Practical equivalent modeling method and device for electromechanical transient state of energy storage power station

Abstract

The invention provides an electromechanical transient practical equivalent modeling method and device for an energy storage power station, and belongs to the field of new energy station simulation modeling. The method comprises the following steps: for each type of energy storage converter in an energy storage power station, acquiring key data of the type of energy storage converter in different working conditions during a fault ride-through period, and based on the key data, a fault ride-through model and a target function, solving to obtain an initial control parameter of the type of energy storage converter, performing iterative solution according to the initial control parameters and limiting conditions of the active current and the reactive current to obtain final control parameters of the energy storage converter of the model; performing equivalent modeling on each energy storage unit and each energy storage converter of the energy storage power station according to the final control parameters of each type of energy storage converter; and performing equivalent modeling on the current collection boost system to complete equivalent modeling of the energy storage power station. According to the method, the accuracy of the energy storage power station simulation model can be improved, and the result credibility of large-scale energy storage grid-connected simulation is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation modeling of new energy stations, and in particular to a practical equivalent modeling method and device for electromechanical transient state of an energy storage power station. Background Art

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

[0003] In the related technologies, most energy storage power stations behave as a power source to the power grid during steady-state operation, and their impact on power grid-related calculations is not yet obvious. However, when a power grid fails, because the low voltage ride through (LVRT) strategies and parameters adopted by converters of different brands and series are different, the power supply will show different external characteristics to the low voltage fault at the access point. However, due to the lack of testing technology and other reasons, most manufacturers are unable to provide accurate models and parameters. Therefore, only typical models and parameters can be used in the transient simulation calculation of the power grid, which makes it difficult to guarantee the accuracy of the energy storage power station simulation model, thereby affecting the credibility of the simulation results of large-scale energy storage grid connection. Summary of the invention

[0004] The embodiment of the present invention provides a practical equivalent modeling method and device for electromechanical transient of an energy storage power station, so as to solve the problem that the existing method can only use typical models and parameters, resulting in difficulty in ensuring the accuracy of the energy storage power station simulation model, thereby affecting the credibility of large-scale energy storage grid-connected simulation results.

[0005] In a first aspect, an embodiment of the present invention provides a practical equivalent modeling method for electromechanical transient of an energy storage power station, wherein the energy storage power station includes an energy storage unit, an energy storage converter, and a collector boost system; the method includes:

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

[0007] According to the final control parameters of each type of energy storage converter, each energy storage unit and each energy storage converter of the energy storage power station are equivalently modeled;

[0008] Carry out equivalent modeling of the power collection and boosting system to complete the equivalent modeling of the energy storage power station.

[0009] In a possible implementation, each energy storage unit and each energy storage converter of the energy storage power station are equivalently modeled according to the final control parameters of each type of energy storage converter, including:

[0010] According to the model of the energy storage converter, the energy storage units and energy storage converters of the energy storage power station are divided into multiple energy storage groups;

[0011] A parameter aggregation method is adopted to perform equivalent modeling on each energy storage group according to the final control parameters of each type of energy storage inverter; wherein the capacity of the equivalent energy storage unit of each energy storage group is the sum of the capacities of all energy storage units in the energy storage group, and the control parameters of the equivalent energy storage inverter of each energy storage group are the final control parameters of the energy storage inverter of the corresponding model of the energy storage group.

[0012] In a possible implementation, the fault ride-through model includes a calculation model of a reactive current reference value and a calculation model of an active current reference value; the objective function includes 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 the energy storage converter of this model are obtained, including:

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

[0015] Based on key data and active objective function, the least square method is used to solve the calculation model of active current reference value to obtain the initial active 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 a possible implementation, the calculation model of the reactive current reference value includes:

[0018]

[0019] The reactive power objective function includes:

[0020] Among them, V setIt is the per unit value of the voltage limit when the energy storage converter of the corresponding model enters the low voltage ride-through state; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I q0,i is the reactive current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the reactive power adjustment coefficient; is the correlation coefficient of reactive current value; is the basic value of reactive current; min L Q is the reactive power objective function value; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

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

[0022]

[0023] The active objective function includes:

[0024] in, is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I p0,i is the active current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the active power adjustment coefficient; is the correlation coefficient of the active current value; is the basic value of active current; min L P is the active objective function value; I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

[0025] In a possible implementation, an iterative solution is performed based on the initial control parameters and the constraints of the active current and the reactive current to obtain the final control parameters of the energy storage converter of this model, including:

[0026] The initial control parameter is taken as a particle in the initial population, and the particle swarm optimization algorithm is used to iteratively solve the final control parameter of the energy storage converter of this model;

[0027] Among them, in the particle swarm optimization algorithm, the calculation formula of fitness is:

[0028]

[0029] In the above formula, I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; I p_max is the maximum value of active current; I max is the maximum value of the total current; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition; is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition; I q_max is the maximum value of reactive current; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition; ε is the maximum error value allowed in energy storage modeling.

[0030] In a possible implementation, the current collection and voltage boosting system includes a current collection circuit;

[0031] Equivalent modeling of the collector boost system, including:

[0032] Based on the grouping results of the energy storage units and energy storage converters, each collection line of the energy storage power station is divided into multiple collection groups;

[0033] Based on the connection structure of each energy storage converter of the energy storage power station, equivalent modeling is performed for each power collection group.

