Energy storage system comprehensive adaptability evaluation method, device and equipment

Abstract

This invention provides a method, apparatus, and equipment for comprehensive adaptability evaluation of energy storage systems, relating to the field of power grid technology. The method includes: executing a frequency adaptability test of the energy storage system through a constructed hardware test loop to generate dynamic data of the energy storage system's frequency response characteristics; converting the frequency response characteristics in the dynamic data into voltage disturbance parameters; driving a simulated power grid device to execute a voltage adaptability test of the energy storage system based on the voltage disturbance parameters to obtain the test results of the voltage recovery curve; extracting the steady-state operating point from the voltage recovery curve based on the test results; executing a power quality adaptability test at the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indices; dynamically setting trigger threshold boundaries for high-voltage or low-voltage ride-through tests; and sequentially executing high-voltage ride-through capability tests and low-voltage ride-through capability tests according to the trigger threshold boundaries. The solution of this invention can realistically simulate the operating environment of an energy storage system in an actual power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method, apparatus and equipment for comprehensive adaptability assessment of energy storage systems. Background Technology

[0002] In integrated power generation, grid, load, and energy storage systems, power sources, grids, loads, and energy storage devices are deeply integrated to achieve efficient energy utilization and flexible allocation through coordinated operation. However, this complex system architecture places higher demands on grid stability, power quality, and equipment adaptability. As a key regulating component, the energy storage system's frequency adaptability, voltage adaptability, power quality adaptability, and high / low voltage ride-through capability directly affect the safe and reliable operation of the entire power grid system.

[0003] Existing power grid testing methods are mostly designed for single devices or simple systems, lacking a comprehensive consideration of the overall performance of energy storage systems in integrated regional power grids. For example, traditional testing methods sometimes conduct frequency, voltage, or power quality tests independently, thus failing to fully consider the correlation and mutual influence between various test items. In terms of high and low voltage ride-through capability testing, the trigger threshold setting lacks dynamism, making it difficult to accurately simulate the complex and ever-changing operating conditions of the actual power grid. These shortcomings result in existing test results failing to truly reflect the actual operating performance of energy storage systems in integrated regional power grids, leading to testing blind spots and evaluation biases. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, device and equipment for comprehensive adaptability evaluation of energy storage systems, which can realistically simulate the operating environment of energy storage systems in actual power grids.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] This invention provides a comprehensive adaptability assessment method for energy storage systems, applicable to integrated source-grid-load-storage systems. The method includes:

[0007] A hardware test circuit is constructed by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test.

[0008] Based on the hardware test loop, frequency adaptability tests of the energy storage system are performed to generate dynamic data containing the frequency response characteristics of the energy storage system.

[0009] The frequency response characteristics in the dynamic data are converted into voltage disturbance parameters. Based on the voltage disturbance parameters, the simulated power grid device is driven to perform voltage adaptability tests of the energy storage system, and test results including voltage recovery curves are obtained.

[0010] Based on the test results, the steady-state operating point in the voltage recovery curve is extracted, and the power quality adaptability test is performed at the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indicators.

[0011] Based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, the trigger threshold boundary for high voltage or low voltage ride-through test is dynamically set, and the high voltage ride-through capability test and low voltage ride-through capability test are executed sequentially according to the trigger threshold boundary.

[0012] Optionally, a hardware test circuit is constructed by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test, including:

[0013] Identify the rated voltage and current parameters on the high-voltage side of the step-up transformer of the energy storage system under test;

[0014] Configure the voltage level matching parameters and current carrying capacity parameters of the simulated power grid device according to the rated voltage and current parameters.

[0015] Based on voltage level matching parameters and current carrying capacity parameters, the receiving end of the simulated power grid device is physically connected in series with the original power grid access point on the high-voltage side of the step-up transformer via a cable to form a unidirectional current transmission path.

[0016] By utilizing a unidirectional current transmission path, a white noise test signal with preset spectral characteristics is injected into the energy storage system under test.

[0017] The feedback waveform of the white noise test signal flowing through the high-voltage side is collected, the spectral similarity between the feedback waveform and the original injected signal is calculated, and a hardware test circuit is constructed based on the spectral similarity.

[0018] Optionally, based on a hardware test loop, a frequency adaptability test of the energy storage system is performed to generate dynamic data containing the frequency response characteristics of the energy storage system, including:

[0019] When the hardware test circuit is in steady-state operation, the simulated power grid device is controlled to obtain a set of frequency disturbance signal sequences that are increased at equal intervals. The frequency disturbance signal sequence covers the frequency operating range specified by the target power grid standard.

[0020] Real-time acquisition of power command tracking curves of energy storage converters in the tested energy storage system;

[0021] Based on the power command tracking curve, determine the dynamic response curve of active power and the actual measured value curve of the energy storage system frequency;

[0022] By comparing the dynamic response curve and the actual measured value curve, the frequency-power regulation slope and regulation dead time are obtained;

[0023] The timing for triggering a step disturbance is set based on the dead time adjustment.

[0024] When the control simulation grid device is running in steady state in the energy storage system, frequency step jumps exceeding the dead zone threshold are applied to the high-frequency and low-frequency directions respectively to generate frequency step response time characteristic parameters.

[0025] Based on the frequency step response time characteristic parameters, dynamic data containing the frequency response characteristics of the energy storage system is constructed.

[0026] Optionally, the frequency response characteristics in the dynamic data are converted into voltage disturbance parameters. Based on these parameters, the simulated grid device is driven to perform voltage adaptability tests on the energy storage system, yielding test results including voltage recovery curves, such as:

[0027] Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the equivalent grid inertia support strength index and active power regulation rate limit of the energy storage system.

[0028] Based on the equivalent power grid inertia support strength index, the characteristic value of power grid impedance fluctuation is mapped;

[0029] Based on the characteristic value of grid impedance fluctuation, and combined with the active power regulation rate limit, the threshold of voltage disturbance amplitude change rate is derived.

[0030] Based on the voltage disturbance amplitude change rate threshold, multiple sets of voltage disturbance sequences with different drop or rise gradient characteristics are generated, and the sequences cover the voltage transient range specified by the target power grid standard.

[0031] The simulated power grid device is controlled to inject the voltage disturbance sequence sequentially under the steady-state operation of the energy storage system to obtain the voltage waveform after disturbance;

[0032] The voltage waveform after each injected disturbance is segmented into transient processes, and the time history curve of the voltage recovering from the disturbance start point to the steady-state allowable deviation band is extracted to form a set of voltage recovery curves containing amplitude-time relationship.

[0033] Based on the voltage recovery curves corresponding to all voltage disturbance sequences, multidimensional test results characterizing the voltage adaptability of the energy storage system are generated.

[0034] Optionally, based on the test results, the steady-state operating point is extracted from the voltage recovery curve. A power quality adaptability test is then performed at the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indices, including:

[0035] From the set of voltage recovery curves, identify the first stable time interval after each curve enters the steady-state allowable deviation band;

[0036] Within the stable time interval, the sliding window variance detection method is used to screen the operating points where the voltage fluctuations continuously meet the national standard steady-state deviation requirements, which are then used as the effective steady-state operating point set.

