A method, system, device and medium for switching operation mode of battery energy storage system

Abstract

The present invention belongs to the field of energy storage, and specifically provides an operation mode switching method, system, device and medium of a battery energy storage system, which can obtain power grid data and battery energy storage system data in real time; construct a power grid dynamic behavior model; construct a battery energy storage system dynamic behavior model; construct a prediction model; construct an objective function, and predict and optimize the power grid state and the output state of the battery energy storage system within a preset time in the future according to the prediction model and the objective function; when the battery energy storage system switches between a grid-connected operation mode and an off-grid operation mode, the operation of the battery energy storage system is controlled according to the results of the prediction and optimization calculation, so that the battery energy storage system is synchronized with the power grid; the method of the present invention is not only applicable to specific battery energy storage systems and power grid environments, but also has strong adaptability. When the power grid conditions or energy storage system parameters change, the present invention can still maintain good synchronization performance and stability.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage technology, and specifically relates to an operation mode switching method, system, device and medium of a battery energy storage system. Background Art

[0002] With the rapid development of renewable energy and the advancement of smart grids, battery energy storage systems, as an important part of the power system, are facing an increasingly prominent problem in switching between grid-connected and off-grid operation modes. In particular, when the battery energy storage system is connected to or disconnected from the grid, how to ensure its synchronization with the grid and avoid impact current and power fluctuations has become a technical problem that needs to be solved urgently.

[0003] Existing battery energy storage system operation mode switching methods mainly rely on simple control strategies and fixed parameter settings. These methods often fail to achieve ideal synchronization effects when facing dynamic changes in the power grid and energy storage system. Specifically, existing methods have the following deficiencies:

[0004] 1. Lack of real-time performance: Existing methods often cannot obtain dynamic data of power grids and energy storage systems in real time, resulting in the inability to accurately predict and respond to changes in power grids and energy storage systems when switching modes;

[0005] 2. Inaccurate models: Existing methods may use simplified grid and energy storage system models, which often cannot accurately reflect the dynamic behavior of the actual system, thus affecting the accuracy and effect of synchronization control;

[0006] 3. Insufficient optimization: Existing methods often lack optimization calculations when switching modes and cannot adjust the optimal control strategy according to the real-time status of the power grid and energy storage system;

[0007] 4. Poor synchronization: Due to the above reasons, existing methods often find it difficult to achieve precise synchronization with the grid when the battery energy storage system is connected to or off the grid, which can easily generate impact current and power fluctuations, affecting the stability of the grid and the safety of the energy storage system.

[0008] In view of this, the art needs an operation mode switching method and system of a battery energy storage system to solve the above problems. Summary of the invention

[0009] The purpose of the present invention is to provide a method, system, device and medium for switching the operation mode of a battery energy storage system, which solves the technical problem that the existing battery energy storage system is difficult to maintain synchronization with the power grid when switching between on-grid and off-grid.

[0010] In order to achieve the above object, the technical solution adopted by the present invention is:

[0011] The present invention provides a method for switching an operation mode of a battery energy storage system, comprising the following steps:

[0012] S1: Real-time acquisition of grid data and battery energy storage system data;

[0013] S2: constructing a power grid dynamic behavior model according to the synchronous generator model, the transmission line model and the load model, and determining parameters of the power grid dynamic behavior model through real-time acquired power grid data;

[0014] S3: constructing a dynamic behavior model of the battery energy storage system according to the battery model, the inverter model and the filter model, and determining parameters of the dynamic behavior model of the battery energy storage system through real-time acquired battery energy storage system data;

[0015] S4: Constructing a prediction model by combining the power grid dynamic behavior model and the battery energy storage system dynamic behavior model, wherein the prediction model is used to predict the power grid state and the output state of the battery energy storage system within a preset time in the future;

[0016] S5: constructing an objective function, and predicting and optimizing the grid state and the output state of the battery energy storage system within a preset time in the future according to the prediction model and the objective function;

[0017] S6: When the battery energy storage system switches between the grid-connected operation mode and the off-grid operation mode, the operation of the battery energy storage system is controlled according to the results of the prediction and optimization calculation so that the battery energy storage system is synchronized with the grid.

