Energy storage power station electromechanical transient practical equivalent modeling method and device

Abstract

The application provides a kind of energy storage power station electromechanical transient practical equivalent modeling method and device, belong to new energy field station simulation modeling.The method comprises: for each type of energy storage converter in energy storage power station, obtain the key data of the type of energy storage converter during fault ride-through under different working conditions, based on key data, fault ride-through model and objective function, the initial control parameters of the type of energy storage converter are solved, according to the initial control parameters and the limit conditions of active current and reactive current, iterative solution is carried out, and the final control parameters of the type of energy storage converter are obtained;According to the final control parameters of each type of energy storage converter, each energy storage unit and each energy storage converter of energy storage power station are equivalent modeled;The equivalent modeling of the power collection and booster system is carried out, and the equivalent modeling of the energy storage power station is completed.The application can improve the accuracy of the simulation model of energy storage power station, and then improve the reliability of the result of large-scale energy storage grid-connected simulation.

Description

Technical Field

[0001] This invention relates to the field of simulation modeling technology for new energy power stations, and in particular to a practical equivalent modeling method and device for electromechanical transients of energy storage power stations. Background Technology

[0002] New energy storage is a crucial equipment foundation and key supporting technology for building new power systems and promoting the green and low-carbon transformation of energy. In recent years, the installed capacity of energy storage has increased significantly, creating possibilities for the application of energy storage technology in power system regulation. Whether promoting energy transformation or supporting the construction of new power systems, in-depth quantitative evaluation of energy storage effectiveness is necessary. Energy storage power station modeling is not only a basic tool for energy storage integration planning and a prerequisite for grid connection evaluation, but also an important analytical means to ensure the large-scale development of energy storage and its role in the new energy system.

[0003] In related technologies, most energy storage power stations, during steady-state operation, function as a power source to the grid, with little impact on grid-related calculations. However, when grid faults occur, the low-voltage ride-through (LVRT) strategies and parameters employed by different brands and series of converters vary, resulting in different external characteristics of the power supply in response to low-voltage faults at the connection point. However, due to a lack of testing technology, most manufacturers cannot provide accurate models and parameters. Consequently, only typical models and parameters can be used in grid transient simulation calculations, making it difficult to guarantee the accuracy of energy storage power station simulation models and thus affecting the reliability of large-scale energy storage grid-connected simulation results. Summary of the Invention

[0004] This invention provides a practical equivalent modeling method and apparatus for electromechanical transients in energy storage power stations, which solves the problem that existing methods can only use typical models and parameters, making it difficult to guarantee the accuracy of energy storage power station simulation models and thus affecting the credibility of large-scale energy storage grid-connected simulation results.

[0005] In a first aspect, embodiments of the present invention provide a practical equivalent modeling method for electromechanical transients in an energy storage power station. The energy storage power station includes an energy storage unit, an energy storage converter, and a current collection and step-up system. The method includes:

[0006] For each type of energy storage converter in the energy storage power station, based on the hardware-in-the-loop test platform of the energy storage converter, key data of the energy storage converter of that type during fault ride-through under different operating conditions are obtained. Based on the key data, fault ride-through model and objective function, the initial control parameters of the energy storage converter of that type are solved. Furthermore, based on the initial control parameters and the constraints of active and reactive current, the final control parameters of the energy storage converter of that type are obtained by iterative solution.

[0007] Based on the final control parameters of each type of energy storage converter, equivalent modeling is performed on each energy storage unit and each energy storage converter of the energy storage power station.

[0008] Equivalent modeling of the current collector step-up system is performed to complete the equivalent modeling of the energy storage power station.

[0009] In one possible implementation, based on the final control parameters of each type of energy storage converter, equivalent modeling is performed on each energy storage unit and each energy storage converter of the energy storage power station, including:

[0010] Based on the model of the energy storage converter, the energy storage power station is divided into multiple energy storage groups, including each energy storage unit and each energy storage converter.

[0011] Using a parameter aggregation method, equivalent modeling is performed for each energy storage group based on the final control parameters of each type of energy storage converter. The capacity of the equivalent energy storage unit in each energy storage group is the sum of the capacities of all energy storage units in the group, and the control parameters of the equivalent energy storage converter in each energy storage group are the final control parameters of the corresponding type of energy storage converter in the group.

[0012] In one possible implementation, the fault ride-through model includes a calculation model for reactive current reference values ​​and a calculation model for active current reference values; the objective functions include a reactive objective function and an active objective function.

[0013] Based on key data, fault ride-through model, and objective function, the initial control parameters of this type of energy storage converter are obtained, including:

[0014] Based on key data and reactive objective function, the least squares method is used to solve the calculation model of reactive current reference value to obtain the initial reactive control parameters of this type of energy storage converter.

[0015] Based on key data and active power objective function, the least squares method is used to solve the calculation model of active current reference value to obtain the initial active power control parameters of this type of energy storage converter.

[0016] The initial control parameters include initial reactive power control parameters and initial active power control parameters.

[0017] In one possible implementation, the calculation model for the reactive current reference value includes:

[0018]

[0019] The reactive power objective function includes:

[0020] Among them, V setThis refers to the per-unit value of the voltage limit when the corresponding energy storage converter enters the low-voltage ride-through state; I q_ref,i This is the reactive current reference value for the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; q0,i The reactive current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the reactive power adjustment factor; The correlation coefficient for reactive current values; I q_set This is the baseline value for reactive current; min L Q The reactive power objective function value; I qi The reactive current fitting value of the corresponding model of energy storage converter under the i-th operating condition.

[0021] In one possible implementation, the calculation model for the active current reference value includes:

[0022]

[0023] The active objective function includes:

[0024] Among them, I p_ref,i This is the reference value of the active current of the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; p0,i The active current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the active power adjustment factor; The correlation coefficient for the active current value; I p_set This is the baseline value for active current; min L P The active objective function value; I pi The active current fitting value of the corresponding model of energy storage converter under the i-th operating condition.

[0025] In one possible implementation, the final control parameters of the energy storage converter are obtained by iteratively solving based on the initial control parameters and the constraints of active and reactive currents, including:

[0026] The initial control parameters are treated as a particle in the initial population. The particle swarm optimization algorithm is used to iteratively solve the problem and obtain the final control parameters of this type of energy storage converter.

