Parameter calibration method, device and equipment for energy storage simulation model based on RT-LAB

Abstract

This invention provides a parameter calibration method, apparatus, and device for an energy storage simulation model based on RT-LAB. The method includes: acquiring the initial operating state of the energy storage system to be calibrated, and inputting the initial operating state data into a pre-constructed energy storage simulation model; acquiring the actual electrical parameter set output by the energy storage system after it is connected to the grid and operates and is tested under preset conditions; acquiring the simulated electrical parameter set output by the energy storage simulation model when it operates and is tested in the RT-LAB simulation system under preset conditions; wherein the electrical parameter values ​​include at least two of the following: power value of the grid-connected node, output voltage value and output current value of the AC side, and battery response speed or frequency; and debugging the energy storage simulation model based on the simulated electrical parameter set and the actual electrical parameter set to determine the calibration parameters of the energy storage simulation model. This invention can accurately calibrate the parameters of the energy storage system.

Description

Technical Field

[0001] This invention relates to the field of energy storage control technology, and in particular to a parameter calibration method, apparatus and equipment for an energy storage simulation model based on RT-LAB. Background Technology

[0002] Energy storage systems encompass the input and output of energy and matter, as well as energy conversion and storage devices. Energy storage systems often involve multiple energy sources, devices, materials, and processes, making them complex energy systems that vary over time and require numerous metrics to describe their performance.

[0003] The fundamental task of energy storage systems is to overcome temporal or localized discrepancies between energy supply and demand. These discrepancies arise in two ways: one is due to sudden changes in energy demand, i.e., peak load issues, where energy storage can regulate or buffer when the rate of load change increases. The other is due to factors such as primary energy sources and energy conversion devices; in this case, the task of the energy storage system is to balance energy production, i.e., not only reducing peak energy output but also filling troughs. Energy storage systems have advantages such as large storage capacity and high economic efficiency, and have been widely used in power grids.

[0004] However, the grid connection and off-grid technology for energy storage systems is still immature, and these processes can cause certain shocks and impacts on the power grid. Currently, the calibration parameters of energy storage models corresponding to energy storage systems are mostly determined by trial and error, requiring continuous experimentation, which is cumbersome and detrimental to grid security. Therefore, how to calibrate the parameters of energy storage models corresponding to energy storage systems during grid connection and off-grid processes has become an urgent technical problem to be solved. Summary of the Invention

[0005] This invention provides a parameter calibration method, apparatus, and device for an energy storage simulation model based on RT-LAB, to solve the problem that the parameters of the energy storage model corresponding to the energy storage system cannot be accurately calibrated during grid connection and disconnection.

[0006] In a first aspect, embodiments of the present invention provide a parameter calibration method for an energy storage simulation model based on RT-LAB, comprising:

[0007] The initial operating state of the energy storage system to be calibrated is obtained, and the data of the initial operating state is input into the pre-built energy storage simulation model. The energy storage simulation model is built in the host computer of the RT-LAB simulation system, and the RT-LAB simulation system is connected to the controller of the energy storage converter.

[0008] Obtain the actual set of electrical parameters output by the energy storage system after it is connected to the power grid, operates under preset conditions, and is tested;

[0009] The electrical parameter set output by the energy storage simulation model in the RT-LAB simulation system when it is run and tested under preset conditions is obtained; the electrical parameter set includes the power value of the grid-connected node, the output voltage and output current value of the AC side, the battery response speed and frequency;

[0010] Based on the simulated electrical parameter set and the real electrical parameter set, the parameters of the energy storage simulation model are adjusted to determine the calibration parameters of the energy storage simulation model.

[0011] In one possible implementation, the initial operating state includes at least two of the following: the battery capacity, power, individual cell voltage, battery internal resistance, and the switching frequency and model of the energy storage converter of the energy storage system to be calibrated.

[0012] In one possible implementation, the parameters of the energy storage simulation model are adjusted based on the simulated electrical parameter set and the actual electrical parameter set to determine the calibration parameters of the energy storage simulation model, including:

[0013] The parameters of the energy storage simulation model are adjusted. When the data of the simulated electrical parameter set is the same as the data of the real electrical parameter set, the adjustment is stopped.

[0014] The parameters of the energy storage simulation model that are identical to the simulated electrical parameter set are determined as the calibration parameters of the energy storage simulation model.

[0015] In one possible implementation, the parameter calibration method further includes a process for detecting calibration parameters, which includes:

[0016] The system acquires three detection parameters after the energy storage simulation model is connected to the simulated power grid device. The first detection parameter is measured during charging and discharging of the energy storage simulation model and includes at least two of the following: charging response time, charging adjustment time, discharging response time, discharging adjustment time, and charge / discharge transition time. The second detection parameter is measured at multiple preset detection points when the output voltage of the simulated power grid device is adjusted to a nominal voltage within multiple different preset ranges during charging and discharging of the energy storage simulation model. The second detection parameter includes the voltage, duration, and whether a trip occurred at each preset detection point. The third detection parameter is measured at multiple preset detection points when the frequency of the simulated power grid device is adjusted within a preset frequency range during charging and discharging of the energy storage simulation model. The second detection parameter includes the operating frequency, operating time, and whether a trip occurred at each preset detection point.

