Control method, system and equipment based on lithium battery energy storage and medium

Abstract

The invention relates to the technical field of unit control, in particular to a control method, system and equipment based on lithium battery energy storage and a medium, and the method comprises the steps: obtaining power grid characteristic parameters, and determining a target transfer function expected to be realized; constructing a power grid frequency response model based on lithium battery energy storage, and solving a target transfer function according to the power grid frequency response model under the condition that a set power grid system frequency response index and frequency adjustment time are used as constraint conditions; deducing a frequency modulation transfer function of the energy storage system according to the target transfer function and the synchronous machine transfer function; and obtaining a control instruction of the energy storage system by using the transfer function, and responding to the power grid according to the control instruction. The method effectively solves the problems of stability and reliability of the lithium battery energy storage system in control.

Description

Technical Field

[0001] The present invention relates to the technical field of unit control, and in particular to a control method, system, equipment and medium based on lithium battery energy storage. Background Art

[0002] There are differences between the charging and discharging characteristics of the ultra-capacity energy storage system and the operating characteristics of the thermal power units. How to achieve coordinated scheduling of the two to achieve the best frequency regulation effect and economic benefits is a problem that needs to be solved.

[0003] Supercapacitors have an extremely fast response speed and can complete charging and discharging within milliseconds. However, due to their own mechanical inertia and thermal inertia, thermal power units have a relatively slow response speed. When thermal power units and supercapacitor energy storage systems operate in coordination, how to achieve effective coordination between the two under different dynamic characteristics to ensure stable operation and rapid response of the system is one of the difficulties in the application of the technology. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a control method, system, device and medium based on lithium battery energy storage in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problems of installation limitations of energy storage systems and low reliability of grid control.

[0005] The objective of the present invention is achieved by the following technical solutions: In a first aspect, the present invention provides a control method based on lithium battery energy storage, comprising: Obtain grid characteristic parameters and determine the target transfer function to be achieved; A grid frequency response model is constructed based on lithium battery energy storage, and a target transfer function is solved according to the grid frequency response model under the condition that the grid system frequency response index and frequency adjustment time are set as constraints; The frequency modulation transfer function of the energy storage system is derived according to the target transfer function and the synchronous machine transfer function; the control instruction of the energy storage system is obtained by using the transfer function, and the power grid system is responded according to the control instruction. As a further improvement of the present invention, the obtaining of power grid characteristic parameters specifically includes: Obtain the total installed capacity of the power grid, load characteristic data, parameter information of synchronous units, and the capacity of the lithium battery energy storage system.

[0006] As a further improvement of the present invention, the determining of the target transfer function to be achieved specifically includes: According to the historical frequency fluctuation data of the power grid, the amplitude, frequency and duration of the frequency fluctuation are analyzed; according to the historical frequency fluctuation data of the power grid, the relationship between the frequency fluctuation and the load change and the power generation adjustment data is obtained, and the frequency fluctuation law is obtained. According to the frequency fluctuation law, the target transfer function expected to be achieved is determined.

[0007] As a further improvement of the present invention, the determining of the target transfer function to be achieved specifically includes: According to the historical frequency fluctuation data of the power grid, the amplitude, frequency and duration of the frequency fluctuation are analyzed; according to the historical frequency fluctuation data of the power grid, the relationship between the frequency fluctuation and the load change and the power generation adjustment data is obtained, and the frequency fluctuation law is obtained. According to the frequency fluctuation law, the target transfer function expected to be achieved is determined.

[0008] As a further improvement of the present invention, solving the target transfer function according to the power grid frequency response model specifically includes: In the case of sudden load change or generator failure, the deviation of the grid frequency is controlled within a set range through the target transfer function; the target transfer function is based on the sum of squares of frequency deviations and mainly includes:

[0009] In the formula, is the actual frequency of the power grid at time t, is the rated frequency, and T is the time range considered.

