Power feature generation method and device of energy storage system, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power feature generation method and device of an energy storage system, computer equipment, a readable storage medium and a program product, and is applied to the technical field of data processing. The method comprises the steps that energy storage power information is acquired, and target energy storage power sub-information is selected from the energy storage power information; according to the target energy storage power sub-information, decomposition power characteristics are generated in a preset transformation mode, the decomposition power characteristics comprise at least one of a first type of decomposition characteristic values and a second type of decomposition characteristic values, the first type of decomposition characteristic values represent the charging state, and the second type of decomposition characteristic values represent the discharging state; according to the decomposed power sub-features, the decomposed power sub-features are split into charging and discharging power features, zero value supplementation is carried out on missing parts in the charging and discharging power features, power supplementation features are obtained, and zero values represent a stagnation state; and generating a target power feature according to the power supplement feature. By adopting the method, the power characteristic generation accuracy of the energy storage system can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for generating power characteristics of an energy storage system. Background Art

[0002] With the widespread adoption of energy storage systems, the demand for their control is increasing. By constructing the power signature of an energy storage system, we can distinguish the sources of power fluctuations in different frequency bands or time periods. For example, short-term fluctuations may be caused by load changes, while long-term fluctuations may be related to the charging and discharging process of the energy storage device or the external grid status. This helps to better understand the operating characteristics of energy storage systems under different circumstances. The power signature of an energy storage system can help analyze the power requirements of the energy storage system at different time scales, thereby formulating more precise energy management strategies.

[0003] At present, when a preset transformation method (for example, Fourier transform) is usually used to generate the power characteristics of an energy storage system, due to the limitations of its transformation characteristics, there is a situation where the generated power characteristics of the energy storage system cannot represent the stagnant state of the energy storage system, resulting in low accuracy in the generation of the power characteristics of the energy storage system. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for generating power characteristics of an energy storage system, which can improve the accuracy of power characteristics generation of the energy storage system, in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for generating a power signature of an energy storage system, comprising:

[0006] Acquire energy storage power information of the energy storage system, and select multiple target energy storage power sub-information from the energy storage power information;

[0007] Generate a decomposed power feature of the energy storage system using a preset transformation method based on the multiple target energy storage power sub-information, wherein the decomposed power feature includes multiple decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value is used to characterize the charge state corresponding to the energy storage device in the energy storage system, and the second-type decomposition feature value is used to characterize the discharge state corresponding to the energy storage device in the energy storage system;

[0008] For each of the decomposed power sub-features, splitting the decomposed power sub-feature into multiple charge and discharge power features based on distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, and supplementing the missing parts of the multiple charge and discharge power features with zero values ​​to obtain a power supplement feature of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnation state corresponding to the energy storage device in the energy storage system;

[0009] A target power feature of the energy storage system is generated according to the power supplement feature of each of the decomposed power sub-features.

[0010] In a second aspect, the present application further provides a power signature generating device for an energy storage system, comprising:

[0011] An acquisition module is used to acquire energy storage power information of the energy storage system and select multiple target energy storage power sub-information from the energy storage power information;

[0012] a generating module, configured to generate, based on the plurality of target energy storage power sub-information, a decomposed power feature of the energy storage system using a preset transformation method, wherein the decomposed power feature includes a plurality of decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value being used to characterize a charge state corresponding to an energy storage device in the energy storage system, and the second-type decomposition feature value being used to characterize a discharge state corresponding to an energy storage device in the energy storage system;

[0013] a splitting and zero-filling module, configured to split each of the decomposed power sub-features into a plurality of charge-discharge power features according to distribution information of the first-category decomposition eigenvalue and the second-category decomposition eigenvalue in the decomposed power sub-feature, and fill in the missing parts of the plurality of charge-discharge power features with zero values, thereby obtaining a power filling feature of the decomposed power sub-feature, wherein the zero value is used to characterize a stagnant state corresponding to the energy storage device in the energy storage system;

[0014] The generating module is further configured to generate a target power feature of the energy storage system according to a power supplement feature of each of the decomposed power sub-features.

[0015] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0016] Acquire energy storage power information of the energy storage system, and select multiple target energy storage power sub-information from the energy storage power information;

[0017] Generate a decomposed power feature of the energy storage system using a preset transformation method based on the multiple target energy storage power sub-information, wherein the decomposed power feature includes multiple decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value is used to characterize the charge state corresponding to the energy storage device in the energy storage system, and the second-type decomposition feature value is used to characterize the discharge state corresponding to the energy storage device in the energy storage system;

[0018] For each of the decomposed power sub-features, splitting the decomposed power sub-feature into multiple charge and discharge power features based on distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, and supplementing the missing parts of the multiple charge and discharge power features with zero values ​​to obtain a power supplement feature of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnation state corresponding to the energy storage device in the energy storage system;

[0019] A target power feature of the energy storage system is generated according to the power supplement feature of each of the decomposed power sub-features.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0021] Acquire energy storage power information of the energy storage system, and select multiple target energy storage power sub-information from the energy storage power information;

[0022] Generate a decomposed power feature of the energy storage system using a preset transformation method based on the multiple target energy storage power sub-information, wherein the decomposed power feature includes multiple decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value is used to characterize the charge state corresponding to the energy storage device in the energy storage system, and the second-type decomposition feature value is used to characterize the discharge state corresponding to the energy storage device in the energy storage system;

[0023] For each of the decomposed power sub-features, splitting the decomposed power sub-feature into multiple charge and discharge power features based on distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, and supplementing the missing parts of the multiple charge and discharge power features with zero values ​​to obtain a power supplement feature of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnation state corresponding to the energy storage device in the energy storage system;

[0024] A target power feature of the energy storage system is generated according to the power supplement feature of each of the decomposed power sub-features.

[0025] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0026] Acquire energy storage power information of the energy storage system, and select multiple target energy storage power sub-information from the energy storage power information;

[0027] Generate a decomposed power feature of the energy storage system using a preset transformation method based on the multiple target energy storage power sub-information, wherein the decomposed power feature includes multiple decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value is used to characterize the charge state corresponding to the energy storage device in the energy storage system, and the second-type decomposition feature value is used to characterize the discharge state corresponding to the energy storage device in the energy storage system;

[0028] For each of the decomposed power sub-features, splitting the decomposed power sub-feature into multiple charge and discharge power features based on distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, and supplementing the missing parts of the multiple charge and discharge power features with zero values ​​to obtain a power supplement feature of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnation state corresponding to the energy storage device in the energy storage system;

[0029] A target power feature of the energy storage system is generated according to the power supplement feature of each of the decomposed power sub-features.

