Energy storage device screening method, electronic device, and storage medium

Abstract

This application discloses a method for screening energy storage devices, an electronic device, and a storage medium, comprising: for any one of a plurality of energy storage devices, obtaining test evaluation results of multiple test items of the energy storage device under corresponding standard indicators, and decision coefficients corresponding to each test item; determining test weights of multiple test items based on the project attributes corresponding to each of the multiple test items; determining the device evaluation result of the energy storage device based on the test weights, decision coefficients, and test evaluation results corresponding to each of the multiple test items; and determining a target energy storage device from the plurality of energy storage devices based on the device evaluation results corresponding to each of the multiple energy storage devices.

Description

Technical Field

[0001] This application relates to the field of energy storage equipment evaluation technology, specifically to screening methods, electronic devices, and storage media for energy storage equipment. Background Technology

[0002] As energy storage applications become increasingly diversified, energy storage devices, such as power conversion systems (PCS), which are the core power conversion devices in electrochemical energy storage systems, need to have comprehensive performance such as high conversion efficiency, fast dynamic response, high reliability and intelligent operation to meet the power regulation and stability support requirements under different operating conditions. Their performance directly affects the efficiency, safety and economic benefits of the entire energy storage system.

[0003] Current evaluations of energy storage converters primarily focus on individual performance indicators such as efficiency and power density, or cost parameters. Therefore, a systematic and standardized evaluation method is urgently needed to achieve accurate and effective selection decisions. Summary of the Invention

[0004] This invention provides a method for screening energy storage devices, an electronic device, and a storage medium, aiming to achieve accurate screening of energy storage devices.

[0005] Firstly, a method for screening energy storage devices is provided, including: For any one of multiple energy storage devices, obtain the test evaluation results of multiple test items of the energy storage device under the corresponding standard indicators, as well as the decision coefficient corresponding to each test item; The test weights of multiple test items are determined based on the project attributes corresponding to each test item. Based on the test weights, decision coefficients and test evaluation results corresponding to multiple test items, the equipment evaluation results of the energy storage equipment are determined. Based on the equipment evaluation results of multiple energy storage devices, the target energy storage device is determined from among the multiple energy storage devices.

[0006] In one embodiment, the test evaluation results of multiple test items of the energy storage device under corresponding standard indicators are obtained, including: Obtain the test data and evaluation intervals corresponding to the standard indicators for the multiple test items of the energy storage device. Based on the discrepancies between the test data and the standard indicators, the test evaluation results corresponding to multiple test items are determined within the evaluation interval.

[0007] In this embodiment, the index gap analysis and scoring calculation of multiple test items can be automatically completed through preset standards, so as to achieve an objective and efficient quantitative evaluation of the performance of energy storage equipment.

[0008] In one embodiment, a specified test item is included among multiple test items; the energy storage device screening method further includes: Based on the test data and standard indicators corresponding to the specified test items, the binarization result is determined, and the test evaluation result corresponding to the specified test items is obtained.

[0009] In this embodiment, by performing binary scoring on the test items for the protection of energy storage devices, the protection function can be efficiently determined.

[0010] In one embodiment, the decision coefficients corresponding to each test item are obtained, including: Obtain the decision parameters of each test item across multiple decision dimensions, as well as the decision weights of the decision user across multiple decision dimensions; Based on the decision parameters and corresponding decision weights of each test item across multiple decision dimensions, the decision coefficients for each test item are determined.

[0011] In this embodiment, by integrating decision parameters and decision weights from multiple decision dimensions, the decision coefficient of each test item is calculated, which can quantify the decision impact under multiple dimensions and improve the authenticity and effectiveness of equipment screening.

[0012] In one embodiment, project attributes include functional attributes and criticality attributes; based on the project attributes corresponding to multiple test items, the test weights of multiple test items are determined, including: Multiple test items are divided based on their respective functional attributes to obtain multiple test item sets; For any set of test items, determine the first-level weight of each test item in the set based on the initial weights corresponding to the set of test items; Obtain the secondary weight of each test item in the keyness attribute in the test item set; Based on the primary and secondary weights corresponding to each test item, the test weight of each test item is determined, thereby obtaining the test weights of multiple test items.

[0013] In this embodiment, by dividing the test item set according to functional attributes and determining the primary and secondary weights of each test item based on the test item set, it is possible to assign weights hierarchically according to the functional system, thereby achieving a more accurate and structured weight allocation and improving the systematicness and objectivity of performance evaluation.

[0014] In one embodiment, based on the initial weights corresponding to the test item set, the first-level weight corresponding to each test item in the test item set is determined, including: The initial weights corresponding to the test item sets are determined based on the number of sets of multiple test items. Based on the user scenario to which each test item belongs in the test item set, determine the scenario correlation coefficient of the test item in the test item set; The initial weights are adjusted based on the scenario correlation coefficient to obtain the first-level weights corresponding to each test item in the test item set.

[0015] In this embodiment, by dynamically adjusting the weights according to different user scenarios, the evaluation results are made to better match the actual needs of customers, thereby improving the accuracy and effectiveness of energy storage device selection.

[0016] In one embodiment, the criticality attribute includes a security attribute and a project association attribute; the secondary weight of each test item in the test item set corresponding to the criticality attribute is obtained, including: Obtain the first weight of each test item in the test item set corresponding to the security attribute, and the second weight corresponding to the project association attribute; Based on the first and second weights, determine the secondary weights corresponding to each test item in the test item set.