[0010] As a further improvement of the present invention, under the condition that the set power grid system frequency response index and frequency adjustment time are used as constraint conditions, solving the target transfer function according to the power grid frequency response model specifically includes: An optimization algorithm is used to solve the target transfer function. During the iteration of the optimization algorithm, the output result of the power grid frequency response model is calculated according to the current parameter value to determine whether the constraint conditions are met. If the constraint conditions are not met, an adaptive adjustment strategy is adopted to dynamically adjust the parameters of the optimization algorithm according to the current number of iterations and the change of the target function value, and calculate the target function value until the convergence condition is met. The convergence condition is that the target function value reaches the minimum or the number of iterations reaches a preset value, and the optimal parameter solution of the target transfer function that meets the constraint conditions is obtained.

[0011] As a further improvement of the present invention, after responding to the power grid system according to the control instruction and meeting the frequency regulation requirements, it also includes performing economic optimization processing according to the cost function, specifically including: constructing a cost function related to the capacity of the energy storage system, and the cost function is:

[0012] In the formula, is the cost function, is the cost coefficient, is the capacity of the lithium battery energy storage system, is the operating power of the lithium battery energy storage system.

[0013] As a further improvement of the present invention, after responding to the power grid system according to the control instruction, it also includes feedback control based on the monitoring information of the lithium battery energy storage system, specifically including: real-time monitoring of the charge state and health state of the lithium battery, feeding back the monitoring data, and adjusting the control strategy of the power grid and energy storage according to the feedback information.

[0014] In a second aspect, the present invention provides a control system based on lithium battery energy storage, which is used to implement the above-mentioned control method based on lithium battery energy storage, including: A data acquisition module is used to obtain the characteristic parameters of the power grid and determine the target transfer function to be achieved; An objective function solving module is used to construct a power grid frequency response model based on lithium battery energy storage, and solve the objective transfer function according to the power grid frequency response model under the condition that the set power grid system frequency response index and frequency adjustment time are used as constraints; The energy storage control module derives the frequency modulation transfer function of the energy storage system according to the target transfer function and the synchronous machine transfer function; obtains the control instruction of the energy storage system by using the transfer function, and responds to the power grid according to the control instruction.

[0015] In a third aspect, the present invention provides an electronic device, comprising a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the control method based on lithium battery energy storage as described above.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the above-mentioned control method based on lithium battery energy storage is implemented.

[0017] The beneficial effect of the present invention is that by accurately acquiring the characteristic parameters of the power grid and determining the target transfer function, the frequency dynamic response of the power grid can be more accurately anticipated and planned. The determination of the target transfer function helps to clarify the frequency response characteristics that the power grid should have under different working conditions, and provides a clear target for subsequent modeling and control. The power grid frequency response model is constructed based on lithium battery energy storage, and the target transfer function is solved with the system frequency response index and the frequency adjustment time as constraints, which can ensure that when the power grid has frequency fluctuations, there are clear quantitative indicators to measure and control the frequency changes. This enables the power grid to maintain the frequency within a relatively stable range when facing various load changes and interferences, thereby improving the power supply quality and reliability of the power grid. The frequency modulation transfer function of the energy storage system is derived and the control instructions are obtained based on it, so that the energy storage system can respond quickly and accurately to the frequency changes of the power grid. The lithium battery energy storage system has a fast power regulation capability, can respond to the frequency changes of the power grid within milliseconds, effectively suppress frequency fluctuations, and improve the frequency stability of the power grid. The frequency modulation transfer function of the energy storage system is derived and the control instructions are obtained based on it, so that the energy storage system can respond quickly and accurately to the frequency changes of the power grid. The lithium battery energy storage system has fast power regulation capability and can respond to grid frequency changes within milliseconds, effectively suppressing frequency fluctuations and improving grid frequency stability. The use of lithium battery energy storage systems to build a grid frequency response model provides a flexible and efficient frequency regulation method for the grid. Compared with traditional frequency regulation methods, energy storage systems are not restricted by geographical location, water resources and other conditions, and can be quickly deployed and adjusted, improving the reliability and flexibility of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of a control method based on lithium battery energy storage in an embodiment of the present invention; Figure 2 is a schematic diagram of the structure of a control system based on lithium battery energy storage in an embodiment of the present invention; Figure 3 is a schematic structural diagram of an electronic device in an embodiment of the present invention; Figure 4 It is a schematic diagram of the chip structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose and technical solution of the present invention clearer and easier to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments, wherein the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0022] Example 1 Obtain grid characteristic parameters and determine the target transfer function to be achieved; Obtain the total installed capacity of the power grid, load characteristic data, parameter information of synchronous units, and the capacity of the lithium battery energy storage system.

