Construction method for optimal configuration model for sodium-ion battery energy storage system

Abstract

Provided are a construction method and apparatus for an optimal configuration model for a sodium-ion battery energy storage system, and a computer device, a computer-readable storage medium, a computer program product and a computer program. The method comprises: initializing model configuration parameters, wherein the model configuration parameters comprise battery parameters, the number of module clusters, a connection mode, charging and discharging strategies, and control logic; on the basis of the model configuration parameters, constructing a system cost function and a system energy efficiency function; on the basis of the model configuration parameters, constructing a system constraint function; and on the basis of the system cost function, the system energy efficiency function and the system constraint function, constructing an optimal configuration model for a sodium-ion battery energy storage system.

Description

Method for constructing an optimal configuration model for sodium-ion battery energy storage systems

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 2024109921636, filed in China on July 23, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of energy storage technology, specifically to a method and apparatus for constructing an optimized configuration model for a sodium-ion battery energy storage system, as well as computer equipment, computer-readable storage media, computer program products, and computer programs. Background Technology

[0004] Battery energy storage is an important component of new energy storage technologies. Against the backdrop of soaring lithium-ion battery prices due to lithium resource shortages, the increasingly mature sodium-ion battery has come into focus. Sodium resources are abundant and inexpensive, and sodium-ion batteries possess excellent low-temperature performance and rate capability, are compatible with lithium-ion battery production lines, and can serve as an important strategic reserve technology for lithium-ion batteries.

[0005] Currently, the commercialization level of sodium-ion batteries is relatively low, with installed capacity of sodium-ion battery energy storage systems only in the tens of megawatt-hours range. Sodium-ion batteries possess excellent low-temperature performance and rate performance, but their battery life, system capacity, and system efficiency are slightly inferior to those of lithium-ion batteries. Furthermore, these multiple indicators are mutually restrictive, making it difficult to achieve optimal performance across multiple objectives simultaneously.

[0006] In related technologies, the parameter configuration of sodium-ion battery energy storage systems is not effective, which affects the overall performance of sodium-ion battery energy storage systems. Summary of the Invention

[0007] The embodiments disclosed herein are intended to at least partially address one of the technical problems in the related art.

[0008] Therefore, the purpose of this disclosure is to propose a method and apparatus for constructing an optimal configuration model for a sodium-ion battery energy storage system, as well as a computer device, a computer-readable storage medium, a computer program product, and a computer program. This method and apparatus can use the system cost function and the system energy efficiency function as objective functions and combine them with system constraint functions to ensure the practicality and reliability of the obtained optimal configuration model for the sodium-ion battery energy storage system, thereby effectively improving the optimization configuration effect of the obtained optimal configuration model for the sodium-ion battery energy storage system.

[0009] To achieve the above objectives, the method for constructing an optimized configuration model for a sodium-ion battery energy storage system, as proposed in the first aspect of this disclosure, includes:

[0010] Initialize model configuration parameters, which include: battery parameters, number of module clusters, connection method, charging and discharging strategy, and control logic;

[0011] Based on the model configuration parameters, construct the system cost function and the system energy efficiency function;

[0012] Based on the model configuration parameters, construct the system constraint function;

[0013] Based on the system cost function, the system energy efficiency function, and the system constraint function, an optimal configuration model for a sodium-ion battery energy storage system is constructed.

[0014] To achieve the above objectives, the apparatus for constructing an optimized configuration model for a sodium-ion battery energy storage system, as proposed in the second aspect of this disclosure, includes:

[0015] The processing module is used to initialize model configuration parameters, which include: battery parameters, number of module clusters, connection method, charging and discharging strategy and control logic;

[0016] The first construction module is used to construct the system cost function and the system energy efficiency function based on the model configuration parameters;

[0017] The second construction module is used to construct system constraint functions based on the model configuration parameters;

[0018] The third construction module is used to construct an optimal configuration model for the sodium-ion battery energy storage system based on the system cost function, the system energy efficiency function, and the system constraint function.

[0019] The computer device proposed in the third aspect of this disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in the first aspect of this disclosure.