[0030] The above-mentioned power feature generation method, device, computer equipment, computer-readable storage medium and computer program product of the energy storage system obtain the energy storage power information of the energy storage system and select multiple target energy storage power sub-information from the energy storage power information; according to the multiple target energy storage power sub-information, a preset transformation method is used to generate the decomposed power feature of the energy storage system, wherein the decomposed power feature includes multiple decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first type of decomposition feature value and a second type of decomposition feature value, the first type of decomposition feature value is used to characterize the charging state corresponding to the energy storage device in the energy storage system, and the decomposed power feature is generated by the preset transformation method. The second type of decomposition eigenvalue is used to characterize the discharge state corresponding to the energy storage device in the energy storage system; for each of the decomposed power sub-features, based on the distribution information of the first type of decomposition eigenvalue and the second type of decomposition eigenvalue in the decomposed power sub-feature, the decomposed power sub-feature is split into multiple charge and discharge power features, and the missing parts of the multiple charge and discharge power features are supplemented with zero values ​​to obtain the power supplement feature of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnation state corresponding to the energy storage device in the energy storage system; based on the power supplement feature of each of the decomposed power sub-features, the target power feature of the energy storage system is generated.

[0031] Thus, first, multiple target energy storage power sub-information is selected from the original power curve of the energy storage system. Based on the multiple target energy storage power sub-information, a conventional preset transformation method is adopted to construct a decomposed power feature. At this time, the decomposed power feature does not have a zero value, that is, the decomposed power feature cannot characterize the stagnant state of the energy storage system. Therefore, based on the power distribution information of the decomposed power feature, the decomposed power feature is split and padded with zeros, so that the obtained target power feature has a zero value, so that the target power feature can characterize the stagnant state of the energy storage system, thereby improving the accuracy of the power feature generation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A diagram illustrating an application environment of a method for generating power characteristics of an energy storage system according to an embodiment;

[0034] Figure 2 1 is a flow chart of a method for generating power characteristics of an energy storage system in one embodiment;

[0035] Figure 3 A schematic diagram of a power linear graph generated in one embodiment;

[0036] Figure 4 1. A flowchart illustrating steps for generating a target power feature of an energy storage system according to a power supplement feature of each decomposed power sub-feature in one embodiment;

[0037] Figure 5 A schematic flow chart of the steps for updating target power characteristics in one embodiment;

[0038] Figure 6 is a structural block diagram of a power signature generating device for an energy storage system in one embodiment;

[0039] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0041] It should be noted that the information involved in this application (for example, energy storage power information, charging and discharging power characteristics, power replenishment characteristics, target power characteristics, remaining power information, energy storage characteristics information and charging and discharging state switching information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the acquisition, transmission, storage, use and processing of relevant data are in compliance with the relevant provisions of national laws and regulations. The content pushed to the user (for example, decomposed power characteristics, target energy storage power sub-information and target power characteristics, etc.) can be rejected by the user or can be easily rejected by the user. In the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0042] It's understandable that the target power signature of an energy storage system is intended to better schedule the system's charging, discharging, and stagnation times, optimizing the system's economic benefits and operational efficiency. By generating a target power signature for an energy storage system, potential instability factors, such as excessive instantaneous power fluctuations or sustained power fluctuations, can be identified in advance, enabling necessary control measures to be taken to enhance the system's control stability. Generating a target power signature for an energy storage system helps analyze the system's charging and discharging patterns, thereby inferring its future performance and lifespan. Because frequent, prolonged deep charging and discharging cycles can cause damage to the energy storage system, generating a target power signature for the energy storage system allows for earlier assessment of the system's health, thereby extending its service life. Furthermore, analyzing the power signature of an energy storage system can identify abnormalities during its operation. For example, abnormal power fluctuations in a specific frequency band may indicate a component failure or maintenance needs. The above analysis demonstrates that generating a target power signature for an energy storage system is closely linked to the system's normal operation, lifespan reduction, and control stability. Therefore, a method for accurately generating a power signature for an energy storage system is urgently needed.

[0043] The power characteristics generation method of the energy storage system provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, energy storage system 102 and terminal 104 communicate with server 106 via a network. The data storage system can store data that server 106 needs to process. The data storage system can be integrated with server 106 or placed on the cloud or other network servers. The server 106 receives the energy storage power information of the energy storage system 102 sent by the energy storage system 102, and selects multiple target energy storage power sub-information from the energy storage power information; based on the multiple target energy storage power sub-information, a preset transformation method is used to generate a decomposed power feature of the energy storage system 102, wherein the decomposed power feature includes multiple decomposed power sub-features, each decomposed power sub-feature includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value is used to characterize the charging state corresponding to the energy storage device in the energy storage system 102, and the second-type decomposition feature value is used to characterize the discharging state corresponding to the energy storage device in the energy storage system 102; for each decomposed power sub-feature, based on the distribution information of the first-type decomposition feature value and the second-type decomposition feature value in the decomposed power sub-feature, the decomposed power sub-feature is split into multiple charge and discharge power features, and the missing parts of the multiple charge and discharge power features are supplemented with zero values ​​to obtain power supplement features of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnant state corresponding to the energy storage device in the energy storage system 102; and the target power feature of the energy storage system 102 is generated based on the power supplement features of each decomposed power sub-feature. Server 106 can push at least one of the decomposed power characteristics, the target energy storage power sub-information, and the target power characteristics to terminal 104. Terminal 104 can include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices can include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices can include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like. Server 106 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0044] In an exemplary embodiment, Figure 2 As shown, a method for generating power characteristics of an energy storage system is provided, and the method is applied to Figure 1 The server 106 in the example is used to illustrate the process, including the following steps 202 to 208.

[0045] Step 202: Obtain energy storage power information of the energy storage system, and select multiple target energy storage power sub-information from the energy storage power information.

[0046] The energy storage system in step 202 includes at least one energy storage device. The energy storage power information is used to characterize the operating power status of the energy storage system at multiple times (times herein can be time points or time periods, without limitation). The energy storage power information can include multiple energy storage power sub-information, each of which includes a specific operating power value of the energy storage system at a specific time. The operating power value can be one of a first-class decomposition eigenvalue, a second-class decomposition eigenvalue, and a zero value. For example, energy storage power sub-information a is (t1, p1). The energy storage power information can also include an energy storage system power curve. The energy storage system power curve characterizes the correspondence between time and the operating power value of the energy storage system. For example, with time as the horizontal axis and the operating power value of the energy storage system as the vertical axis, a curve drawn from multiple coordinate points (coordinate points consisting of multiple times and corresponding operating power values) serves as the power curve of the energy storage system.

[0047] Among them, when the first type of decomposition eigenvalue is a positive number, the second type of decomposition eigenvalue is a negative number; when the first type of decomposition eigenvalue is a negative number, the second type of decomposition eigenvalue is a positive number.

[0048] Exemplarily, obtaining energy storage power information of the energy storage system includes: sending an energy storage power query request to the energy storage system, and obtaining energy storage power information of the energy storage system sent by the energy storage system after receiving the energy storage power query request.

[0049] Exemplarily, selecting a plurality of target energy storage power sub-information from the energy storage power information includes: acquiring an information selection quantity, and selecting a plurality of target energy storage power sub-information from the energy storage power information according to the information selection quantity.