[0023] According to the historical frequency fluctuation data of the power grid, the amplitude, frequency and duration of the frequency fluctuation are analyzed; according to the historical frequency fluctuation data of the power grid, the relationship between the frequency fluctuation and the load change and the power generation adjustment data is obtained, and the frequency fluctuation law is obtained. According to the frequency fluctuation law, the target transfer function expected to be achieved is determined.

[0024] In the process of solving the target transfer function, the set frequency response index and frequency adjustment time are included in the solution process as constraints. Optimization algorithms, such as particle swarm optimization algorithm, genetic algorithm, etc., can be used to construct the fitness function with the parameters of the target transfer function as optimization variables. Specific optimization objectives can include the following aspects: By optimizing the target transfer function, the deviation of the grid frequency can be controlled within a very small range in the case of sudden load changes or generator failures, thereby improving the power supply quality of the grid. An objective function with the sum of square frequency deviations as the main component can be constructed:

[0025] In the formula, is the actual frequency of the power grid at time t, is the rated frequency, and T is the time range considered.

[0026] In addition, the power grid can quickly recover to a stable state when facing various disturbances, avoiding frequency oscillation or instability. The anti-interference ability of the power grid can be enhanced by adjusting the parameters of the target transfer function, optimizing the damping characteristics and inertial response of the system.

[0027] The load characteristic data can be used to estimate the impact of load changes on frequency under different conditions, providing a basis for determining the input variables of the model; while the generator set parameters help to understand the original frequency response characteristics of the system, providing a reference for determining the initial state and boundary conditions of the model.

[0028] An optimization algorithm is used to solve the target transfer function. During the iteration of the optimization algorithm, the output result of the power grid frequency response model is calculated according to the current parameter value to determine whether the constraint conditions are met. If the constraint conditions are not met, an adaptive adjustment strategy is adopted to dynamically adjust the parameters of the optimization algorithm according to the current number of iterations and the change of the target function value, and calculate the target function value until the convergence condition is met. The convergence condition is that the target function value reaches the minimum or the number of iterations reaches a preset value, and the optimal parameter solution of the target transfer function that meets the constraint conditions is obtained.

[0029] A grid frequency response model is constructed based on lithium battery energy storage, and a target transfer function is solved according to the grid frequency response model under the condition that the grid system frequency response index and frequency adjustment time are set as constraints; Specifically, in a specific grid operation scenario, the frequency deviation shall not exceed ±0.2Hz, and the frequency change rate shall be controlled within a certain value to ensure the stable operation of the grid and the normal operation of power equipment. At the same time, the frequency adjustment time will also be clearly specified according to the actual needs and operation standards of the grid. For example, after a frequency disturbance occurs, the grid must return to a stable state within a specific time range (such as 10-30 seconds).

[0030] Based on a carefully constructed grid frequency response model, the model comprehensively considers various components and equipment in the grid, including generators, loads, energy storage systems, and power transmission lines. Through the mathematical description and analysis of each part of the model, a set of equations or transfer function relationships that can accurately reflect the dynamic changes in grid frequency are established.

[0031] In determining the target transfer function and solving the problem, the characteristics and constraints of the energy storage system are taken into account, such as energy storage capacity, charging and discharging power limits, etc. This enables the energy storage system to give full play to its advantages, avoid overcharging and discharging, extend the service life of the energy storage system, and improve its utilization efficiency while meeting the grid frequency regulation requirements.

[0032] The frequency modulation transfer function of the energy storage system is derived according to the target transfer function and the synchronous machine transfer function; the control instruction of the energy storage system is obtained by using the transfer function, and the power grid is responded according to the control instruction.