[0018] Preferably, in S2, the expression for constructing the power grid dynamic behavior model is:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] in, For generator The phase angle, For generator The angular velocity of is the synchronous angular velocity, For generator The transient electromotive force, is the time constant, For generator The maximum electromotive force, For generator The direct-axis synchronous reactance, For generator The direct axis current, For generator The direct-axis current time constant is For generator The terminal voltage, For generator The stator resistance, For Node The voltage, For Node The equivalent capacitance of is the number of nodes in the power grid, For slave nodes Transfer to Node Voltage Through admittance After conversion, the node The voltage contribution of is the load impedance, is the load power, For Line The current, For Line The inductance, For Line The resistance, and Line The starting node and the terminal node voltage.

[0025] Preferably, in S2, the parameters of the power grid dynamic behavior model are determined by combining the power grid data acquired in real time with a system identification method.

[0026] Preferably, in S3, the expression of the dynamic behavior model of the battery energy storage system is:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] in, is the terminal voltage of the battery, is the open circuit voltage of the battery, is the internal resistance of the battery, is the capacitance of the battery, is the inductance of the battery, is the output current of the battery, is the output voltage of the inverter, is the DC bus voltage, is the modulation angle of the inverter, is the inductor current of the filter, is the capacitor voltage of the filter, is the inductance of the filter, is the capacitance of the filter.

[0033] Preferably, in S3, the parameters of the dynamic behavior model of the battery energy storage system are determined by combining the battery energy storage system data acquired in real time with a system identification method.

[0034] Preferably, in S4, the expression of the prediction model is:

[0035]

[0036] in, is the prediction function, , , , and are model parameters, is the number of nodes in the power grid, is the number of lines, For generator The phase angle, For generator The transient electromotive force, For generator The direct axis current, For Node The voltage, For Line The current, is the terminal voltage of the battery, is the output current of the battery, is the inductor current of the filter, is the capacitor voltage of the filter.

[0037] Preferably, in S5, the objective function is:

[0038]

[0039] in, is the objective function, are decision variables, including the control parameters of the battery energy storage system, , , , and are weight coefficients, is the number of nodes in the power grid, is the number of lines, For generator The phase angle, For generator The angular velocity of is the synchronous angular velocity, For generator The transient electromotive force, is the time constant, For generator The maximum electromotive force, For generator The direct-axis synchronous reactance, For generator The direct axis current, For generator The direct-axis current time constant is For generator The terminal voltage, For generator The stator resistance, For Node The voltage, For Node The equivalent capacitance of For slave nodes Transfer to Node Voltage Through admittance After conversion, the node The voltage contribution of is the load impedance, is the load power, For Line The current, For Line The inductance, For Line The resistance, and Line The starting node and the terminal node The voltage, is the terminal voltage of the battery, is the open circuit voltage of the battery, is the internal resistance of the battery, is the capacitance of the battery, is the inductance of the battery, is the output current of the battery, is the reference output current of the battery, is the output voltage of the inverter, is the reference output voltage of the inverter, is the DC bus voltage, is the modulation angle of the inverter, is the inductor current of the filter, is the reference inductor current of the filter, is the capacitor voltage of the filter, is the reference capacitor voltage of the filter, is the inductance of the filter, is the capacitance of the filter.

