Energy storage system safety assessment method and system

Abstract

The invention discloses an energy storage system safety evaluation method and system. The method comprises the following steps: constructing an energy storage system safety risk evaluation index system according to a to-be-evaluated energy storage system, and calculating the weight of each index; obtaining a cloud feature value of each index according to the calculated weight value of each index, and calculating a comprehensive cloud feature value of each index according to the cloud feature value of each index; and generating a comprehensive evaluation cloud atlas and a secondary index evaluation cloud atlas based on the obtained comprehensive cloud feature values of the indexes, and calculating the membership degree of the comprehensive evaluation cloud to each safety evaluation grade based on the comprehensive evaluation cloud atlas, so that various factors influencing the safety of the energy storage system can be comprehensively considered, multi-dimensional evaluation is performed on the energy storage system, and the safety of the energy storage system is improved. The one-sidedness of single-factor evaluation is avoided, qualitative safety evaluation is converted into quantitative analysis, the evaluation result is more accurate and scientific, interference of human factors is reduced, the evaluation result is displayed in a visual graphic mode, related personnel can understand and analyze the safety condition of the energy storage system conveniently, and the safety of the energy storage system is improved. And even non-professionals can roughly know the security risk level of the system through the cloud atlas.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage system safety, and particularly relates to a method and system for safety assessment of energy storage systems. Background Art

[0002] In recent years, with the rapid growth of the installation of new energy storage, the safety issues of energy storage systems have become prominent.

[0003] On the one hand, in recent years, the scale of energy storage projects has been continuously expanding. From large-scale grid-level energy storage power stations to distributed energy storage facilities, the application scenarios of energy storage systems have become increasingly diverse and complex. Whether it is the energy storage configuration in densely populated areas or the energy storage application in industrial environments, higher requirements for safety are put forward. On the other hand, during the rapid development of energy storage technology itself, there are many battery chemical systems involved, including lithium-ion batteries, flow batteries, etc. Each system has its own unique safety risks. For example, lithium-ion batteries may have problems such as thermal runaway and short circuits. Once an accident occurs, it may lead to serious consequences such as fires and explosions.

[0004] A safety accident of a primary energy storage system will not only cause huge economic losses, but also pose a serious threat to the environment and the safety of personnel's lives. Therefore, in order to effectively improve the safety of energy storage systems and strengthen the safety management of energy storage systems, it is necessary to regularly carry out safety assessments of energy storage systems, timely discover the risks existing in the systems and make corresponding rectifications; currently, there is no technology that can effectively achieve the safety evaluation of energy storage systems. Therefore, it is urgent to effectively evaluate the safety performance of energy storage systems. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for safety assessment of energy storage systems to overcome the problem that the prior art cannot effectively evaluate energy storage systems. To achieve the above purpose, the technical solution adopted by the present invention is as follows: In the first aspect of the present invention, a method for safety assessment of an energy storage system includes the following steps: Construct an energy storage system safety risk evaluation index system according to the energy storage system to be evaluated and preset safety evaluation grades, and calculate the weight values of each index in the constructed index system; Construct a cloud model evaluation standard cloud, obtain the cloud characteristic values of each index according to the cloud model evaluation standard cloud and the calculated weight values of each index, and calculate the comprehensive cloud characteristic value of each index according to the cloud characteristic values of each index; Generate a comprehensive evaluation cloud diagram and a secondary index evaluation cloud diagram based on the obtained comprehensive cloud characteristic values of each index, calculate the membership degrees of the comprehensive cloud characteristic values of each index belonging to each safety evaluation grade based on the comprehensive evaluation cloud diagram; calculate the safety risk level of the energy storage system to be evaluated by using the principle of the maximum membership degree for the secondary index evaluation cloud diagram and the obtained membership degrees.

[0006] Preferably, the construction of the energy storage system safety risk evaluation index system according to the energy storage system to be evaluated specifically includes: evaluating the indicators that constitute the risk assessment elements of the energy storage system, and selecting the key indicators that affect the safety of the energy storage system as the energy storage system safety risk evaluation index system, specifically including on-site test indicators, key equipment and facility indicators, rules and regulations indicators, and operation and maintenance inspection indicators.

