A method and system for evaluating the performance of an electrochemical energy storage system in grid-connected test

Abstract

The application relates to the field of grid-connected test technology and discloses a kind of electrochemical energy storage system grid-connected test performance evaluation method and system, obtain grid-connected test instruction and reference working condition parameter set, determine multiple simulation running data sets;Obtain real-time electrical data set and physical state data set, synchronize mapping real-time electrical data set, physical state data set and simulation running data set, obtain deviation analysis result set;According to the deviation analysis result set, index decoupling is carried out, energy conversion sub-index, power tracking sub-index, cycle life sub-index and power grid support sub-index are obtained, and weight allocation processing is carried out, to obtain comprehensive performance evaluation result, on the premise of guaranteeing that evaluation result and engineering demand are highly matched, the grid-connected test performance evaluation precision of electrochemical energy storage system is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of grid-connected testing technology, and more specifically, to a method and system for evaluating the grid-connected performance of an electrochemical energy storage system. Background Technology

[0002] Grid-connected performance evaluation of electrochemical energy storage systems is a key technical step to ensure the safe grid connection of energy storage power stations. By quantitatively analyzing the multi-dimensional performance parameters of the system under grid-connected operation, it is determined whether it meets the requirements of grid dispatch and power market operation, which plays an important supporting role in the construction of new power systems.

[0003] Existing grid-connected testing and evaluation methods typically employ a separate, independent testing approach, measuring the energy conversion efficiency, power response speed, cycle degradation characteristics, and grid support capacity of the energy storage system separately, and determining its pass / fail status based on fixed thresholds. However, existing technologies lack integrated operational condition simulations in these separate tests, making it difficult for evaluation results to reflect the comprehensive performance under real-world operating scenarios. Furthermore, the static threshold determination method does not consider the functional differences of the energy storage system in different application scenarios, resulting in insufficient matching between the evaluation conclusions and engineering application requirements, thus affecting the guiding value of the test data for system optimization and improvement. Summary of the Invention

[0004] This invention provides a method and system for evaluating the grid-connected performance of electrochemical energy storage systems. This addresses the technical challenges of existing independent testing modes, such as the lack of operational condition simulation and the difficulty in adapting static threshold determination to different application scenarios. It achieves the technical effects of hierarchical decomposition of grid-connected commands, dynamic simulation of benchmark operating conditions, real-time data synchronization mapping, and adaptive weighting of performance indicators for application scenarios. This significantly improves the accuracy of grid-connected performance evaluation of electrochemical energy storage systems while ensuring a high degree of alignment between the evaluation results and engineering requirements.

[0005] To achieve the above objectives, the present invention provides a method for evaluating the grid-connected performance of an electrochemical energy storage system, comprising: Obtain grid connection test instructions and a set of baseline operating condition parameters, and determine multiple simulation operation datasets based on the grid connection test instructions and the set of baseline operating condition parameters; The real-time electrical data set and physical state data set of the electrochemical energy storage system during grid-connected testing are obtained, and the real-time electrical data set and physical state data set are synchronously mapped with the simulated operation data set to obtain a deviation analysis result set. Based on the deviation analysis result set, the performance indexes are decoupled to obtain the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index. The comprehensive performance evaluation result is obtained by weighting the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index.

[0006] Furthermore, when acquiring grid connection test commands and baseline operating condition parameter sets, the following are included: The grid connection test commands include power ramp rate commands, frequency adjustment commands, and voltage support commands. The reference operating condition parameter set includes the initial value of the state of charge, the temperature reference range, and the charge / discharge switching interval threshold. The test execution timing is obtained by sorting the power ramp rate command, frequency adjustment command, and voltage support command according to the command priority. The boundary conditions are marked based on the initial state of charge, the temperature reference range, and the charge / discharge switching interval threshold to obtain a test constraint mark set.

[0007] Furthermore, when determining multiple simulated operation datasets based on the grid connection test command and the baseline operating condition parameter set, the process includes: The test layer is divided according to the grid connection test command to obtain a steady-state characteristic test layer, a dynamic response test layer and an abnormal operating condition tolerance test layer. Based on the baseline operating condition parameter set, the steady-state characteristic test layer, dynamic response test layer, and abnormal operating condition tolerance test layer are respectively subjected to operating condition simulation processing to obtain the simulation operation dataset corresponding to each test layer.

