A multi-dimensional adjustment capability-based energy storage device characterization aggregation method and system

Abstract

The application discloses a multi-dimensional adjustment capacity-based energy storage equipment characterization aggregation method and system, and the method comprises the following steps: obtaining and preprocessing operation data of various energy storage equipments; calculating characterization indexes corresponding to energy storage equipment adjustment capacity characterization based on the preprocessed operation data; performing multi-dimensional feature analysis on an energy storage equipment cluster in which all the energy storage equipments are located to obtain an analysis result; calculating an adjustment weight of the energy storage equipment based on the analysis result; and calculating multi-dimensional total adjustment capacity of the energy storage equipment cluster by using the characterization indexes and the adjustment weight of the energy storage equipment.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, and mainly to a method and system for characterizing and aggregating energy storage devices based on multi-dimensional regulation capabilities. Background Technology

[0002] With the advancement of the "dual carbon" goals and the high proportion of new energy sources (wind power, photovoltaics, etc.) integrated into the power system, the "source-grid-load" characteristics of the power system are undergoing profound changes: the randomness and volatility of new energy output exacerbate the risk of active / reactive power imbalance in the power grid, and reduce the frequency and voltage stability margins. This places multi-dimensional, highly precise, and large-scale demands on the regulation capabilities of energy storage devices. Massive energy storage devices (such as battery energy storage, flywheel energy storage, supercapacitors, etc.) serve as flexible regulation resources, providing support in scenarios such as power balance, voltage stability, and frequency stability, and have become a core means to resolve the contradiction between new energy consumption and the safe operation of the power grid.

[0003] However, existing technologies struggle to meet the demands of large-scale, multi-scenario energy storage coordinated regulation. The main shortcomings are as follows: 1. Incomplete characterization of individual device regulation capabilities: Existing methods primarily describe regulation capabilities using basic parameters such as "power and time," lacking quantitative characterization of multi-dimensional characteristics. 2. Poor adaptability of large-scale cluster aggregation models: When the number of energy storage devices is large (hundreds or thousands), their types are mixed (batteries / flywheels / supercapacitors), and they are spatially dispersed (distributed across different voltage levels or regions), existing "simple weighting" methods fail to consider spatial distribution differences (e.g., devices in remote areas contribute less to the core grid regulation than those near load centers), fail to differentiate device type characteristics (e.g., the value difference between the fast response characteristics of flywheels and the large capacity characteristics of batteries in different scenarios), and fail to correlate with dynamic operating states (e.g., devices with low remaining capacity cannot continuously participate in regulation), leading to significant deviations between aggregation results and actual regulation capabilities. 3. Insufficient coordination of multi-dimensional regulation capabilities: Existing technologies often design regulation models for a single dimension, lacking synergistic considerations.

[0004] Therefore, there is an urgent need for a method that can fully characterize the multi-dimensional adjustment capabilities of a single device, accurately aggregate the characteristics of large-scale clusters, and adapt to collaborative control in multiple scenarios. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method and system for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities.

[0006] The technical solution of the present invention is as follows:

[0007] On one hand, this invention proposes a method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities, the method comprising:

[0008] Acquire and preprocess operational data from various energy storage devices;

[0009] Calculate the corresponding characterization index for the regulation capability of energy storage equipment based on the preprocessed operating data.

[0010] A multi-dimensional feature analysis was performed on the energy storage clusters containing all energy storage devices, and the analysis results were obtained.

[0011] Based on the analysis results, the regulation weights of energy storage devices are calculated, and the multi-dimensional total regulation capability of the energy storage device cluster is obtained by using the characterization indicators and regulation weights of the energy storage devices.

[0012] Preferably, the preprocessing includes denoising and normalization, wherein denoising is performed using Kalman filtering.

[0013] Preferably, the regulation capability characterization includes power balance regulation capability, voltage frequency stability regulation capability, and frequency stability regulation capability, and the corresponding characterization indicators are active power regulation capacity index, reactive power regulation capacity index, and frequency response coefficient.

