A network-type energy storage semi-physical simulation test platform and method

Abstract

The present invention discloses a semi-physical simulation test platform and method for grid-type energy storage, which specifically relates to the field of simulation testing, including a digital twin collaborative modeling module, an impedance adaptive hardware coupling module, a grid control depth verification module, a multi-dimensional performance evaluation module, and an iterative optimization module. The present invention integrates a dynamic impedance network model and a virtual energy storage unit by constructing a grid-energy storage digital twin. The dynamic impedance network adopts a frequency-varying parameter transmission line equation and is generated by fitting measured impedance scan data. In the in-loop test, a full-band disturbance injection method is used to monitor the impedance characteristics. Under a preset weak grid operating condition, the virtual inertia and physical droop control parameters are coordinated. The multi-dimensional performance evaluation system quantifies system performance from three dimensions: dynamic response, stability margin, and transient energy function. When the phase margin deviation is greater than 15°, an iterative optimization mechanism is triggered, the virtual inertia coefficient is reconstructed, and verification is repeated until the stability threshold is met.

Description

Technical Field

[0001] The present invention relates to the field of simulation testing technology, and more specifically, to a network-type energy storage semi-physical simulation testing platform and method. Background Art

[0002] With the rapid development of renewable energy and the construction of smart grids, energy storage systems are becoming increasingly widely used in power systems. Grid-forming energy storage systems (GFES), as a new energy storage technology, can actively support grid voltage and frequency, improving grid stability and reliability, and have become a research hotspot in the energy storage field. However, the complexity and dynamic characteristics of GFES present numerous challenges in their testing and verification.

[0003] However, it still has some shortcomings in actual use. For example, traditional physical testing usually needs to be carried out in an actual power grid environment, which is costly and risky, and it is difficult to simulate various extreme working conditions and fault scenarios. In addition, the repeatability and controllability of physical testing are poor, making it difficult to fully evaluate the dynamic characteristics of the system. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a network-type energy storage semi-physical simulation test platform and method to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] Digital twin collaborative modeling module: Builds a grid-energy storage digital twin in a real-time simulation unit. The digital twin includes a dynamic impedance network model and a virtual grid-based energy storage unit. The virtual grid-based energy storage unit synchronizes real-time data with the physical energy storage device controller through an error compensation algorithm.

[0007] Impedance adaptive hardware coupling module: connects the physical energy storage device to the simulation loop via the power amplifier, and collects the impedance characteristics of the device output in real time; dynamically adjusts the damping compensation parameters of the power amplifier based on the impedance scan results;

[0008] Network control deep verification module: Injects preset weak grid conditions into the digital twin to activate the network control strategy of the physical equipment; synchronously adjusts the inertia simulation coefficient of the virtual energy storage unit and the droop control parameters of the physical equipment to generate bidirectional parameter interaction instructions;

[0009] Multi-dimensional performance evaluation module: Calculates the active power response rate of physical equipment under frequency disturbances; analyzes the phase margin deviation of virtual-physical dual-terminal data at impedance mutation points to determine the control stability boundary;

[0010] Iterative optimization module: When the phase margin deviation is greater than 15°, the virtual inertia coefficient is reconstructed based on the Lyapunov function, and the network control depth verification module to the multi-dimensional performance evaluation module are repeated until the stability threshold is met.

[0011] Preferably, in the digital twin collaborative modeling module, a grid-energy storage digital twin is constructed in the real-time simulation unit. The twin includes a dynamic impedance network model and a virtual grid-type energy storage unit. Real-time data synchronization with the physical energy storage device controller is achieved through an error compensation algorithm. The dynamic impedance network model adopts a frequency-varying parameter transmission line equation.

[0012] The virtual grid-type energy storage unit is constructed based on a multi-time-scale state-space model. It uses the IGBT loss-thermal coupling equation to simulate the switching dynamics of power devices at the millisecond level and executes the virtual synchronous machine rotor motion equation at the second level.

[0013] At the same time, the integrated control instruction precompiler converts the PWM signal output by the physical controller into an equivalent voltage source that can be analyzed by simulation;

[0014] The error compensation real-time synchronization mechanism adopts a bidirectional delay observation-compensation channel design. The forward channel timestamps the physical device control signal and inputs it into the digital twin, and estimates the transmission delay through sliding window filtering.

