An energy storage power station lean operation evaluation method based on power grid disturbance response

Abstract

This invention provides a lean operation evaluation method for energy storage power stations based on grid disturbance response, comprising: collecting operating parameters of the grid-connected point of the energy storage power station and preprocessing the operating parameters to obtain an operating data sequence; identifying grid disturbance events based on the operating data sequence; extracting disturbance features of the grid disturbance events and constructing a disturbance characteristic vector based on the disturbance features; extracting the power response sequence of the energy storage power station corresponding to the grid disturbance event, calculating response features based on the output power response sequence, and constructing a response feature vector based on the response features; calculating a disturbance response adaptability index based on the disturbance characteristic vector and the response feature vector; and generating an operation evaluation result of the energy storage power station based on the disturbance response adaptability index. This method reflects the relationship between disturbance characteristics and energy storage response behavior, avoids evaluation bias caused by directly comparing response values ​​under different disturbance conditions, and achieves a lean evaluation of the operational adaptability and response consistency of the energy storage power station.

Description

Technical Field

[0001] This invention relates to the field of power storage operation control technology, and in particular to a lean operation evaluation method for energy storage power stations based on grid disturbance response. Background Technology

[0002] With the continuous expansion of new energy grid connection, the operating characteristics of the power system have undergone significant changes, and the volatility and uncertainty of operating parameters such as grid frequency and voltage have been continuously increasing. Energy storage power stations, with their rapid adjustment capabilities, are widely used in grid frequency regulation, voltage regulation, and stability support scenarios. The grid-connected operation performance of energy storage power stations is directly related to the safety and stability of the power grid; therefore, a scientific and reasonable evaluation of their operating status has become an important research topic in the field of power system operation and management.

[0003] However, existing evaluation methods still have significant shortcomings: First, the evaluation dimensions are limited, often focusing on steady-state operation indicators or statistical performance indicators, based on long-term statistical data or ideal operating condition assumptions, making it difficult to reflect the actual dynamic response capabilities of energy storage in complex grid environments. Second, existing methods do not adequately consider grid disturbance factors, failing to incorporate disturbance characteristics into the evaluation system, and thus cannot match the diverse and frequent real-world operating scenarios of grid disturbances. Third, existing research often focuses on quantifying the response results of a single disturbance event, lacking a systematic characterization of the intrinsic relationship between disturbance characteristics and energy storage response behavior; direct numerical comparisons under different disturbance intensities and types can easily lead to evaluation biases. Summary of the Invention

[0004] This invention provides a lean operation evaluation method for energy storage power stations based on grid disturbance response, which solves the technical problems of single evaluation dimensions and insufficient consideration of disturbance factors in the existing technology.

[0005] On the one hand, this invention provides a lean operation evaluation method for energy storage power stations based on grid disturbance response, including: The operating parameters of the energy storage power station grid connection point are collected, and the operating parameters are preprocessed to obtain an operating data sequence; Based on the operational data sequence, identify power grid disturbance events; Extract the disturbance features of the power grid disturbance event, and construct a disturbance characteristic vector based on the disturbance features; Extract the power response sequence of the energy storage power station corresponding to the power grid disturbance event, calculate the response characteristics based on the output power response sequence, and construct a response feature vector based on the response characteristics; Based on the disturbance characteristic vector and the response characteristic vector, calculate the disturbance response adaptability index; Based on the aforementioned disturbance response adaptability index, an operational evaluation result for the energy storage power station is generated.

[0006] Optionally, identifying power grid disturbance events based on the operational data sequence includes: Calculate the amount and rate of change of each operating parameter in the operating data sequence between adjacent sampling times; Based on the amount of change and the rate of change, a disturbance discrimination function is constructed; When the function value of the disturbance discrimination function is greater than the preset disturbance threshold and continues for a preset duration, a power grid disturbance event is determined to have occurred.

[0007] Optionally, calculating the perturbation response adaptability index based on the perturbation characteristic vector and the response feature vector includes: The disturbance characteristic vector and the response characteristic vector are normalized respectively to obtain the disturbance normalization result and the response normalization result; Based on the perturbation normalization result and the response normalization result, a perturbation-response correspondence is constructed; Based on the disturbance-response correspondence, the disturbance response adaptability index is obtained.