[0014] The active power regulation capacity index is obtained based on the difference between the product of maximum charging power and charging time and the product of maximum discharging power and discharging time.

[0015] The reactive power regulation capacity index is obtained based on the difference between the maximum leading reactive power and the maximum lagging reactive power.

[0016] The frequency response coefficient is obtained by differentiating the active power with respect to the frequency.

[0017] Preferably, the multi-dimensional features are spatial distribution characteristics, type difference characteristics, and operating status characteristics, and the corresponding analysis results are distribution uniformity index, equipment type feature vector, and status distribution matrix;

[0018] The distribution uniformity index is calculated based on the average density of the energy storage device cluster and the energy storage density within the region.

[0019] Establish a feature vector for equipment type based on the power response speed, rated energy capacity, and cycle life of energy storage equipment clusters;

[0020] A state distribution matrix is ​​established based on the proportion of energy storage devices in different remaining capacity ranges.

[0021] Preferably, the adjustment weights include the aggregate weight, spatial weight, type weight, and frequency regulation contribution weight of the energy storage device;

[0022] The location importance score of each energy storage device was obtained using the analytic hierarchy process (AHP).

[0023] The aggregate weight of each energy storage device is calculated based on the location importance score, the remaining capacity of the energy storage device, the preset health status index, and the preset adjustment factor.

[0024] Spatial weights are calculated based on the regional load importance and distribution uniformity index;

[0025] Based on the current energy storage equipment type, the corresponding equipment type features are extracted from the equipment type feature vector and normalized to obtain the type weight;

[0026] The frequency regulation contribution weight is calculated based on the state distribution matrix and the remaining capacity of the energy storage device.

[0027] Preferably, the multi-dimensional total regulation capability includes active power total regulation capability, reactive power total regulation capability, and frequency response characteristic aggregation capability;

[0028] The active power regulation capacity index of each energy storage device is accumulated and multiplied with the corresponding aggregate weight to obtain the total active power regulation index, which is used to characterize the total active power regulation capability.

[0029] The total reactive power regulation index is obtained by summing the product of the reactive power regulation capacity index, spatial weight, and type weight of each energy storage device, which is used to characterize the total reactive power regulation capability.

[0030] Frequency response characteristics are calculated based on the frequency response coefficient and the frequency regulation contribution weight, which are used to characterize the aggregation capability of frequency response characteristics.

[0031] Preferably, the method further includes evaluating the multi-dimensional overall regulation capability of the energy storage device cluster based on preset evaluation indicators to obtain an evaluation value;

[0032] The evaluation indicators include power balance error, voltage stability margin, and frequency deviation;

[0033] If the evaluated value is greater than the preset threshold, the corresponding optimization strategy will be triggered.

[0034] On the other hand, the present invention also proposes an energy storage device characterization aggregation system based on multi-dimensional adjustment capabilities, the system comprising:

[0035] The data acquisition module is used to acquire and preprocess operational data from various energy storage devices.

[0036] The characterization index calculation module is used to calculate the characterization index corresponding to the regulation capability of energy storage equipment based on the preprocessed operating data.

[0037] The feature analysis module is used to perform multi-dimensional feature analysis on the energy storage device cluster containing all energy storage devices and obtain the analysis results;

[0038] The regulation capacity calculation module calculates the regulation weight of energy storage devices based on the analysis results, and uses the characterization indicators of energy storage devices and regulation weights to calculate the multi-dimensional total regulation capacity of the energy storage device cluster.

[0039] In another aspect, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities as described in any one of the present invention.

[0040] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities as described in any one of the present invention.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. This invention provides a method and system for characterizing and aggregating energy storage devices based on multi-dimensional regulation capabilities. By quantifying the multi-dimensional characteristics of a single device through differentiated indicators, it solves the problem of traditional methods only partially characterizing the regulation potential of devices, improves the quantitative accuracy of the regulation capabilities of a single device, and enhances the matching efficiency between device characteristics and grid demand.