[0027] In the particle swarm optimization algorithm, the formula for calculating fitness is:

[0028]

[0029] In the above formula, I pi Let I be the fitted value of the active current of the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; p_max I represents the maximum value of the active current. max I represents the maximum value of the total current. qi I represents the fitted reactive current value of the corresponding model of energy storage converter under the i-th operating condition; p_ref,i I represents the active current reference value for the corresponding model of energy storage converter under the i-th operating condition; q_max I represents the maximum value of the reactive current. q_ref,i ε is the reactive current reference value of the corresponding model of energy storage converter under the i-th operating condition; ε is the maximum allowable error value in energy storage modeling.

[0030] In one possible implementation, the collector boost system includes collector lines;

[0031] Equivalent modeling of the current collector boost system includes:

[0032] Based on the clustering results of energy storage units and energy storage converters, the collector lines of the energy storage power station are divided into multiple collector groups.

[0033] Based on the connection structure of each energy storage converter in the energy storage power station, an equivalent model is performed for each collector group.

[0034] In one possible implementation, based on the connection structure of each energy storage converter in the energy storage power station, equivalent modeling is performed for each collector group, including:

[0035] If the connection structure is a trunk-type structure, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n1 S represents the equivalent impedance of the collector line of the collector group corresponding to the trunk-type structure; k Z represents the capacity of the k-th energy storage unit corresponding to this collector group; j Let S be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group; S j Let be the capacity of the j-th energy storage unit corresponding to this collector group;

[0036] If the connection structure is radial, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n2 S is the equivalent impedance of the collector line of the collector group corresponding to the radial structure; j Z represents the capacity of the j-th energy storage unit corresponding to this collector group; jLet l be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group.

[0037] The equivalent susceptance of the equivalent collector line in each collector group is the sum of the susceptances of each collector line in that collector group.

[0038] In one possible implementation, the collector step-up system includes a main transformer, a box-type transformer, and a static var generator (SVG).

[0039] Equivalent modeling of the current collector boost system includes:

[0040] Based on the clustering results of energy storage units and energy storage converters, each box-type transformer in the energy storage power station is divided into multiple box-type transformer groups, and equivalent modeling is performed for each box-type transformer group.

[0041] Based on the nodes connected to each main transformer of the energy storage power station, the main transformers of the energy storage power station are divided into multiple main transformer groups, and equivalent modeling is performed for each main transformer group; among them, main transformers with the same connected nodes are grouped into one main transformer group.

[0042] Based on the busbars connected to each SVG of the energy storage power station, each SVG of the energy storage power station is divided into multiple SVG groups, and the parameter aggregation method is used to perform equivalent modeling for each SVG group.

[0043] Secondly, embodiments of the present invention provide a practical equivalent modeling device for electromechanical transients in an energy storage power station. The energy storage power station includes an energy storage unit, an energy storage converter, and a current collection and step-up system. The device includes:

[0044] The parameter identification module is used to acquire key data of each type of energy storage converter in the energy storage power station under different operating conditions during fault ride-through based on the hardware-in-the-loop test platform of the energy storage converter. Based on the key data, fault ride-through model and objective function, the module solves for the initial control parameters of the energy storage converter of that type. Furthermore, based on the initial control parameters and the constraints of active and reactive current, the module iteratively solves for the final control parameters of the energy storage converter of that type.

[0045] The first equivalent modeling module is used to perform equivalent modeling of each energy storage unit and each energy storage converter of the energy storage power station based on the final control parameters of each type of energy storage converter.

[0046] The second equivalent modeling module is used to perform equivalent modeling of the current collector step-up system, thus completing the equivalent modeling of the energy storage power station.

[0047] This application provides a practical equivalent modeling method and apparatus for electromechanical transients in energy storage power stations. Through a hardware-in-the-loop test platform for energy storage converters, key data of various models of energy storage converters under different operating conditions during fault ride-through are obtained. This accurately reproduces the actual operating conditions during fault ride-through and solves problems such as difficulty in simulating operating conditions and limited conditions in on-site online measurements. Furthermore, this application fully considers the differences in fault ride-through control strategies for different models of energy storage converters when performing equivalent modeling of energy storage power stations, which improves the accuracy of the energy storage power station simulation model and thus enhances the credibility of large-scale energy storage grid-connected simulation results. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating the implementation of an embodiment of the practical equivalent modeling method for electromechanical transients in energy storage power stations provided by the present invention.

[0050] Figure 2 This is a schematic diagram of a typical structure of an energy storage power station provided in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of the hardware-in-the-loop test platform for an energy storage converter provided in an embodiment of the present invention;

[0052] Figure 4 This is a schematic diagram of a trunk line structure provided in an embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of a radial structure provided in an embodiment of the present invention;

[0054] Figure 6 This is a schematic diagram of an equivalent model of an energy storage power station including a main transformer, provided in an embodiment of the present invention.

[0055] Figure 7 This is a schematic diagram of an equivalent model of an energy storage power station that does not include a main transformer, provided in an embodiment of the present invention.

[0056] Figure 8 This is a schematic diagram of the structure of an equivalent modeling device for practical application of electromechanical transients in energy storage power stations, provided in an embodiment of the present invention. Detailed Implementation

[0057] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0059] As mentioned earlier, only typical models and parameters can be used in grid transient simulation calculations, making it difficult to guarantee the accuracy of energy storage power station simulation models, which in turn affects the credibility of large-scale energy storage grid-connected simulation results. Furthermore, the data used for energy storage converter parameter identification mainly comes from laboratory "all-digital" model simulations and field tests. "All-digital" model simulations, due to overly idealized experimental conditions, yield parameters that are difficult to apply in engineering practice; while field tests require specific conditions, resulting in high data acquisition costs. For numerous types of energy storage converters, it is difficult to obtain experimental data through field tests for each one individually.

[0060] To address the aforementioned issues, this invention provides a practical equivalent modeling method for electromechanical transients in energy storage power stations.

[0061] Figure 1 This is a flowchart illustrating the implementation of the practical equivalent modeling method for electromechanical transients in energy storage power stations provided in this embodiment of the invention. Figure 2 A typical structural diagram of an energy storage power station is shown. (See attached diagram) Figure 2 The energy storage power station includes an energy storage unit 21, an energy storage converter 22, and a current collector boost system 23. The energy storage unit 21 is connected to the corresponding energy storage converter 22, and the energy storage converter 22 is connected to the upstream power grid through the corresponding current collector boost system 23. The energy storage unit 21 includes an energy storage battery pack. The energy storage power station may also include a control system, which can generate corresponding control strategies based on parameters of the energy storage unit 21 and the current collector boost system 23, etc., to control the corresponding energy storage converter 22, ensuring that the operating state of the energy storage converter 22 meets usage requirements.