[0020] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in the first aspect of this disclosure.

[0021] A fifth aspect of this disclosure provides a computer program product in which, when instructions are executed by a processor, a method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in a first aspect of this disclosure is performed.

[0022] A sixth aspect of this disclosure provides a computer program comprising computer program code that, when executed on a computer, causes the computer to perform a method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in a first aspect of this disclosure.

[0023] The present disclosure provides a method, apparatus, computer device, and storage medium for constructing an optimized configuration model for a sodium-ion battery energy storage system. This involves initializing model configuration parameters, including battery parameters, the number of module clusters, connection methods, charging / discharging strategies, and control logic; constructing a system cost function and a system energy efficiency function based on these parameters; constructing a system constraint function based on the same parameters; and finally, constructing an optimized configuration model for the sodium-ion battery energy storage system based on these three functions. Therefore, by using the system cost function and system energy efficiency function as objective functions and combining them with the system constraint function, the practicality and reliability of the resulting optimized configuration model for the sodium-ion battery energy storage system can be guaranteed, thereby effectively improving the optimization effect of the model on the sodium-ion battery energy storage system.

[0024] Additional aspects and advantages of embodiments of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this disclosure. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0026] Figure 1 is a flowchart illustrating the method for constructing an optimized configuration model for a sodium-ion battery energy storage system according to an embodiment of this disclosure.

[0027] Figure 2 is a flowchart illustrating the construction method of an optimized configuration model for a sodium-ion battery energy storage system according to another embodiment of this disclosure;

[0028] Figure 3 is a schematic flowchart of solving the Pareto optimal solution set using NSGA-II according to an embodiment of this disclosure;

[0029] Figure 4 is a schematic diagram of the structure of a device for constructing an optimized configuration model of a sodium-ion battery energy storage system according to an embodiment of this disclosure;

[0030] Figure 5 shows a block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure. Detailed Implementation

[0031] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0032] Figure 1 is a flowchart illustrating the construction method of an optimized configuration model for a sodium-ion battery energy storage system according to an embodiment of this disclosure.

[0033] It should be noted that the execution subject of the method for constructing the sodium-ion battery energy storage system optimization configuration model in this embodiment is the sodium-ion battery energy storage system optimization configuration model construction device. This device can be implemented by software and / or hardware. This device can be configured in a computer device, which may include, but is not limited to, a terminal, a server, etc. For example, the terminal may be a mobile phone, a PDA, etc.

[0034] As shown in Figure 1, the method for constructing the optimized configuration model of the sodium-ion battery energy storage system includes steps S101 to S104.

[0035] S101: Initialize model configuration parameters, which include: battery parameters, number of module clusters, connection method, charging and discharging strategy and control logic.

[0036] Among them, the model configuration parameters can refer to the relevant parameters of the performance prediction model of sodium-ion battery energy storage system.

[0037] Among them, battery parameters can refer to the relevant parameters of the battery configured in the sodium-ion battery energy storage system, such as rated capacity, rated voltage, etc., without limitation.

[0038] In this embodiment of the disclosure, multiple batteries can be combined to form a battery module, and multiple modules can be jointly configured to form a module cluster.

[0039] The connection method can refer to the connection method between different batteries in a sodium-ion battery energy storage system, such as series connection or parallel connection, and there are no restrictions on this.

[0040] Among them, the charge and discharge strategy can be used to describe the control strategy of sodium-ion battery energy storage system during the charge and discharge process, such as charging power, charging cut-off voltage, discharging power, discharging cut-off voltage, etc., without any restrictions.

[0041] The control logic can be used to describe the control strategy for the entire sodium-ion battery energy storage system, such as temperature control strategy and continuous working time, without any restrictions.

[0042] In other words, in this embodiment of the present disclosure, before constructing the optimized configuration model of the sodium-ion battery energy storage system, the model configuration parameters can be initialized first, thereby providing a reliable parameter adjustment object for subsequent optimized configuration.

[0043] S102: Construct the system cost function and system energy efficiency function based on the model configuration parameters.

[0044] The system cost function can be used to describe how the configuration cost of a sodium-ion battery energy storage system changes with one or more parameters in the model configuration parameters.

