Comprehensive evaluation method of energy storage power station, medium and electronic equipment

Abstract

The invention provides a comprehensive evaluation method of an energy storage power station, a medium and electronic equipment, which can obtain evaluation index data of a plurality of energy storage power stations for clustering, perform clustering, determine a plurality of energy storage power station classifications and classification centers, and determine a to-be-evaluated energy storage power station according to the distance between the evaluation index data of the to-be-evaluated energy storage power station and the classification centers. The method comprises the following steps: determining the classification of the energy storage power station, determining the corresponding weight of the energy storage power station evaluation index of the to-be-evaluated energy storage power station through an analytic hierarchy process, determining the comprehensive score according to the corresponding weight and the evaluation index data, constructing a multi-dimensional and hierarchical energy storage power station index system, and providing a quantitative index basis for the evaluation of the energy storage power station. The method achieves the step-by-step evaluation from the single battery to the battery cluster to the battery system, maintains the relative independence of the evaluation of each layer, can reduce the influence of human subjective factors, avoids the problem that different types of energy storage power stations are difficult to compare, gives consideration to the multi-dimensional and hierarchical indexes, and more comprehensively evaluates the comprehensive score of the energy storage power station.

Description

Technical Field

[0001] This invention relates to the field of energy storage power station technology, and in particular to a comprehensive evaluation method, medium and electronic equipment for energy storage power stations. Background Technology

[0002] Energy storage power stations are equipment systems that use electrochemical cells or electromagnetic energy storage media to cyclically store, convert, and release electrical energy, providing a unique and indispensable flexible resource for the construction of new power systems. To improve the technical requirements for monitoring the performance of energy storage power station equipment, it is necessary to rationally evaluate the performance of energy storage power stations and ensure their quality and economic efficiency.

[0003] Chinese patent application "A Safety Evaluation Method for Grid-Connected Energy Storage Power Stations Based on Entropy Weight-Analytic Hierarchy Process" (Publication No. CN114971427A) proposes a scheme that combines entropy weight method and analytic hierarchy process (AHP) to calculate index weights and calculate the values ​​of each index based on operational data to comprehensively evaluate the grid-connected safety status. This scheme can provide a safety reference for the grid-connected operation of large-capacity energy storage power stations and improve the safety of grid dispatch. Chinese patent application "A Comprehensive Evaluation Method for Energy Storage Power Stations Based on Improved AHP-WRSR" (Publication No. CN119443778A) proposes a scheme that overcomes the strong subjectivity and reliance on expert experience of the traditional analytic hierarchy process (AHP) through multi-factor comprehensive analysis. This scheme can establish a more robust safety risk assessment system and improve the reliability and accuracy of the evaluation results. Chinese patent application "Comprehensive Evaluation Method, Equipment, and Readable Medium for Energy Storage Power Station Systems" (Publication No. CN114024328A) proposes a scheme that analyzes the consistency indicators of battery modules in terms of voltage, temperature, SOC, usable capacity, and overall SOH by acquiring charge and discharge data, and determines the weight of each indicator to calculate the comprehensive performance index. This scheme can comprehensively and effectively evaluate the current state of the system, improving evaluation efficiency and accuracy. The shortcomings of the first two patent applications lie in the fact that the analytic hierarchy process (AHP) used is greatly influenced by subjective human factors, resulting in subjectivity and ambiguity in the evaluation process, leading to low credibility and accuracy of the evaluation results and a lack of practical application value. The shortcoming of the latter patent application is that it only considers battery consistency indicators, which fails to comprehensively reflect the real-time operating status of the energy storage power station system, reducing the evaluation accuracy.

[0004] Therefore, a comprehensive evaluation scheme for energy storage power stations is needed, which takes into account multiple influencing factors, has a comprehensive evaluation index system, and a reasonable evaluation method. Summary of the Invention

[0005] One objective of this application is to provide a comprehensive evaluation method for energy storage power stations, in order to solve the problem of low efficiency in the use of laser positioning with reflective markers in the prior art.

[0006] To achieve the above objectives, some embodiments of this application provide a comprehensive evaluation method for energy storage power stations, the method comprising:

[0007] Obtain multiple evaluation index data for energy storage power stations for clustering. The evaluation index data includes the corresponding values ​​of the evaluation indexes of energy storage power stations. The evaluation indexes of energy storage power stations include at least the safety index, health index, consistency index, charge and discharge capacity index, and energy efficiency level index of the battery system.

[0008] Clustering is performed on multiple evaluation index data of energy storage power stations used for clustering to determine multiple energy storage power station categories and corresponding classification centers;

[0009] Based on the distance between the evaluation index data of the energy storage power station to be evaluated and the energy storage power station classification center, the energy storage power station to which the energy storage power station to be evaluated belongs is determined.

[0010] The weights of the evaluation indicators for the energy storage power station to be evaluated are determined by the analytic hierarchy process (AHP), and the comprehensive score of the energy storage power station to be evaluated is determined based on the corresponding weights and evaluation indicator data.

[0011] Furthermore, the corresponding values ​​of the safety indicators of the battery system are determined by the weighted sum of the corresponding values ​​of the safety indicators of multiple battery clusters in the battery system. The corresponding values ​​of the safety indicators of the battery cluster are determined by the weighted sum of the corresponding values ​​of the fault level indicators of the battery cluster. The corresponding values ​​of the fault level indicators of the battery cluster are determined by the weighted sum of the corresponding values ​​of the corresponding fault level indicators of multiple cells in the battery cluster. The fault level indicators include level zero fault indicators, level one fault indicators, level two fault indicators, and level three fault indicators.