[0007] Preferably, the calculation of the weight values of each indicator in the constructed index system specifically includes the following steps: Construct a judgment matrix according to the importance degree of the indicators; Calculate the eigenvector corresponding to the largest eigenvalue of the judgment matrix; Calculate the index weight vector according to the obtained eigenvector, and the weight values of each indicator can be obtained.

[0008] Preferably, constructing a judgment matrix according to the importance degree of the indicators specifically includes: scoring each indicator in the energy storage system safety risk evaluation index system by using the 9-scale method to obtain the index score vector , construct a matrix according to the index score vector , , according to the reciprocity of the matrix, use the exponential function to construct a judgment matrix A： (1) In the formula, for any i , j , k = 1, 2, 3, …, n , there is a ij = 1 / a ji , a ik = a ij * a jk .

[0009] Preferably, the construction of the cloud model evaluation standard cloud specifically includes: constructing a standard evaluation cloud according to the energy storage system safety risk evaluation index system, adopting a percentage system for the safety risk assessment grading, and classifying the safety risks of the energy storage power station into five levels from low to high according to the scores, namely high risk (score ≤ 60), relatively high risk (60 ≤ score < 70), medium risk (70 ≤ score < 80), relatively low risk (80 ≤ score < 90), and low risk (score ≥ 90); obtaining the digital characteristics of the standard cloud according to formula (4), generating cloud droplets in the forward cloud generator, and obtaining the standard cloud map, (4)

[0010] Preferably, cloud eigenvalue of each index is obtained according to the calculated weight value of each index: the mean value and variance of each index score are obtained according to formula (5), and then cloud digital eigenvalue is generated by using the inverse cloud generator: (5) In the formula, Z ij is the evaluation results of m experts on n indexes.

[0011] Preferably, the comprehensive cloud eigenvalue of each index is calculated according to the cloud eigenvalue of each index: (6) In the formula, w j is the analytic hierarchy process weight. The cloud parameters of the index are weighted according to formula (6) to obtain the cloud digital eigenvalue of the secondary index and the comprehensive evaluation cloud digital eigenvalue.

[0012] An energy storage system safety assessment system, including an index weight module, an index comprehensive cloud eigenvalue and evaluation module; The index weight module constructs an energy storage system safety risk evaluation index system according to the energy storage system to be evaluated and presets a safety evaluation level, and calculates the weight value of each index in the constructed index system; The index comprehensive cloud eigenvalue constructs a cloud model evaluation standard cloud, obtains the cloud eigenvalue of each index according to the cloud model evaluation standard cloud and the calculated weight value of each index, and calculates the comprehensive cloud eigenvalue of each index according to the cloud eigenvalue of each index; The evaluation module calculates the membership degree of the comprehensive cloud eigenvalue of each index belonging to each safety evaluation level based on the comprehensive evaluation cloud map; the membership degree maximum principle is used to calculate the safety risk level of the energy storage system to be evaluated from the secondary index evaluation cloud map and the obtained membership degree.

[0013] Preferably, the construction of the energy storage system safety risk evaluation index system according to the energy storage system to be evaluated specifically includes: evaluating the indexes that make up the risk assessment elements of the energy storage system, and selecting the key indexes affecting the safety of the energy storage system as the energy storage system safety risk evaluation index system, specifically including on-site test indexes, key equipment and facilities indexes, rules and regulations indexes and operation and maintenance inspection indexes.

[0014] Preferably, the calculation of the weight of each index in the constructed index system specifically includes the following steps: Construct a judgment matrix according to the importance degree of the index; Calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix; Calculate the index weight vector according to the obtained eigenvector to obtain the corresponding weight.

[0015] Preferably, constructing the judgment matrix according to the importance degree of the indicators specifically includes: scoring each indicator in the safety risk evaluation index system of the energy storage system by using the 9-scale method to obtain the indicator score vector , constructing a matrix according to the indicator score vector , , according to the reciprocity of the matrix, using the exponential function to construct the judgment matrix A： (1) In the formula, for any i , j , k = 1, 2, 3, …, n , there is a ij = 1 / a ji , a ik = a ij * a jk .

