Whole-process testing method for network construction type energy storage performance

Abstract

The invention relates to a full-process testing method for the performance of a network-building type energy storage system, and the method achieves the comprehensive performance testing of the network-building type energy storage system through the innovative data collection, model building, multi-working-condition testing and performance evaluation technologies. The method comprises the following specific steps: firstly, carrying out system initialization and data acquisition, setting initial parameters of each component and monitoring operation data in real time; secondly, constructing a dynamic response model, and ensuring the accuracy of the model through data preprocessing and a machine learning algorithm; then, implementing three test modes of off-grid, grid-connected and grid-construction, and comprehensively evaluating the dynamic response characteristics and stability of the energy storage system in different environments; performance evaluation and optimization are carried out, a comprehensive report is generated, and optimization suggestions are provided; and finally, storing the test data in a distributed database, and outputting a detailed report. The method has the advantages of comprehensiveness, accuracy and high efficiency, and an intelligent solution is provided for a user, so that the overall performance of the networking type energy storage system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage performance testing, and specifically relates to a full-process testing method for network-type energy storage performance. Background Art

[0002] With the continuous development of the field of energy storage performance testing, existing energy storage performance testing products have been widely used. However, these products still have some problems in actual use. For example, existing energy storage performance testing methods usually have problems such as complex testing process, low testing efficiency, and inaccurate test results, which makes it difficult to fully evaluate the performance of energy storage systems in practical applications.

[0003] In order to solve these problems, there have been some attempts to improve test efficiency and accuracy by introducing automated test equipment and optimizing test processes. Although these methods have improved relevant aspects to a certain extent, they still have limitations, such as high test equipment costs, incomplete test methods, and inability to adapt to a variety of energy storage systems.

[0004] For example, an energy storage test system and energy storage battery performance test method (application number: CN202311256073.2). The design uses an auxiliary battery module and an off-grid test module to achieve full charge and discharge testing of large-capacity energy storage batteries through electrical isolation. Although off-grid testing can be achieved to avoid harm to the power grid, its testing method mainly focuses on the charging and discharging performance of the battery, lacking a comprehensive evaluation of the overall performance of the energy storage system, especially the dynamic response and stability testing of the grid-type energy storage system. Therefore, this design cannot meet the needs of comprehensive performance testing of the grid-type energy storage system.

[0005] Another example is the grid-related performance test method and system for energy storage power stations (application number: CN202310784510.1). This product uses real-time data acquisition and prediction models to evaluate the performance of energy storage power stations by comparing the power surplus value with the reference power surplus value. Although it has certain automation capabilities, it mainly focuses on the prediction of power and power, and lacks dynamic response and stability testing of energy storage systems under actual working conditions, especially in grid-type energy storage systems. Therefore, this design is difficult to meet the needs of comprehensive performance testing of grid-type energy storage systems.

[0006] The above problems show that the current energy storage performance test methods are difficult to effectively cope with the new requirements for comprehensive performance evaluation and dynamic response under complex working conditions. Therefore, the present invention provides a full-process test method for network-based energy storage performance to overcome these shortcomings and provide a new solution that is more intelligent, efficient and adaptable to changing environments. Summary of the invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide a full-process testing method for the performance of a grid-type energy storage system. This method overcomes the problems of complex testing process, low testing efficiency, inaccurate test results and inability to comprehensively evaluate the performance of a grid-type energy storage system in the prior art by introducing innovative data collection, model building, multi-condition testing and performance evaluation technologies.

[0008] A full-process testing method for grid-type energy storage performance provided by the present invention comprises the following steps:

[0009] Step S1: System initialization and data collection.

[0010] The system initialization and data collection include:

[0011] Acquisition module: The acquisition module sets the initial parameters of each component in the test system, including but not limited to the basic parameters of the energy storage system (such as battery type, rated capacity, charge and discharge rate, etc.) and test environment parameters (such as temperature, humidity, etc.). The operating data of the energy storage system under different working conditions is collected in real time through sensors and data collectors, including real-time charge and discharge power, charge and discharge amount, terminal voltage, internal resistance, etc. The data acquisition module is connected to each sensor and data collector wirelessly or wired to ensure the real-time and accuracy of the data.