[0012] Furthermore, the corresponding value of the battery system's health index is determined by the weighted sum of the corresponding values ​​of the health indices of multiple battery clusters in the battery system. The corresponding value of the battery cluster's health index is determined by the weighted sum of the corresponding values ​​of the SOHC index and SOHR index of the battery cluster. The corresponding value of the battery cluster's SOHC index is determined by the weighted sum of the corresponding values ​​of the SOHC index of multiple cells in the battery cluster. The corresponding value of the battery cluster's SOHR index is determined by the weighted sum of the corresponding values ​​of the SOHR index of multiple cells in the battery cluster.

[0013] Furthermore, the corresponding values ​​of the battery system's consistency indicators are determined by the weighted sum of the corresponding values ​​of the consistency indicators of multiple battery clusters within the battery system. The corresponding values ​​of the battery cluster's consistency indicators are determined by the weighted sum of the corresponding values ​​of the battery cluster's voltage consistency indicator, temperature consistency indicator, internal resistance consistency indicator, SOC consistency indicator, and capacity consistency indicator. The corresponding value of the battery cluster's voltage consistency indicator is determined by the normalization operation of the voltage difference between multiple cells within the battery cluster. The corresponding value of the battery cluster's temperature consistency indicator is determined by the normalization operation of the temperature difference between multiple cells within the battery cluster. The corresponding value of the battery cluster's internal resistance consistency indicator is determined by the normalization operation of the internal resistance difference between multiple cells within the battery cluster. The corresponding value of the battery cluster's SOC consistency indicator is determined by the normalization operation of the SOC difference between multiple cells within the battery cluster. The corresponding value of the battery cluster's capacity consistency indicator is determined by the normalization operation of the capacity difference between multiple cells within the battery cluster.

[0014] Furthermore, the corresponding value of the charge and discharge capacity index of the battery system is determined by the weighted sum of the corresponding values ​​of the charge and discharge capacity index of multiple battery clusters in the battery system. The corresponding value of the charge and discharge capacity index of a battery cluster is determined by the weighted sum of the corresponding values ​​of the peak charging duration index and the peak discharging duration index of the battery cluster.

[0015] Furthermore, the corresponding value of the energy efficiency level index of the battery system is determined by the weighted sum of the corresponding values ​​of the energy efficiency level index of multiple battery clusters in the battery system. The corresponding value of the energy efficiency level index of the battery cluster is determined by the weighted sum of the corresponding values ​​of the energy efficiency index and the thermal management energy consumption level index of the battery cluster.

[0016] Furthermore, the method also includes:

[0017] The optimal number of clusters for classifying energy storage power stations was determined using the silhouette coefficient method.

[0018] Furthermore, the weights of the evaluation indicators for the energy storage power station to be evaluated are determined using the analytic hierarchy process (AHP), including:

[0019] Establish a hierarchical model based on decision-making objectives and energy storage power station evaluation indicators;

[0020] Construct an evaluation matrix based on the hierarchical structure model;

[0021] Based on the calculation results of the consistency ratio, the reasonableness of the setting of the evaluation matrix is ​​determined;

[0022] The weights of the evaluation indicators for energy storage power stations are determined based on the largest eigenvalue and the corresponding eigenvector of the evaluation matrix.

[0023] Some embodiments of this application also provide a computer-readable medium having computer-readable instructions stored thereon, which can be executed by a processor to implement the aforementioned comprehensive evaluation method for energy storage power stations.

[0024] Some embodiments of this application also provide an electronic device, which includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the electronic device performs the aforementioned comprehensive evaluation method for energy storage power stations.

[0025] Compared with existing technologies, the solution provided in this application can acquire and cluster multiple evaluation index data of energy storage power stations, determine multiple energy storage power station categories and corresponding classification centers, and determine the category of the energy storage power station to be evaluated based on the distance between the evaluation index data of the energy storage power station to be evaluated and the classification center of the energy storage power station. Then, the corresponding weights of the energy storage power station evaluation indexes of the energy storage power station to be evaluated are determined through the analytic hierarchy process (AHP), and the comprehensive score of the energy storage power station to be evaluated is determined based on the corresponding weights and evaluation index data. This constructs a multi-dimensional and hierarchical energy storage power station index system, providing quantitative index basis for the evaluation of energy storage power stations. It realizes the step-by-step evaluation from individual battery cells to battery clusters and then to battery systems while maintaining the relative independence of each level of evaluation. It can reduce the influence of subjective human factors, avoid the problem of difficulty in comparing different types of energy storage power stations, and at the same time take into account multi-dimensional and hierarchical indicators to more comprehensively evaluate the comprehensive score of energy storage power stations. Attached Figure Description

[0026] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0027] Figure 1 A flowchart illustrating a comprehensive evaluation method for an energy storage power station, provided for some embodiments of this application.

[0028] Figure 2 A schematic diagram showing the relationship between the K value and the silhouette coefficient of a clustering algorithm provided for some embodiments of this application. Detailed Implementation

[0029] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0030] Here, the comprehensive evaluation method for energy storage power stations in this application embodiment is suitable for scenarios that comprehensively evaluate different types of energy storage power stations.