[0016] Preferably, the construction of the cloud model evaluation standard cloud specifically includes: constructing a standard evaluation cloud according to the safety risk evaluation index system of the energy storage system, adopting a hundred-mark system for the safety risk assessment grading, and classifying the safety risk of the energy storage power station into five levels from low to high according to the score, namely high risk (score ≤ 60), relatively high risk (60 ≤ score < 70), medium risk (70 ≤ score < 80), relatively low risk (80 ≤ score < 90), and low risk (score ≥ 90); obtaining the digital characteristics of the standard cloud according to formula (4), generating cloud droplets in the forward cloud generator, and obtaining the standard cloud diagram (4)

[0017] Preferably, obtaining the cloud characteristic values of each indicator according to the calculated weight values of each indicator: obtaining the average score and variance of each indicator according to formula (5), and then generating cloud digital characteristic values by using the reverse cloud generator: (5) In the formula, Z ij is the evaluation results of m experts on n indicators.

[0018] Preferably, calculating the comprehensive cloud characteristic values of each indicator according to the cloud characteristic values of each indicator: (6) In the formula, w j is the analytic hierarchy process weight, and the digital characteristics of the secondary index cloud and the digital characteristics of the comprehensive evaluation cloud are obtained by weighting and processing the index cloud parameters according to formula (6).

[0019] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the cascade hydropower planning method are implemented.

[0020] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the cascade hydropower planning method are implemented.

[0021] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention provides a method for evaluating the safety of an energy storage system. An evaluation index system for the safety risk of the energy storage system to be evaluated is constructed, and the weights of the various indexes in the constructed index system are calculated; a standard cloud for cloud model evaluation is constructed, the cloud characteristic values of the various indexes are obtained according to the calculated weight values of the various indexes, and the comprehensive cloud characteristic values of the various indexes are calculated according to the cloud characteristic values of the various indexes; based on the obtained comprehensive cloud characteristic values of the various indexes, a comprehensive evaluation cloud map and a secondary index evaluation cloud map are generated. Based on the comprehensive evaluation cloud map, the membership degrees of the comprehensive evaluation cloud belonging to each safety evaluation level are calculated. According to the secondary index evaluation cloud map and the principle of the maximum membership degree, the safety risk level of the energy storage system is obtained. It can comprehensively consider various factors affecting the safety of the energy storage system, conduct multi-dimensional evaluation of the energy storage system, avoid the one-sidedness of single-factor evaluation, transform qualitative safety evaluation into quantitative analysis, make the evaluation results more accurate and scientific, reduce the interference of human factors, and display the evaluation results in an intuitive graphical manner, facilitating relevant personnel to understand and analyze the safety status of the energy storage system. Even non-professionals can roughly understand the safety risk level of the system through the cloud map.

[0022] The present invention calculates the membership degrees of the comprehensive evaluation cloud belonging to each safety evaluation level based on the comprehensive evaluation cloud map, and obtains the safety risk level of the energy storage system according to the secondary index evaluation cloud map and the principle of the maximum membership degree. It can clearly determine the safety risk level of the energy storage system, provide a clear basis for taking corresponding safety measures, help formulate risk prevention and control strategies targeted, and improve the safety of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic flow chart of the method for evaluating the safety of the energy storage system in an embodiment of the present invention.

[0024] Figure 2 It is the standard cloud map obtained in an embodiment of the present invention.

[0025] Figure 3 It is a schematic diagram of the evaluation index system for the safety risk of the energy storage system obtained in an embodiment of the present invention.

[0026] Figure 4This is the comprehensive evaluation cloud map of the energy storage system safety risk obtained in the embodiment of the present invention.

[0027] Figure 5 This is the secondary index evaluation cloud map obtained in the embodiment of the present invention. Detailed implementation manners

[0028] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] In the specific implementation manner of the present invention, a method for evaluating the safety of an energy storage system is provided. The improved analytic hierarchy process (AHP) is used to calculate the subjective weight. Instead of comparing the importance of each pair of indicators, the importance degree of the indicators is directly scored, and the result is calculated through the specified constructed matrix without the need for consistency verification, which can greatly improve the accuracy of the evaluation of the energy storage system safety. The specific steps are as follows: S1. According to the energy storage system to be evaluated, construct an energy storage system safety risk evaluation index system and preset a safety evaluation level, and calculate the weight value of each index in the constructed index system. S2. Construct a cloud model evaluation standard cloud, obtain the cloud characteristic values of each index according to the calculated weight values of each index, and calculate the comprehensive cloud characteristic value of each index according to the cloud characteristic values of each index. S3. Generate a comprehensive evaluation cloud map and a secondary index evaluation cloud map based on the obtained comprehensive cloud feature values of each index, and calculate the membership degrees of the comprehensive cloud feature values of each index belonging to each safety evaluation level based on the comprehensive evaluation cloud map; calculate the safety risk level of the energy storage system to be evaluated by using the principle of the maximum membership degree for the secondary index evaluation cloud map and the obtained membership degrees.