[0015] Preferably, in the impedance adaptive hardware coupling module, the physical energy storage device is connected to the real-time simulation loop via the power amplifier through an interface to construct a hardware-in-the-loop test system, and the impedance characteristics of the device output end are collected in real time and the system parameters are dynamically adjusted. Specifically, a full-band disturbance injection method is used to inject a white noise test signal with an amplitude not exceeding 2% of the rated voltage within a wide frequency range of 0.1 to 2000 Hz, and the voltage and current responses are monitored in real time through a high-sampling rate data acquisition card;

[0016] The full-band perturbation injection method is used to perform broadband excitation testing on the device under test. The test signal is generated by a high-performance arbitrary waveform generator, injecting a white noise signal with an amplitude strictly controlled within 2% of the rated voltage within the frequency range of 0.1 to 2000 Hz. This signal is shaped by a 6th-order Butterworth bandpass filter and coupled to the port of the device under test through an isolation amplifier. A 24-bit high-precision data acquisition card with a synchronous sampling rate of 500 kS / s is used to synchronously acquire three-phase voltage and current signals using hardware triggering. The sampling time window is set to 10 power frequency cycles. The collected time domain data is converted into frequency domain impedance characteristics through real-time FFT processing.

[0017] The ratio curve of the physical device output impedance to the grid equivalent impedance is calculated using a frequency domain analysis method. A closed-loop analysis of the impedance ratio trajectory is performed using the Nyquist stability criterion. When the trajectory approaches the (-1, j0) point and the phase margin is less than 30 degrees, a risk of mutual resonance is determined.

[0018] For the identified resonance point, the module automatically activates a multi-stage compensation mechanism. In the low-frequency band below 500Hz, a passive damping strategy is adopted by adjusting the output filter cutoff frequency of the power amplifier. In the mid-frequency band of 500-1500Hz, an active damper is enabled to dynamically generate a reverse compensation current according to the resonance point frequency.

[0019] Preferably, in the grid control depth verification module, a closed-loop test environment with virtual-real interaction is established to realize the control performance verification of the grid-type energy storage system under extremely weak grid conditions; the module first presets two typical weak grid test scenarios in the digital twin: one is the impedance phase jump condition, which quickly switches the grid equivalent impedance angle from 30° to 75° within 100ms to simulate the sudden change of grid characteristics caused by the sudden switching of transmission lines; the other is the SCR gradual disturbance condition, which linearly reduces the system short-circuit capacity ratio from SCR=3 to SCR=1.5, with a change rate of not less than 1SCR / second, simulating the continuous deterioration of grid strength caused by large-scale disconnection of new energy sources;

[0020] During the test, the module synchronously coordinates the interaction of control parameters of the virtual and physical systems: the virtual energy storage unit adjusts the inertia simulation coefficient in real time, and its adjustment algorithm is designed based on Lyapunov stability theory;

[0021] In the steady-state characteristic analysis, the THD change rate of the common connection point voltage before and after the impedance phase jump is calculated to evaluate the improvement effect of the control strategy on power quality;

[0022] In the steady-state characteristic analysis, the voltage waveform at the common connection point is monitored in real time using a high-precision power quality analyzer with a sampling rate set to 256 points / cycle. When a preset impedance phase jump (such as a 30°→75° step change) is detected, the THD dynamic analysis process is automatically triggered. First, the voltage waveform of the five power frequency cycles before the jump is collected as baseline data, and then the dynamic process of the 20 cycles after the jump is continuously recorded. An improved IEC 61000-4-7 standard algorithm is used to calculate the content of each harmonic, with particular attention paid to characteristic harmonics of orders 2 to 25. When the THD change rate is detected to be greater than 15% and continues to exceed the limit, control parameter optimization is automatically triggered, adjusting the phase-locked loop bandwidth. If convergence is not achieved within three cycles, the system switches to harmonic compensation mode, injecting reverse harmonic current components into the dq axis current loop.