[0031] In this scenario, the performance of the energy storage power station is comprehensively, objectively, and accurately evaluated based on various indicators, thereby providing a foundation for the energy storage power station to effectively play its energy storage role and ensure its safe and reliable operation.

[0032] The comprehensive evaluation method for energy storage power stations provided in this application can acquire and cluster multiple evaluation index data for energy storage power stations, determine multiple energy storage power station categories and corresponding classification centers, and determine the category to which the energy storage power station to be evaluated belongs based on the distance between the evaluation index data of the energy storage power station to be evaluated and the energy storage power station classification center. Then, the corresponding weights of the energy storage power station evaluation indexes of the energy storage power station to be evaluated are determined through the analytic hierarchy process (AHP), and the comprehensive score of the energy storage power station to be evaluated is determined based on the corresponding weights and evaluation index data. This constructs a multi-dimensional and hierarchical energy storage power station index system, providing quantitative index basis for the evaluation of energy storage power stations. It realizes the step-by-step evaluation from individual battery cells to battery clusters and then to battery systems while maintaining the relative independence of each level of evaluation. It can reduce the influence of subjective human factors, avoid the problem of difficulty in comparing different types of energy storage power stations, and at the same time take into account multi-dimensional and hierarchical indicators to more comprehensively evaluate the comprehensive score of energy storage power stations.

[0033] Figure 1 The present application illustrates the flowchart of a comprehensive evaluation method for energy storage power stations executed via electronic devices in some embodiments of this application. The electronic devices serve as the executing entity of this method, such as... Figure 1 As shown, the method may include the following steps:

[0034] Step S101: Obtain multiple evaluation index data for energy storage power stations used for clustering.

[0035] It is understood that electronic devices may include, but are not limited to, laptops, desktop computers, tablets, mobile phones, wearable devices, head-mounted displays, servers, mobile email devices, portable game consoles, portable music players, e-reader devices, televisions in which one or more processors are embedded or coupled, or other electronic devices capable of accessing networks.

[0036] Here, evaluation index data for multiple energy storage power stations are obtained. This evaluation index data is used for subsequent clustering operations to classify the multiple energy storage power stations into different categories.

[0037] In some embodiments of this application, the evaluation index data of the energy storage power station are the corresponding values ​​of the energy storage power station evaluation index. There are multiple energy storage power station evaluation indexes, which may include, but are not limited to, battery system safety indexes, health indexes, consistency indexes, charge and discharge capacity indexes, and energy efficiency level indexes. Each energy storage power station evaluation index has a corresponding value, which is used to describe the score of the index.

[0038] Here, energy storage power stations are divided into three layers: the battery system layer, the battery cluster layer, and the battery cell (i.e., the cell) layer, each with corresponding evaluation indicators. The evaluation indicators for the battery system layer may include, but are not limited to: battery system safety indicators, health indicators, consistency indicators, charge / discharge capacity indicators, and energy efficiency level indicators.

[0039] Evaluation indicators for battery clusters can be divided into primary and secondary indicators. Primary indicators for battery clusters may include, but are not limited to, safety indicators, health indicators, consistency indicators, charge / discharge capacity indicators, and energy efficiency indicators.

[0040] The safety indicators of a battery cluster include multiple secondary indicators, namely the battery cluster fault level indicators. These fault level indicators may include, but are not limited to, level zero fault indicators, level one fault indicators, level two fault indicators, and level three fault indicators. The battery cluster fault level indicators are used to describe the frequency scores of different levels of faults within the battery cluster.

[0041] The health indicators of a battery cluster can include, but are not limited to, secondary indicators such as SOHC (State of Health of Cells) and SOHR (State of Health for Remaining Capacity). SOHC describes the health status of the battery cluster, while SOHR describes the health status of the battery cluster based on its remaining capacity.

[0042] Battery cluster consistency metrics can include, but are not limited to, several secondary metrics: voltage consistency, temperature consistency, internal resistance consistency, state of charge (SOC) consistency, and capacity consistency. Voltage consistency describes the voltage differences among multiple cells in a battery cluster; temperature consistency describes the temperature differences; internal resistance consistency describes the internal resistance differences; SOC consistency describes the state of charge (SOC) differences; and capacity consistency describes the capacity differences.

[0043] The charge and discharge capacity indicators of a battery cluster may include, but are not limited to, secondary indicators: peak charging duration and peak discharging duration. The peak charging duration indicator describes the percentage of time during which the charging power of the battery cluster is at its peak power within a specified time period (e.g., one month). The peak discharging duration indicator describes the percentage of time during which the discharging power of the battery cluster is at its peak power within a specified time period.

[0044] The energy efficiency level indicators of a battery cluster may include, but are not limited to, secondary indicators: energy efficiency indicators and thermal management energy consumption level indicators. The energy efficiency indicator describes the ratio of the cumulative discharge capacity to the cumulative charge capacity of the battery cluster within a specified time range (such as one month), while the thermal management energy consumption level indicator describes the ratio of the cooling capacity (or heating capacity) of the thermal management system of the battery cluster to its input power within a specified time range.

[0045] Evaluation metrics for individual battery cells may include, but are not limited to: cell fault level, SOHC, SOHR, voltage, temperature, internal resistance, SOC, and capacity.