[0031] In the specific implementation manner of the present application, by analyzing the energy storage system to be evaluated, evaluate the indicators constituting the risk assessment elements of the energy storage system, and select the key indicators affecting the safety of the energy storage system as the safety risk evaluation index system of the energy storage system, specifically including on-site test indicators, key equipment and facility indicators, rules and regulations indicators, and operation and maintenance and repair indicators.

[0032] The acquisition of the specific system safety risk evaluation index system is specifically obtained through the summary of historical data: collect the past operation data, monitoring data, maintenance records, etc. of the system, and analyze the information related to safety risks therein, such as the number of equipment failures, the occurrence frequency of safety incidents, the occurrence of system vulnerabilities, etc. Through the statistical analysis of these data, the key factors and laws affecting the system safety can be found, so as to determine the corresponding evaluation indicators.

[0033] Or obtain it through the analysis of accident cases: study the accident cases of the same type of system at home and abroad, analyze the causes, processes and consequences of the accidents, summarize the key factors and weak links leading to the system safety risks therefrom, and convert them into evaluation indicators.

[0034] In the specific implementation manner of the present invention, an improved AHP method is used to calculate the subjective weight. Instead of comparing the importance of each pair of indicators, directly score the importance degree of the indicators, and calculate the result through the specified matrix construction, without the need for consistency verification. The calculation of the weights of each indicator in the constructed indicator system specifically includes the following steps: First, construct a judgment matrix: According to the importance degree of the indicators, score each indicator in the safety risk evaluation index system of the energy storage system by using the 9-scale method as shown in Table 1 to obtain the index score vector , and construct a matrix according to the index score vector , , and according to the reciprocity of the matrix, use the exponential function to construct a judgment matrix A： (1) In the formula, for any i , j , k = 1, 2, 3,..., n , there is a ij = 1 / aji , a ik = a ij * a jk The judgment matrix constructed by the present invention using the above method satisfies the definition of consistency and does not require consistency verification.

[0035] Table 19 Scale Method Judgment Scale

[0036] Calculate the eigenvector corresponding to the largest eigenvalue of the judgment matrix: (2) Calculate the index weight vector according to the obtained eigenvector: (3) In the specific embodiment of the present application, according to the construction standard of the energy storage system safety risk assessment index system, a standard evaluation cloud is constructed. The standard evaluation cloud is the benchmark comparison of the cloud model. The safety risk assessment grading adopts a percentile system. According to the scores from low to high, the safety risks of the energy storage power station in the present application are divided into five levels: high risk (score ≤ 60), relatively high risk (60 ≤ score < 70), medium risk (70 ≤ score < 80), relatively low risk (80 ≤ score < 90), and low risk (score ≥ 90). Then, according to Equation (4), the digital characteristics of the standard cloud are obtained, and cloud droplets are generated in the forward cloud generator to obtain the standard cloud map, as Figure 2 shown.

[0037] (4) Table 2 Digital Characteristics of the Standard Cloud

[0038] Obtain the cloud characteristic values of each index according to the calculated weight values of each index: Calculate the average value and variance of each index score according to Equation (5), and then generate cloud digital characteristic values using the inverse cloud generator: (5) where Z ij is the evaluation results of m experts on n indexes.

[0039] Calculate the comprehensive cloud characteristic values of each index according to the cloud characteristic values of each index: (6) where w j is the analytic hierarchy process weight. According to Equation (6), the index cloud parameters are weighted to obtain the digital characteristics of the secondary index cloud and the digital characteristics of the comprehensive evaluation cloud.

[0040] Generate a comprehensive evaluation cloud map based on the obtained comprehensive cloud feature values of each index: According to the forward cloud model, this invention uses MATLAB to draw the secondary index cloud map and the comprehensive evaluation cloud map, and conducts a comparative analysis with the standard cloud map.