[0050] Further, as an embodiment, obtaining the number of information selections includes: obtaining the number of information selections set by the user.

[0051] As another embodiment, obtaining the number of selected information includes: obtaining operating characteristics of the energy storage system, and determining the number of selected information according to the operating characteristics.

[0052] In this way, it can be ensured that the time distribution corresponding to the power characteristics of the generated energy storage system meets user expectations.

[0053] As one embodiment, determining the number of information to be selected based on operating characteristics includes: extracting periodic change features in the operating characteristics, and determining the number of information to be selected based on the periodic change features, wherein the more frequent the periodic changes represented by the periodic change features, the greater the number of information to be selected.

[0054] In this way, considering that the energy storage power information of the energy storage system is essentially the charging and discharging conditions of the energy storage system, the energy storage power change of the energy storage system is a time-series change. Therefore, by analyzing the periodic change characteristics in the operating characteristics of the energy storage system, it is possible to ensure that the power characteristics of the constructed energy storage system can represent the frequency change conditions of the periodic energy storage system.

[0055] As another embodiment, determining the number of selected information based on the operating characteristics includes: extracting power distribution features from the operating characteristics, and determining the number of selected information based on the power distribution features.

[0056] In this way, the amount of information to be selected is determined based on the power distribution characteristics, thereby ensuring that the power characteristics of the constructed energy storage system can characterize the frequency change status of the complete energy storage system as much as possible.

[0057] As another embodiment, obtaining the number of selected information includes: determining the number of selected information according to the selected preset transformation mode. Specifically, when the preset transformation mode is the Hadamard-Walsh transformation mode or the wavelet transformation mode, the number of selected information is a power of 2 (for example, 2 n ).

[0058] In this way, it can be ensured that the multiple target energy storage power sub-information obtained by selecting the information selection quantity is compatible with the preset transformation method, thereby avoiding the need to fill zero values ​​or remove part of the target energy storage power sub-information to ensure the normal operation of the subsequent preset transformation method.

[0059] Step 204: Generate a decomposed power feature of the energy storage system using a preset transformation method based on the multiple target energy storage power sub-information. The decomposed power feature includes multiple decomposed power sub-features, each of which includes at least one of a first-type decomposition feature value and a second-type decomposition feature value. The first-type decomposition feature value is used to characterize the charging state of the energy storage device in the energy storage system, and the second-type decomposition feature value is used to characterize the discharging state of the energy storage device in the energy storage system.

[0060] The preset transformation method in step 204 includes but is not limited to Fourier transformation, Hadamard-Walsh transformation, and wavelet transformation.

[0061] The decomposed power features in step 204 are used to characterize the charge and discharge power distribution of each type of energy storage device at each time. The decomposed power features may include characteristic values ​​of each type of energy storage device at each time. The decomposed power features may also include multiple decomposed power linear images, each power linear image corresponding to a type of energy storage device. Each power linear image represents the correspondence between a time period and the characteristic value of the corresponding energy storage device. For example, with time as the horizontal axis and the characteristic value of the corresponding energy storage device as the vertical axis, a line drawn from multiple coordinate points (coordinate points consisting of multiple times and corresponding characteristic values) is used as a power linear image. The power linear image can be a broken line graph or a curve graph, without limitation.

[0062] As an embodiment, step 204 includes: performing Fourier transform on multiple target energy storage power sub-information respectively to obtain multiple frequency domain power sub-information; identifying spectrum information in the multiple frequency domain power sub-information to obtain frequency component information and amplitude information corresponding to the multiple frequency domain power sub-information; and performing time domain restoration on the frequency component information and amplitude information corresponding to the multiple frequency domain power sub-information respectively to obtain the decomposed power characteristics of the energy storage system.

[0063] As another embodiment, step 204 includes: constructing a Hadamard matrix, and performing a Walsh transform on the Hadamard matrix according to a plurality of target energy storage power sub-information to obtain a decomposed power characteristic of the energy storage system.

[0064] As another embodiment, step 204 includes: performing multiple Haar wavelet transforms on multiple target energy storage power sub-information to obtain decomposed power characteristics of the energy storage system.

[0065] In step 206, for each decomposed power sub-feature, the decomposed power sub-feature is split into multiple charge-discharge power features based on the distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature. The missing parts of the multiple charge-discharge power features are supplemented with zero values ​​to obtain power supplement features of the decomposed power sub-feature, wherein the zero value is used to represent the stagnation state corresponding to the energy storage device in the energy storage system.

[0066] As an embodiment, the decomposed power sub-feature is split into multiple charging and discharging power features based on the distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, including: the multiple charging and discharging power features include a charging power feature and a discharging power feature; the part of the decomposed power sub-feature belonging to the first type of decomposition eigenvalue is split into a charging power feature, wherein the charging power feature completely overlaps with the part of the decomposed power sub-feature belonging to the first type of decomposition eigenvalue; the feature of the decomposed power sub-feature belonging to the second type of decomposition eigenvalue is split into a discharging power feature, wherein the discharging power feature completely overlaps with the part of the decomposed power sub-feature belonging to the second type of decomposition eigenvalue.

[0067] For example, when the first type of decomposition eigenvalue is a positive number and the decomposed power sub-feature is (-1, 1, 1, -1, 1, -1), the charging power feature obtained by splitting is (-1, missing part, missing part, -1, missing part, -1), and the discharging power feature obtained by splitting is (missing part, 1, 1, missing part, 1, missing part); the power supplement feature obtained by padding the charging power feature with zeros is (-1, 0, 0, -1, 0, -1), and the power supplement feature obtained by padding the discharging power feature with zeros is (0, 1, 1, 0, 1, 0). When the decomposed power sub-feature is (0, -1, 1, 0, 1, -1), the charging power feature obtained by splitting is (missing part, -1, missing part, missing part, missing part, -1), and the discharging power feature obtained by splitting is (missing part, missing part, 1, missing part, 1, missing part); the power supplement feature obtained by padding the charging power feature with zero is (0, -1, 0, 0, 0, -1), and the power supplement feature obtained by padding the discharging power feature with zero is (0, 0, 1, 0, 1, 0).

[0068] It is understandable that when generating decomposed power features based on multiple target energy storage power sub-information and using a preset transformation method, there may be a situation where the charging and discharging states of the decomposed power sub-features in the decomposed power features switch slowly. For the decomposed power sub-features with slow charging and discharging state switching, the subsequent splitting and zero-filling steps are redundant, resulting in low power feature generation efficiency.

[0069] Optionally, before step 206, the method further includes: respectively determining the charge and discharge state switching information of each decomposed power sub-feature; and eliminating the decomposed power sub-features whose charge and discharge state switching information does not meet the preset switching frequency condition in the decomposed power features.