[0046] Cell fault level indicators may include, but are not limited to: Level 0 fault indicators, Level 1 fault indicators, Level 2 fault indicators, and Level 3 fault indicators. These indicators describe the frequency scores of different levels of faults within the cell. The SOHC indicator describes the cell's health status, and the SOHR indicator describes the cell's health status based on remaining capacity. Voltage indicators describe the cell's voltage condition, temperature indicators describe its temperature condition, internal resistance indicators describe its internal resistance, SOC indicators describe its SOC status, and capacity indicators describe its capacity status.

[0047] In some embodiments of this application, the corresponding values ​​of the safety indicators of the battery system are determined by the weighted sum of the corresponding values ​​of the safety indicators of multiple battery clusters in the battery system; the corresponding values ​​of the health indicators of the battery system are determined by the weighted sum of the corresponding values ​​of the health indicators of multiple battery clusters in the battery system; the corresponding values ​​of the consistency indicators of the battery system are determined by the weighted sum of the corresponding values ​​of the consistency indicators of multiple battery clusters in the battery system; the corresponding values ​​of the charge / discharge capacity indicators of the battery system are determined by the weighted sum of the corresponding values ​​of the charge / discharge capacity indicators of multiple battery clusters in the battery system; and the corresponding values ​​of the energy efficiency level indicators of the battery system are determined by the weighted sum of the corresponding values ​​of the energy efficiency level indicators of multiple battery clusters in the battery system.

[0048] This can be expressed by the following formula:

[0049]

[0050] Where f() is the scoring function of each indicator of the battery system (i.e., the corresponding value of each indicator), g1() is the scoring function of the first-level indicator of the battery cluster, idx is each indicator of the battery system, i is the battery cluster number, and clu_w i Let f() be the ratio of the capacity of battery cluster i to the total capacity of the battery system. Here, the values ​​of f() and g1() are both in the interval [0, 1].

[0051] In some embodiments of this application, the corresponding value of the primary index of the battery cluster is determined by the weighted sum of the corresponding values ​​of the secondary indexes corresponding to the primary index of the battery cluster. This can be expressed by the following formula:

[0052]

[0053] Where g1() is the scoring function of the primary indicator of the battery cluster, g2() is the scoring function of the secondary indicator corresponding to the primary indicator, idx is the primary indicator of the battery cluster, i is the number of the secondary indicator of the battery cluster, and idx_w i is the weight of the secondary indicator i of the battery cluster, and secidx is the secondary indicator corresponding to the primary indicator.

[0054] In some embodiments of this application, the corresponding value of the safety index of the battery cluster is determined by the weighted sum of the corresponding values ​​of multiple fault level indices of the battery cluster. Here, the multiple fault level indices of the battery cluster are, for example, a level 0 fault index, a level 1 fault index, a level 2 fault index, and a level 3 fault index. Each index has a corresponding value (i.e., a score) and a weight. The corresponding value of each index multiplied by its weight is added together to obtain the corresponding value of the battery cluster's safety index. For example, if the score of the level 0 fault index is 90% and the weight is 0.1, the score of the level 1 fault index is 80% and the weight is 0.2, the score of the level 0 fault index is 70% and the weight is 0.3, and the score of the level 0 fault index is 80% and the weight is 0.4, then the corresponding value of the safety index is 90% + 0.1 + 80% + 0.2 + 70% + 0.3 + 80% + 0.4 = 78%.

[0055] In some embodiments of this application, the corresponding values ​​of the secondary indicators of the battery cluster can be determined by the weighted sum of the corresponding secondary indicators of multiple cells in the battery cluster and the battery cluster itself. Here, the evaluation indicators related to the secondary indicators of the cells and the battery cluster may include, but are not limited to, the cell's fault level indicator, SOHC indicator, and SOHR indicator. This can be expressed by the following formula:

[0056]

[0057] Where g2() is the scoring function of the secondary index of the battery cluster, h() is the scoring function of the cell index corresponding to the secondary index of the battery cluster, i is the cell number, and cell_w i is the capacity weight of cell i, and secidx is the cell index corresponding to the secondary index of the battery cluster.

[0058] Specifically, the corresponding value of the fault level index of a battery cluster is determined by the weighted sum of the corresponding values ​​of the fault level indices of multiple cells within that cluster. Each cell's fault level index corresponds one-to-one with the corresponding fault level indices of the battery cluster; for example, there might be a zero-level fault index, a first-level fault index, a second-level fault index, and a third-level fault index. The corresponding value of a specific fault level index of a battery cluster, such as the zero-level fault index, is determined by the weighted sum of the corresponding values ​​of the zero-level fault indices of all cells within that cluster.

[0059] The corresponding values ​​of the battery cell's fault level index are determined according to a predetermined scoring rule. The scoring rule includes the score settings corresponding to different fault levels. The score settings are determined based on the number of times the battery cell experiences the corresponding fault level within a preset time range. For example, if the preset time range is one month and the fault level is level zero, then if a battery cell experiences zero level faults 0 times within a month, the corresponding value of the level zero fault index for that battery cell is 100%. If the number of zero level faults is 0-10 times, the corresponding value of the level zero fault index is 90%. If the number of zero level faults is 10-20 times, the corresponding value of the level zero fault index is 80%. If the number of zero level faults is 20-30 times, the corresponding value of the level zero fault index is 70%. If the number of zero level faults is more than 30 times, the corresponding value of the level zero fault index is 60%. Therefore, the scoring function h() for the battery cell's level zero fault index is a piecewise function.

