Method, device and equipment for evaluating comprehensive adaptability of energy storage system

Abstract

The invention provides a comprehensive adaptability evaluation method, device and equipment for an energy storage system, and relates to the technical field of power grids, and the method comprises the steps: executing a frequency adaptability test of the energy storage system through a constructed hardware test loop, and generating dynamic data of frequency response characteristics of the energy storage system; frequency response characteristics in the dynamic data are converted into voltage disturbance parameters, a simulation power grid device is driven to execute an energy storage system voltage adaptability test according to the voltage disturbance parameters, and a test result of a voltage recovery curve is obtained; extracting a steady-state operating point in the voltage recovery curve according to a test result, executing an electric energy quality adaptability test under the steady-state operating point to obtain quantized harmonic distortion rate and voltage volatility indexes, dynamically setting a trigger threshold boundary of a high-voltage or low-voltage ride-through test, and obtaining a high-voltage or low-voltage ride-through test according to the trigger threshold boundary. And the high-voltage ride-through capability test and the low-voltage ride-through capability test are executed in sequence. According to the scheme, the operation environment of the energy storage system in an actual power grid can be truly simulated.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a method, device and equipment for evaluating the comprehensive adaptability of an energy storage system. Background Art

[0002] In an integrated power-grid-load-storage system, power sources, grids, loads, and energy storage devices are deeply integrated, achieving efficient energy utilization and flexible allocation through coordinated operation. However, this complex system architecture places higher demands on grid stability, power quality, and device adaptability. As a key regulatory link, energy storage systems' frequency adaptability, voltage adaptability, power quality adaptability, and high and low voltage ride-through capabilities directly impact the safe and reliable operation of the entire power grid.

[0003] Existing grid testing methods mostly target single devices or simple systems, lacking comprehensive consideration of the comprehensive performance of energy storage systems in regional power grids with integrated power sources, grids, loads, and storage. For example, some traditional testing methods independently conduct frequency, voltage, or power quality tests, and therefore fail 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 dynamics, making it difficult to accurately simulate the complex and changeable operating conditions of actual power grids. These deficiencies result in the existing test results being unable to truly reflect the actual operating performance of energy storage systems in regional power grids with integrated power sources, grids, loads, and storage, resulting in testing blind spots and evaluation deviations. Summary of the Invention

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

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: The present invention provides a comprehensive adaptability evaluation method for an energy storage system, which is applied to a source-grid-load-storage integrated system. The method comprises: A hardware test loop is constructed by connecting a simulated grid device in series to the high-voltage side of the step-up transformer of the energy storage system being tested. Based on the hardware test loop, perform frequency adaptability testing of the energy storage system and generate dynamic data containing the frequency response characteristics of the energy storage system; Convert the frequency response characteristics in the dynamic data into voltage disturbance parameters. Use the voltage disturbance parameters to drive the simulated grid device to perform the energy storage system voltage adaptability test, and obtain test results including the voltage recovery curve. 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 under the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indicators; According to the quantified harmonic distortion rate and voltage fluctuation rate indicators, the trigger threshold boundary of the 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 performed in sequence according to the trigger threshold boundary.

[0006] Optionally, a simulated grid device is connected in series to the high-voltage side of the step-up transformer of the energy storage system being tested to construct a hardware test loop, including: Identify the rated voltage and current parameters of the high-voltage side of the step-up transformer of the energy storage system being tested; According to the rated voltage and current parameters, configure the voltage level matching parameters and current carrying capacity parameters of the simulated power grid device; Based on the voltage level matching parameters and current carrying capacity parameters, the obtained end of the simulated grid device is physically connected in series to the original grid access point on the high-voltage side of the step-up transformer through a cable to form a unidirectional current transmission path; Using a unidirectional current transmission path, a white noise test signal with a preset spectrum characteristic 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 injection signal is calculated, and a hardware test loop is constructed based on the spectral similarity.

[0007] Optionally, based on the hardware test loop, perform an energy storage system frequency adaptability test to generate dynamic data containing the energy storage system frequency response characteristics, including: When the hardware test loop is in a steady-state operation state, controlling the simulated power grid device to obtain a set of frequency disturbance signal sequences that increase at equal intervals, the frequency disturbance signal sequences covering the frequency operation range specified by the target power grid standard; Real-time acquisition of the power command tracking curve of the energy storage converter in the energy storage system being tested; According to the power command tracking curve, the dynamic response curve of active power and the actual measurement value curve of energy storage system frequency are determined; By comparing the dynamic response curve and the actual measurement value curve, the frequency-power adjustment slope and adjustment dead time are obtained; According to the adjustment dead time, set the step disturbance triggering time; Controlling the simulated grid device to apply frequency step mutations exceeding the dead zone threshold in the high-frequency direction and the low-frequency direction when the energy storage system is in steady-state operation, thereby generating frequency step response time characteristic parameters; According to the frequency step response time characteristic parameters, dynamic data including the frequency response characteristics of the energy storage system is constructed.

[0008] Optionally, the frequency response characteristics in the dynamic data are converted into voltage disturbance parameters, and a simulated grid device is driven according to the voltage disturbance parameters to perform a voltage adaptability test of the energy storage system, thereby obtaining test results including a voltage recovery curve, including: Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the energy storage system equivalent grid inertia support strength index and active power regulation rate limit; Mapping a grid impedance fluctuation characteristic value according to the equivalent grid inertia support strength index; Derived a voltage disturbance amplitude change rate threshold value based on the grid impedance fluctuation characteristic value and the active power regulation rate limit; Based on the voltage disturbance amplitude change rate threshold, generating multiple groups 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; Controlling the simulated power grid device to sequentially inject the voltage disturbance sequence under the steady-state operation of the energy storage system to obtain a voltage waveform after disturbance; The transient process of the voltage waveform after each disturbance injection is segmented, and the time history curve of the voltage recovery from the starting point of the disturbance to the steady-state allowable deviation band is extracted to form a set of voltage recovery curves containing the amplitude-time relationship; Based on the voltage recovery curves corresponding to all voltage disturbance sequences, multi-dimensional test results are generated to characterize the voltage adaptability of the energy storage system.