[0008] Furthermore, when performing test layer division processing according to the grid connection test instructions to obtain a steady-state characteristic test layer, a dynamic response test layer, and an abnormal operating condition tolerance test layer, the process includes: Based on the test execution sequence and test constraint tag set, test type identification processing is performed to obtain continuous test tasks, transient test tasks, and impact test tasks; The continuous testing tasks are classified as the steady-state characteristic testing layer, the transient testing tasks as the dynamic response testing layer, and the impact testing tasks as the abnormal operating condition tolerance testing layer.

[0009] Furthermore, when performing operating condition simulation processing on the steady-state characteristic test layer, dynamic response test layer, and abnormal operating condition tolerance test layer respectively based on the benchmark operating condition parameter set to obtain the simulation operation dataset corresponding to each test layer, the process includes: Based on the aforementioned benchmark operating condition parameter set, construct the ideal power output curve of the steady-state characteristic test layer, the standard frequency adjustment curve of the dynamic response test layer, and the extreme operating condition tolerance curve of the abnormal operating condition tolerance test layer; Discrete sampling is performed on the ideal power output curve to obtain a steady-state simulation dataset; The standard frequency adjustment curve is segmented into time windows to obtain a dynamic simulation dataset; The stress point annotation process is performed on the extreme condition tolerance curve to obtain an abnormal simulation dataset.

[0010] Furthermore, when acquiring real-time electrical and physical state datasets of the electrochemical energy storage system during grid-connected testing, the following are included: The instantaneous values ​​of three-phase voltage and three-phase current at the grid connection interface of the electrochemical energy storage system are collected to obtain the raw electrical dataset; The temperature field distribution data and state of charge distribution data of each battery cluster inside the electrochemical energy storage system were collected to obtain the raw physical dataset. The original electrical dataset is normalized to obtain a normalized electrical dataset; The original physical dataset is subjected to consistency verification to obtain a valid physical dataset.

[0011] Furthermore, when synchronously mapping the real-time electrical dataset, physical state dataset, and simulated operation dataset to obtain the deviation analysis result set, the process includes: Establish a timestamp alignment benchmark based on the test execution sequence; The normalized electrical dataset and the effective physical dataset are paired point-by-point with the steady-state simulation dataset, the dynamic simulation dataset and the abnormal simulation dataset according to the timestamp alignment reference to obtain a set of data pairs. The data set is subjected to interpolation to obtain a response deviation sequence; The response deviation sequence is subjected to exceedance judgment processing to obtain the deviation analysis result set.

[0012] Furthermore, when performing performance index decoupling processing based on the aforementioned deviation analysis result set to obtain the energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index, the following are included: Based on the maximum deviation amplitude and the number of out-of-standard points in the deviation analysis result set, energy loss is quantitatively assessed to obtain the energy conversion sub-index. Based on the duration of exceeding the standard and the maximum deviation amplitude in the deviation analysis result set, the response timeliness is evaluated to obtain the power tracking sub-index; Based on the cumulative growth trend of the number of out-of-standard points in the deviation analysis result set, the lifespan decay is extrapolated to obtain the cycle life sub-index. The support strength is assessed based on the maximum deviation amplitude and the duration of exceeding the standard in the deviation analysis result set, and the power grid support sub-index is obtained.

[0013] Furthermore, when performing weighted adjustments based on the energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index to obtain the comprehensive performance evaluation result, the following are included: Obtain the application scenario identifier of the electrochemical energy storage system, which includes frequency regulation application scenario, peak shaving application scenario and emergency support application scenario; The first weighting coefficient of the energy conversion sub-index, the second weighting coefficient of the power tracking sub-index, the third weighting coefficient of the cycle life sub-index, and the fourth weighting coefficient of the grid support sub-index are determined based on the application scenario identifier. The energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index are weighted and summed based on the first, second, third, and fourth weighting coefficients to obtain the comprehensive performance evaluation result.