[0037] The control simulation power grid device locks the voltage amplitude corresponding to the effective steady-state operating point set and maintains constant voltage to obtain the mode;

[0038] In constant voltage mode, the source and load sides of the tested energy storage system are simultaneously started to operate at full power, and voltage and current waveform data at the grid connection point are continuously collected.

[0039] Based on voltage and current waveform data, the voltage fluctuation rate within a preset interval is calculated to obtain quantified harmonic distortion rate and voltage fluctuation rate indices.

[0040] Optionally, based on quantified harmonic distortion rate and voltage fluctuation rate indices, the trigger threshold boundaries for high-voltage or low-voltage ride-through tests are dynamically set, and based on the trigger threshold boundaries, including:

[0041] When the quantized harmonic distortion rate exceeds the target power grid standard limit, calculate the margin correction amount of the voltage trigger threshold boundary;

[0042] Based on the statistical distribution characteristics of the voltage fluctuation rate index and the margin correction, the trigger thresholds for high voltage ride-through tests and low voltage ride-through tests are calculated.

[0043] Optional, dynamic data on the frequency response characteristics of the energy storage system, including:

[0044] Full-band power regulation sensitivity distribution diagram, K value and dead time under continuous ramp disturbance, regulation delay time and energy storage system recovery time under step disturbance.

[0045] Embodiments of the present invention also propose a comprehensive adaptability evaluation device for energy storage systems, comprising:

[0046] The acquisition module is used to construct a hardware test circuit by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test.

[0047] The processing module is used to perform frequency adaptability tests on the energy storage system based on a hardware test loop, generating dynamic data containing the frequency response characteristics of the energy storage system; converting the frequency response characteristics in the dynamic data into voltage disturbance parameters, driving the simulated grid device to perform voltage adaptability tests on the energy storage system according to the voltage disturbance parameters, and obtaining test results including voltage recovery curves; extracting the steady-state operating point from the voltage recovery curve based on the test results, performing power quality adaptability tests at the steady-state operating point, and obtaining quantified harmonic distortion rate and voltage fluctuation rate indices; dynamically setting the trigger threshold boundaries for high-voltage or low-voltage ride-through tests based on the quantified harmonic distortion rate and voltage fluctuation rate indices, and sequentially performing high-voltage ride-through capability tests and low-voltage ride-through capability tests according to the trigger threshold boundaries.

[0048] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when run by the processor, executes the method described above.

[0049] Embodiments of the present invention also provide a computer-readable storage medium comprising: storage instructions that, when executed on a computer, cause the computer to perform the method described above.

[0050] The above-described solution of the present invention has at least the following beneficial effects:

[0051] The comprehensive adaptability evaluation method for energy storage systems described in this invention constructs a hardware test loop by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test. Based on the hardware test loop, a frequency adaptability test of the energy storage system is performed, generating dynamic data containing the frequency response characteristics of the energy storage system. The frequency response characteristics in the dynamic data are converted into voltage disturbance parameters, and the simulated power grid device is driven to perform a voltage adaptability test of the energy storage system according to the voltage disturbance parameters, obtaining test results including a voltage recovery curve. Based on the test results, the steady-state operating point in the voltage recovery curve is extracted, and a power quality adaptability test is performed at the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indices. Based on the quantified harmonic distortion rate and voltage fluctuation rate indices, the trigger threshold boundary for high-voltage or low-voltage ride-through tests is dynamically set, and high-voltage ride-through capability tests and low-voltage ride-through capability tests are performed sequentially according to the trigger threshold boundary. This method can realistically simulate the operating environment of the energy storage system in the actual power grid, ensuring the authenticity and reliability of the test data. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the comprehensive adaptability assessment method for energy storage systems according to the present invention.

[0053] Figure 2 This is a schematic diagram of the modules of the energy storage system comprehensive adaptability evaluation device of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0055] like Figure 1 As shown, embodiments of the present invention propose a comprehensive adaptability evaluation method for energy storage systems, applied to integrated source-grid-load-storage systems. The method includes:

[0056] Step 11: Construct a hardware test circuit by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test;

[0057] Step 12: Based on the hardware test loop, perform frequency adaptability testing on the energy storage system to generate dynamic data containing the frequency response characteristics of the energy storage system;

[0058] Step 13: Convert the frequency response characteristics in the dynamic data into voltage disturbance parameters, and drive the simulated grid device to perform voltage adaptability test of the energy storage system based on the voltage disturbance parameters to obtain test results including voltage recovery curves;

[0059] Step 14: Based on the test results, extract the steady-state operating point from the voltage recovery curve, and perform a power quality adaptability test at the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indices.

[0060] Step 15: Based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, dynamically set the trigger threshold boundary for high voltage or low voltage ride-through test, and execute the high voltage ride-through capability test and low voltage ride-through capability test in sequence according to the trigger threshold boundary.

[0061] In this embodiment, a hardware test circuit is constructed by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test. This allows for a realistic simulation of the operating environment of the energy storage system in the actual power grid, ensuring the authenticity and reliability of the test data. During the test process, frequency adaptability testing, voltage adaptability testing, power quality adaptability testing, and high and low voltage ride-through capability testing are sequentially conducted based on the hardware test circuit. Each test stage is closely related and progressively advanced. Converting frequency response characteristics into voltage disturbance parameters for voltage adaptability testing breaks the limitations of traditional independent testing projects, fully considering the mutual influence between frequency and voltage characteristics, and achieving a systematic evaluation of the energy storage system's performance. By extracting the steady-state operating point of the voltage recovery curve for power quality adaptability testing, the power quality indicators of the energy storage system under stable operating conditions can be accurately captured, obtaining quantified harmonic distortion rate and voltage fluctuation rate indicators.

[0062] The trigger threshold boundaries for high and low voltage ride-through tests are dynamically set based on quantified harmonic distortion rate and voltage fluctuation rate indicators, making the trigger threshold more consistent with the actual power grid operating conditions. This effectively avoids the evaluation bias caused by traditional fixed threshold tests and greatly improves the accuracy of high and low voltage ride-through capability tests.

[0063] In an optional embodiment of the present invention, step 11, constructing a hardware test circuit by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test, includes:

[0064] Step 111: Identify the rated voltage and current parameters of the high-voltage side of the step-up transformer of the energy storage system under test. The rated voltage and current parameters of the high-voltage side of the step-up transformer of the energy storage system under test can be obtained directly from the equipment nameplate, factory technical documents or system design drawings.

[0065] Step 112: Configure the voltage level matching parameters and current carrying capacity parameters of the simulated power grid device according to the rated voltage and current parameters;

[0066] Step 113: Based on the voltage level matching parameters and current carrying capacity parameters, the obtained end of the simulated power grid device is physically connected in series with the original power grid access point on the high voltage side of the step-up transformer through a cable to form a unidirectional current transmission path.