[0045] The system energy efficiency function can be used to indicate how the system energy efficiency function of a sodium-ion battery energy storage system follows changes in one or more parameters in the model configuration parameters.

[0046] In other words, in this embodiment of the present disclosure, the system cost function and the system energy efficiency function can be constructed based on the model configuration parameters as objective functions in the process of constructing the optimal configuration model of the sodium-ion battery energy storage system.

[0047] S103: Construct system constraint functions based on model configuration parameters.

[0048] Among them, the system constraint function can be used to constrain relevant parameters in the process of constructing the optimal configuration model of sodium-ion battery energy storage system.

[0049] Understandably, in the process of optimizing the configuration of a sodium-ion battery energy storage system, it is necessary to ensure that the values ​​of relevant system parameters are within the normal range. Therefore, it is necessary to construct system constraint functions based on the model configuration parameters to ensure the practicality and reliability of the resulting sodium-ion battery energy storage system optimization configuration model.

[0050] S104: Based on the system cost function, system energy efficiency function, and system constraint function, construct an optimal configuration model for a sodium-ion battery energy storage system.

[0051] Among them, the sodium-ion battery energy storage system optimization configuration model refers to the model that obtains the optimal configuration of the sodium-ion battery energy storage system by adjusting parameters such as battery parameters, number of module clusters, connection method, charging and discharging strategy and control logic.

[0052] In this embodiment of the disclosure, when constructing the optimal configuration model of the sodium-ion battery energy storage system based on the system cost function, system energy efficiency function, and system constraint function, a fast elite multi-objective genetic algorithm (NSGA-II) can be used to solve for the Pareto optimal solution set. Based on the actual needs of the project, the optimal control strategy is obtained from the Pareto optimal solution set. The optimization configuration objective of the sodium-ion battery energy storage system optimal configuration model can include minimizing system cost and maximizing system energy efficiency.

[0053] In this embodiment, model configuration parameters are initialized, including battery parameters, number of module clusters, connection method, charging / discharging strategy, and control logic. Based on these parameters, a system cost function and a system energy efficiency function are constructed. A system constraint function is also constructed. Finally, an optimized configuration model for the sodium-ion battery energy storage system is built based on these parameters. This approach uses the system cost function and system energy efficiency function as objective functions, combined with the system constraint function, to ensure the practicality and reliability of the resulting optimized configuration model. This effectively improves the optimization performance of the sodium-ion battery energy storage system for the system.

[0054] Figure 2 is a flowchart illustrating the construction method of an optimized configuration model for a sodium-ion battery energy storage system proposed in another embodiment of this disclosure.

[0055] As shown in Figure 2, the method for constructing the optimized configuration model of the sodium-ion battery energy storage system includes steps S201 to S208.

[0056] S201: Initialize model configuration parameters, which include: battery parameters, number of module clusters, connection method, charging and discharging strategy and control logic.

[0057] For a detailed description of S201, please refer to the above embodiments, which will not be repeated here.

[0058] S202: Construct the battery cost function based on battery parameters, charging and discharging strategies, and control logic.

[0059] Among them, the battery cost function can be used to describe how the battery configuration cost changes with battery parameters, charging and discharging strategies, and control logic.

[0060] In this embodiment of the disclosure, when constructing the battery cost function based on battery parameters, charging and discharging strategies, and control logic, the correlation information between battery parameters, charging and discharging strategies, control logic, and battery cost can be determined separately, and then the battery cost function can be constructed based on the correlation information.

[0061] S203: Construct an integration cost function based on battery parameters, number of module clusters, connection method, charging and discharging strategy, and control logic.

[0062] Integration cost refers to the cost of integrating a sodium-ion battery energy storage system. The integration cost function can be used to describe the relationship between battery parameters, the number of module clusters, connection methods, charging / discharging strategies, control logic, and integration cost.

[0063] S204: Construct a system cost function based on the battery cost function and the integration cost function.

[0064] In this embodiment of the disclosure, when constructing the system cost function based on the battery cost function and the integration cost function, the battery cost function and the integration cost function can be summed to obtain the system cost function.