[0034] In a possible implementation, based on the connection structure of each energy storage converter of the energy storage power station, each power collection group is modeled equivalently, including:

[0035] If the connection structure is a trunk structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n1 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the trunk structure; S k is the capacity of the kth energy storage unit corresponding to the power collection group; Z j is the impedance of the jth collector line of the collector group; l is the number of collector lines in the collector group; S j is the capacity of the jth energy storage unit corresponding to the power collection group;

[0036] If the connection structure is a radial structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n2 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the radial structure; S j is the capacity of the jth energy storage unit corresponding to the power collection group; Z jis the impedance of the jth collector line of the collector group; l is the number of collector lines of the collector group;

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

[0038] In a possible implementation, the power collection and boosting system includes a main transformer, a box transformer and a static VAR generator SVG;

[0039] Equivalent modeling of the collector boost system, including:

[0040] Based on the grouping results of energy storage units and energy storage converters, each box-type transformer of the energy storage power station is divided into multiple box-type transformer groups, and each box-type transformer group is modeled with equivalent value;

[0041] According to the nodes to which the main transformers of the energy storage power station are connected, the main transformers of the energy storage power station are divided into multiple main transformer groups, and each main transformer group is modeled with equivalent value; among them, the main transformers with the same connected nodes are divided into one main transformer group;

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

[0043] In a second aspect, an embodiment of the present invention provides a practical equivalent modeling device for electromechanical transient of an energy storage power station, wherein the energy storage power station includes an energy storage unit, an energy storage converter, and a collector boost system; the device includes:

[0044] The parameter identification module is used for obtaining the key data of the energy storage converter of this type during the fault ride-through under different working conditions for each type of energy storage converter in the energy storage power station based on the energy storage converter hardware-in-the-loop test platform, and solving the initial control parameters of the energy storage converter of this type based on the key data, the fault ride-through model and the objective function, and performing iterative solution according to the initial control parameters and the constraints of the active current and the reactive current to obtain the final control parameters of the energy storage converter of this type;

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

[0046] The second equivalent modeling module is used to perform equivalent modeling on the power collection and boosting system to complete the equivalent modeling of the energy storage power station.

[0047] The embodiment of the present application provides a practical equivalent modeling method and device for electromechanical transient of an energy storage power station. Through a hardware-in-the-loop test platform for an energy storage converter, key data of various types of energy storage converters during fault ride-through under different operating conditions are obtained, and the actual operating conditions during fault ride-through can be truly reproduced, and the problems of difficult simulation of operating conditions and limited conditions in on-site online measurements can be solved. In addition, when performing equivalent modeling of an energy storage power station, the embodiment of the present application fully considers the differences in fault ride-through control strategies of energy storage converters of different types, and can improve the accuracy of the simulation model of the energy storage power station, thereby improving the credibility of the results of large-scale energy storage grid-connected simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a flow chart for implementing a practical equivalent modeling method for electromechanical transient state of an energy storage power station provided by an embodiment of the present invention;

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

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

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

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

[0054] Figure 6 is a schematic diagram of an equivalent model corresponding to an energy storage power station including a main transformer provided in one embodiment of the present invention;

[0055] Figure 7 is a schematic diagram of an equivalent model corresponding to an energy storage power station not including a main transformer provided by an embodiment of the present invention;

[0056] Figure 8 It is a structural schematic diagram of a practical equivalent modeling device for electromechanical transient state of an energy storage power station provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] 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 invention. However, it should be clear to those skilled in the art that the present invention may 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 invention.

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0059] As mentioned above, only typical models and parameters can be used in transient simulation calculations of power grids, which makes it difficult to ensure the accuracy of the energy storage power station simulation model, thereby affecting the credibility of large-scale energy storage grid-connected simulation results. In addition, the data used for parameter identification of energy storage converters mainly comes from laboratory "all-digital" model simulation and field testing. Since the experimental conditions of "all-digital" model simulation are too ideal, the parameters identified are difficult to use in engineering practice; and field testing needs to be carried out under specific circumstances, and the cost of obtaining experimental data is high. For many types of energy storage converters, it is difficult to obtain experimental data through field tests one by one.

[0060] In order to solve the above problems, an embodiment of the present invention provides a practical equivalent modeling method for electromechanical transient of an energy storage power station.

[0061] Figure 1 The flowchart of the practical equivalent modeling method of electromechanical transient state of energy storage power station provided by the embodiment of the present invention is as follows: Figure 2 shows a typical structural diagram of an energy storage power station, see Figure 2 The energy storage power station includes an energy storage unit 21, an energy storage converter 22 and a power collection and boosting 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 upper power grid through the corresponding power collection and boosting 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 may generate a corresponding control strategy based on parameters of the energy storage unit 21 and the power collection and boosting system 23, etc., for controlling the corresponding energy storage converter 22 so that the operating state of the energy storage converter 22 meets the use requirements.