[0017] Based on the first detection parameter, the second detection parameter, the third detection parameter, the preset response time test standard, the preset voltage response standard, and the preset frequency response test standard, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0018] In one possible implementation, the parameter calibration method further includes a process for detecting calibration parameters, which includes:

[0019] The fourth detection parameter of the energy storage simulation model under no-load and rated resistive load is obtained. The fourth detection parameter includes the output voltage imbalance of the AC side, the output voltage and amplitude deviation of the AC side.

[0020] The fifth detection parameter of the energy storage simulation model is obtained under multiple different preset input / output power conditions. The fifth detection parameter includes the output voltage and output current harmonic distortion rate on the AC side.

[0021] Based on the fourth and fifth detection parameters, as well as the power quality detection standards, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0022] In one possible implementation, the parameter calibration method further includes a process for detecting calibration parameters, which includes:

[0023] The sixth detection parameter of the energy storage simulation model after the electrochemical energy storage system is connected to the distribution network with a preset voltage is obtained. The sixth detection parameter is the detection time obtained by conducting no-load and load experiments based on the low voltage ride-through curve.

[0024] Based on the sixth detection parameter and the low voltage ride-through curve, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0025] In one possible implementation, the parameter calibration method further includes a process for detecting calibration parameters, which includes:

[0026] After the energy storage simulation model is connected to the simulated power grid device, the seventh detection parameter is obtained by adjusting the voltage amplitude or frequency on the simulated power grid device side. The seventh detection parameter is the disconnection time between the energy storage simulation model and the simulated power grid device.

[0027] Based on the seventh detection parameter and the preset island disconnection time, determine whether the calibration parameters meet the requirements for disconnection.

[0028] Secondly, embodiments of the present invention provide a parameter calibration device for an energy storage simulation model based on RT-LAB, comprising:

[0029] The input data module is used to obtain the initial operating state of the energy storage system to be calibrated and input the data of the initial operating state into the pre-built energy storage simulation model. The energy storage simulation model is built in the host computer of the RT-LAB simulation system, and the RT-LAB simulation system is connected to the controller of the energy storage converter.

[0030] The module for obtaining the actual parameter set is used to obtain the actual electrical parameter set output by the energy storage system when it is connected to the power grid, operates under preset conditions, and is tested.

[0031] The simulation parameter set acquisition module is used to acquire the electrical parameter set output by the energy storage simulation model when it runs and is tested in the RT-LAB simulation system according to the preset conditions; wherein, the electrical parameter set includes the power value of the grid-connected node, the output voltage and output current value of the AC side, the battery response speed and frequency;

[0032] The parameter determination module is used to debug the energy storage simulation model based on the simulated electrical parameter set and the actual electrical parameter set, and to determine the calibration parameters of the energy storage simulation model.

[0033] In one possible implementation, the initial operating state includes at least two of the following: the battery capacity, power, individual cell voltage, battery internal resistance, and the switching frequency and model of the energy storage converter of the energy storage system to be calibrated.

[0034] In one possible implementation, a parameter determination module is used to debug the parameters of the energy storage simulation model. When the data of the simulated electrical parameter set is the same as the data of the real electrical parameter set, the debugging stops.

[0035] The parameters of the energy storage simulation model that are identical to the simulated electrical parameter set are determined as the calibration parameters of the energy storage simulation model.

[0036] In one possible implementation, a detection module is used to acquire a first detection parameter, a second detection parameter, and a third detection parameter of the energy storage simulation model after it is connected to the simulated power grid device. The first detection parameter is detected by the energy storage simulation model during charging and discharging, and includes at least two of the following: charging response time, charging adjustment time, discharging response time, discharging adjustment time, and charge-discharge conversion time. The second detection parameter is detected at multiple preset detection points when the output voltage of the simulated power grid device is adjusted to a nominal voltage within multiple different preset ranges during charging and discharging, and includes the voltage, duration, and whether a trip occurred at each preset detection point. The third detection parameter is detected at multiple preset detection points when the frequency of the simulated power grid device is adjusted within a preset frequency range during charging and discharging, and includes the operating frequency, operating time, and whether a trip occurred at each preset detection point.

[0037] Based on the first detection parameter, the second detection parameter, the third detection parameter, the preset response time test standard, the preset voltage response standard, and the preset frequency response test standard, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0038] In one possible implementation, a detection module is used to acquire a fourth detection parameter of the energy storage simulation model under no-load and rated resistive load conditions, wherein the fourth detection parameter includes the output voltage imbalance on the AC side, the output voltage on the AC side, and the amplitude deviation.

[0039] The fifth detection parameter of the energy storage simulation model is obtained under multiple different preset input / output power conditions. The fifth detection parameter includes the output voltage and output current harmonic distortion rate on the AC side.