[0008] Optionally, based on the perturbation normalization result and the response normalization result, a perturbation-response correspondence is constructed, including: Each feature dimension in the perturbation normalization result is added to a preset positive number to obtain the final dimension; The ratio of the feature dimension in the response normalization result to the corresponding final dimension in the perturbation normalization result is determined as the perturbation-response correspondence. Based on the disturbance-response correspondence, disturbance response adaptability indices are obtained, including: The results of each ratio are multiplied by the corresponding preset weight coefficients and then summed to obtain the summation result, which is used as the disturbance response adaptability index.

[0009] Optionally, generating the energy storage power station operation evaluation result based on the disturbance response adaptability index includes: Calculate the mean and standard deviation of the disturbance response adaptability index; The energy storage power station operation evaluation results are generated based on the average value and the standard deviation.

[0010] Optionally, generating the energy storage power station operation evaluation result based on the average value and the standard deviation includes: Determine the product of the standard deviation and the preset consistency adjustment coefficient; The difference between the average value and the product is determined as the evaluation result of the energy storage power station operation.

[0011] Optionally, the disturbance characteristics include: Disturbance amplitude, disturbance rate of change, and disturbance duration; Wherein, the disturbance amplitude is the maximum deviation of the operating parameter from its steady-state value during the disturbance period; The disturbance change rate is the maximum value of the rate of change of the operating parameter during the disturbance; The duration of the disturbance is the length of time from the start time of the disturbance to the end time of the disturbance.

[0012] Optionally, the response features include: Response amplitude characteristics, response rate of change characteristics, and response recovery characteristics; The response amplitude characteristic is the maximum adjustment amplitude of the energy storage power station's output power during the response process; The response change rate characteristic is the maximum rate of change of the output power of the energy storage power station. The response recovery characteristic is the shortest time required for the output power of the energy storage power station to recover to the steady-state range.

[0013] Optionally, the preprocessing includes: Time synchronization and data smoothing.

[0014] Optionally, the time synchronization processing and data smoothing processing include: Each operating parameter is aligned according to a unified time base to obtain time synchronization parameters; The time synchronization parameters are smoothed using a sliding window averaging method to obtain the running data sequence.

[0015] This invention provides a lean operation evaluation method for energy storage power stations based on grid disturbance response. Based on operational data sequences, it identifies grid disturbance events; extracts disturbance characteristics of these events and constructs a disturbance characteristic vector; extracts the power response sequence of the energy storage power station corresponding to the grid disturbance event, calculates response characteristics based on the output power response sequence, and constructs a response characteristic vector based on these characteristics; calculates a disturbance response adaptability index based on the disturbance characteristic vector and the response characteristic vector; and generates an energy storage power station operation evaluation result based on the disturbance response adaptability index. This method evaluates the actual dynamic response capability of energy storage power stations in complex grid environments. By incorporating grid disturbance characteristics into the evaluation system, it can match the diverse and frequent actual operation scenarios of grid disturbances. By constructing a correspondence between disturbances and responses and calculating adaptability indices, it reflects the connection between disturbance characteristics and energy storage response behavior, avoiding evaluation bias caused by directly comparing response values ​​under different disturbance conditions, and achieving a lean evaluation of the operational adaptability and response consistency of energy storage power stations. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the lean operation evaluation method for energy storage power stations based on grid disturbance response provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the process for identifying power grid disturbance events provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] Figure 1 This is a flowchart illustrating the lean operation evaluation method for energy storage power stations based on grid disturbance response provided in this embodiment of the invention.

[0020] See Figure 1 The lean operation evaluation method for energy storage power stations based on grid disturbance response includes the following steps.

[0021] Step 101: Collect the operating parameters of the energy storage power station's grid connection point and preprocess the operating parameters to obtain the operating data sequence.