[0040] An operation mode switching system of a battery energy storage system, comprising:

[0041] Data acquisition module, used to obtain power grid data and battery energy storage system data in real time;

[0042] A power grid dynamic behavior model building module, used to build a power grid dynamic behavior model according to a synchronous generator model, a transmission line model and a load model, and determine parameters of the power grid dynamic behavior model through real-time acquired power grid data;

[0043] A battery energy storage system dynamic behavior model construction module is used to construct a battery energy storage system dynamic behavior model according to a battery model, an inverter model and a filter model, and determine parameters of the battery energy storage system dynamic behavior model through real-time acquired battery energy storage system data;

[0044] A prediction model building module, used to build a prediction model by combining the power grid dynamic behavior model and the battery energy storage system dynamic behavior model, wherein the prediction model is used to predict the power grid state and the output state of the battery energy storage system within a preset time in the future;

[0045] An objective function construction and calculation module is used to construct an objective function, and to predict and optimize the grid state and the output state of the battery energy storage system within a preset time in the future according to the prediction model and the objective function;

[0046] A control module is used to control the operation of the battery energy storage system according to the results of the prediction and optimization calculation when the battery energy storage system switches between the grid-connected operation mode and the off-grid operation mode, so as to synchronize the battery energy storage system with the grid.

[0047] A computer device comprising:

[0048] a processor suitable for executing a computer program;

[0049] A computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method described is performed.

[0050] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention provides an operation mode switching method for a battery energy storage system. The method obtains dynamic data of a power grid and an energy storage system in real time, builds an accurate dynamic behavior model, and uses the dynamic behavior model to predict the future behavior of the power grid and the energy storage system. In this way, when the energy storage system is switched between grid connection and off-grid connection, fluctuations will occur due to the mismatch between the two in the optimal state. The present invention predicts the future operation states of the power grid and the energy storage system through two behavior models, and calculates the optimal state of the energy storage system through an optimization algorithm. This state can be highly matched with the operation of the power grid. In this way, there will be no large fluctuations when switching between grid connection and off-grid connection, and the synchronization accuracy of the battery energy storage system with the power grid when connected to the grid and off-grid connection is achieved, which helps to reduce impact current and power fluctuations, and improve the stability of the power grid. Then, through prediction and optimization calculation, when the battery energy storage system switches the operation mode, the output of the energy storage system can be adjusted in advance to match it with the power grid state. This helps reduce the impact on the power grid when the system switches, and enhances the stability of the entire power system. Compared with the existing methods, the response speed is slow when facing dynamic changes in the power grid and energy storage system. The present invention can quickly respond to changes in the power grid and energy storage system through real-time data acquisition and model prediction, and improve the response speed and control effect of the system. At the same time, the present invention can optimize the operating state of the battery energy storage system while meeting the needs of the power grid by constructing an objective function and performing optimization calculations. This helps to extend the service life of the energy storage system and improve the economy and reliability of the system.

[0053] In summary, the method of the present invention is not only applicable to specific battery energy storage systems and power grid environments, but also has strong adaptability. When the power grid conditions or energy storage system parameters change, the present invention can still maintain good synchronization performance and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the present invention;

[0055] Figure 2 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0057] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0058] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0059] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0060] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0061] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0062] In order to solve the problem that the existing battery energy storage system has poor synchronization with the power grid when being connected to or disconnected from the power grid, which affects the stability of the power grid and the safety of the energy storage system, the present application provides an operation mode switching method and system for a battery energy storage system, aiming to achieve precise synchronization between the battery energy storage system and the power grid when switching between being connected to and disconnected from the power grid, thereby improving the stability of the power grid and the safety of the energy storage system.

[0063] Example 1

[0064] like Figure 1 , Figure 2 As shown, this embodiment provides an operation mode switching method for a battery energy storage system, the method comprising the following steps:

[0065] S1: Real-time acquisition of grid data and battery energy storage system data;

[0066] S2: constructing a power grid dynamic behavior model according to the synchronous generator model, the transmission line model and the load model, and determining parameters of the power grid dynamic behavior model by using real-time acquired power grid data and a system identification method;

[0067] S3: constructing a dynamic behavior model of the battery energy storage system according to the battery model, the inverter model and the filter model, and determining the parameters of the dynamic behavior model of the battery energy storage system by acquiring the battery energy storage system data in real time and adopting a system identification method; S4: constructing a prediction model in combination with the dynamic behavior model of the power grid and the dynamic behavior model of the battery energy storage system, and the prediction model is used to predict the power grid state and the output state of the battery energy storage system within a preset time in the future;

[0068] S5: constructing an objective function, and predicting and optimizing the grid state and the output state of the battery energy storage system within a preset time in the future according to the prediction model and the objective function;

[0069] S6: When the battery energy storage system switches between the grid-connected operation mode and the off-grid operation mode, the operation of the battery energy storage system is controlled according to the results of the prediction and optimization calculation so that the battery energy storage system is synchronized with the grid.