[0014] To achieve the above objectives, the present invention also provides a grid-connected performance evaluation system for electrochemical energy storage systems, comprising: The parameter processing module is used to acquire grid connection test instructions and baseline operating condition parameter sets, and determine multiple simulation operation datasets based on the grid connection test instructions and the baseline operating condition parameter sets; The deviation analysis module is used to acquire real-time electrical data sets and physical state data sets of the electrochemical energy storage system during grid-connected testing, and to perform synchronous mapping processing between the real-time electrical data sets, physical state data sets and the simulated operation data sets to obtain a deviation analysis result set. The index decoupling module is used to perform performance index decoupling processing based on the deviation analysis result set to obtain energy conversion sub-index, power tracking sub-index, cycle life sub-index and grid support sub-index; The performance evaluation module is used to perform weighting processing on the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index to obtain a comprehensive performance evaluation result.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses a method and system for evaluating the grid-connected performance of electrochemical energy storage systems. The method involves acquiring grid-connected test commands and a set of baseline operating parameters to determine multiple simulated operation datasets; acquiring real-time electrical datasets and physical state datasets; synchronously mapping the real-time electrical datasets and physical state datasets with the simulated operation datasets to obtain a deviation analysis result set; decoupling the indicators based on the deviation analysis result set to obtain energy conversion sub-indices, power tracking sub-indices, cycle life sub-indices, and grid support sub-indices; and performing weight adjustment to obtain a comprehensive performance evaluation result. This significantly improves the accuracy of grid-connected performance evaluation of electrochemical energy storage systems while ensuring a high degree of matching between the evaluation results and engineering requirements. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for evaluating the grid-connected performance of an electrochemical energy storage system according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of the structure of an electrochemical energy storage system grid-connected test performance evaluation system is shown in an embodiment of the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a method for evaluating the grid-connected performance of an electrochemical energy storage system, comprising: S110: Obtain grid connection test instructions and a set of baseline operating condition parameters, and determine multiple simulation operation datasets based on the grid connection test instructions and the set of baseline operating condition parameters; S120: Acquire the real-time electrical data set and physical state data set of the electrochemical energy storage system during grid-connected testing, and perform synchronous mapping processing between the real-time electrical data set, physical state data set and the simulated operation data set to obtain a deviation analysis result set; S130: Based on the deviation analysis result set, the performance index decoupling process is performed to obtain the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index; S140: The weights of the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index are adjusted to obtain the comprehensive performance evaluation result.

[0023] In some embodiments of this application, obtaining grid-connected test instructions and baseline operating condition parameter sets includes: The grid connection test commands include power ramp rate commands, frequency adjustment commands, and voltage support commands. The reference operating condition parameter set includes the initial value of the state of charge, the temperature reference range, and the charge / discharge switching interval threshold. The test execution timing is obtained by sorting the power ramp rate command, frequency adjustment command, and voltage support command according to the command priority. The boundary conditions are marked based on the initial state of charge, the temperature reference range, and the charge / discharge switching interval threshold to obtain a test constraint mark set.

[0024] In this embodiment, the grid connection test command is a standardized test command sequence issued by the power grid dispatch center or test master station. The power ramp rate command specifically specifies the rate of change of the active power of the energy storage system, such as increasing from 0MW to 100MW at a rate of 5% rated power / second. The frequency adjustment command specifically specifies the simulation of grid frequency deviation events, such as injecting a frequency step deviation of +0.2Hz at t=30 seconds. The voltage support command specifically specifies the voltage fluctuation scenario at the grid connection point, such as simultaneously reducing the three-phase voltage to 90% of the rated voltage at t=60 seconds and maintaining it for 5 seconds. The initial state of charge (SOC) value is set as the SOC reference point at the start of the test, and is set to 50% to ensure bidirectional charging and discharging regulation capability. The temperature reference range is set as the temperature range within which the battery cluster can operate, such as 25℃ to 35℃. When the temperature exceeds this range, the test is automatically paused. The charge / discharge switching interval threshold is set as the minimum time interval for continuous charge / discharge switching, such as 30 seconds, to prevent frequent switching of the battery cluster from causing performance misjudgment.

[0025] In this embodiment, instruction priority sorting refers to determining the execution order based on the dependencies and safety requirements of the test items. Specifically, the frequency adjustment instruction is executed before the voltage support instruction, and the power ramp-up rate instruction is executed first within each test layer. The test execution sequence is ultimately presented as a sequence of instructions with timestamps, such as [0s: power ramp-up instruction, 30s: frequency adjustment instruction, 60s: voltage support instruction, 90s: power ramp-up instruction]. Boundary condition marking refers to converting the baseline operating condition parameters into executable monitoring tags, such as marking the initial state of charge as "initial SOC=50%", marking the temperature baseline range as "allowable temperature [25, 35]℃", and marking the charge / discharge switching interval threshold as "minimum interval 30s". Finally, a test constraint tag set containing 9 boundary tags is formed for real-time comparison during the test.