[0009] Optionally, based on the test results, a steady-state operating point is extracted from the voltage recovery curve, and a power quality adaptability test is performed under the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indicators, including: From the voltage recovery curve set, identify the first stable time interval after each curve enters the steady-state allowable deviation band; In the stable time interval, a sliding window variance detection method is used to select operating points whose voltage fluctuations continuously meet the national standard steady-state deviation requirements as a valid steady-state operating point set; Controlling the simulated power grid device to lock the voltage amplitude corresponding to the effective steady-state operating point set to maintain a constant voltage to obtain a mode; In constant voltage acquisition mode, the source and load sides of the energy storage system under test are simultaneously started to operate at full power, and the voltage and current waveform data of the grid connection point are continuously collected; Based on the 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 indicators.

[0010] Optionally, based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, the trigger threshold boundary of the high voltage or low voltage ride through test is dynamically set, and based on the trigger threshold boundary, the following are included: When the quantified harmonic distortion rate exceeds the target grid standard limit, the margin correction amount of the voltage trigger threshold boundary is calculated; The trigger thresholds of the high voltage ride through test and the low voltage ride through test are calculated based on the statistical distribution characteristics of the voltage fluctuation rate index and the margin correction amount.

[0011] Optional dynamic data on the frequency response characteristics of the energy storage system, including: Full-band power regulation sensitivity distribution diagram, K value and dead time under continuous ramp disturbance, and regulation delay time under step disturbance, and energy storage system recovery time.

[0012] An embodiment of the present invention further provides a device for evaluating the comprehensive adaptability of an energy storage system, comprising: An acquisition module is used to build a hardware test loop by connecting a simulated grid device in series to the high-voltage side of the step-up transformer of the energy storage system under test; The processing module is used to perform a frequency adaptability test of the energy storage system based on a hardware test loop, and generate dynamic data containing the frequency response characteristics of the energy storage system; convert the frequency response characteristics in the dynamic data into voltage disturbance parameters, and drive the simulated power grid device to perform the voltage adaptability test of the energy storage system according to the voltage disturbance parameters, and obtain test results including a voltage recovery curve; based on the test results, extract the steady-state operating point in the voltage recovery curve, and perform the power quality adaptability test under 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, dynamically set the trigger threshold boundary of the high voltage or low voltage ride-through test, and according to the trigger threshold boundary, sequentially perform the high voltage ride-through capability test and the low voltage ride-through capability test.

[0013] An embodiment of the present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the above-mentioned method when executed by the processor.

[0014] An embodiment of the present invention further provides a computer-readable storage medium, comprising: storing instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method.

[0015] The above solution of the present invention includes at least the following beneficial effects: The comprehensive adaptability assessment method for an energy storage system described in the present invention constructs a hardware test loop by connecting a simulated power grid device in series to the high-voltage side of a step-up transformer of a tested energy storage system; based on the hardware test loop, a frequency adaptability test of the energy storage system is performed to generate dynamic data including 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 according to the voltage disturbance parameters to perform a voltage adaptability test of the energy storage system to obtain a test result including a voltage recovery curve; based on the test results, a steady-state operating point in the voltage recovery curve is extracted, and a power quality adaptability test is performed under 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 of the high voltage or low voltage ride-through test is dynamically set, and according to the trigger threshold boundary, the high voltage ride-through capability test and the low voltage ride-through capability test are performed in sequence; the operating environment of the energy storage system in the actual power grid can be truly simulated to ensure the authenticity and reliability of the test data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the comprehensive adaptability evaluation method of the energy storage system of the present invention.

[0017] Figure 2 It is a module schematic diagram of the energy storage system comprehensive adaptability evaluation device of the present invention. DETAILED DESCRIPTION

[0018] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0019] like Figure 1 As shown, an embodiment of the present invention provides a comprehensive adaptability evaluation method for an energy storage system, which is applied to a source-grid-load-storage integrated system. The method includes: Step 11: construct a hardware test loop by connecting a simulated power grid device in series to the high-voltage side of the step-up transformer of the energy storage system under test; Step 12: Performing a frequency adaptability test of the energy storage system based on the hardware test loop to generate dynamic data including frequency response characteristics of the energy storage system; Step 13: Convert the frequency response characteristics in the dynamic data into voltage disturbance parameters, and drive the simulated grid device according to the voltage disturbance parameters to perform a voltage adaptability test of the energy storage system to obtain a test result including a voltage recovery curve; Step 14: extract the steady-state operating point from the voltage recovery curve based on the test results, perform a power quality adaptability test at the steady-state operating point, and obtain quantified harmonic distortion rate and voltage fluctuation rate indicators; Step 15: Dynamically set the trigger threshold boundary of the high voltage or low voltage ride through test according to the quantified harmonic distortion rate and voltage fluctuation rate indicators, and perform the high voltage ride through capability test and the low voltage ride through capability test in sequence according to the trigger threshold boundary.

[0020] In this embodiment, by connecting a simulated power grid device in series to the high-voltage side of the step-up transformer of the energy storage system being tested to construct a hardware test loop, the operating environment of the energy storage system in the actual power grid can be truly simulated, ensuring the authenticity and reliability of the test data; in the test process, frequency adaptability test, voltage adaptability test, power quality adaptability test and high and low voltage ride-through capability test are carried out in sequence based on the hardware test loop, and each test link is closely related and progressive; the frequency response characteristics are converted into voltage disturbance parameters to carry out voltage adaptability test, breaking the limitation of traditional test items being carried out independently, fully considering the mutual influence between frequency and voltage characteristics, and realizing a systematic evaluation of the performance of the energy storage system; by extracting the steady-state operating point of the voltage recovery curve for power quality adaptability test, it is possible to accurately capture the power quality indicators of the energy storage system under stable operation, and obtain quantified harmonic distortion rate and voltage fluctuation rate indicators; The trigger threshold boundaries of high and low voltage ride-through tests are dynamically set based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, making the trigger threshold more closely aligned with actual grid operating conditions. This effectively avoids the assessment bias caused by traditional fixed threshold testing and greatly improves the accuracy of high and low voltage ride-through capability tests.