[0060] The calculation methods for the corresponding values ​​of the first-level, second-level, and third-level fault indicators of the battery cell are the same as those for the corresponding values ​​of the zero-level fault indicator.

[0061] In addition, when calculating the corresponding value of a certain fault level index of a battery cluster, such as the zero-level fault index, the weight of the cell (i.e., the capacity weight) is the ratio of the capacity of that cell to the total capacity of the battery cluster.

[0062] In some embodiments of this application, the corresponding value of the battery cluster health index is determined by the weighted sum of the corresponding values ​​of the SOHC index and SOHR index of the battery cluster. Here, the SOHC index of the battery cluster reflects the capacity capability, and the SOHR index of the battery cluster reflects the internal resistance (power) capability. The weights of the two can be equal, such as 0.5, or they can be different. For example, if a certain energy storage power station is more concerned with power, the weight of the SOHR index can be increased to 0.6, and the weight of the SOHC index can be decreased to 0.4. The specific weight can be determined according to the actual evaluation needs of the energy storage power station, and this application does not impose specific limitations on this.

[0063] Furthermore, the SOHC index value of a battery cluster is determined by the weighted sum of the SOHC index values ​​of multiple cells within that cluster. Each cell in the battery cluster has a corresponding SOHC index with a corresponding value, and the weight corresponding to the SOHC index value of each cell is the capacity weight of that cell. The corresponding h() function is min(SOHC, 100) / 100, where SOHC is the corresponding value of the cell's SOHC index.

[0064] Similarly, the SOHR index of a battery cluster is determined by the weighted sum of the SOHR index values ​​of multiple cells within that cluster. Each cell in the cluster has a corresponding SOHR index with a corresponding value, and the weight corresponding to the SOHR index value of each cell is the capacity weight of that cell. The corresponding h() function is min(SOHR, 100) / 100, where SOHR is the corresponding SOHR index value of the cell.

[0065] In some embodiments of this application, the corresponding value of the battery cluster consistency index is determined by the weighted sum of the corresponding values ​​of the battery cluster voltage consistency index, the battery cluster temperature consistency index, the battery cluster internal resistance consistency index, the battery cluster SOC consistency index, and the battery cluster capacity consistency index.

[0066] Here, the weights corresponding to the values ​​of these five consistency indicators can be the same, for example, all of them are 0.2, or they can be different weights set as needed. This application embodiment does not impose specific restrictions on this.

[0067] In some embodiments of this application, the corresponding value of the voltage consistency index of the battery cluster is determined by a normalization operation of the voltage difference of multiple cells in the battery cluster. First, the corresponding values ​​of the voltage indices of multiple cells in the battery cluster are obtained. Then, the voltage difference is determined based on the corresponding values ​​of the multiple voltage indices. Finally, the voltage difference is normalized to obtain the corresponding value of the voltage consistency index of the battery cluster. The voltage difference can be expressed as the following formula:

[0068] Voltage_diff=Voltage_Max–Voltage_Min

[0069] Wherein, Voltage_diff is the voltage difference among multiple cells, Voltage_Max is the maximum value of the corresponding voltage index among multiple cells, and Voltage_Min is the minimum value of the corresponding voltage index among multiple cells.

[0070] Here, the scoring function g2() of the secondary index of the battery cluster, namely the voltage consistency index, is a normalization function used to normalize the voltage difference to the [0, 1] interval, which can be expressed by the following formula:

[0071] g2(secidx)=e ax+b

[0072] Where x is the voltage difference (i.e., Voltage_diff) among multiple cells, g2(secidx) is the corresponding value of the voltage consistency index, secidx is the voltage consistency index, and a and b are predetermined coefficients.

[0073] Specifically, the following method can be used to determine the coefficients of a and b:

[0074] When x = 0mV, there is no voltage difference between the multiple cells, and g2 = 1; when x = 100mV, this is the maximum voltage difference between the multiple cells when the battery system is operating normally, and g2 = 0.6. Therefore, the following system of equations can be obtained:

[0075] e a×0+b =1

[0076] e a×100+b =0.6

[0077] We can solve for a = -0.0051 and b = 0;

[0078] Therefore, when x = 20mV, substituting x into the above formula yields g2 = 0.9.

[0079] In some embodiments of this application, the corresponding value of the temperature consistency index of the battery cluster is determined by the normalization operation of the temperature difference of multiple cells in the battery cluster. Here, the calculation method of the corresponding value of the temperature consistency index of the battery cluster is similar to the calculation method of the corresponding value of the voltage consistency index of the battery cluster, and can be referred to the calculation process of the corresponding value of the voltage consistency index of the battery cluster described above, and will not be repeated here.

[0080] In some embodiments of this application, the corresponding value of the internal resistance consistency index of the battery cluster is determined by the normalization operation of the internal resistance difference of multiple cells in the battery cluster. Here, the calculation method of the corresponding value of the internal resistance consistency index of the battery cluster is similar to the calculation method of the corresponding value of the voltage consistency index of the battery cluster, and the calculation process of the corresponding value of the voltage consistency index of the battery cluster described above can be referred to, and will not be repeated here.