[0041] Generate a comprehensive evaluation cloud map based on the obtained comprehensive cloud feature values of each index: Basis of the forward cloud model The forward cloud model is an effective tool for realizing the conversion between qualitative concepts and quantitative representations, based on three digital features: expected value Ex (reflecting the central position of cloud droplets), entropy En (reflecting the measurable granularity of qualitative concepts, the larger the entropy, the more ambiguous the concept), and hyperentropy He (representing the uncertainty of entropy). In the safety assessment of energy storage systems, these characteristic values can be obtained through the processing and analysis of each index data.

[0042] Data preparation: Before drawing the cloud map, it is necessary to first obtain the data of each index for the safety assessment of the energy storage system. For secondary indexes, the cloud feature values (Ex, En, He) of each secondary index need to be calculated separately, and at the same time, the cloud feature values of the comprehensive evaluation are calculated.

[0043] Draw the secondary index cloud map Define the cloud model function. In MATLAB, a function needs to be written to generate cloud droplets.

[0044] Example is as follows: function [x, y] = cloud_model(Ex, En, He, N) % Ex: Expected value % En: Entropy % He: Hyperentropy % N: Number of generated cloud droplets En_prime = normrnd(En, He, [1, N]); x = normrnd(Ex, abs(En_prime), [1, N]); y = exp(-(x - Ex).^2 . / (2 * En_prime.^2)); end Obtain the secondary index cloud feature values based on the cloud droplets; Suppose the cloud feature values of each secondary index have been calculated by a certain method and stored in a matrix; % Assume there are 3 secondary indexes, and each index has three characteristic values of Ex, En, and He secondary_cloud_features = [Ex1, En1, He1; Ex2, En2, He2; Ex3, En3,He3]; Draw the secondary index cloud chart: num_secondary_indices = size(secondary_cloud_features, 1); N = 1000; % Number of cloud droplets generated figure; for i = 1:num_secondary_indices Ex = secondary_cloud_features(i, 1); En = secondary_cloud_features(i, 2); He = secondary_cloud_features(i, 3); [x, y] = cloud_model(Ex, En, He, N); subplot(num_secondary_indices, 1, i); scatter(x, y, 'filled'); title(['Secondary Index ', num2str(i),'Cloud Chart']); xlabel('Index Value'); ylabel('Degree of Certainty'); end Through the drawn secondary index cloud charts, the uncertainty and distribution of each secondary index can be visually seen. The more dispersed the cloud chart is, the greater the uncertainty of the index. The comprehensive evaluation cloud chart comprehensively reflects the results of the safety assessment of the entire energy storage system, helping decision-makers to have an overall understanding of the safety status of the energy storage system. At the same time, based on the shape and distribution of the cloud chart, the key factors affecting the safety of the energy storage system can be further analyzed, providing a basis for subsequent safety management and improvement.

[0045] Based on the comprehensive evaluation cloud chart, the calculation process of the membership degree of the comprehensive evaluation cloud belonging to each safety evaluation level is as follows: The calculation method of the membership degree of each evaluation cloud with respect to each evaluation level is as follows: ① Generate a random number yi that conforms to a normal distribution, .

[0046] ② Generate a random number xi that conforms to a normal distribution, 。

[0047] ③ Calculate the membership degree

[0048] ④ Repeat ①~③ until the required n is generated, where n is the number of cloud droplets. 。

[0049] ⑤ Adopt the method of taking the average value through multiple calculations to reduce errors , where m is the number of repeated calculations.

[0050] ⑥ Conduct normalization processing to obtain the corresponding membership degree values: 。

[0051] According to the principle of maximum membership degree, determine the risk levels of the energy storage system and secondary indicators.

[0052] In the specific implementation manner of the present invention, the specific process of determining the risk levels of the energy storage system and secondary indicators according to the principle of maximum membership degree: Establish a fuzzy set of risk levels First, it is necessary to determine the classification of risk levels. The risk levels are divided into five levels: "low risk", "relatively low risk", "medium risk", "relatively high risk", and "high risk", which are represented by the fuzzy sets A1, A2, A3, A4, and A5 respectively.

[0053] Define the corresponding membership functions for each level, and these functions will determine the degree to which each element belongs to each risk level according to specific indicators and data.

[0054] Determine the membership relationship between secondary indicators and risk levels For each secondary indicator of the energy storage system, collect relevant data or information. According to these data, use the membership function to calculate the membership degree of this secondary indicator for each risk level.