[0023] Preferably, in the multidimensional performance evaluation module, the multidimensional performance evaluation module constructs a three-dimensional evaluation system including dynamic response, stability margin and energy characteristics, and comprehensively quantitatively evaluates the performance of the grid-type energy storage system; in the dynamic response evaluation dimension, the module monitors the active power change rate of the physical equipment under frequency disturbance in real time, and its calculation adopts the least squares method to fit the slope of the power-time curve to meet the dynamic response benchmark of the active power change rate greater than 10% of the rated power of the equipment per second; at the same time, the response time difference between the virtual model and the physical equipment is collected to establish a dynamic consistency index;

[0024] The specific calculation method is:

[0025]

[0026] in, Expressed as a dynamic consistency indicator, Indicates the actual response time, Expressed as the reference time constant, The value is 20ms; when DCI < 0.9, control parameter re-tuning is triggered, that is, when the dynamic consistency index is lower than the threshold of 0.9, the control parameters are automatically or manually re-adjusted;

[0027] In the stability margin analysis, for the impedance mutation condition, a closed-loop transfer function matrix including virtual and physical systems is constructed based on the dual-end data synchronization acquisition unit. , by solving the characteristic equation The root locus of the phase margin deviation Δφ at the critical frequency point is calculated; when Δφ>15° is detected, it is automatically marked as a stability risk point.

[0028] Preferably, in the iterative optimization module, during the iterative optimization stage, when it is detected that the phase margin deviation exceeds 15°, the system automatically triggers a parameter adjustment mechanism based on the Lyapunov stability theory; first, a Lyapunov function including a phase margin error term and a virtual inertia offset term is constructed;

[0029] The updated virtual inertia will be sent to the virtual energy storage unit in the digital twin in real time, and the droop coefficient of the physical device will be adjusted synchronously through the two-way parameter interaction interface; then the dynamic test process of the network control depth verification module will be re-executed to collect new phase margin data under the same disturbance conditions. If it does not meet the requirements, |≤5°, the iteration continues until the maximum number of iterations is reached; during each iteration, record The evolution trajectory of , g and key state variables is used to form a parameter optimization path report.

[0030] Technical effects and advantages of the present invention:

[0031] The present invention uses a digital twin collaborative modeling module to construct a grid-energy storage digital twin containing a dynamic impedance network model and a virtual energy storage unit, and achieves real-time data synchronization with the physical device controller through an error compensation algorithm. The impedance adaptive hardware coupling module connects the physical energy storage device to the simulation loop, collects impedance characteristics in real time, and dynamically adjusts the power amplifier parameters to suppress high-frequency oscillations. The network control depth verification module presets weak grid conditions and synchronously adjusts virtual and physical system parameters. The multi-dimensional performance evaluation module constructs a three-dimensional evaluation system to quantitatively evaluate system performance. The iterative optimization module reconstructs the virtual inertia coefficient based on the Lyapunov function when the phase margin deviation is greater than 15 degrees, repeats the verification and evaluation until the stability threshold is met, and records the parameter evolution trajectory to form a report.

[0032] Through the closed-loop coupling architecture of "digital simulation-power interface-physical equipment", the present invention breaks through the limitations of traditional methods in dynamic performance verification, provides a high-reliability testing method for the grid-connected stability of grid-type energy storage, and improves the control performance and stability assessment accuracy of the grid-type energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the platform module connection of the present invention.

[0034] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] See also Figure 1 As shown, the present invention provides a network-type energy storage semi-physical simulation test platform, including a digital twin collaborative modeling module, an impedance adaptive hardware coupling module, a network control depth verification module, a multi-dimensional performance evaluation module, and an iterative optimization module.

[0037] Digital twin collaborative modeling module: Builds a grid-energy storage digital twin in a real-time simulation unit. The digital twin includes a dynamic impedance network model and a virtual grid-based energy storage unit. The virtual grid-based energy storage unit synchronizes real-time data with the physical energy storage device controller through an error compensation algorithm.