[0043] 2. This invention provides a method and system for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities. It combines spatial, type, and state three-dimensional features to calculate weights, determines spatial weights based on distribution uniformity and regional load importance, extracts key parameters from type feature vectors according to scenarios to calculate type weights, corrects frequency adjustment weights through state distribution matrix, and finally aggregates to obtain the total cluster capacity. This solves the problem of poor adaptability of traditional "simple weighting" to massive, heterogeneous, and distributed energy storage, and improves the accuracy of large-scale cluster aggregation results.

[0044] 3. This invention provides a method and system for characterizing and aggregating energy storage devices based on multi-dimensional regulation capabilities. It constructs a closed loop of "total regulation capability - evaluation index - optimization strategy". When the index exceeds the limit, it automatically triggers the device's charging and discharging adjustment or compensation strategy, which solves the problem of insufficient coordination in traditional "single-dimensional regulation". It realizes the dynamic matching of energy storage regulation and grid security needs, improves the grid's ability to resist new energy fluctuations, and enhances the utilization efficiency of energy storage resources. Attached Figure Description

[0045] Figure 1 This is a detailed flowchart of an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0048] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0049] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0050] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0051] Example 1:

[0052] See details Figure 1 This invention provides a method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities, the method comprising:

[0053] S1. Acquire and preprocess operational data from various energy storage devices;

[0054] The types of energy storage devices include battery energy storage, flywheel energy storage, and supercapacitors;

[0055] The preprocessing includes denoising and normalization, wherein denoising is performed using Kalman filtering;

[0056] S2. Calculate the characterization index corresponding to the regulation capability of the energy storage device based on the preprocessed operating data.

[0057] The regulation capability is characterized by power balance regulation capability, voltage and frequency stability regulation capability, and frequency stability regulation capability, and the corresponding characterization indicators are active power regulation capacity index, reactive power regulation capacity index, and frequency response coefficient.

[0058] The active power regulation capacity index is obtained based on the difference between the product of maximum charging power and charging time and the product of maximum discharging power and discharging time. This index characterizes the net active power regulation that the energy storage device can provide per unit time. A positive value indicates the advantage of charging regulation, while a negative value indicates the advantage of discharging regulation. The formula is as follows:

[0059] ;

[0060] In the formula, Indicates the first Active power regulation capacity index of an energy storage device; Indicates the first The maximum charging power of each energy storage device; Indicates the first The continuous charging time of an energy storage device; Indicates the first The maximum discharge power of each energy storage device; Indicates the first The continuous discharge time of an energy storage device;

[0061] Based on the difference between the maximum leading reactive power and the maximum lagging reactive power, a reactive power regulation capacity index is obtained, where a positive value indicates the advantage of capacitive reactive power regulation and a negative value indicates the advantage of inductive reactive power regulation, expressed by the formula:

[0062] ;

[0063] In the formula, Indicates the first The reactive power regulation capacity index of an energy storage device; Indicates the first The maximum advanced reactive power of each energy storage device; Indicates the first The maximum lagging reactive power of an energy storage device;

[0064] The frequency response coefficient is obtained by differentiating the active power with respect to frequency, which characterizes the active power adjustment rate corresponding to a unit frequency deviation at the rated frequency. It is expressed by the following formula:

[0065] ;

[0066] In the formula, Indicates the first Frequency response coefficient of an energy storage device; Indicates the first The active power of each energy storage device; Indicates the rated frequency;

[0067] S3. Perform multi-dimensional feature analysis on the energy storage equipment clusters containing all energy storage devices to obtain the analysis results;

[0068] The multi-dimensional features are specifically spatial distribution characteristics, type difference characteristics, and operating status characteristics, and the corresponding analysis results are distribution uniformity index, equipment type feature vector, and status distribution matrix.