[0055] Comprehensively consider the membership degrees of all secondary indicators for each risk level. Adopt the weighted average method to assign corresponding weights to each secondary indicator, and then calculate the comprehensive membership degree of the energy storage system for each risk level.

[0056] Suppose the energy storage system has n secondary indicators, and the membership degrees of the i-th secondary indicator for the levels of "low risk", "relatively low risk", "medium risk", "relatively high risk", and "high risk" are μi1, μi2, μi3, μi4, and μi5 respectively, and its weight is wi. Then the comprehensive membership degree μ1 of the energy storage system for the "low risk" level is: μ1 = ∑i = 1nwi × μi1. Similarly, the comprehensive membership degrees μ2, μ3, μ4, and μ5 for the levels of "relatively low risk", "medium risk", "relatively high risk", and "high risk" can be calculated.

[0057] Determine the risk level using the principle of maximum membership degree Compare the comprehensive membership degrees of the energy storage system for each risk level. Select the level with the largest comprehensive membership degree as the risk level of the energy storage system. For example, if μ2 is the largest, then determine the risk level of the energy storage system as "lower risk".

[0058] For the determination of the risk level of the secondary indicators, it is also to compare their membership degrees for each risk level and select the level with the largest membership degree as the risk level of the secondary indicator. Through the above process, the risk levels of the energy storage system and the secondary indicators can be objectively determined according to the principle of maximum membership degree, providing strong support for risk assessment and management.

[0059] In another embodiment of the present invention, there is provided an energy storage system safety assessment system, an index weight module, an index comprehensive cloud feature and evaluation module; The index weight module constructs an energy storage system safety risk evaluation index system according to the energy storage system to be evaluated, and calculates the weights of each index in the constructed index system; The index comprehensive cloud feature constructs a cloud model evaluation standard cloud, obtains the cloud feature values of each index according to the calculated weight values of each index, and calculates the comprehensive cloud feature values of each index according to the cloud feature values of each index; The evaluation module generates a comprehensive evaluation cloud map and a secondary indicator evaluation cloud map based on the obtained comprehensive cloud feature values of each index, calculates the membership degrees of the comprehensive evaluation cloud belonging to each safety evaluation level based on the comprehensive evaluation cloud map, and obtains the safety risk level of the energy storage system according to the secondary indicator evaluation cloud map and the principle of maximum membership degree.

[0060] Preferably, the construction of the energy storage system safety risk evaluation index system according to the energy storage system to be evaluated specifically includes: evaluating the indicators constituting the risk assessment elements of the energy storage system, and selecting the key indicators affecting the safety of the energy storage system as the energy storage system safety risk evaluation index system, specifically including on-site test indicators, key equipment and facility indicators, rules and regulations indicators, and operation and maintenance inspection indicators.

[0061] Embodiment In the specific embodiment of the present invention, a medium-sized lithium iron phosphate battery energy storage system is taken as an example for safety risk assessment, and the information required for safety assessment is obtained by analyzing the relevant materials and historical data of the system.

[0062] First, establish an energy storage system safety assessment index system through the historical data summary acquisition method, as Figure 3 shown.

[0063] Weight calculation: The index weights are calculated by expert scoring. A set number (the set number in this application is 10) of senior experts in energy storage and power are invited. Based on the basic information of the energy storage system provided and combined with their rich work experience, the experts score the indicators according to the nine-scale table to obtain the determined weights. w j As shown in Table 3.

[0064] Table 3 Index Weights

[0065] Based on the data and information obtained above, the non-quantifiable evaluation indicators are scored. When scoring, both the actual operation situation of the energy storage system and the experience level of each expert are considered. Therefore, the scoring can be regarded as scientific and reasonable. The obtained scoring values are input into the reverse cloud generator, and the cloud digital characteristic parameters of the tertiary indicators are calculated through Equation (5) as shown in Table 4: Table 4 Cloud Digital Characteristics of Tertiary Indicators

[0066] Through Equation (6), the cloud digital characteristics of the energy storage system safety risk assessment are calculated as (77.398, 3.11, 1.182), and the generated evaluation cloud diagram is as Figure 4 shown.

[0067] From Figure 4 it can be seen that the cloud droplets are relatively dense and stable. The safety risk level of this energy storage system is between medium risk and lower risk. The membership degrees of the comprehensive evaluation cloud belonging to each safety evaluation level are calculated as shown in Table 5. According to the principle of the largest membership degree, it can be determined that the safety risk level of this energy storage system belongs to medium risk, and the conclusion is consistent with the actual situation.