[0040] Based on the fourth and fifth detection parameters, as well as the power quality detection standards, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0041] In one possible implementation, a detection module is used to acquire a sixth detection parameter of the energy storage simulation model after the electrochemical energy storage system is connected to a distribution network with a preset voltage. The sixth detection parameter is the detection time obtained by conducting no-load and load experiments based on the low voltage ride-through curve.

[0042] Based on the sixth detection parameter and the low voltage ride-through curve, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0043] In one possible implementation, the detection module is used to obtain a seventh detection parameter after adjusting the voltage amplitude or frequency on the side of the simulated power grid device after the energy storage simulation model is connected to the simulated power grid device, wherein the seventh detection parameter is the disconnection time between the energy storage simulation model and the simulated power grid device;

[0044] Based on the seventh detection parameter and the preset island disconnection time, determine whether the calibration parameters meet the requirements for disconnection.

[0045] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0046] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0047] This invention provides a parameter calibration method, apparatus, and device for an energy storage simulation model based on RT-LAB. First, the initial operating state of the energy storage system to be calibrated is acquired, and the data is input into a pre-constructed energy storage simulation model. Next, the actual electrical parameter set output by the energy storage system after connection to the grid, operating under preset conditions and undergoing testing, is acquired. Then, the simulated electrical parameter set output by the energy storage simulation model in the RT-LAB simulation system, operating under preset conditions and undergoing testing, is acquired. Finally, based on the simulated electrical parameter set and the actual electrical parameter set, the parameters of the energy storage simulation model are adjusted to determine the calibration parameters. This makes the calibration parameters of the energy storage simulation model closer to the parameters of the actual energy storage system, and the debugging time can be shortened by modifying control parameters online in real time. 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 schematic diagram of the RT-LAB system provided in an embodiment of the present invention;

[0050] Figure 2 This is a flowchart illustrating the implementation of the parameter calibration method for the energy storage simulation model based on RT-LAB provided in this embodiment of the invention.

[0051] Figure 3 This is a schematic diagram of low voltage ride-through capability simulation provided in an embodiment of the present invention;

[0052] Figure 4 This is a schematic diagram of the low voltage ride-through capability of the electrochemical energy storage system provided in an embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of the anti-islanding experiment provided in an embodiment of the present invention;

[0054] Figure 6 This is a schematic diagram of the parameter calibration device for the energy storage simulation model based on RT-LAB provided in an embodiment of the present invention;

[0055] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0056] 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.

[0057] 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.

[0058] As described in the background section, energy storage systems are playing an increasingly important role in the power grid. However, the technologies related to grid connection and disconnection of energy storage systems are not yet mature. When energy storage systems are connected to or disconnected from the grid, they can cause certain impacts and influences on the grid. Therefore, how to calibrate the parameters of the corresponding energy storage model during the grid connection and disconnection process has become an urgent technical problem to be solved.

[0059] To address the problems of existing technologies, embodiments of the present invention provide a parameter calibration method, apparatus, and device for an energy storage simulation model based on RT-LAB. The parameter calibration method for an energy storage simulation model based on RT-LAB provided by the embodiments of the present invention will be described first.

[0060] First, a brief introduction to the RT-LAB system:

[0061] like Figure 1As shown, the RT-LAB system comprises a computation subsystem and a display subsystem. The computation subsystem includes a computation section, mathematical operations, I / O interfaces, a signal generator, a physical model, etc. The display subsystem includes a user module, an oscilloscope, a monitor, manual switches, and constants. The computation subsystem performs real-time calculations on the CPU core of the target machine, while the display subsystem displays the results on the host computer.

[0062] All system inputs, such as those to the arithmetic and display subsystems, must first pass through the OpComm module; otherwise, the connected signals will not function. The OpComm module is primarily used for communication between the various arithmetic subsystems and between the arithmetic and display subsystems.

[0063] The PCS controller is connected via the RT-LAB hardware IO board. The signals required by the control part are transmitted and interacted with the external hardware control circuit in the form of analog and digital signals. The simulator outputs analog signals to the controller. The controller performs grid-connected control calculations based on the input analog signals and finally outputs a digital PWM wave, which is sent to the RT-LAB real-time simulator.

[0064] Connect the analog output interface of the RT-LAB real-time emulator to the input of the PCS control board, and connect the PWM output of the PCS to the digital input of the RT-LAB real-time emulator.

[0065] The simulation environment constructed by this invention includes a host computer monitoring system, an RT-LAB simulation host, an RT-LAB real-time simulator, and a controller for the energy storage converter.

[0066] See Figure 2 The document illustrates a flowchart of the parameter calibration method for an energy storage simulation model based on RT-LAB, as provided in an embodiment of the present invention. Details are as follows:

[0067] Step S210: Obtain the initial operating state of the energy storage system to be calibrated, and input the data of the initial operating state into the pre-built energy storage simulation model.

[0068] The energy storage simulation model is built in the host computer of the RT-LAB simulation system, and the RT-LAB simulation system is connected to the controller of the energy storage converter.