[0038] In the digital twin collaborative modeling module, a grid-energy storage digital twin is constructed in the real-time simulation unit. The twin includes a dynamic impedance network model and a virtual grid-type energy storage unit. Real-time data synchronization with the physical energy storage device controller is achieved through an error compensation algorithm. The dynamic impedance network model uses a frequency-varying parameter transmission line equation:

[0039]

[0040] in, Expressed as the grid impedance, Expressed as the real part of impedance resistance, Expressed as the imaginary reactance of impedance, Expressed as the amplitude of the kth frequency-varying component, It represents the time constant of the kth frequency-varying component, f represents the frequency, k represents the index number of the frequency-varying component, and j represents the imaginary unit;

[0041] and Generated by fitting the measured impedance scan data; j is used to distinguish the real and imaginary parts of the impedance, the real part represents resistance, and the imaginary part represents reactance;

[0042] The virtual grid-type energy storage unit is constructed based on a multi-time-scale state-space model. It uses the IGBT loss-thermal coupling equation to simulate the switching dynamics of power devices at the millisecond level and executes the virtual synchronous machine rotor motion equation at the second level:

[0043]

[0044] in, Expressed as the rotor angular acceleration, is the rotor angular velocity, H is the inertia constant, Expressed as the input mechanical torque, Expressed as the output electromagnetic torque, Expressed as the damping coefficient, Expressed as angular velocity deviation;

[0045] At the same time, the integrated control instruction precompiler converts the PWM signal output by the physical controller into an equivalent voltage source that can be analyzed by simulation;

[0046] The error compensation real-time synchronization mechanism adopts a bidirectional delay observation-compensation channel design. The forward channel timestamps the physical device control signal and inputs it into the digital twin, and estimates the transmission delay through sliding window filtering.

[0047] The forward channel uses an FPGA-based hardware timestamp unit to insert 16-bit precision synchronization timestamps on both the rising and falling edges of the PWM drive signal output by the physical device controller. These timestamps are then transmitted to the digital twin via a dedicated communication protocol with CRC checksum. An adaptive sliding window delay estimation algorithm is deployed on the digital twin end, which uses a variable weight exponential forgetting strategy.

[0048] The feedback channel injects the virtual unit output into the physical controller after dynamic gain compensation. At the same time, a data consistency verification protocol is established to compare the terminal voltage phase difference between the physical device and the virtual unit every 10ms. When the phase difference is greater than 2°, the model parameter online correction is triggered.

[0049] Impedance adaptive hardware coupling module: Connects the physical energy storage device to the simulation loop via a power amplifier to collect the impedance characteristics of the device output in real time; dynamically adjusts the power amplifier's damping compensation parameters based on the impedance scan results to suppress high-frequency oscillations;

[0050] In the impedance adaptive hardware coupling module, the physical energy storage device is connected to the real-time simulation loop via the power amplifier through an interface to build a hardware-in-the-loop test system. The impedance characteristics of the device output are collected in real time and the system parameters are dynamically adjusted. Specifically, a full-band disturbance injection method is used to inject a white noise test signal with an amplitude not exceeding 2% of the rated voltage within a wide frequency range of 0.1 to 2000 Hz. The voltage and current responses are monitored in real time using a high-sampling-rate data acquisition card.

[0051] The full-band perturbation injection method is used to perform broadband excitation testing on the device under test. The test signal is generated by a high-performance arbitrary waveform generator, injecting a white noise signal with an amplitude strictly controlled within 2% of the rated voltage within the frequency range of 0.1 to 2000 Hz. This signal is shaped by a 6th-order Butterworth bandpass filter and coupled to the port of the device under test through an isolation amplifier. A 24-bit high-precision data acquisition card with a synchronous sampling rate of 500 kS / s is used to synchronously acquire three-phase voltage and current signals using hardware triggering. The sampling time window is set to 10 power frequency cycles. The collected time domain data is converted into frequency domain impedance characteristics through real-time FFT processing.

[0052] The ratio curve of the physical device output impedance to the grid equivalent impedance is calculated using a frequency domain analysis method. A closed-loop analysis of the impedance ratio trajectory is performed using the Nyquist stability criterion. When the trajectory approaches the (-1, j0) point and the phase margin is less than 30 degrees, a risk of mutual resonance is determined.

[0053] For the identified resonance point, the module automatically activates a multi-stage compensation mechanism. In the low-frequency band below 500Hz, a passive damping strategy is adopted by adjusting the output filter cutoff frequency of the power amplifier. In the mid-frequency band of 500-1500Hz, an active damper is enabled to dynamically generate a reverse compensation current based on the resonance point frequency. The specific calculation method is as follows:

[0054]

[0055] in, represents the reverse compensation current generated for the active damper, Expressed as the damping gain coefficient, Expressed as PCC point voltage, Expressed as the rate of change of the voltage at the PCC point;

[0056] The calculation method of the gain coefficient is as follows:

[0057]

[0058] in, Expressed as the damping gain coefficient, Expressed as the frequency of the resonance point, Expressed as equivalent capacitance parameter;

[0059] For high-frequency resonance above 1500Hz, the system switches to digital notch filter mode and inserts a second-order IIR filter into the control loop to attenuate the gain in a specific frequency band.