[0017] In this embodiment, by integrating the dual influence of safety attributes and project-related attributes, the comprehensive secondary weight of each test item is calculated, achieving a more accurate weight allocation that better reflects actual risks and relevance, thereby improving the accuracy of energy storage device screening.

[0018] In one embodiment, the equipment evaluation result of the energy storage device is determined based on the test weights, decision coefficients, and test evaluation results corresponding to multiple test items, including: Based on the test weights and decision coefficients corresponding to multiple test items, the target weights corresponding to multiple test items are determined. Based on the target weights corresponding to multiple test items, the corresponding test evaluation results are weighted and fused to obtain the equipment evaluation results of the energy storage equipment.

[0019] In this embodiment, the test evaluation results of the test items are weighted and summed by calculating the target weight to obtain the equipment evaluation results. Then, the energy storage equipment is screened based on the equipment evaluation results, which ensures the accuracy of the energy storage equipment screening.

[0020] Secondly, a screening device for energy storage equipment is also provided, comprising: The data acquisition module is used to acquire the test evaluation results of multiple test items of any one of the multiple energy storage devices under the corresponding standard indicators, as well as the decision coefficient corresponding to each test item; The weight determination module is used to determine the test weights of multiple test items based on the project attributes corresponding to each test item. The equipment evaluation result module is used to determine the equipment evaluation result of the energy storage equipment based on the test weights, decision coefficients and test evaluation results corresponding to multiple test items; The equipment screening module is used to determine the target energy storage device from multiple energy storage devices based on the equipment evaluation results corresponding to each of the multiple energy storage devices.

[0021] Thirdly, this application also provides an electronic device, including a memory and a processor, the memory storing a computer program for controlling the processor to operate in order to perform the methods in any of the embodiments of any of the above aspects.

[0022] Fourthly, this application also provides a computer-readable storage medium including computer instructions that, when executed by a processor, implement the methods in any of the embodiments described above.

[0023] Fifthly, the present application provides a computer program product that, when executed by a processor, implements the method in any of the above-described embodiments.

[0024] Beneficial effects: By obtaining the test evaluation results of multiple test items for any energy storage device under corresponding standard indicators and the decision coefficient of each test item, and then determining the corresponding test weights based on the project attributes of multiple test items, the device evaluation result of the energy storage device can be determined by the test weights, decision coefficients, and test evaluation results of multiple test items. This allows for the introduction of test evaluation results at the objective performance level and decision coefficients at the subjective demand level of the energy storage device, ensuring the data diversity of the device evaluation results. Furthermore, by combining the test weights, test evaluation results, and decision coefficients of the test items to determine the device evaluation result, the device evaluation result can be accurately reflected under both objective and subjective factors, improving the accuracy of the device evaluation results and thus enhancing the accuracy and effectiveness of energy storage device selection. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating the energy storage device selection method provided by an exemplary embodiment of this disclosure; Figure 2 This is another flowchart illustrating the energy storage device screening method provided by an exemplary embodiment of this disclosure; Figure 3This is a schematic diagram of the screening process for energy storage devices provided by an exemplary embodiment of this disclosure; Figure 4 This is a schematic diagram of the overall evaluation process of the energy storage device provided in the exemplary embodiments of this disclosure; Figure 5 This is a schematic diagram of a screening device for energy storage equipment provided in an exemplary embodiment of this disclosure; Figure 6 This is an internal structural diagram of an electronic device provided by an exemplary embodiment of this disclosure. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0029] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0030] On the one hand, this embodiment provides a method for screening energy storage devices, such as Figure 1 As shown, it includes the following steps: S101, for any one of the multiple energy storage devices, obtain the test evaluation results of multiple test items of the energy storage device under the corresponding standard indicators, as well as the decision coefficient corresponding to each test item.

[0031] Here, "energy storage equipment" refers to energy storage devices within an electrochemical energy storage system, including but not limited to energy storage converters (PCS). An energy storage converter is a bidirectional converter responsible for controlling the bidirectional conversion of electrical energy between the energy storage battery and the grid (or load). "Test items" refer to mandatory test items specifically designated for energy storage equipment. "Standard indicators" refer to the standard values ​​set for each test item in the professional technical specifications, such as the minimum required values. "Test evaluation results" refer to the evaluation values ​​obtained based on the test performance of the test items according to the standard indicators. "Decision coefficient" is a parameter characterizing the influence of the test item on user decision-making.

[0032] For example, in response to a screening command for multiple energy storage devices, the terminal obtains the test evaluation results of multiple test items for each energy storage device under corresponding standard indicators, as well as the test item itself. The test items and corresponding standard indicators are identical for each energy storage device. The test evaluation result for each test item can be determined by acquiring the test data of the energy storage device for the corresponding test item and then comparing the test data with the standard indicator; alternatively, it can be an evaluation value obtained by linearly transforming the test data based on the benchmark score corresponding to the standard index. The decision coefficient can be obtained by acquiring the decision coefficients for each test item under multiple decision dimensions and then merging the decision coefficients across multiple decision dimensions.

[0033] S102, determine the test weights of multiple test items based on the project attributes corresponding to each test item.

[0034] Among them, project attributes describe the operational states involved in maintaining the normal operation of energy storage equipment, such as functional attributes, safety attributes, and collaborative operation attributes that affect the operation of energy storage equipment. Test weights are quantitative data used to measure the degree of influence or importance of test items in equipment evaluation.