[0070] The charge-discharge state switching information may include the charge-discharge cycle switching frequency, and the charge-discharge state switching information may also include the charge-discharge state switching count. When the charge-discharge state switching information is the charge-discharge cycle switching frequency, the preset switching frequency condition is greater than a preset cycle switching frequency threshold. The preset cycle switching frequency threshold can be set by the user as needed, or can be an empirical value, or can be determined by the distribution of the charge-discharge cycle switching frequencies of each decomposed power sub-feature. When the charge-discharge state switching information is the charge-discharge state switching count, the preset switching frequency condition is greater than a preset switching count threshold. The preset switching count threshold can be set by the user as needed, or can be an empirical value, or can be determined by the distribution of the charge-discharge state switching counts of each decomposed power sub-feature.

[0071] In this way, before performing step 206 (subsequent feature splitting and zero-filling content), the decomposed power sub-features that do not meet the preset switching frequency conditions are eliminated, so that the decomposed power sub-features with slower charging and discharging state switching are eliminated, so that redundant splitting and zero-filling steps are not performed on this type of decomposed power sub-features, thereby improving the efficiency of power feature generation.

[0072] As an embodiment, respectively determining the charge and discharge state switching information of each decomposed power sub-feature includes: for each decomposed power sub-feature, accumulating the number of charge and discharge state switching times of the decomposed power sub-feature to obtain the charge and discharge state switching information of the decomposed power sub-feature.

[0073] As another embodiment, the charge and discharge state switching information of each decomposed power sub-feature is determined separately, including: for each decomposed power sub-feature, accumulating the number of charge and discharge state switching times of the decomposed power sub-feature, and determining the ratio between the number of charge and discharge state switching times and the total time period corresponding to the decomposed power sub-feature (that is, the total time period of multiple target energy storage power sub-information) as the charge and discharge state switching information of the decomposed power sub-feature.

[0074] Step 208: Generate a target power feature of the energy storage system based on the power supplement feature of each decomposed power sub-feature.

[0075] The target power feature in step 208 is used to characterize the target charge and discharge power distribution of each energy storage device at each time. The target power feature may include the target characteristic value of each energy storage device (corresponding to each type of energy storage device or each energy storage device described above) at each time. The target power feature may also include multiple decomposed target power linear graphs, each target power linear graph corresponding to an energy storage device, and each power linear graph characterizing the correspondence between a time period and the characteristic value of the corresponding energy storage device. For example, with time as the horizontal axis and the characteristic value of the corresponding energy storage device as the vertical axis, a line drawn from multiple coordinate points (coordinate points consisting of multiple times and corresponding characteristic values) serves as a power linear graph. The power linear graph may be a broken line graph or a curve graph, without limitation herein.

[0076] As an embodiment, step 208 includes: determining the power supplement feature of each decomposed power sub-feature as the operating power value of each energy storage device at each time.

[0077] As another embodiment, step 208 includes: generating a plurality of power linear graphs according to the power supplement feature of each decomposed power sub-feature, wherein each point in each power linear graph corresponds to the power supplement feature of each decomposed power sub-feature.

[0078] Alternatively, the distribution of power characteristics can be decomposed into the following matrix: , and the time interval corresponding to the energy storage power information is 1 / 9, and the range is 0-1, for example, then the generated power linear graph can be referred to Figure 3 .

[0079] Optionally, after step 208, the method further includes: controlling the energy storage system according to the target power characteristics of the energy storage system.

[0080] Furthermore, the energy storage system is controlled according to the target power characteristics of the energy storage system. Specifically, each energy storage device in the energy storage system is controlled according to the charge and discharge power distribution of each energy storage device represented by the target power characteristics of the energy storage system.

[0081] In the above-mentioned method for generating the power signature of the energy storage system, multiple target energy storage power sub-information is first selected from the original power curve of the energy storage system. Based on the multiple target energy storage power sub-information, a conventional preset transformation method is adopted to construct a decomposed power signature. At this time, the decomposed power signature does not have a zero value, that is, the decomposed power signature cannot represent the stagnant state of the energy storage system. Therefore, based on the power distribution information of the decomposed power signature, the decomposed power signature is split and padded with zeros so that the obtained target power signature has a zero value, so that the target power signature can represent the stagnant state of the energy storage system, thereby improving the accuracy of the power signature generation of the energy storage system.

[0082] In an exemplary embodiment, Figure 4 As shown, a method for accurately generating a target power feature is provided, wherein the power supplement feature of the decomposed power sub-feature includes decomposed feature values ​​and supplemented zero values ​​under multiple time intervals; based on the power supplement feature of each decomposed power sub-feature, the target power feature of the energy storage system is generated, including steps 302 to 308.

[0083] Step 302 : For each decomposed power sub-feature, sort the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature to obtain eigenvalue sorting information of the decomposed power sub-feature.

[0084] As an embodiment, step 302 includes: for each decomposed power sub-feature, sorting all decomposed eigenvalues ​​in all time intervals in the decomposed power sub-feature from small to large to obtain eigenvalue sorting information of the decomposed power sub-feature.

[0085] As another embodiment, step 302 includes: for each decomposed power sub-feature, sorting all decomposed eigenvalues ​​in all time intervals in the decomposed power sub-feature from large to small to obtain eigenvalue sorting information of the decomposed power sub-feature.

[0086] For example, the power supplement features of the decomposed power sub-features include (1, 3, 0, 0, 2, 5), the decomposed eigenvalues ​​represent 1, 3, 2 and 5 in the first time interval, the second time interval, the fifth time interval and the sixth time interval, and the decomposed eigenvalues ​​are sorted from large to small, and the obtained eigenvalue sorting information is 5, 3, 2, 1.

[0087] Step 304 : According to the eigenvalue sorting information of the decomposed power sub-feature, the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature are divided to obtain a plurality of eigenvalue intervals.

[0088] The eigenvalue sorting information in step 304 includes the decomposed eigenvalues ​​arranged in sorting order.

[0089] As an embodiment, step 304 includes: evenly dividing the decomposed eigenvalues ​​in each time interval according to the eigenvalue sorting information of the decomposed power sub-features, to obtain a plurality of eigenvalue intervals in each time interval.

[0090] As another embodiment, step 304 includes: obtaining the number of energy storage devices in the energy storage system, dividing the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature according to the eigenvalue sorting information and the number of devices of the decomposed power sub-feature, and obtaining multiple eigenvalue intervals, wherein the greater the number of devices, the greater the number of intervals of the divided eigenvalue intervals.

[0091] In this way, the number of intervals of the divided characteristic value intervals is ensured to match the number of energy storage devices, ensuring that the target power characteristics generated subsequently can correspond to each energy storage device respectively.

[0092] Further, as an embodiment, the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature are divided according to the eigenvalue sorting information of the decomposed power sub-feature and the number of devices to obtain multiple eigenvalue intervals, including: preliminarily dividing the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature according to the eigenvalue sorting information of the decomposed power sub-feature to obtain multiple interval intervals, wherein the number of decomposed eigenvalues ​​in each interval interval is the same; according to the number of devices, merging multiple first-type interval intervals in the multiple interval intervals that are adjacent and whose characteristic difference values ​​are lower than a preset difference threshold to obtain multiple eigenvalue intervals, wherein the preset difference threshold can be set by the user as needed, and can also correspond to the distribution status of each decomposed eigenvalue of the decomposed power sub-feature.

