A method and system for controlling an energy storage system of a supercapacitor coupled lithium battery

Abstract

The present invention discloses a control method and system for an energy storage system of a supercapacitor coupled lithium battery, which relates to the field of energy storage control technology, including battery data collection, modal decomposition of the collected data; extraction of approximate periodic components and non-approximate periodic components; and internal product combination optimization to update frequency modulation instructions. The energy storage system control method of a supercapacitor coupled lithium battery provided by the present invention minimizes the error of the non-approximate periodic component through iterative optimization, so that the system can more accurately capture the non-periodic changes in the load data and improve the control accuracy. By giving priority to using SCES to respond to high-frequency, small-amplitude frequency modulation instructions, combined with BESS to respond to long-term, large-amplitude frequency modulation instructions, the charge and discharge frequency of the lithium battery is reduced, and the battery life is extended. The present invention achieves better results in terms of control accuracy and optimization of battery life.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage control, and in particular to a method and system for controlling an energy storage system of a supercapacitor coupled lithium battery. Background Art

[0002] As an important energy storage technology, lithium batteries are increasingly used in power systems. However, under high-frequency and high-power charging and discharging conditions, the cycle life and performance of lithium batteries will decrease significantly. In order to improve the service life of lithium batteries and the economy of the system, supercapacitors (SCES), as an energy storage device with high power density and long life, have gradually attracted the attention of researchers. The coupled energy storage system of supercapacitors and lithium batteries can make full use of the advantages of both, and show good application prospects in power frequency regulation, energy management, etc. At present, data processing methods based on variational mode decomposition (VMD) have been widely used in power systems. By performing modal decomposition on power load data, intrinsic mode functions (IMFs) of different frequency components can be extracted to provide data support for further prediction and control.

[0003] Although the existing technology has improved the comprehensive utilization efficiency of lithium batteries and supercapacitors to a certain extent, there are still some shortcomings in practical applications. First, traditional methods usually ignore the processing of non-periodic components when performing modal decomposition and data prediction, resulting in low prediction accuracy. In addition, when optimizing the frequency modulation command response, the existing technology lacks consideration of real-time environmental data (such as temperature, humidity, etc.), which may lead to a decrease in the response performance of the energy storage system under extreme conditions. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing variational modal decomposition technology has insufficient processing of non-approximate periodic components, resulting in low prediction accuracy and unstable frequency modulation response performance, as well as the problem of how to achieve accurate prediction and efficient frequency modulation control by optimizing modal decomposition and compensation functions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a control method for an energy storage system of a supercapacitor coupled lithium battery, comprising collecting battery data, performing modal decomposition on the collected data, extracting approximate periodic components and extracting non-approximate periodic components, and performing inner multiplication combination optimization to update frequency modulation instructions.

[0007] As a preferred solution of the energy storage system control method of the ultra-capacitor coupled lithium battery described in the present invention, wherein: the battery data acquisition includes load data: obtaining the load data of the battery, including real-time load and extracting the time series of load data The frequency modulation command data obtained from the charging end includes automatic power generation control commands and primary frequency modulation commands. and Obtain the real-time power of the supercapacitor energy storage system SCES and the lithium battery energy storage system BESS from the energy storage control system And the real-time power corresponding to the lithium battery . Get the ambient temperature and humidity data.

[0008] As a preferred solution of the energy storage system control method of the ultra-capacitor coupled lithium battery of the present invention, wherein: the modal decomposition of the collected data includes using the variational modal decomposition method to decompose the load data Decomposed into multiple modal functions IMFs, expressed as:

[0009] ,

[0010] in, For the IMF subsequences.

[0011] As a preferred solution of the energy storage system control method of the ultra-capacitor coupled lithium battery of the present invention, wherein: the extraction of approximate periodic components and non-approximate periodic components includes extracting each decomposed real-time load modal function IMF subsequence Extracting approximately periodic functions , expressed as:

[0012] ,

[0013] in, is the compensation function, satisfying is a constant ranging from 0.5 to 1.5. For the cycle, Represents the cycle, Represents the time offset, compensation function It is expressed as:

[0014] ,

[0015] in, is the compensation adjustment coefficient.

[0016] As a preferred solution of the energy storage system control method of the supercapacitor coupled lithium battery of the present invention, wherein: the extraction of approximate periodic components and the extraction of non-approximate periodic components also include calculating the initial non-approximate periodic components , expressed as:

[0017] ,

[0018] Combined with the error term, each IMF subsequence is iteratively optimized to select the minimum non-approximate periodic component , the error term is expressed as:

[0019] ,

[0020] The compensation function and constant are updated during the iteration process, and the gradient of the error term to the compensation function parameter is calculated including:

[0021] right The gradient of is:

[0022] ,

[0023] right The gradient of is:

[0024] ,

[0025] right The gradient of is:

[0026] ,

[0027] right The gradient of is:

[0028] ,

[0029] right The gradient of is:

[0030] ,

[0031] Update coefficient based on learning rate , if the error term The change in is less than the preset threshold, or the maximum number of iterations is reached , the iteration stops.