[0069] RT-LAB is a real-time simulation framework software package, mainly used for hardware-in-the-loop simulation. Modeling is performed using the modeling software on the host computer of the RT-LAB simulation system, and the energy storage simulation model is run on the real-time simulation platform through the target computer. Then, the controller of the actual energy storage converter is combined with the RT-LAB simulation system to realize the real-time simulation of the energy storage system.

[0070] The initial model parameters for the energy storage simulation model can be either classic parameters or empirical values; no specific restrictions are imposed here.

[0071] In this embodiment, the initial operating state includes at least two of the following: the battery capacity, power, voltage of individual battery cells, internal resistance of the battery, and the switching frequency and model of the energy storage converter of the energy storage system to be calibrated.

[0072] Step S220: Obtain the actual set of electrical parameters output by the energy storage system when it is connected to the power grid, operates under preset conditions, and is tested.

[0073] The energy storage system is connected to the power grid, and the system is tested according to preset operating conditions to measure the actual electrical parameter set of the energy storage system to be calibrated in real application scenarios.

[0074] The preset operating conditions include power grid conditions such as voltage and line impedance.

[0075] The electrical parameter set includes at least two of the following when the energy storage system is connected to the grid: the power value of the grid-connected node, the output voltage and output current values ​​on the AC side, and the battery response speed or frequency.

[0076] Step S230: Obtain the set of electrical parameters output by the energy storage simulation model when it is run and tested in the RT-LAB simulation system according to preset conditions.

[0077] Since the application of calibration parameters focuses on testing whether the parameters of the energy storage system meet the predetermined requirements, the main tests include low voltage ride-through, anti-islanding protection capability, grid adaptability, etc. Therefore, the set of decisive electrical parameters selected includes at least two of the following: power value of the grid-connected node, output voltage and output current value of the AC side, and battery response speed or frequency.

[0078] The same preset operating conditions as the power grid are input into the pre-built energy storage simulation model for initialization, compilation, and download. The model is then executed in the RT-LAB lower-level target machine. The detected AC and DC voltage and current control signals are transmitted to the controller of the energy storage converter through the analog output board. The PWM wave generated in the controller is then transmitted to the lower-level target machine through the digital input board, thereby controlling the switching of the energy storage inverter to meet the requirements of power regulation and charging / discharging. At the same time, the set of simulated electrical parameters is obtained.

[0079] Step S240: Based on the simulated electrical parameter set and the actual electrical parameter set, adjust the parameters of the energy storage simulation model to determine the calibration parameters of the energy storage simulation model.

[0080] In some embodiments, the energy storage simulation model is debugged until the simulated electrical parameter set data matches the actual electrical parameter set data, at which point the debugging stops. Then, the parameters of the energy storage simulation model when the simulated electrical parameter set matches the actual electrical parameter set are determined as the calibration parameters of the energy storage simulation model.

[0081] By combining the simulated electrical parameter set data with the actual electrical parameter set data, the energy storage simulation model built in the RT-LAB system can be debugged to obtain a debugged model. Through repeated debugging, the output of the simulated electrical parameter set data and the actual electrical parameter set data are made consistent. During the debugging process, performance evaluation indicators set according to the actual electrical parameter set can be used for judgment. When the parameters calibrated under the simulated electrical parameter set meet the set performance evaluation indicators, the debugging is complete. When the parameters calibrated under the simulated electrical parameter set do not meet the set performance evaluation indicators, the simulated electrical parameter set is adjusted further.

[0082] By combining the RT-LAB system with hardware simulation, the environmental constraints of the entire calibration process were resolved, achieving both realism and controllability. Furthermore, it allows for convenient and safe multiple independent repeatable experiments, making the calibration parameters of the energy storage simulation model essentially closer to real-world scenarios. Based on the model parameter calibration, the functional parameters of the energy storage system are further tested. The test results are compared with the corresponding set parameter standards to determine whether the tested functional parameters of the energy storage system meet the standards.

[0083] In addition, after determining the calibration parameters of the energy storage simulation model, it is necessary to test the rationality of the obtained calibration parameters. The main testing methods include: response time testing, power quality testing, low voltage ride-through capability testing, anti-islanding testing, frequency response testing, and voltage response testing. If the calibration parameters of the energy storage simulation model do not meet the test results after passing the above tests, the calibration parameters of the energy storage simulation model need to be readjusted.

[0084] In the first detection method, response time testing, the energy storage simulation model is first connected to a simulated power grid. The simulation model is then set to operate in charging / discharging mode. The charging response time, charging adjustment time, discharging response time, discharging adjustment time, and charge / discharge transition time are measured, and waveform data during the transition process is recorded using an oscilloscope. The test standard for response time is: both charging and discharging response times should not exceed 180ms, and the time from full charge to full discharge should be less than 400ms.

[0085] The second detection method, power quality detection, includes detection under four conditions: three-phase voltage imbalance (off-grid), voltage deviation (off-grid), harmonics (on-grid), and DC component (on-grid).