[0012] System initialization: Initialize the test system and set the test parameters, including the test voltage range, test current range, test time interval, etc. During the initialization process, the system automatically checks the working status of each sensor and data collector to ensure their normal operation.

[0013] Step S2: Construct a dynamic response model.

[0014] The constructing of the dynamic response model comprises:

[0015] Data preprocessing: preprocess the collected data, including data cleaning, denoising, normalization and other operations to ensure data quality. During the preprocessing process, the system uses filtering algorithms and statistical methods to eliminate outliers and retain valid data.

[0016] Model construction: Based on the preprocessed data, a dynamic response model of the energy storage system is constructed. The model includes the dynamic response characteristics during the charging and discharging process and the stability of the energy storage system under different working conditions. Specifically, the model adopts a nonlinear dynamic model, including parameters such as the open circuit voltage, polarization voltage, and ohmic internal resistance of the battery, as well as the dynamic response characteristics of the energy storage system at different charging and discharging rates. The model is trained and optimized through machine learning algorithms (such as support vector machines, neural networks, etc.) to ensure the accuracy and robustness of the model.

[0017] Step S3: Full process testing.

[0018] The full-process test tests the actual performance of the energy storage system, including off-grid test, grid-connected test and grid-building test, to verify the performance of the energy storage system in actual operation, including key performance indicators such as its charging and discharging efficiency, cycle life, dynamic response time, etc. Specifically include:

[0019] Off-grid test: In off-grid mode, the energy storage system is fully charged and discharged through auxiliary battery modules and electrical isolation devices. During the test, the system monitors the charge and discharge status of the energy storage system in real time, and records key parameters such as charge and discharge curves, temperature changes, and internal resistance changes. The off-grid test is designed to verify the performance of the energy storage system when it is operating independently and ensure that it can operate stably in the event of a grid failure.

[0020] Grid-connected test: In the grid-connected mode, the actual operating conditions of the energy storage system in the grid are simulated through real-time data collection and prediction models. During the test, the system collects real-time parameters such as the voltage, frequency, and power factor of the grid, as well as parameters such as the charging and discharging power and the amount of charge and discharge of the energy storage system. The grid-connected test is designed to evaluate the dynamic response characteristics and stability of the energy storage system in the grid to ensure that it can effectively maintain the stability of the grid.

[0021] Network construction test: In the network construction mode, the actual operating conditions of the energy storage system in the network construction system are simulated by introducing a virtual grid. During the test, the system collects the voltage, frequency, power factor and other parameters of the virtual grid in real time, as well as the charging and discharging power, charging and discharging charge and other parameters of the energy storage system. The network construction test is designed to evaluate the dynamic response characteristics and stability of the energy storage system in the network construction system to ensure that it can effectively support the stable operation of the power grid.

[0022] Step S4: Performance evaluation and optimization.

[0023] The performance evaluation and optimization include:

[0024] Performance evaluation: Based on the data collected from the full-process test, the performance of the energy storage system is comprehensively evaluated. Evaluation indicators include but are not limited to charge and discharge efficiency, cycle life, dynamic response time, stability, etc. The system processes the test data through the data analysis module and generates a performance evaluation report. During the evaluation process, the system uses a multi-objective optimization algorithm to comprehensively consider the impact of each evaluation indicator to ensure the comprehensiveness and accuracy of the evaluation results.

[0025] Optimization suggestions: Based on the performance evaluation results, the system provides optimization suggestions. Optimization suggestions include but are not limited to suggestions on adjusting the charging and discharging strategy of the energy storage system, optimizing system parameter settings, and improving system design. The system automatically generates optimization suggestions through optimization algorithms for users to refer to and implement.

[0026] Step S5: Data storage and result output.

[0027] The data storage and result output include:

[0028] Data storage: Store the data of the entire process test and the performance evaluation results in the database for subsequent analysis and use. The data storage module uses distributed storage technology to ensure the security and reliability of the data.

[0029] Result output: The system generates test reports and performance evaluation reports, which are output through the user interface or data interface. The reports include detailed data of the test process, performance evaluation results, optimization suggestions, etc. for users to review and use.