[0035] For example, after obtaining the test evaluation results and decision coefficients corresponding to multiple test items, the terminal acquires the item attributes corresponding to each test item, wherein the item attributes include a first attribute and a second attribute. When determining the test weight corresponding to each test item based on the item attributes, it may be possible to acquire the primary weight corresponding to the first attribute and the secondary weight corresponding to the second attribute for each test item, and determine the test weight corresponding to each test item based on the primary weight and the secondary weight corresponding to each test item.

[0036] Specifically, primary weights can be determined based on the first attribute to which multiple test items belong. The first attribute can represent the performance aspect of the test item, such as a functional attribute. Secondary weights, on the other hand, are obtained by further assigning weights to each test item within the same primary attribute, based on its secondary attribute. The secondary attribute can represent the criticality of the test item's function during device operation, such as a safety attribute. Therefore, there is a hierarchical relationship between primary and secondary weights, indicating the weight of test items under different levels of project attributes.

[0037] S103, based on the test weights, decision coefficients and test evaluation results corresponding to multiple test items, determine the equipment evaluation result of the energy storage device.

[0038] S104, Based on the equipment evaluation results corresponding to multiple energy storage devices, determine the target energy storage device from the multiple energy storage devices.

[0039] The equipment evaluation results are quantitative data that evaluates energy storage equipment from multiple dimensions. The target energy storage equipment refers to the energy storage equipment that was finally selected.

[0040] For example, after obtaining the test weights, decision coefficients, and test evaluation results corresponding to multiple test items for any energy storage device, the terminal obtains the target test evaluation result for each test item based on its test weight, decision coefficient, and test evaluation result. Then, based on the target test evaluation results of multiple test items, the terminal determines the device evaluation result of the energy storage device. This can be achieved by summing the target test evaluation results of any test device across multiple test items. Among the device evaluation results corresponding to multiple energy storage devices, the energy storage device corresponding to the evaluation result that reaches a preset evaluation threshold is identified as the target energy storage device. Alternatively, the energy storage device corresponding to the evaluation result with the largest value can be identified as the target energy storage device.

[0041] In this embodiment, by obtaining the test evaluation results of multiple test items for any energy storage device under corresponding standard indicators and the decision coefficient of each test item, and then determining the corresponding test weights based on the project attributes of multiple test items, the device evaluation result of the energy storage device is determined by the test weights, decision coefficients, and test evaluation results of multiple test items. This approach can introduce the test evaluation results of the energy storage device at the objective performance level and the decision coefficients at the subjective demand level, ensuring the data diversity of the device evaluation results. Furthermore, by combining the test weights, test evaluation results, and decision coefficients of the test items to determine the device evaluation result, the device evaluation result of the energy storage device can be accurately reflected under both objective and subjective factors, improving the accuracy of the device evaluation results and thus improving the accuracy and effectiveness of energy storage device selection.

[0042] In one embodiment, the test evaluation results of multiple test items of the energy storage device under corresponding standard indicators are obtained, including: Obtain the test data and evaluation intervals corresponding to the standard indicators for the multiple test items of the energy storage device. Based on the discrepancies between the test data and the standard indicators, the test evaluation results corresponding to multiple test items are determined within the evaluation interval.

[0043] The evaluation interval refers to the range of evaluation values ​​corresponding to the pre-set test evaluation results.

[0044] For example, the terminal acquires test data corresponding to multiple test items of the energy storage device, as well as the benchmark evaluation value and evaluation range of the standard indicator corresponding to each test item. Generally, the standard indicator is the minimum requirement value under the professional specification, and the evaluation range is, for example, [0, 100]. The benchmark evaluation value represents the score when the test data of the test item meets the standard indicator, and serves as the passing score for that test item, for example, 60 points is the passing score.

[0045] For any test item, calculate the difference between the test data corresponding to that test item and the standard indicator to obtain the indicator gap. Then, perform linear interpolation within the evaluation interval based on the benchmark evaluation value and the indicator gap to obtain the test evaluation result corresponding to that test item, thereby obtaining the test evaluation results corresponding to multiple test items respectively. Specifically, it can be to obtain the qualified indicator interval between the optimal indicator (i.e., the optimal ideal value) and the benchmark indicator corresponding to the test item, as well as the qualified evaluation interval (e.g., [60, 100]). Based on the ratio between the qualified evaluation intervals, obtain the unit evaluation increment. For example, the response time (test item) specified in the national technical specification GB / T 34120-2023 "Performance Testing and Evaluation Methods for Microgrid Energy Storage Systems" is no more than 100ms (standard indicator, i.e., the minimum requirement value). Then, 100ms is taken as the passing score of 60 points, and 0ms (theoretical optimal value) is taken as the full score of 100 points. Then, with 100ms as the benchmark, the score increases by 0.4 points for every 1ms decrease in response time ((100-60) / (100-0)=0.4).

[0046] Then, based on the indicator gap, the unit score increment, and the benchmark evaluation value, the test evaluation result corresponding to the test item is obtained. Specifically, the evaluation gain can be obtained by multiplying the indicator gap and the unit evaluation increment, and then the sum of the benchmark evaluation value and the score gain is calculated to obtain the test evaluation result corresponding to the test item. The score gain represents the additional evaluation value corresponding to the indicator gap compared to the benchmark indicator. For example, if the response time of the active power control test item is 20.74ms when the discharge power is 80% of the rated power, then the test evaluation result of the response time at this point is: (100-20.74)×0.4+60=91.704.