[0032] As a preferred solution of the energy storage system control method of the ultra-capacitive coupled lithium battery of the present invention, the internal multiplication combination optimization update frequency modulation instruction includes: As input, the non-approximate periodic components of all IMF subsequences are combined by inner multiplication to form a non-periodic component sequence , expressed as:

[0033] ,

[0034] ,

[0035] in, Represents the intrinsic multiplication operator.

[0036] As a preferred solution of the energy storage system control method of the ultra-capacitive coupled lithium battery of the present invention, wherein: the internal multiplication combination optimization update frequency modulation instruction also includes inputting the optimized non-periodic component sequence and frequency modulation command data and , according to the frequency modulation instruction data and , adjust the power output of SCES and BESS, and use SCES charging and discharging to respond to the AGC frequency modulation command of the single frequency modulation command and the AGC frequency modulation command that is not greater than the single frequency modulation command duration threshold and not greater than the single frequency modulation command fluctuation range threshold. Use SCES charging and discharging to respond to the AGC command that is greater than the single frequency modulation command duration threshold and greater than the single frequency modulation command fluctuation range threshold. SCES and BESS charging and discharging jointly respond, and SCES has a higher priority than BESS, which is expressed as:

[0037] ,

[0038] in, represents the duration threshold, Indicates the power fluctuation range threshold.

[0039] Another object of the present invention is to provide a supercapacitor coupled lithium battery energy storage system control system, which can minimize the error of non-approximate periodic components through iterative optimization, so that the system can more accurately capture the non-periodic changes in load data and improve the prediction accuracy. This solves the problem of insufficient accuracy of the current variational mode decomposition technology.

[0040] As a preferred solution of the energy storage system control system of the ultra-capacitor coupled lithium battery described in the present invention, it includes: a data acquisition module, a component extraction module, and an instruction optimization module. The data acquisition module is used to collect battery data and perform modal decomposition on the collected data. The component extraction module is used to extract approximate periodic components and non-approximate periodic components. The instruction optimization module is used to perform internal multiplication combination optimization to update the frequency modulation instruction.

[0041] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a method for controlling an energy storage system of a supercapacitor coupled lithium battery.

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for controlling an energy storage system of a supercapacitor coupled lithium battery.

[0043] Beneficial effects of the present invention: The energy storage system control method of the ultra-capacitor coupled lithium battery provided by the present invention minimizes the error of the non-approximate periodic component through iterative optimization, so that the system can more accurately capture the non-periodic changes in the load data and improve the control accuracy. By giving priority to using SCES to respond to high-frequency, small-amplitude frequency modulation instructions, combined with BESS to respond to long-term, large-amplitude frequency modulation instructions, the charging and discharging frequency of the lithium battery is reduced and the battery life is extended. The present invention achieves better results in terms of control accuracy and optimization of battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 An overall flow chart of a method for controlling an energy storage system of a supercapacitor coupled lithium battery provided in the first embodiment of the present invention.

[0046] Figure 2 A schematic diagram of a module of a control system for an energy storage system of a supercapacitor coupled lithium battery provided in accordance with a third embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a method for controlling an energy storage system of a supercapacitor coupled lithium battery, comprising:

[0049] S1: Collect battery data and perform modal decomposition on the collected data.

[0050] Furthermore, battery data collection includes load data: obtain battery load data, including real-time load and extract the time series of load data .

[0051] Frequency modulation command data obtained from the charging end, including automatic power generation control command and primary frequency modulation command and .

[0052] Obtain the real-time power of the supercapacitor energy storage system SCES and the lithium battery energy storage system BESS from the energy storage control system And the real-time power corresponding to the lithium battery .

[0053] Get the ambient temperature and humidity data.

[0054] It should be noted that modal decomposition of the collected data includes using the variational modal decomposition method to convert the load data into Decomposed into multiple modal functions IMFs, expressed as:

[0055] ,

[0056] in, For the IMF subsequences.

[0057] ,

[0058] in, is the Dirac function, It is The center frequency of the mode, represents convolution, Is an imaginary unit.

[0059] After VMD decomposition, the obtained mode function can be expressed as:

[0060] ,

[0061] Each They are all intrinsic mode functions of the original load data and can be further processed separately.

[0062] In this way, the VMD method effectively decomposes complex load data into modal functions of different frequency components, providing a more refined analysis basis for subsequent prediction and optimization control.

[0063] S2: Perform approximate periodic component extraction and non-approximate periodic component extraction.