[0081] In some embodiments of this application, the corresponding value of the SOC consistency index of the battery cluster is determined by the normalization operation of the SOC difference of multiple cells in the battery cluster. Here, the calculation method of the corresponding value of the SOC consistency index of the battery cluster is similar to the calculation method of the corresponding value of the voltage consistency index of the battery cluster, and the calculation process of the corresponding value of the voltage consistency index of the battery cluster described above can be referred to, and will not be repeated here.

[0082] In some embodiments of this application, the corresponding value of the capacity consistency index of the battery cluster is determined by the normalization operation of the capacity difference among multiple cells in the battery cluster. Here, the calculation method of the corresponding value of the capacity consistency index of the battery cluster is similar to the calculation method of the corresponding value of the voltage consistency index of the battery cluster, and the calculation process of the corresponding value of the voltage consistency index of the battery cluster described above can be referred to, and will not be repeated here.

[0083] In some embodiments of this application, the corresponding values ​​of the battery cluster's charge / discharge capacity indicators are determined by the weighted sum of the corresponding values ​​of the battery cluster's peak charging duration indicator and peak discharging duration indicator. Here, the peak charging duration indicator refers to the percentage of time the battery cluster's charging power is at its peak power within a specified time period (e.g., one month) of charging cycles, and the peak discharging duration indicator refers to the percentage of time the battery cluster's discharging power is at its peak power within a specified time period of discharging cycles. The weights corresponding to the corresponding values ​​of the two indicators can be the same, for example, both being 0.5, or they can be different weights set as needed; this application does not impose specific limitations on this.

[0084] In some embodiments of this application, the corresponding value of the energy efficiency level index of the battery cluster is determined by the weighted sum of the corresponding value of the energy efficiency index and the corresponding value of the thermal management energy consumption level index of the battery cluster. Here, the energy efficiency index refers to the ratio of the cumulative discharge capacity to the cumulative charge capacity of the battery cluster within a specified time range (such as one month), and the thermal management energy consumption level index refers to the ratio of the cooling capacity (or heating capacity) of the thermal management system of the battery cluster to its input power within a specified time range. The corresponding weights of the two indices can be the same, for example, both being 0.5, or they can be different weights set as needed. This application does not impose specific limitations on this.

[0085] Step S102: Cluster the evaluation index data of multiple energy storage power stations used for clustering to determine multiple energy storage power station categories and corresponding classification centers.

[0086] In some embodiments of this application, the clustering algorithm used to cluster multiple evaluation index data can be any algorithm capable of data clustering, such as K-Means clustering algorithm, hierarchical clustering algorithm, density-based spatial clustering of applications with noise (DBSCAN), etc.

[0087] In some embodiments of this application, the evaluation index data may include, in addition to the corresponding values ​​of the battery system's safety index, health index, consistency index, charge / discharge capacity index, and energy efficiency index, the corresponding values ​​of additional indicators of the battery system. These additional indicators may include, but are not limited to, the battery system's geographical location, service time, and power plant scale.

[0088] In some embodiments of this application, the optimal number of clusters for classifying energy storage power stations can also be determined using the silhouette coefficient method. Here, the silhouette coefficient S... i The calculation formula can be expressed as follows:

[0089]

[0090] Among them, a i Let b be the average distance between sample point i and all other points in the same cluster, used to reflect the similarity (i.e., cohesion) between point i and other points in the same cluster. i The silhouette coefficient is the average distance between sample point i and all points in its nearest neighbor cluster, reflecting the degree of separation between sample point i and other categories. The value of the silhouette coefficient ranges from [-1, 1], with values ​​closer to 1 indicating better clustering results.

[0091] Figure 2 This application illustrates the relationship between the K-value and the silhouette coefficient of the clustering algorithm in some embodiments, such as... Figure 2 As shown in the figure, when the number of clusters K for energy storage power stations is set to different values, the corresponding clustering results are different based on the contour coefficient obtained from the contour coefficient formula. It can be seen from the figure that the contour coefficient is the largest when K is 4, indicating that the clustering effect is best when the number of clusters K for energy storage power stations is set to 4.

[0092] In some embodiments of this application, the corresponding weights of the evaluation indicators for energy storage power stations in the energy storage power station classification center are determined using the analytic hierarchy process (AHP), and the comprehensive score of the energy storage power station classification center is determined based on the corresponding weights and the evaluation indicator data of the energy storage power station classification center. The formula for calculating the comprehensive score of the energy storage power station classification center can be expressed as follows:

[0093]

[0094] Where F (centroid) is the comprehensive score of the energy storage power station classification center, cen_w i The weights, f(idx), are calculated using the analytic hierarchy process. i This is the evaluation index data for the energy storage power station classification center.

[0095] The Analytic Hierarchy Process (AHP), proposed in the early 1970s by Thomas Seti, an operations researcher at the University of Pittsburgh, is a hierarchical weighted decision analysis method that applies network system theory and multi-objective comprehensive evaluation methods.

[0096] In addition, after obtaining the comprehensive score of each energy storage power station classification center, the classification center can be labeled with Class I, Class II, Class III, etc., in descending order of the comprehensive score, with m classifications corresponding to m classification labels.

[0097] Step S103: Determine the energy storage power station category to which the energy storage power station to be evaluated belongs based on the distance between the evaluation index data of the energy storage power station to be evaluated and the energy storage power station classification center.