[0047] In this embodiment, the index gap analysis and scoring calculation of multiple test items can be automatically completed through preset standards, so as to achieve an objective and efficient quantitative evaluation of the performance of energy storage equipment.

[0048] In one embodiment, a specified test item is included among multiple test items; the energy storage device screening method further includes: Based on the test data and standard indicators corresponding to the specified test items, the binarization result is determined, and the test evaluation result corresponding to the specified test items is obtained.

[0049] Among them, the designated test items refer to the test items with protection functions in energy storage equipment, such as Boolean items in national technical specifications, such as the correctness of the protection function operation.

[0050] For example, for a specific test item among multiple test items, the terminal obtains the benchmark index corresponding to the specified test item, compares the test data corresponding to the specified test item with the benchmark index, determines the binarized result based on the comparison result, and obtains the test evaluation result corresponding to the specified test item. Specifically, if the test data of the specified test item exceeds the benchmark index, the test evaluation result of the specified test item is determined to be a full score, such as 100; if the test data of the specified test item does not exceed the benchmark index, the test evaluation result of the specified test item is determined to be zero.

[0051] In this embodiment, by performing binary scoring on the test items for the protection of energy storage devices, the protection function can be efficiently determined.

[0052] In one embodiment, such as Figure 2 As shown, obtain the decision coefficients corresponding to each test item, including: S201, obtain the decision parameters of each test item under multiple decision dimensions, and the decision weights of the decision user under multiple decision dimensions; S202, based on the decision parameters and corresponding decision weights of each test item under multiple decision dimensions, determine the decision coefficient corresponding to each test item.

[0053] In this context, "decision dimension" refers to the influencing factors that are pre-set for each test item, affecting the device selection process by decision-making users. These factors may include cost, security, and technology. "Decision-making users" are the users who make decisions regarding device selection, specifically group users. "Decision parameters" are the parameter values ​​pre-set for each test item under its corresponding decision dimension. "Decision weights" refer to the degree of attention decision-making users pay to any given test item across multiple test dimensions.

[0054] For example, the terminal obtains the decision parameters of each test item under multiple decision dimensions, and obtains the user information of the decision user, including the user's department, job level, etc., and obtains the decision weight of each decision user under multiple decision dimensions based on the user information of the decision user.

[0055] Understandably, due to differences in the departments or job levels of decision-makers, their focus on each decision dimension will also differ. For example, procurement-level personnel may prioritize cost, while operations-level personnel may prioritize security. Therefore, for each decision-maker's corresponding job level, we can set the decision focus level for each of the multiple decision dimensions, as well as the weight of each decision-maker's own identity. Thus, each decision-maker's decision weight across multiple decision dimensions can be derived from their decision focus level and identity weight across those dimensions. For example, the product of each decision-maker's decision focus level and identity weight in each decision dimension can be used as their decision weight for that dimension.

[0056] Then, based on the decision weights of each test item across multiple decision dimensions, the corresponding decision parameters are weighted and summed to obtain the decision coefficients for each test item.

[0057] For example, to integrate customer concerns with the company's internal decision-making structure (job level requirements) into the evaluation system, a dynamic adjustment coefficient is introduced as a decision coefficient. Decision-making users, such as those at different job levels or departments within the company (e.g., management, technical department, sales department, operations department), have different decision-making focuses. A "job level-concern" weight matrix is ​​constructed, as shown in Table 1. Each test item is mapped to decision parameters under different decision dimensions (e.g., cost, security, technological leadership, market competitiveness), as shown in Table 2, which includes the mapping basis for each test item. Then, based on the current job level composition of the decision-making users, a comprehensive decision coefficient related to the job level of the decision-making users is calculated, which is used to fine-tune the final test evaluation results or weights.

[0058] Table 1

[0059] Table 2

[0060] Assuming the decision-making team composition, for example, a bidding committee includes: Management: 2 people (weight 0.3), Technology Department: 3 people (weight 0.3), Sales Department: 1 person (weight 0.2), and Operations Department: 1 person (weight 0.2). Decision dimensions include: cost, security, technology, market, and reliability. Referring to Table 1, obtain the decision focus of each department across multiple decision dimensions and calculate the corresponding decision weight for each dimension. .

[0061] Calculation example: Cost: 0.3 × 0.35 + 0.3 × 0.15 + 0.2 × 0.25 + 0.2 × 0.10 = 0.225 Safety: 0.3 × 0.25 + 0.3 × 0.30 + 0.2 × 0.15 + 0.2 × 0.35 = 0.275 Technical calculation: 0.3 × 0.15 + 0.3 × 0.35 + 0.2 × 0.10 + 0.2 × 0.10 = 0.205 Market: 0.3 × 0.20 + 0.3 × 0.10 + 0.2 × 0.40 + 0.2 × 0.10 = 0.190 Reliability: 0.3×0.05 + 0.3×0.10 + 0.2×0.10 + 0.2×0.35 = 0.165.

[0062] Then, normalization is performed, for example: 0.225 / (0.225+0.275+0.205+0.19+0.165)= 0.212, resulting in decision weights for multiple decision dimensions: [0.213, 0.260, 0.194, 0.180, 0.156].

[0063] Calculate the decision coefficient for each test item. Taking conversion efficiency as an example, = 0.8×0.213 + 0.1×0.260 + 0.6×0.194 + 0.3×0.180 + 0.2×0.156=0.397. Then, the multiple decision coefficients are normalized to make the average value 1 or maintain a relative proportion, so as to obtain the final decision coefficients used.