[0062] See Figure 1 The above-mentioned practical equivalent modeling method for electromechanical transients of energy storage power stations is described in detail below:

[0063] Step 101: For each type of energy storage converter in the energy storage power station, based on the hardware-in-the-loop test platform of the energy storage converter, acquire key data of the energy storage converter of that type during fault ride-through under different operating conditions. Based on the key data, fault ride-through model and objective function, solve for the initial control parameters of the energy storage converter of that type. Then, based on the initial control parameters and the constraints of active and reactive current, perform iterative solution to obtain the final control parameters of the energy storage converter of that type.

[0064] Because energy storage power stations contain a large number of energy storage units—sometimes dozens in large-scale stations—modeling and identifying each unit individually would result in an excessive number of parameters for the entire station model, potentially exceeding 1000. Therefore, when studying dynamic problems involving energy storage power stations, the focus is on the overall dynamic characteristics of the station, rather than the specific dynamics of each individual unit. Consequently, considering the limited types of energy storage converters used in a single power station (i.e., a limited number of converter models), equivalent modeling is performed for different converter models.

[0065] There are multiple models of energy storage converters in an energy storage power station. The control parameters of different models of energy storage converters are different. The same model of energy storage converter can use the same control parameters. Therefore, in the embodiments of this application, the corresponding final control parameters are determined by corresponding methods for different models of energy storage converters in the energy storage power station.

[0066] The following explanation uses a specific energy storage converter (model A) as an example to illustrate the process of determining its final control parameters. The process for determining the final control parameters for other models of energy storage converters is the same and will not be repeated here. Note that A is only a hypothetical model and does not represent the actual model of the energy storage converter.

[0067] First, based on a hardware-in-the-loop test platform for energy storage converters, key data of model A energy storage converters during fault ride-through under different operating conditions were obtained. The hardware-in-the-loop test platform for energy storage converters was built using MATLAB / Simulink and RT-LAB, and includes both hardware and software components. See [link to relevant documentation] Figure 3 The hardware component includes an energy storage converter controller, an RT-LAB simulator, and a host computer. The software component is a simulation model of the energy storage grid-connected system built based on MATLAB / Simulink, and its simple structure is as follows: Figure 3 The RT-LAB digital model is shown. The control and protection components of the energy storage converter are provided by the energy storage converter controller hardware, while the grid model and the main circuit of the energy storage unit are implemented using RT-LAB simulation models. The RT-LAB simulator and the energy storage converter controller interact via an analog / digital channel.

[0068] Using a hardware-in-the-loop test platform for energy storage converters, fault ride-through tests can be conducted on the Model A energy storage converter under various operating conditions to obtain measured response data. These operating conditions include high-power and low-power charging operation with two-phase and three-phase voltage drops to 5%, 20%, 40%, 60%, and 80% respectively, and high-power and low-power discharging operation with two-phase and three-phase voltage drops to 5%, 20%, 40%, 60%, and 80% respectively, totaling 40 sets of tests. The obtained measured response data can include: bus voltage, active and reactive power output of the energy storage converter, and active and reactive current response curves under each operating condition.

[0069] Based on the above measured response data, key data under each operating condition can be calculated, including the active power, reactive power, active current, reactive current, and grid connection point voltage amplitude of the energy storage converter under each operating condition.

[0070] It should be noted that the above measured response data and key data only illustrate a portion of the data they contain. Depending on actual needs, they can contain more data, and no specific restrictions are imposed here.

[0071] The embodiments of this application use the above-mentioned hardware-in-the-loop test platform for energy storage converter to obtain response data for parameter identification. This can solve the problems that the parameters obtained by simulation identification using the "all-digital" model are difficult to use in engineering practice due to the overly idealized experimental conditions, as well as the difficulties in simulating operating conditions, limited conditions, and high acquisition costs in on-site online data measurement.

[0072] Next, based on the aforementioned key data, fault ride-through model, and objective function, the initial control parameters of the type A energy storage converter are obtained. Then, based on the initial control parameters of the type A energy storage converter and the constraints on active and reactive currents, the final control parameters of the type A energy storage converter are obtained through iterative solutions. Note that the constraints on active and reactive currents are not considered when solving for the initial control parameters.

[0073] Step 102: Based on the final control parameters of each type of energy storage converter, perform equivalent modeling of each energy storage unit and each energy storage converter in the energy storage power station.

[0074] Based on the final control parameters of the various types of energy storage converters mentioned above, this application embodiment can perform equivalent modeling of all energy storage units and all energy storage converters in the energy storage power station to obtain the corresponding equivalent energy storage units and corresponding equivalent energy storage converters.

[0075] Step 103: Perform equivalent modeling of the current collector boosting system to complete the equivalent modeling of the energy storage power station.

[0076] This application embodiment can continue to perform equivalent modeling on all collector-boost systems of the energy storage power station to obtain the corresponding equivalent collector-boost systems. After performing equivalent modeling on the energy storage units, energy storage converters, and collector-boost systems of the energy storage power station, the equivalent model of the energy storage power station can be obtained, thus completing the equivalent modeling of the energy storage power station.

[0077] This application embodiment uses a hardware-in-the-loop test platform for energy storage converters to acquire key data of various models of energy storage converters during fault ride-through under different operating conditions. This can realistically reproduce the actual operating conditions during fault ride-through and solve problems such as difficulty in simulating operating conditions and limited conditions in on-site online measurements. In addition, when performing equivalent modeling of energy storage power stations, this application embodiment fully considers the differences in fault ride-through control strategies of different models of energy storage converters, which can improve the accuracy of the energy storage power station simulation model and thus improve the credibility of the results of large-scale energy storage grid connection simulation.

[0078] In some embodiments, step 102 above may include:

[0079] Based on the model of the energy storage converter, the energy storage power station is divided into multiple energy storage groups, including each energy storage unit and each energy storage converter.

[0080] Using a parameter aggregation method, equivalent modeling is performed for each energy storage group based on the final control parameters of each type of energy storage converter. The capacity of the equivalent energy storage unit in each energy storage group is the sum of the capacities of all energy storage units in the group, and the control parameters of the equivalent energy storage converter in each energy storage group are the final control parameters of the corresponding type of energy storage converter in the group.

[0081] In this embodiment, energy storage converters of the same model can be grouped into one energy storage group, and the energy storage units connected to those converters can also be grouped into the same group. This allows the energy storage units and converters of the energy storage power station to be divided into multiple energy storage groups based on the converter model. Specifically, each model of energy storage converter corresponds to one energy storage group. Then, a parameter aggregation method is used to perform equivalent modeling on each energy storage group, obtaining the corresponding equivalent energy storage units and equivalent energy storage converters.

