greatly shortened, and the lithium battery energy storage system can respond to the frequency modulation requirement of a power system more rapidly.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery energy storage, and relates to a frequency modulation instruction prediction method for lithium battery energy storage and related products. Background Art

[0002] With the large-scale access of renewable energy and the increasing openness of the power market, the frequency stability of the power system faces unprecedented challenges. Traditionally, the power system mainly relies on large thermal power units and hydropower units for frequency regulation, but these traditional regulation methods have problems such as slow response speed and low regulation accuracy, which are difficult to meet the high requirements of modern power systems for frequency stability.

[0003] In order to meet this challenge, lithium-ion battery energy storage system (LIBESS) has gradually become an important means of power system frequency regulation due to its advantages of fast response speed, high regulation accuracy, and flexible charging and discharging. However, to give full play to the advantages of lithium-ion battery energy storage system in frequency regulation, it is necessary to accurately predict its frequency regulation instructions so as to formulate energy storage charging and discharging strategies in advance, thereby ensuring the frequency stability of the power system.

[0004] At present, the prediction of frequency regulation instructions mainly relies on traditional statistical methods and physical models. However, these methods often have problems such as low prediction accuracy and poor adaptability. On the one hand, traditional statistical methods are difficult to accurately capture the complexity and nonlinearity of power system frequency fluctuations; on the other hand, although physical models can reflect the physical characteristics of power systems, they are often limited by model complexity and difficulty in accurately obtaining parameters, resulting in unsatisfactory prediction results in practical applications. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problems in the prior art that the frequency modulation instruction prediction is complex, the prediction accuracy is low and the effect is unsatisfactory, and to provide a frequency modulation instruction prediction method for lithium battery energy storage and related products.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A first aspect of the present invention provides a method for predicting frequency modulation instructions for lithium battery energy storage, comprising the following steps: Obtain the historical frequency modulation instruction sequence of the lithium battery energy storage system; Input the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence; The training method of the pre-trained prediction model is: The collected historical frequency modulation instructions are used as a training set and input into the prediction model for training to obtain a trained prediction model.

[0007] Furthermore, the prediction model is an LSTM model.

[0008] Furthermore, the historical frequency modulation instruction sequence is input into a pre-trained prediction model to obtain a predicted frequency modulation instruction sequence, specifically: Preprocess the historical frequency modulation instructions to obtain a processed frequency modulation instruction sequence; Dividing the processed frequency modulation instruction sequence into N sub-frequency modulation instruction sequences; Inputting N sub-FM instruction sequences into the prediction model respectively to obtain N predicted FM instruction sequences; Based on the N predicted frequency modulation instruction sequences, a final predicted frequency modulation instruction sequence is obtained.

[0009] Furthermore, the preprocessing of the historical frequency modulation instructions adopts a median filtering method.

[0010] Furthermore, the historical frequency modulation instruction sequence is divided into N sub-frequency modulation instruction sequences by using a random sampling method.

[0011] Further, the final predicted frequency modulation instruction sequence is obtained based on the N predicted frequency modulation instruction sequences, specifically: The N predicted frequency modulation instruction sequences are averaged to obtain the final predicted frequency modulation instruction sequence.

[0012] A second aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for predicting the frequency modulation instructions for lithium battery energy storage when executing the computer program.

[0013] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the frequency modulation instruction prediction method for lithium battery energy storage is implemented.

[0014] A fourth aspect of the present invention provides a computer program product, which includes computer instructions, and the computer instructions instruct a computer to execute the above-mentioned frequency modulation instruction prediction method for lithium battery energy storage.

[0015] A fifth aspect of the present invention provides a frequency modulation instruction prediction system for lithium battery energy storage, comprising: A data acquisition module is used to obtain the historical frequency modulation instruction sequence of the lithium battery energy storage system; The prediction module inputs the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for predicting frequency modulation instructions for lithium battery energy storage. By using a historical frequency modulation instruction sequence as training data, the prediction model in the present invention can learn the potential laws and changing trends of the frequency modulation instructions. Compared with traditional physical models or empirical formulas, this data-driven method can more accurately capture the complexity and nonlinear characteristics of frequency modulation instructions, thereby significantly improving the accuracy of prediction. Since the prediction of frequency modulation instructions is performed after the model training is completed, in practical applications, it is only necessary to input new historical frequency modulation instructions into the model to quickly obtain the prediction results. This efficient prediction method greatly shortens the system response time, allowing the lithium battery energy storage system to respond more quickly to the frequency modulation needs of the power system. Accurate frequency modulation instruction prediction helps to plan the charging and discharging strategy of the lithium battery energy storage system in advance. By reasonably arranging the allocation and use of energy storage resources, the frequency modulation capability of the energy storage system can be maximized, while avoiding unnecessary energy loss and battery wear, and improving the economy and service life of the energy storage system.