[0086] Three-phase voltage imbalance (off-grid) testing involves measuring and recording the output voltage imbalance on the AC side of the energy storage simulation model under no-load and rated resistive load (balanced load) conditions. The three-phase voltage imbalance (off-grid) testing standard is: the negative sequence voltage imbalance at the point of common coupling should not exceed 2%, and for short periods should not exceed 4%.

[0087] Voltage deviation (off-grid) detection involves measuring and recording the AC output voltage and amplitude deviation of the energy storage simulation model under no-load and rated resistive load (balanced load) conditions. Voltage deviation (off-grid) detection standards: ① Three-phase voltage deviation of 20kV and below shall not exceed ±7% of the nominal voltage; ② Single-phase voltage deviation of 220V shall not exceed -10% to +7% of the nominal voltage.

[0088] Harmonic (grid-connected) testing involves measuring and recording the AC side voltage / current harmonics of the energy storage simulation model under input / output conditions of 33%, 66%, and 100% of rated power. The harmonic (grid-connected) testing standards are shown in Tables 1 and 2.

[0089] Table 1. Harmonic distortion rate and content of voltage at the point of common coupling.

[0090]

[0091] Table 2 Harmonic distortion rate and content of current at the point of common coupling.

[0092]

[0093]

[0094] DC component (grid-connected) detection involves measuring and recording the DC component of the AC side current in the energy storage simulation model under input / output conditions of 33%, 66%, and 100%. The DC component (grid-connected) detection standards are: ① not exceeding 0.5% of the AC rated value of the electrochemical energy storage system (via transformer connection); ② not exceeding 1% of the AC rated value of the electrochemical energy storage system (via converter connection).

[0095] The third testing method, low voltage ride-through capability testing, such as... Figure 3 As shown, no-load and load tests were conducted according to the low voltage ride-through curve, and the experimental curves were recorded. For electrochemical energy storage systems connected to 6kV~10(20)kV distribution networks, when the grid connection point voltage is below 85% of the rated voltage, the energy storage simulation model should have the following characteristics: Figure 4 The low voltage ride-through capability shown.

[0096] The fourth detection method, in anti-islanding detection, such as... Figure 5As shown, the grid-connected energy storage power station should operate normally when the voltage amplitude or frequency of the simulated grid side is adjusted to vary within a specified range, and the duration of the maximum and minimum values ​​is not less than 1 minute. When the grid-side voltage or frequency changes exceed a certain range, the grid-connected energy storage simulation model should respond to voltage or frequency anomalies as required. Simultaneously, the electrochemical energy storage system should have anti-islanding protection; in the event of unplanned islanding, it should disconnect from the distribution network within 2 seconds.

[0097] The fifth testing method, frequency response testing, involves the following steps: First, connecting the energy storage simulation model to the simulated power grid device and setting the simulation model to a charging state. Then, adjusting the frequency of the simulated power grid device to the range of 49.52Hz to 50.18Hz, selecting several points within this range (at least three points, with critical points required), and running each point continuously for at least 5 minutes without tripping; otherwise, the test is stopped. Next, setting the energy storage system to a discharging state and repeating the frequency adjustment steps. Then, setting the energy storage system to a charging state, adjusting the frequency of the simulated power grid device to the ranges of 49.32Hz to 49.48Hz and 50.22Hz to 50.48Hz respectively, selecting several points within these ranges (at least three points, with critical points required), and running each point continuously for at least 4 seconds. The operating status of the energy storage simulation model and the corresponding action frequency and action time are recorded. Finally, the energy storage simulation model was set to operate in a discharge state, and the frequency of the simulated power grid device was adjusted to the ranges of 49.32Hz~49.48Hz and 50.22Hz~50.48Hz respectively. Several points were reasonably selected within this range (at least 3 points, and the critical point must be measured), and each point was continuously operated for at least 4 seconds. The operating status of the energy storage simulation model and the corresponding action frequency and action time were recorded respectively.

[0098] The standards for frequency response testing are as follows: ① For electrochemical energy storage systems connected to a 220V / 380V distribution network, charging should be stopped when the frequency at the grid connection point is lower than 49.5Hz; when the frequency at the grid connection point is higher than 50.2Hz, power supply to the distribution network should be stopped; ② The frequency response characteristics of electrochemical energy storage systems connected to a 6kV~10(20)kV distribution network should meet the requirements of Table 3 below:

[0099] Table 3 Frequency response characteristics requirements for electrochemical energy storage systems

[0100] f<48 The electrochemical energy storage system should be immediately disconnected from the power distribution network. 48≤f≤49.5 Electrochemical energy storage systems should not draw power from the power distribution network. 49.5≤f≤50.2 Normal operation f>50.2 Electrochemical energy storage systems should not supply electrical energy to the power distribution network.