[0067] Step 114: Using a unidirectional current transmission path, inject a white noise test signal with preset spectral characteristics into the energy storage system under test.

[0068] Step 115: Acquire the feedback waveform of the white noise test signal when it flows through the high-voltage side, calculate the spectral similarity between the feedback waveform and the original injected signal, and construct a hardware test circuit based on the spectral similarity.

[0069] In this embodiment, the output voltage range of the simulated power grid device in step 112 needs to cover the rated voltage of the high-voltage side of the step-up transformer. For example, if the rated voltage is 10kV, the voltage regulation range of the device usually needs to be set to no less than 8kV to 12kV to ensure a 20% margin above and below the rated value to meet the voltage fluctuation requirements during testing. Current carrying capacity parameter configuration: The current carrying capacity of the simulated power grid device needs to be configured according to 1.5 times the rated current of the step-up transformer. For example, when the rated current is 500A, the current capacity of the device should be at least 750A to cope with the short-term overload situation that may occur during the test and ensure equipment safety.

[0070] In this embodiment, step 113 specifically involves selecting the cable specification based on the current carrying capacity of the simulated power grid device. For example, if the maximum current carrying capacity of the device is 750A, a copper core cable with a current carrying capacity of not less than 750A should be selected (usually referring to the current carrying capacity table provided by the cable manufacturer, considering factors such as ambient temperature and laying method). One end of the cable is connected to the output terminal of the simulated power grid device, and the other end is connected to the original power grid access point on the high-voltage side of the step-up transformer, ensuring a firm physical connection and avoiding excessive contact resistance that could affect the unidirectional current transmission. In step 114, the white noise test signal is a signal with uniform energy distribution over a wide frequency range. The spectral characteristics of its output signal are set using a signal generation device (such as an arbitrary waveform generator) to ensure uniform power distribution within a specified frequency range (e.g., 50Hz ± 20%). After signal generation, it is injected into the tested energy storage system through a unidirectional current transmission path to simulate complex interference signals in the actual power grid.

[0071] In this embodiment, the feedback waveform acquisition in step 115 specifically involves using a high-precision oscilloscope or data acquisition equipment to acquire the voltage and current waves after the white noise signal passes through the high-voltage side of the step-up transformer in real time; spectrum similarity analysis involves comparing the acquired feedback waveform with the original injected white noise signal to observe whether the energy distribution of the two at each frequency point is consistent; for example, if the energy proportion of the original signal at 100Hz is 10%, the energy proportion of the feedback signal at that frequency point should not be less than 9%. The integrity of the loop is judged by qualitatively analyzing the consistency of the spectrum shape. When the difference in spectrum distribution is less than 10%, the hardware test loop is considered to be successfully constructed.

[0072] This invention ensures a high degree of electrical matching between the simulated power grid device and the system under test by identifying rated voltage and current and configuring device parameters, avoiding test errors caused by parameter incompatibility. The scientific selection of cable specifications and physical series connection methods form a stable current transmission path, reducing problems such as poor contact or cable overheating, and ensuring the safety of the testing process. Injecting white noise signals and analyzing the feedback spectrum allows for direct verification of the test circuit's transmission capability for signals of different frequencies, ensuring the authenticity of signal transmission in subsequent tests. This method, through quantitative spectral similarity evaluation, can identify potential problems in circuit connections or parameter configurations in real time, facilitating timely adjustments and improving the test system's ability to simulate complex power grid conditions.

[0073] In an optional embodiment of the present invention, step 12, based on a hardware test loop, performs a frequency adaptability test on the energy storage system to generate dynamic data containing the frequency response characteristics of the energy storage system, including:

[0074] Step 121: When the hardware test circuit is in steady-state operation, control the simulated power grid device to obtain a set of frequency disturbance signal sequences that are increased at equal intervals. The frequency disturbance signal sequence covers the frequency operating range specified by the target power grid standard. Real-time acquisition of the power command tracking curve of the energy storage converter in the tested energy storage system.

[0075] Step 122: Based on the power command tracking curve, determine the dynamic response curve of active power and the actual measured value curve of the energy storage system frequency; by comparing the dynamic response curve and the actual measured value curve, obtain the frequency-power regulation slope and regulation dead time.

[0076] Step 123: Based on the adjustment dead time, set the triggering timing of the step disturbance; control the simulated grid device to apply frequency step changes exceeding the dead time threshold to the high-frequency direction and the low-frequency direction respectively when the energy storage system is in steady state operation, so as to generate frequency step response time characteristic parameters.

[0077] Step 124: Construct dynamic data containing the frequency response characteristics of the energy storage system based on the frequency step response time characteristic parameters.

[0078] In this embodiment, after the hardware test circuit is running stably, step 121 sets the frequency disturbance signal sequence generated by the simulated power grid device according to the target power grid standard (such as the national standard for the normal operating range of the power grid frequency is 50Hz±0.2Hz). Assuming an interval of 0.05Hz, a series of frequency signals are output sequentially, increasing from 49.8Hz to 50.2Hz. During the output of each frequency signal, a high-precision power measurement device is used to acquire the power command tracking curve of the energy storage converter in the tested energy storage system in real time. During the acquisition process, it is necessary to ensure that the sampling frequency is high enough (e.g., more than 100 times per second) to completely record the trend of power command changes over time. Step 122 specifically involves analyzing the acquired power command tracking curve, extracting the active power values ​​at different times, and plotting the dynamic response curve of active power changing over time. Simultaneously, the actual frequency value of the energy storage system during operation is obtained through a frequency measurement device, and the actual frequency measurement value curve is plotted. Parameters are acquired, and the two curves are compared to observe the response of active power when the frequency changes. In the range where the frequency change is relatively smooth, multiple frequency-power corresponding points are selected, and the frequency-power adjustment slope is estimated by analyzing the changing trends of these points. The frequency range where the frequency changes but the active power does not respond significantly is identified; the duration of this range is the adjustment dead zone time.

[0079] In this embodiment, step 123 specifically involves selecting an appropriate moment to trigger a frequency step disturbance based on the adjustment dead time obtained in step 122. For example, when the energy storage system has been running stably for a period of time (e.g., 5 minutes) and the frequency is in a stable state, a step disturbance is prepared to be applied. The simulated grid device is controlled to first apply a frequency step change exceeding the dead time threshold (e.g., 0.3Hz) to the high-frequency direction (e.g., from 50Hz directly to 50.3Hz), and at the same time, a high-speed data acquisition device is used to record the energy storage system's response process to the change, including changes in parameters such as power and frequency, thereby generating frequency step response time characteristic parameters in the high-frequency direction. Similarly, a step change is applied to the low-frequency direction (e.g., from 50Hz to 49.7Hz), and the frequency step response time characteristic parameters in the low-frequency direction are recorded and generated. These characteristic parameters mainly include the time from the occurrence of the frequency change to the start of effective power adjustment in the energy storage system, the time to reach a stable adjustment state, etc.