[0082] The method for equivalent modeling of energy storage units and energy storage converters in this application embodiment can significantly reduce the number of equivalent energy storage units and equivalent energy storage converters in the equivalent model of an energy storage power station, thereby reducing the number of parameters that need to be considered.

[0083] In some embodiments, in step 101, the fault ride-through model includes a calculation model for reactive current reference values ​​and a calculation model for active current reference values; the objective function includes a reactive objective function and an active objective function.

[0084] Based on key data, fault ride-through model, and objective function, the initial control parameters of this type of energy storage converter are obtained, including:

[0085] Based on key data and reactive objective function, the least squares method is used to solve the calculation model of reactive current reference value to obtain the initial reactive control parameters of this type of energy storage converter.

[0086] Based on key data and active power objective function, the least squares method is used to solve the calculation model of active current reference value to obtain the initial active power control parameters of this type of energy storage converter.

[0087] The initial control parameters include initial reactive power control parameters and initial active power control parameters.

[0088] This application uses the energy storage power station model in the PSD-BPA transient stability procedure and employs the least squares method to identify the control parameters of the energy storage converter using key data.

[0089] In some embodiments, the calculation model for the reactive current reference value includes:

[0090]

[0091] The reactive power objective function includes:

[0092] Among them, V set This refers to the per-unit value of the voltage limit when the corresponding energy storage converter enters the low-voltage ride-through state, typically taken as 0.9; I q_ref,i This is the reactive current reference value for the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; q0,i The reactive current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the reactive power adjustment factor; The correlation coefficient for reactive current values; I q_set This is the baseline value for reactive current; min L Q The reactive power objective function value; I qi The reactive current fitting value of the corresponding model of energy storage converter under the i-th operating condition.

[0093] The reactive power control parameters to be solved in the above model include: and I q_set .

[0094] In some embodiments, the calculation model for the active current reference value includes:

[0095]

[0096] The active objective function includes:

[0097] Among them, I p_ref,i This is the reference value of the active current of the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; p0,i The active current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the active power adjustment factor; The correlation coefficient for the active current value; I p_set This is the baseline value for active current; min L P The active objective function value; I pi The active current fitting value of the corresponding model of energy storage converter under the i-th operating condition.

[0098] The active power control parameters to be solved in the above model include: and I p_set .

[0099] The aforementioned calculation models for reactive current reference values ​​and active current reference values ​​are both calculation models that do not consider the constraints of active and reactive current.

[0100] When considering the constraints of active and reactive current, the fault ride-through model is as follows:

[0101] During fault ride-through, energy storage power stations have two control modes: active power priority control mode and reactive power priority control mode.

[0102] In reactive power priority control mode, the calculation model for reactive current reference values ​​considering reactive current constraints includes:

[0103]

[0104] Among them, V set This refers to the per-unit value of the voltage limit when the corresponding energy storage converter enters the low-voltage ride-through state; I q_ref This refers to the reactive current reference value for the corresponding model of energy storage converter under a specific operating condition; V t I is the per-unit value of the grid connection point voltage amplitude under a certain operating condition; q0 This represents the reactive current value of the corresponding model of energy storage converter before a fault occurs under a certain operating condition. This is the reactive power adjustment factor; The correlation coefficient for reactive current values ​​is I. q0 Correlation coefficient; I q_setThis is the basic value for reactive current; I q_max This represents the maximum value of the reactive current.

[0105] In reactive power priority control mode, the calculation model for the active current reference value considering active current constraints includes:

[0106]

[0107] Among them, I p_ref This refers to the reference value of the active current of the corresponding model of energy storage converter under a certain operating condition; V t I is the per-unit value of the grid connection point voltage amplitude under a certain operating condition; p0 This refers to the active current value of the corresponding model of energy storage converter before a fault occurs under a certain operating condition. This is the active power adjustment factor; The correlation coefficient of the active current value is I. p0 Correlation coefficient; I p_set This is the baseline value for active current; I p_max I represents the maximum value of the active current. max This represents the maximum value of the total current, which is calculated based on the active and reactive currents; I q_ref This is the reference value for the reactive current of the corresponding model of energy storage converter under a certain operating condition.

[0108] In active power priority control mode, the calculation model for the active current reference value considering active current constraints includes:

[0109]

[0110] Among them, I p_ref This refers to the reference value of the active current of the corresponding model of energy storage converter under a certain operating condition; V t I is the per-unit value of the grid connection point voltage amplitude under a certain operating condition; p0 This refers to the active current value of the corresponding model of energy storage converter before a fault occurs under a certain operating condition. This is the active power adjustment factor; The correlation coefficient of the active current value is I. p0 Correlation coefficient; I p_set This is the baseline value for active current; I p_max This represents the maximum value of the active current.

[0111] In active power priority control mode, the calculation model for reactive current reference values ​​considering reactive current constraints includes:

[0112]

[0113] Among them, V setThis refers to the per-unit value of the voltage limit when the corresponding energy storage converter enters the low-voltage ride-through state; I q_ref This refers to the reactive current reference value for the corresponding model of energy storage converter under a specific operating condition; V t I is the per-unit value of the grid connection point voltage amplitude under a certain operating condition; q0 This represents the reactive current value of the corresponding model of energy storage converter before a fault occurs under a certain operating condition. This is the reactive power adjustment factor; The correlation coefficient for reactive current values ​​is I. q0 Correlation coefficient; I q_set This is the basic value for reactive current; I q_max I represents the maximum value of the reactive current. max This represents the maximum value of the total current, which is calculated based on the active and reactive currents; I p_ref This is the reference value of the active current for the corresponding model of energy storage converter under a certain operating condition.

[0114] As can be seen from the above model description, the two control modes are very similar. Considering that energy storage power stations need to provide dynamic reactive power support to the grid during faults to assist in voltage recovery within the fault's affected area, a reactive power priority control mode is usually adopted.

[0115] Without considering the constraints of active and reactive currents, the fault ride-through model can be written as the corresponding model in the aforementioned embodiments. A set of initial active control parameters and a set of initial reactive control parameters are obtained using the least squares method, forming the initial control parameters.

[0116] In some embodiments, in step 101, based on the initial control parameters and the constraints of active and reactive currents, an iterative solution is performed to obtain the final control parameters of the energy storage converter of this type, including:

[0117] The initial control parameters are treated as a particle in the initial population. The particle swarm optimization algorithm is used to iteratively solve the problem and obtain the final control parameters of this type of energy storage converter.