[0065] In other words, in this embodiment of the present disclosure, a battery cost function can be constructed based on battery parameters, charging and discharging strategies, and control logic; an integrated cost function can be constructed based on battery parameters, the number of module clusters, connection methods, charging and discharging strategies, and control logic; and a system cost function can be constructed based on the battery cost function and the integrated cost function. Therefore, combining the battery cost function and the integrated cost function can effectively improve the accuracy of the resulting system cost function in indicating the optimized configuration cost of the sodium-ion battery energy storage system.

[0066] S205: Based on the model configuration parameters, construct the battery cell energy efficiency function and the non-battery system integration efficiency function.

[0067] Battery cell energy efficiency refers to the energy conversion efficiency of a single battery cell or battery pack, that is, the energy ratio between input and output. It is usually expressed as a percentage or decimal and is used to measure the energy loss of a battery during the storage and release of energy.

[0068] Among them, the battery cell energy efficiency function can be used to describe the relationship between one or more parameters in the model configuration parameters and the battery cell energy efficiency.

[0069] Among them, non-battery system integration efficiency can refer to the energy conversion and transmission efficiency of other components and devices in the system (such as inverters, charge and discharge controllers, sensors, cooling systems, etc.) other than the battery itself.

[0070] Among them, the non-battery system integration efficiency function can be used to describe the relationship between one or more parameters in the model configuration parameters and the non-battery system integration efficiency.

[0071] In some embodiments, when constructing the battery cell energy efficiency function based on model configuration parameters, the system material characteristics can be determined according to the model configuration parameters, and a first function can be constructed based on the system material characteristics, wherein the first function is used to indicate the correlation information between the system material characteristics and the battery cell energy efficiency; the operating voltage can be determined according to the model configuration parameters, and a second function can be constructed based on the operating voltage, wherein the second function is used to indicate the correlation information between the operating voltage and the battery cell energy efficiency; the battery cell energy efficiency function is constructed based on the first function and the second function. Therefore, the resulting battery cell energy efficiency function can accurately indicate the correlation information between the battery cell energy efficiency and the system material characteristics and operating voltage, thereby ensuring the practicality of the battery cell energy efficiency function in the subsequent model construction process.

[0072] The system material characteristics can be information used to describe the material properties of the sodium-ion battery energy storage system, determined according to one or more parameters in the model configuration parameters in this embodiment of the disclosure.

[0073] The operating voltage can be the voltage at which the sodium-ion battery energy storage system is in operation, as determined by one or more parameters in the model configuration parameters in this embodiment of the disclosure.

[0074] In some embodiments, when constructing the non-battery system integration efficiency function based on model configuration parameters, the following steps can be taken: First, a system energy storage conversion strategy is determined based on the model configuration parameters. Then, a third function is constructed based on the system energy storage conversion strategy, whereby the third function indicates the correlation information between the system energy storage conversion strategy and the non-battery system integration efficiency. Next, a system integration architecture is determined based on the model configuration parameters. Then, a fourth function is constructed based on the system integration architecture, whereby the fourth function indicates the correlation information between the system integration architecture and the non-battery system integration efficiency. Finally, the non-battery system integration efficiency function is constructed based on the third and fourth functions. This approach combines the system energy storage conversion strategy and the system integration architecture to ensure the accuracy and reliability of the obtained non-battery system integration efficiency function.

[0075] Among these, system energy storage conversion strategies refer to how battery energy storage systems manage and schedule the charging and discharging processes of batteries to maximize system efficiency, lifespan, and economics. These strategies can be formulated based on factors such as energy demand, grid conditions, battery type, and technical characteristics.

[0076] System integration architecture refers to the design framework and organizational structure of the entire system, including the various subsystems, components, and their connection methods and interactions.

[0077] In this embodiment of the disclosure, when constructing the battery cell energy efficiency function and the non-battery system integration efficiency function according to the model configuration parameters, reliable reference information can be provided for the subsequent construction of the system energy efficiency function.

[0078] S206: Construct a system energy efficiency function based on the battery cell energy efficiency function and the non-battery system integration efficiency function.

