A battery charging and discharging control method, system, device and medium of an energy storage system

Abstract

The application belongs to the field of energy storage, and specifically provides a battery charging and discharging control method, system, device and medium of an energy storage system, comprising the following steps: step 1, obtaining energy storage battery data and grid data of a target energy storage system; step 2, inputting the obtained energy storage battery data and grid data into a preset battery charging and discharging control model; step 3, calculating an optimal charging and discharging control strategy at the current time by using an optimization algorithm; and step 4, controlling the charging and discharging of the energy storage system based on the obtained optimal charging and discharging control strategy; wherein the battery charging and discharging control model is constructed with the optimization objectives of minimizing cost and maximizing battery life; the application realizes the reduction of the operation cost of the energy storage system, the prolongation of the service life of the energy storage battery, the effective reduction of the phenomena of overcharging, overdischarging and frequent charging and discharging of the battery, and the adaptation to the charging and discharging requirements of the energy storage system under different working conditions.

Description

Technical Field

[0001] This invention belongs to the field of direct-connected energy storage systems, specifically relating to a battery charging and discharging control method, system, equipment, and medium for an energy storage system. Background Technology

[0002] Against the backdrop of rapid development of new energy sources, energy storage systems, as an indispensable part of the power system, directly affect the stable operation of the power grid and the efficient utilization of renewable energy. However, current battery charging and discharging control strategies have revealed many problems in practical applications, especially the overly simplistic design of these strategies, which fails to fully meet the complex needs of energy storage systems.

[0003] Existing energy storage system battery charging and discharging control strategies often rely solely on the grid's immediate power demand or the current battery charge level for simple charging and discharging scheduling, neglecting long-term optimization of energy storage system operating costs and battery lifespan maintenance. This simplified control method proves inadequate when facing complex operating conditions such as large grid load fluctuations and unstable renewable energy output, easily leading to frequent charging and discharging of the energy storage system, increasing operating costs, accelerating battery aging, and shortening its lifespan.

[0004] Therefore, there is a need in the art for a battery charging and discharging control method and system for energy storage systems to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a battery charging and discharging control method, system, device and medium for energy storage systems, so as to solve the defects of existing battery charging and discharging control in energy storage systems, which result in frequent charging and discharging and increased operating costs.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The present invention provides a battery charging and discharging control method for an energy storage system, comprising the following steps: Step 1: Obtain the energy storage battery data and grid data of the target energy storage system; Step 2: Input the obtained energy storage battery data and grid data into the preset battery charge / discharge control model, where: the expression of the battery charge / discharge control model is:

[0007] in, It is a function of the battery charge and discharge control model; , These are the weighting coefficients; It is the total cost function; It is a function of battery life loss; Step 3: Optimize the output of the preset battery charge and discharge control model using an optimization algorithm to obtain the optimal charge and discharge control strategy at the current moment. Step 4: Control the charging and discharging of the energy storage system based on the obtained optimal charging and discharging control strategy; The battery charge-discharge control model is constructed with the optimization objectives of minimizing cost and maximizing battery life. The constraints of the battery charge-discharge control model are constructed using battery charge-discharge power, battery operating temperature, battery internal resistance, battery cycle count, environmental cost, revenue obtained through grid ancillary services, total cost, and battery life loss.

[0008] Preferably, the total cost function for:

[0009] in, It is the total time period. It is a moment Electricity price, It is a moment The power of electricity purchased from or sold to the grid. It is a moment Battery maintenance costs, It is a moment Battery replacement cost, It is a moment Environmental costs, It is a moment Revenue generated from providing ancillary services to the power grid.

[0010] Preferably, the battery life loss function for:

[0011] in, This refers to the battery's rated cycle life. It is a moment depth of discharge, It is the exponential coefficient of the effect of the depth of discharge on battery life. It is a moment Battery temperature, This is a reference temperature. It is a moment The internal resistance of the battery, It is the initial internal resistance of the battery. It is the exponential coefficient of the effect of battery internal resistance on battery life. It is a moment Battery charging and discharging power, It is the exponential coefficient of the effect of battery charging and discharging power on battery life.