[0017] Furthermore, the present invention significantly improves the prediction accuracy and stability of frequency modulation instructions by adopting an advanced LSTM (Long Short-Term Memory) model as a prediction model. The LSTM model, with its powerful sequence data processing capability and long-term dependency capture capability, has demonstrated excellent performance in dealing with the prediction problem of time series data such as power system frequency modulation instructions. By acquiring and preprocessing the historical frequency modulation instruction sequence, the present invention effectively removes noise and outliers in the original data and improves the data quality. In particular, the median filtering method is adopted, which can effectively smooth data fluctuations while retaining the main trend of the data, further enhancing the robustness of the prediction model. The present invention divides the processed historical frequency modulation instruction sequence into multiple subsequences and selects them by random sampling. This strategy not only increases the diversity of data, but also helps the model learn the characteristics of the frequency modulation instruction sequence at different time scales, thereby improving the generalization ability of the model. By inputting N sub-frequency modulation instruction sequences into the LSTM model for prediction, and averaging the N prediction results, the present invention realizes the integrated prediction of frequency modulation instructions. This method can make full use of the information of multiple prediction results, effectively reduce the accidental errors of a single prediction result, and make the final predicted frequency modulation instruction sequence more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0019] Figure 1A block diagram of a method for predicting frequency modulation instructions for lithium battery energy storage according to the present invention; Figure 2 This is a block diagram of the frequency modulation instruction prediction system for lithium battery energy storage of the present invention; Figure 3 A schematic diagram of a computer device provided by an embodiment.

[0020] Among them: 201-data acquisition module; 202-prediction module; 60-computer device; 61-processor; 62-memory; 63-computer program. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention described and marked in the drawings here can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0024] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0025] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0026] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings: See also Figure 1 The present invention discloses a method for predicting frequency modulation instructions for lithium battery energy storage, comprising the following steps: S1, obtaining the historical frequency modulation instruction sequence of the lithium battery energy storage system; extracting the historical frequency modulation instruction sequence from the database of the lithium battery energy storage system, which instruction sequences record the frequency modulation instructions received by the system in the past period of time.

[0028] S2, input the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence; select a suitable prediction model; use the collected historical frequency modulation instruction sequence as a training set to train the prediction model. During the training process, by adjusting the parameters of the model (such as learning rate, number of hidden layer units, etc.), the model can accurately capture the changing trend and potential rules of the frequency modulation instruction. After the training is completed, a trained prediction model is obtained, which can receive a new historical frequency modulation instruction sequence as input and output a predicted frequency modulation instruction sequence. When it is necessary to predict future frequency modulation instructions, the new historical frequency modulation instruction sequence is input into the trained LSTM prediction model. The model calculates the future frequency modulation instruction sequence based on the input historical frequency modulation instruction sequence, that is, the final predicted frequency modulation instruction sequence, for use by the lithium battery energy storage system; these predicted instructions will guide the charging and discharging strategy of the energy storage system to ensure the frequency stability of the power system.

[0029] An embodiment of the present invention discloses a method for predicting frequency modulation instructions for lithium battery energy storage, comprising the following steps: S1, extracts the historical frequency modulation instruction sequence from the database of the lithium battery energy storage system. These instructions record the frequency modulation instructions received by the system in the past period of time.

[0030] S2, pre-process the historical frequency modulation instructions using the median filtering method to remove noise and outliers. Median filtering is a nonlinear smoothing technology that can effectively protect the edge information of the frequency modulation instructions and remove high-frequency noise.

[0031] S3, dividing the processed historical frequency modulation instruction sequence into N sub-frequency modulation instruction sequences. A random sampling method is used here to ensure that each sub-sequence contains enough frequency modulation instructions while maintaining data diversity.

[0032] S4, taking the collected historical frequency modulation instructions (subsequences after preprocessing and segmentation) as a training set, inputting it into the LSTM prediction model for training, and obtaining a trained LSTM prediction model.

[0033] S5, inputting the N sub-FM instruction sequences into the trained LSTM prediction model respectively to obtain N predicted FM instruction sequences. Then, averaging the N predicted FM instruction sequences to obtain the final predicted FM instruction sequence.

[0034] The present invention captures the long-term dependency of frequency modulation instructions through the LSTM model, thereby improving the accuracy of prediction; by averaging N predicted frequency modulation instruction sequences, the volatility of a single prediction result is reduced and the stability of the prediction is improved; the sub-frequency modulation instruction sequence generated by the random sampling method increases the diversity of data, enabling the model to better adapt to the frequency modulation instruction prediction under different circumstances, and providing strong support for the formulation of frequency modulation strategies for lithium battery energy storage systems.

[0035] An embodiment of the present invention provides a frequency modulation instruction prediction system for lithium battery energy storage, comprising: The data acquisition module 201 acquires the historical frequency modulation instruction sequence of the lithium battery energy storage system; The prediction module 202 inputs the historical frequency modulation instruction sequence into a pre-trained prediction model to obtain a final predicted frequency modulation instruction sequence.