[0079] In other words, in this embodiment of the disclosure, a battery cell energy efficiency function and a non-battery system integrated efficiency function can be constructed based on model configuration parameters; and a system energy efficiency function can be constructed based on the battery cell energy efficiency function and the non-battery system integrated efficiency function. This effectively improves the comprehensiveness and accuracy of the resulting system energy efficiency function.

[0080] S207: Construct system constraint functions based on model configuration parameters.

[0081] S208: Based on the system cost function, system energy efficiency function, and system constraint function, an optimal configuration model for sodium-ion battery energy storage system is constructed.

[0082] For a detailed description of S207 and S208, please refer to the above embodiments, which will not be repeated here.

[0083] In this embodiment, a battery cost function is constructed based on battery parameters, charging / discharging strategies, and control logic; an integrated cost function is constructed based on battery parameters, the number of module clusters, connection methods, charging / discharging strategies, and control logic; and a system cost function is constructed based on the battery cost function and the integrated cost function. Therefore, combining the battery cost function and the integrated cost function can effectively improve the accuracy of the resulting system cost function in indicating the optimized configuration cost of the sodium-ion battery energy storage system. Furthermore, a battery cell energy efficiency function and a non-battery system integrated efficiency function are constructed based on model configuration parameters; and a system energy efficiency function is constructed based on the battery cell energy efficiency function and the non-battery system integrated efficiency function. This can effectively improve the comprehensiveness and accuracy of the resulting system energy efficiency function.

[0084] In some embodiments, this disclosure also proposes a method for constructing an optimized configuration model for a sodium-ion battery energy storage system, wherein the system constraint function includes at least one of the following: a battery energy efficiency constraint function; an operating voltage constraint function; and a system energy efficiency constraint function. This effectively improves the practicality and applicability of the system constraint function.

[0085] Among them, the battery energy efficiency constraint function is used to constrain the range of battery energy efficiency values ​​during the optimization configuration of sodium-ion battery energy storage systems.

[0086] The operating voltage constraint function is used to constrain the range of operating voltage values ​​during the optimization configuration of the sodium-ion battery energy storage system.

[0087] Among them, the system energy efficiency constraint function is used to constrain the range of system energy efficiency values ​​during the optimization configuration of the sodium-ion battery energy storage system.

[0088] In summary, to achieve the goal of optimal performance of sodium-ion battery energy storage systems, this disclosure proposes a CMOP-based optimization configuration model for sodium-ion battery energy storage systems. Based on the performance prediction model of energy storage system equipment, the model optimizes the performance of sodium-ion battery energy storage systems by adjusting parameters such as battery parameters, number of module clusters, connection method, charging and discharging strategy, and control logic.

[0089] Sodium-ion battery systems have lower energy density, and due to the lower energy density and wider voltage range of individual sodium-ion cells, their system energy efficiency is also lower than that of lithium-ion batteries. Furthermore, sodium-ion batteries are currently less commercially available, and economies of scale in production capacity cannot amortize their costs; additionally, integration costs are higher. Therefore, sodium-ion battery energy storage systems are more expensive than lithium-ion batteries. Thus, the objective function is set as minimizing system cost and maximizing system energy efficiency. The objective function is as follows:

[0090] (1) Lowest system cost

[0091] The system cost includes two categories: battery cost (Cell(x)) and integration cost (ASSEM(x)). Ω represents the system configuration optimization parameter set, including battery parameters, number of module clusters, connection method, charging and discharging strategy, control logic, etc.

[0092] (2) The system has the highest energy efficiency.

[0093] System energy efficiency is the sum of the energy efficiency of battery cells and the integrated efficiency of non-battery systems.

[0094] The output capacity and energy efficiency of sodium-ion batteries are limited by the material system and operating voltage. Therefore, the energy efficiency of the battery cell is a function Ecell(x) = G(M(x), V(x)) with variables being the material and the operating voltage. M(x) is the material-efficiency function, and V(x) is the voltage-efficiency function.