[0033] Reduce the capacity demand and operating cost of the energy storage system. Under the premise of meeting the frequency regulation requirements, minimize the configuration capacity of the energy storage system, improve the utilization efficiency of the energy storage system, and reduce investment costs and operation and maintenance costs. Specifically, after the control command responds to the power grid system and meets the frequency regulation requirements, it also includes economic optimization processing based on the cost function, specifically including: constructing a cost function related to the capacity of the energy storage system, the cost function is:

[0034] In the formula, is the cost function, is the cost coefficient, is the capacity of the lithium battery energy storage system, is the operating power of the lithium battery energy storage system.

[0035] After responding to the power grid system according to the control instruction, it also includes feedback control based on the monitoring information of the lithium battery energy storage system, specifically including: real-time monitoring of the charge state and health state of the lithium battery, feeding back the monitoring data, and adjusting the control strategy of the power grid and energy storage according to the feedback information.

[0036] By optimizing the control instructions of the energy storage system, the energy storage system can work in coordination with other equipment in the power grid. For example, when the power grid frequency fluctuates greatly, the energy storage system can participate in frequency regulation together with traditional power generation equipment such as synchronous machines, improving the frequency regulation capability of the entire power grid. At the same time, it also gives full play to the rapid response advantage of the energy storage system and optimizes its utilization efficiency in the power grid.

[0037] By solving the target transfer function and deriving the frequency modulation transfer function, the grid frequency can be accurately controlled and optimized, which not only improves the power supply quality of the grid, but also reduces the damage to user equipment caused by frequency fluctuations, and enhances the reliability of the grid and user satisfaction.

[0038] This method uses advanced control theories and technologies, such as transfer function solution and system identification, to provide technical support for the development of smart grids. Through precise control and optimization of grid frequency, it can realize intelligent management and operation of grids and improve the automation level and operation efficiency of grids.

[0039] Example 2 like Figure 2 As shown, this embodiment provides a control system based on lithium battery energy storage, which is used to implement the control method based on lithium battery energy storage described in Example 1, including: A data acquisition module is used to obtain the characteristic parameters of the power grid and determine the target transfer function to be achieved; An objective function solving module is used to construct a power grid frequency response model based on lithium battery energy storage, and solve the objective transfer function according to the power grid frequency response model under the condition that the set power grid system frequency response index and frequency adjustment time are used as constraints; The energy storage control module derives the frequency modulation transfer function of the energy storage system according to the target transfer function and the synchronous machine transfer function; obtains the control instruction of the energy storage system by using the transfer function, and responds to the power grid according to the control instruction.

[0040] Example 3 In another embodiment of the present invention, the present invention also 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.

[0041] 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.

[0042] 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).

[0043] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the control method based on lithium battery energy storage in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: Obtain grid characteristic parameters and determine the target transfer function to be achieved; A grid frequency response model is constructed based on lithium battery energy storage, and a target transfer function is solved according to the grid frequency response model under the condition that the grid system frequency response index and frequency adjustment time are set as constraints; The frequency modulation transfer function of the energy storage system is derived according to the target transfer function and the synchronous machine transfer function; the control instruction of the energy storage system is obtained by using the transfer function, and the power grid system is responded according to the control instruction.

[0044] Example 4 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 method for calculating the composition of fluid in the reservoir transformation wellbore in the embodiment is realized. 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 calculation system for the composition of fluid in the reservoir transformation wellbore in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0045] 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 3This 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.

[0046] 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. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0047] 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 memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 60.

[0048] 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.

[0049] Any reference to a memory, database or other medium used in the embodiments provided in this 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.

[0050] 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.

[0051] Example 5 Figure 4 The block diagram is a chip provided according to an embodiment of the present invention.