[0069] The distribution uniformity index is calculated based on the average density of the energy storage device cluster and the energy storage density within the region, expressed by the formula:

[0070] ;

[0071] ;

[0072] In the formula, Represents the distribution evenness index, if The closer it is to 1, the more uniform the distribution. Indicates the total number of regions; Indicates the first Index values ​​for each region; Indicates the first Energy storage density in each region; Indicates the first Number of energy storage devices in each region; Indicates the first The area of ​​each region; This indicates the average density of the energy storage device cluster;

[0073] Based on the power response speed, rated energy capacity, and cycle life of energy storage device clusters, a feature vector for each device type is established, expressed by the formula:

[0074] ;

[0075] In the formula, Indicates the first Equipment type feature vector of an energy storage equipment cluster; Indicates the first The power response speed of an energy storage device cluster; Indicates the first The rated energy capacity of an energy storage device cluster; Indicates the first Cycle life of an energy storage device cluster;

[0076] A state distribution matrix is ​​established based on the proportion of energy storage devices in different remaining capacity ranges, expressed by the formula:

[0077] ;

[0078] In the formula, Represents the state distribution matrix; This indicates the proportion of high-energy-storage equipment, where equipment with a remaining capacity greater than 80% is considered to have a high-energy-storage equipment proportion. This indicates the proportion of medium-sized energy storage devices, with the remaining capacity between 20% and 80% representing the proportion of medium-sized energy storage devices. This indicates the proportion of low-capacity energy storage devices, with those having less than 20% remaining capacity representing the proportion of medium-capacity energy storage devices.

[0079] S4. Calculate the regulation weight of the energy storage equipment based on the analysis results, and use the characterization index and regulation weight of the energy storage equipment to calculate the multi-dimensional total regulation capability of the energy storage equipment cluster.

[0080] S41, The adjustment weights include the aggregate weight, spatial weight, type weight, and frequency regulation contribution weight of the energy storage device;

[0081] The location importance score of each energy storage device was obtained using the analytic hierarchy process (AHP).

[0082] The aggregate weight of each energy storage device is calculated based on its location importance score, remaining capacity, preset health status index, and preset adjustment factor, and is expressed by the following formula:

[0083] ;

[0084] ;

[0085] In the formula, Indicates the first Aggregate weight of individual energy storage devices; Indicates the first Location importance score for each energy storage device; Indicates the first Normalized value of the remaining capacity of an energy storage device; Indicates the first Health status index of an energy storage device; Indicates the first The remaining capacity of the energy storage device; Indicates the first The rated capacity of each energy storage device; Indicates the number of energy storage devices;

[0086] The spatial weight is calculated based on the regional load importance and distribution evenness index, and is expressed by the following formula:

[0087] ;

[0088] In the formula, Indicates the first Spatial weight of each energy storage device; Indicates the first Regional load importance of each area;

[0089] Based on the current energy storage equipment type, the corresponding equipment type features are extracted from the equipment type feature vector and normalized to obtain the type weight;

[0090] If the current energy storage device is a flywheel energy storage device, the power response speed is the core influencing factor for type differences. The power response speed is extracted, normalized, and the type weight is obtained, expressed by the formula:

[0091] ;

[0092] In the formula, Indicates the first The type weight of each energy storage device; Indicates the first Energy conversion efficiency of an energy storage device cluster;

[0093] If the current energy storage device is battery energy storage, the rated energy capacity is extracted and normalized to obtain the type weight, which is expressed by the formula:

[0094] ;

[0095] If the current energy storage device is a supercapacitor, the cycle life is extracted and normalized to obtain the type weight, which is expressed by the formula:

[0096] ;

[0097] In the formula, Indicates the first The purchase cost of an energy storage equipment cluster;

[0098] The frequency regulation contribution weight is calculated based on the state distribution matrix and the remaining capacity of the energy storage device, and is expressed by the following formula:

[0099] ;

[0100] In the formula, Indicates the first The frequency regulation contribution weight of each energy storage device;

[0101] S42, The multi-dimensional total regulation capability includes active power total regulation capability, reactive power total regulation capability, and frequency response characteristic aggregation capability;

[0102] The total active power regulation index is obtained by summing the product of the active power regulation capacity index and the corresponding aggregate weight of each energy storage device. This index characterizes the total active power regulation capability and is expressed by the formula:

[0103] ;

[0104] In the formula, This represents the total active power regulation index;

[0105] The total reactive power regulation index is obtained by summing the products of the reactive power regulation capacity index, spatial weight, and type weight of each energy storage device. This index characterizes the total reactive power regulation capability and is expressed by the formula:

[0106] ;

[0107] In the formula, This represents the total reactive power regulation index;

[0108] The frequency response characteristics are calculated based on the frequency response coefficient and the frequency regulation contribution weight, which are used to characterize the aggregation capability of the frequency response characteristics. This is expressed by the following formula:

[0109] ;

[0110] In the formula, Indicates frequency response characteristics;

[0111] S5. The method further includes evaluating the multi-dimensional overall regulation capability of the energy storage equipment cluster based on preset evaluation indicators to obtain an evaluation value.

[0112] The evaluation indicators include power balance error, voltage stability margin, and frequency deviation;

[0113] The power balance error is expressed by the formula:

[0114] ;

[0115] In the formula, This indicates the power balance error value; This indicates the active power demand.

[0116] The voltage stability margin is expressed by the formula:

[0117] ;

[0118] In the formula, This represents the voltage stability margin value; Indicates the maximum voltage at a critical node in the power grid; This represents the minimum voltage at a critical node in the power grid. Indicates the rated voltage of critical nodes in the power grid;

[0119] The frequency deviation is expressed by the formula:

[0120] ;

[0121] In the formula, Indicates the frequency deviation value; Indicates the rated frequency;

[0122] If the evaluated value is greater than a preset threshold, the corresponding optimization strategy is triggered, where the evaluated value is the power balance error value, voltage stability margin value, and frequency deviation value; the optimization strategy is specifically as follows:

[0123] like > Then, the charging and discharging plans of energy storage devices with high aggregation weights will be adjusted, prioritizing the use of energy storage devices with remaining capacity > 50% and a health status index > 0.8. This indicates the preset power balance error threshold;

[0124] like <0.1 or If the frequency is >0.2Hz, a reactive power or frequency compensation strategy will be triggered, prioritizing the scheduling of energy storage devices with a power response speed >10kW / ms.

[0125] Example 2:

[0126] This embodiment provides a system for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities. The system includes:

[0127] The data acquisition module is used to acquire and preprocess operational data from various energy storage devices.

[0128] The characterization index calculation module is used to calculate the characterization index corresponding to the regulation capability of energy storage equipment based on the preprocessed operating data.

[0129] The feature analysis module is used to perform multi-dimensional feature analysis on the energy storage device cluster containing all energy storage devices and obtain the analysis results;

[0130] The regulation capacity calculation module calculates the regulation weight of energy storage devices based on the analysis results, and uses the characterization indicators of energy storage devices and regulation weights to calculate the multi-dimensional total regulation capacity of the energy storage device cluster.

[0131] Example 3:

[0132] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention.