[0080] In this embodiment, step 124 specifically involves integrating all the data obtained in steps 121 to 123, including frequency disturbance signal sequences, power command tracking curves, dynamic response curves, actual measurement curves, frequency-power regulation slopes, regulation dead time, and frequency step response time characteristic parameters in the high-frequency and low-frequency directions. These data are then organized into a complete dataset according to a certain format to construct dynamic data containing the frequency response characteristics of the energy storage system.

[0081] This invention comprehensively simulates various frequency changes that energy storage systems may encounter in actual power grids through equally spaced incremental disturbances and step abrupt changes covering the frequency range specified by the target power grid standard, thus fully evaluating their frequency response capabilities. By analyzing the power command tracking curve and the actual frequency measurement curve, key performance parameters such as frequency-power regulation slope and regulation dead time can be accurately obtained. The frequency step disturbance test simulates sudden frequency changes in the power grid, and the obtained frequency step response time characteristic parameters reflect the energy storage system's response speed and regulation capability to rapid frequency changes, making the test results closer to actual operating conditions. The constructed dynamic data records the frequency response characteristics of the energy storage system in detail, providing rich and accurate data support for subsequent optimization of control strategies and parameter adjustments, which helps improve the operational stability and reliability of the energy storage system in an integrated power grid.

[0082] In an optional embodiment of the present invention, step 13 converts the frequency response characteristics in the dynamic data into voltage disturbance parameters, drives the simulated power grid device to perform an energy storage system voltage adaptability test based on the voltage disturbance parameters, and obtains test results including a voltage recovery curve, including:

[0083] Step 131: Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the equivalent grid inertia support strength index and active power regulation rate limit of the energy storage system.

[0084] Step 132: Map the grid impedance fluctuation characteristic value according to the equivalent grid inertia support strength index; derive the voltage disturbance amplitude change rate threshold according to the grid impedance fluctuation characteristic value and the active power regulation rate limit.

[0085] Step 133: Based on the voltage disturbance amplitude change rate threshold, generate multiple sets of voltage disturbance sequences with different drop or rise gradient characteristics, the sequences covering the voltage transient range specified by the target power grid standard;

[0086] Step 134: Control the simulated power grid device to inject the voltage disturbance sequence sequentially under the steady-state operation of the energy storage system to obtain the disturbed voltage waveform;

[0087] Step 135: Perform transient process segmentation on the voltage waveform after each injected disturbance, extract the time history curve of the voltage recovering from the disturbance start point to the steady-state allowable deviation band, and form a set of voltage recovery curves containing amplitude-time relationship;

[0088] Step 136: Generate multidimensional test results characterizing the voltage adaptability of the energy storage system based on the voltage recovery curves corresponding to all voltage disturbance sequences.

[0089] In this embodiment, step 131 specifically involves extracting the frequency-power regulation slope from the dynamic data obtained in step 12. This slope reflects the energy storage system's ability to adjust active power when the frequency changes. Simultaneously, frequency step response time characteristic parameters are extracted, such as the time it takes for power regulation to begin after a frequency jump and the time to reach a stable regulation state. Based on the frequency-power regulation slope and the frequency step response time characteristic parameters, the energy storage system's response capability to grid frequency changes is comprehensively evaluated. For example, by analyzing the power change during the frequency step response time and combining it with the system capacity, the equivalent grid inertia support strength index of the energy storage system is estimated. This index reflects the energy storage system's ability to provide inertia support when the grid frequency changes. Based on the maximum rate of change of active power during the frequency change process, an active power regulation rate limit is determined, reflecting the upper limit of the energy storage system's speed in regulating active power.

[0090] In this example, a higher equivalent grid inertia support strength index in step 132 means a stronger ability of the energy storage system to suppress grid frequency fluctuations, and correspondingly, a smaller grid impedance fluctuation; conversely, a lower index indicates a larger fluctuation. Based on historical data and empirical relationships, the equivalent grid inertia support strength index is mapped to grid impedance fluctuation characteristic values, and a correspondence table between the two is established, such as assigning a high inertia support strength to a low impedance fluctuation characteristic value. Thresholds are derived, and combined with grid impedance fluctuation characteristic values ​​and active power regulation rate limits, considering the stability requirements of grid operation, the voltage change law of the grid under different impedance and power regulation conditions is analyzed to derive the voltage disturbance amplitude change rate threshold. For example, when the grid impedance fluctuation is large and the active power regulation rate is fast, the allowable voltage disturbance amplitude change rate is relatively small to ensure the safe and stable operation of the grid.

[0091] In this example, step 133 specifically involves designing multiple voltage disturbance sequences with different drop or rise gradient characteristics based on the derived voltage disturbance amplitude change rate threshold and the voltage transient range specified by the target power grid standard (e.g., voltage drop to 0.8-1.2 times the rated voltage). For example, using 0.05 times the rated voltage as the gradient, multiple voltage drop sequences are generated from the rated voltage dropping to 0.8 times the rated voltage, and multiple voltage rise sequences are generated from the rated voltage rising to 1.2 times the rated voltage. This ensures that these sequences can comprehensively cover the voltage transient changes that may occur in actual operation. The process involves several steps: Step 134 specifically involves, under steady-state operation of the energy storage system, controlling the simulated grid device to sequentially inject the voltage disturbance sequence generated in step 133 into the energy storage system according to a preset sequence. During the injection of each voltage disturbance sequence, a high-precision voltage measurement device is used to collect the voltage waveform of the energy storage system in real time, ensuring that the collected waveform data can accurately reflect the response of the energy storage system under voltage disturbances. Step 135 specifically involves analyzing the voltage waveform after each voltage disturbance injection, and determining the start and end points of the voltage transient process based on the characteristics of the voltage waveform changes. For example, the transient process is segmented by taking the moment when the voltage begins to deviate from the steady-state value as the starting point and the moment when the voltage recovers to within the allowable deviation range of the steady state (such as ±5% of the rated voltage) and remains stable as the ending point. The time history curve is extracted, and the voltage amplitude corresponding to each time point when the voltage recovers from the disturbance start point to within the allowable deviation range of the steady state is extracted during the transient process. The time history curve of the voltage amplitude changing with time is plotted, and the time history curves obtained after each voltage disturbance are summarized to form a set of voltage recovery curves containing the amplitude-time relationship. Step 36 specifically involves comprehensively analyzing the voltage recovery curves corresponding to all voltage disturbance sequences, extracting key parameters such as voltage recovery time and maximum voltage deviation during the recovery process, and integrating these parameters according to a certain logic to form multi-dimensional test results. For example, using different voltage disturbance sequences as dimensions, the voltage recovery time, maximum voltage deviation, and other parameters corresponding to each sequence are arranged and combined to intuitively demonstrate the adaptability of the energy storage system under different voltage disturbance conditions.