[0021] In an optional embodiment of the present invention, step 11, by connecting a simulated power grid device in series to the high-voltage side of a step-up transformer of the energy storage system being tested, to construct a hardware test loop, includes: Step 111: Identify the rated voltage and current parameters of the high-voltage side of the step-up transformer of the energy storage system being tested. The rated voltage and current parameters of the high-voltage side of the step-up transformer of the energy storage system being tested can be directly obtained from the equipment nameplate, factory technical documents, or system design drawings. Step 112, configuring voltage level matching parameters and current carrying capacity parameters of the simulated power grid device according to the rated voltage and current parameters; Step 113: Based on the voltage level matching parameter and the current carrying capacity parameter, the obtained end of the simulated grid device is physically connected in series to the original grid access point on the high-voltage side of the step-up transformer through a cable to form a unidirectional current transmission path; Step 114: injecting a white noise test signal with a preset spectrum characteristic into the energy storage system under test using a unidirectional current transmission path; Step 115 , collecting the feedback waveform when the white noise test signal flows through the high voltage side, calculating the spectrum similarity between the feedback waveform and the original injection signal, and constructing a hardware test loop based on the spectrum similarity.

[0022] In this embodiment, the output voltage range of the simulated power grid device in step 112 must cover the rated voltage of the high-voltage side of the step-up transformer. For example, if the rated voltage is 10 kV, the voltage adjustment range of the device must generally be set to no less than 8 kV to 12 kV to ensure a 20% margin above and below the rated value to meet the voltage fluctuation requirements during the test. Current carrying capacity parameter configuration: The current carrying capacity of the simulated power grid device must be configured to 1.5 times the rated current of the step-up transformer. For example, if the rated current is 500 A, the current capacity of the device should be at least 750 A to cope with short-term overload conditions that may occur during the test and ensure equipment safety. In this embodiment, step 113 specifically involves selecting cable specifications based on the current-carrying capacity of the simulated power grid device. For example, if the device's maximum current carrying capacity is 750A, a copper-core cable with a current carrying capacity of at least 750A is required (usually referring to the current carrying capacity table provided by the cable manufacturer, taking into account factors such as ambient temperature and installation method). One end of the cable is connected to the output 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. This ensures a secure physical connection and prevents excessive contact resistance from affecting unidirectional current transmission. In step 114, the white noise test signal is a signal with uniform energy distribution over a wide frequency range. Using a signal generation device (such as an arbitrary waveform generator), the spectral characteristics of the output signal are configured to ensure uniform power distribution within a specified frequency range (e.g., 50Hz±20%). After the signal is generated, it is injected into the energy storage system being tested through the unidirectional current transmission path to simulate the complex interference signals found in an actual power grid.

[0023] In this embodiment, the feedback waveform acquisition in step 115 is specifically performed by using a high-precision oscilloscope or data acquisition equipment to collect the voltage and current waves after the white noise signal passes through the high-voltage side of the step-up transformer in real time; spectral similarity analysis is performed to compare the spectrum of the collected 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 100 Hz is 10%, the energy proportion of the feedback signal at this frequency point should be no less than 9%. The loop integrity is judged by qualitatively analyzing the consistency of the spectrum shape. When the spectrum distribution difference is less than 10%, it is considered that the hardware test loop is successfully constructed.

[0024] The present invention ensures a high degree of electrical matching between the simulated power grid device and the system under test through the identification of rated voltage and current and configuration of device parameters, thus avoiding test errors caused by parameter incompatibility; the scientific selection of cable specifications and the physical series connection method form a stable current transmission path, reduce problems such as poor contact or cable overheating, and ensure the safety of the test process; injecting white noise signals and analyzing the feedback spectrum can intuitively verify the transmission capability of the test loop for signals of different frequencies, ensuring the authenticity of signal transmission in subsequent tests. This method can detect potential problems in loop connection or parameter configuration in real time through quantitative spectrum similarity evaluation, facilitate timely adjustment, and enhance the test system's simulation capability for complex power grid conditions.

[0025] In an optional embodiment of the present invention, step 12, performing a frequency adaptability test of the energy storage system based on the hardware test loop to generate dynamic data including frequency response characteristics of the energy storage system, includes: Step 121, when the hardware test loop is in a steady-state operating state, controlling the simulated power grid device to obtain a set of frequency disturbance signal sequences that increase at equal intervals, wherein the frequency disturbance signal sequences cover the frequency operating range specified by the target power grid standard; and collecting a power command tracking curve of the energy storage converter in the energy storage system being tested in real time; Step 122: Determine the dynamic response curve of active power and the actual measured value curve of energy storage system frequency based on the power command tracking curve; and obtain the frequency-power regulation slope and regulation dead time by comparing the dynamic response curve and the actual measured value curve. Step 123: Setting a step disturbance triggering timing based on the adjusted dead time; controlling the simulated power grid device to apply a frequency step mutation exceeding the dead zone threshold in both the high-frequency and low-frequency directions when the energy storage system is in steady-state operation, thereby generating a frequency step response time characteristic parameter; Step 124 : constructing dynamic data including frequency response characteristics of the energy storage system according to the frequency step response time characteristic parameters.

[0026] In this embodiment, after the hardware test loop is running stably, step 121 sets the simulated grid device to generate a frequency disturbance signal sequence according to the target grid standard (eg, the national standard grid frequency normal operating range is 50 Hz ± 0.2 Hz). Assume that a series of frequency signals are output sequentially, starting from 49.8 Hz and increasing to 50.2 Hz, at intervals of 0.05 Hz. When each frequency signal is output, a high-precision power measurement device is used to collect, in real time, a power command tracking curve of the energy storage converter in the energy storage system being tested. During the collection process, the sampling frequency must be sufficiently high (e.g., more than 100 times per second) to fully record the changing trend of the power command over time. Step 122 specifically involves analyzing the collected power command tracking curve, extracting active power values ​​at different times, and plotting a dynamic response curve of the 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 a curve of the actual measured frequency value is plotted. Parameters are obtained, the two curves are compared, and the response of the active power to the frequency change is observed. Within a range where the frequency change is relatively gentle, multiple frequency-power corresponding points are selected. By analyzing the changing trend of these points, the frequency-power regulation slope is estimated. A frequency range where the frequency change but the active power does not significantly respond is identified. The duration of this range is the regulation dead time.