[0095] The efficiency of non-battery system integration is constrained by the energy storage converter conversion strategy and system integration architecture. Therefore, the efficiency of non-battery system integration is constrained by the function ESSEM(x) = T(ET(x), ST(x)) of the energy storage conversion strategy and system integration architecture. ET(x) is the energy storage conversion strategy-efficiency function, and ST(x) is the system integration architecture-efficiency function.

[0096] The constraints are as follows:

[0097] 1) Define a battery cell as a basic unit containing individual battery cells, i.e., a battery cluster. Referring to existing lithium-ion battery standards, the battery energy efficiency should be no less than 95%, therefore, the value of ECell(x) should range from 95% to 100%.

[0098] 2) Since the controllable voltage range of a sodium-ion battery cell is 1.5 to 4V, its output capacity and energy efficiency are limited by the operating voltage range. That is, 1.5 ≤ V(x) ≤ 4.

[0099] 3) The system efficiency is usually higher than 85%, but it is difficult to achieve 100%, so 85% ≤ E(x) ≤ 100%.

[0100] Therefore, this system model optimizes system cost and system energy efficiency as objective functions. The multi-objective optimization configuration model for the sodium-ion battery energy storage system is: min F(C(x),-E(x))

[0101] For the aforementioned multi-objective optimization problem, the Fast Elite Multi-Objective Genetic Algorithm (NSGA-II) can be used to solve for the Pareto optimal solution set. Based on the actual needs of the project, the optimal control strategy, i.e., the optimal parameter set of x, is obtained from the Pareto optimal solution set. Figure 3 is a flowchart illustrating the process of solving the Pareto optimal solution set using NSGA-II according to an embodiment of this disclosure.

[0102] Figure 4 is a schematic diagram of the structure of the device for constructing an optimized configuration model of a sodium-ion battery energy storage system according to an embodiment of this disclosure.

[0103] As shown in Figure 4, the construction device 40 for the optimized configuration model of the sodium-ion battery energy storage system includes a processing module 401, a first construction module 402, a second construction module 403, and a third construction module 404.

[0104] The processing module 401 is used to initialize the model configuration parameters, which include: battery parameters, number of module clusters, connection method, charging and discharging strategy and control logic.

[0105] The first building module 402 is used to build the system cost function and the system energy efficiency function based on the model configuration parameters.

[0106] The second construction module 403 is used to construct system constraint functions based on model configuration parameters.

[0107] The third building module 404 is used to construct an optimal configuration model for a sodium-ion battery energy storage system based on the system cost function, the system energy efficiency function, and the system constraint function.

[0108] It should be noted that the aforementioned explanation of the method for constructing the optimal configuration model of the sodium-ion battery energy storage system also applies to the construction device for the optimal configuration model of the sodium-ion battery energy storage system in this embodiment, and will not be repeated here.

[0109] In this embodiment, model configuration parameters are initialized, including battery parameters, number of module clusters, connection method, charging / discharging strategy, and control logic. Based on these parameters, a system cost function and a system energy efficiency function are constructed. A system constraint function is also constructed. Finally, an optimized configuration model for the sodium-ion battery energy storage system is built based on these parameters. This approach uses the system cost function and system energy efficiency function as objective functions, combined with the system constraint function, to ensure the practicality and reliability of the resulting optimized configuration model. This effectively improves the optimization performance of the sodium-ion battery energy storage system for the system.

[0110] To implement the above embodiments, this disclosure also proposes a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in the first aspect of this disclosure.

[0111] Figure 5 shows a block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure. The computer device 12 shown in Figure 5 is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present disclosure.

[0112] As shown in Figure 5, the computer device 12 is presented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0113] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0114] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0115] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (not shown in Figure 5, commonly referred to as a "hard disk drive").

[0116] Although not shown in Figure 5, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0117] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0118] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0119] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the method for constructing the sodium-ion battery energy storage system optimization configuration model mentioned in the foregoing embodiments.

[0120] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in the foregoing embodiments of this disclosure.

[0121] To implement the above embodiments, this disclosure also proposes a computer program product, which, when executed by an instruction processor, performs a method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in the foregoing embodiments of this disclosure.