[0030] Technical principle

[0031] A full-process testing method for the performance of a grid-type energy storage system of the present invention realizes a comprehensive performance test of a grid-type energy storage system through multi-level data collection and processing, multi-condition test modes and comprehensive performance evaluation. First, the operating data of the energy storage system under different working conditions is collected in real time through high-precision sensors and data collectors to ensure the accuracy and real-time nature of the data. Secondly, through data preprocessing and nonlinear dynamic model construction, a dynamic response model of the energy storage system is established to ensure the robustness and accuracy of the model. Then, through the three test modes of off-grid, grid-connected and grid-connected, the performance of the energy storage system under different operating environments is comprehensively evaluated. Finally, through the multi-objective optimization algorithm and performance evaluation module, a comprehensive performance evaluation report is generated, and optimization suggestions are provided to help users improve the performance of the energy storage system.

[0032] Beneficial effects:

[0033] Comprehensiveness: The present invention comprehensively evaluates the dynamic response and stability of the grid-connected energy storage system through three test modes: off-grid, grid-connected and grid-connected, thus overcoming the problem of incomplete testing in the prior art.

[0034] Accuracy: The dynamic response model of the energy storage system is constructed through nonlinear dynamic models and machine learning algorithms to ensure the accuracy and robustness of the test results and avoid the problem of inaccurate test results in existing technologies.

[0035] High efficiency: The multi-objective optimization algorithm and real-time data acquisition technology are used to significantly improve test efficiency, reduce test time, reduce test costs, and overcome the problem of low test efficiency in existing technologies.

[0036] Multi-level data acquisition and processing: The present invention uses high-precision sensors and data collectors to collect real-time operating data of the energy storage system under different working conditions, and ensures the accuracy and real-time nature of the data through data preprocessing and nonlinear dynamic model construction. During the data preprocessing process, the system uses filtering algorithms and statistical methods to eliminate outliers and retain valid data, thereby improving data quality.

[0037] Multi-operating condition test mode: This invention introduces three test modes: off-grid, grid-connected and grid-building, to comprehensively evaluate the performance of the energy storage system in different operating environments. The off-grid test verifies the performance of the energy storage system when it is operating independently, ensuring its stability in the event of a grid failure; the grid-connected test evaluates the dynamic response characteristics and stability of the energy storage system in the grid; the grid-building test evaluates the dynamic response characteristics and stability of the energy storage system in a grid-building system.

[0038] Nonlinear dynamic model construction: The present invention adopts a nonlinear dynamic model, including parameters such as the open circuit voltage, polarization voltage, and ohmic internal resistance of the battery, as well as the dynamic response characteristics of the energy storage system at different charge and discharge rates. The model is trained and optimized through machine learning algorithms (such as support vector machines, neural networks, etc.) to ensure the accuracy and robustness of the model.

[0039] Multi-objective optimization algorithm: This invention adopts a multi-objective optimization algorithm to comprehensively consider the influence of various evaluation indicators to ensure the comprehensiveness and accuracy of the evaluation results. The system automatically generates optimization suggestions through the optimization algorithm for users to refer to and implement.

[0040] Distributed data storage: The data storage module of the present invention adopts distributed storage technology to ensure the security and reliability of data. Test data and performance evaluation results are stored in the database for subsequent analysis and use.

[0041] Through the above innovations, the present invention provides a new solution that is more intelligent, efficient and adaptable to changing environments, overcomes the shortcomings of the prior art, and realizes all-round performance testing of the grid-type energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The present invention is a general flow chart of a full-process testing method for grid-type energy storage performance.

[0043] Figure 2 It is a structural diagram of the system initialization and data acquisition module.

[0044] Figure 3 Flowchart for data preprocessing and dynamic response model construction.

[0045] Figure 4 Schematic diagram of off-grid test mode.

[0046] Figure 5 This is a schematic diagram of the grid-connected test mode.

[0047] Figure 6 Schematic diagram of the network test mode.

[0048] Figure 7Schematic diagram of the performance evaluation structure.

[0049] Figure 8 This is a structural diagram of the data storage and result output module. DETAILED DESCRIPTION

[0050] In conjunction with the drawings in the embodiments of the present application, the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0051] Example 1

[0052] System initialization and data collection (step S1).