[0022] In this step, the operating parameters may include at least the grid frequency, voltage, and the output power of the energy storage power station. The operating parameters can be represented by the following formula (1): (1); in, For the runtime parameter vector; This refers to the time when the operating parameters were collected. The grid frequency at the point where the energy storage power station is connected to the grid; This refers to the voltage at the grid connection point of the energy storage power station. This refers to the output power of the energy storage power station to the power grid.

[0023] Preprocessing includes time synchronization and data smoothing.

[0024] Specifically, time synchronization and data smoothing processes include: Each operating parameter is aligned according to a unified time base to obtain the time synchronization parameters, as shown in the following formula (2): (2); in, These are the time synchronization parameters after alignment; This is a time synchronization compensation amount, used to uniformly map data from different sampling channels to the same time base.

[0025] The time synchronization parameters are smoothed using a sliding window averaging method to obtain the running data sequence, as shown in formula (3) below: (3); in, The smoothed running data; This represents the number of sampling points within the sliding time window. The sampling time interval; The summation index represents the index within the sliding window. Number of sampling points; For a moment Previous The parameter values ​​for each sampling period. For the time-synchronized operating parameters at time The parameter value.

[0026] Step 102: Identify power grid disturbance events based on the operational data sequence.

[0027] Based on operational data sequences, identify power grid disturbance events, including: Calculate the amount and rate of change of each operating parameter in the running data sequence between adjacent sampling times; The change is shown in the following formula (4): (4); in, The change; For a moment The parameter value at the previous moment; The rate of change is shown in the following formula (5): (5); in, The rate of change.

[0028] Based on the amount of change and the rate of change, a disturbance discrimination function is constructed; Specifically, the disturbance discrimination function is shown in the following formula (6): (6); in, Let be the perturbation discrimination function; , , These represent the rates of change of frequency, voltage, and power, respectively. and They are respectively and The weighting coefficients are used to balance the influence of different operating parameters in disturbance discrimination; When the function value of the disturbance discrimination function is greater than the preset disturbance threshold and continues for a preset duration, a power grid disturbance event is determined to occur, as shown in the following formula (7): (7); in, This is the preset perturbation threshold.

[0029] This step constructs a disturbance discrimination function by calculating the amount and rate of change of operating parameters, and combines it with preset disturbance thresholds and duration to accurately identify power grid disturbance events, thereby improving the accuracy of disturbance event identification.

[0030] Step 103: Extract the disturbance features of power grid disturbance events and construct a disturbance characteristic vector based on the disturbance features.

[0031] Disturbance characteristics include: Disturbance amplitude, disturbance rate of change, and disturbance duration; Wherein, the disturbance amplitude is the maximum deviation of the operating parameter from the steady-state value during the disturbance period, as shown in the following formula (8): (8); in, for The corresponding steady-state value; This is the maximum offset; The rate of change of the disturbance is the maximum value of the rate of change of the operating parameter during the disturbance, as shown in the following formula (9): (9); in, This represents the maximum rate of change. The duration of the disturbance is the time from the start time of the disturbance to the end time of the disturbance, as shown in the following formula (10): (10); in, The duration; This is the time when the disturbance ends; This is the start time of the disturbance.

[0032] The disturbance characteristic vector is shown in the following formula (11): (11); in, This is the disturbance characteristic vector.

[0033] Step 104: Extract the power response sequence of the energy storage power station corresponding to the grid disturbance event, calculate the response characteristics based on the output power response sequence, and construct the response feature vector based on the response characteristics.

[0034] Response characteristics include: response amplitude characteristics, response rate of change characteristics, and response recovery characteristics; Among them, the response amplitude characteristic is the maximum adjustment amplitude of the output power of the energy storage power station during the response process; The response rate of change characteristic is the maximum rate of change of the output power of the energy storage power station; The response recovery characteristic is the shortest time required for the output power of the energy storage power station to recover to the steady-state range.