[0060] Network control deep verification module: Injects preset weak grid conditions into the digital twin to activate the network control strategy of the physical equipment; synchronously adjusts the inertia simulation coefficient of the virtual energy storage unit and the droop control parameters of the physical equipment to generate bidirectional parameter interaction instructions;

[0061] In the grid control deep verification module, a closed-loop test environment with virtual-real interaction is established to verify the control performance of the grid-type energy storage system under extremely weak grid conditions. The module first presets two typical weak grid test scenarios in the digital twin: one is the impedance phase jump condition, which quickly switches the grid equivalent impedance angle from 30° to 75° within 100ms, simulating the sudden change in grid characteristics caused by the sudden switching of transmission lines; the other is the SCR gradual disturbance condition, which linearly reduces the system short-circuit capacity ratio from SCR=3 to SCR=1.5, with a change rate of no less than 1SCR / second, simulating the continuous deterioration of grid strength caused by large-scale disconnection of new energy sources.

[0062] During the test, the module synchronously coordinates the interaction of control parameters of the virtual and physical systems: the virtual energy storage unit adjusts the inertia simulation coefficient in real time. Its adjustment algorithm is designed based on Lyapunov stability theory. The specific calculation method is as follows:

[0063]

[0064] in, Expressed as the real-time inertia simulation coefficient of the virtual energy storage unit, Expressed as the initial inertia value, Expressed as the adaptive gain coefficient, Frequency deviation;

[0065] At the same time, the calculation method of the droop control parameters on the physical device side is as follows:

[0066]

[0067] in, Expressed as the real-time droop control parameter on the physical device side, Expressed as the reference droop reference value, Expressed as the change sensitivity factor, Expressed as the change in short-circuit capacity ratio; bidirectional parameter interaction is achieved through a high-speed communication bus, ensuring that the control characteristics of the virtual model and the physical device evolve synchronously;

[0068] In transient process evaluation, comprehensive performance indicators are used to quantify the system dynamic response:

[0069]

[0070] Among them, CPI is expressed as a comprehensive performance index. Expressed as the maximum frequency deviation during the dynamic process, Expressed as rated frequency, Represented as recovery time, Expressed as a baseline value;

[0071] In the steady-state characteristic analysis, the THD change rate of the common connection point voltage before and after the impedance phase jump is calculated to evaluate the improvement effect of the control strategy on power quality;

[0072] In the steady-state characteristic analysis, the voltage waveform at the common connection point is monitored in real time using a high-precision power quality analyzer with a sampling rate set to 256 points / cycle. When a preset impedance phase jump (such as a 30°→75° step change) is detected, the THD dynamic analysis process is automatically triggered. First, the voltage waveform of the five power frequency cycles before the jump is collected as baseline data, and then the dynamic process of the 20 cycles after the jump is continuously recorded. An improved IEC 61000-4-7 standard algorithm is used to calculate the content of each harmonic, with particular attention paid to characteristic harmonics of orders 2 to 25. When the THD change rate is detected to be greater than 15% and continues to exceed the limit, control parameter optimization is automatically triggered, adjusting the phase-locked loop bandwidth. If convergence is not achieved within three cycles, the system switches to harmonic compensation mode, injecting reverse harmonic current components into the dq axis current loop.