[0026] The beneficial effects of the above technical solution are: by prioritizing instructions and marking boundary conditions, a structured test execution framework is established, ensuring that the test process conforms to engineering logic and security constraints, and improving the standardization and repeatability of the test process.

[0027] In some embodiments of this application, determining multiple simulated operation datasets based on the grid connection test command and the baseline operating condition parameter set includes: The test layer is divided according to the grid connection test command to obtain a steady-state characteristic test layer, a dynamic response test layer and an abnormal operating condition tolerance test layer. Based on the baseline operating condition parameter set, the steady-state characteristic test layer, dynamic response test layer, and abnormal operating condition tolerance test layer are respectively subjected to operating condition simulation processing to obtain the simulation operation dataset corresponding to each test layer.

[0028] In some embodiments of this application, when performing test layer division processing according to the grid-connected test instructions to obtain a steady-state characteristic test layer, a dynamic response test layer, and an abnormal operating condition tolerance test layer, the process includes: Based on the test execution sequence and test constraint tag set, test type identification processing is performed to obtain continuous test tasks, transient test tasks, and impact test tasks; The continuous testing tasks are classified as the steady-state characteristic testing layer, the transient testing tasks as the dynamic response testing layer, and the impact testing tasks as the abnormal operating condition tolerance testing layer.

[0029] In this embodiment, the test type identification process refers to classifying test tasks according to the time and intensity characteristics of the instructions. Among them, continuous test tasks are defined as instructions with a duration of more than 5 minutes and a power change rate of less than 10% of the rated power / minute (such as a full-power charge and discharge test lasting 2 hours). Transient test tasks are defined as fast adjustment instructions with a response time requirement of less than 10 seconds (such as a frequency regulation instruction requiring a response within 5 seconds). Impulsive test tasks are defined as tolerance tests simulating extreme faults in the power grid (such as a voltage drop to 70% of the rated voltage for 3 seconds).

[0030] In this embodiment, the classification process follows mapping rules. Continuous test tasks are classified into the steady-state characteristic test layer because they examine long-term operational stability; transient test tasks are classified into the dynamic response test layer because they examine rapid response capability; and impact test tasks are classified into the abnormal operating condition tolerance test layer because they examine survivability under extreme conditions. In specific implementation, the system has a built-in task classification mapping table. When the test execution sequence is input, the predefined classification criteria in the mapping table are automatically read, and the instruction corresponding to each timestamp is labeled with a test layer. Finally, a three-layer test structure containing 58 test points is output, of which the steady-state characteristic test layer contains 20 continuous test points, the dynamic response test layer contains 25 transient test points, and the abnormal operating condition tolerance test layer contains 13 impact test points.

[0031] The beneficial effects of the above technical solution are: by identifying and automatically classifying test task types, intelligent hierarchical management of test projects is achieved, ensuring that test projects with different characteristics are executed within the corresponding layer, and avoiding mutual interference between test projects.

[0032] In some embodiments of this application, when performing operating condition simulation processing on the steady-state characteristic test layer, dynamic response test layer, and abnormal operating condition tolerance test layer according to the benchmark operating condition parameter set to obtain the simulation operation dataset corresponding to each test layer, the process includes: Based on the aforementioned benchmark operating condition parameter set, construct the ideal power output curve of the steady-state characteristic test layer, the standard frequency adjustment curve of the dynamic response test layer, and the extreme operating condition tolerance curve of the abnormal operating condition tolerance test layer; Discrete sampling is performed on the ideal power output curve to obtain a steady-state simulation dataset; The standard frequency adjustment curve is segmented into time windows to obtain a dynamic simulation dataset; The stress point annotation process is performed on the extreme condition tolerance curve to obtain an abnormal simulation dataset.

[0033] In this embodiment, the ideal power output curve is constructed based on the rated capacity and energy conversion efficiency benchmark. Specifically, at a rated power of 100MW, the charging efficiency curve maintains a plateau characteristic of 98% efficiency in the range from 0 to 100% SOC, and the discharging efficiency curve maintains 96% efficiency in the range from 20% to 80% SOC. The curve is discretized with a step size of 1% SOC. The standard frequency regulation curve is constructed based on the grid primary frequency regulation dead zone of ±0.033Hz and the droop coefficient of 4%. When the frequency deviation is +0.2Hz, the power response value should be -20MW, and the response time should not exceed 5 seconds. The curve depicts the complete frequency response process with a step size of 0.01 seconds. The extreme operating condition tolerance curve is constructed based on the grid standard to construct a low voltage ride-through scenario. It is set that the grid connection point voltage drops sharply to 70% of the rated voltage at t=0 seconds and recovers after 0.5 seconds. The energy storage system is required not to disconnect from the grid during this process and the reactive current support is not less than 50% of the rated current.