[0093] For example, the eigenvalue sorting information is 5, 3, 3, 3, 2, 1; the multiple intervals obtained by preliminary division include interval 1: (5), interval 2: (3), interval 3: (3), interval 4: (3), interval 5: (2), interval 6: (1); when the preset difference threshold is 2, intervals 2, 3, 4 and 5 are merged, and the obtained eigenvalue intervals include: eigenvalue interval 1: (5), eigenvalue interval 2: (3, 3, 3, 2), eigenvalue interval 3: (1).

[0094] Step 306 : updating the eigenvalues ​​of the multiple eigenvalue intervals according to the statistical eigenvalues ​​in the multiple eigenvalue intervals.

[0095] Among them, statistical eigenvalues ​​include but are not limited to maximum eigenvalue, average eigenvalue and median eigenvalue, etc.

[0096] As an embodiment, step 306 includes: the statistical eigenvalues ​​include the maximum eigenvalue; for the first eigenvalue interval, all the decomposed eigenvalues ​​in the first eigenvalue interval are updated to the maximum eigenvalue in the first eigenvalue interval; for the non-first eigenvalue interval, all the decomposed eigenvalues ​​of the first eigenvalue subinterval in the non-first eigenvalue interval are updated to the decomposed eigenvalues ​​of the first updated eigenvalue interval; according to the difference between each decomposed eigenvalue in the second power subinterval and the decomposed eigenvalue of the first updated eigenvalue interval, the decomposed eigenvalue of the second eigenvalue subinterval in the non-first eigenvalue interval is updated, wherein the decomposed eigenvalues ​​in the first eigenvalue subinterval are all less than or equal to the decomposed eigenvalue of the first updated eigenvalue interval, and the decomposed eigenvalues ​​in the second eigenvalue subinterval are all greater than the decomposed eigenvalue of the first updated eigenvalue interval.

[0097] Among them, each eigenvalue interval is sorted according to the number of decomposition eigenvalues ​​contained in each eigenvalue interval. Specifically, the more decomposition eigenvalues ​​contained in the eigenvalue interval, the higher the ranking. Then, the first eigenvalue interval contains the largest number of decomposition eigenvalues, and the last eigenvalue interval contains the least number of decomposition eigenvalues.

[0098] Furthermore, based on the differences between each decomposition eigenvalue in the second power sub-interval and the decomposition eigenvalue of the first updated eigenvalue interval, the decomposition eigenvalue of the second eigenvalue sub-interval in the non-first eigenvalue interval is updated, including: the second sub-interval includes a first subset and a second subset, the decomposition eigenvalue of the first updated eigenvalue interval is determined as the first subset, and the differences between each decomposition eigenvalue in the second power sub-interval and the decomposition eigenvalue of the first updated eigenvalue interval are determined as the second subset.

[0099] For example, the eigenvalue intervals include: eigenvalue interval 1: (5), eigenvalue interval 2: (3, 3, 3, 2), eigenvalue interval 3: (1), set eigenvalue interval 2 as the first eigenvalue interval, the maximum eigenvalue of eigenvalue interval 2 is 3, so, update eigenvalue interval 2 to: (3, 3, 3, 3), 5 in eigenvalue interval 1 is greater than 3 in the updated eigenvalue interval 2, so, divide eigenvalue interval 1 into the first subset (3) and the second subset (2), 1 in eigenvalue interval 3 is less than 3 in the updated eigenvalue interval 2, so, update eigenvalue interval 3 to (3).

[0100] Step 308 : Generate a target power feature of the energy storage system based on the supplemented zero value of each decomposed power sub-feature and the corresponding updated multiple feature value intervals.

[0101] Exemplarily, step 308 includes: for each updated eigenvalue interval, combining the updated eigenvalue interval with the corresponding supplementary zero value to obtain the power feature corresponding to each updated eigenvalue interval; fusing the power features corresponding to all updated eigenvalue intervals to obtain the target power feature of the energy storage system.

[0102] In this embodiment, considering that the data source of the decomposed power sub-features is the target energy storage power sub-information, the generated target power features may be relatively complex. By sorting the decomposed eigenvalues ​​of each decomposed power sub-power in all intervals, subsequent eigenvalue interval division and updating are performed, so that the elements contained in the updated eigenvalue intervals are simpler, and thus the generated target power features are also simpler, thereby improving the power configuration efficiency of the subsequent energy storage system.

[0103] In an exemplary embodiment, Figure 5 As shown, a method for accurately updating a target power feature is provided, wherein the target power feature includes multiple target power sub-features; after generating the target power feature of the energy storage system according to the power supplement feature of each decomposed power sub-feature, the method includes steps 402 to 408.

[0104] Step 402: Allocate multiple target power sub-features to each energy storage device in the energy storage system.

[0105] Exemplarily, step 402 includes: the target power sub-feature includes decomposed characteristic values ​​in multiple time intervals; obtaining energy storage characteristic information of each energy storage device in the energy storage system; respectively determining charge and discharge state switching information of each target power sub-feature; and allocating the multiple target power sub-features to each energy storage device in the energy storage system based on the charge and discharge state switching information, the decomposed characteristic values ​​of each target power sub-feature, and the energy storage characteristic information of each energy storage device.

[0106] The energy storage characteristic information is used to characterize the power configuration type of the energy storage device. The power configuration type is used to characterize the speed of the power configuration of the energy storage device. The power configuration type includes but is not limited to the power type and the energy type. The power configuration of the energy storage device belonging to the power type is faster than that of the energy storage device belonging to the energy type.

[0107] Furthermore, according to the charge and discharge state switching information of each target power sub-feature, the decomposed eigenvalue and the energy storage characteristic information of each energy storage device, multiple target power sub-features are allocated to each energy storage device in the energy storage system, including: according to the charge and discharge state switching information of each target power sub-feature, each target power sub-feature is divided into a first type of power sub-feature and a second type of power sub-feature, the degree represented by the charge and discharge state switching information corresponding to the first type of power sub-feature is higher than the degree represented by the charge and discharge state switching information corresponding to the second type of power sub-feature; the target power sub-feature belonging to the second type of power sub-feature is allocated to , the power configuration type represented by the energy storage characteristic information belongs to the energy type energy storage device; obtain the rated power of the energy storage device whose power configuration type represented by the energy storage characteristic information belongs to the power type; according to the decomposition characteristic value and rated power of the target power sub-feature belonging to the first type of power sub-feature, respectively assign the target power sub-feature belonging to the first type of power sub-feature to the energy storage device whose power configuration type represented by the energy storage characteristic information belongs to the power type, wherein, the higher the rated power of the energy storage device whose power configuration type represented by the energy storage characteristic information belongs to the power type, the higher the decomposition characteristic value of the assigned target power sub-feature.