[0036] In one embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0037] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0038] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0039] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the frequency modulation instruction prediction method for lithium battery energy storage in the above embodiment; the processor may load and execute the following steps of one or more instructions in the computer-readable storage medium: Obtain the historical frequency modulation instruction sequence of the lithium battery energy storage system; Input the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence; The training method of the pre-trained prediction model is: The collected historical frequency modulation instructions are used as a training set and input into the prediction model for training to obtain a trained prediction model.

[0040] One embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for predicting frequency modulation instructions for lithium battery energy storage when executing the computer program. The memory may be any type of storage device, such as a random access memory (RAM), a read-only memory (ROM), a hard disk drive (HDD), a solid-state drive (SSD), or cloud storage. In this embodiment, a computer program for implementing a photovoltaic power generation power prediction method is stored in the memory. The processor is responsible for executing the computer program stored in the memory. The processor may be any type of computing device, such as a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In this embodiment, the processor executes a computer program to implement the method for predicting frequency modulation instructions for lithium battery energy storage: Obtain the historical frequency modulation instruction sequence of the lithium battery energy storage system; Input the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence; The training method of the pre-trained prediction model is: The collected historical frequency modulation instructions are used as a training set and input into the prediction model for training to obtain a trained prediction model.

[0041] One embodiment of the present invention provides a computer program product, comprising a series of computer instructions. These instructions are designed to instruct a computer to perform a series of operations to achieve the prediction of frequency modulation instructions for lithium battery energy storage. These instructions can be loaded into any type of computer system, including but not limited to personal computers, servers, cloud computing platforms, etc., to execute the frequency modulation instruction prediction method for lithium battery energy storage. The computer instructions in the computer program product of this embodiment have the main function of instructing the computer to perform the following steps to implement the frequency modulation instruction prediction method for lithium battery energy storage: Obtain the historical frequency modulation instruction sequence of the lithium battery energy storage system; Input the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence; The training method of the pre-trained prediction model is: The collected historical frequency modulation instructions are used as a training set and input into the prediction model for training to obtain a trained prediction model.

[0042] Figure 3 A schematic diagram of a computer device provided by an embodiment of the present invention.

[0043] See also Figure 3 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the frequency modulation instruction prediction method for lithium battery energy storage in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the frequency modulation instruction prediction system for lithium battery energy storage in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0044] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 3 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0045] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0046] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0047] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0048] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0049] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0050] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting frequency modulation instructions for lithium battery energy storage, characterized in that: The following steps are involved: Obtain the historical frequency modulation instruction sequence of the lithium battery energy storage system; Input the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence; The training method of the pre-trained prediction model is: The collected historical frequency modulation instructions are used as a training set and input into the prediction model for training to obtain a trained prediction model.

2. The method for predicting frequency modulation instructions for lithium battery energy storage according to claim 1, characterized in that: The prediction model is an LSTM model.

3. The method for predicting frequency modulation instructions for lithium battery energy storage according to claim 1, characterized in that: The historical frequency modulation instruction sequence is input into the pre-trained prediction model to obtain the predicted frequency modulation instruction sequence, specifically: Preprocess the historical frequency modulation instructions to obtain a processed frequency modulation instruction sequence; Dividing the processed frequency modulation instruction sequence into N sub-frequency modulation instruction sequences; Inputting N sub-FM instruction sequences into the prediction model respectively to obtain N predicted FM instruction sequences; Based on the N predicted frequency modulation instruction sequences, a final predicted frequency modulation instruction sequence is obtained.

4. The method for predicting frequency modulation instructions for lithium battery energy storage according to claim 3, characterized in that: The preprocessing of the historical frequency modulation instructions adopts a median filtering method.

5. The method for predicting frequency modulation instructions for lithium battery energy storage according to claim 3, characterized in that: The historical frequency modulation instruction sequence is divided into N sub-frequency modulation instruction sequences by using a random sampling method.

6. The method for predicting frequency modulation instructions for lithium battery energy storage according to claim 3, characterized in that: The final predicted frequency modulation instruction sequence is obtained based on the N predicted frequency modulation instruction sequences, specifically: The N predicted frequency modulation instruction sequences are averaged to obtain the final predicted frequency modulation instruction sequence.

7. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for predicting the frequency modulation instructions of lithium battery energy storage according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting frequency modulation instructions for lithium battery energy storage according to any one of claims 1 to 6 is implemented.

9. A computer program product, comprising computer instructions, characterized in that: The computer instructions instruct the computer to execute the frequency modulation instruction prediction method for lithium battery energy storage as described in any one of claims 1-6.

10. A frequency modulation instruction prediction system for lithium battery energy storage, based on the frequency modulation instruction prediction method for lithium battery energy storage according to claim 1, characterized in that: include: A data acquisition module is used to obtain the historical frequency modulation instruction sequence of the lithium battery energy storage system; The prediction module inputs the historical frequency modulation instruction sequence into the pre-trained prediction model to obtain the final predicted frequency modulation instruction sequence.

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