[0034] In this embodiment, discrete sampling processing refers to extracting data points from the ideal power output curve at 10-second intervals to form a steady-state simulation dataset containing 720 sampling points; time window segmentation processing refers to segmenting the standard frequency adjustment curve into segments with a response time of 5 seconds, each segment containing 500 data points to form a dynamic simulation dataset containing 15 time windows; stress point annotation processing refers to marking three key stress locations—the voltage drop start point, the lowest point, and the recovery point—on the extreme condition withstand curve, and annotating the expected current support value for each point to form an abnormal simulation dataset containing 3 stress annotation points.

[0035] The beneficial effects of the above technical solution are: by constructing ideal operating curves in layers and performing differentiated data processing, a benchmark dataset matching the characteristics of each test layer is generated, providing an accurate reference standard for subsequent real-time data comparison.

[0036] In some embodiments of this application, acquiring real-time electrical data sets and physical state data sets of an electrochemical energy storage system during grid-connected testing includes: The instantaneous values ​​of three-phase voltage and three-phase current at the grid connection interface of the electrochemical energy storage system are collected to obtain the raw electrical dataset; The temperature field distribution data and state of charge distribution data of each battery cluster inside the electrochemical energy storage system were collected to obtain the raw physical dataset. The original electrical dataset is normalized to obtain a normalized electrical dataset; The original physical dataset is subjected to consistency verification to obtain a valid physical dataset.

[0037] In this embodiment, the sampling frequency of the instantaneous three-phase voltage is set to 10kHz, and 200 data points are collected per cycle (0.02 seconds); the sampling frequency of the instantaneous three-phase current is also 10kHz, and the digital range corresponding to the current range of 0 to 2000A is 0 to 4095.

[0038] In this embodiment, temperature field distribution data is collected by three PT100 temperature sensors installed in each battery cluster, with a sampling period of 1 second; state of charge (SOC) distribution data is obtained through the battery management system, with an update period of 100 milliseconds; normalization refers to dividing the original electrical dataset by a reference value (the voltage reference value is the rated voltage of 35kV, and the current reference value is the rated current of 1650A) to convert it into a dimensionless normalized value, which facilitates the unified comparison of parameters with different dimensions; consistency verification processing refers to performing a rationality check on the original physical dataset, eliminating outliers that exceed the range of engineering experience, retaining valid data points, and forming a valid physical dataset containing temperature values ​​and SOC values.

[0039] The beneficial effects of the above technical solution are: through multi-channel high-precision data acquisition and standardized processing, a complete dataset covering electrical interfaces and internal states is constructed, providing a reliable raw data foundation for performance evaluation.

[0040] In some embodiments of this application, when synchronously mapping the real-time electrical dataset, physical state dataset, and simulated operation dataset to obtain a deviation analysis result set, the following steps are included: Establish a timestamp alignment benchmark based on the test execution sequence; The normalized electrical dataset and the effective physical dataset are paired point-by-point with the steady-state simulation dataset, the dynamic simulation dataset and the abnormal simulation dataset according to the timestamp alignment reference to obtain a set of data pairs. The data set is subjected to interpolation to obtain a response deviation sequence; The response deviation sequence is subjected to exceedance judgment processing to obtain the deviation analysis result set.

[0041] In this embodiment, the timestamp alignment reference is based on the PPS second pulse of the GPS synchronization clock. The test start time is marked as t=0 seconds, and all subsequent data points are marked incrementally according to the sampling period. For example, the timestamp of the nth sampling point of voltage data is n×0.0001 seconds. Point-by-point pairing processing refers to pairing the normalized real-time electrical data (such as the normalized value of active power of 0.98 at t=30 seconds) with the steady-state simulation data of the same timestamp (such as the normalized value of the ideal power output curve of 1.00 at t=30 seconds) to form a data pair structure of [real-time value, simulation value]. The pairing accuracy requires a time deviation of less than 1 millisecond.