[0062] See also Figure 1 The practical equivalent modeling method of the electromechanical transient state of the above energy storage power station is described in detail as follows:

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

[0064] Due to the large number of energy storage units in energy storage power stations, the number of energy storage units in large energy storage power stations can reach dozens. If each energy storage unit is modeled and identified, the number of parameters of the entire energy storage power station model is too large, possibly exceeding 1,000. When studying dynamic problems involving energy storage power stations, the focus is on the overall dynamic characteristics of the energy storage power station, rather than the specific dynamics of each energy storage unit in the station. Therefore, considering that the types of energy storage inverters used in the same energy storage power station are limited, that is, the models of energy storage inverters are limited, equivalent modeling is performed for different models of energy storage inverters.

[0065] There are multiple types of energy storage inverters in the energy storage power station. The control parameters of different types of energy storage inverters are different. The same type of energy storage inverters can use the same control parameters. Therefore, the embodiments of the present application determine the corresponding final control parameters for different types of energy storage inverters in the energy storage power station through corresponding methods.

[0066] The following takes a certain energy storage converter A as an example to explain the process of determining its final control parameters. The process of determining the final control parameters of other types of energy storage converters is the same and will not be repeated. Among them, A is just an assumed model and does not represent the actual model of the energy storage converter.

[0067] First, based on the energy storage converter hardware-in-the-loop test platform, key data of the A-type energy storage converter during the fault ride-through under different working conditions are obtained. The energy storage converter hardware-in-the-loop test platform is a platform built based on MATLAB / Simulink and RT-LAB, including hardware and software parts. Figure 3 The hardware part includes the energy storage converter controller, RT-LAB simulator and host computer. The software part is a simulation model of the energy storage grid-connected system based on MATLAB / Simulink. Its simple structure is as follows Figure 3 The RT-LAB digital model is shown in the figure. Among them, the energy storage converter control and protection are provided by the energy storage converter controller hardware, while the power grid model and the main circuit of the energy storage unit are realized by the simulation model built by RT-LAB. The RT-LAB simulator and the energy storage converter controller realize signal interaction through analog / digital channels.

[0068] By using the energy storage converter hardware-in-the-loop test platform, the A-type energy storage converter can be subjected to fault ride-through experiments under various operating conditions to obtain measured response data. Among them, the above-mentioned operating conditions can include high power and low power in charging operation mode, two-phase and three-phase voltage drops to 5%, 20%, 40%, 60% and 80% respectively, and high power and low power in discharging operation mode, two-phase and three-phase voltage drops to 5%, 20%, 40%, 60% and 80% respectively, a total of 40 groups of experiments. The measured response data obtained may include: bus voltage under various operating conditions, active power and reactive power output by the energy storage converter, and output active current and reactive current response curves.

[0069] Based on the above measured response data, key data under various working conditions can be calculated, which may include active power, reactive power, active current, reactive current and grid-connected point voltage amplitude of the energy storage converter under various working conditions.

[0070] It should be noted that the above-mentioned measured response data and key data only illustrate part of the data contained in each. According to actual needs, both can contain more data, and no specific limitation is made here.

[0071] The embodiment of the present application adopts the above-mentioned energy storage converter hardware-in-the-loop test platform to obtain response data for parameter identification, which can solve the problems that the parameters obtained by using "all-digital" model simulation identification are difficult to use in engineering practice due to overly idealized experimental conditions, and the working conditions in on-site online data measurement are difficult to simulate, the conditions are limited, and the acquisition cost is high.

[0072] Next, based on the above key data, fault ride-through model and objective function, the initial control parameters of the energy storage converter of type A are solved, and according to the initial control parameters of the energy storage converter of type A and the constraints of active current and reactive current, an iterative solution is performed to obtain the final control parameters of the energy storage converter of type A. Among them, the constraints of active current and reactive current are not considered when solving the initial control parameters.

[0073] Step 102 , according to the final control parameters of the energy storage converters of various models, equivalent modeling is performed on each energy storage unit and each energy storage converter of the energy storage power station.

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

[0075] Step 103, perform equivalent modeling on the power collection and boosting system to complete the equivalent modeling of the energy storage power station.

[0076] The embodiment of the present application can continue to perform equivalent modeling on all the power collection and boosting systems of the energy storage power station to obtain the corresponding equivalent power collection and boosting system. After performing equivalent modeling on the energy storage unit, energy storage converter and power collection and boosting system of the energy storage power station, the equivalent model of the energy storage power station can be obtained, thereby completing the equivalent modeling of the energy storage power station.

[0077] The embodiment of the present application obtains key data of various types of energy storage inverters during fault ride-through under different working conditions through the energy storage inverter hardware-in-the-loop test platform, can truly reproduce the actual operating conditions during fault ride-through, and can solve the problems of difficult simulation of working conditions and limited conditions in on-site online measurements; in addition, when performing equivalent modeling of the energy storage power station, the embodiment of the present application fully considers the differences in fault ride-through control strategies of energy storage inverters of different types, which can improve the accuracy of the energy storage power station simulation model, and thereby improve the credibility of the results of large-scale energy storage grid-connected simulation.