[0070] Example 2

[0071] On the basis of Example 1, this embodiment provides an operation mode switching method for a battery energy storage system. In S2, the dynamic behavior model of the power grid is:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] in, For generator The phase angle, For generator The angular velocity of is the synchronous angular velocity, For generator The transient electromotive force, is the time constant, For generator The maximum electromotive force, For generator The direct-axis synchronous reactance, For generator The direct axis current, For generator The direct-axis current time constant is For generator The terminal voltage, For generator The stator resistance, For Node The voltage, For Node The equivalent capacitance of is the number of nodes in the power grid, For slave nodes Transfer to Node Voltage Through admittance After conversion, the node The voltage contribution of is the load impedance, is the load power, For Line The current, For Line The inductance, For Line The resistance, and Line The starting node and the terminal node voltage.

[0078] Example 3

[0079] On the basis of Example 1, this embodiment provides an operation mode switching method for a battery energy storage system. In S3, the dynamic behavior model of the battery energy storage system is:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] in, is the terminal voltage of the battery, is the open circuit voltage of the battery, is the internal resistance of the battery, is the capacitance of the battery, is the inductance of the battery, is the output current of the battery, is the output voltage of the inverter, is the DC bus voltage, is the modulation angle of the inverter, is the inductor current of the filter, is the capacitor voltage of the filter, is the inductance of the filter, is the capacitance of the filter.

[0086] Example 4

[0087] On the basis of Example 1, this embodiment provides a method for switching the operation mode of a battery energy storage system. In S2, the parameters of the dynamic behavior model of the power grid are determined by using the real-time acquired power grid data and a system identification method. The specific method is:

[0088] Assigning initial values ​​to the power grid dynamic behavior model;

[0089] Using the acquired power grid data to adjust the parameters of the power grid dynamic behavior model through the least square method;

[0090] Calculating the deviation value between the output data of the power grid dynamic behavior model and the actual data;

[0091] If the deviation value between the output data of the power grid dynamic behavior model and the actual data is greater than a first preset value, continue to adjust the parameters of the power grid dynamic behavior model until the deviation value between the output data of the power grid dynamic behavior model and the actual data is less than or equal to the first preset value.

[0092] Example 5

[0093] On the basis of Example 1, this embodiment provides a method for switching the operation mode of a battery energy storage system. In S3, the parameters of the dynamic behavior model of the battery energy storage system are determined by obtaining the battery energy storage system data in real time and using a system identification method. The specific method is:

[0094] Assigning initial values ​​to the dynamic behavior model of the battery energy storage system;

[0095] Using the acquired battery energy storage system data to adjust the parameters of the dynamic behavior model of the battery energy storage system through the least squares method;

[0096] Calculating the deviation value between the output data of the dynamic behavior model of the battery energy storage system and the actual data;

[0097] If the deviation value between the output data of the dynamic behavior model of the battery energy storage system and the actual data is greater than a second preset value, continue to adjust the parameters of the dynamic behavior model of the battery energy storage system until the deviation value between the output data of the dynamic behavior model of the battery energy storage system and the actual data is less than or equal to the second preset value.

[0098] Example 6

[0099] Based on Example 1, this embodiment provides an operation mode switching method for a battery energy storage system. In S4, the prediction model is:

[0100]

[0101] in, is the prediction function, , , , and are model parameters, is the number of nodes in the power grid, is the number of lines, For generator The phase angle, For generator The transient electromotive force, For generator The direct axis current, For Node The voltage, For Line The current, is the terminal voltage of the battery, is the output current of the battery, is the inductor current of the filter, is the capacitor voltage of the filter, is the continuous product symbol.