[0098] Here, the evaluation index data of the energy storage power station to be evaluated is obtained. The evaluation index data of the energy storage power station to be evaluated includes at least the corresponding values ​​of the battery system's safety index, health index, consistency index, charge and discharge capacity index, and energy efficiency index. It may also include the corresponding values ​​of additional indicators of the battery system, which may include, but are not limited to, the battery system's geographical location, service time, and power station scale.

[0099] In some embodiments of this application, the distance between the evaluation index data of the energy storage power station to be evaluated and multiple energy storage power station classification centers is calculated, and the energy storage power station classification corresponding to the energy storage power station classification center with the smallest distance is determined as the classification to which the energy storage power station to be evaluated belongs.

[0100] Step S104: Determine the corresponding weights of the evaluation indicators of the energy storage power station to be evaluated using the analytic hierarchy process (AHP), and determine the comprehensive score of the energy storage power station to be evaluated based on the corresponding weights and evaluation indicator data.

[0101] In some embodiments of this application, determining the corresponding weights of the evaluation indicators for the energy storage power station to be evaluated using the analytic hierarchy process (AHP) may include the following steps:

[0102] 1) Establish a hierarchical model based on decision-making objectives and evaluation indicators for energy storage power stations;

[0103] Here, the comprehensive score of the energy storage power station is taken as the decision-making objective, and the evaluation indicators of the energy storage power station, such as the safety indicators, health indicators, consistency indicators, charge and discharge capacity indicators and energy efficiency level indicators of the battery system, are taken as factors. The two are divided into the objective layer and the criterion layer.

[0104] 2) Construct the evaluation matrix based on the hierarchical structure model;

[0105] The evaluation matrix is ​​used to represent the relative importance of all indicators in this layer relative to a certain indicator in the previous layer. The element a of the evaluation matrix... ij This represents the comparison result of the i-th indicator relative to the j-th indicator, as shown in Table 1 below:

[0106] Table 1

[0107]

[0108] For example, assuming the importance ranking is safety indicators > health indicators > consistency indicators > charge / discharge capacity indicators > energy efficiency indicators, then according to the scoring rules in Table 1, the evaluation matrix in Table 2 can be obtained as follows:

[0109] Table 2

[0110]

[0111]

[0112] 3) Based on the calculation results of the consistency ratio, determine whether the setting of the evaluation matrix is ​​reasonable;

[0113] The consistency ratio is a criterion in the Analytic Hierarchy Process (AHP) used to determine whether the evaluation matrix is ​​reasonably set. For example, the consistency ratio of the evaluation matrix in Table 2 is calculated to be 0.053, with the corresponding weights as follows: safety index 0.513, health index 0.261, consistency index 0.129, charging / discharging capacity index 0.063, and energy efficiency level index 0.033. Generally speaking, a consistency ratio less than 0.1 is considered to indicate that the evaluation matrix has acceptable consistency.

[0114] 4) Determine the corresponding weights of the evaluation indicators for energy storage power stations based on the largest eigenvalue and the corresponding eigenvector of the evaluation matrix.

[0115] Here, the weight vector is obtained by solving for the maximum eigenvalue and the corresponding eigenvector of the evaluation matrix. The data in the weight vector are the corresponding weights of the evaluation indicators of the energy storage power station.

[0116] Similarly, the formula for calculating the comprehensive score of the energy storage power station to be evaluated can be expressed as follows:

[0117]

[0118] Where F(station) is the overall score of the energy storage power station to be evaluated, sta_w i The weights, f(idx), are calculated using the analytic hierarchy process. i These are the evaluation indicator data for the energy storage power station to be evaluated.

[0119] In summary, the solution provided in this application can acquire and cluster multiple evaluation index data for energy storage power stations, determine multiple energy storage power station categories and corresponding classification centers, and determine the category to which the energy storage power station to be evaluated belongs based on the distance between the evaluation index data of the energy storage power station to be evaluated and the energy storage power station classification center. Then, the corresponding weights of the energy storage power station evaluation indexes of the energy storage power station to be evaluated are determined through the analytic hierarchy process (AHP), and the comprehensive score of the energy storage power station to be evaluated is determined based on the corresponding weights and evaluation index data. This constructs a multi-dimensional and hierarchical energy storage power station index system, providing quantitative index basis for the evaluation of energy storage power stations. It realizes the step-by-step evaluation from individual battery cells to battery clusters and then to battery systems while maintaining the relative independence of each level of evaluation. It can reduce the influence of subjective human factors, avoid the problem of difficulty in comparing different types of energy storage power stations, and at the same time take into account multi-dimensional and hierarchical indicators to more comprehensively evaluate the comprehensive score of energy storage power stations.

[0120] It should be noted that this application can be implemented in software and / or a combination of software and hardware, for example, using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In one embodiment, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, and similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that cooperates with a processor to perform the various steps or functions.

[0121] In a typical configuration of this application, both the terminal and the network device include one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0123] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media, such as modulated data signals and carrier waves.

[0124] Furthermore, a portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. The program instructions invoking the methods of this application may be stored in a fixed or removable recording medium, and / or transmitted via a data stream in a broadcast or other signal carrying medium, and / or stored in the working memory of a computer device operating according to the program instructions. Here, one embodiment of this application includes a device comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run methods and / or technical solutions based on the foregoing embodiments of this application.