[0108] Step 404: monitor the remaining power information of each energy storage device in the energy storage system in real time.

[0109] The remaining power information in step 404 may be a remaining power value or a remaining power percentage.

[0110] As an embodiment, step 404 includes: sending an energy storage device power query request to the energy storage system in real time, and receiving remaining power information of each energy storage device in the energy storage system sent by the energy storage system after receiving the energy storage device power query request.

[0111] In step 406, for each energy storage device, if the remaining power information of the first device in the energy storage device satisfies the preset low power condition, then when the charge and discharge state represented by the target power sub-feature assigned to the first device is a discharge state, the charge and discharge state of the target power sub-feature assigned to the first device is switched to a stagnant state.

[0112] The preset low battery condition in step 406 may be set by the user as needed, or may be an empirical value, for example, less than 20%.

[0113] As one embodiment, switching the charge and discharge state of the target power sub-feature assigned to the first device to a stagnant state includes: updating the decomposed characteristic value of the target power sub-feature assigned to the first device in the interval after the time interval corresponding to the current time to zero.

[0114] It is understandable that although this can ensure the normal operation of the energy storage device, the time interval after the time interval corresponding to the current time cannot meet the power supply demand of the grid. Therefore, it is necessary to add a first supplementary power sub-feature to characterize the charging and discharging state of the auxiliary discharge device.

[0115] Optionally, the method also includes: determining the zero value as the decomposition characteristic value of the interval before the time interval corresponding to the current time in the first supplementary power sub-feature, and determining the decomposition characteristic value of the interval after the time interval corresponding to the current time in the target power sub-feature assigned to the first device before the update as the decomposition characteristic value of the interval after the time interval corresponding to the current time in the first supplementary power sub-feature.

[0116] For example, the target power sub-features assigned to the first device include (1, 0, 2, 1, 1, 1), where the time interval is 1 minute. When it is found that the remaining power information of the first device meets the preset low-power condition in the third minute, the target power sub-feature is updated to (1, 0, 2, 0, 0, 0), and the newly added first supplementary power sub-feature is (0, 0, 0, 1, 1, 1).

[0117] In step 408, if the remaining power information of the second device in the energy storage device satisfies the preset high power condition, then when the charge and discharge state represented by the target power sub-feature assigned to the second device is the charging state, the charge and discharge state of the target power sub-feature assigned to the second device is switched to the stagnant state.

[0118] The preset high power condition in step 408 may be set by the user as needed, or may be an empirical value, for example, higher than 80%.

[0119] As one embodiment, switching the charge and discharge state of the target power sub-feature assigned to the second device to a stagnant state includes: updating the decomposed characteristic value of the target power sub-feature assigned to the second device in the interval after the time interval corresponding to the current time to zero.

[0120] Optionally, the method also includes: determining the zero value as the decomposition characteristic value of the interval before the time interval corresponding to the current time in the second supplementary power sub-feature, and determining the decomposition characteristic value of the interval after the time interval corresponding to the current time in the target power sub-feature assigned to the second device before the update as the decomposition characteristic value of the interval after the time interval corresponding to the current time in the second supplementary power sub-feature.

[0121] For example, the target power sub-features assigned to the second device include (-1, 0, -2, -1, -1, -1), where the time interval is 1 minute. When it is found that the remaining power information of the second device meets the preset high power condition in the third minute, the target power sub-feature is updated to (-1, 0, -2, 0, 0, 0), and the newly added second supplementary power sub-feature is (0, 0, 0, -1, -1, -1).

[0122] Taking into account the situation that, during the above-mentioned decomposition feature generation and configuration process, the real-time remaining power information of the energy storage device may not correspond to the assigned target power sub-feature, specifically, the energy storage device needs to be configured to discharge according to the target power sub-feature, but the real-time remaining power information of the energy storage device indicates that the remaining power is too little, or the energy storage device needs to be configured to charge according to the target power sub-feature, but the real-time remaining power information of the energy storage device indicates that the remaining power is too much, that is, the generated target power sub-feature does not match the energy storage device.

[0123] In this embodiment, by assigning target power sub-signatures to physical energy storage devices respectively, the remaining power information of the energy storage devices is monitored in real time. When the target power sub-signature does not match the real-time state of the energy storage device, the state of the target power sub-signature is switched in time, so that the switched target power sub-signature matches the energy storage device, thereby improving the accuracy of the generated target power sub-signature.

[0124] As a detailed embodiment, energy storage power information of an energy storage system is obtained, and multiple target energy storage power sub-information is selected from the energy storage power information; based on the multiple target energy storage power sub-information, a preset transformation method is used to generate a decomposed power feature of the energy storage system, wherein the decomposed power feature includes multiple decomposed power sub-features, each decomposed power sub-feature includes at least one of a first-type decomposition eigenvalue and a second-type decomposition eigenvalue, the first-type decomposition eigenvalue is used to characterize the charging state corresponding to the energy storage device in the energy storage system, and the second-type decomposition eigenvalue is used to characterize the discharging state corresponding to the energy storage device in the energy storage system; for each decomposed power sub-feature, based on distribution information of the first-type decomposition eigenvalue and the second-type decomposition eigenvalue in the decomposed power sub-feature, the decomposed power sub-feature is split into multiple charge and discharge power features, and the missing parts of the multiple charge and discharge power features are supplemented with zero values ​​to obtain power supplement features of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnation state corresponding to the energy storage device in the energy storage system; charge and discharge state switching information of each decomposed power sub-feature is determined respectively; and decomposed power sub-features whose charge and discharge state switching information in the decomposed power feature does not meet the preset switching frequency condition are eliminated.

[0125] Furthermore, for each decomposed power sub-feature, the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature are sorted by size to obtain the eigenvalue sorting information of the decomposed power sub-feature; according to the eigenvalue sorting information of the decomposed power sub-feature, the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature are divided to obtain multiple eigenvalue intervals; for the first eigenvalue interval, all the decomposed eigenvalues ​​in the first eigenvalue interval are updated to the maximum eigenvalue in the first eigenvalue interval; for the non-first eigenvalue interval, all the decomposed eigenvalues ​​of the first eigenvalue sub-interval in the non-first eigenvalue interval are updated to the first The decomposed eigenvalues ​​of the updated eigenvalue interval; updating the decomposed eigenvalues ​​of the second eigenvalue subinterval in the non-first eigenvalue interval according to the differences between each decomposed eigenvalue in the second power subinterval and the decomposed eigenvalue of the first updated eigenvalue interval, wherein the decomposed eigenvalues ​​in the first eigenvalue subinterval are all less than or equal to the decomposed eigenvalue of the first updated eigenvalue interval, and the decomposed eigenvalues ​​in the second eigenvalue subinterval are all greater than the decomposed eigenvalue of the first updated eigenvalue interval; generating the target power characteristics of the energy storage system according to the supplementary zero value of each decomposed power subfeature and the corresponding updated multiple eigenvalue intervals.