[0102] Example 7

[0103] Based on Example 1, this embodiment provides an operation mode switching method for a battery energy storage system. In S5, the objective function is:

[0104]

[0105] in, is the objective function, are decision variables, including the control parameters of the battery energy storage system, , , , and are weight coefficients, is the number of nodes in the power grid, is the number of lines, For generator The phase angle, For generator The angular velocity of is the synchronous angular velocity, For generator The transient electromotive force, is the time constant, For generator The maximum electromotive force, For generator The direct-axis synchronous reactance, For generator The direct axis current, For generator The direct-axis current time constant is For generator The terminal voltage, For generator The stator resistance, For Node The voltage, For Node The equivalent capacitance of is the number of nodes in the power grid, For slave nodes Transfer to Node Voltage Through admittance After conversion, the node The voltage contribution of is the load impedance, is the load power, For Line The current, For Line The inductance, For Line The resistance, and Line The starting node and the terminal node The voltage, is the terminal voltage of the battery, is the open circuit voltage of the battery, is the internal resistance of the battery, is the capacitance of the battery, is the inductance of the battery, is the output current of the battery, is the reference output current of the battery, is the output voltage of the inverter, is the reference output voltage of the inverter, is the DC bus voltage, is the modulation angle of the inverter, is the inductor current of the filter, is the reference inductor current of the filter, is the capacitor voltage of the filter, is the reference capacitor voltage of the filter, is the inductance of the filter, is the capacitance of the filter, is the continuous product symbol.

[0106] Example 8

[0107] On the basis of Example 1, this embodiment provides a method for switching the operation mode of a battery energy storage system. In S5, the state of the power grid and the output state of the battery energy storage system within a preset time in the future are predicted and optimized according to the prediction model and the objective function. The specific method is:

[0108] Initializing parameters of the prediction model and the objective function;

[0109] Dividing the preset time into multiple time steps;

[0110] The solution of the prediction model is calculated at each time step using numerical integration methods;

[0111] Calculating the objective function at each time step based on the prediction model and the value of the decision variable;

[0112] An optimization algorithm is employed to minimize the objective function and determine the optimal values ​​of the decision variables.

[0113] The numerical integration method includes at least one of the Euler method and the Runge-Kutta method; and / or, the optimization algorithm includes at least one of the gradient descent algorithm, the genetic algorithm, and the particle swarm optimization algorithm.

[0114] Example 8

[0115] The present invention also provides an operation mode switching system for a battery energy storage system, the system comprising:

[0116] The data acquisition module 100 is used to acquire the power grid data and the battery energy storage system data in real time;

[0117] A power grid dynamic behavior model building module 200 is used to build a power grid dynamic behavior model according to a synchronous generator model, a transmission line model and a load model, and determine parameters of the power grid dynamic behavior model through power grid data acquired in real time;

[0118] The power grid dynamic behavior model is:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] in, For generator The phase angle, For generator The angular velocity of is the synchronous angular velocity, For generator The transient electromotive force, is the time constant, For generator The maximum electromotive force, For generator The direct-axis synchronous reactance, For generator The direct axis current, For generator The direct-axis current time constant is For generator The terminal voltage, For generator The stator resistance, For Node The voltage, For Node The equivalent capacitance of is the number of nodes in the power grid, For slave nodes Transfer to Node Voltage Through admittance After conversion, the node The voltage contribution of is the load impedance, is the load power, For Line The current, For Line The inductance, For Line The resistance, and Line The starting node and the terminal node voltage.