[0027] In this embodiment, step 123 specifically includes selecting an appropriate moment to trigger a frequency step disturbance based on the regulation dead-band time obtained in step 122. For example, when the energy storage system has been operating stably for a period of time (e.g., 5 minutes) and the frequency is in a stable state, preparing to apply a step disturbance; controlling the simulated power grid device to first apply a frequency step mutation exceeding the dead-band threshold (e.g., 0.3 Hz) in the high-frequency direction (e.g., directly from 50 Hz to 50.3 Hz), and simultaneously using a high-speed data acquisition device to record the energy storage system's response to the mutation, including changes in parameters such as power and frequency, to generate frequency step response time characteristic parameters in the high-frequency direction; similarly, applying a step mutation in the low-frequency direction (e.g., from 50 Hz to 49.7 Hz), recording and generating frequency step response time characteristic parameters in the low-frequency direction. These characteristic parameters mainly include the time from the occurrence of the frequency mutation to the start of effective power regulation and the time to reach a stable regulation state.

[0028] In this embodiment, step 124 specifically integrates all the data obtained in steps 121 to 123, including the frequency disturbance signal sequence, the power command tracking curve, the dynamic response curve, the actual measurement value curve, the frequency-power regulation slope, the regulation dead time, and the frequency step response time characteristic parameters in the high-frequency and low-frequency directions, and organizes these data into a complete data set according to a certain format, thereby constructing dynamic data containing the frequency response characteristics of the energy storage system.

[0029] The present invention can comprehensively simulate various frequency changes that the energy storage system may encounter in the actual power grid and fully evaluate its frequency response capability by conducting equally spaced incremental disturbance and step mutation tests covering the frequency range specified by the target power grid standard. By analyzing the power command tracking curve and the actual frequency measurement value curve, key performance parameters such as the frequency-power regulation slope and the regulation dead time can be accurately obtained. The frequency step disturbance test can simulate sudden frequency changes in the power grid, and the obtained frequency step response time characteristic parameters can reflect the response speed and regulation capability of the energy storage system to rapid frequency changes, making the test results closer to the 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 the subsequent control strategy optimization and parameter adjustment of the energy storage system, which is conducive to improving the operating stability and reliability of the energy storage system in the source-grid-load-storage integrated power grid.

[0030] In an optional embodiment of the present invention, step 13 converts the frequency response characteristics in the dynamic data into voltage disturbance parameters, and drives the simulated grid device according to the voltage disturbance parameters to perform the energy storage system voltage adaptability test, thereby obtaining a test result including a voltage recovery curve, including: Step 131: Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the energy storage system equivalent grid inertia support strength index and active power regulation rate limit; Step 132: mapping a grid impedance fluctuation characteristic value according to the equivalent grid inertia support strength index; and deriving a voltage disturbance amplitude change rate threshold value according to the grid impedance fluctuation characteristic value and in combination with the active power regulation rate limit. Step 133: generating a plurality of voltage disturbance sequences with different drop or rise gradient characteristics based on the voltage disturbance amplitude change rate threshold, wherein the sequences cover the voltage transient range specified by the target power grid standard; Step 134, controlling the simulated power grid device to sequentially inject the voltage disturbance sequence under the steady-state operation of the energy storage system to obtain a voltage waveform after disturbance; Step 135 , performing transient process segmentation on the voltage waveform after each disturbance injection, extracting a time history curve of the voltage recovering from the disturbance starting point to within the steady-state allowable deviation band, and forming a voltage recovery curve set including an amplitude-time relationship; Step 136 : Generate a multi-dimensional test result representing the voltage adaptability of the energy storage system based on the voltage recovery curves corresponding to all voltage disturbance sequences.

[0031] 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 during frequency changes. Frequency step response time characteristic parameters are also extracted, such as the time it takes for power regulation to begin after a frequency mutation and the time it takes to reach a stable regulation state. Based on the frequency-power regulation slope and frequency step response time characteristic parameters, a comprehensive assessment is made of the energy storage system's ability to respond to grid frequency changes. For example, by analyzing the power change within the frequency step response time and combining it with the system capacity, an 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 during grid frequency mutations. Based on the maximum rate of change of active power during the frequency change, an active power regulation rate limit is determined, reflecting the upper limit of the speed at which the energy storage system can regulate active power.

[0032] In this example, a higher equivalent grid inertia support strength index in step 132 indicates a stronger ability of the energy storage system to suppress grid frequency fluctuations, resulting in relatively smaller grid impedance fluctuations. Conversely, a higher equivalent grid inertia support strength index is mapped to grid impedance fluctuation characteristic values ​​based on historical data and empirical relationships, and a corresponding relationship table is established between the two. For example, high inertia support strength is associated with low impedance fluctuation characteristic values. Thresholds are derived by combining the grid impedance fluctuation characteristic values ​​and the active power regulation rate limit, taking into account the stability requirements of grid operation. By analyzing the voltage variation patterns of the grid under different impedance and power regulation conditions, a threshold for the voltage disturbance amplitude change rate is derived. 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 safe and stable grid operation.