[0042] In this embodiment, the difference operation processing refers to performing an arithmetic operation of subtracting the simulated value from the real-time value for each data pair. For example, if the above data pair is calculated to obtain a deviation value of -0.02, it means that the actual power is 2% lower than the ideal value. The response deviation sequence is composed of the deviation values ​​of all time points arranged in chronological order. The out-of-limit judgment processing refers to comparing the response deviation sequence with a preset qualified threshold, wherein the energy conversion deviation threshold is set to ±3% and the power tracking deviation threshold is set to ±5%. When the absolute value of the deviation exceeds the corresponding threshold, it is marked as an out-of-limit point. The final deviation analysis result set contains information in three dimensions: the number of out-of-limit points, the duration of the out-of-limit, and the maximum deviation amplitude.

[0043] The beneficial effects of the above technical solution are: through point-by-point comparison and over-limit judgment in time synchronization, the test deviation is accurately located and quantitatively analyzed, providing structured deviation data for performance index extraction.

[0044] In some embodiments of this application, when performing performance index decoupling processing based on the deviation analysis result set to obtain the energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index, the following is included: Based on the maximum deviation amplitude and the number of out-of-standard points in the deviation analysis result set, energy loss is quantitatively assessed to obtain the energy conversion sub-index. Based on the duration of exceeding the standard and the maximum deviation amplitude in the deviation analysis result set, the response timeliness is evaluated to obtain the power tracking sub-index; Based on the cumulative growth trend of the number of out-of-standard points in the deviation analysis result set, the lifespan decay is extrapolated to obtain the cycle life sub-index. The support strength is assessed based on the maximum deviation amplitude and the duration of exceeding the standard in the deviation analysis result set, and the power grid support sub-index is obtained.

[0045] In this embodiment, the deviation analysis result set includes three structured dimensions: number of points exceeding the standard (range 0 to 500), duration of exceeding the standard (time type, in seconds, cumulative value range 0 to 3600 seconds), and maximum deviation magnitude (percentage type, range 0% to 50%). In this embodiment, the energy conversion sub-index is calculated based on the deviation data of the steady-state characteristic test layer. The specific evaluation rule is as follows: when the maximum deviation amplitude > 5% or the number of exceedance points > 10, the energy conversion sub-index = 100 - (maximum deviation amplitude × 500 + number of exceedance points × 2). The power tracking sub-index is calculated based on the deviation data of the dynamic response test layer. The specific evaluation rule is as follows: when the exceedance duration > 30 seconds or the maximum deviation amplitude > 3%, the power tracking sub-index = 100 - (exceedance duration × 1.5 + maximum deviation amplitude). ×300); The cycle life sub-index is calculated based on the changing trend of the number of out-of-specification points in the abnormal operating condition tolerance test layer. The specific evaluation rule is: count the increase in the number of out-of-specification points per 100 cycles. If the increase is >3, then the cycle life sub-index = 80 - (increase × 5); The power grid support sub-index is calculated based on the deviation data under the voltage support command. The specific evaluation rule is: when the maximum deviation amplitude is >10% or the out-of-specification duration is >5 seconds, the power grid support sub-index = 100 - (maximum deviation amplitude × 200 + out-of-specification duration × 5).

[0046] The beneficial effects of the above technical solution are: by directly utilizing the three-dimensional deviation analysis results generated in the preceding steps, a precise mapping from deviation characteristics to performance indicators is achieved, ensuring a complete closed loop of the internal logic chain of the technical solution.

[0047] In some embodiments of this application, when performing weighted adjustment processing based on the energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index to obtain the comprehensive performance evaluation result, the following is included: Obtain the application scenario identifier of the electrochemical energy storage system, which includes frequency regulation application scenario, peak shaving application scenario and emergency support application scenario; The first weighting coefficient of the energy conversion sub-index, the second weighting coefficient of the power tracking sub-index, the third weighting coefficient of the cycle life sub-index, and the fourth weighting coefficient of the grid support sub-index are determined based on the application scenario identifier. The energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index are weighted and summed based on the first, second, third, and fourth weighting coefficients to obtain the comprehensive performance evaluation result.