[0125] A battery energy storage system dynamic behavior model construction module 300 is used to construct a battery energy storage system dynamic behavior model according to a battery model, an inverter model and a filter model, and determine parameters of the battery energy storage system dynamic behavior model through real-time acquired battery energy storage system data;

[0126] In S3, the dynamic behavior model of the battery energy storage system is:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] in, is the terminal voltage of the battery, is the open circuit voltage of the battery, is the internal resistance of the battery, is the capacitance of the battery, is the inductance of the battery, is the output current of the battery, is the output voltage of the inverter, is the DC bus voltage, is the modulation angle of the inverter, is the inductor current of the filter, is the capacitor voltage of the filter, is the inductance of the filter, is the capacitance of the filter.

[0133] A prediction model building module 400, for building a prediction model by combining the power grid dynamic behavior model and the battery energy storage system dynamic behavior model, wherein the prediction model is used to predict the power grid state and the output state of the battery energy storage system within a preset time in the future;

[0134] The prediction model is:

[0135]

[0136] in, is the prediction function, , , , and are model parameters, is the number of nodes in the power grid, is the number of lines, For generator The phase angle, For generator The transient electromotive force, For generator The direct axis current, For Node The voltage, For Line The current, is the terminal voltage of the battery, is the output current of the battery, is the inductor current of the filter, is the capacitor voltage of the filter.

[0137] The objective function construction and calculation module 500 is used to construct an objective function, and to predict and optimize the grid state and the output state of the battery energy storage system within a preset time in the future according to the prediction model and the objective function;

[0138] The objective function is:

[0139]

[0140] in, is the objective function, are decision variables, including the control parameters of the battery energy storage system, , , , and are weight coefficients, is the number of nodes in the power grid, is the number of lines, For generator The phase angle, For generator The angular velocity of is the synchronous angular velocity, For generator The transient electromotive force, is the time constant, For generator The maximum electromotive force, For generator The direct-axis synchronous reactance, For generator The direct axis current, For generator The direct-axis current time constant is For generator The terminal voltage, For generator The stator resistance, For Node The voltage, For Node The equivalent capacitance of is the number of nodes in the power grid, For slave nodes Transfer to Node Voltage Through admittance After conversion, the node The voltage contribution of is the load impedance, is the load power, For Line The current, For Line The inductance, For Line The resistance, and Line The starting node and the terminal node The voltage, is the terminal voltage of the battery, is the open circuit voltage of the battery, is the internal resistance of the battery, is the capacitance of the battery, is the inductance of the battery, is the output current of the battery, is the reference output current of the battery, is the output voltage of the inverter, is the reference output voltage of the inverter, is the DC bus voltage, is the modulation angle of the inverter, is the inductor current of the filter, is the reference inductor current of the filter, is the capacitor voltage of the filter, is the reference capacitor voltage of the filter, is the inductance of the filter, is the capacitance of the filter.

[0141] The control module 600 is used to control the operation of the battery energy storage system according to the results of the prediction and optimization calculation when the battery energy storage system switches between the grid-connected operation mode and the off-grid operation mode, so as to synchronize the battery energy storage system with the grid.

[0142] Example 9

[0143] This embodiment 9 provides a computer device, including: a memory for storing a computer program; and a processor for implementing the steps of a computer method when executing the computer program.

[0144] When the processor executes the computer program, the steps of the above computer method are implemented.

[0145] Alternatively, when the processor executes the computer program, the functions of each module in the above system are realized.

[0146] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above are examples of computer devices and do not constitute a limitation on computer devices. The computer device may include more components than the above, 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.

[0147] The processor may be a central processing unit (CPU), other general-purpose 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, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0148] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0149] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0150] Example 10

[0151] This embodiment 10 also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described are implemented.

[0152] If the module / unit integrated in the computer system is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0153] Based on such understanding, the present invention implements all or part of the processes in the above method, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above computer method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0154] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0155] It should be noted that the content contained in the computer-readable storage medium can 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, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0156] It can be seen from the above that the method and system for switching the operation mode of a battery energy storage system provided by the present invention have the following beneficial technical effects:

[0157] 1. Improve synchronization accuracy: By acquiring dynamic data of the power grid and energy storage system in real time and building an accurate dynamic behavior model, the present invention can significantly improve the synchronization accuracy of the battery energy storage system with the power grid when connected to the grid or off the grid. This helps to reduce impact current and power fluctuations and improve the stability of the power grid.