[0033] In this example, step 133 specifically designs multiple sets of 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 in the target power grid standard (such as voltage drop to 0.8-1.2 times the rated voltage). For example, with 0.05 times the rated voltage as the gradient, multiple voltage drop sequences are generated from the rated voltage drop to 0.8 times the rated voltage, and multiple voltage rise sequences are generated from the rated voltage rise to 1.2 times the rated voltage, to ensure that these sequences can fully cover the voltage transient changes that may occur in actual operation. Step 134 specifically involves, when the energy storage system is in steady-state operation, controlling the simulated power grid device to sequentially inject the voltage disturbance sequence generated in step 133 into the energy storage system in a preset order. 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 to ensure 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 starting and ending points of the voltage transient process based on the changing characteristics of the voltage waveform. 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 steady-state allowable deviation band (e.g., ±5% of the rated voltage) and remains stable as the ending point. Time history curves are extracted. During the transient process, the voltage amplitude corresponding to each time point from the disturbance starting point to the steady-state allowable deviation band is extracted, and a time history curve of the voltage amplitude changing with time is plotted. The time history curves obtained after each voltage disturbance injection are summarized to form a set of voltage recovery curves containing the amplitude-time relationship. Step 36 specifically involves performing a comprehensive analysis of 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 a multi-dimensional test result. For example, using different voltage disturbance sequences as dimensions, parameters such as voltage recovery time and maximum voltage deviation corresponding to each sequence are arranged and combined to intuitively demonstrate the adaptability of the energy storage system under different voltage disturbance conditions.

[0034] By converting frequency response characteristics into voltage disturbance parameters, the present invention establishes an intrinsic connection between the frequency characteristics and voltage adaptability of the energy storage system, realizes the transition from frequency performance evaluation to voltage performance testing, and makes the test more systematic; derives voltage disturbance parameters based on the performance indicators of the energy storage system itself, and generates a disturbance sequence covering the actual operating voltage transient range, which can accurately simulate various voltage fluctuations that may occur in power grid operation and improve the authenticity and validity of the test results; generates multi-dimensional test results by extracting key parameters from the voltage recovery curve; this test method comprehensively considers the response process of the energy storage system to voltage changes, which helps to accurately evaluate the compatibility of the energy storage system with the power grid.

[0035] In an optional embodiment of the present invention, step 14 extracts a steady-state operating point from the voltage recovery curve based on the test results, performs a power quality adaptability test at the steady-state operating point, and obtains quantified harmonic distortion rate and voltage fluctuation rate indicators, including: Step 141 , identifying, from the voltage recovery curve set, the first stable time interval after each curve enters the steady-state allowable deviation band; Step 142 , within the stable time interval, using a sliding window variance detection method to select operating points whose voltage fluctuations continuously meet the national standard steady-state deviation requirements as a valid steady-state operating point set; Step 143, controlling the simulated power grid device to lock the voltage amplitude corresponding to the effective steady-state operating point set to maintain a constant voltage to obtain a mode; Step 144 , in the constant voltage acquisition mode, synchronously start the source side and the load side of the energy storage system under inspection to operate at full power, and continuously collect the voltage and current waveform data of the grid connection point; Step 145 , based on the voltage and current waveform data, calculate the voltage fluctuation rate within a preset interval to obtain a quantized harmonic distortion rate and voltage fluctuation rate index.

[0036] In this embodiment, step 141 specifically involves traversing the voltage recovery curve set. For each curve, the voltage amplitude is checked point by point starting from the starting point of the curve according to the steady-state allowable deviation band (such as ±5% of the rated voltage). When the voltage amplitude is within the steady-state allowable deviation band for the first time for multiple consecutive sampling points (such as 10 sampling points), the start time and end time of the interval are recorded. This interval is the first stable time interval after entering the steady state. For example, if the voltage of a certain curve first continuously meets the deviation requirement at t=10s and continues until t=12s, the stable time interval of the curve is [10s, 12s]. Step 142 specifically involves setting a sliding window of fixed length (such as [10s, 12s]) within the stable time interval. 100ms), traversing the voltage data within the interval in window units. The variance of the voltage amplitude within each sliding window is calculated. The 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 variances of multiple consecutive windows (e.g., five windows) are all less than the threshold, the operating point in that window is deemed to meet the steady-state requirements and is included in the set of valid steady-state operating points. 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 selects a representative operating point from the set of valid steady-state operating points (e.g., the operating point corresponding to the middle moment) and obtains its corresponding voltage amplitude. The control module of the simulated power grid device is used to precisely adjust the output voltage to this amplitude, and the constant voltage maintenance function is activated. The device continuously monitors the output voltage and immediately compensates for any fluctuations through a feedback regulation mechanism, ensuring that the voltage amplitude remains stable within an error range of ±0.5% of the target value during the test. Step 144 specifically involves simultaneously activating both the power supply and load sides of the energy storage system being tested, while maintaining a constant voltage at the simulated grid device. High-precision data acquisition equipment (such as an oscilloscope with a sampling frequency of 10kHz or higher) is used to continuously collect voltage and current waveform data at the grid connection point. During the acquisition process, the triggering conditions of the data acquisition equipment are synchronized with the constant voltage state of the simulated grid device to avoid data loss or deviation. Data is continuously collected for at least one minute to ensure sufficient samples.

[0037] 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 dividing the difference by the rated voltage to obtain the voltage fluctuation rate within the interval. For example, if the maximum voltage within a 1 second interval is 390 V and the minimum voltage is 385 V, and the rated voltage is 400 V, then the voltage fluctuation rate within the interval is [(390-385) / 400]×100%. Harmonic distortion rate calculation involves performing a fast Fourier transform (FFT) on the collected current and voltage waveform data to separate the harmonic components. The square root of the sum of the squares of the amplitudes of the harmonic components is then divided by the amplitude of the fundamental component to obtain the harmonic distortion rate. Statistical analysis is then performed on the voltage fluctuation rate and the harmonic distortion rate within all preset intervals, and the average or maximum value is taken as the final quantitative indicator.

[0038] By identifying stable time intervals and combining them with sliding window variance detection, the present invention can accurately screen out 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. It also starts full-power operation on the source and load sides in constant voltage mode, highly restoring the full-load working 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.

[0039] In an optional embodiment of the present invention, step 15 dynamically sets a trigger threshold boundary for a high voltage or low voltage ride through test based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, and according to the trigger threshold boundary, includes: Step 151 , when the quantized harmonic distortion rate exceeds the target grid standard limit, calculating the margin correction amount of the voltage trigger threshold boundary; Step 152 : Calculate the triggering threshold of the high voltage ride through test and the triggering threshold of the low voltage ride through test according to the statistical distribution characteristics of the voltage fluctuation rate indicator and the margin correction amount.