[0118] In the particle swarm optimization algorithm, the formula for calculating fitness is:

[0119]

[0120] In the above formula, I pi Let I be the fitted value of the active current of the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; p_max I represents the maximum value of the active current. max I represents the maximum value of the total current. qiI represents the reactive current fitting value of the corresponding model of energy storage converter under the i-th operating condition; p_ref,i I represents the active current reference value for the corresponding model of energy storage converter under the i-th operating condition; q_max I represents the maximum value of the reactive current. q_ref,i ε is the reactive current reference value of the corresponding model of energy storage converter under the i-th operating condition; ε is the maximum allowable error value in energy storage modeling.

[0121] Among them, the active current limiting conditions include the above-mentioned I p_max and The limiting conditions for reactive current can include the above-mentioned I q_max .

[0122] In this embodiment of the application, a set of initial control parameters obtained by the least squares method is used as a set of initial values ​​for the particle swarm optimization algorithm, that is, as a particle in the initial population. Considering the constraints of active current and reactive current, the algorithm continues to iterate and solve to obtain the final control parameters.

[0123] The specific process is as follows:

[0124] Step 1: Initialize the particle swarm and add the initial control parameters obtained by the least squares algorithm to the initial population as the current population (the particle positions are the 6 parameters to be solved).

[0125] Step 2: Substitute the relevant parameters into the aforementioned calculation models for reactive current reference values ​​and active current reference values, respectively, without considering active and reactive current limitations, to obtain I. pi and I qi And according to the fitness calculation formula above, calculate the fitness of each particle in the population (the smaller the fitness value, the better);

[0126] Step 3: Update the individual optimal solution and the group optimal solution;

[0127] Step 4: Update the position and velocity of particles within the population;

[0128] Step 5: Determine if the preset termination condition is met. If yes, exit the iteration and proceed to Step 6; otherwise, go back to Step 2 and continue iterating.

[0129] Step 6: The parameters corresponding to the optimal solution at this point are the final control parameters.

[0130] It should be noted that the above fitness calculation formula is for the reactive power priority control mode. If operating in the active power priority control mode, the fitness calculation formula can be adaptively modified to obtain the fitness calculation formula for the active power priority control mode, and the fitness calculation should be performed using the fitness calculation formula for the active power priority control mode.

[0131] This application embodiment uses a combination of least squares method and intelligent optimization algorithm (i.e. particle swarm optimization algorithm) to identify the control parameters of the energy storage converter. This method uses least squares method to obtain better initial values ​​for optimization, improves the optimization efficiency of intelligent optimization algorithm, and has good global optimization capability.

[0132] In some embodiments, the current collector boost system includes current collector lines;

[0133] In step 103, equivalent modeling of the current collector boost system is performed, including:

[0134] Based on the clustering results of energy storage units and energy storage converters, the collector lines of the energy storage power station are divided into multiple collector groups.

[0135] Based on the connection structure of each energy storage converter in the energy storage power station, an equivalent model is performed for each collector group.

[0136] When an energy storage power station includes a main transformer, the current collection and step-up system comprises the main transformer (referred to as the main transformer), a box-type transformer (referred to as the box transformer), current collection lines (referred to as current collection lines), and a static var generator (SVG). The energy storage converter is connected to the upstream power grid sequentially through the corresponding box-type transformer, current collection lines, and main transformer. When an energy storage power station does not include a main transformer, the current collection and step-up system comprises a box-type transformer, current collection lines, and an SVG. The energy storage converter is connected to the upstream power grid sequentially through the corresponding box-type transformer and current collection lines.

[0137] This application embodiment can group the collector lines corresponding to the same type of energy storage converter into one collector group, thereby dividing the collector lines of the energy storage power station into multiple collector groups. Then, based on the connection structure of each energy storage converter in the energy storage power station, different methods are used to perform equivalent modeling for each collector group with different connection structures.

[0138] In some embodiments, based on the connection structure of each energy storage converter in the energy storage power station, equivalent modeling is performed on each collector group, including:

[0139] If the connection structure is a trunk-type structure, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n1 S represents the equivalent impedance of the collector line of the collector group corresponding to the trunk-type structure; k Z represents the capacity of the k-th energy storage unit corresponding to this collector group; j Let S be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group; S j Let be the capacity of the j-th energy storage unit corresponding to this collector group;

[0140] If the connection structure is radial, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n2 S is the equivalent impedance of the collector line of the collector group corresponding to the radial structure; j Z represents the capacity of the j-th energy storage unit corresponding to this collector group; j Let l be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group.

[0141] The equivalent susceptance of the equivalent collector line in each collector group is the sum of the susceptances of each collector line in that collector group.

[0142] Considering the topology of an energy storage power station, the connection structure of each energy storage converter within the station can generally be divided into two types: trunk type and radial type. The equivalent impedance and equivalent admittance of the collector lines are calculated according to the connection structure type. The calculation of the collector line parameters is based on the following two assumptions: due to the short length of the collector lines and the small voltage drop along the lines, it is assumed that the terminal voltages of each energy storage converter are equal; and it is assumed that the power factor of each energy storage converter is the same.

[0143] For the trunk line structure, see [link / reference]. Figure 4 For radial structures, see Figure 5 The calculation methods for the equivalent impedance of the equivalent collector lines differ between trunk-type and radial-type structures; please refer to the calculation formulas mentioned above for details. The equivalent susceptance of the equivalent collector line is the sum of the susceptances of all collector lines in the corresponding collector group.

[0144] The energy storage unit corresponding to the collector group is the energy storage unit in the energy storage group of the energy storage converter of the corresponding model of the collector group.

[0145] In some embodiments, the current collector step-up system includes a main transformer, a box-type transformer, and a static var generator (SVG).

[0146] Equivalent modeling of the current collector boost system includes:

[0147] Based on the clustering results of energy storage units and energy storage converters, each box-type transformer in the energy storage power station is divided into multiple box-type transformer groups, and equivalent modeling is performed for each box-type transformer group.

[0148] Based on the nodes connected to each main transformer of the energy storage power station, the main transformers of the energy storage power station are divided into multiple main transformer groups, and equivalent modeling is performed for each main transformer group; among them, main transformers with the same connected nodes are grouped into one main transformer group.

[0149] Based on the busbars connected to each SVG of the energy storage power station, each SVG of the energy storage power station is divided into multiple SVG groups, and the parameter aggregation method is used to perform equivalent modeling for each SVG group.

[0150] This application embodiment can group the box-type transformers corresponding to the same type of energy storage converter into one box-type transformer group, thereby dividing the box-type transformers of the energy storage power station into multiple box-type transformer groups. Then, equivalent modeling can be performed on each box-type transformer group separately.