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

[0079] According to the model of the energy storage converter, the energy storage units and energy storage converters of the energy storage power station are divided into multiple energy storage groups;

[0080] A parameter aggregation method is adopted to perform equivalent modeling on each energy storage group according to the final control parameters of each type of energy storage inverter; wherein the capacity of the equivalent energy storage unit of each energy storage group is the sum of the capacities of all energy storage units in the energy storage group, and the control parameters of the equivalent energy storage inverter of each energy storage group are the final control parameters of the energy storage inverter of the corresponding model of the energy storage group.

[0081] In the embodiment of the present application, energy storage converters of the same model can be grouped into one energy storage group, and the energy storage units connected to the energy storage converters of the same model can also be grouped into the energy storage group, so that the energy storage units and energy storage converters of the energy storage power station can be divided into multiple energy storage groups according to the model of the energy storage converter. That is, each model of the energy storage converter corresponds to an energy storage group. Then, the parameter aggregation method is used to perform equivalent modeling on each energy storage group to obtain 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 the embodiment of the present application can significantly reduce the number of equivalent energy storage units and equivalent energy storage converters in the equivalent model of the energy storage power station, thereby reducing the number of parameters that require attention.

[0083] In some embodiments, in step 101, the fault ride-through model includes a calculation model of a reactive current reference value and a calculation model of an active current reference value; 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 the energy storage converter of this model are obtained, including:

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

[0086] Based on key data and active objective function, the least square method is used to solve the calculation model of active current reference value to obtain the initial active 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] The embodiment of the present application adopts the energy storage power station model in the PSD-BPA transient stability program, and uses key data and the least squares method to identify the control parameters of the energy storage converter.

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

[0090]

[0091] The reactive power objective function includes:

[0092] Among them, V set It is the per unit value of the voltage limit when the energy storage converter of the corresponding model enters the low voltage ride-through state, and is generally taken as 0.9; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I q0,i is the reactive current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the reactive power adjustment coefficient; is the correlation coefficient of reactive current value; is the basic value of reactive current; min L Q is the reactive power objective function value; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

[0093] Among them, the reactive power control parameters to be solved in the above model include and

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

[0095]

[0096] The active objective function includes:

[0097] in, is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I p0,i is the active current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the active power adjustment coefficient; is the correlation coefficient of the active current value; is the basic value of active current; min L P is the active objective function value; I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

[0098] Among them, the active control parameters to be solved in the above model include and

[0099] The aforementioned calculation model of the reactive current reference value and the calculation model of the active current reference value are both calculation models under the limiting conditions of the active current and the reactive current without considering them.

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

[0101] The control modes of the energy storage power station during fault ride-through are divided into two types: active power priority control mode and reactive power priority control mode.

[0102] In the reactive power priority control mode, the calculation model of the reactive current reference value considering the reactive current limitation condition includes:

[0103]

[0104] Among them, V set I q_ref V is the reactive current reference value of the corresponding type of energy storage converter under a certain working condition; t is the per unit value of the grid connection point voltage amplitude under a certain working condition; I q0 is the reactive current value of the energy storage converter of the corresponding model before the fault under a certain working condition; is the reactive power adjustment coefficient; is the correlation coefficient of the reactive current value, that is, I q0 The correlation coefficient of is the reactive current basic value; I q_max is the maximum value of reactive current.

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

[0106]

[0107] Among them, I p_ref is the active current reference value of the corresponding type of energy storage converter under a certain working condition; V t is the per unit value of the grid connection point voltage amplitude under a certain working condition; I p0 is the active current value of the energy storage converter of the corresponding model before the fault under a certain working condition; is the active power adjustment coefficient; is the correlation coefficient of the active current value, that is, I p0 The correlation coefficient of is the basic value of active current; I p_max is the maximum value of active current; I max is the maximum value of the total current, which is the current calculated based on the active current and reactive current; I q_ref It is the reactive current reference value of the energy storage converter of the corresponding model under a certain working condition.

[0108] In the active power priority control mode, the calculation model of the active current reference value considering the active current limitation condition includes:

[0109]

[0110] Among them, I p_ref is the active current reference value of the corresponding type of energy storage converter under a certain working condition; V t is the per unit value of the grid connection point voltage amplitude under a certain working condition; I p0 is the active current value of the energy storage converter of the corresponding model before the fault under a certain working condition; is the active power adjustment coefficient; is the correlation coefficient of the active current value, that is, I p0 The correlation coefficient of is the basic value of active current; I p_max is the maximum value of active current.

[0111] In the active power priority control mode, the calculation model of the reactive current reference value considering the reactive current limitation condition includes:

[0112]

[0113] Among them, V set I q_ref V is the reactive current reference value of the corresponding type of energy storage converter under a certain working condition;t is the per unit value of the grid connection point voltage amplitude under a certain working condition; I q0 is the reactive current value of the energy storage converter of the corresponding model before the fault under a certain working condition; is the reactive power adjustment coefficient; is the correlation coefficient of the reactive current value, that is, I q0 The correlation coefficient of is the reactive current basic value; I q_max is the maximum value of reactive current; I max is the maximum value of the total current, which is the current calculated based on the active current and reactive current; I p_ref It is the active current reference value of the energy storage converter of the corresponding model under a certain working condition.

[0114] From the above model introduction, it can be seen that the two control modes are very similar. Considering that the energy storage power station needs to provide dynamic reactive power support to the grid during the fault to assist in voltage recovery within the fault affected area, the reactive power priority control mode is usually adopted.