[0158] 2. Enhance system stability: Through prediction and optimization calculation, the present invention can adjust the output of the energy storage system in advance when the battery energy storage system switches the operating mode to match the power grid state. This helps to reduce the impact on the power grid when the system switches and enhance the stability of the entire power system.

[0159] 3. Improve response speed: Existing methods often have slow response speeds when facing dynamic changes in power grids and energy storage systems. However, the present invention can quickly respond to changes in power grids and energy storage systems through real-time data acquisition and model prediction, thereby improving the system's response speed and control effect.

[0160] 4. Optimize system operation: By constructing an objective function and performing optimization calculations, the present invention can optimize the operating state of the battery energy storage system while meeting the needs of the power grid. This helps to extend the service life of the energy storage system and improve the economy and reliability of the system.

[0161] 5. Strong adaptability: The method of the present invention is not only applicable to specific battery energy storage systems and power grid environments, but also has strong adaptability. When the power grid conditions or energy storage system parameters change, the present invention can still maintain good synchronization performance and stability.

[0162] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for switching an operation mode of a battery energy storage system, characterized in that: The following steps are involved: S1: Real-time acquisition of grid data and battery energy storage system data; S2: constructing a power grid dynamic behavior model according to the synchronous generator model, the transmission line model and the load model, and determining parameters of the power grid dynamic behavior model through real-time acquired power grid data; S3: constructing a dynamic behavior model of the battery energy storage system according to the battery model, the inverter model and the filter model, and determining parameters of the dynamic behavior model of the battery energy storage system through real-time acquired battery energy storage system data; S4: Constructing a prediction model by combining the power grid dynamic behavior model and the battery energy storage system dynamic behavior model, wherein the prediction model is used to predict the power grid state and the output state of the battery energy storage system within a preset time in the future; S5: constructing an objective function, and predicting and optimizing the grid state and the output state of the battery energy storage system within a preset time in the future according to the prediction model and the objective function; S6: When the battery energy storage system switches between the grid-connected operation mode and the off-grid operation mode, the battery energy storage system is controlled to operate according to the results of the prediction and optimization calculation so that the battery energy storage system is synchronized with the grid; In S2, the expression for constructing the dynamic behavior model of the power grid is: Among them, θ g is the phase angle of the generator g, ω g is the angular velocity of the generator g, ω s is the synchronous angular velocity, E g is the transient electromotive force of generator g, T do is the time constant, E max is the maximum electromotive force of the generator g, x d is the direct-axis synchronous reactance of generator g, I d is the direct axis current of generator g, T d is the direct axis current time constant of generator g, V t is the terminal voltage of generator g, R a is the stator resistance of the generator g, V n is the voltage at node n, C m is the equivalent capacitance of node n, N is the number of nodes in the power grid, and Y nk V tk is the voltage V transmitted from node k to node n tk Through the admittance Y nk The voltage contribution to node n after conversion, Z l is the load impedance, S l is the load power, I l is the current in line l, L l is the inductance of line l, R l is the resistance of line l, and are the voltages of the starting node i and the ending node j of line l respectively; In S3, the expression of the dynamic behavior model of the battery energy storage system is: V inv =V dc ·sin(φ) Among them, V b is the terminal voltage of the battery, V o is the open circuit voltage of the battery, R b is the internal resistance of the battery, C b is the capacitance of the battery, L b is the inductance of the battery, I b is the output current of the battery, V inv is the output voltage of the inverter, V dc is the DC bus voltage, φ is the modulation angle of the inverter, I LV is the filter inductor current, V L is the capacitor voltage of the filter, L f is the inductance of the filter, C f is the capacitance of the filter; In S4, the expression of the prediction model is: Among them, F(t) is the prediction function, A, α1, α2, α3 and α4 are model parameters, N is the number of nodes in the power grid, L is the number of lines, θ g is the phase angle of generator g, E g is the transient electromotive force of generator g, I d is the direct axis current of generator g, V n is the voltage at node n, I l is the current in line l, V b is the terminal voltage of the battery, I b is the output current of the battery, I LV is the filter inductor current, V L is the capacitor voltage of the filter, Π( ) is the continuous product sign; In S5, the objective function is: Among them, G(x,t) is the objective function, x is the decision variable, β1, β2, β3, β4 and β5 are weight coefficients, N is the number of nodes in the power grid, L is the number of lines, θ g is the phase angle of the generator g, ω g is the angular velocity of the generator g, ω s is the synchronous angular velocity, E g is the transient electromotive force of generator g, T do is the time constant, E max is the maximum electromotive force of the generator g, x d is the direct-axis synchronous reactance of generator g, I d is the direct axis current of generator g, T d is the direct axis current time constant of generator g, V t is the terminal voltage of generator g, R a is the stator resistance of the generator g, V n is the voltage at node n, C m is the equivalent capacitance of node n, Y nk V tk is the voltage V transmitted from node k to node n tk Through the admittance Y nk The voltage contribution to node n after conversion, Z l is the load impedance, S l is the load power, I l is the current in line l, L l is the inductance of line l, R l is the resistance of line l, and are the voltages of the starting node i and the ending node j of line l, V b is the terminal voltage of the battery, V o is the open circuit voltage of the battery, R b is the internal resistance of the battery, C b is the capacitance of the battery, L b is the inductance of the battery, I b is the output current of the battery, is the reference output current of the battery, V inv is the output voltage of the inverter, is the reference output voltage of the inverter, V dc is the DC bus voltage, φ is the modulation angle of the inverter, I LV is the inductor current of the filter, is the reference inductor current of the filter, V L is the capacitor voltage of the filter, is the reference capacitor voltage of the filter, L f is the inductance of the filter, C f is the capacitance of the filter, and Π() is the continuous product sign.