[0064] In this embodiment, by integrating decision parameters and decision weights from multiple decision dimensions, the decision coefficient of each test item is calculated, which can quantify the decision impact under multiple dimensions and improve the authenticity and effectiveness of equipment screening.

[0065] In one embodiment, such as Figure 3As shown, project attributes include functional attributes and criticality attributes; based on the project attributes corresponding to multiple test items, the test weights of multiple test items are determined, including: S301, based on the functional attributes corresponding to multiple test items, divide multiple test items into multiple test item sets; S302, For any set of test items, determine the first-level weight corresponding to each test item in the set of test items based on the initial weights corresponding to the set of test items; S303, obtain the secondary weight of each test item in the keyness attribute in the test item set; S304. Based on the primary and secondary weights corresponding to each test item, determine the test weight of each test item, thereby obtaining the test weights of multiple test items.

[0066] Among them, functional attributes indicate the specific role that the test item plays in the operation of the energy storage device, such as response speed and charging power. Criticality attributes indicate the degree of impact (or importance) of the test item on the overall performance, safety, or compliance of the energy storage device.

[0067] For example, the terminal acquires the project attributes corresponding to multiple test items, including functional attributes and criticality attributes. Based on the functional attributes corresponding to each test item, the multiple test items are divided into multiple test item sets. Test item sets with similar functional attributes are used as the first attribute of that test item set to determine the primary weight of the test items. For example, the division of test item sets can be based on the technical principles of the PCS (Power Conversion System) and its core functions in energy storage systems. All test items are categorized into several similar functional attributes with clear technical connotations. For instance, they can be clustered into test item sets under the first attribute such as "Core Electrical Performance and Economy," "Grid Interaction and Safety Compliance," "Power Quality and Grid Support," and "Environmental Adaptability and Reliability." It is important to understand that the division of test items is not a subjective division, but rather based on the commonality of the technical functions reflected by the test items and the correlation of their impact on the system. For example, test items such as "low voltage ride-through", "high voltage ride-through", and "frequency adaptability" are classified under "B. Grid Interaction and Safety Compliance" because they jointly assess the survivability and support capability of the PCS under abnormal grid operating conditions. Their underlying control strategies are interconnected, and they are often assessed simultaneously in actual grid faults.

[0068] After the terminal obtains multiple test item sets, it determines the initial weight of each test item set based on the number of test item sets. For example, if there are 4 test item sets, the initial weight of each test item set is 1 / 4 = 0.25. For any given test item set, based on the user scenario to which each test item belongs (e.g., user-side peak-valley arbitrage, grid-side peak shaving), the scenario correlation coefficient of the test item within the test item set is determined. The scenario correlation coefficient represents the weight adjustment coefficient of the test item in the corresponding user scenario. The product of the initial weight corresponding to each test item and the scenario correlation coefficient is calculated to obtain the first-level weight corresponding to each test item.

[0069] Obtain the secondary weight corresponding to the criticality attribute for each test item in the test item set. The criticality attribute is used as a second attribute under a certain functional attribute (first attribute) of the same type to determine the secondary weight of the test item. Then, based on the product of the primary weight and the secondary weight corresponding to each test item, obtain the test weight of each test item, thus obtaining the test weights of multiple test items.

[0070] In this embodiment, by dividing the test item set according to functional attributes and determining the primary and secondary weights of each test item based on the test item set, it is possible to assign weights hierarchically according to the functional system, thereby achieving a more accurate and structured weight allocation and improving the systematicness and objectivity of performance evaluation.

[0071] In one embodiment, based on the initial weights corresponding to the test item set, the first-level weight corresponding to each test item in the test item set is determined, including: The initial weights corresponding to the test item sets are determined based on the number of sets of multiple test items. Based on the user scenario to which each test item belongs in the test item set, determine the scenario correlation coefficient of the test item in the test item set; The initial weights are adjusted based on the scenario correlation coefficient to obtain the first-level weights corresponding to each test item in the test item set.

[0072] For example, the terminal calculates the initial weight corresponding to each test item set based on the number of sets of multiple test items. Generally, the initial weight is obtained by calculating the ratio of unit 1 to the number of sets. For any test item set, the user scenario to which each test item belongs is obtained. Different user scenarios have different test items that users care about. For example, the test items for PCS (energy storage converter) equipment will include all the test items in GB / T 34133-2023. The test items have corresponding user scenarios, and different user scenarios have different test items that users care about. For example, for the user scenario of peak-valley arbitrage on the user side, the test item of concern is charging and discharging efficiency; for the user scenario of primary frequency regulation of new energy power plants, the test item of concern is primary frequency regulation, etc. For different user scenarios (such as user-side peak-valley arbitrage, primary frequency regulation of new energy power plants, grid-side peak regulation, etc.), the intensity of user concern for each first attribute is pre-quantified, such as the user's core needs, scenario basis, etc., to obtain the scenario correlation coefficients corresponding to multiple first attributes under each user scenario (as shown in Table 3).

[0073] Table 3

[0074] Where A represents core electrical performance and economy, B represents grid interaction and safety compliance, C represents power quality and grid support, and D represents environmental adaptability and reliability.