[0151] A main transformer with identical connected nodes is considered as a main transformer group. If there are no other main transformers with identical connected nodes, then a single main transformer is considered as a main transformer group. Then, equivalent modeling is performed for each main transformer group.

[0152] Equivalent modeling of the main transformer can include equivalence of the winding parameters and excitation parameters of the main transformer.

[0153] Winding parameters include short-circuit loss and short-circuit voltage. The short-circuit loss of the equivalent main transformer is the sum of the short-circuit losses of each main transformer in the corresponding main transformer group and the active power loss of the feeders within the station; the equivalent reactance X of the equivalent main transformer... ∑ Equivalent reactance X calculated based on the total reactive power loss of the line ∑1 The parallel reactance X of each main transformer winding in the corresponding main transformer group ∑2 The sum, according to X ∑ Calculate the short-circuit voltage U of the equivalent main transformer. k %.

[0154] Excitation parameters include no-load loss and no-load current. The no-load loss of the equivalent main transformer is the sum of the no-load losses of all main transformers in the corresponding main transformer group; the no-load current I0% of the equivalent transformer is based on the excitation susceptance B of each main transformer in the corresponding main transformer group. m(i) Parallel susceptance B m∑ To obtain.

[0155] Based on the busbars connected to each SVG in the energy storage power station, SVGs connected to the same busbar are grouped into one SVG group, thus dividing the SVGs of the energy storage power station into multiple SVG groups. Then, a parameter aggregation method is used to perform equivalent modeling on each SVG group. The capacity of the equivalent SVG in each SVG group is the sum of the capacities of all SVGs within that group, and the control parameters of the equivalent SVG in each SVG group are the classic control parameters of the SVG.

[0156] After performing equivalent modeling on the energy storage unit, energy storage converter, and collector step-up system respectively, an equivalent model of the energy storage power station can be obtained. For an energy storage power station including a main transformer, the corresponding equivalent model is as follows: Figure 6 As shown, the equivalent model for an energy storage power station that does not include a main transformer is as follows: Figure 7 As shown in the figure. Where n is the number of energy storage units in the energy storage power station. Figure 6 and Figure 7 In this context, the equivalent collector line is the equivalent collector line, the equivalent box transformer is the equivalent box-type transformer, the equivalent converter is the equivalent energy storage converter, and the SVG is the equivalent SVG.

[0157] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0158] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0159] Figure 8 The diagram shows a schematic representation of the practical equivalent modeling device for electromechanical transients in energy storage power stations provided in an embodiment of the present invention. For ease of explanation, only the parts relevant to the embodiment of the present invention are shown, and are described in detail below:

[0160] An energy storage power station includes energy storage units, energy storage converters, and a current collection and step-up system; such as Figure 8 As shown, the practical equivalent modeling device 80 for electromechanical transients of energy storage power stations includes: a parameter identification module 81, a first equivalent modeling module 82, and a second equivalent modeling module 83.

[0161] The parameter identification module 81 is used to acquire key data of each type of energy storage converter in the energy storage power station under different operating conditions during fault ride-through based on the hardware-in-the-loop test platform of the energy storage converter. Based on the key data, fault ride-through model and objective function, the initial control parameters of the energy storage converter of that type are solved. Furthermore, based on the initial control parameters and the constraints of active and reactive current, the final control parameters of the energy storage converter of that type are obtained by iterative solution.

[0162] The first equivalent modeling module 82 is used to perform equivalent modeling of each energy storage unit and each energy storage converter of the energy storage power station based on the final control parameters of each type of energy storage converter.

[0163] The second equivalent modeling module 83 is used to perform equivalent modeling of the collector step-up system and complete the equivalent modeling of the energy storage power station.

[0164] In one possible implementation, the first equivalent modeling module 82 is specifically used for:

[0165] Based on the model of the energy storage converter, the energy storage power station is divided into multiple energy storage groups, including each energy storage unit and each energy storage converter.

[0166] Using a parameter aggregation method, equivalent modeling is performed for each energy storage group based on the final control parameters of each type of energy storage converter. The capacity of the equivalent energy storage unit in each energy storage group is the sum of the capacities of all energy storage units in the group, and the control parameters of the equivalent energy storage converter in each energy storage group are the final control parameters of the corresponding type of energy storage converter in the group.

[0167] In one possible implementation, the fault ride-through model includes a calculation model for reactive current reference values ​​and a calculation model for active current reference values; the objective functions include a reactive objective function and an active objective function.

[0168] In parameter identification module 81, based on key data, fault ride-through model, and objective function, the initial control parameters of this type of energy storage converter are solved, including:

[0169] Based on key data and reactive objective function, the least squares method is used to solve the calculation model of reactive current reference value to obtain the initial reactive control parameters of this type of energy storage converter.

[0170] Based on key data and active power objective function, the least squares method is used to solve the calculation model of active current reference value to obtain the initial active power control parameters of this type of energy storage converter.

[0171] The initial control parameters include initial reactive power control parameters and initial active power control parameters.

[0172] In one possible implementation, the calculation model for the reactive current reference value includes:

[0173]

[0174] The reactive power objective function includes:

[0175] Among them, V set This refers to the per-unit value of the voltage limit when the corresponding energy storage converter enters the low-voltage ride-through state; I q_ref,i This is the reactive current reference value for the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; q0,i The reactive current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the reactive power adjustment factor; The correlation coefficient for reactive current values; I q_set This is the baseline value for reactive current; min L Q The reactive power objective function value; I qiThe reactive current fitting value of the corresponding model of energy storage converter under the i-th operating condition.

[0176] In one possible implementation, the calculation model for the active current reference value includes:

[0177]

[0178] The active objective function includes:

[0179] Among them, I p_ref,i This is the reference value of the active current of the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; p0,i The active current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the active power adjustment factor; The correlation coefficient for the active current value; I p_set This is the baseline value for active current; min L P The active objective function value; I pi The active current fitting value of the corresponding model of energy storage converter under the i-th operating condition.

[0180] In one possible implementation, the parameter identification module 81 iteratively solves the initial control parameters and the constraints on active and reactive currents to obtain the final control parameters of the energy storage converter, including:

[0181] The initial control parameters are treated as a particle in the initial population. The particle swarm optimization algorithm is used to iteratively solve the problem and obtain the final control parameters of this type of energy storage converter.

[0182] In the particle swarm optimization algorithm, the formula for calculating fitness is:

[0183]

[0184] In the above formula, I pi Let I be the fitted value of the active current of the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; p_max I represents the maximum value of the active current. max I represents the maximum value of the total current. qi I represents the fitted reactive current value of the corresponding model of energy storage converter under the i-th operating condition; p_ref,i I represents the active current reference value for the corresponding model of energy storage converter under the i-th operating condition; q_max I represents the maximum value of the reactive current. q_ref,iε is the reactive current reference value of the corresponding model of energy storage converter under the i-th operating condition; ε is the maximum allowable error value in energy storage modeling.