[0133] Example 4:

[0134] This embodiment proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0135] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0136] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0137] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0138] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities, characterized in that, The method includes: Acquire and preprocess operational data from various energy storage devices; The energy storage device's regulation capacity is represented by the preprocessed operating data, and the corresponding performance indicators are calculated. The regulation capacity includes power balance regulation capacity, voltage and frequency stability regulation capacity, and frequency stability regulation capacity. The corresponding performance indicators are active power regulation capacity, reactive power regulation capacity and frequency response coefficient. The active power regulation capacity index is obtained based on the difference between the product of maximum charging power and charging time and the product of maximum discharging power and discharging time. The reactive power regulation capacity index is obtained based on the difference between the maximum leading reactive power and the maximum lagging reactive power. The frequency response coefficient is obtained by differentiating the active power with respect to the frequency. A multi-dimensional feature analysis was performed on the energy storage clusters containing all energy storage devices, and the analysis results were obtained. The multi-dimensional features are specifically spatial distribution characteristics, type difference characteristics, and operating status characteristics, and the corresponding analysis results are distribution uniformity index, equipment type feature vector, and status distribution matrix. The distribution uniformity index is calculated based on the average density of the energy storage device cluster and the energy storage density within the region. Establish a feature vector for equipment type based on the power response speed, rated energy capacity, and cycle life of energy storage equipment clusters; A state distribution matrix is ​​established based on the proportion of energy storage devices in different remaining capacity ranges; Based on the analysis results, the regulation weight of the energy storage equipment is calculated, and the multi-dimensional total regulation capability of the energy storage equipment cluster is calculated using the characterization index and regulation weight of the energy storage equipment. The adjustment weights include the aggregation weight, spatial weight, type weight, and frequency regulation contribution weight of the energy storage device; The location importance score of each energy storage device was obtained using the analytic hierarchy process (AHP). The aggregate weight of each energy storage device is calculated based on the location importance score, the remaining capacity of the energy storage device, the preset health status index, and the preset adjustment factor. Spatial weights are calculated based on the regional load importance and distribution uniformity index; Based on the current energy storage equipment type, the corresponding equipment type features are extracted from the equipment type feature vector and normalized to obtain the type weight; The frequency regulation contribution weight is calculated based on the state distribution matrix and the remaining capacity of the energy storage device. The multi-dimensional total regulation capability includes active power total regulation capability, reactive power total regulation capability, and frequency response characteristic aggregation capability. The active power regulation capacity index of each energy storage device is accumulated and multiplied with the corresponding aggregate weight to obtain the total active power regulation index, which is used to characterize the total active power regulation capability. The total reactive power regulation index is obtained by summing the product of the reactive power regulation capacity index, spatial weight, and type weight of each energy storage device, which is used to characterize the total reactive power regulation capability. Frequency response characteristics are calculated based on the frequency response coefficient and the frequency regulation contribution weight, which are used to characterize the aggregation capability of frequency response characteristics.

2. The method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities according to claim 1, characterized in that, The preprocessing includes denoising and normalization, wherein denoising is performed using Kalman filtering.

3. The method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities according to claim 1, characterized in that, The method also includes evaluating the multi-dimensional overall regulation capability of the energy storage equipment cluster based on preset evaluation indicators to obtain evaluation values; The evaluation indicators include power balance error, voltage stability margin, and frequency deviation; If the evaluated value is greater than the preset threshold, the corresponding optimization strategy will be triggered.

4. A characterization and aggregation system for energy storage devices based on multi-dimensional adjustment capabilities, employing the energy storage device characterization and aggregation method as described in claim 1, characterized in that, The system includes: The data acquisition module is used to acquire and preprocess operational data from various energy storage devices. The characterization index calculation module is used to calculate the characterization index corresponding to the regulation capability of energy storage equipment based on the preprocessed operating data. The feature analysis module is used to perform multi-dimensional feature analysis on the energy storage device cluster containing all energy storage devices and obtain the analysis results; The regulation capacity calculation module calculates the regulation weight of energy storage devices based on the analysis results, and uses the characterization indicators of energy storage devices and regulation weights to calculate the multi-dimensional total regulation capacity of the energy storage device cluster.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the energy storage device characterization aggregation method based on multi-dimensional adjustment capability as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a method for characterizing and aggregating energy storage devices based on multi-dimensional adjustment capabilities as described in any one of claims 1 to 3.

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