[0092] This invention establishes an intrinsic link between the frequency characteristics and voltage adaptability of an energy storage system by converting frequency response characteristics into voltage disturbance parameters, thus transitioning from frequency performance evaluation to voltage performance testing and making the testing more systematic. It derives voltage disturbance parameters based on the energy storage system's own performance indicators and generates a disturbance sequence covering the actual operating voltage transient range, accurately simulating various voltage fluctuations that may occur during grid operation, improving the authenticity and validity of the test results. By extracting key parameters from the voltage recovery curve, it generates multi-dimensional test results. This testing method comprehensively considers the energy storage system's response to voltage changes, helping to accurately assess the compatibility of the energy storage system with the grid.

[0093] In an optional embodiment of the present invention, step 14 involves extracting the steady-state operating point from the voltage recovery curve based on the test results, performing a power quality adaptability test at the steady-state operating point, and obtaining quantified harmonic distortion rate and voltage fluctuation rate indices, including:

[0094] Step 141: From the set of voltage recovery curves, identify the first stable time interval after each curve enters the steady-state allowable deviation band;

[0095] Step 142: Within the stable time interval, the sliding window variance detection method is used to screen the operating points where the voltage fluctuation continuously meets the national standard steady-state deviation requirements, and these points are used as the effective steady-state operating point set.

[0096] Step 143: Control the simulated power grid device to lock the voltage amplitude corresponding to the effective steady-state operating point set, and maintain constant voltage to obtain the mode;

[0097] Step 144: In constant voltage mode, the source and load sides of the tested energy storage system are simultaneously started to operate at full power, and voltage and current waveform data at the grid connection point are continuously collected.

[0098] Step 145: Based on voltage and current waveform data, calculate the voltage fluctuation rate within a preset interval to obtain quantified harmonic distortion rate and voltage fluctuation rate indices.

[0099] In this embodiment, step 141 specifically involves traversing the set of voltage recovery curves. For each curve, based on the steady-state allowable deviation range (e.g., ±5% of the rated voltage), the voltage amplitude is checked point by point starting from the curve's starting point. When the voltage amplitude is within the steady-state allowable deviation range for the first time at multiple consecutive sampling points (e.g., 10 sampling points), the start and end times of this interval are recorded. This interval is the first stable time interval after entering steady state. For example, if a curve's voltage first meets the deviation requirement at t=10s and continues until t=12s, then the stable time interval of this curve is [10s, 12s]. Step 142 specifically involves setting a fixed-duration sliding window (e.g., within the stable time interval) within the stable time interval. Step 143 involves iterating through the voltage data within each window (100ms) sequentially. The variance of the voltage amplitude within each sliding window is calculated; this variance reflects the severity of voltage fluctuations. The variance of each window is compared with the steady-state voltage deviation fluctuation threshold specified in the national standard. If the variance of multiple consecutive windows (e.g., 5 windows) is less than the threshold, the operating point of that window is considered to meet the steady-state requirements and is included in the effective steady-state operating point set. For example, if the variance of a window is 0.02, which is less than the national standard threshold of 0.03, the operating point corresponding to that window is retained. Step 143 specifically involves selecting representative operating points (e.g., operating points corresponding to intermediate moments) from the effective steady-state operating point set and obtaining their corresponding voltage amplitudes. The output voltage is precisely adjusted to this amplitude using the control module of the simulated power grid device, and the constant voltage holding function is activated. The device continuously monitors the output voltage, and immediately compensates for any fluctuations through a feedback adjustment mechanism to ensure that the voltage amplitude remains stable within ±0.5% of the target value during the test. Step 144 specifically involves simultaneously starting the power supply and load sides of the tested energy storage system while the simulated power grid device maintains a constant voltage, bringing it to full-power operation. Using high-precision data acquisition equipment (such as an oscilloscope with a sampling frequency of 10kHz or higher), voltage and current waveform data are continuously collected at the grid connection point. During the acquisition process, it is ensured that the triggering conditions of the data acquisition equipment are synchronized with the constant voltage state of the simulated power grid device to avoid data loss or deviation. Data is continuously collected for at least 1 minute to ensure sufficient sample size.

[0100] In this example, step 145 specifically involves dividing the collected voltage waveform data into preset time intervals (e.g., 1 second), calculating the difference between the maximum and minimum voltage amplitudes within each interval, and then dividing by the rated voltage to obtain the voltage fluctuation rate within that interval. For example, if the maximum voltage value within a 1-second interval is 390V, the minimum is 385V, and the rated voltage is 400V, then the voltage fluctuation rate for that interval is [(390-385) / 400]×100%. The harmonic distortion rate is calculated by performing a Fast Fourier Transform (FFT) on the collected current and voltage waveform data to separate each harmonic component. The square root of the sum of the squares of the amplitudes of each harmonic component is then divided by the amplitude of the fundamental component to obtain the harmonic distortion rate. Statistical analysis is performed on the voltage fluctuation rate and harmonic distortion rate within all preset intervals, and the average or maximum value is taken as the final quantitative indicator.

[0101] This invention identifies stable time intervals and combines them with sliding window variance detection to accurately screen out the operating points that are truly in steady-state operation from the voltage recovery curve, avoiding test errors caused by transient fluctuations and ensuring that subsequent tests are based on stable operating conditions. By starting the source and load sides to full power operation in constant voltage mode, it highly replicates the full-load operating state of the energy storage system in the actual power grid, making the collected data closer to the actual operating conditions and improving the reliability of the test results.

[0102] In an optional embodiment of the present invention, step 15 involves dynamically setting the trigger threshold boundary for high-voltage or low-voltage ride-through testing based on quantified harmonic distortion rate and voltage fluctuation rate indices, and, based on the trigger threshold boundary, including:

[0103] Step 151: When the quantized harmonic distortion rate exceeds the target power grid standard limit, calculate the margin correction amount of the voltage trigger threshold boundary.

[0104] Step 152: Calculate the trigger thresholds for high-voltage ride-through test and low-voltage ride-through test based on the statistical distribution characteristics of the voltage fluctuation rate index and the margin correction amount.

[0105] In this embodiment, step 51 specifically involves comparing the quantized harmonic distortion rate obtained in step 14 with the target power grid standard limit. For example, if the target power grid standard specifies a harmonic distortion rate limit of 5%, and the calculated harmonic distortion rate is 6%, it is determined to exceed the standard limit. A margin correction is then calculated. Based on the degree to which the harmonic distortion rate exceeds the standard, combined with historical test data and engineering experience, the margin correction is determined. Generally, the greater the exceedance of the harmonic distortion rate, the larger the margin correction. For example, if the harmonic distortion rate exceeds the standard by 1%, the margin correction at the voltage trigger threshold boundary can be set to 3% of the rated voltage, referring to previous test results of similar equipment. If it exceeds the standard by 2%, the correction is increased to 5% of the rated voltage, thereby enhancing the test's adaptability to abnormal system conditions.