[0035] The power response sequence is shown in the following formula (12): (12); in, For the first Secondary disturbance event; The active power output of the energy storage power station; For the first The output power response sequence of the energy storage power station corresponding to the disturbance event; Using the start time of the disturbance as the time reference, the response sequence is time-aligned as shown in the following formula (13): (13); in, This is the time-aligned power response sequence; It is a time variable relative to the start time of the disturbance; This is the start time of the disturbance; The response amplitude characteristics are shown in the following formula (14): (14); in, This represents the maximum adjustment amplitude of the energy storage power station's output power during the response process. For the first Steady-state reference value of the output power of the energy storage power station before the occurrence of the disturbance event.

[0036] The response rate of change is characterized by the following formula (15): (15); in, The maximum rate of change in the output power of the energy storage power station; The rate of change of power; The response recovery characteristics are shown in the following formula (16): (16); in, The shortest time required for the output power of an energy storage power station to recover to the steady-state range; The preset steady-state judgment threshold is used.

[0037] The response feature vector is shown in the following formula (17): (17); in, The response feature vector.

[0038] Step 105: Calculate the perturbation response adaptability index based on the perturbation characteristic vector and the response characteristic vector.

[0039] Step 106: Generate the operation evaluation results of the energy storage power station based on the disturbance response adaptability index.

[0040] In this embodiment, grid disturbance events are identified based on operational data sequences; disturbance characteristics of grid disturbance events are extracted, and disturbance characteristic vectors are constructed based on these characteristics; power response sequences of energy storage power stations corresponding to grid disturbance events are extracted, response characteristics are calculated based on the output power response sequences, and response characteristic vectors are constructed based on these response characteristics; disturbance response adaptability indices are calculated based on the disturbance characteristic vectors and response characteristic vectors; and operational evaluation results of energy storage power stations are generated based on the disturbance response adaptability indices, thus realizing the evaluation of the actual dynamic response capability of energy storage power stations in complex grid environments. By incorporating grid disturbance characteristics into the evaluation system, the system can match the diverse and frequent actual operational scenarios of grid disturbances. By constructing the correspondence between disturbances and responses and calculating adaptability indices, the system reflects the connection between disturbance characteristics and energy storage response behavior, avoiding evaluation bias caused by directly comparing response values ​​under different disturbance conditions, and achieving a refined evaluation of the operational adaptability and response consistency of energy storage power stations.

[0041] In one embodiment of this specification, a perturbation response adaptability index is calculated based on the perturbation characteristic vector and the response feature vector, including: The disturbance characteristic vector and the response characteristic vector are normalized respectively to obtain the disturbance normalization result and the response normalization result; The perturbation normalization result is shown in the following formula (18): (18); in, This is the result of perturbation normalization; and These represent the minimum and maximum values ​​of the disturbance characteristic parameters, respectively. The response normalization result is shown in the following formula (19): (19); in, In response to the normalization results; and These represent the minimum and maximum values ​​of the response characteristic parameters, respectively.

[0042] Based on the perturbation normalization results and the response normalization results, a perturbation-response correspondence is constructed; Based on the disturbance-response correspondence, the disturbance response adaptability index is obtained.

[0043] In this embodiment, by normalizing the disturbance characteristic vector and the response characteristic vector, a disturbance-response correspondence is constructed, and then the disturbance response adaptability index is calculated, which enhances the comparability of response capability evaluation under different disturbance conditions.

[0044] In one embodiment of this specification, a perturbation-response correspondence is constructed based on the perturbation normalization result and the response normalization result, including: Each feature dimension in the perturbation normalization result is added to a preset positive number to obtain the final dimension; The ratio of the feature dimension in the response normalization result to the corresponding final dimension in the perturbation normalization result is determined as the perturbation-response correspondence. Based on the disturbance-response correspondence, disturbance response adaptability indices are obtained, including: The results of each ratio are multiplied by the corresponding preset weight coefficients and then summed to obtain the summation result, which is used as the disturbance response adaptability index.

[0045] Specifically, the disturbance response adaptability index is shown in the following formula (20): (20); in, For the first The perturbation response adaptability index for the sub-disturbance event; The eigenvector of the eigenvector One dimension; For the first In the second disturbance event, the energy storage power station The normalized value of each response feature; For the first The second disturbance event The normalized value of the disturbance characteristic; For the first The weight coefficients corresponding to each feature; For extremely small positive numbers, such as ; This represents the total number of dimensions of the feature vectors.