[0185] In one possible implementation, the collector boost system includes collector lines;

[0186] In the second equivalent modeling module 83, equivalent modeling of the collector boost system is performed, including:

[0187] Based on the clustering results of energy storage units and energy storage converters, the collector lines of the energy storage power station are divided into multiple collector groups.

[0188] Based on the connection structure of each energy storage converter in the energy storage power station, an equivalent model is performed for each collector group.

[0189] In one possible implementation, in the second equivalent modeling module 83, based on the connection structure of each energy storage converter in the energy storage power station, equivalent modeling is performed on each collector group, including:

[0190] If the connection structure is a trunk-type structure, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n1 S represents the equivalent impedance of the collector line of the collector group corresponding to the trunk-type structure; k Z represents the capacity of the k-th energy storage unit corresponding to this collector group; j Let S be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group; S j Let be the capacity of the j-th energy storage unit corresponding to this collector group;

[0191] If the connection structure is radial, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n2 S is the equivalent impedance of the collector line of the collector group corresponding to the radial structure; j Z represents the capacity of the j-th energy storage unit corresponding to this collector group; j Let l be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group.

[0192] The equivalent susceptance of the equivalent collector line in each collector group is the sum of the susceptances of each collector line in that collector group.

[0193] In one possible implementation, the collector step-up system includes a main transformer, a box-type transformer, and a static var generator (SVG).

[0194] In the second equivalent modeling module 83, equivalent modeling of the collector boost system is performed, including:

[0195] Based on the clustering results of energy storage units and energy storage converters, each box-type transformer in the energy storage power station is divided into multiple box-type transformer groups, and equivalent modeling is performed for each box-type transformer group.

[0196] Based on the nodes connected to each main transformer of the energy storage power station, the main transformers of the energy storage power station are divided into multiple main transformer groups, and equivalent modeling is performed for each main transformer group; among them, main transformers with the same connected nodes are grouped into one main transformer group.

[0197] Based on the busbars connected to each SVG of the energy storage power station, each SVG of the energy storage power station is divided into multiple SVG groups, and the parameter aggregation method is used to perform equivalent modeling for each SVG group.

[0198] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0199] Those skilled in the art will recognize that the templates, units, and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0200] If the module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above embodiments of the practical equivalent modeling method for electromechanical transients of energy storage power stations. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0201] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A practical equivalent modeling method for electromechanical transients in energy storage power stations, characterized in that, The energy storage power station includes an energy storage unit, an energy storage converter, and a current collector step-up system; the method includes: For each type of energy storage converter in the energy storage power station, key data of the energy storage converter during fault ride-through under different operating conditions are obtained based on the hardware-in-the-loop test platform of the energy storage converter. Based on the key data, fault ride-through model and objective function, the initial control parameters of the energy storage converter are solved. According to the initial control parameters and the constraints of active current and reactive current, the final control parameters of the energy storage converter are obtained by iterative solution. Based on the final control parameters of each type of energy storage converter, equivalent modeling is performed on each energy storage unit and each energy storage converter of the energy storage power station. Equivalent modeling is performed on the current collector boosting system to complete the equivalent modeling of the energy storage power station; The fault ride-through model includes a calculation model for reactive current reference values ​​and a calculation model for active current reference values; the objective function includes a reactive objective function and an active objective function. The calculation model for the reactive current reference value includes: The reactive power objective function includes: Among them, V set This refers to the per-unit value of the voltage limit when the corresponding energy storage converter enters the low voltage ride-through state. This is the reactive current reference value for the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; q0,i The reactive current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the reactive power adjustment factor; The correlation coefficient for reactive current values; I q_set This is the baseline value for reactive current; min L Q The reactive power objective function value; I qi The reactive current fitting value of the corresponding model of energy storage converter under the i-th operating condition; The calculation model for the active current reference value includes: The active power objective function includes: in, I represents the active current reference value for the corresponding model of energy storage converter under the i-th operating condition; p0,i The active current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the active power adjustment factor; The correlation coefficient for the active current value; I p_set This is the baseline value for active current; min L P The active objective function value; I pi The active current fitting value of the corresponding model of energy storage converter under the i-th operating condition; In reactive power priority control mode, the calculation model for reactive current reference values ​​considering reactive current constraints includes: Among them, I q_ref This refers to the reactive current reference value for the corresponding model of energy storage converter under a specific operating condition; V t I is the per-unit value of the grid connection point voltage amplitude under a certain operating condition; q0 This refers to the reactive current value of the corresponding model of energy storage converter before a fault under a certain operating condition; I q_max This represents the maximum value of the reactive current. In reactive power priority control mode, the calculation model for the active current reference value considering active current constraints includes: Among them, I p_ref This refers to the active current reference value for the corresponding model of energy storage converter under a specific operating condition; I p0 This refers to the active current value of the corresponding model of energy storage converter before a fault under a certain operating condition; I p_max I represents the maximum value of the active current. max This is the maximum value of the total current, which is calculated based on the active and reactive currents. In active power priority control mode, the calculation model for the active current reference value considering active current constraints includes: In active power priority control mode, the calculation model for reactive current reference values ​​considering reactive current constraints includes:

2. The practical equivalent modeling method for electromechanical transients of energy storage power stations according to claim 1, characterized in that, The process of performing equivalent modeling of each energy storage unit and each energy storage converter in the energy storage power station based on the final control parameters of each type of energy storage converter includes: Based on the model of the energy storage converter, the energy storage power station is divided into multiple energy storage groups, including each energy storage unit and each energy storage converter. Using a parameter aggregation method, equivalent modeling is performed for each energy storage group based on the final control parameters of each type of energy storage converter. The capacity of the equivalent energy storage unit in each energy storage group is the sum of the capacities of all energy storage units in the group, and the control parameters of the equivalent energy storage converter in each energy storage group are the final control parameters of the corresponding type of energy storage converter in the group.

3. The practical equivalent modeling method for electromechanical transients of energy storage power stations according to claim 1, characterized in that, Based on the key data, fault ride-through model, and objective function, the initial control parameters of this type of energy storage converter are obtained by solving, including: Based on the key data and the reactive objective function, the least squares method is used to solve the calculation model of the reactive current reference value to obtain the initial reactive control parameters of the energy storage converter of this model. Based on the key data and the active power objective function, the calculation model of the active current reference value is solved by the least squares method to obtain the initial active power control parameters of this type of energy storage converter. The initial control parameters include the initial reactive power control parameters and the initial active power control parameters.