[0101] The sixth testing method, voltage response testing, involves first connecting the energy storage simulation model to a simulated power grid device and setting the model to charging mode. Next, the output voltage of the simulated power grid device is adjusted to within 86%–109% of the nominal voltage of the grid to be connected. Within this range, several points are reasonably selected (at least three points, and critical points must be measured). Each point is run continuously for at least 5 minutes without tripping; otherwise, the test is stopped. Then, the output voltage of the simulated power grid device is adjusted to 111%–119%, 51%–84%, 121%, and 49% of the nominal voltage of the grid to be connected. Within this range, several points are reasonably selected (at least three points, and critical points must be measured). Each point is run continuously for at least 4 seconds, and the operating status of the energy storage simulation model and the corresponding action voltage and action time are recorded. Finally, the energy storage simulation model is set to discharging mode, and the above steps are repeated.

[0102] The standards for voltage response testing are shown in Table 4:

[0103] Table 4 Characteristics of Voltage Response Detection

[0104]

[0105] Therefore, by using the above six tests, after obtaining the calibration parameters of the energy storage simulation model, only these six methods are needed to verify the accuracy of the calibration parameters.

[0106] The parameter calibration method provided by this invention first obtains the initial operating state of the energy storage system to be calibrated and inputs it into a pre-constructed energy storage simulation model. Next, it obtains the actual electrical parameter set output by the energy storage system when it is connected to the grid and operates under preset conditions and is tested. Then, it obtains the simulated electrical parameter set output by the energy storage simulation model when it operates and is tested under preset conditions in an RT-LAB simulation system. Finally, based on the simulated electrical parameter set and the actual electrical parameter set, the energy storage simulation model is debugged to determine the calibration parameters. This makes the calibration parameters of the energy storage simulation model closer to the parameters of the actual energy storage system, and the debugging time can be shortened by modifying control parameters online in real time.

[0107] The energy storage simulation model is run on a real-time simulation platform using a target machine. Then, the controller of the actual energy storage converter is combined with the RT-LAB simulation system to achieve real-time simulation of the energy storage system. By adding hardware components to the control or test loops, it essentially approximates a real energy storage system and also solves the environmental constraints in the entire experiment, achieving both realism and controllability. Furthermore, the experimental process is convenient and safe, allowing for multiple independent and repeated tests. In addition, control parameters can be modified online in real time, shortening the development cycle and saving R&D costs.

[0108] 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.

[0109] Based on the parameter calibration method for the RT-LAB-based energy storage simulation model provided in the above embodiments, the present invention also provides a specific implementation of a parameter calibration device for the RT-LAB-based energy storage simulation model applied to the parameter calibration method of the RT-LAB-based energy storage simulation model. Please refer to the following embodiments.

[0110] like Figure 6 As shown, a parameter calibration device 600 for an energy storage simulation model based on RT-LAB is provided. The device includes:

[0111] The input data module 610 is used to acquire the initial operating state of the energy storage system to be calibrated and input the data of the initial operating state into the pre-built energy storage simulation model. The RT-LAB simulation system is connected to the controller of the energy storage converter.

[0112] The module 620 for acquiring the real parameter set is used to acquire the real electrical parameter set output by the energy storage system when it is connected to the power grid, operates under preset conditions, and is tested.

[0113] The simulation parameter set acquisition module 630 is used to acquire the electrical parameter set output by the energy storage simulation model when it runs and is tested in the RT-LAB simulation system according to preset conditions; wherein, the electrical parameter set includes at least two of the following: power value of the grid-connected node, output voltage value and output current value of the AC side, and battery response speed or frequency;

[0114] The parameter determination module 640 is used to debug the energy storage simulation model based on the simulated electrical parameter set and the actual electrical parameter set, and to determine the calibration parameters of the energy storage simulation model.

[0115] In one possible implementation, the initial operating parameter values ​​include at least two of the following: the battery capacity, power, individual cell voltage, battery internal resistance, and the switching frequency and model of the energy storage converter of the energy storage system to be calibrated.

[0116] In one possible implementation, parameter determination module 640 is used to debug the energy storage simulation model. When the data of the simulated electrical parameter set is the same as the data of the real electrical parameter set, the debugging is stopped.

[0117] The parameters of the energy storage simulation model that are identical to the simulated electrical parameter set are determined as the calibration parameters of the energy storage simulation model.

[0118] In one possible implementation, a detection module is used to acquire a first detection parameter, a second detection parameter, and a third detection parameter of the energy storage simulation model after it is connected to the simulated power grid device. The first detection parameter is detected by the energy storage simulation model during charging and discharging, and includes at least two of the following: charging response time, charging adjustment time, discharging response time, discharging adjustment time, and charge-discharge conversion time. The second detection parameter is detected at multiple preset detection points when the output voltage of the simulated power grid device is adjusted to a nominal voltage within multiple different preset ranges during charging and discharging, and includes the voltage, duration, and whether a trip occurred at each preset detection point. The third detection parameter is detected at multiple preset detection points when the frequency of the simulated power grid device is adjusted within a preset frequency range during charging and discharging, and includes the operating frequency, operating time, and whether a trip occurred at each preset detection point.