[0012] Preferably, in step 2, the constraints of the battery charge / discharge control model are:

[0013] in, and These are the minimum and maximum values ​​of the battery's charging and discharging power, respectively. and These are the lowest and highest operating temperatures of the battery, respectively. and These are the minimum and maximum values ​​of the battery's internal resistance, respectively. and These are the minimum and maximum battery cycle counts, respectively. and These are the minimum and maximum values ​​of environmental cost, respectively. and These are the minimum and maximum values ​​of ancillary service revenue for the power grid, respectively. and These are the minimum and maximum values ​​of the total cost, respectively. and These are the minimum and maximum values ​​of battery life loss; For a moment The battery charging and discharging power; For a moment The battery temperature; For a moment The internal resistance of the battery; For a moment Battery cycle count; For a moment Electricity price; For a moment Revenue generated from providing ancillary services to the power grid; It is a function of total cost; This is a function representing battery life loss.

[0014] Preferably, the total cost function for:

[0015] in, It is the total time period. It is a moment Electricity price, It is a moment The power of electricity purchased from or sold to the grid. It is a moment Battery maintenance costs, It is a moment Battery replacement cost, It is a moment Environmental costs, It is a moment Revenue generated from providing ancillary services to the power grid; The battery life loss function for:

[0016] in, It is the total time period. This refers to the battery's rated cycle life. It is a moment depth of discharge, It is the exponential coefficient of the effect of the depth of discharge on battery life. It is a moment Battery temperature, This is a reference temperature. It is a moment The internal resistance of the battery, It is the initial internal resistance of the battery. It is the exponential coefficient of the effect of battery internal resistance on battery life. It is a moment Battery charging and discharging power, It is the exponential coefficient of the effect of battery charging and discharging power on battery life; In step 2, the constraints of the battery charge / discharge control model are:

[0017] in, and These are the minimum and maximum values ​​of the battery's charging and discharging power, respectively. and These are the lowest and highest operating temperatures of the battery, respectively. and These are the minimum and maximum values ​​of the battery's internal resistance, respectively. and These are the minimum and maximum battery cycle counts, respectively. and These are the minimum and maximum values ​​of environmental cost, respectively. and These are the minimum and maximum values ​​of ancillary service revenue for the power grid, respectively. and These are the minimum and maximum values ​​of the total cost, respectively. and These are the minimum and maximum values ​​of battery life loss; For a moment The battery charging and discharging power; For a moment The battery temperature; For a moment The internal resistance of the battery; For a moment Battery cycle count; For a moment Electricity price; For a moment Revenue generated from providing ancillary services to the power grid; It is a function of total cost; This is a function representing battery life loss.

[0018] Preferably, in step 3, a genetic algorithm is used to calculate the optimal charging and discharging control strategy at the current moment.

[0019] A battery charging and discharging control system for an energy storage system, comprising: The acquisition module is used to acquire energy storage battery data and grid data of the target energy storage system; The input module is used to input the obtained energy storage battery data and grid data into a preset battery charge and discharge control model. The battery charge and discharge control model is constructed with the optimization objectives of minimizing cost and maximizing battery life. The constraints of the battery charge and discharge control model are constructed with battery charge and discharge power, battery operating temperature, battery internal resistance, battery cycle number, environmental cost, revenue obtained through grid ancillary services, total cost, and battery life loss. The calculation module is used to optimize the output of the preset battery charge and discharge control model using optimization algorithms to obtain the optimal charge and discharge control strategy at the current moment. The control module is used to control the charging and discharging of the energy storage system based on the obtained optimal charging and discharging control strategy.

[0020] A computer device, comprising: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method.

[0021] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.

[0022] A computer program product comprising a computer program that, when executed by a processor, implements the method.

[0023] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a battery charging and discharging control method for an energy storage system. By constructing a battery charging and discharging control model based on minimizing cost and maximizing battery life, and employing an optimization algorithm to calculate the optimal charging and discharging control strategy in real time, this method can significantly reduce the operating cost of the energy storage system, improve overall economic efficiency, and effectively reduce overcharging, over-discharging, and frequent charging and discharging phenomena, thus extending the lifespan of the energy storage battery. Furthermore, this method can acquire grid data in real time, more accurately determine the real-time demand of the grid, and adjust the charging and discharging strategy of the energy storage system accordingly, contributing to enhanced grid stability and reliability. Simultaneously, the use of advanced optimization algorithms and a refined control model improves the system's intelligence and automation level, giving the method strong adaptability and robustness, enabling it to meet the charging and discharging needs of the energy storage system under different operating conditions. Attached Figure Description

[0024] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0027] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0029] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0031] To address the aforementioned technical problems, namely the overly simplistic battery charging and discharging control strategies in existing energy storage systems, which easily lead to frequent charging and discharging, increasing operating costs, accelerating battery aging, and shortening lifespan, this application provides a battery charging and discharging control method and system for energy storage systems. The aim is to reduce the operating costs of energy storage systems, extend the lifespan of energy storage batteries, effectively reduce overcharging, over-discharging, and frequent charging and discharging phenomena, and adapt to the charging and discharging needs of energy storage systems under different operating conditions.