4. The practical equivalent modeling method for electromechanical transients of energy storage power stations according to claim 1, characterized in that, The final control parameters of this type of energy storage converter are obtained by iteratively solving based on the initial control parameters and the constraints of active and reactive currents, including: The initial control parameters are used as a particle in the initial population. The particle swarm optimization algorithm is used to iteratively solve the problem and obtain the final control parameters of this type of energy storage converter. In the particle swarm optimization algorithm, the formula for calculating fitness is: In the above formula, I pi Let I be the fitted value of the active current of the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; p_max I represents the maximum value of the active current. max I represents the maximum value of the total current. qi The reactive current fitting value of the corresponding model of energy storage converter under the i-th operating condition; I represents the active current reference value for the corresponding model of energy storage converter under the i-th operating condition; q_max This represents the maximum value of the reactive current. ε is the reactive current reference value of the corresponding model of energy storage converter under the i-th operating condition; ε is the maximum allowable error value in energy storage modeling.

5. The practical equivalent modeling method for electromechanical transients of energy storage power stations according to any one of claims 1 to 4, characterized in that, The current collector boosting system includes current collector lines; The equivalent modeling of the current collector boost system includes: Based on the grouping results of the energy storage unit and the energy storage converter, each collector line of the energy storage power station is divided into multiple collector groups; Based on the connection structure of each energy storage converter in the energy storage power station, an equivalent model is performed for each collector group.

6. The practical equivalent modeling method for electromechanical transients of energy storage power stations according to claim 5, characterized in that, The connection structure of each energy storage converter in the energy storage power station is used to perform equivalent modeling for each collector group, including: If the connection structure is a trunk-type structure, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n1 S represents the equivalent impedance of the collector line of the collector group corresponding to the trunk-type structure; k Z represents the capacity of the k-th energy storage unit corresponding to this collector group; j Let S be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group; S j Let be the capacity of the j-th energy storage unit corresponding to this collector group; If the connection structure is a radial structure, then the equivalent impedance of the equivalent collector line for each collector group is: Among them, Z eq-n2 S is the equivalent impedance of the collector line of the collector group corresponding to the radial structure; j Z represents the capacity of the j-th energy storage unit corresponding to this collector group; j Let l be the impedance of the j-th collector line in the collector group; l is the number of collector lines in the collector group. The equivalent susceptance of the equivalent collector line in each collector group is the sum of the susceptances of each collector line in that collector group.

7. The practical equivalent modeling method for electromechanical transients of energy storage power stations according to any one of claims 1 to 4, characterized in that, The current collection and step-up system includes a main transformer, a box-type transformer, and a static var generator (SVG). The equivalent modeling of the current collector boost system includes: Based on the clustering results of the energy storage unit and the energy storage converter, each box-type transformer of the energy storage power station is divided into multiple box-type transformer groups, and equivalent modeling is performed for each box-type transformer group. Based on the nodes connected to each main transformer of the energy storage power station, each main transformer of the energy storage power station is divided into multiple main transformer groups, and equivalent modeling is performed on each main transformer group; wherein, main transformers with the same connected nodes are grouped into one main transformer group. Based on the busbars connected to each SVG of the energy storage power station, each SVG of the energy storage power station is divided into multiple SVG groups, and an equivalent model is performed on each SVG group using a parameter aggregation method.

8. A practical equivalent modeling device for electromechanical transients in energy storage power stations, characterized in that, The energy storage power station includes an energy storage unit, an energy storage converter, and a current collector step-up system; the device includes: The parameter identification module is used to acquire key data of each type of energy storage converter in the energy storage power station during fault ride-through under different operating conditions based on the hardware-in-the-loop test platform of the energy storage converter. Based on the key data, fault ride-through model and objective function, the module solves for the initial control parameters of the energy storage converter and iteratively solves for the final control parameters of the energy storage converter based on the initial control parameters and the constraints of active and reactive current. The first equivalent modeling module is used to perform equivalent modeling of each energy storage unit and each energy storage converter of the energy storage power station based on the final control parameters of each type of energy storage converter. The second equivalent modeling module is used to perform equivalent modeling on the current collection and step-up system, thereby completing the equivalent modeling of the energy storage power station. The fault ride-through model includes a calculation model for reactive current reference values ​​and a calculation model for active current reference values; the objective function includes a reactive objective function and an active objective function. The calculation model for the reactive current reference value includes: The reactive power objective function includes: Among them, V set This refers to the per-unit value of the voltage limit when the corresponding energy storage converter enters the low voltage ride-through state. This is the reactive current reference value for the corresponding model of energy storage converter under the i-th operating condition, 1≤i≤m, where m is the number of operating conditions; V ti I is the per-unit value of the grid connection point voltage amplitude under the i-th operating condition; q0,i The reactive current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the reactive power adjustment factor; The correlation coefficient for reactive current values; I q_set This is the baseline value for reactive current; min L Q The reactive power objective function value; I qi The reactive current fitting value of the corresponding model of energy storage converter under the i-th operating condition; The calculation model for the active current reference value includes: The active power objective function includes: in, I represents the active current reference value for the corresponding model of energy storage converter under the i-th operating condition; p0,i The active current value of the corresponding model of energy storage converter before the fault under the i-th operating condition; This is the active power adjustment factor; The correlation coefficient for the active current value; I p_set This is the baseline value for active current; min L P The active objective function value; I pi The active current fitting value of the corresponding model of energy storage converter under the i-th operating condition; In reactive power priority control mode, the calculation model for reactive current reference values ​​considering reactive current constraints includes: Among them, I q_ref This refers to the reactive current reference value for the corresponding model of energy storage converter under a specific operating condition; V t I is the per-unit value of the grid connection point voltage amplitude under a certain operating condition; q0 This refers to the reactive current value of the corresponding model of energy storage converter before a fault under a certain operating condition; I q_max This represents the maximum value of the reactive current. In reactive power priority control mode, the calculation model for the active current reference value considering active current constraints includes: Among them, I p_ref This refers to the active current reference value for the corresponding model of energy storage converter under a specific operating condition; I p0 This refers to the active current value of the corresponding model of energy storage converter before a fault under a certain operating condition; I p_max I represents the maximum value of the active current. max This is the maximum value of the total current, which is calculated based on the active and reactive currents. In active power priority control mode, the calculation model for the active current reference value considering active current constraints includes: In active power priority control mode, the calculation model for reactive current reference values ​​considering reactive current constraints includes:

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