[0064] Furthermore, the extraction of approximate periodic components and the extraction of non-approximate periodic components include the following steps: Extracting approximately periodic functions , expressed as:

[0065] ,

[0066] in, is the compensation function, satisfying is a constant ranging from 0.5 to 1.5. For the cycle, Represents the cycle, Represents the time offset, compensation function It is expressed as:

[0067] ,

[0068] in, is the compensation adjustment coefficient.

[0069] It should be noted that the extraction of approximate periodic components and the extraction of non-approximate periodic components also include calculating the initial non-approximate periodic components , expressed as:

[0070] ,

[0071] Combined with the error term, each IMF subsequence is iteratively optimized to select the minimum non-approximate periodic component , the error term is expressed as:

[0072] ,

[0073] The compensation function and constant are updated during the iteration process, and the gradient of the error term to the compensation function parameter is calculated including:

[0074] right The gradient of is:

[0075] ,

[0076] right The gradient of is:

[0077] ,

[0078] right The gradient of is:

[0079] ,

[0080] right The gradient of is:

[0081] ,

[0082] right The gradient of is:

[0083] ,

[0084] Update coefficient based on learning rate , if the error term The change in is less than the preset threshold, or the maximum number of iterations is reached , the iteration stops. The power load data contains periodic and non-periodic components. Extracting and optimizing these components helps to improve the accuracy of load forecasting and ensure the effective execution of frequency regulation instructions.

[0085] S3: Perform internal multiplication combination optimization to update the frequency modulation instruction.

[0086] Furthermore, the inner multiplication combination optimization is performed to update the frequency modulation instruction including the non-approximate periodic component of each IMF subsequence As input, the non-approximate periodic components of all IMF subsequences are combined by inner multiplication to form a non-periodic component sequence , expressed as:

[0087] ,

[0088] ,

[0089] in, Represents the intrinsic multiplication operator.

[0090] It should be noted that the internal multiplication combination optimization update frequency modulation instruction also includes inputting the optimized non-periodic component sequence and frequency modulation command data and , according to the frequency modulation instruction data and , adjust the power output of SCES and BESS, and use SCES charging and discharging to respond to the AGC frequency modulation command of the single frequency modulation command and the AGC frequency modulation command that is not greater than the single frequency modulation command duration threshold and not greater than the single frequency modulation command fluctuation range threshold. Use SCES charging and discharging to respond to the AGC command that is greater than the single frequency modulation command duration threshold and greater than the single frequency modulation command fluctuation range threshold. SCES and BESS charging and discharging jointly respond, and SCES has a higher priority than BESS, which is expressed as:

[0091] ,

[0092] in, represents the duration threshold, Indicates the power fluctuation range threshold. The internal multiplication combination optimization can integrate the information of each non-periodic component to form a more accurate load forecast sequence. The SCES is used first for short-term high-frequency frequency modulation response, and the BESS is used for long-term high-power frequency modulation response, which can effectively improve the stability and response speed of the frequency modulation system, extend the service life of the lithium battery, and reduce the operating cost of the system.

[0093] Example 2, an embodiment of the present invention, provides a method for controlling an energy storage system of an ultra-capacitor coupled lithium battery. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0094] First, the test objects include supercapacitor energy storage system (SCES) and lithium battery energy storage system (BESS). For comparative analysis, a control group is introduced. The control group adopts the existing system control method of using VMD to decompose the sequence and then predict the subsequences separately, and then superimpose the prediction results to obtain the final prediction result. The real-time load data and predicted load data of the power system are obtained, and the recording time interval is 1 day.

[0095] The experimental group adopted the control method of the ultra-capacitor coupled lithium battery energy storage system of the present invention, and adopted the approximate periodic component extraction method and the inner multiplication rule after decomposition.

[0096] Table 1 Experimental data comparison table

[0097] time Real-time load (kW) Forecast load (kW) SCES power output (kW) BESS power output (kW) Control group lithium battery power output (kW) Ambient temperature (℃) humidity(%) 0 1000 950 500 200 700 25 60 1 1050 1000 550 250 750 24 62 2 1100 1050 600 300 800 24 65 3 1150 1100 650 350 850 23 63 4 1200 1150 700 400 900 22 61 5 1250 1200 750 450 950 21 60 6 1300 1250 800 500 1000 20 58 7 1350 1300 850 550 1050 19 57 8 1400 1350 900 600 1100 18 55 9 1450 1400 950 650 1150 17 54 10 1500 1450 1000 700 1200 16 52

[0098] When the load is low, SCES mainly undertakes most of the frequency regulation tasks, and the power output gradually increases, which reflects the rapid response capability of SCES in short-term and high-frequency frequency regulation.

[0099] As the load increases, BESS gradually participates in the frequency regulation task, SCES and BESS respond jointly, and the power output stabilizes at a high level, ensuring the frequency regulation requirements of the system.