[0046] In this embodiment, the ratio of the feature dimension in the response normalization result to the corresponding final dimension in the disturbance normalization result is determined as the disturbance-response correspondence. Each ratio result is multiplied by a preset corresponding weight coefficient and then summed to obtain the summation result, which serves as the disturbance response adaptability index. This approach provides a more comprehensive consideration of power grid disturbance factors, can match the diverse and frequent actual operating scenarios of power grid disturbances, reduces evaluation bias, and improves the accuracy of evaluation results.

[0047] In one embodiment of this specification, an operational evaluation result for an energy storage power station is generated based on a disturbance response adaptability index, including: Calculate the mean and standard deviation of the disturbance response adaptability index; The operation evaluation results of the energy storage power station are generated based on the mean and standard deviation.

[0048] The response performance of an energy storage power station under a single disturbance condition is insufficient to fully reflect its operational level. Therefore, a unified analysis is conducted on the adaptive indices obtained under multiple disturbance events. The set of adaptive indices obtained under U disturbance events is shown in the following formula (21): (twenty one); The average value is shown in the following formula (22): (twenty two); The standard deviation is shown in the following formula (23): (twenty three); in, A set of adaptive indicators; These are the adaptive indicators for each disturbance response; This represents the average of the set of adaptation indicators; Let i be the adaptive index for the disturbance response; The standard deviation of the set of adaptive indicators.

[0049] In this embodiment, by calculating the average value and standard deviation of the disturbance response adaptability index, and generating the energy storage power station operation evaluation results accordingly, the overall adaptability and response consistency of the energy storage power station can be reflected simultaneously.

[0050] In one embodiment of this specification, an operational evaluation result for an energy storage power station is generated based on the average value and standard deviation, including: Determine the product of the standard deviation and the preset consistency adjustment coefficient; The difference between the average value and the product is determined as the evaluation result of the energy storage power station operation.

[0051] The evaluation results of the energy storage power station operation are shown in the following formula (24): (twenty four); in, The results of the energy storage power station operation evaluation; This is a preset consistency adjustment coefficient.

[0052] In this embodiment, the difference between the average value and the standard deviation after adjustment by the consistency adjustment coefficient is used as the operation evaluation result, which directly assesses the consistency of the response behavior of the energy storage power station, thereby achieving a more accurate and stable lean operation evaluation.

[0053] Figure 2 This is a schematic flowchart illustrating the process of identifying power grid disturbance events according to an embodiment of the present invention. See also... Figure 2 Specifically, it includes the following steps: Step 201: Collect the operating parameters of the energy storage power station's grid connection point and preprocess the operating parameters to obtain the operating data sequence.

[0054] Step 202: Calculate the amount and rate of change of each running parameter in the running data sequence between adjacent sampling times.

[0055] Step 203: Construct a disturbance discrimination function based on the amount of change and the rate of change.

[0056] Step 204: Determine whether the function value of the disturbance discrimination function is greater than the preset disturbance threshold and continues for a preset duration.

[0057] Step 205: If yes, confirm that a power grid disturbance event has occurred and output the power grid disturbance event. If no, return to step 201.

[0058] In some other embodiments of this specification, the method further includes: The disturbance characteristic vector, response feature vector, and corresponding disturbance response adaptability index are associated and stored to obtain a disturbance response case library; When evaluating a new primary power grid disturbance event, similar historical disturbance cases are matched from the disturbance response case library, and their adaptability indicators are used as the reference benchmark for the evaluation results.

[0059] In some other embodiments of this specification, the method further includes: Establish a reliability curve for the service capability of an energy storage power station based on multiple disturbance events; the curve describes the probability distribution of obtaining a specific evaluation level under different disturbance intensities. Based on the credibility curve, construct an assessment report on the performance capability of this energy storage in the power ancillary services market; The assessment report will be quantified into a market credit score, which will be used as a capacity conversion factor or a basis for reducing or waiving security deposits when participating in service bidding.