[0068] Table 5 Calculation of Membership Degrees of Comprehensive Evaluation Cloud

[0069] Analyze the specific situation of the energy storage system safety risk, find out the links where risks exist, and obtain the cloud digital characteristics and membership degrees of the secondary indicators as shown in Table 6. The comprehensive cloud diagram of the secondary indicators is as Figure 5 shown.

[0070] Table 6 Calculation of Cloud Digital Characteristics and Membership Degrees of Secondary Indicators

[0071] Combined with the evaluation cloud diagram of the secondary indicators and the principle of the largest membership degree, it can be determined that the on-site test index B1 is in a lower risk, the key equipment and facilities index B2 and the operation and maintenance inspection index B4 are in a medium risk, and the rules and regulations index B3 is in a higher risk.

[0072] In another embodiment of the present invention, a terminal device is provided. The terminal device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of the energy storage system safety assessment method.

[0073] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in the terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the energy storage system safety assessment method in the above embodiment.

[0074] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program codes.

[0075] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0076] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A safety assessment method for an energy storage system, characterized in that, It includes the following steps: Construct an energy storage system safety risk evaluation index system according to the energy storage system to be evaluated, preset the safety evaluation level, and calculate the weight values of each index in the constructed index system; Construct a cloud model evaluation standard cloud, obtain the cloud characteristic values of each index according to the cloud model evaluation standard cloud and the calculated weight values of each index, and calculate the comprehensive cloud characteristic value of each index according to the cloud characteristic values of each index; Generate a comprehensive evaluation cloud map and a secondary index evaluation cloud map based on the obtained comprehensive cloud characteristic values of each index, calculate the membership degrees of the comprehensive cloud characteristic values of each index belonging to each safety evaluation level based on the comprehensive evaluation cloud map; calculate the safety risk level of the energy storage system to be evaluated by using the principle of the largest membership degree for the secondary index evaluation cloud map and the obtained membership degrees.

2. The safety assessment method of an energy storage system according to claim 1, wherein The construction of the energy storage system safety risk evaluation index system according to the energy storage system to be evaluated specifically includes: evaluating the indexes that constitute the risk assessment elements of the energy storage system, and selecting the key indexes that affect the safety of the energy storage system as the energy storage system safety risk evaluation index system, specifically including on-site test indexes, key equipment and facility indexes, rules and regulations indexes, and operation and maintenance inspection indexes.

3. A method for safety assessment of an energy storage system according to claim 1, characterized in that, The calculation of the weight values of each index in the constructed index system specifically includes the following steps: Construct a judgment matrix according to the importance degree of the indexes; Calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix; Calculate the index weight vector according to the obtained eigenvector to obtain the weight values of each index.

4. A safety assessment method for an energy storage system according to claim 3, characterized in that, Constructing the judgment matrix according to the importance degree of indicators specifically includes: scoring each indicator in the safety risk assessment index system of the energy storage system by using the 9-scale method to obtain the index score vector , constructing a matrix according to the index score vector , , according to the reciprocity of the matrix, constructing the judgment matrix by using the exponential function A： (1) where, for any i , j , k = 1, 2, 3, …,[ n , there is a ij = 1 / a ji , a ik = a ij * a jk .

5. A safety assessment method for an energy storage system according to claim 1, characterized in that The construction of the cloud model evaluation standard cloud specifically includes: constructing a standard evaluation cloud according to the energy storage system safety risk evaluation index system, adopting a percentile system for the safety risk assessment grading, and dividing the safety risks of the energy storage power station into five levels: high risk (score ≤ 60), relatively high risk (60 ≤ score < 70), medium risk (70 ≤ score < 80), relatively low risk (80 ≤ score < 90), and low risk (score ≥ 90) from low to high according to the scores; obtain the digital characteristics of the standard cloud according to formula (4), generate cloud droplets in the forward cloud generator, and obtain the standard cloud map. (4)。 6. A safety assessment method for an energy storage system according to claim 5, characterized in that, Obtain the cloud characteristic values of each index according to the calculated weight values of each index: obtain the average score and variance of each index according to formula (5), and then generate cloud digital characteristic values by using the reverse cloud generator. (5) where, Z ij is the evaluation results of m experts on n indicators.