[0100] In the control group, although the power output increased steadily, the high frequency and high power charging and discharging of lithium batteries may cause their life and performance to decline. The ambient temperature and humidity gradually decreased over time, which may affect the performance of the energy storage system. The experimental group effectively reduced the impact of environmental factors on system performance by adjusting the compensation function, while the control group did not have a corresponding compensation mechanism.

[0101] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a control system for an energy storage system of an ultra-capacitor coupled lithium battery, including a data acquisition module, a component extraction module, and an instruction optimization module.

[0102] The data acquisition module is used to collect battery data and perform modal decomposition on the collected data. The component extraction module is used to extract approximate periodic components and non-approximate periodic components. The instruction optimization module is used to perform internal multiplication combination optimization to update the frequency modulation instruction.

[0103] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it 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 methods of 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. Various media that can store program codes.

[0104] 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.

[0105] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), 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.

[0106] 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 logical 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. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 controlling an energy storage system of a supercapacitor coupled lithium battery, characterized in that: include: Collect battery data and perform modal decomposition on the collected data; Perform approximate periodic component extraction and non-approximate periodic component extraction; Perform internal multiplication combination optimization to update frequency modulation instructions; The extraction of approximate periodic components and non-approximate periodic components includes: i (t) Extract the approximate periodic function f iT (t), expressed as: in, is the compensation function, satisfying A is a constant with a value range of 0.5 to 1.5, T is the period, t represents the period, Δt represents the time offset, and the compensation function It is expressed as: Among them, B, C, D, E are compensation adjustment coefficients; The extracting of approximate periodic components and extracting of non-approximate periodic components further comprises calculating an initial non-approximate periodic component δ i (t), expressed as: δ i (t)=f i (t)-f iT (t) Combined with the error term, each IMF subsequence is iteratively optimized to select the minimum non-approximate periodic component δ i (t), the error term is expressed as: ε i =∫f i (t)-f iT (t) 2 dt The compensation function and constant are updated during the iteration process, and the gradient of the error term to the compensation function parameter is calculated including: The gradient of B is: The gradient of C is: The gradient of D is: The gradient of E is: The gradient of A is: Based on the learning rate update coefficients A, B, C, D, E, if the error term ε i If the change in is less than the preset threshold or reaches the maximum number of iterations M, the iteration is stopped; The inner multiplication combination optimization update frequency modulation instruction includes: N (t) is used as input, and the non-approximate periodic components of all IMF subsequences are combined by inner multiplication to form a non-periodic component sequence f TP (t), expressed as: in, represents the inner multiplication operator; The method of performing internal multiplication combination optimization and updating frequency modulation instructions also includes inputting the optimized non-periodic component sequence f TP (t) and frequency modulation command data AGC(t) and Freq(t), according to the frequency modulation command data AGC(t) and Freq(t), adjust the power output of SCES and BESS, for a single frequency modulation command and an AGC frequency modulation command not greater than a single command duration threshold and not greater than a single command fluctuation range threshold, adopt SCES charging and discharging to respond separately, for an AGC command greater than a single command duration threshold and greater than a single command fluctuation range threshold, adopt SCES and BESS charging and discharging to respond jointly, and SCES has a higher priority than BESS, expressed as: Among them, t threshold represents the duration threshold, P threshold Indicates the power fluctuation range threshold.

2. The method for controlling the energy storage system of the supercapacitor coupled lithium battery according to claim 1, characterized in that: The battery data collection includes load data: obtaining the battery load data, including real-time load and extracting the time series P of the load data. load (t); Frequency modulation command data obtained from the charging end, including automatic generation control command and primary frequency modulation command AGC(t) and Freq(t); Obtain the real-time power P of the supercapacitor energy storage system SCES and the lithium battery energy storage system BESS from the energy storage control system SCES (t) and the real-time power P corresponding to the lithium battery BESS (t); Get the ambient temperature T(t) and humidity H(t) data.

3. The energy storage system control method of the supercapacitor coupled lithium battery according to claim 2, characterized in that: The modal decomposition of the collected data includes using a variational modal decomposition method to transform the load data P load (t) is decomposed into multiple modal functions IMFs, expressed as: Among them, f i (t) is the i-th IMF subsequence.

4. A system using the energy storage system control method of the supercapacitor coupled lithium battery according to any one of claims 1 to 3, characterized in that: Including data acquisition module, component extraction module, instruction optimization module; The data acquisition module is used to collect battery data and perform modal decomposition on the collected data; The component extraction module is used to extract approximately periodic components and non-approximate periodic components; The instruction optimization module is used to perform internal multiplication combination optimization and update frequency modulation instructions.

5. 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 energy storage system control method of the super-capacitor coupled lithium battery described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for controlling an energy storage system of a supercapacitor coupled lithium battery according to any one of claims 1 to 3 are implemented.

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