[0115] When the active current and reactive current limiting conditions are not considered, the fault ride-through model can be written as the corresponding model in the above embodiment. A set of initial active control parameters and a set of initial reactive control parameters are respectively solved by the least square method to form initial control parameters.

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

[0117] The initial control parameter is taken as a particle in the initial population, and the particle swarm optimization algorithm is used to iteratively solve the final control parameter of the energy storage converter of this model;

[0118] Among them, in the particle swarm optimization algorithm, the calculation formula of fitness is:

[0119]

[0120] In the above formula, I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; I p_max is the maximum value of active current; I max is the maximum value of the total current; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition; is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition; I q_max is the maximum value of reactive current; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition; ε is the maximum error value allowed in energy storage modeling.

[0121] Among them, the limiting conditions of active current include the above I p_max and The reactive current limiting conditions may include the above I q_max .

[0122] In an embodiment of the present application, a set of initial control parameters obtained by the least squares method is used as a set of initial values ​​of the particle swarm optimization algorithm, that is, as a particle in the initial population, considering the constraints of active current and reactive current, and continuing 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 position is the 6 parameters to be solved);

[0125] Step 2: Substitute the relevant parameters into the calculation model of the reactive current reference value and the calculation model of the active current reference value under the aforementioned limiting conditions without considering the active current and reactive current, and obtain I pi and I qi , and according to the above fitness calculation formula, calculate the fitness of each particle in the population (the smaller the fitness value, the better);

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

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

[0128] Step 5: Determine whether the preset termination condition is met. If so, jump out of the iteration phase and execute step 6; otherwise, go to step 2 and continue iterating;

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

[0130] It should be noted that the above fitness calculation formula is the fitness calculation formula under the reactive power priority control mode. If working in the active power priority control mode, the fitness calculation formula can be adaptively modified to obtain the fitness calculation formula under the active power priority control mode, and the fitness calculation formula under the active power priority control mode is used to perform fitness calculation.

[0131] The embodiment of the present application adopts a method combining the least squares method with an intelligent optimization algorithm (i.e., a particle swarm optimization algorithm) to identify the control parameters of the energy storage inverter. This method uses the least squares method to obtain a better initial value for optimization, thereby improving the optimization efficiency of the intelligent optimization algorithm and having a better global optimization capability.

[0132] In some embodiments, the current collection and voltage boosting system includes a current collection circuit;

[0133] In step 103, equivalent modeling is performed on the current collection and boosting system, including:

[0134] Based on the grouping results of the energy storage units and energy storage converters, each collection line of the energy storage power station is divided into multiple collection groups;

[0135] Based on the connection structure of each energy storage converter of the energy storage power station, equivalent modeling is performed for each power collection group.

[0136] When the energy storage power station includes a main transformer, the collection and boosting system includes a main transformer (referred to as the main transformer), a box transformer (referred to as the box transformer), a collection line (referred to as the collection line) and a static var generator (SVG), and the energy storage converter is connected to the upper power grid through the corresponding box transformer, the collection line and the main transformer in sequence. When the energy storage power station does not include a main transformer, the collection and boosting system includes a box transformer, a collection line and SVG, and the energy storage converter is connected to the upper power grid through the corresponding box transformer and the collection line in sequence.

[0137] The embodiment of the present application can divide the collector lines corresponding to the same type of energy storage converter into one collector group, so that the collector lines of the energy storage power station can be divided into multiple collector groups. Then, based on the connection structure of each energy storage converter of the energy storage power station, different connection structures are used to perform equivalent modeling on each collector group respectively.

[0138] In some embodiments, based on the connection structure of each energy storage converter of the energy storage power station, each power collection group is respectively subjected to equivalent modeling, including:

[0139] If the connection structure is a trunk structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n1 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the trunk structure; S k is the capacity of the kth energy storage unit corresponding to the power collection group; Z j is the impedance of the jth collector line of the collector group; l is the number of collector lines in the collector group; S j is the capacity of the jth energy storage unit corresponding to the power collection group;

[0140] If the connection structure is a radial structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n2 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the radial structure; S j is the capacity of the jth energy storage unit corresponding to the power collection group; Z j is the impedance of the jth collector line of the collector group; l is the number of collector lines of the collector group;

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

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

[0143] Among them, the trunk structure see Figure 4 , radial structure see Figure 5 The calculation methods of the equivalent impedance of the equivalent collector line corresponding to the trunk structure and the radial structure are different. For details, please refer to the above calculation formula. The equivalent susceptance of the equivalent collector line is the sum of the susceptances of each collector line in the corresponding collector group.

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

[0145] In some embodiments, the power collection and boosting system includes a main transformer, a box transformer, and a static VAR generator SVG;

[0146] Equivalent modeling of the collector boost system, including:

[0147] Based on the grouping results of energy storage units and energy storage converters, each box-type transformer of the energy storage power station is divided into multiple box-type transformer groups, and each box-type transformer group is modeled with equivalent value;

[0148] According to the nodes to which the main transformers of the energy storage power station are connected, the main transformers of the energy storage power station are divided into multiple main transformer groups, and each main transformer group is modeled with equivalent value; among them, the main transformers with the same connected nodes are divided into one main transformer group;

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

[0150] The embodiment of the present application can divide the box-type transformers corresponding to the energy storage converters of the same model into one box-type transformer group, so that the box-type transformers of the energy storage power station can be divided into multiple box-type transformer groups. Afterwards, each box-type transformer group can be modeled with equivalent value.