[0073] Multi-dimensional performance evaluation module: Calculates the active power response rate of physical equipment under frequency disturbances; analyzes the phase margin deviation of virtual-physical dual-terminal data at impedance mutation points to determine the control stability boundary;

[0074] The multi-dimensional performance evaluation module constructs a three-dimensional evaluation system including dynamic response, stability margin and energy characteristics to comprehensively and quantitatively evaluate the performance of the grid-type energy storage system. In the dynamic response evaluation dimension, the module monitors the active power change rate of the physical equipment under frequency disturbance in real time. The calculation adopts the least squares method to fit the slope of the power-time curve to meet the dynamic response benchmark of the active power change rate greater than 10% of the rated power of the equipment per second. At the same time, the response time difference between the virtual model and the physical equipment is collected to establish a dynamic consistency index. The calculation method is as follows:

[0075]

[0076] in, Expressed as a dynamic consistency indicator, Indicates the actual response time, Expressed as the reference time constant, The value is 20ms; when DCI < 0.9, control parameter re-tuning is triggered, that is, when the dynamic consistency index is lower than the threshold of 0.9, the control parameters are automatically or manually re-adjusted;

[0077] In the stability margin analysis, for the impedance mutation condition, a closed-loop transfer function matrix including virtual and physical systems is constructed based on the dual-end data synchronization acquisition unit. , by solving the characteristic equation The root locus of the phase margin deviation Δφ at the key frequency point is calculated; when Δφ>15° is detected, it is automatically marked as a stability risk point;

[0078] The small signal stability evaluation uses the singular value decomposition method in the dq coordinate system. First, the state space equation of the control loop is established:

[0079]

[0080] Among them, x represents the state vector, u represents the input vector, M represents the state matrix, and N represents the input matrix;

[0081] u

[0082] y represents the output vector, P represents the output matrix, and Q represents the direct transfer matrix.

[0083] The state matrix M includes the dynamics of the virtual synchronous machine rotor, the dynamics of the phase-locked loop, and the dynamics of the current control loop; the system matrix A is subjected to singular value decomposition to obtain , set the stability threshold , when the limit is exceeded, an analysis report containing the dominant oscillation mode is generated;

[0084] The transient energy function evaluation dimension uses the improved Lyapunov function, and the calculation method is as follows:

[0085]

[0086] in, Expressed as a transient energy function, Expressed as equivalent inertia, Expressed as the voltage sensitivity coefficient, Expressed as power angle deviation, Expressed as the power angular velocity deviation, Expressed as mechanical torque;

[0087] The module calculates the rate of change of the energy function in real time. When the rate of change of the energy function is continuously greater than 0 and lasts for more than 5 cycles, it is determined that the platform is at risk of transient instability.

[0088] Iterative optimization module: When the phase margin deviation is greater than 15°, the virtual inertia coefficient is reconstructed based on the Lyapunov function, and the network control depth verification module and the multi-dimensional performance evaluation module are repeated until the stability threshold is met;

[0089] In the iterative optimization module, during the iterative optimization phase, when the phase margin deviation is detected to be greater than 15°, the system automatically triggers a parameter adjustment mechanism based on the Lyapunov stability theory. First, a Lyapunov function containing a phase margin error term and a virtual inertia offset term is constructed. The calculation method is as follows:

[0090]

[0091] in, Expressed as a Lyapunov function, Phase margin tracking error, g is expressed as the actual phase, Expressed as a weight factor, Expressed as virtual inertia update amount;

[0092]

[0093] in, It is represented as the phase margin tracking error, g is represented as the actual phase, It is represented as the reference phase;

[0094] By solving the derivative of the Lyapunov function The negative definite condition of is used to derive the virtual inertia update law, and the specific calculation method is:

[0095]

[0096] in, It is represented by the virtual inertia update amount, m is represented by the convergence factor, Expressed as the partial derivative of the phase with respect to the virtual inertia, Expressed as phase margin tracking error;

[0097] Obtained through online table lookup of pre-established frequency domain sensitivity model;

[0098] The updated virtual inertia will be sent to the virtual energy storage unit in the digital twin in real time, and the droop coefficient of the physical device will be adjusted synchronously through the two-way parameter interaction interface; then the dynamic test process of the network control depth verification module will be re-executed to collect new phase margin data under the same disturbance conditions. If it does not meet the requirements, |≤5°, the iteration continues until the maximum number of iterations is reached; during each iteration, record The evolution trajectory of , g and key state variables is used to form a parameter optimization path report.

[0099] See also Figure 2 As shown, in this embodiment, it should be specifically explained that the present invention provides a semi-physical simulation test method for network-type energy storage, including:

[0100] Step A1: Construct a grid-energy storage digital twin in a real-time simulation unit. The digital twin includes a dynamic impedance network model and a virtual grid-based energy storage unit. The virtual grid-based energy storage unit synchronizes real-time data with the physical energy storage device controller through an error compensation algorithm.