[0126] Furthermore, energy storage characteristic information of each energy storage device in the energy storage system is obtained; the charge and discharge state switching information of each target power sub-feature is determined respectively; multiple target power sub-features are allocated to each energy storage device in the energy storage system according to the charge and discharge state switching information of each target power sub-feature, the decomposed characteristic value and the energy storage characteristic information of each energy storage device; the remaining power information of each energy storage device in the energy storage system is monitored in real time; for each energy storage device, if the remaining power information of a first device in the energy storage device meets a preset low power condition, then when the charge and discharge state represented by the target power sub-feature corresponding to the first device is a discharge state, the charge and discharge state of the target power sub-feature corresponding to the first device is switched to a stagnant state; if the remaining power information of a second device in the energy storage device meets a preset high power condition, then when the charge and discharge state represented by the target power sub-feature corresponding to the second device is a charge state, the charge and discharge state of the target power sub-feature corresponding to the second device is switched to a stagnant state.

[0127] Thus, first, multiple target energy storage power sub-information is selected from the original power curve of the energy storage system. Based on the multiple target energy storage power sub-information, a conventional preset transformation method is adopted to construct a decomposed power feature. At this time, the decomposed power feature does not have a zero value, that is, the decomposed power feature cannot characterize the stagnant state of the energy storage system. Therefore, based on the power distribution information of the decomposed power feature, the decomposed power feature is split and padded with zeros, so that the obtained target power feature has a zero value, so that the target power feature can characterize the stagnant state of the energy storage system, thereby improving the accuracy of the power feature generation of the energy storage system.

[0128] Furthermore, considering that the data source of the decomposed power sub-feature is the target energy storage power sub-information, there may be a situation where the generated target power feature is relatively complex. By sorting the decomposed eigenvalues ​​of each decomposed power sub-power in all intervals, the subsequent eigenvalue interval division and update are performed, so that the elements contained in the updated eigenvalue interval are simpler, and the generated target power feature is also simpler, which can improve the power configuration efficiency of the subsequent energy storage system; and, by assigning the target power sub-features to the physical energy storage devices respectively, the remaining power information of the energy storage devices is monitored in real time. When the target power sub-feature does not match the real-time state of the energy storage device, the state of the target power sub-feature is switched in time, so that the switched target power sub-feature matches the energy storage device, thereby improving the accuracy of the generated target power sub-feature.

[0129] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0130] Based on the same inventive concept, embodiments of the present application also provide a power signature generation device for an energy storage system for implementing the aforementioned method for generating a power signature for an energy storage system. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in the embodiments of the power signature generation device for one or more energy storage systems provided below can be found in the limitations of the method for generating a power signature for an energy storage system described above and will not be further elaborated here.

[0131] In an exemplary embodiment, Figure 6 As shown, a power characteristic generation device 600 of an energy storage system is provided, comprising: an acquisition module 602, a generation module 604 and a splitting and zero-filling module 606, wherein:

[0132] An acquisition module 602 is configured to acquire energy storage power information of the energy storage system and select multiple target energy storage power sub-information from the energy storage power information;

[0133] A generating module 604 is configured to generate a decomposed power feature of the energy storage system using a preset transformation method based on the multiple target energy storage power sub-information, wherein the decomposed power feature includes multiple decomposed power sub-features, each decomposed power sub-feature includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value is used to represent the charge state corresponding to the energy storage device in the energy storage system, and the second-type decomposition feature value is used to represent the discharge state corresponding to the energy storage device in the energy storage system;

[0134] A splitting and zero-filling module 606 is configured to split each decomposed power sub-feature into multiple charge and discharge power features based on the distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, and fill the missing parts of the multiple charge and discharge power features with zero values ​​to obtain power filling features of the decomposed power sub-features, wherein the zero value is used to represent the stagnant state corresponding to the energy storage device in the energy storage system;

[0135] The generating module 604 is further configured to generate a target power feature of the energy storage system according to the power supplement feature of each decomposed power sub-feature.

[0136] In one embodiment, the power supplementary feature of the decomposed power sub-feature includes decomposed eigenvalues ​​and supplementary zero values ​​in multiple time intervals; the generation module 604 is further used to sort the decomposed eigenvalues ​​in all time intervals in the decomposed power sub-feature for each decomposed power sub-feature, and obtain eigenvalue sorting information of the decomposed power sub-feature; according to the eigenvalue sorting information of the decomposed power sub-feature, the decomposed eigenvalues ​​in all time intervals in the decomposed power sub-feature are divided to obtain multiple eigenvalue intervals; according to the statistical eigenvalues ​​in the multiple eigenvalue intervals, the eigenvalues ​​of the multiple eigenvalue intervals are updated; and according to the supplementary zero value of each decomposed power sub-feature and the corresponding updated multiple eigenvalue intervals, the target power feature of the energy storage system is generated.

[0137] In one embodiment, the statistical eigenvalue includes the maximum eigenvalue; the generation module 604 is further used to update, for the first eigenvalue interval, all decomposed eigenvalues ​​in the first eigenvalue interval to the maximum eigenvalue in the first eigenvalue interval; for the non-first eigenvalue interval, update all decomposed eigenvalues ​​of the first eigenvalue subinterval in the non-first eigenvalue interval to the decomposed eigenvalue of the first updated eigenvalue interval; update the decomposed eigenvalue of the second eigenvalue subinterval in the non-first eigenvalue interval according to the difference between each decomposed eigenvalue in the second power subinterval and the decomposed eigenvalue of the first updated eigenvalue interval, wherein the decomposed eigenvalues ​​in the first eigenvalue subinterval are all less than or equal to the decomposed eigenvalue of the first updated eigenvalue interval, and the decomposed eigenvalues ​​in the second eigenvalue subinterval are all greater than the decomposed eigenvalue of the first updated eigenvalue interval.

[0138] In one embodiment, the target power feature includes multiple target power sub-features; after generating the target power feature of the energy storage system according to the power supplement feature of each decomposed power sub-feature, the above-mentioned device also includes a switching module for assigning the multiple target power sub-features to each energy storage device in the energy storage system; real-time monitoring of the remaining power information of each energy storage device in the energy storage system; for each energy storage device, if the remaining power information of a first device in the energy storage device meets a preset low power condition, then when the charge and discharge state represented by the target power sub-feature assigned to the first device is a discharge state, the charge and discharge state of the target power sub-feature assigned to the first device is switched to a stagnant state; if the remaining power information of a second device in the energy storage device meets a preset high power condition, then when the charge and discharge state represented by the target power sub-feature assigned to the second device is a charging state, the charge and discharge state of the target power sub-feature assigned to the second device is switched to a stagnant state.

[0139] In one embodiment, the target power sub-feature includes decomposed characteristic values ​​in multiple time intervals; the switching module is further used to obtain energy storage characteristic information of each energy storage device in the energy storage system; respectively determine the charge and discharge state switching information of each target power sub-feature; and allocate the multiple target power sub-features to each energy storage device in the energy storage system based on the charge and discharge state switching information, the decomposed characteristic values, and the energy storage characteristic information of each energy storage device of each target power sub-feature.