[0119] Based on the first detection parameter, the second detection parameter, the third detection parameter, the preset response time test standard, the preset voltage response standard, and the preset frequency response test standard, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0120] In one possible implementation, a detection module is used to acquire a fourth detection parameter of the energy storage simulation model under no-load and rated resistive load conditions, wherein the fourth detection parameter includes the output voltage imbalance on the AC side, the output voltage on the AC side, and the amplitude deviation.

[0121] The fifth detection parameter of the energy storage simulation model is obtained under multiple different preset input / output power conditions. The fifth detection parameter includes the output voltage and output current harmonic distortion rate on the AC side.

[0122] Based on the fourth and fifth detection parameters, as well as the power quality detection standards, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0123] In one possible implementation, a detection module is used to acquire a sixth detection parameter of the energy storage simulation model after the electrochemical energy storage system is connected to a distribution network with a preset voltage. The sixth detection parameter is the detection time obtained by conducting no-load and load experiments based on the low voltage ride-through curve.

[0124] Based on the sixth detection parameter and the low voltage ride-through curve, determine whether the calibration parameters meet the requirements for disconnection from the grid.

[0125] In one possible implementation, the detection module is used to obtain a seventh detection parameter after adjusting the voltage amplitude or frequency on the side of the simulated power grid device after the energy storage simulation model is connected to the simulated power grid device, wherein the seventh detection parameter is the disconnection time between the energy storage simulation model and the simulated power grid device;

[0126] Based on the seventh detection parameter and the preset island disconnection time, determine whether the calibration parameters meet the requirements for disconnection.

[0127] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 7 As shown, the electronic device 7 in this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70. When the processor 70 executes the computer program 72, it implements the steps in the above-described embodiments of the parameter calibration methods for various RT-LAB-based energy storage simulation models, for example... Figure 2 Steps 210 to 240 are shown. Alternatively, when the processor 70 executes the computer program 72, it implements the functions of each module in the above-described device embodiments, for example... Figure 6 The functions of modules 610 to 640 are shown.

[0128] For example, the computer program 72 can be divided into one or more modules, which are stored in the memory 71 and executed by the processor 70 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 72 in the electronic device 7. For example, the computer program 72 can be divided into... Figure 6 Modules 610 to 640 are shown.

[0129] The electronic device 7 may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7 This is merely an example of electronic device 7 and does not constitute a limitation on electronic device 7. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0130] The processor 70 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0131] The memory 71 can be an internal storage unit of the electronic device 7, such as a hard disk or memory. The memory 71 can also be an external storage device of the electronic device 7, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 71 can include both internal and external storage units of the electronic device 7. The memory 71 is used to store the computer program and other programs and data required by the electronic device. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0133] 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.

[0134] Those skilled in the art will recognize that the 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.

[0135] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0138] If the integrated 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 parameter calibration methods of the various RT-LAB-based energy storage simulation models described above. 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 (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0139] 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 parameter calibration method of an RT-LAB-based energy storage simulation model, characterized in that, include: The initial operating state of the energy storage system to be calibrated is obtained, and the data of the initial operating state is input into the pre-built energy storage simulation model. The energy storage simulation model is built in the host computer of the RT-LAB simulation system, and the RT-LAB simulation system is connected to the controller of the energy storage converter. The system obtains the actual electrical parameter set output by the energy storage system after it is connected to the grid and operates and is tested under preset conditions; it also obtains the simulated electrical parameter set output by the energy storage simulation model in the RT-LAB simulation system when it operates and is tested under the preset conditions; wherein, the electrical parameter set includes the power value of the grid-connected node, the output voltage and output current value of the AC side, the battery response speed and frequency; The parameters of the energy storage simulation model are adjusted. When the data of the simulated electrical parameter set is the same as the data of the real electrical parameter set, the adjustment is stopped. The parameters of the energy storage simulation model when the data of the simulated electrical parameter set is the same as the data of the real electrical parameter set are determined as the calibration parameters of the energy storage simulation model. The detection process for the calibration parameters includes: The system acquires a first detection parameter, a second detection parameter, and a third detection parameter after the energy storage simulation model is connected to a simulated power grid device. The first detection parameter is detected during charging and discharging of the energy storage simulation model and includes at least two of the following: charging response time, charging adjustment time, discharging response time, discharging adjustment time, and charge / discharge conversion time. The second detection parameter is detected at multiple preset detection points when the output voltage of the simulated power grid device is adjusted to a nominal voltage within multiple different preset ranges during charging and discharging of the energy storage simulation model. The second detection parameter includes the voltage, duration, and whether a trip occurred at each preset detection point. The third detection parameter is detected at multiple preset detection points when the frequency of the simulated power grid device is adjusted within a preset frequency range during charging and discharging of the energy storage simulation model. The third detection parameter includes the operating frequency, operating time, and whether a trip occurred at each preset detection point. Based on the first detection parameter, the second detection parameter, the third detection parameter, a preset response time verification standard, a preset voltage response standard, and a preset frequency response detection standard, the system determines whether the calibration parameters meet the grid connection and disconnection requirements. The fourth detection parameter of the energy storage simulation model is obtained under no-load and rated resistive load conditions, wherein the fourth detection parameter includes the output voltage imbalance, output voltage and amplitude deviation on the AC side; the fifth detection parameter of the energy storage simulation model is obtained under multiple different preset input / output power conditions, wherein the fifth detection parameter includes the output voltage and output current harmonic distortion rate on the AC side; based on the fourth detection parameter, the fifth detection parameter, and the power quality detection standard, it is determined whether the calibration parameters meet the off-grid requirements; The sixth detection parameter of the energy storage simulation model after the electrochemical energy storage system is connected to the distribution network with a preset voltage is obtained. The sixth detection parameter is the detection time obtained by conducting no-load and load experiments based on the low voltage ride-through curve. Based on the sixth detection parameter and the low voltage ride-through curve, it is determined whether the calibration parameters meet the off-grid requirements. After the energy storage simulation model is connected to the simulated power grid device, a seventh detection parameter is obtained after adjusting the voltage amplitude or frequency on the simulated power grid device side. The seventh detection parameter is the disconnection time between the energy storage simulation model and the simulated power grid device. Based on the seventh detection parameter and the preset islanding disconnection time, it is determined whether the calibration parameter meets the grid connection and disconnection requirements.