[0101] Step A2: Connect the physical energy storage device to the simulation loop via the power amplifier to collect the impedance characteristics of the device output in real time; dynamically adjust the damping compensation parameters of the power amplifier based on the impedance scan results;

[0102] Step A3: Inject the preset weak grid condition into the digital twin to activate the grid-based control strategy of the physical equipment; synchronously adjust the inertia simulation coefficient of the virtual energy storage unit and the droop control parameters of the physical equipment to generate a bidirectional parameter interaction instruction;

[0103] Step A4: Calculate the active power response rate of the physical device under frequency disturbance; analyze the phase margin deviation of the virtual-physical two-terminal data at the impedance mutation point to determine the control stability boundary;

[0104] Step A5: When the phase margin deviation is greater than 15°, the virtual inertia coefficient is reconstructed based on the Lyapunov function, and the network control depth verification module to the multi-dimensional performance evaluation module is repeated until the stability threshold is met.

[0105] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A network-type energy storage semi-physical simulation test platform, characterized in that: include: Digital twin collaborative modeling module: Builds a grid-energy storage digital twin in a real-time simulation unit. The digital twin includes a dynamic impedance network model and a virtual grid-based energy storage unit. The virtual grid-based energy storage unit synchronizes real-time data with the physical energy storage device controller through an error compensation algorithm. In the digital twin collaborative modeling module, a grid-energy storage digital twin is constructed in the real-time simulation unit. The twin includes a dynamic impedance network model and a virtual grid-type energy storage unit. Real-time data synchronization with the physical energy storage device controller is achieved through an error compensation algorithm. The dynamic impedance network model uses a frequency-varying parameter transmission line equation: ； in, Expressed as the grid impedance, Expressed as the real part of impedance resistance, Expressed as the imaginary reactance of impedance, Expressed as the amplitude of the kth frequency-varying component, It represents the time constant of the kth frequency-varying component, f represents the frequency, k represents the index number of the frequency-varying component, and j represents the imaginary unit; The virtual grid-type energy storage unit is constructed based on a multi-time-scale state-space model. It uses the IGBT loss-thermal coupling equation to simulate the switching dynamics of power devices at the millisecond level and executes the virtual synchronous machine rotor motion equation at the second level: ； in, Expressed as the rotor angular acceleration, is the rotor angular velocity, H is the inertia constant, Expressed as the input mechanical torque, Expressed as the output electromagnetic torque, Expressed as the damping coefficient, Expressed as angular velocity deviation; At the same time, the integrated control instruction precompiler converts the PWM signal output by the physical controller into an equivalent voltage source that can be analyzed by simulation; Impedance adaptive hardware coupling module: connects the physical energy storage device to the simulation loop via the power amplifier, and collects the impedance characteristics of the device output in real time; dynamically adjusts the damping compensation parameters of the power amplifier based on the impedance scan results; In the impedance adaptive hardware coupling module, the physical energy storage device is connected to the real-time simulation loop through the power amplifier through the interface to build a hardware-in-the-loop test system, and the impedance characteristics of the device output end are collected in real time and the system parameters are dynamically adjusted; The ratio curve of the physical device output impedance to the grid equivalent impedance is calculated using a frequency domain analysis method. A closed-loop analysis of the impedance ratio trajectory is performed using the Nyquist stability criterion. When the trajectory approaches the (-1, j0) point and the phase margin is less than 30 degrees, a risk of mutual resonance is determined. For the identified resonance point, the module automatically activates a multi-stage compensation mechanism. In the low-frequency band below 500Hz, a passive damping strategy is adopted by adjusting the output filter cutoff frequency of the power amplifier. In the mid-frequency band of 500-1500Hz, an active damper is enabled to dynamically generate a reverse compensation current based on the resonance point frequency. The specific calculation method is as follows: ； in, represents the reverse compensation current generated for the active damper, Expressed as the damping gain coefficient, Expressed as PCC point voltage, Expressed as the rate of change of the voltage at the PCC point; Network control deep verification module: Injects preset weak grid conditions into the digital twin to activate the network control strategy of the physical equipment; synchronously adjusts the inertia simulation coefficient of the virtual energy storage unit and the droop control parameters of the physical equipment to generate bidirectional parameter interaction instructions; In the grid control deep verification module, a closed-loop test environment with virtual-real interaction is established to verify the control performance of the grid-connected energy storage system under extremely weak grid conditions. This module first presets two typical weak grid test scenarios in the digital twin. During the test, the module synchronously coordinates the interaction of control parameters of the virtual and physical systems: the virtual energy storage unit adjusts the inertia simulation coefficient in real time. Its adjustment algorithm is designed based on Lyapunov stability theory. The specific calculation method is as follows: ； in, Expressed as the real-time inertia simulation coefficient of the virtual energy storage unit, Expressed as the initial inertia value, Expressed as the adaptive gain coefficient, Frequency deviation; At the same time, the calculation method of the droop control parameters on the physical device side is as follows: ； in, Expressed as the real-time droop control parameter on the physical device side, Expressed as the reference droop reference value, Expressed as the change sensitivity factor, Expressed as the change in short-circuit capacity ratio; Multi-dimensional performance evaluation module: Calculates the active power response rate of physical equipment under frequency disturbances; analyzes the phase margin deviation of virtual-physical dual-terminal data at impedance mutation points to determine the control stability boundary; The multi-dimensional performance evaluation module constructs a three-dimensional evaluation system including dynamic response, stability margin and energy characteristics to comprehensively and quantitatively evaluate the performance of the grid-type energy storage system. In the dynamic response evaluation dimension, the module monitors the active power change rate of the physical equipment under frequency disturbance in real time. The calculation adopts the least squares method to fit the slope of the power-time curve to meet the dynamic response benchmark of the active power change rate greater than 10% of the rated power of the equipment per second. At the same time, the response time difference between the virtual model and the physical equipment is collected to establish a dynamic consistency index. The calculation method is as follows: ； in, Expressed as a dynamic consistency indicator, Indicates the actual response time, Expressed as the base time constant, The value is 20ms; Iterative optimization module: When the phase margin deviation is greater than 15°, the virtual inertia coefficient is reconstructed based on the Lyapunov function, and the network control depth verification module and the multi-dimensional performance evaluation module are repeated until the stability threshold is met; In the iterative optimization module, during the iterative optimization phase, when the phase margin deviation is detected to be greater than 15°, the system automatically triggers a parameter adjustment mechanism based on the Lyapunov stability theory. First, a Lyapunov function containing a phase margin error term and a virtual inertia offset term is constructed. The calculation method is as follows: ； in, Expressed as a Lyapunov function, Phase margin tracking error, g is expressed as the actual phase, Expressed as a weight factor, Expressed as virtual inertia update amount; By solving the derivative of the Lyapunov function The negative definite condition of is used to derive the update law of virtual inertia, and the specific calculation method is: ； in, It is represented by the virtual inertia update amount, m is represented by the convergence factor, Expressed as the partial derivative of the phase with respect to the virtual inertia, Expressed as phase margin tracking error.