[0053] like Figure 1 As shown, the present invention provides a full-process test method for grid-type energy storage performance, including system initialization and data collection steps. The specific steps are as follows:

[0054] S11, acquisition module settings:

[0055] The acquisition module sets the initial parameters of each component in the test system, including but not limited to the basic parameters of the energy storage system (such as battery type, rated capacity, charge and discharge rate, etc.) and the test environment parameters (such as temperature, humidity, etc.).

[0056] For example, battery type identification: Through the built-in identification algorithm, the battery type (such as lithium-ion battery, lead-acid battery, etc.) is automatically identified, and specific parameters are preset according to the battery type. Rated capacity setting: Enter the rated capacity of the battery, which is the maximum amount of electricity that the battery can store under standard conditions. Charge and discharge rate setting: Set the battery charge and discharge rate according to the battery specification to ensure that the safe operating range of the battery is not exceeded during the test. Environmental parameter monitoring: Deploy temperature and humidity sensors to monitor the temperature and humidity of the test environment in real time to evaluate the impact of environmental factors on the performance of the energy storage system.

[0057] The operating data of the energy storage system under different working conditions is collected in real time through sensors and data collectors, including real-time charging and discharging power, charging and discharging charge, terminal voltage, internal resistance, etc.

[0058] The data acquisition module is connected to each sensor and data collector by wireless or wired means to ensure the real-time and accuracy of the data.

[0059] S12, system initialization:

[0060] Initialize the test system and set the test parameters, including the test voltage range, test current range, test time interval, etc. According to the design specifications of the energy storage system, set the upper and lower limits of the test voltage to simulate different grid voltage conditions. Set the upper and lower limits of the test current to simulate different load conditions. Set the time interval for data collection to ensure data continuity and cover the entire test cycle.

[0061] During the initialization process, the system automatically checks the working status of each sensor and data collector to ensure their normal operation. When the system is initialized, the sensor self-test program is automatically executed to check the sensor's response time and accuracy. At the same time, the storage capacity and battery status of the data collector are checked to ensure that data will not be lost during the entire test cycle. During the initialization process, the communication link is tested to ensure that the connection between the data acquisition module and the sensor and data collector is stable and reliable.

[0062] A dynamic response model is constructed (step S2).

[0063] like Figure 3 As shown in Figure 2, building a dynamic response model includes the following steps:

[0064] Data preprocessing:

[0065] Preprocess the collected data, including data cleaning, denoising, normalization and other operations to ensure the quality of the data. Specifically, identify and process missing values ​​in the data, fill them by interpolation, deletion or using statistical values ​​(such as mean, median), and the system uses filtering algorithms and statistical methods to remove outliers to prevent them from having an adverse effect on model training. Apply low-pass filters (such as Butterworth filters and Kalman filters) to reduce high-frequency noise in the data, and use wavelet transforms to perform multi-scale decomposition of the signal to identify and remove noise components. Finally, normalize the data, such as minimum-maximum scaling or Z-score normalization, to ensure that all features are on the same scale, improving the stability and convergence speed of model training.

[0066] Model construction:

[0067] Based on the preprocessed data, a dynamic response model of the energy storage system is constructed, which includes the dynamic response characteristics during the charging and discharging process and the stability of the energy storage system under different working conditions.

[0068] Specifically, the model adopts a nonlinear dynamic model, including parameters such as the open circuit voltage, polarization voltage, and ohmic internal resistance of the battery, as well as the dynamic response characteristics of the energy storage system at different charge and discharge rates.

[0069] Train and optimize the model through machine learning algorithms to ensure the accuracy and robustness of the model. For example, use support vector machines (SVM) for classification or regression tasks to predict the behavior of the energy storage system under specific operating conditions. Design and train neural network models, such as deep learning models, to capture the complex nonlinear relationships of the energy storage system. Use the preprocessed data to train the selected machine learning algorithm and minimize the prediction error by adjusting the model parameters.

[0070] Full process test (step S3).

[0071] like Figure 4 , Figure 5 and Figure 6 As shown, the full process test includes the following steps:

[0072] Off-grid test( Figure 4 ):

[0073] In off-grid mode, the energy storage system is fully charged and discharged through auxiliary battery modules and electrical isolation devices. During the test, the system monitors the charge and discharge status of the energy storage system in real time, records key parameters such as charge and discharge curves, temperature changes, and internal resistance changes, and uses high-precision sensors and data acquisition systems to record these parameters to ensure the accuracy and reliability of the data. The collected data is analyzed to verify the performance of the energy storage system in an off-grid state, including charge and discharge efficiency, response time, etc. The off-grid test is designed to evaluate the stability and reliability of the energy storage system by comparing the data before and after the test, verify the performance of the energy storage system when it is operating independently, and ensure that it can operate stably in the event of a grid failure.