[0060] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units.

[0061] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as OM / AM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A lean operation evaluation method for energy storage power stations based on grid disturbance response, characterized in that, include: The operating parameters of the energy storage power station grid connection point are collected, and the operating parameters are preprocessed to obtain an operating data sequence; Based on the operational data sequence, identify power grid disturbance events; Extract the disturbance features of the power grid disturbance event, and construct a disturbance characteristic vector based on the disturbance features; Extract the power response sequence of the energy storage power station corresponding to the power grid disturbance event, calculate the response characteristics based on the output power response sequence, and construct a response feature vector based on the response characteristics; Based on the disturbance characteristic vector and the response characteristic vector, calculate the disturbance response adaptability index; Based on the aforementioned disturbance response adaptability index, an operational evaluation result for the energy storage power station is generated.

2. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response as described in claim 1, characterized in that, The process of identifying power grid disturbance events based on the operational data sequence includes: Calculate the amount and rate of change of each operating parameter in the operating data sequence between adjacent sampling times; Based on the amount of change and the rate of change, a disturbance discrimination function is constructed; When the function value of the disturbance discrimination function is greater than the preset disturbance threshold and continues for a preset duration, a power grid disturbance event is determined to have occurred.

3. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 1, characterized in that, The calculation of the perturbation response adaptability index based on the perturbation characteristic vector and the response feature vector includes: The disturbance characteristic vector and the response characteristic vector are normalized respectively to obtain the disturbance normalization result and the response normalization result; Based on the perturbation normalization result and the response normalization result, a perturbation-response correspondence is constructed; Based on the disturbance-response correspondence, the disturbance response adaptability index is obtained.

4. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 3, characterized in that, Based on the perturbation normalization result and the response normalization result, a perturbation-response correspondence is constructed, including: Each feature dimension in the perturbation normalization result is added to a preset positive number to obtain the final dimension; The ratio of the feature dimension in the response normalization result to the corresponding final dimension in the perturbation normalization result is determined as the perturbation-response correspondence. Based on the disturbance-response correspondence, disturbance response adaptability indices are obtained, including: The results of each ratio are multiplied by the corresponding preset weight coefficients and then summed to obtain the summation result, which is used as the disturbance response adaptability index.

5. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 1, characterized in that, The process of generating an operational evaluation result for the energy storage power station based on the disturbance response adaptability index includes: Calculate the mean and standard deviation of the disturbance response adaptability index; The energy storage power station operation evaluation results are generated based on the average value and the standard deviation.

6. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 5, characterized in that, The step of generating an energy storage power station operation evaluation result based on the average value and the standard deviation includes: Determine the product of the standard deviation and the preset consistency adjustment coefficient; The difference between the average value and the product is determined as the evaluation result of the energy storage power station operation.

7. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 1, characterized in that, The disturbance features include: Disturbance amplitude, disturbance rate of change, and disturbance duration; Wherein, the disturbance amplitude is the maximum deviation of the operating parameter relative to the steady-state value during the disturbance; The disturbance change rate is the maximum value of the rate of change of the operating parameter during the disturbance; The duration of the disturbance is the length of time from the start time of the disturbance to the end time of the disturbance.

8. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 1, characterized in that, The response features include: Response amplitude characteristics, response rate of change characteristics, and response recovery characteristics; The response amplitude characteristic is the maximum adjustment amplitude of the energy storage power station's output power during the response process; The response change rate characteristic is the maximum rate of change of the output power of the energy storage power station; The response recovery characteristic is the shortest time required for the output power of the energy storage power station to recover to the steady-state range.

9. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 1, characterized in that, The preprocessing includes: Time synchronization and data smoothing.

10. The method for evaluating the lean operation of energy storage power stations based on grid disturbance response according to claim 9, characterized in that, The time synchronization processing and data smoothing processing include: Each operating parameter is aligned according to a unified time base to obtain time synchronization parameters; The time synchronization parameters are smoothed using a sliding window averaging method to obtain the running data sequence.

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