[0106] In this example, step 152 specifically involves statistically analyzing the voltage fluctuation rate index obtained in step 14, calculating its average, maximum, and minimum values. For example, if the average voltage fluctuation rate is 2%, the maximum is 5%, and the minimum is 1%, the overall level and extreme cases of system voltage fluctuation can be determined. The high-voltage ride-through test trigger threshold is adjusted based on the high-voltage ride-through trigger threshold specified in the target power grid standard, taking into account the maximum voltage fluctuation rate and the margin correction. If the standard high-voltage ride-through trigger threshold is 1.1 times the rated voltage, the maximum voltage fluctuation rate is 5%, and the margin correction is... If the voltage fluctuation rate is 3%, the trigger threshold will be increased to 1.18 times the rated voltage (1.1 + 0.05 + 0.03) to ensure that the energy storage system can still pass the test when the voltage fluctuates and harmonics exceed the standard. Similarly, the trigger threshold for the low voltage ride-through test will be adjusted downward based on the standard low voltage ride-through trigger threshold (such as 0.9 times the rated voltage) and the minimum voltage fluctuation rate and margin correction. If the minimum voltage fluctuation rate is 1% and the margin correction is 3%, the trigger threshold will be set to 0.86 times the rated voltage (0.9 - 0.01 - 0.03) to make the test more closely match the actual low voltage abnormal conditions of the power grid.

[0107] This invention adjusts the trigger thresholds for high and low voltage ride-through tests in real time based on harmonic distortion rate and voltage fluctuation rate, overcoming the limitations of traditional fixed threshold tests. This makes the test conditions more consistent with the complex and variable power quality conditions in actual power grid operation. By setting thresholds that consider the combined effects of harmonic exceedance and voltage fluctuations, the ride-through capability of energy storage systems under harsh power quality environments can be more rigorously tested. This ensures that energy storage devices can still operate stably when the grid voltage is abnormal, improving the overall reliability of the power grid. The threshold calculation method based on statistical characteristics and correction quantities avoids the one-sidedness of single-index evaluation, allowing the test results to more comprehensively reflect the performance of the energy storage system.

[0108] In an optional embodiment of the present invention, the dynamic data of the frequency response characteristics of the energy storage system includes:

[0109] Full-band power regulation sensitivity distribution diagram, K value and dead time under continuous ramp disturbance, regulation delay time and energy storage system recovery time under step disturbance.

[0110] In this embodiment, during the frequency adaptability test in step 12, the active power response values ​​of the energy storage system are recorded at different frequency points (such as 49.8Hz, 49.9Hz, 50Hz, 50.1Hz, 50.2Hz, etc.). For two adjacent frequency points, the ratio of the power change to the frequency change is calculated to obtain the power regulation sensitivity of that frequency range. For example, when the frequency changes from 49.9Hz to 50Hz, the power increases from 100kW to 120kW, then the sensitivity of that range is (120-10). 0) / (50-49.9)=200kW / Hz; With frequency as the horizontal axis and sensitivity as the vertical axis, mark the sensitivity values ​​of each frequency range on the coordinate system, and connect the points with a smooth curve to form a power regulation sensitivity distribution map covering the entire test frequency range; In the continuous ramp disturbance test (such as the frequency rising from 50Hz to 50.2Hz at a rate of 0.1Hz / s), record the slope of the linear segment of the power response curve. This slope is the K value, which represents the power regulation caused by a unit frequency change in the energy storage system. For example, if the power increases from 100kW to 140kW during a 0.2Hz frequency increase, then the value of K is (140-100) / 0.2 = 200kW / Hz; the dead time is determined by observing the time interval from when the frequency begins to change to when the power begins to respond. For example, if the frequency starts to rise at t=0s, but the power does not start to change until t=0.5s, then the dead time is 0.5s; when a frequency step disturbance (such as a sudden change from 50Hz to 50.3Hz) occurs, record the time interval from the moment the step occurs to when the power response reaches 10% of the steady-state value. For example, if the step occurs at t=0s and the power reaches 10% of the steady-state value at t=0.3s, then the adjustment delay time is 0.3s; record the time from the moment the step occurs to when the power response enters and remains within ±5% of the steady-state value. For example, if the power stabilizes within ±5% of the steady-state value after t=2.5s, then the recovery time is 2.5s.

[0111] This invention provides a full-band power regulation sensitivity distribution map that visually demonstrates the differences in regulation capabilities of an energy storage system across different frequency ranges, helping to analyze the system's adaptability throughout the entire frequency range. Parameters such as K-value, dead time, regulation delay time, and recovery time transform the frequency response characteristics of the energy storage system into quantifiable technical indicators, facilitating performance comparisons between different devices. These dynamic data provide key parameters for grid stability assessment. For example, the K-value can be used to calculate the system's frequency regulation effect, and the recovery time can assess the system's ability to recover from frequency disturbances, aiding grid dispatch decisions. Based on the dead time and regulation delay time, the control algorithm of the energy storage system can be optimized in a targeted manner to shorten the response time and improve the system's suppression effect on grid frequency fluctuations.

[0112] like Figure 2 As shown, embodiments of the present invention also provide a comprehensive adaptability evaluation device 20 for energy storage systems, characterized in that it includes:

[0113] The acquisition module 21 is used to construct a hardware test circuit by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test;

[0114] Processing module 22 is used to perform frequency adaptability testing of energy storage system based on hardware test loop, and generate dynamic data containing frequency response characteristics of energy storage system; convert the frequency response characteristics in dynamic data into voltage disturbance parameters, drive the simulated grid device to perform voltage adaptability testing of energy storage system according to voltage disturbance parameters, and obtain test results including voltage recovery curve; extract steady-state operating point from voltage recovery curve according to test results, perform power quality adaptability testing under steady-state operating point, and obtain quantified harmonic distortion rate and voltage fluctuation rate indicators; dynamically set the trigger threshold boundary of high voltage or low voltage ride-through test according to the quantified harmonic distortion rate and voltage fluctuation rate indicators, and perform high voltage ride-through capability test and low voltage ride-through capability test sequentially according to the trigger threshold boundary.

[0115] Optionally, a hardware test circuit is constructed by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test, including:

[0116] Identify the rated voltage and current parameters on the high-voltage side of the step-up transformer of the energy storage system under test;

[0117] Configure the voltage level matching parameters and current carrying capacity parameters of the simulated power grid device according to the rated voltage and current parameters.

[0118] Based on voltage level matching parameters and current carrying capacity parameters, the receiving end of the simulated power grid device is physically connected in series with the original power grid access point on the high-voltage side of the step-up transformer via a cable to form a unidirectional current transmission path.

[0119] By utilizing a unidirectional current transmission path, a white noise test signal with preset spectral characteristics is injected into the energy storage system under test.

[0120] The feedback waveform of the white noise test signal flowing through the high-voltage side is collected, the spectral similarity between the feedback waveform and the original injected signal is calculated, and a hardware test circuit is constructed based on the spectral similarity.