[0074] Grid connection test( Figure 5 ):

[0075] In the grid-connected mode, the actual operating conditions of the energy storage system in the power grid are simulated through real-time data collection and prediction models. During the test, the system collects real-time parameters such as the voltage, frequency, and power factor of the power grid, as well as parameters such as the charging and discharging power and the amount of charge and discharge of the energy storage system. The grid-connected test is designed to evaluate the dynamic response characteristics and stability of the energy storage system in the power grid to ensure that it can effectively maintain the stability of the power grid. By simulating the load changes and power fluctuations of the power grid, the adaptability and regulation capabilities of the energy storage system to changes in the power grid are tested.

[0076] Network test Figure 6 ):

[0077] In the grid-building mode, a virtual grid is introduced to simulate the actual operating conditions of the energy storage system in the grid-building system. During the test, the system collects parameters such as the voltage, frequency, and power factor of the virtual grid in real time, as well as parameters such as the charging and discharging power and the amount of charge and discharge of the energy storage system. Evaluate the dynamic response characteristics and stability of the energy storage system in the grid-building system to ensure that it can effectively support the stable operation of the grid. Test the performance of the energy storage system in the grid-building system by simulating different grid operation scenarios. Conduct in-depth analysis of the collected data to evaluate the performance of the energy storage system in the grid-building system, including dynamic response time and stability. The grid-building test is designed to evaluate the dynamic response characteristics and stability of the energy storage system in the grid-building system to ensure that it can effectively support the stable operation of the grid.

[0078] Performance evaluation and optimization (step S4).

[0079] like Figure 7 and Figure 8 As shown, performance evaluation and optimization includes the following steps:

[0080] Performance evaluation:

[0081] Based on the data collected from the full-process test, the performance of the energy storage system is comprehensively evaluated. Statistical analysis methods such as variance analysis and correlation analysis are used. Evaluation indicators include but are not limited to charge and discharge efficiency, cycle life, dynamic response time, stability, etc.

[0082] The system processes the test data through the data analysis module and generates a performance evaluation report. During the evaluation process, the system uses a multi-objective optimization algorithm, comprehensively considers the economy, reliability and environmental impact of the energy storage system, and uses a multi-dimensional evaluation system to evaluate the overall performance of the energy storage system. For example, the system combines the charging and discharging characteristics of the energy storage system with the grid demand to evaluate its role in peak load shaving and valley filling in the grid.

[0083] Optimization suggestions:

[0084] Based on the performance evaluation results, the system provides optimization suggestions. Based on the performance evaluation results, optimize the charging and discharging strategy of the energy storage system. Adopt the valley power peak strategy, that is, charge during the period of low electricity price and discharge during the period of high electricity price to reduce the power grid electricity cost. Optimize the parameter settings of the energy storage system, such as charging and discharging power, charging and discharging depth, etc., to improve system efficiency and extend battery life. Based on the energy balance model of the energy storage system, adjust the charging and discharging strategy to ensure the charging and discharging balance of the energy storage system within 24 hours. According to the performance evaluation results, put forward improvement suggestions for the design of the energy storage system. Including battery type selection, battery arrangement, thermal management system design, etc., to improve the overall performance and reliability of the energy storage system. Implement an intelligent monitoring system to monitor the working status of the energy storage equipment in real time and make adjustments based on the monitoring results. Regular maintenance and care to ensure that the equipment is in the best working condition and improve system efficiency. The system automatically generates optimization suggestions through optimization algorithms for users to refer to and implement.

[0085] Data storage and result output (step S5).

[0086] like Figure 8 As shown, data storage and result output include the following steps:

[0087] Data Storage:

[0088] The data and performance evaluation results of the full process test are stored in the database for subsequent analysis and use. The data storage module uses distributed storage technology to ensure the security and reliability of the data.

[0089] Result output:

[0090] The system generates test reports and performance evaluation reports, which are output through the user interface or data interface. The reports include detailed data of the test process, performance evaluation results, optimization suggestions, etc. for users to review and use.