[0040] In this embodiment, step 51 specifically compares the quantified harmonic distortion rate obtained in step 14 with the target grid standard limit. For example, if the target grid standard stipulates that the harmonic distortion rate limit is 5%, when the calculated harmonic distortion rate is 6%, it is determined to exceed the standard limit; calculate the margin correction amount, and determine the margin correction amount based on the degree of harmonic distortion rate exceeding the standard, combined with historical test data and engineering experience. Generally speaking, the more the harmonic distortion rate exceeds the standard, the greater the margin correction amount; if the harmonic distortion rate exceeds the standard by 1%, refer to the test results of similar equipment in the past and set the margin correction amount of the voltage trigger threshold boundary to 3% of the rated voltage; if it exceeds the standard by 2%, the correction amount is increased to 5% of the rated voltage, thereby enhancing the adaptability of the test to abnormal system conditions; In this example, step 152 specifically performs statistical analysis on the voltage fluctuation rate index obtained in step 14, and calculates its average value, maximum value, minimum value and other statistical quantities; for example, if the average value of the voltage fluctuation rate is 2%, the maximum value is 5%, and the minimum value is 1%, the overall level and extreme conditions of the system voltage fluctuation can be judged; 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 value of the voltage fluctuation rate and the margin correction amount. If the standard high voltage ride through trigger threshold is 1.1 times the rated voltage, the maximum value of the voltage fluctuation rate is 5%, and the margin correction amount is If the voltage fluctuation rate is 3%, the trigger threshold is raised 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 the harmonics exceed the standard. Similarly, the low voltage ride-through test trigger threshold is lowered based on the standard low voltage ride-through trigger threshold (such as 0.9 times the rated voltage) in combination with the minimum voltage fluctuation rate and the margin correction amount. If the minimum voltage fluctuation rate is 1% and the margin correction amount is 3%, the trigger threshold is set to 0.86 times the rated voltage (0.9-0.01-0.03), making the test more in line with the actual low voltage abnormal operating conditions of the power grid.

[0041] The present invention adjusts the trigger thresholds of high and low voltage ride-through tests in real time based on the harmonic distortion rate and voltage fluctuation rate, changing the limitations of traditional fixed threshold tests and making the test conditions more consistent with the complex and changeable power quality conditions in actual grid operation. By setting thresholds based on the combined effects of harmonic excess and voltage fluctuation, the ride-through capability of the energy storage system in harsh power quality environments can be more rigorously tested, ensuring that the energy storage equipment can still operate stably when the grid voltage is abnormal, thereby improving the overall reliability of the grid. The threshold calculation method based on statistical characteristics and correction amounts avoids the one-sidedness of single indicator evaluation and enables the test results to more comprehensively reflect the performance of the energy storage system.

[0042] In an optional embodiment of the present invention, the dynamic data of the frequency response characteristics of the energy storage system includes: Full-band power regulation sensitivity distribution diagram, K value and dead time under continuous ramp disturbance, and regulation delay time under step disturbance, and energy storage system recovery time.

[0043] In this embodiment, in the frequency adaptability test of step 12, the active power response value of the energy storage system at different frequency points (such as 49.8Hz, 49.9Hz, 50Hz, 50.1Hz, 50.2Hz, etc.) is recorded; for two adjacent frequency points, the ratio of the power change to the frequency change is calculated to obtain the power regulation sensitivity of the frequency interval. For example, when the frequency changes from 49.9Hz to 50Hz, the power increases from 100kW to 120kW, then the sensitivity of the interval is (120-10 0) / (50-49.9)=200kW / Hz; with frequency as the horizontal axis and sensitivity as the vertical axis, the sensitivity values ​​of each frequency interval are marked in the coordinate system, and each point is connected with a smooth curve to form a power regulation sensitivity distribution diagram covering the entire test frequency range; in a continuous ramp disturbance test (such as increasing the frequency from 50Hz to 50.2Hz at a rate of 0.1Hz / s), the slope of the linear segment of the power response curve is recorded. This slope is the K value, which represents the power regulation caused by the unit frequency change of the energy storage system. For example, if the power increases from 100 kW to 140 kW while the frequency increases by 0.2 Hz, the K value is (140 - 100) / 0.2 = 200 kW / Hz. The dead time is determined by observing the interval from the start of the frequency change to the start of the power response. For example, if the frequency begins to rise at t = 0 s, but the power does not begin to change until t = 0.5 s, the dead time is 0.5 s. When a frequency step disturbance occurs (such as a sudden change from 50 Hz to 50.3 Hz), record the interval from the moment the step occurs to the time the power response reaches 10% of the steady-state value. For example, if the step occurs at t = 0 s and the power reaches 10% of the steady-state value at t = 0.3 s, the adjustment delay time is 0.3 s. The time from the moment the step occurs to the time the power response enters and remains within ±5% of the steady-state value is recorded. For example, if the power stabilizes within ±5% of the steady-state value after t = 2.5 s, the recovery time is 2.5 s.

[0044] The full-band power regulation sensitivity distribution diagram of the present invention intuitively displays the differences in the regulation capabilities of the energy storage system in different frequency intervals, helping to analyze the adaptability of the system within the entire frequency range; parameters such as K value, dead time, regulation delay time and recovery time convert the frequency response characteristics of the energy storage system into quantifiable technical indicators, facilitating performance comparison between different devices. These dynamic data provide key parameters for grid stability assessment. For example, the K value can be used to calculate the frequency regulation effect of the system, and the recovery time can evaluate the system's ability to recover from frequency disturbances, assisting in grid dispatching 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, shortening the response time and improving the system's suppression effect on grid frequency fluctuations.