[0075] For example, referring to Table 3, the first attribute of the test item "Response Time" is "B Power Grid Interaction and Security Compliance", and the user scenario corresponding to the response time is "Primary Frequency Regulation of New Energy Power Plant". Then the scenario correlation coefficient corresponding to the test item "Response Time" is "B (35%)" corresponding to "Primary Frequency Regulation of New Energy Power Plant".

[0076] Then, the initial weights are adjusted using the scenario correlation coefficient. This can be done by calculating the product of the scenario correlation coefficient and the initial weights to obtain the first-level weights corresponding to each test item in the test item set.

[0077] In this embodiment, by dynamically adjusting the weights according to different user scenarios, the evaluation results are made to better match the actual needs of customers, thereby improving the accuracy and effectiveness of energy storage device selection.

[0078] In one embodiment, the criticality attribute includes a security attribute and a project association attribute; the secondary weight of each test item in the test item set corresponding to the criticality attribute is obtained, including: Obtain the first weight of each test item in the test item set corresponding to the security attribute, and the second weight corresponding to the project association attribute; Based on the first and second weights, determine the secondary weights corresponding to each test item in the test item set.

[0079] Among them, safety attributes refer to the characteristics of energy storage devices in preventing risks such as personal injury, equipment damage, and data leakage during operation. Project correlation attributes refer to the characteristics of whether test items of energy storage devices operate collaboratively during operation.

[0080] For example, the criticality attribute includes a safety attribute and a project-related attribute. Within the test item set corresponding to each first attribute, a secondary weight is assigned based on the safety attribute and the project-related attribute (second attribute) corresponding to each test item. This weight consists of the first weight of each test item corresponding to the safety attribute and the second weight corresponding to the project-related attribute. Specifically, the first weight corresponding to the safety attribute is determined by the degree of damage caused to the function of the first attribute by the test item in the event of failure; that is, the criticality of the test item under different damage levels, as shown in Table 4. For example, within the first attribute "Grid Interaction and Safety Compliance," "Anti-Islanding Protection" has an extremely high safety level because it is directly related to personal and equipment safety, and should be assigned a high secondary weight. The project-related attribute refers to whether the performance evaluated by the test item can be indirectly characterized by other test items in the same test item set. The second weight corresponding to the project-related attribute is determined by the degree of independence of the test item at its corresponding independent level within the test item set of the same first attribute, as shown in Table 5.

[0081] Then, based on the product of the first and second weights corresponding to each test item, the secondary weights corresponding to each test item in the test item set are obtained.

[0082] Table 4

[0083] Table 5

[0084] The secondary weights for each test item The calculation can be expressed as: (Normalized to a 0-1 scale). Among them, Indicates the first weight. This is represented as the second weight.

[0085] In this embodiment, by integrating the dual influence of safety attributes and project-related attributes, the comprehensive secondary weight of each test item is calculated, achieving a more accurate weight allocation that better reflects actual risks and relevance, thereby improving the accuracy of energy storage device screening.

[0086] In one embodiment, the equipment evaluation result of the energy storage device is determined based on the test weights, decision coefficients, and test evaluation results corresponding to multiple test items, including: Based on the test weights and decision coefficients corresponding to multiple test items, the target weights corresponding to multiple test items are determined. Based on the target weights corresponding to multiple test items, the corresponding test evaluation results are weighted and fused to obtain the equipment evaluation results of the energy storage equipment.

[0087] For example, for any energy storage device, after the terminal obtains the test weights and decision coefficients corresponding to multiple test items, it calculates the product of the test weight and decision coefficient for each test item to obtain the target weight for each test item. Then, the target weights corresponding to the multiple test items are used to perform a weighted fusion calculation on the corresponding test evaluation results to obtain the device evaluation result for the energy storage device, thereby obtaining the device evaluation results for multiple energy storage devices.

[0088] Specifically, the target test evaluation result for each test item can be obtained by multiplying its target weight and the test evaluation result. The sum of the target test evaluation results for multiple test items can then be calculated and normalized to obtain the equipment evaluation result for the energy storage device. For example, the calculation of the target score for the j-th test item in the set of test items corresponding to the k-th first attribute can be expressed as: Target test evaluation result = (Test evaluation result) × (First weight) ×Scene correlation coefficient )×(Decision coefficient) ).

[0089] In this embodiment, the test evaluation results of the test items are weighted and summed by calculating the target weight to obtain the equipment evaluation results. Then, the energy storage equipment is screened based on the equipment evaluation results, which ensures the accuracy of the energy storage equipment screening.

[0090] In one specific embodiment, such as Figure 4 The diagram illustrates a comprehensive evaluation process for energy storage devices. The example used is the comprehensive evaluation of PCS (Power Conversion System) devices from multiple PCS (Power Conversion System) manufacturers to select target PCS devices.

[0091] Obtain test reports for PCS equipment from multiple PCS manufacturers, such as PCS manufacturer 1, PCS manufacturer 2, ..., PCS manufacturer n. Extract test data for multiple comparable test items under the same working conditions from the test reports, such as test data for test item 1, test item 2, ..., test item n. Score each test item based on the scoring rules established according to national standard values ​​(i.e., standard indicators), and obtain the test score for each test item, which is the test evaluation result. For example, PCS manufacturer 1's score for test item 1 is a1, the score for test item 2 is a2, ..., the score for test item n is an, and similarly, PCS manufacturer 2's score for test item 1 is b1, the score for test item 2 is b2, ..., the score for test item n is bn, and PCS manufacturer n's score for test item 1 is c1, the score for test item 2 is c2, ..., the score for test item n is cn, etc.