[0032] Example 1 like Figure 1 As shown, this embodiment provides a battery charging and discharging control method for an energy storage system, the control method including the following steps: Step 1: Obtain the energy storage battery data and grid data of the energy storage system; Step 2: Input the energy storage battery data and the power grid data into the preset battery charging and discharging control model; Step 3: Optimize the output of the preset battery charge and discharge control model using an optimization algorithm to obtain the optimal charge and discharge control strategy at the current moment. Step 4: Control the charging and discharging of the energy storage system based on the obtained optimal charging and discharging control strategy; The battery charge / discharge control model is constructed based on cost minimization and battery life maximization as optimization objectives.

[0033] Example 2 Based on Example 1, this example provides a battery charging and discharging control method for an energy storage system. In step 2, the battery charging and discharging control model expression is as follows:

[0034] in, It is a function of the battery charge / discharge control model. , These are the weighting coefficients; It is the total cost function; It is a function of battery life loss.

[0035] The total cost function for:

[0036] in, It is the total time period. It is a moment Electricity price, It is a moment The power of electricity purchased from or sold to the grid. It is a moment Battery maintenance costs, It is a moment Battery replacement cost, It is a moment Environmental costs, It is a moment Revenue generated from providing ancillary services to the power grid.

[0037] Battery maintenance costs The formula is:

[0038] in, , , , and These are the coefficients. It is a constant. It is a moment Battery temperature, It is a moment The state of charge of the battery, It is a moment The battery charge / discharge rate.

[0039] The battery life loss function for:

[0040] in, This refers to the battery's rated cycle life. It is a moment depth of discharge, It is the exponential coefficient of the effect of the depth of discharge on battery life. It is a moment Battery temperature, This is a reference temperature. It is a moment The internal resistance of the battery, It is the initial internal resistance of the battery. It is the exponential coefficient of the effect of battery internal resistance on battery life. It is a moment Battery charging and discharging power, It is the exponential coefficient of the effect of battery charging and discharging power on battery life.

[0041] The constraints of the battery charge / discharge control model are as follows:

[0042] in, and These are the minimum and maximum values ​​of the battery's charging and discharging power, respectively. and These are the lowest and highest operating temperatures of the battery, respectively. and These are the minimum and maximum values ​​of the battery's internal resistance, respectively. and These are the minimum and maximum battery cycle counts, respectively. and These are the minimum and maximum values ​​of environmental cost, respectively. and These are the minimum and maximum values ​​of grid ancillary service revenue, respectively. and These are the minimum and maximum values ​​of the total cost, respectively. and These are the minimum and maximum values ​​for battery life loss.

[0043] Example 3 Based on Example 1, this example provides a battery charging and discharging control method for an energy storage system. In step 3, the optimization algorithm is a genetic algorithm; the step of using the optimization algorithm to calculate the optimal charging and discharging control strategy at the current moment includes: Encode the variables, determine the number of individuals in the population, and randomly generate the initial population; According to the function of the battery charge and discharge control model Define the fitness function and apply constraints; Individuals are selected to participate in the next generation of reproduction based on their fitness. Select two parent individuals and exchange some genes using a uniform crossover method to generate new offspring individuals; Set the probability of crossover operation and perform mutation operation on the selected individuals with a preset probability; Set the probability of the mutation operation and iterate; After the iteration is complete, the individual with the highest fitness is selected as the optimal solution; The optimal individual's encoding is decoded into actual decision variable values ​​to obtain the optimal charging and discharging control strategy.

[0044] The encoding of variables includes: Determine the type of the variable; for continuous variables, use real number encoding, and for discrete variables, use binary encoding. The constraint processing includes: For individuals that do not meet the constraints, the fitness of the individual is reduced by using a penalty function to satisfy the constraints.