7. The safety assessment method of an energy storage system according to claim 6, wherein Calculate the comprehensive cloud characteristic value of each index according to the cloud characteristic values of each index. (6) In the formula, w j is the analytic hierarchy process weight. The cloud parameters of the secondary indicators and the comprehensive evaluation cloud digital characteristics are obtained by weighting the index cloud parameters according to formula (6).

8. A safety assessment system for an energy storage system, characterized in that, Index weight module, index comprehensive cloud characteristic and evaluation module; The index weight module constructs an energy storage system safety risk evaluation index system according to the energy storage system to be evaluated, presets the safety evaluation level, and calculates the weight values of each index in the constructed index system. The index comprehensive cloud characteristic constructs a cloud model evaluation standard cloud, obtains the cloud characteristic values of each index according to the cloud model evaluation standard cloud and the calculated weight values of each index, and calculates the comprehensive cloud characteristic value of each index according to the cloud characteristic values of each index. The evaluation module generates a comprehensive evaluation cloud chart and a secondary index evaluation cloud chart based on the obtained comprehensive cloud feature values of each index, and calculates the membership degrees of the comprehensive cloud feature values of each index belonging to each safety evaluation level based on the comprehensive evaluation cloud chart; the membership degree maximum principle is adopted to calculate the secondary index evaluation cloud chart and the obtained membership degrees to obtain the safety risk level of the energy storage system to be evaluated.

9. The safety assessment system for an energy storage system according to claim 8, wherein The construction of the safety risk evaluation index system for the energy storage system to be evaluated specifically includes: evaluating the indexes that constitute the risk assessment elements of the energy storage system, and selecting the key indexes that affect the safety of the energy storage system as the safety risk evaluation index system for the energy storage system, specifically including on-site test indexes, key equipment and facility indexes, rules and regulations indexes, and operation and maintenance and repair indexes.

10. The safety assessment system for an energy storage system according to claim 8, characterized in that, The calculation of the weights of each index in the constructed index system specifically includes the following steps: Construct a judgment matrix according to the importance degree of the indexes; Calculate the eigenvector corresponding to the largest eigenvalue of the judgment matrix; Calculate the index weight vector according to the obtained eigenvector, and the corresponding weight can be obtained.

11. A safety assessment system for an energy storage system according to claim 10, characterized in that Constructing the judgment matrix according to the importance degree of indicators specifically includes: scoring each indicator in the safety risk assessment index system of the energy storage system by using the 9-scale method to obtain the indicator score vector , constructing a matrix according to the indicator score vector , , according to the reciprocity of the matrix, constructing the judgment matrix by using the exponential function A： (1) Where, for any i , j , k = 1, 2, 3, …,[[]]END]] n , there is a ij = 1 / a ji , a ik = a ij * a jk .

12. The safety evaluation system for an energy storage system according to claim 8, wherein The construction of the cloud model evaluation standard cloud specifically includes: constructing a standard evaluation cloud according to the safety risk evaluation index system of the energy storage system, adopting a percentage system for the safety risk assessment grading, and dividing the safety risks of the energy storage power station into five levels from low to high according to the scores, namely high risk (score ≤ 60), relatively high risk (60 ≤ score < 70), medium risk (70 ≤ score < 80), relatively low risk (80 ≤ score < 90), and low risk (score ≥ 90); obtain the digital characteristics of the standard cloud according to formula (4), generate cloud droplets in the forward cloud generator, and obtain the standard cloud chart. (4)。 13. The safety assessment system for an energy storage system according to claim 12, wherein Obtain the cloud feature values of each index according to the calculated weight values of each index: obtain the average score and variance of each index according to formula (5), and then generate cloud digital characteristic values with the inverse cloud generator. (5) where Z ij is the evaluation results of m experts on n indicators.

14. The safety assessment system for an energy storage system according to claim 13, characterized in that Calculate the comprehensive cloud feature values of each index according to the cloud feature values of each index. (6) In the formula, w j are the analytic hierarchy process weights. The cloud parameters of the secondary indicators and the comprehensive evaluation cloud digital characteristics are obtained by weighting the index cloud parameters according to Equation (6).

15. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the cascade hydropower plan preparation method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the cascade hydropower plan preparation method according to any one of claims 1 to 7 are implemented.

Images