2. A method for semi-physical simulation testing of a network-type energy storage, using a semi-physical simulation testing platform for a network-type energy storage according to claim 1, characterized in that: Step A1: Construct a grid-energy storage digital twin in a real-time simulation unit. The digital twin includes a dynamic impedance network model and a virtual grid-based energy storage unit. The virtual grid-based energy storage unit synchronizes real-time data with the physical energy storage device controller through an error compensation algorithm. Step A2: Connect the physical energy storage device to the simulation loop via the power amplifier to collect the impedance characteristics of the device output in real time; dynamically adjust the damping compensation parameters of the power amplifier based on the impedance scan results; Step A3: Inject the preset weak grid condition into the digital twin to activate the grid-based control strategy of the physical equipment; Synchronously adjust the inertia simulation coefficient of the virtual energy storage unit and the droop control parameters of the physical device to generate two-way parameter interaction instructions; Step A4: Calculate the active power response rate of the physical device under frequency disturbance; analyze the phase margin deviation of the virtual-physical two-terminal data at the impedance mutation point to determine the control stability boundary; Step A5: When the phase margin deviation is greater than 15°, the virtual inertia coefficient is reconstructed based on the Lyapunov function, and the network control depth verification module to the multi-dimensional performance evaluation module is repeated until the stability threshold is met.

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