[0122] To implement the above embodiments, this disclosure also proposes a computer program, which includes computer program code. When the computer program code is run on a computer, the computer executes the method for constructing an optimized configuration model for a sodium-ion battery energy storage system as proposed in the first aspect of this disclosure.

[0123] It should be noted that the foregoing explanations of the method and apparatus embodiments also apply to the electronic devices, computer-readable storage media, computer program products and computer programs of the above embodiments, and will not be repeated here.

[0124] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0125] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

[0126] It should be noted that in the description of this disclosure, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0127] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0128] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0129] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0130] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0131] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0132] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0133] Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

[0134] All embodiments disclosed herein can be executed individually or in combination with other embodiments, and are all considered to be within the scope of protection claimed by this disclosure.

Claims

1. A method for constructing an optimal configuration model of a sodium-ion battery energy storage system, characterized in that, The method comprises the following steps: initializing model configuration parameters, wherein the model configuration parameters comprise battery parameters, module cluster quantity, connection mode, charging and discharging strategy, and control logic; constructing a system cost function and a system energy efficiency function according to the model configuration parameters; constructing a system constraint function according to the model configuration parameters; constructing a sodium-ion battery energy storage system optimization configuration model based on the system cost function, the system energy efficiency function, and the system constraint function.

2. The method of claim 1, wherein, constructing the system cost function according to the model configuration parameters, comprising: constructing a battery cost function according to the battery parameters, the charging and discharging strategy, and the control logic; constructing an integration cost function according to the battery parameters, the module cluster quantity, the connection mode, the charging and discharging strategy, and the control logic; constructing the system cost function based on the battery cost function and the integration cost function.

3. The method of claim 1 or 2, wherein, constructing the system energy efficiency function according to the model configuration parameters, comprising: constructing a battery unit energy efficiency function and a non-battery system integration efficiency function according to the model configuration parameters; constructing the system energy efficiency function based on the battery unit energy efficiency function and the non-battery system integration efficiency function.

4. The method of claim 3, wherein, constructing the battery unit energy efficiency function according to the model configuration parameters, comprising: determining system material characteristics according to the model configuration parameters, and constructing a first function based on the system material characteristics, wherein the first function is used to indicate the association information between the system material characteristics and the battery unit energy efficiency; determining working voltage according to the model configuration parameters, and constructing a second function based on the working voltage, wherein the second function is used to indicate the association information between the working voltage and the battery unit energy efficiency; constructing the battery unit energy efficiency function based on the first function and the second function.

5. The method of claim 3, wherein, constructing the non-battery system integration efficiency function according to the model configuration parameters, comprising: determining system energy storage transformation strategy according to the model configuration parameters, and constructing a third function based on the system energy storage transformation strategy, wherein the third function is used to indicate the association information between the system energy storage transformation strategy and the non-battery system integration efficiency; determining system integration architecture according to the model configuration parameters, and constructing a fourth function based on the system integration architecture, wherein the fourth function is used to indicate the association information between the system integration architecture and the non-battery system integration efficiency; constructing the non-battery system integration efficiency function based on the third function and the fourth function.

6. The method of any one of claims 1 to 5, wherein, The system constraint function comprises at least one of the following: a battery energy efficiency constraint function; a working voltage constraint function; a system energy efficiency constraint function.

7. A device for constructing an optimal configuration model of a sodium-ion battery energy storage system, characterized in that, The method comprises the following steps: a processing module for initializing model configuration parameters, wherein the model configuration parameters comprise battery parameters, module cluster quantity, connection mode, charging and discharging strategy, and control logic; a first construction module for constructing a system cost function and a system energy efficiency function according to the model configuration parameters; a second constructing module configured to construct a system constraint function according to the model configuration parameters; a third constructing module configured to construct a sodium-ion battery energy storage system optimization configuration model based on the system cost function, the system energy efficiency function and the system constraint function.

8. A computer device, comprising: comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein, the memory has stored instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, wherein, the computer instructions are for causing the computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterised in that, a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

11. A computer program, characterized in that, the computer program comprises computer program code which, when run on a computer, causes the computer to perform the method of any one of claims 1 to 6.

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