[0045] like Figure 2 As shown, an embodiment of the present invention further provides an energy storage system comprehensive adaptability evaluation device 20, characterized by comprising: An acquisition module 21 is configured to construct a hardware test loop by connecting a simulated power grid device in series to the high-voltage side of a step-up transformer of a tested energy storage system; The processing module 22 is used to perform a frequency adaptability test of the energy storage system based on a hardware test loop, and generate dynamic data including the frequency response characteristics of the energy storage system; convert the frequency response characteristics in the dynamic data into voltage disturbance parameters, and drive the simulated power grid device to perform the voltage adaptability test of the energy storage system according to the voltage disturbance parameters, and obtain a test result including a voltage recovery curve; based on the test results, extract the steady-state operating point in the voltage recovery curve, and perform the power quality adaptability test under 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, dynamically set the trigger threshold boundary of the high voltage or low voltage ride-through test, and according to the trigger threshold boundary, perform the high voltage ride-through capability test and the low voltage ride-through capability test in sequence.

[0046] Optionally, a simulated grid device is connected in series to the high-voltage side of the step-up transformer of the energy storage system being tested to construct a hardware test loop, including: Identify the rated voltage and current parameters of the high-voltage side of the step-up transformer of the energy storage system being tested; According to the rated voltage and current parameters, configure the voltage level matching parameters and current carrying capacity parameters of the simulated power grid device; Based on the voltage level matching parameters and current carrying capacity parameters, the obtained end of the simulated grid device is physically connected in series to the original grid access point on the high-voltage side of the step-up transformer through a cable to form a unidirectional current transmission path; Using a unidirectional current transmission path, a white noise test signal with a preset spectrum characteristic 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 injection signal is calculated, and a hardware test loop is constructed based on the spectral similarity.

[0047] Optionally, based on the hardware test loop, perform an energy storage system frequency adaptability test to generate dynamic data containing the energy storage system frequency response characteristics, including: When the hardware test loop is in a steady-state operation state, controlling the simulated power grid device to obtain a set of frequency disturbance signal sequences that increase at equal intervals, the frequency disturbance signal sequences covering the frequency operation range specified by the target power grid standard; Real-time acquisition of the power command tracking curve of the energy storage converter in the energy storage system being tested; According to the power command tracking curve, the dynamic response curve of active power and the actual measurement value curve of energy storage system frequency are determined; By comparing the dynamic response curve and the actual measurement value curve, the frequency-power adjustment slope and adjustment dead time are obtained; According to the adjustment dead time, set the step disturbance triggering time; Controlling the simulated grid device to apply frequency step mutations exceeding the dead zone threshold in the high-frequency direction and the low-frequency direction when the energy storage system is in steady-state operation, thereby generating frequency step response time characteristic parameters; According to the frequency step response time characteristic parameters, dynamic data including the frequency response characteristics of the energy storage system is constructed.

[0048] Optionally, the frequency response characteristics in the dynamic data are converted into voltage disturbance parameters, and a simulated grid device is driven according to the voltage disturbance parameters to perform a voltage adaptability test of the energy storage system, thereby obtaining test results including a voltage recovery curve, including: Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the energy storage system equivalent grid inertia support strength index and active power regulation rate limit; Mapping a grid impedance fluctuation characteristic value according to the equivalent grid inertia support strength index; Derived a voltage disturbance amplitude change rate threshold value based on the grid impedance fluctuation characteristic value and the active power regulation rate limit; Based on the voltage disturbance amplitude change rate threshold, generating multiple groups 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; Controlling the simulated power grid device to sequentially inject the voltage disturbance sequence under the steady-state operation of the energy storage system to obtain a voltage waveform after disturbance; The transient process of the voltage waveform after each disturbance injection is segmented, and the time history curve of the voltage recovery from the starting point of the disturbance to the steady-state allowable deviation band is extracted to form a set of voltage recovery curves containing the amplitude-time relationship; Based on the voltage recovery curves corresponding to all voltage disturbance sequences, multi-dimensional test results are generated to characterize the voltage adaptability of the energy storage system.

[0049] Optionally, based on the test results, a steady-state operating point is extracted from the voltage recovery curve, and a power quality adaptability test is performed under the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indicators, including: From the voltage recovery curve set, identify the first stable time interval after each curve enters the steady-state allowable deviation band; In the stable time interval, a sliding window variance detection method is used to select operating points whose voltage fluctuations continuously meet the national standard steady-state deviation requirements as a valid steady-state operating point set; Controlling the simulated power grid device to lock the voltage amplitude corresponding to the effective steady-state operating point set to maintain a constant voltage to obtain a mode; In constant voltage acquisition mode, the source and load sides of the energy storage system under test are simultaneously started to operate at full power, and the voltage and current waveform data of the grid connection point are continuously collected; Based on the 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 indicators.

[0050] Optionally, based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, the trigger threshold boundary of the high voltage or low voltage ride through test is dynamically set, and based on the trigger threshold boundary, the following are included: When the quantified harmonic distortion rate exceeds the target grid standard limit, the margin correction amount of the voltage trigger threshold boundary is calculated; The trigger thresholds of the high voltage ride through test and the low voltage ride through test are calculated based on the statistical distribution characteristics of the voltage fluctuation rate index and the margin correction amount.

[0051] Optional dynamic data on the frequency response characteristics of the energy storage system, including: Full-band power regulation sensitivity distribution diagram, K value and dead time under continuous ramp disturbance, and regulation delay time under step disturbance, and energy storage system recovery time.

[0052] It should be noted that this device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0053] An embodiment of the present invention further provides a computing device comprising: a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction implements the steps of the above-described method when executed by the processor.

[0054] An embodiment of the present invention further provides a computer-readable storage medium comprising instructions, which, when executed on a computer, cause the computer to execute the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0055] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0056] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0057] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0058] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0059] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0060] If the functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art, or part 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 for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0061] In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but they do not necessarily need to be performed in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including a processor, storage medium, etc.) or a network of computing devices. This can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0062] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, 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 well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0063] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A comprehensive adaptability evaluation method for an energy storage system, characterized in that: Applied to a source-grid-load-storage integrated system, the method includes: A hardware test loop is constructed by connecting a simulated grid device in series to the high-voltage side of the step-up transformer of the energy storage system being tested. Based on the hardware test loop, perform frequency adaptability testing of the energy storage system and generate dynamic data containing the frequency response characteristics of the energy storage system; Convert the frequency response characteristics in the dynamic data into voltage disturbance parameters. Use the voltage disturbance parameters to drive the simulated grid device to perform the energy storage system voltage adaptability test, and obtain test results including the voltage recovery curve. 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 under the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indicators; According to the quantified harmonic distortion rate and voltage fluctuation rate indicators, the trigger threshold boundary of the 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 performed in sequence according to the trigger threshold boundary.