[0092] Multiple test items are weighted once (technical dimension clustering and weighting) to obtain multiple test item sets and corresponding initial weights p. For example, p1: test item 1, test item 2, test item 3; p2: test item 4, test item 5, test item 6, etc. Then, for any set of test items, the customer scenario coefficient S (i.e., scenario correlation coefficient) of each test item is obtained. The initial weights p are adjusted using the customer scenario coefficient S. For example, p1×S1: test item 1, test item 2, test item 3; p2×S2: test item 4, test item 5, test item 6, etc.

[0093] For any set of test items, perform secondary weighting (weighting test items within the technical dimension) to obtain the secondary weight q of each test item. For example, p1×S1×q1: test item 1, test item 2, test item 3, p2×S2×q2: test item 4, test item 5, test item 6, etc.

[0094] Obtain the decision coefficient D corresponding to each test item. Based on the initial weight p, customer scenario coefficient S, secondary weight q, and decision coefficient D corresponding to each test item, obtain the target weight corresponding to each test item. For example, p1×S1×q1×D1: test item 1, test item 2, test item 3; p2×S2×q2×D2: test item 4, test item 5, test item 6, etc.

[0095] Based on the target weights of multiple test items and the test evaluation results, the equipment evaluation result of the energy storage device is obtained. For example, PCS1 = a1×p1×S1×q1×D1 + a2×p2×S2×q2×D2 + … + an×p4×S4×an×Dn. The sum of the equipment evaluation results of multiple PCS devices is calculated as the full score of the device, which can be expressed as: .

[0096] Then, the equipment evaluation results of multiple PCS devices were normalized, and the final score was: , ...

[0097] In this embodiment, PCS equipment is evaluated from multiple dimensions using national standard test data to avoid "generalization from limited evidence" and achieve a systematic evaluation. The scoring criteria are strictly based on national standards, and the weighting is based on technical logic and functional contribution to minimize subjective arbitrariness and achieve objective evaluation. Through customer scenario coefficients and job level decision coefficients, the evaluation results can be flexibly adapted to the actual needs of specific projects and the value orientation of decision-makers, achieving dynamic and adaptive customized evaluation.

[0098] On the other hand, this embodiment provides a screening device for energy storage equipment. Figure 5This is a schematic diagram of a screening device for an energy storage device according to an embodiment of this application, such as... Figure 5 As shown, the energy storage equipment screening device 500 includes: a data acquisition module 501, a weight determination module 502, an equipment evaluation result module 503, and an equipment screening module 504. The device will be described below.

[0099] The data acquisition module 501 is used to acquire the test evaluation results of multiple test items of the energy storage device under the corresponding standard indicators, as well as the decision coefficient corresponding to each test item, for any one of the multiple energy storage devices. The weight determination module 502 is used to determine the test weights of multiple test items based on the project attributes corresponding to each test item. The equipment evaluation result module 503 is used to determine the equipment evaluation result of the energy storage equipment based on the test weights, decision coefficients and test evaluation results corresponding to multiple test items respectively. The equipment screening module 504 is used to determine the target energy storage device from multiple energy storage devices based on the equipment evaluation results corresponding to each of the multiple energy storage devices.

[0100] In one embodiment, the data acquisition module is further configured to acquire test data corresponding to multiple test items of the energy storage device and evaluation intervals corresponding to standard indicators; and determine the test evaluation results corresponding to multiple test items within the evaluation interval based on the indicator gap between the test data and the standard indicators.

[0101] In one embodiment, a specified test item is included among multiple test items; the screening device for energy storage equipment is further configured to determine the binarization result based on the test data and standard indicators corresponding to the specified test item, and obtain the test evaluation result corresponding to the specified test item.

[0102] In one embodiment, the data acquisition module is further configured to acquire the decision parameters of each test item under multiple decision dimensions, as well as the decision weights of the decision user under multiple decision dimensions; and determine the decision coefficient corresponding to each test item based on the decision parameters and corresponding decision weights of each test item under multiple decision dimensions.

[0103] In one embodiment, the project attributes include functional attributes and criticality attributes; the weight determination module is further configured to divide the multiple test items based on the functional attributes corresponding to the multiple test items respectively, to obtain multiple test item sets; for any test item set, based on the initial weights corresponding to the test item set, determine the primary weight corresponding to each test item in the test item set; obtain the secondary weight corresponding to the criticality attribute for each test item in the test item set; and determine the test weight of each test item based on the primary weight and secondary weight corresponding to each test item, thereby obtaining the test weights of multiple test items.

[0104] In one embodiment, the weight determination module is further configured to determine the initial weight corresponding to the test item set based on the number of sets of multiple test item sets; determine the scenario correlation coefficient of the test item under the test item set based on the user scenario to which each test item belongs in the test item set; and adjust the initial weight based on the scenario correlation coefficient to obtain the first-level weight corresponding to each test item in the test item set.

[0105] In one embodiment, the criticality attribute includes a security attribute and a project association attribute; the weight determination module 502 is further configured to obtain the first weight of each test item in the test item set corresponding to the security attribute, and the second weight corresponding to the project association attribute; and determine the secondary weight of each test item in the test item set based on the first weight and the second weight.

[0106] In one embodiment, the equipment screening module is further configured to determine the target weights corresponding to the multiple test items based on the test weights and decision coefficients corresponding to the multiple test items respectively; and to perform weighted fusion calculation on the corresponding test evaluation results based on the target weights corresponding to the multiple test items respectively, so as to obtain the equipment evaluation results of the energy storage equipment.