[0091] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

[0092] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of components as the criteria for distinction. For example, "including" mentioned throughout the specification and claims is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve technical problems within a certain error range and basically achieve technical effects.

[0093] It should be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0094] The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the invention concept described herein through the above teachings or the technology or knowledge of the relevant field. Changes and variations made by those skilled in the art do not depart from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A full-process testing method for grid-type energy storage performance, characterized in that: The following steps are involved: Step S1: System initialization and data collection, including: Collect the operating data of the energy storage system under different working conditions in real time through sensors and data collectors, including one or more of the following real-time charging and discharging power, charging and discharging charge, terminal voltage, and internal resistance; initialize the system and set test parameters, including one or more of the test voltage range, test current range, and test time interval; Step S2: construct a dynamic response model, including: Model construction: Based on the preprocessed operating data, a dynamic response model of the energy storage system is constructed, and the dynamic response model is trained and optimized through machine learning algorithms to ensure the accuracy and robustness of the model; Step S3: Full process testing, including: In off-grid mode, the energy storage system is fully charged and discharged through auxiliary battery modules and electrical isolation devices; in grid-connected mode, the actual operating conditions of the energy storage system in the grid are simulated through real-time data collection and prediction models; in grid-building mode, the actual operating conditions of the energy storage system in a grid-building system are simulated by introducing a virtual grid; Step S4: Performance evaluation and optimization, including: Based on the data collected from the full-process test, the performance of the energy storage system is comprehensively evaluated. The evaluation indicators include but are not limited to charging and discharging efficiency, cycle life, dynamic response time, stability, etc. The test data is processed through the data analysis module to generate a performance evaluation report; Step S5: Data storage and result output, including: The data and performance evaluation results of the entire test process are stored in the database; the system generates a test report and a performance evaluation report, which are output through the user interface or data interface. The report includes detailed data of the test process, performance evaluation results, optimization suggestions, etc. for users to review and use.

2. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The step S1: system initialization and data acquisition, includes an acquisition module and a system initialization module. The data acquisition module is connected to each sensor and data collector in a wireless or wired manner to ensure the real-time and accuracy of the data.

3. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The step S2: constructing a dynamic response model includes data preprocessing and model building. The data preprocessing includes operations such as data cleaning, denoising, and normalization to ensure the quality of the data.

4. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The dynamic response model adopts a nonlinear dynamic model, including parameters such as the open circuit voltage, polarization voltage, and ohmic internal resistance of the battery, as well as the dynamic response characteristics of the energy storage system at different charge and discharge rates.

5. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The step S3: full process test, including off-grid test, grid-connected test and grid-building test. The off-grid test fully charges and discharges the energy storage system through the auxiliary battery module and the electrical isolation device. During the test, the system monitors the charge and discharge status of the energy storage system in real time, and records key parameters such as the charge and discharge curve, temperature change, and internal resistance change.

6. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The grid-connected test simulates the actual operating conditions of the energy storage system in the power grid through real-time data collection and prediction models. During the test, the system collects parameters such as the voltage, frequency, power factor of the power grid, as well as parameters such as the charging and discharging power and charging and discharging charge of the energy storage system in real time.

7. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The network construction test simulates the actual operating conditions of the energy storage system in the network construction system by introducing a virtual power grid. During the test, the system collects the voltage, frequency, power factor and other parameters of the virtual power grid in real time, as well as the charging and discharging power, charging and discharging charge and other parameters of the energy storage system.

8. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The step S4: performance evaluation and optimization includes performance evaluation and optimization suggestions. The performance evaluation processes the test data through a data analysis module to generate a performance evaluation report. The evaluation indicators include but are not limited to charge and discharge efficiency, cycle life, dynamic response time, stability, etc.

9. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The optimization suggestions are automatically generated through an optimization algorithm, and the optimization suggestions include but are not limited to suggestions on adjusting the charging and discharging strategy of the energy storage system, optimizing system parameter settings, improving system design, etc.

10. A full-process testing method for grid-type energy storage performance according to claim 1, characterized in that: The step S5: data storage and result output includes a data storage and result output module. The data storage module adopts distributed storage technology to ensure the security and reliability of data.

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