2. The method for evaluating the comprehensive adaptability of an energy storage system according to claim 1, wherein: By connecting a simulated grid device in series to the high-voltage side of the step-up transformer of the energy storage system being tested, a hardware test loop is constructed, including: Identify the rated voltage and current parameters of the high-voltage side of the step-up transformer of the energy storage system being tested; According to the rated voltage and current parameters, configure the voltage level matching parameters and current carrying capacity parameters of the simulated power grid device; Based on the voltage level matching parameters and current carrying capacity parameters, the obtained end of the simulated grid device is physically connected in series to the original grid access point on the high-voltage side of the step-up transformer through a cable to form a unidirectional current transmission path; Using a unidirectional current transmission path, a white noise test signal with a preset spectrum characteristic 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 injection signal is calculated, and a hardware test loop is constructed based on the spectral similarity.

3. The method for evaluating the comprehensive adaptability of an energy storage system according to claim 2, wherein: Based on the hardware test loop, the 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: When the hardware test loop is in a steady-state operation state, controlling the simulated power grid device to obtain a set of frequency disturbance signal sequences that increase at equal intervals, the frequency disturbance signal sequences covering the frequency operation range specified by the target power grid standard; Real-time acquisition of the power command tracking curve of the energy storage converter in the energy storage system being tested; According to the power command tracking curve, the dynamic response curve of active power and the actual measurement value curve of energy storage system frequency are determined; By comparing the dynamic response curve and the actual measurement value curve, the frequency-power adjustment slope and adjustment dead time are obtained; According to the adjustment dead time, set the step disturbance triggering time; Controlling the simulated grid device to apply frequency step mutations exceeding the dead zone threshold in the high-frequency direction and the low-frequency direction when the energy storage system is in steady-state operation, thereby generating frequency step response time characteristic parameters; According to the frequency step response time characteristic parameters, dynamic data including the frequency response characteristics of the energy storage system is constructed.

4. The method for evaluating the comprehensive adaptability of an energy storage system according to claim 2, wherein: The frequency response characteristics in the dynamic data are converted into voltage disturbance parameters. Based on the voltage disturbance parameters, the simulated grid device is driven to perform the energy storage system voltage adaptability test, and the test results including the voltage recovery curve are obtained, including: Analyze the frequency-power regulation slope and frequency step response time characteristic parameters in the dynamic data, and calculate the energy storage system equivalent grid inertia support strength index and active power regulation rate limit; Mapping a grid impedance fluctuation characteristic value according to the equivalent grid inertia support strength index; Derived a voltage disturbance amplitude change rate threshold value based on the grid impedance fluctuation characteristic value and the active power regulation rate limit; Based on the voltage disturbance amplitude change rate threshold, generating multiple groups 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; Controlling the simulated power grid device to sequentially inject the voltage disturbance sequence under the steady-state operation of the energy storage system to obtain a voltage waveform after disturbance; The transient process of the voltage waveform after each disturbance injection is segmented, and the time history curve of the voltage recovery from the starting point of the disturbance to the steady-state allowable deviation band is extracted to form a set of voltage recovery curves containing the amplitude-time relationship; Based on the voltage recovery curves corresponding to all voltage disturbance sequences, multi-dimensional test results are generated to characterize the voltage adaptability of the energy storage system.

5. The method for evaluating the comprehensive adaptability of an energy storage system according to claim 4, wherein: Based on the test results, the steady-state operating point in the voltage recovery curve is extracted. The power quality adaptability test is performed under the steady-state operating point to obtain quantified harmonic distortion rate and voltage fluctuation rate indicators, including: From the voltage recovery curve set, identify the first stable time interval after each curve enters the steady-state allowable deviation band; In the stable time interval, a sliding window variance detection method is used to select operating points whose voltage fluctuations continuously meet the national standard steady-state deviation requirements as a valid steady-state operating point set; Controlling the simulated power grid device to lock the voltage amplitude corresponding to the effective steady-state operating point set to maintain a constant voltage to obtain a mode; In constant voltage acquisition mode, the source and load sides of the energy storage system under test are simultaneously started to operate at full power, and the voltage and current waveform data of the grid connection point are continuously collected; Based on the 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 indicators.

6. The method for evaluating the comprehensive adaptability of an energy storage system according to claim 2, wherein: Based on the quantified harmonic distortion rate and voltage fluctuation rate indicators, the trigger threshold boundary of the high voltage or low voltage ride through test is dynamically set. Based on the trigger threshold boundary, the following are included: When the quantified harmonic distortion rate exceeds the target grid standard limit, the margin correction amount of the voltage trigger threshold boundary is calculated; The trigger thresholds of the high voltage ride through test and the low voltage ride through test are calculated based on the statistical distribution characteristics of the voltage fluctuation rate index and the margin correction amount.

7. The method for evaluating the comprehensive adaptability of an energy storage system according to claim 6, wherein: Dynamic data on the frequency response characteristics of the energy storage system, including: Full-band power regulation sensitivity distribution diagram, K value and dead time under continuous ramp disturbance, and regulation delay time under step disturbance, and energy storage system recovery time.

8. A comprehensive adaptability evaluation device for an energy storage system, characterized in that: include: An acquisition module is used to build a hardware test loop by connecting a simulated grid device in series to the high-voltage side of the step-up transformer of the energy storage system under test; A processing module is configured to perform a frequency adaptability test of the energy storage system based on a hardware test loop, generate dynamic data containing the frequency response characteristics of the energy storage system, convert the frequency response characteristics in the dynamic data into voltage disturbance parameters, and drive a simulated power grid device to perform a voltage adaptability test of the energy storage system based on the voltage disturbance parameters, thereby 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 the power quality adaptability test is performed under 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 of the 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 performed in sequence according to the trigger threshold boundary.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that The device stores instructions, which, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 7.

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