[0107] Each module in the screening device of the aforementioned energy storage equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0108] Thirdly, this embodiment provides an electronic device, including a memory and a processor. The memory stores computer instructions, and when the computer instructions are executed by the processor, they implement the method of any of the above embodiments.

[0109] In one embodiment, this embodiment also provides an electronic device, which may be a server, and its internal structure diagram may be as follows. Figure 6As shown, this electronic device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer instructions, and a database. The internal memory provides the environment for the operation of the operating system and computer instructions stored in the non-volatile storage media. The database is used for data involved in the cell quality testing method. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer instructions are executed by the processor, a screening method for energy storage devices is implemented.

[0110] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0112] Therefore, embodiments of this application provide a computer-readable storage medium storing multiple computer programs that can be loaded by a processor to execute any of the energy storage device selection methods provided in this application. The computer program can execute the steps of the energy storage device selection method as follows: For any one of multiple energy storage devices, obtain the test evaluation results of multiple test items of the energy storage device under the corresponding standard indicators, as well as the decision coefficient corresponding to each test item; determine the test weight of multiple test items based on the project attributes corresponding to each test item; determine the equipment evaluation result of the energy storage device based on the test weight, decision coefficient and test evaluation results corresponding to each test item; and determine the target energy storage device from the multiple energy storage devices based on the equipment evaluation results corresponding to each energy storage device.

[0113] The specific implementation of each of the above operations can be found in the preceding embodiments, and will not be repeated here. The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), a disk, or an optical disk, etc.

[0114] Since the computer program stored in the computer-readable storage medium can execute any of the energy storage device screening methods provided in the embodiments of this application, the beneficial effects that any of the energy storage device screening methods provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0115] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations of the above embodiments.

[0116] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0117] The above provides a detailed description of the energy storage device screening method, electronic device, and storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of screening energy storage devices, the method comprising: include: ​ For any one of multiple energy storage devices, obtain the test evaluation results of multiple test items of the energy storage device under the corresponding standard indicators, and the decision coefficient corresponding to each test item; Based on the project attributes corresponding to the multiple test items, the test weights of the multiple test items are determined; Based on the test weights, decision coefficients, and test evaluation results corresponding to the multiple test items, the equipment evaluation result of the energy storage device is determined. Based on the equipment evaluation results corresponding to the multiple energy storage devices, the target energy storage device is determined from the multiple energy storage devices.

2. The method according to claim 1, characterized in that, Obtain the test evaluation results of multiple test items of the energy storage device under the corresponding standard indicators, including: Obtain the test data corresponding to multiple test items of the energy storage device and the evaluation intervals corresponding to the standard indicators; Based on the index gap between the test data and the standard index, test evaluation results corresponding to multiple test items are determined within the evaluation interval.

3. The method according to claim 1, characterized in that, The method further includes: a specified test item among the multiple test items; Based on the test data and standard indicators corresponding to the specified test item, the binarization result is determined, and the test evaluation result corresponding to the specified test item is obtained.

4. The method according to claim 1, characterized in that, Obtain the decision coefficients corresponding to each of the test items, including: Obtain the decision parameters of each test item under multiple decision dimensions, and the decision weights of the decision user under the multiple decision dimensions; Based on the decision parameters and corresponding decision weights of each test item across multiple decision dimensions, the decision coefficients corresponding to each test item are determined.

5. The method according to claim 1, characterized in that, The project attributes include functional attributes and criticality attributes; determining the test weights of multiple test items based on the project attributes corresponding to each test item includes: The multiple test items are divided based on their respective functional attributes to obtain multiple test item sets; For any set of test items, based on the initial weights corresponding to the set of test items, determine the first-level weights corresponding to each test item in the set of test items; Obtain the secondary weight of each test item in the set of test items corresponding to the criticality attribute; Based on the primary and secondary weights corresponding to each test item, the test weight of each test item is determined, thereby obtaining the test weights of multiple test items.

6. The method according to claim 5, characterized in that, Based on the initial weights corresponding to the test item set, determine the first-level weight corresponding to each test item in the test item set, including: The initial weights corresponding to the test item sets are determined based on the number of sets of multiple test item sets. Based on the user scenario to which each test item belongs in the test item set, determine the scenario association coefficient of the test item under the test item set; The initial weights are adjusted based on the scenario correlation coefficients to obtain the first-level weights corresponding to each test item in the test item set.

7. The method according to claim 5, characterized in that, The criticality attribute includes security attributes and project association attributes; obtaining the secondary weight of each test item in the test item set corresponding to the criticality attribute includes: Obtain the first weight of each test item in the test item set corresponding to the security attribute, and the second weight corresponding to the project association attribute; Based on the first weight and the second weight, determine the secondary weight corresponding to each test item in the test item set.

8. The method according to any one of claims 1-7, characterized in that, The process of determining the equipment evaluation result of the energy storage device based on the test weights, decision coefficients, and test evaluation results corresponding to the multiple test items includes: Based on the test weights and decision coefficients corresponding to the multiple test items, the target weights corresponding to the multiple test items are determined. Based on the target weights corresponding to the multiple test items, the corresponding test evaluation results are weighted and fused to obtain the equipment evaluation result of the energy storage device.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the steps of the method according to any one of claims 1-8.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program configured to be executed by a processor to implement the method of any one of claims 1-8.

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