[0121] Optionally, based on a hardware test loop, a frequency adaptability test of the energy storage system is performed to generate dynamic data containing the frequency response characteristics of the energy storage system, including:

[0122] When the hardware test circuit is in steady-state operation, the simulated power grid device is controlled to obtain a set of frequency disturbance signal sequences that are increased at equal intervals. The frequency disturbance signal sequence covers the frequency operating range specified by the target power grid standard.

[0123] Real-time acquisition of power command tracking curves of energy storage converters in the tested energy storage system;

[0124] Based on the power command tracking curve, determine the dynamic response curve of active power and the actual measured value curve of the energy storage system frequency;

[0125] By comparing the dynamic response curve and the actual measured value curve, the frequency-power regulation slope and regulation dead time are obtained;

[0126] The timing for triggering a step disturbance is set based on the dead time adjustment.

[0127] When the control simulation grid device is running in steady state in the energy storage system, frequency step jumps exceeding the dead zone threshold are applied to the high-frequency and low-frequency directions respectively to generate frequency step response time characteristic parameters.

[0128] Based on the frequency step response time characteristic parameters, dynamic data containing the frequency response characteristics of the energy storage system is constructed.

[0129] Optionally, the frequency response characteristics in the dynamic data are converted into voltage disturbance parameters. Based on these parameters, the simulated grid device is driven to perform voltage adaptability tests on the energy storage system, yielding test results including voltage recovery curves, such as:

[0130] Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the equivalent grid inertia support strength index and active power regulation rate limit of the energy storage system.

[0131] Based on the equivalent power grid inertia support strength index, the characteristic value of power grid impedance fluctuation is mapped;

[0132] Based on the characteristic value of grid impedance fluctuation, and combined with the active power regulation rate limit, the threshold of voltage disturbance amplitude change rate is derived.

[0133] Based on the voltage disturbance amplitude change rate threshold, multiple sets of voltage disturbance sequences with different drop or rise gradient characteristics are generated, and the sequences cover the voltage transient range specified by the target power grid standard.

[0134] The simulated power grid device is controlled to inject the voltage disturbance sequence sequentially under the steady-state operation of the energy storage system to obtain the voltage waveform after disturbance;

[0135] The voltage waveform after each injected disturbance is segmented into transient processes, and the time history curve of the voltage recovering from the disturbance start point to the steady-state allowable deviation band is extracted to form a set of voltage recovery curves containing amplitude-time relationship.

[0136] Based on the voltage recovery curves corresponding to all voltage disturbance sequences, multidimensional test results characterizing the voltage adaptability of the energy storage system are generated.

[0137] Optionally, based on the test results, the steady-state operating point is extracted from the voltage recovery curve. A power quality adaptability test is then performed at the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indices, including:

[0138] From the set of voltage recovery curves, identify the first stable time interval after each curve enters the steady-state allowable deviation band;

[0139] Within the stable time interval, the sliding window variance detection method is used to screen the operating points where the voltage fluctuations continuously meet the national standard steady-state deviation requirements, which are then used as the effective steady-state operating point set.

[0140] The control simulation power grid device locks the voltage amplitude corresponding to the effective steady-state operating point set and maintains constant voltage to obtain the mode;

[0141] In constant voltage mode, the source and load sides of the tested energy storage system are simultaneously started to operate at full power, and voltage and current waveform data at the grid connection point are continuously collected.

[0142] Based on voltage and current waveform data, the voltage fluctuation rate within a preset interval is calculated to obtain quantified harmonic distortion rate and voltage fluctuation rate indices.

[0143] Optionally, based on quantified harmonic distortion rate and voltage fluctuation rate indices, the trigger threshold boundaries for high-voltage or low-voltage ride-through tests are dynamically set, and based on the trigger threshold boundaries, including:

[0144] When the quantized harmonic distortion rate exceeds the target power grid standard limit, calculate the margin correction amount of the voltage trigger threshold boundary;

[0145] Based on the statistical distribution characteristics of the voltage fluctuation rate index and the margin correction, the trigger thresholds for high voltage ride-through tests and low voltage ride-through tests are calculated.

[0146] Optional, dynamic data on the frequency response characteristics of the energy storage system, including:

[0147] Full-band power regulation sensitivity distribution diagram, K value and dead time under continuous ramp disturbance, regulation delay time and energy storage system recovery time under step disturbance.

[0148] It should be noted that this device is a device corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0149] Embodiments of the present invention also provide a computing device, including: a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described above.

[0150] Embodiments of the present invention also provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0151] 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.

[0152] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0153] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of 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.

[0154] 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.

[0155] In addition, 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.

[0156] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0157] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0158] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0159] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A comprehensive adaptability evaluation method for energy storage systems, characterized in that, Applied to an integrated power generation, grid, load, and storage system, the method includes: A hardware test circuit is constructed by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test. Based on a hardware test loop, frequency adaptability tests of the energy storage system are performed, generating dynamic data containing the frequency response characteristics of the energy storage system, including: When the hardware test circuit is in steady-state operation, the simulated power grid device is controlled to obtain a set of frequency disturbance signal sequences that are increased at equal intervals. The frequency disturbance signal sequence covers the frequency operating range specified by the target power grid standard. Real-time acquisition of power command tracking curves of energy storage converters in the tested energy storage system; Based on the power command tracking curve, determine the dynamic response curve of active power and the actual measured value curve of the energy storage system frequency; By comparing the dynamic response curve and the actual measured value curve, the frequency-power regulation slope and regulation dead time are obtained; The timing for triggering a step disturbance is set based on the dead time adjustment. When the control simulation grid device is running in steady state in the energy storage system, frequency step jumps exceeding the dead zone threshold are applied to the high-frequency and low-frequency directions respectively to generate frequency step response time characteristic parameters. Based on the frequency step response time characteristic parameters, dynamic data containing the frequency response characteristics of the energy storage system is constructed. Specifically, the frequency disturbance signal sequence, power command tracking curve, dynamic response curve, actual measurement curve, frequency-power regulation slope, regulation dead time, and frequency step response time characteristic parameters in the high-frequency and low-frequency directions are organized into a complete dataset according to a preset format to construct dynamic data containing the frequency response characteristics of the energy storage system. The frequency response characteristics in the dynamic data are converted into voltage disturbance parameters, and the voltage disturbance parameters are used to drive the simulated grid device to perform voltage adaptability tests on the energy storage system, obtaining test results including voltage recovery curves, including: Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the equivalent grid inertia support strength index and active power regulation rate limit of the energy storage system. Based on the equivalent power grid inertia support strength index, the characteristic value of power grid impedance fluctuation is mapped; Based on the characteristic value of grid impedance fluctuation, and combined with the active power regulation rate limit, the threshold of voltage disturbance amplitude change rate is derived. Based on the voltage disturbance amplitude change rate threshold, multiple sets of voltage disturbance sequences with different drop or rise gradient characteristics are generated, and the sequences cover the voltage transient range specified by the target power grid standard. The simulated power grid device is controlled to inject the voltage disturbance sequence sequentially under the steady-state operation of the energy storage system to obtain the voltage waveform after disturbance; The voltage waveform after each injected disturbance is segmented into transient processes, and the time history curve of the voltage recovering from the disturbance start point to the steady-state allowable deviation band is extracted to form a set of voltage recovery curves containing amplitude-time relationship. Based on the voltage recovery curves corresponding to all voltage disturbance sequences, multi-dimensional test results characterizing the voltage adaptability of the energy storage system are generated. Based on the test results, the steady-state operating point in the voltage recovery curve is extracted, and the power quality adaptability test is performed at the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indicators. Based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, the trigger threshold boundary for high voltage or low voltage ride-through test is dynamically set, and the high voltage ride-through capability test and low voltage ride-through capability test are executed sequentially according to the trigger threshold boundary.