[0151] The main transformers with the same connected nodes are regarded as a main transformer group. If there are no other main transformers with the same connected nodes, a single main transformer is a main transformer group. After that, each main transformer group is modeled with equivalent value.

[0152] The equivalent modeling of the main transformer may include equivalent values ​​for the winding parameters and excitation parameters of the main transformer.

[0153] Winding parameters include short-circuit loss and short-circuit voltage. Among them, the short-circuit loss of the equivalent main transformer is the sum of the short-circuit loss of each main transformer in the corresponding main transformer group and the active loss of the feeder power transmission in the station; the equivalent reactance X of the equivalent main transformer is ∑ The equivalent reactance X converted from 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 of X ∑ Obtain the equivalent short-circuit voltage U of the main transformer k %.

[0154] Excitation parameters include no-load loss and no-load current. Among them, the no-load loss of the equivalent main transformer is the sum of the no-load losses of each main transformer in the corresponding main transformer group; the no-load current I0% of the equivalent transformer is calculated according to the excitation susceptance B of each main transformer in the corresponding main transformer group. m(i) Parallel Susceptance B m∑ To obtain.

[0155] According to the busbars to which each SVG of the energy storage power station is connected, the SVGs connected to the same busbar are grouped into one SVG group, thereby dividing each SVG of the energy storage power station into multiple SVG groups, and then using the parameter aggregation method, each SVG group is modeled as equivalent. The capacity of the equivalent SVG of each SVG group is the sum of the capacities of all SVGs in the SVG group, and the control parameters of the equivalent SVG of each SVG group are the classic control parameters of SVG.

[0156] After the energy storage unit, energy storage converter and power collection boost system are modeled respectively, the equivalent model of the energy storage power station can be obtained. The equivalent model corresponding to the energy storage power station including the main transformer is as follows: Figure 6 As shown in the figure, the equivalent model corresponding to the energy storage power station without the main transformer is as follows Figure 7 As shown in FIG. 1 , n is the number of energy storage units in the energy storage power station. Figure 6 and Figure 7In the figure, the equivalent collector line is the equivalent collector line, the equivalent box transformer is the equivalent box transformer, the equivalent converter is the equivalent energy storage converter, and the SVG is the equivalent SVG.

[0157] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean 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 embodiment of the present invention.

[0158] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0159] Figure 8 The schematic diagram of the structure of the electromechanical transient practical equivalent modeling device for energy storage power station provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0160] The energy storage power station includes energy storage units, energy storage converters and power collection and boosting systems; Figure 8 As shown, the practical equivalent modeling device 80 for electromechanical transient state of energy storage power station 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 for obtaining the key data of the energy storage converter of this type during the fault ride-through under different working conditions for each type of energy storage converter in the energy storage power station based on the energy storage converter hardware-in-the-loop test platform, and solving the initial control parameters of the energy storage converter of this type based on the key data, the fault ride-through model and the objective function, and performing iterative solving according to the initial control parameters and the constraints of the active current and the reactive current to obtain the final control parameters of the energy storage converter of this type;

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

[0163] The second equivalent modeling module 83 is used to perform equivalent modeling on the power collection and boosting system to complete the equivalent modeling of the energy storage power station.

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

[0165] According to the model of the energy storage converter, the energy storage units and energy storage converters of the energy storage power station are divided into multiple energy storage groups;

[0166] A parameter aggregation method is adopted to perform equivalent modeling on each energy storage group according to the final control parameters of each type of energy storage inverter; wherein the capacity of the equivalent energy storage unit of each energy storage group is the sum of the capacities of all energy storage units in the energy storage group, and the control parameters of the equivalent energy storage inverter of each energy storage group are the final control parameters of the energy storage inverter of the corresponding model of the energy storage group.

[0167] In a possible implementation, the fault ride-through model includes a calculation model of a reactive current reference value and a calculation model of an active current reference value; the objective function includes a reactive objective function and an active objective function;

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

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

[0170] Based on key data and active objective function, the least square method is used to solve the calculation model of active current reference value to obtain the initial active 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 a possible implementation, the calculation model of the reactive current reference value includes:

[0173]

[0174] The reactive power objective function includes:

[0175] Among them, V set It is the per unit value of the voltage limit when the energy storage converter of the corresponding model enters the low voltage ride-through state; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I q0,i is the reactive current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the reactive power adjustment coefficient; is the correlation coefficient of reactive current value; is the reactive current basic value; min L Q is the reactive power objective function value; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

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

[0177]

[0178] The active objective function includes:

[0179] in, is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I p0,i is the active current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the active power adjustment coefficient; is the correlation coefficient of the active current value; is the basic value of active current; min L P is the active objective function value; I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

[0180] In a possible implementation, in the parameter identification module 81, an iterative solution is performed according to the initial control parameters and the constraints of the active current and the reactive current to obtain the final control parameters of the energy storage converter of this model, including:

[0181] The initial control parameter is taken as a particle in the initial population, and the particle swarm optimization algorithm is used to iteratively solve the final control parameter of the energy storage converter of this model;

[0182] Among them, in the particle swarm optimization algorithm, the calculation formula of fitness is:

[0183]

[0184] In the above formula, I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; I p_max is the maximum value of active current; I max is the maximum value of the total current; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition; is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition; I q_max is the maximum value of reactive current; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition; ε is the maximum error value allowed in energy storage modeling.