[0140] In one embodiment, before splitting the decomposed power sub-feature into multiple charge and discharge power features based on the distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, the above-mentioned device also includes: a elimination module for respectively determining the charge and discharge state switching information of each decomposed power sub-feature; and eliminating the decomposed power sub-features whose charge and discharge state switching information in the decomposed power feature does not meet the preset switching frequency conditions.

[0141] Each module in the power signature generation device for the energy storage system described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0142] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, and the wireless communication can be achieved via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for generating power characteristics of an energy storage system. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0143] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0144] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0146] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0147] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0148] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0149] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for generating power characteristics of an energy storage system, characterized in that: The method comprises: Acquire energy storage power information of the energy storage system, and select multiple target energy storage power sub-information from the energy storage power information; Generate a decomposed power feature of the energy storage system using a preset transformation method based on the multiple target energy storage power sub-information, wherein the decomposed power feature includes multiple decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value is used to characterize the charge state corresponding to the energy storage device in the energy storage system, and the second-type decomposition feature value is used to characterize the discharge state corresponding to the energy storage device in the energy storage system; For each of the decomposed power sub-features, splitting the decomposed power sub-feature into multiple charge and discharge power features based on distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, and supplementing the missing parts of the multiple charge and discharge power features with zero values ​​to obtain a power supplement feature of the decomposed power sub-feature, wherein the zero value is used to characterize the stagnation state corresponding to the energy storage device in the energy storage system; A target power feature of the energy storage system is generated according to the power supplement feature of each of the decomposed power sub-features.

2. The method according to claim 1, characterized in that The power supplement feature of the decomposed power sub-feature includes decomposed feature values ​​and supplemented zero values ​​in multiple time intervals; generating the target power feature of the energy storage system according to the power supplement feature of each decomposed power sub-feature includes: For each decomposed power sub-feature, sorting the decomposed eigenvalues ​​in all time intervals of the decomposed power sub-feature to obtain eigenvalue sorting information of the decomposed power sub-feature; Dividing the decomposed eigenvalues ​​of all time intervals in the decomposed power sub-feature according to the eigenvalue sorting information of the decomposed power sub-feature to obtain a plurality of eigenvalue intervals; updating the characteristic values ​​of the plurality of characteristic value intervals according to the statistical characteristic values ​​in the plurality of characteristic value intervals; The target power feature of the energy storage system is generated according to the supplementary zero value of each of the decomposed power sub-features and the corresponding updated multiple feature value intervals.

3. The method according to claim 2, characterized in that The statistical eigenvalue includes a maximum eigenvalue; and updating the eigenvalues ​​of the multiple eigenvalue intervals according to the statistical eigenvalues ​​in the multiple eigenvalue intervals includes: For the first eigenvalue interval, all decomposed eigenvalues ​​in the first eigenvalue interval are updated to the maximum eigenvalue in the first eigenvalue interval; For the non-first eigenvalue interval, all decomposed eigenvalues ​​of the first eigenvalue subinterval in the non-first eigenvalue interval are updated to the decomposed eigenvalues ​​of the first updated eigenvalue interval; according to the differences between each decomposed eigenvalue in the second power subinterval and the decomposed eigenvalue of the first updated eigenvalue interval, the decomposed eigenvalue of the second eigenvalue subinterval in the non-first eigenvalue interval is updated, wherein the decomposed eigenvalues ​​in the first eigenvalue subinterval are all less than or equal to the decomposed eigenvalue of the first updated eigenvalue interval, and the decomposed eigenvalues ​​in the second eigenvalue subinterval are all greater than the decomposed eigenvalue of the first updated eigenvalue interval.

4. The method according to claim 1, wherein The target power feature includes a plurality of target power sub-features; after generating the target power feature of the energy storage system according to the power supplement feature of each of the decomposed power sub-features, the method further includes: Allocating the plurality of target power sub-signatures to each energy storage device in the energy storage system; Real-time monitoring of the remaining power information of each energy storage device in the energy storage system; For each of the energy storage devices, if the remaining power information of a first device among the energy storage devices satisfies a preset low-power condition, then, if the charge-discharge state represented by the target power sub-feature assigned to the first device is a discharge state, switching the charge-discharge state of the target power sub-feature assigned to the first device to a stagnant state; If the remaining power information of a second device in the energy storage device meets the preset high power condition, then when the charge and discharge state represented by the target power sub-feature assigned to the second device is a charging state, the charge and discharge state of the target power sub-feature assigned to the second device is switched to a stagnant state.

5. The method according to claim 4, characterized in that The target power sub-features include decomposed feature values ​​in multiple time intervals; and allocating the multiple target power sub-features to each energy storage device in the energy storage system includes: Obtaining energy storage characteristic information of each energy storage device in the energy storage system; respectively determining charge and discharge state switching information of each target power sub-feature; The multiple target power sub-features are allocated to each energy storage device in the energy storage system according to the charge and discharge state switching information, the decomposed feature value and the energy storage characteristic information of each energy storage device of each target power sub-feature.

6. The method according to any one of claims 1 to 5, characterized in that Before splitting the decomposed power sub-feature into a plurality of charge and discharge power features based on distribution information of the first type of decomposition eigenvalues ​​and the second type of decomposition eigenvalues ​​in the decomposed power sub-feature, the method further includes: respectively determining charge and discharge state switching information of each of the decomposed power sub-features; The decomposed power sub-features whose charge and discharge state switching information does not meet the preset switching frequency condition in the decomposed power feature are eliminated.

7. A power characteristic generating device for an energy storage system, characterized in that: The device comprises: An acquisition module is used to acquire energy storage power information of the energy storage system and select multiple target energy storage power sub-information from the energy storage power information; a generating module, configured to generate, based on the plurality of target energy storage power sub-information, a decomposed power feature of the energy storage system using a preset transformation method, wherein the decomposed power feature includes a plurality of decomposed power sub-features, each of the decomposed power sub-features includes at least one of a first-type decomposition feature value and a second-type decomposition feature value, the first-type decomposition feature value being used to characterize a charge state corresponding to an energy storage device in the energy storage system, and the second-type decomposition feature value being used to characterize a discharge state corresponding to an energy storage device in the energy storage system; a splitting and zero-filling module, configured to split each of the decomposed power sub-features into a plurality of charge-discharge power features according to distribution information of the first-category decomposition eigenvalue and the second-category decomposition eigenvalue in the decomposed power sub-feature, and fill in the missing parts of the plurality of charge-discharge power features with zero values, thereby obtaining a power filling feature of the decomposed power sub-feature, wherein the zero value is used to characterize a stagnant state corresponding to the energy storage device in the energy storage system; The generating module is further configured to generate a target power feature of the energy storage system according to a power supplement feature of each of the decomposed power sub-features.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images