2. The comprehensive adaptability evaluation method for energy storage systems according to claim 1, characterized in that, A hardware test circuit is constructed by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test, including: Identify the rated voltage and current parameters on the high-voltage side of the step-up transformer of the energy storage system under test; Configure the voltage level matching parameters and current carrying capacity parameters of the simulated power grid device according to the rated voltage and current parameters. Based on voltage level matching parameters and current carrying capacity parameters, the receiving end of the simulated power grid device is physically connected in series with the original power grid access point on the high-voltage side of the step-up transformer via a cable to form a unidirectional current transmission path. By utilizing a unidirectional current transmission path, a white noise test signal with preset spectral characteristics is injected into the energy storage system under test. The feedback waveform of the white noise test signal flowing through the high-voltage side is collected, the spectral similarity between the feedback waveform and the original injected signal is calculated, and a hardware test circuit is constructed based on the spectral similarity.

3. The comprehensive adaptability evaluation method for energy storage systems according to claim 1, characterized in that, Based on the test results, the steady-state operating point is extracted from the voltage recovery curve. Power quality adaptability testing is then performed at this steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indices, including: From the set of voltage recovery curves, identify the first stable time interval after each curve enters the steady-state allowable deviation band; Within the stable time interval, the sliding window variance detection method is used to screen the operating points where the voltage fluctuations continuously meet the national standard steady-state deviation requirements, which are then used as the effective steady-state operating point set. The control simulation power grid device locks the voltage amplitude corresponding to the effective steady-state operating point set and maintains constant voltage to obtain the mode; In constant voltage mode, the source and load sides of the tested energy storage system are simultaneously started to operate at full power, and voltage and current waveform data at the grid connection point are continuously collected. Based on voltage and current waveform data, the voltage fluctuation rate within a preset interval is calculated to obtain quantified harmonic distortion rate and voltage fluctuation rate indices.

4. The comprehensive adaptability evaluation method for energy storage systems according to claim 1, characterized in that, Based on quantified harmonic distortion rate and voltage fluctuation rate indices, the trigger threshold boundaries for high-voltage or low-voltage ride-through tests are dynamically set, including: When the quantized harmonic distortion rate exceeds the target power grid standard limit, calculate the margin correction amount of the voltage trigger threshold boundary; Based on the statistical distribution characteristics of the voltage fluctuation rate index and the margin correction, the trigger thresholds for high voltage ride-through tests and low voltage ride-through tests are calculated.

5. A comprehensive adaptability evaluation device for energy storage systems, characterized in that, include: The acquisition module is used to construct a hardware test circuit by connecting a simulated power grid device in series with the high-voltage side of the step-up transformer of the energy storage system under test. The processing module is used to perform frequency adaptability testing of the energy storage system based on a hardware test loop, generating dynamic data containing the frequency response characteristics of the energy storage system. This includes: controlling the simulated power grid device to obtain a set of equally spaced, incrementally increasing frequency disturbance signal sequences when the hardware test loop is in steady-state operation, the frequency disturbance signal sequences covering the frequency operating range specified by the target power grid standard; real-time acquisition of the power command tracking curve of the energy storage converter in the tested energy storage system; determining the dynamic response curve of active power and the actual measured value curve of the energy storage system frequency based on the power command tracking curve; obtaining the frequency-power regulation slope and regulation dead time by comparing the dynamic response curve and the actual measured value curve; and determining the regulation dead time based on the... The timing of the step disturbance trigger is set; the simulated grid device is controlled to apply frequency step changes exceeding the dead zone threshold in both the high-frequency and low-frequency directions during the steady-state operation of the energy storage system, generating frequency step response time characteristic parameters; based on these parameters, dynamic data containing the frequency response characteristics of the energy storage system is constructed; specifically, the frequency disturbance signal sequence, power command tracking curve, dynamic response curve, actual measurement curve, frequency-power regulation slope, regulation dead time, and frequency step response time characteristic parameters in the high-frequency and low-frequency directions are organized into a complete dataset according to a preset format to construct dynamic data containing the frequency response characteristics of the energy storage system; the frequency in the dynamic data is... The response characteristics are converted into voltage disturbance parameters. Based on these parameters, the simulated grid device is driven to perform a voltage adaptability test of the energy storage system, obtaining test results including a voltage recovery curve. This includes: analyzing the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data; calculating the equivalent grid inertia support strength index and active power regulation rate limit of the energy storage system; mapping grid impedance fluctuation characteristic values ​​based on the equivalent grid inertia support strength index; deriving the voltage disturbance amplitude change rate threshold based on the grid impedance fluctuation characteristic values ​​and the active power regulation rate limit; and generating multiple sets of voltage disturbance sequences with different drop or rise gradient characteristics based on the voltage disturbance amplitude change rate threshold. The system covers the voltage transient range specified by the target power grid standard; it controls the simulated power grid device to sequentially inject the voltage disturbance sequence under the steady-state operation state of the energy storage system, obtaining the voltage waveform after disturbance; it performs transient process segmentation on the voltage waveform after each disturbance injection, extracts the time history curve of the voltage recovering from the disturbance start point to the steady-state allowable deviation band, forming a set of voltage recovery curves containing amplitude-time relationship; based on the voltage recovery curves corresponding to all voltage disturbance sequences, it generates multi-dimensional test results characterizing the voltage adaptability of the energy storage system; based on the test results, it extracts the steady-state operating point from the voltage recovery curve, performs power quality adaptability test under the steady-state operating point, and obtains quantified harmonic distortion rate and voltage fluctuation rate indicators;Based on quantified harmonic distortion rate and voltage fluctuation rate indices, the trigger threshold boundaries for high-voltage or low-voltage ride-through tests are dynamically set, and high-voltage ride-through capability tests and low-voltage ride-through capability tests are executed sequentially according to the trigger threshold boundaries.

6. A computing device, characterized in that, include: A processor, a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, A storage instruction that, when executed on a computer, causes the computer to perform the method as described in any one of claims 1 to 4.

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