[0185] In a possible implementation, the current collection and voltage boosting system includes a current collection circuit;

[0186] In the second equivalent modeling module 83, equivalent modeling is performed on the current collection and boosting system, including:

[0187] Based on the grouping results of the energy storage units and energy storage converters, each collection line of the energy storage power station is divided into multiple collection groups;

[0188] Based on the connection structure of each energy storage converter of the energy storage power station, equivalent modeling is performed for each power collection group.

[0189] In a possible implementation, in the second equivalent modeling module 83, based on the connection structure of each energy storage converter of the energy storage power station, each power collection group is respectively subjected to equivalent modeling, including:

[0190] If the connection structure is a trunk structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n1 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the trunk structure; S k is the capacity of the kth energy storage unit corresponding to the power collection group; Z j is the impedance of the jth collector line of the collector group; l is the number of collector lines in the collector group; S j is the capacity of the jth energy storage unit corresponding to the power collection group;

[0191] If the connection structure is a radial structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n2 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the radial structure; S j is the capacity of the jth energy storage unit corresponding to the power collection group; Z j is the impedance of the jth collector line of the collector group; l is the number of collector lines of the collector group;

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

[0193] In a possible implementation, the power collection and boosting system includes a main transformer, a box transformer and a static VAR generator SVG;

[0194] In the second equivalent modeling module 83, equivalent modeling is performed on the current collection and boosting system, including:

[0195] Based on the grouping results of energy storage units and energy storage converters, each box-type transformer of the energy storage power station is divided into multiple box-type transformer groups, and each box-type transformer group is modeled with equivalent value;

[0196] According to the nodes to which the main transformers of the energy storage power station are connected, the main transformers of the energy storage power station are divided into multiple main transformer groups, and each main transformer group is modeled with equivalent value; among them, the main transformers with the same connected nodes are divided into one main transformer group;

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

[0198] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0199] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0200] If the module / unit 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. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned embodiments of the practical equivalent modeling method of the electromechanical transient state of each energy storage power station. 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 some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0201] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those 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 invention, and should all be included in the protection scope of the present invention.

Claims

1. A practical equivalent modeling method for electromechanical transient state of energy storage power station, characterized in that: The energy storage power station includes an energy storage unit, an energy storage converter and a power collection and boosting system; the method includes: For each type of energy storage converter in the energy storage power station, based on the energy storage converter hardware-in-the-loop test platform, key data of the energy storage converter of this type during fault ride-through under different working conditions are obtained, and based on the key data, the fault ride-through model and the objective function, the initial control parameters of the energy storage converter of this type are solved, and, according to the initial control parameters and the constraints of the active current and the reactive current, the final control parameters of the energy storage converter of this type are obtained by iterative solution; According to the final control parameters of the energy storage converters of various models, equivalent modeling is performed on each energy storage unit and each energy storage converter of the energy storage power station; The current collection and boosting system is equivalently modeled to complete the equivalent modeling of the energy storage power station.

2. The practical equivalent modeling method of electromechanical transient state of energy storage power station according to claim 1 is characterized in that: The equivalent modeling of each energy storage unit and each energy storage converter of the energy storage power station according to the final control parameters of each type of energy storage converter includes: According to the model of the energy storage converter, the energy storage units and energy storage converters of the energy storage power station are divided into a plurality of energy storage groups; A parameter aggregation method is adopted to perform equivalent modeling on each energy storage group according to the final control parameters of each type of energy storage inverter; wherein the capacity of the equivalent energy storage unit of each energy storage group is the sum of the capacities of all energy storage units in the energy storage group, and the control parameters of the equivalent energy storage inverter of each energy storage group are the final control parameters of the energy storage inverter of the corresponding model of the energy storage group.

3. The practical equivalent modeling method of electromechanical transient state of energy storage power station according to claim 1 is characterized in that: The fault ride-through model includes a calculation model of a reactive current reference value and a calculation model of an active current reference value; the objective function includes a reactive objective function and an active objective function; The initial control parameters of the energy storage converter of this model are obtained based on the key data, the fault ride-through model and the objective function, including: Based on the key data and the reactive objective function, the calculation model of the reactive current reference value is solved by using the least square method to obtain the initial reactive control parameters of the energy storage converter of this model; Based on the key data and the active objective function, the calculation model of the active current reference value is solved by the least square method to obtain the initial active control parameters of the energy storage converter of this model; The initial control parameters include the initial reactive power control parameters and the initial active power control parameters.