2. The method for switching the operation mode of a battery energy storage system according to claim 1, characterized in that: In S2, the parameters of the power grid dynamic behavior model are determined by combining the power grid data acquired in real time with a system identification method.

3. The method for switching the operation mode of a battery energy storage system according to claim 1, characterized in that: In S3, the parameters of the dynamic behavior model of the battery energy storage system are determined by combining the battery energy storage system data acquired in real time with a system identification method.

4. An operation mode switching system for a battery energy storage system, characterized in that: The switching method according to any one of claims 1 to 3 comprises: Data acquisition module, used to obtain power grid data and battery energy storage system data in real time; A power grid dynamic behavior model building module, used to build a power grid dynamic behavior model according to a synchronous generator model, a transmission line model and a load model, and determine parameters of the power grid dynamic behavior model through real-time acquired power grid data; A battery energy storage system dynamic behavior model construction module is used to construct a battery energy storage system dynamic behavior model according to a battery model, an inverter model and a filter model, and determine parameters of the battery energy storage system dynamic behavior model through real-time acquired battery energy storage system data; A prediction model building module, used to build a prediction model by combining the power grid dynamic behavior model and the battery energy storage system dynamic behavior model, wherein the prediction model is used to predict the power grid state and the output state of the battery energy storage system within a preset time in the future; An objective function construction and calculation module is used to construct an objective function, and to predict and optimize the grid state and the output state of the battery energy storage system within a preset time in the future according to the prediction model and the objective function; A control module is used to control the operation of the battery energy storage system according to the results of the prediction and optimization calculation when the battery energy storage system switches between the grid-connected operation mode and the off-grid operation mode, so as to synchronize the battery energy storage system with the grid.

5. A computer device, characterized in that: include: a processor suitable for executing a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the method according to any one of claims 1 to 3 is performed.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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