[0045] The formula for the penalty function method is as follows:

[0046] in, It is the fitness value after the constraint penalty. It is an individual's fitness value. It refers to the number of constraints. It is the first One constraint condition. Is with the first The penalty coefficient associated with each constraint.

[0047] Example 4 This embodiment provides a battery charging and discharging control system for an energy storage system, the control system comprising: The acquisition module 100 is used to acquire energy storage battery data and grid data of the target energy storage system. The input module 200 is used to input the obtained energy storage battery data and grid data into a preset battery charge-discharge control model. The battery charge-discharge control model is constructed with cost minimization and battery life maximization as optimization objectives. The constraints of the battery charge-discharge control model are constructed using battery charge-discharge power, battery operating temperature, battery internal resistance, battery cycle count, environmental cost, revenue obtained through grid ancillary services, total cost, and battery life loss. Specifically: The battery charging and discharging control model expression is as follows:

[0048] in, It is a function of the battery charge / discharge control model. , These are the weighting coefficients; It is the total cost function; It is a function of battery life loss.

[0049] The total cost function for:

[0050] in, It is the total time period. It is a moment Electricity price, It is a moment The power of electricity purchased from or sold to the grid. It is a moment Battery maintenance costs, It is a moment Battery replacement cost, It is a moment Environmental costs, It is a moment Revenue generated from providing ancillary services to the power grid.

[0051] Battery maintenance costs The formula is:

[0052] in, , , , and These are the coefficients. It is a constant. It is a moment Battery temperature, It is a moment The state of charge of the battery, It is a moment The battery charge / discharge rate.

[0053] The battery life loss function for:

[0054] in, This refers to the battery's rated cycle life. It is a moment depth of discharge, It is the exponential coefficient of the effect of the depth of discharge on battery life. It is a moment Battery temperature, This is a reference temperature. It is a moment The internal resistance of the battery, It is the initial internal resistance of the battery. It is the exponential coefficient of the effect of battery internal resistance on battery life. It is a moment Battery charging and discharging power, It is the exponential coefficient of the effect of battery charging and discharging power on battery life.

[0055] The constraints of the battery charge / discharge control model are as follows:

[0056] in, and These are the minimum and maximum values ​​of the battery's charging and discharging power, respectively. and These are the lowest and highest operating temperatures of the battery, respectively. and These are the minimum and maximum values ​​of the battery's internal resistance, respectively. and These are the minimum and maximum battery cycle counts, respectively. and These are the minimum and maximum values ​​of environmental cost, respectively. and These are the minimum and maximum values ​​of grid ancillary service revenue, respectively. and These are the minimum and maximum values ​​of the total cost, respectively. and These are the minimum and maximum values ​​for battery life loss.

[0057] The calculation module 300 is used to calculate the optimal charging and discharging control strategy at the current moment using an optimization algorithm. Specifically: The optimal charge / discharge control strategy at the current moment is calculated using a genetic algorithm. The control module 400 is used to control the charging and discharging of the energy storage system based on the obtained optimal charging and discharging control strategy.

[0058] This application constructs a battery charge-discharge control model based on minimizing cost and maximizing battery life, and employs an optimization algorithm to calculate the optimal charge-discharge control strategy in real time. This method can significantly reduce the operating cost of energy storage systems, improve overall economic efficiency, and effectively reduce overcharging, over-discharging, and frequent charge-discharge phenomena, thus extending the lifespan of energy storage batteries. Furthermore, this method can acquire grid data in real time, more accurately determine the real-time demand of the grid, and adjust the charge-discharge strategy of the energy storage system accordingly, contributing to enhanced grid stability and reliability. Simultaneously, the use of advanced optimization algorithms and a refined control model improves the system's intelligence and automation level, giving the method strong adaptability and robustness, enabling it to meet the charge-discharge requirements of energy storage systems under different operating conditions.

[0059] Example 5 This embodiment 5 provides a computer device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of a computer method.

[0060] When the processor executes the computer program, it implements the steps of the above-described computer method.

[0061] Alternatively, the processor may execute the computer program to implement the functions of each module in the aforementioned system. The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above are examples of computer devices and do not constitute a limitation on the computer device; it may include more components than described above, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0062] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor, or any conventional processor, etc. The processor is the control center of the computer device, connecting various parts of the computer device through various interfaces and lines.