[0048] In this embodiment, the application scenario identifier is determined by reading the "application type" field in the project filing file. The first weighting coefficient is set to 0.2 in the frequency regulation scenario, 0.3 in the peak shaving scenario, and 0.15 in the emergency support scenario; the second weighting coefficient is set to 0.4 in the frequency regulation scenario, 0.25 in the peak shaving scenario, and 0.2 in the emergency support scenario; the third weighting coefficient is set to 0.1 in the frequency regulation scenario, 0.25 in the peak shaving scenario, and 0.15 in the emergency support scenario; and the fourth weighting coefficient is set to 0.3 in the frequency regulation scenario, 0.2 in the peak shaving scenario, and 0.5 in the emergency support scenario.

[0049] In this embodiment, the weighted summation process refers to multiplying the four sub-indices by their corresponding weights and then adding them together. For example, the comprehensive performance evaluation result in the frequency regulation scenario = energy conversion index 78 × 0.2 + power tracking index 85 × 0.4 + cycle life index 80 × 0.1 + grid support index 92 × 0.3 = 84.6 points.

[0050] The beneficial effects of the above technical solution are: by adaptively adjusting the weights driven by application scenarios, the comprehensive evaluation results can reflect the true value of the energy storage system in actual service scenarios, avoiding the evaluation distortion problem caused by a single fixed weight.

[0051] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0052] Correspondingly, such as Figure 2 As shown, this application also provides a grid-connected testing performance evaluation system for electrochemical energy storage systems, comprising: The parameter processing module is used to acquire grid connection test instructions and baseline operating condition parameter sets, and determine multiple simulation operation datasets based on the grid connection test instructions and the baseline operating condition parameter sets; The deviation analysis module is used to acquire real-time electrical data sets and physical state data sets of the electrochemical energy storage system during grid-connected testing, and to perform synchronous mapping processing between the real-time electrical data sets, physical state data sets and the simulated operation data sets to obtain a deviation analysis result set. The index decoupling module is used to perform performance index decoupling processing based on the deviation analysis result set to obtain energy conversion sub-index, power tracking sub-index, cycle life sub-index and grid support sub-index; The performance evaluation module is used to perform weighting processing on the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index to obtain a comprehensive performance evaluation result.

[0053] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0054] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0055] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the grid-connected performance of an electrochemical energy storage system, characterized in that, include: Obtain grid connection test instructions and a set of baseline operating condition parameters, and determine multiple simulation operation datasets based on the grid connection test instructions and the set of baseline operating condition parameters; The real-time electrical data set and physical state data set of the electrochemical energy storage system during grid-connected testing are obtained, and the real-time electrical data set and physical state data set are synchronously mapped with the simulated operation data set to obtain a deviation analysis result set. Based on the deviation analysis result set, the performance indexes are decoupled to obtain the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index. The comprehensive performance evaluation result is obtained by weighting the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index.

2. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 1, characterized in that, When obtaining grid connection test commands and baseline operating condition parameter sets, the following is included: The grid connection test commands include power ramp rate commands, frequency adjustment commands, and voltage support commands. The reference operating condition parameter set includes the initial value of the state of charge, the temperature reference range, and the charge / discharge switching interval threshold. The test execution timing is obtained by sorting the power ramp rate command, frequency adjustment command, and voltage support command according to the command priority. The boundary conditions are marked based on the initial state of charge, the temperature reference range, and the charge / discharge switching interval threshold to obtain a test constraint mark set.

3. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 2, characterized in that, When determining multiple simulated operation datasets based on the grid connection test instructions and the baseline operating condition parameter set, the following are included: The test layer is divided according to the grid connection test command to obtain a steady-state characteristic test layer, a dynamic response test layer and an abnormal operating condition tolerance test layer. Based on the baseline operating condition parameter set, the steady-state characteristic test layer, dynamic response test layer, and abnormal operating condition tolerance test layer are respectively subjected to operating condition simulation processing to obtain the simulation operation dataset corresponding to each test layer.

4. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 3, characterized in that, When performing test layer division processing according to the grid connection test instructions to obtain a steady-state characteristic test layer, a dynamic response test layer, and an abnormal operating condition tolerance test layer, the process includes: Based on the test execution sequence and test constraint tag set, test type identification processing is performed to obtain continuous test tasks, transient test tasks, and impact test tasks; The continuous testing tasks are classified as the steady-state characteristic testing layer, the transient testing tasks as the dynamic response testing layer, and the impact testing tasks as the abnormal operating condition tolerance testing layer.

5. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 3, characterized in that, When performing operating condition simulation processing on the steady-state characteristic test layer, dynamic response test layer, and abnormal operating condition tolerance test layer according to the benchmark operating condition parameter set to obtain the simulation operation dataset corresponding to each test layer, the following steps are included: Based on the aforementioned benchmark operating condition parameter set, construct the ideal power output curve of the steady-state characteristic test layer, the standard frequency adjustment curve of the dynamic response test layer, and the extreme operating condition tolerance curve of the abnormal operating condition tolerance test layer; Discrete sampling is performed on the ideal power output curve to obtain a steady-state simulation dataset; The standard frequency adjustment curve is segmented into time windows to obtain a dynamic simulation dataset; The stress point annotation process is performed on the extreme condition tolerance curve to obtain an abnormal simulation dataset.

6. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 1, characterized in that, When acquiring real-time electrical and physical state datasets of electrochemical energy storage systems during grid-connected testing, the following should be included: The instantaneous values ​​of three-phase voltage and three-phase current at the grid connection interface of the electrochemical energy storage system are collected to obtain the raw electrical dataset; The temperature field distribution data and state of charge distribution data of each battery cluster inside the electrochemical energy storage system were collected to obtain the raw physical dataset. The original electrical dataset is normalized to obtain a normalized electrical dataset; The original physical dataset is subjected to consistency verification to obtain a valid physical dataset.

7. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 6, characterized in that, When performing synchronous mapping processing between the real-time electrical dataset, the physical state dataset, and the simulated operation dataset to obtain the deviation analysis result set, the process includes: Establish a timestamp alignment benchmark based on the test execution sequence; The normalized electrical dataset and the effective physical dataset are paired point-by-point with the steady-state simulation dataset, the dynamic simulation dataset and the abnormal simulation dataset according to the timestamp alignment reference to obtain a set of data pairs. The data set is subjected to interpolation to obtain a response deviation sequence; The response deviation sequence is subjected to exceedance judgment processing to obtain the deviation analysis result set.

8. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 1, characterized in that, When performing performance index decoupling processing based on the aforementioned deviation analysis result set to obtain the energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index, the following are included: Based on the maximum deviation amplitude and the number of out-of-standard points in the deviation analysis result set, energy loss is quantitatively assessed to obtain the energy conversion sub-index. Based on the duration of exceeding the standard and the maximum deviation amplitude in the deviation analysis result set, the response timeliness is evaluated to obtain the power tracking sub-index; Based on the cumulative growth trend of the number of out-of-standard points in the deviation analysis result set, the lifespan decay is extrapolated to obtain the cycle life sub-index. The support strength is assessed based on the maximum deviation amplitude and the duration of exceeding the standard in the deviation analysis result set, and the power grid support sub-index is obtained.

9. The method for evaluating the grid-connected performance of an electrochemical energy storage system according to claim 1, characterized in that, When performing weighted adjustments based on the energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index to obtain the comprehensive performance evaluation result, the following are included: Obtain the application scenario identifier of the electrochemical energy storage system, which includes frequency regulation application scenario, peak shaving application scenario and emergency support application scenario; The first weighting coefficient of the energy conversion sub-index, the second weighting coefficient of the power tracking sub-index, the third weighting coefficient of the cycle life sub-index, and the fourth weighting coefficient of the grid support sub-index are determined based on the application scenario identifier. The energy conversion sub-index, power point tracking sub-index, cycle life sub-index, and grid support sub-index are weighted and summed based on the first, second, third, and fourth weighting coefficients to obtain the comprehensive performance evaluation result.

10. A grid-connected performance evaluation system for an electrochemical energy storage system, applied to the grid-connected performance evaluation method for an electrochemical energy storage system as described in any one of claims 1-9, characterized in that, include: The parameter processing module is used to acquire grid connection test instructions and baseline operating condition parameter sets, and determine multiple simulation operation datasets based on the grid connection test instructions and the baseline operating condition parameter sets; The deviation analysis module is used to acquire real-time electrical data sets and physical state data sets of the electrochemical energy storage system during grid-connected testing, and to perform synchronous mapping processing between the real-time electrical data sets, physical state data sets and the simulated operation data sets to obtain a deviation analysis result set. The index decoupling module is used to perform performance index decoupling processing based on the deviation analysis result set to obtain energy conversion sub-index, power tracking sub-index, cycle life sub-index and grid support sub-index; The performance evaluation module is used to perform weighting processing on the energy conversion sub-index, power tracking sub-index, cycle life sub-index, and grid support sub-index to obtain a comprehensive performance evaluation result.

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