2. The parameter calibration method of claim 1, wherein, The initial operating state includes at least two of the following: the battery capacity, power, voltage of individual battery terminals, and internal resistance of the energy storage system to be calibrated, as well as the switching frequency and model of the energy storage converter.

3. A parameter calibration device for an RT-LAB-based energy storage simulation model, characterized in that include: The input data module is used to obtain the initial operating state of the energy storage system to be calibrated and input the data of the initial operating state into the pre-built energy storage simulation model. The energy storage simulation model is built in the host computer of the RT-LAB simulation system and the RT-LAB simulation system is connected to the controller of the energy storage converter. The module for obtaining the actual parameter set is used to obtain the actual electrical parameter set output by the energy storage system when it is connected to the power grid, operates under preset conditions, and is tested. The simulation parameter set acquisition module is used to acquire the electrical parameter set output by the energy storage simulation model when it runs and is tested in the RT-LAB simulation system according to the preset conditions; wherein, the electrical parameter set includes the power value of the grid-connected node, the output voltage and output current value of the AC side, the battery response speed and frequency; The parameter determination module is used to debug the parameters of the energy storage simulation model. When the data of the simulated electrical parameter set is the same as the data of the real electrical parameter set, the debugging is stopped. The parameters of the energy storage simulation model when the data of the simulated electrical parameter set is the same as the data of the real electrical parameter set are determined as the calibration parameters of the energy storage simulation model. The parameter determination module is further used to acquire a first detection parameter, a second detection parameter, and a third detection parameter of the energy storage simulation model after it is connected to the simulated power grid device. The first detection parameter is detected by the energy storage simulation model during charging and discharging, and includes at least two of the following: charging response time, charging adjustment time, discharging response time, discharging adjustment time, and charge / discharge conversion time. The second detection parameter is detected at multiple preset detection points when the output voltage of the simulated power grid device is adjusted to a nominal voltage within multiple different preset ranges during charging and discharging, and includes the voltage, duration, and whether a trip occurred at each preset detection point. The third detection parameter is detected at multiple preset detection points when the frequency of the simulated power grid device is adjusted within a preset frequency range during charging and discharging, and includes the operating frequency, operating time, and whether a trip occurred at each preset detection point. Based on the first detection parameter, the second detection parameter, the third detection parameter, a preset response time verification standard, a preset voltage response standard, and a preset frequency response detection standard, it is determined whether the calibration parameters meet the grid disconnection requirements. The following steps are taken: First, obtain the fourth detection parameters of the energy storage simulation model under no-load and rated resistive load conditions, including the AC side output voltage imbalance, AC side output voltage, and amplitude deviation. Second, obtain the fifth detection parameters of the energy storage simulation model under multiple different preset input / output power conditions, including the AC side output voltage and output current harmonic distortion rate. Third, based on the fourth and fifth detection parameters and power quality testing standards, determine whether the calibration parameters meet the off-grid requirements. Fourth, obtain the electrochemical energy storage of the energy storage simulation model in a distribution network connected to a preset voltage. The system then uses a sixth detection parameter, which is the detection time obtained during no-load and load experiments based on the low-voltage ride-through curve. Based on the sixth detection parameter and the low-voltage ride-through curve, it is determined whether the calibration parameters meet the grid connection and disconnection requirements. A seventh detection parameter is obtained after adjusting the voltage amplitude or frequency on the simulated grid device side after connecting the energy storage simulation model to the simulated grid device. This seventh detection parameter is the disconnection time between the energy storage simulation model and the simulated grid device. Based on the seventh detection parameter and the preset islanding disconnection time, it is determined whether the calibration parameters meet the grid connection and disconnection requirements.

4. An electronic device, comprising: The method includes a memory and a processor, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the method as described in any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 4. When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 2.

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