[0052] See also Figure 4 The chip is an electronic device 600, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0053] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0054] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0055] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0056] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0057] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0058] 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, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

Claims

1. A control method based on lithium battery energy storage, characterized in that: include: Obtain grid characteristic parameters and determine the target transfer function to be achieved; A grid frequency response model is constructed based on lithium battery energy storage, and a target transfer function is solved according to the grid frequency response model under the condition that the grid system frequency response index and frequency adjustment time are set as constraints; The frequency modulation transfer function of the energy storage system is derived according to the target transfer function and the synchronous machine transfer function; the control instruction of the energy storage system is obtained by using the transfer function, and the power grid system is responded according to the control instruction.

2. The control method based on lithium battery energy storage according to claim 1, characterized in that: The obtaining of the grid characteristic parameters specifically includes: Obtain the total installed capacity of the power grid, load characteristic data, parameter information of synchronous units, and the capacity of the lithium battery energy storage system.

3. The control method based on lithium battery energy storage according to claim 2, characterized in that: The determining of the target transfer function to be achieved specifically includes: According to the historical frequency fluctuation data of the power grid, the amplitude, frequency and duration of the frequency fluctuation are analyzed; according to the historical frequency fluctuation data of the power grid, the relationship between the frequency fluctuation and the load change and the power generation adjustment data is obtained, and the frequency fluctuation law is obtained. According to the frequency fluctuation law, the target transfer function expected to be achieved is determined.

4. The control method based on lithium battery energy storage according to claim 1, characterized in that: Solving the target transfer function according to the power grid frequency response model specifically includes: In the case of sudden load change or generator failure, the deviation of the grid frequency is controlled within a set range through the target transfer function; the target transfer function is based on the sum of squares of frequency deviations and mainly includes: In the formula, is the actual frequency of the power grid at time t, is the rated frequency, and T is the time range considered.

5. The control method based on lithium battery energy storage according to claim 1, characterized in that: The target transfer function is solved according to the grid frequency response model under the condition that the grid system frequency response index and the frequency adjustment time are set as constraints, specifically including: An optimization algorithm is used to solve the target transfer function. During the iteration of the optimization algorithm, the output result of the power grid frequency response model is calculated according to the current parameter value to determine whether the constraint conditions are met. If the constraint conditions are not met, an adaptive adjustment strategy is adopted to dynamically adjust the parameters of the optimization algorithm according to the current number of iterations and the change of the target function value, and calculate the target function value until the convergence condition is met. The convergence condition is that the target function value reaches the minimum or the number of iterations reaches a preset value, and the optimal parameter solution of the target transfer function that meets the constraint conditions is obtained.

6. The control method based on lithium battery energy storage according to claim 5, characterized in that: After the power grid system responds to the control instruction and meets the frequency regulation requirements, the process further includes performing economic optimization processing according to the cost function, specifically including: constructing a cost function related to the capacity of the energy storage system, wherein the cost function is: In the formula, is the cost function, is the cost coefficient, is the capacity of the lithium battery energy storage system, is the operating power of the lithium battery energy storage system.

7. The control method based on lithium battery energy storage according to claim 1, characterized in that: After responding to the power grid system according to the control instruction, it also includes feedback control based on the monitoring information of the lithium battery energy storage system, specifically including: real-time monitoring of the charge state and health state of the lithium battery, feeding back the monitoring data, and adjusting the control strategy of the power grid and energy storage according to the feedback information.

8. A control system based on lithium battery energy storage, used to implement the control method based on lithium battery energy storage according to any one of claims 1 to 7, characterized in that: include: A data acquisition module is used to obtain the characteristic parameters of the power grid and determine the target transfer function to be achieved; An objective function solving module is used to construct a power grid frequency response model based on lithium battery energy storage, and solve the objective transfer function according to the power grid frequency response model under the condition that the set power grid system frequency response index and frequency adjustment time are used as constraints; The energy storage control module derives the frequency modulation transfer function of the energy storage system according to the target transfer function and the synchronous machine transfer function; obtains the control instruction of the energy storage system by using the transfer function, and responds to the power grid according to the control instruction.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the control method based on lithium battery energy storage as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the control method based on lithium battery energy storage as described in any one of claims 1 to 7 is implemented.

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