[0125] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

Claims

1. A comprehensive evaluation method of energy storage power stations, characterized in that, The method comprises: obtaining a plurality of evaluation index data of energy storage power stations for clustering, wherein the evaluation index data comprises corresponding values of energy storage power station evaluation indexes, and the energy storage power station evaluation indexes at least include safety indexes, health indexes, consistency indexes, charge-discharge capability indexes and energy efficiency level indexes of a battery system; clustering the plurality of evaluation index data of energy storage power stations for clustering to determine a plurality of energy storage power station classifications and corresponding classification centers; determining the energy storage power station classification to which a to-be-evaluated energy storage power station belongs according to the distance between the evaluation index data of the to-be-evaluated energy storage power station and the energy storage power station classification center; determining the corresponding weight of the energy storage power station evaluation indexes of the to-be-evaluated energy storage power station by an analytic hierarchy process, and determining the comprehensive score of the to-be-evaluated energy storage power station according to the corresponding weight and the evaluation index data.

2. The method of claim 1, wherein, The corresponding value of the safety index of the battery system is determined by the weighted sum of the corresponding values of the safety indexes of a plurality of battery clusters in the battery system, the corresponding value of the safety index of the battery cluster is determined by the weighted sum of the corresponding values of a plurality of fault level indexes of the battery cluster, and the corresponding value of the fault level index of the battery cluster is determined by the weighted sum of the corresponding values of the corresponding fault level indexes of a plurality of battery cells in the battery cluster, wherein the fault level indexes include zero-level fault indexes, first-level fault indexes, second-level fault indexes and third-level fault indexes.

3. The method of claim 1, wherein, The corresponding value of the health index of the battery system is determined by the weighted sum of the corresponding values of the health indexes of a plurality of battery clusters in the battery system, the corresponding value of the health index of the battery cluster is determined by the weighted sum of the corresponding values of a SOHC index and a SOHR index of the battery cluster, the corresponding value of the SOHC index of the battery cluster is determined by the weighted sum of the corresponding values of the SOHC indexes of a plurality of battery cells in the battery cluster, and the corresponding value of the SOHR index of the battery cluster is determined by the weighted sum of the corresponding values of the SOHR indexes of a plurality of battery cells in the battery cluster.

4. The method of claim 1, wherein, The corresponding value of the consistency index of the battery system is determined by a weighted sum of corresponding values of the consistency index of a plurality of battery clusters in the battery system, the corresponding value of the consistency index of the battery cluster being determined by a weighted sum of the corresponding value of the voltage consistency index of the battery cluster, the corresponding value of the temperature consistency index of the battery cluster, the corresponding value of the internal resistance consistency index of the battery cluster, the corresponding value of the SOC consistency index of the battery cluster, and the corresponding value of the capacity consistency index of the battery cluster, the corresponding value of the voltage consistency index of the battery cluster being determined by a normalization operation of the voltage difference of a plurality of battery cells in the battery cluster, the corresponding value of the temperature consistency index of the battery cluster being determined by a normalization operation of the temperature difference of a plurality of battery cells in the battery cluster, the corresponding value of the internal resistance consistency index of the battery cluster being determined by a normalization operation of the internal resistance difference of a plurality of battery cells in the battery cluster, the corresponding value of the SOC consistency index of the battery cluster being determined by a normalization operation of the SOC difference of a plurality of battery cells in the battery cluster, and the corresponding value of the capacity consistency index of the battery cluster being determined by a normalization operation of the capacity difference of a plurality of battery cells in the battery cluster.

5. The method of claim 1, wherein, The corresponding value of the charge-discharge capability index of the battery system is determined by a weighted sum of corresponding values of the charge-discharge capability index of a plurality of battery clusters in the battery system, the corresponding value of the charge-discharge capability index of the battery cluster being determined by a weighted sum of the corresponding value of the peak charge duration capability index of the battery cluster and the corresponding value of the peak discharge duration capability index of the battery cluster.

6. The method of claim 1, wherein, The corresponding value of the energy efficiency level index of the battery system is determined by a weighted sum of corresponding values of the energy efficiency level index of a plurality of battery clusters in the battery system, the corresponding value of the energy efficiency level index of the battery cluster being determined by a weighted sum of the corresponding value of the energy efficiency index of the battery cluster and the corresponding value of the thermal management energy consumption level index of the battery cluster.

7. The method of claim 1, wherein, The method further comprises: determining the optimal cluster number of the energy storage power station classification by the profile coefficient method.

8. The method of claim 1, wherein, determining the corresponding weight of the energy storage power station evaluation index of the to-be-evaluated energy storage power station by the analytic hierarchy process, comprising: establishing a hierarchical structure model according to the decision target and the energy storage power station evaluation index; constructing an evaluation matrix according to the hierarchical structure model; determining that the setting condition of the evaluation matrix is reasonable according to the calculation result of the consistency ratio; determining the corresponding weight of the energy storage power station evaluation index according to the maximum eigenvalue and the corresponding eigenvector of the evaluation matrix. 9.A computer readable medium having computer readable instructions stored thereon, the computer readable instructions being executable by a processor to implement the method of any one of claims 1 to 8.

10. An electronic device comprising a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein, The computer program instructions, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 8. The computer program instructions, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 8.

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