[0063] The memory can be used to store the computer program and / or module, and the processor implements various functions of the computer device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0064] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0065] Example 6 This embodiment 6 also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described.

[0066] If the modules / units integrated in the computer system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0067] Based on this understanding, all or part of the processes in the above-described method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described computer method. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0068] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0069] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0070] Example 7 This embodiment 7 provides a computer product, which includes a computer program stored in a computer-readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium and executes the computer program, so that the computer device can perform the method in embodiment 1, which will not be described again here.

[0071] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.

[0072] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A battery charging and discharging control method for an energy storage system, characterized in that, Includes the following steps: Step 1: Obtain energy storage battery data and grid data for the target energy storage system; Step 2: Input the obtained energy storage battery data and grid data into the preset battery charge / discharge control model, where the expression of the battery charge / discharge control model is: in, It is a function of the battery charge and discharge control model; , These are the weighting coefficients; It is the total cost function; It is a function of battery life loss; Step 3: Optimize the output of the preset battery charge and discharge control model using an optimization algorithm to obtain the optimal charge and discharge control strategy at the current moment. Step 4: Control the charging and discharging of the energy storage system based on the obtained optimal charging and discharging control strategy; The battery charge / discharge control model is constructed with cost minimization and battery life maximization as optimization objectives. Constraints on the model are established using battery charge / discharge power, battery operating temperature, battery internal resistance, battery cycle count, environmental cost, revenue from grid ancillary services, total cost, and battery life loss. The total cost function... for: in, It is the total time period. It is a moment Electricity price, It is a moment The power of electricity purchased from or sold to the grid. It is a moment Battery maintenance costs, It is a moment Battery replacement cost, It is a moment Environmental costs, It is a moment Revenue generated from providing ancillary services to the power grid.

2. The battery charging and discharging control method for an energy storage system according to claim 1, characterized in that, The battery life loss function for: in, It is the total time period. This refers to the battery's rated cycle life. It is a moment depth of discharge, It is the exponential coefficient of the effect of the depth of discharge on battery life. It is a moment Battery temperature, This is a reference temperature. It is a moment The internal resistance of the battery, It is the initial internal resistance of the battery. It is the exponential coefficient of the effect of battery internal resistance on battery life. It is a moment Battery charging and discharging power, It is the exponential coefficient of the effect of battery charging and discharging power on battery life.

3. The battery charging and discharging control method for an energy storage system according to claim 1, characterized in that, In step 2, the constraints of the battery charge / discharge control model are: in, and These are the minimum and maximum values ​​of the battery's charging and discharging power, respectively. and These are the lowest and highest operating temperatures of the battery, respectively. and These are the minimum and maximum values ​​of the battery's internal resistance, respectively. and These are the minimum and maximum battery cycle counts, respectively. and These are the minimum and maximum values ​​of environmental cost, respectively. and These are the minimum and maximum values ​​of ancillary service revenue for the power grid, respectively. and These are the minimum and maximum values ​​of the total cost, respectively. and These are the minimum and maximum values ​​of battery life loss; For a moment The battery charging and discharging power; For a moment The battery temperature; For a moment The internal resistance of the battery; For a moment The number of battery cycles; For a moment Electricity price; For a moment Revenue generated from providing ancillary services to the power grid; It is a function of total cost; This is a function representing battery life loss.

4. The battery charging and discharging control method for an energy storage system according to claim 1, characterized in that, In step 3, a genetic algorithm is used to calculate the optimal charging and discharging control strategy at the current moment.

5. A battery charging and discharging control system for an energy storage system, characterized in that, The control method based on claim 1 includes: The acquisition module is used to acquire energy storage battery data and grid data of the target energy storage system; The input module is used to input the obtained energy storage battery data and grid data into a preset battery charge and discharge control model. The battery charge and discharge control model is constructed with the optimization objectives of minimizing cost and maximizing battery life. The constraints of the battery charge and discharge control model are constructed with battery charge and discharge power, battery operating temperature, battery internal resistance, battery cycle number, environmental cost, revenue obtained through grid ancillary services, total cost, and battery life loss. The calculation module is used to optimize the output of the preset battery charge and discharge control model using optimization algorithms to obtain the optimal charge and discharge control strategy at the current moment. The control module is used to control the charging and discharging of the energy storage system based on the obtained optimal charging and discharging control strategy.

6. A computer device, characterized in that, include: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method as described in any one of claims 1-4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-4.

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