4. The practical equivalent modeling method of electromechanical transient state of energy storage power station according to claim 3 is characterized in that: The calculation model of the reactive current reference value includes: The reactive power objective function includes: Among them, V set It is the per unit value of the voltage limit when the energy storage converter of the corresponding model enters the low voltage ride-through state; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I q0,i is the reactive current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the reactive power adjustment coefficient; is the correlation coefficient of reactive current value; is the reactive current basic value; min L Q is the reactive power objective function value; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

5. The practical equivalent modeling method of electromechanical transient state of energy storage power station according to claim 3 is characterized in that: The calculation model of the active current reference value includes: The active objective function includes: in, is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; V ti is the per unit value of the grid connection point voltage amplitude under the i-th working condition; I p0,i is the active current value of the energy storage converter of the corresponding model before the fault under the i-th working condition; is the active power adjustment coefficient; is the correlation coefficient of the active current value; is the basic value of active current; min L P is the active objective function value; I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition.

6. The practical equivalent modeling method for electromechanical transient state of energy storage power station according to claim 1 is characterized in that: The iterative solution is performed according to the initial control parameters and the active current and reactive current limiting conditions to obtain the final control parameters of the energy storage converter of this model, including: The initial control parameter is used as a particle in the initial population, and a particle swarm optimization algorithm is used to iteratively solve the final control parameter of the energy storage converter of this model; Among them, in the particle swarm optimization algorithm, the calculation formula of fitness is: In the above formula, I pi is the active current fitting value of the energy storage converter of the corresponding model under the i-th working condition, 1≤i≤m, m is the number of working conditions; I p_max is the maximum value of active current; I max is the maximum value of the total current; I qi is the reactive current fitting value of the energy storage converter of the corresponding model under the i-th working condition; is the active current reference value of the energy storage converter of the corresponding model under the i-th working condition; I q_max is the maximum value of reactive current; is the reactive current reference value of the energy storage converter of the corresponding model under the i-th working condition; ε is the maximum error value allowed in energy storage modeling.

7. The practical equivalent modeling method for electromechanical transient state of energy storage power station according to any one of claims 1 to 6 is characterized in that: The current collection and voltage boosting system comprises a current collection circuit; The equivalent modeling of the current collecting and boosting system comprises: Based on the grouping results of the energy storage units and the energy storage converters, each collection line of the energy storage power station is divided into a plurality of collection groups; Based on the connection structure of each energy storage converter of the energy storage power station, equivalent modeling is performed on each power collection group.

8. The practical equivalent modeling method for electromechanical transient state of energy storage power station according to claim 7 is characterized in that: Based on the connection structure of each energy storage converter of the energy storage power station, each power collection group is respectively subjected to equivalent modeling, including: If the connection structure is a trunk structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n1 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the trunk structure; S k is the capacity of the kth energy storage unit corresponding to the power collection group; Z j is the impedance of the jth collector line of the collector group; l is the number of collector lines in the collector group; S j is the capacity of the jth energy storage unit corresponding to the power collection group; If the connection structure is a radial structure, the equivalent impedance of the equivalent collector line of each collector group is: Among them, Z eq-n2 is the equivalent impedance of the equivalent collector line of the collector group corresponding to the radial structure; S j is the capacity of the jth energy storage unit corresponding to the power collection group; Z j is the impedance of the jth collector line of the collector group; l is the number of collector lines of the collector group; The equivalent susceptance of the equivalent collector line of each collector group is the sum of the circuits of each collector line in the collector group.

9. The practical equivalent modeling method for electromechanical transient state of energy storage power station according to any one of claims 1 to 6, characterized in that: The power collection and boosting system includes a main transformer, a box transformer and a static VAR generator SVG; The equivalent modeling of the current collecting and boosting system comprises: Based on the grouping results of the energy storage unit and the energy storage converter, each box-type transformer of the energy storage power station is divided into a plurality of box-type transformer groups, and each box-type transformer group is respectively subjected to equivalent modeling; According to the nodes to which the main transformers of the energy storage power station are connected, the main transformers of the energy storage power station are divided into a plurality of main transformer groups, and each main transformer group is respectively subjected to equivalent modeling; wherein the main transformers with the same connected nodes are divided into one main transformer group; According to the buses connected to the SVGs of the energy storage power station, the SVGs of the energy storage power station are divided into a plurality of SVG groups, and an equivalent modeling is performed on each SVG group by using a parameter aggregation method.

10. A practical equivalent modeling device for electromechanical transient state of energy storage power station, characterized in that: The energy storage power station includes an energy storage unit, an energy storage converter and a power collection and boosting system; the device includes: A parameter identification module is used for obtaining key data of the energy storage converter of this type during fault ride-through under different working conditions for each type of energy storage converter in the energy storage power station based on the energy storage converter hardware-in-the-loop test platform, and solving the initial control parameters of the energy storage converter of this type based on the key data, the fault ride-through model and the objective function, and performing iterative solution according to the initial control parameters and the constraints of the active current and the reactive current to obtain the final control parameters of the energy storage converter of this type; A first equivalent modeling module is used to perform equivalent modeling on each energy storage unit and each energy storage converter of the energy storage power station according to the final control parameters of each type of energy storage converter; The second equivalent modeling module is used to perform equivalent modeling on the power collection and boosting system to complete the equivalent modeling of the energy storage power station.

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