Battery energy storage power station comprehensive evaluation method based on improved AHP-WRSR

Abstract

The invention relates to the field of battery energy storage power station safety risk evaluation, in particular to a battery energy storage power station comprehensive evaluation method based on improved AHP-WRSR. As an important component of a modern energy system, the safety risk evaluation of a battery energy storage power station is crucial. A battery energy storage power station safety risk evaluation system in the existing research adopts an analytic hierarchy process, is greatly influenced by human subjective factors, can only give a relative advantage and disadvantage relationship of an evaluation scheme, and lacks practical application value. The expert experience needs to be judged by an expert according to the experience, is greatly influenced by the professional degree of the expert, and does not reflect objective reality. According to the battery energy storage power station comprehensive evaluation method based on the improved AHP-WRSR, factors influencing the safety risk of the battery energy storage power station are comprehensively considered from multiple aspects, a battery energy storage power station safety evaluation system is perfected, and the reliability and accuracy of a safety evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the field of safety risk assessment of battery energy storage power stations, and in particular to a comprehensive assessment method of battery energy storage power stations based on improved AHP-WRSR. Background Art

[0002] As an important part of the modern energy system, the safety risk assessment of battery energy storage power stations is crucial. Battery energy storage systems involve a large number of battery cells and complex control systems. If there are defects in the system or improper operation leads to serious consequences such as battery overheating, fire or explosion, it will not only cause damage to the equipment, but also threaten the surrounding environment and personnel safety. As part of the power grid, the stable operation of battery energy storage power stations is crucial to the safety and reliability of the entire power system.

[0003] In existing research, the safety risk assessment system of battery energy storage power stations usually adopts the hierarchical analysis method. The traditional hierarchical analysis method uses a 1-9 scale quantitative judgment matrix, which is greatly affected by human subjective factors, and the evaluation results are not accurate enough. It can only give the relative advantages and disadvantages of the evaluation schemes and lacks practical application value; expert experience requires experts to make judgments based on experience, which is cumbersome and greatly affected by the professional level of experts, and does not reflect objective reality. In view of the existing problems, the present invention aims to solve the following problems: subjectivity, randomness, ambiguity and other problems that are prone to occur in the process of safety risk assessment of battery energy storage power stations. Summary of the invention

[0004] The technical problem of the present invention is to comprehensively consider the factors affecting the safety risk of battery energy storage power stations from multiple aspects, improve the safety assessment system of battery energy storage power stations, and improve the reliability and accuracy of safety assessment results.

[0005] The purpose of the present invention is to solve the above problems and provide a comprehensive evaluation method for a battery energy storage power station based on an improved AHP-WRSR, comprising the following steps: S1: Obtain evaluation indicators based on historical data, accident investigation reports, consulting risk management personnel and the logical relationship between risk accidents, and preliminarily establish an evaluation indicator system with comprehensive evaluation of battery energy storage power stations as the target indicator, which includes a criterion layer under the target indicator layer; S2: Calculate the weights of evaluation indicators using fuzzy analytic hierarchy process and laboratory evaluation method; S3: Calculate the weighted rank sum ratio of the evaluation index using the evaluation index weight; S4: Substitute the probability unit as the independent variable into the regression equation to obtain the evaluation level and form a complete comprehensive evaluation of the battery energy storage power station.

[0006] Preferably, in step S1, the criterion layer includes evaluation indicators, and the evaluation indicators include battery module, electrical protection system, safety protection system, environmental monitoring and staff; the evaluation indicators under the battery module include: battery module status, battery cell temperature consistency, battery overcharge status and battery overdischarge status; the evaluation indicators of the electrical protection system include: charge and discharge management system, battery management system, lightning protection and grounding protection system and insulation monitoring system; the evaluation indicators under the safety protection system include: gas monitoring system, fire safety system and safety linkage system; the evaluation indicators under environmental monitoring include: external fire source, battery compartment temperature, humidity and poor ventilation; the evaluation indicators of staff include: misoperation, technical level, psychological condition and physical quality.

[0007] Preferably, in step S2, the weight determination method based on fuzzy analytic hierarchy process-decision making and laboratory evaluation method comprises the following sub-steps: S21. Calculate the initial weights of evaluation indicators using fuzzy analytic hierarchy process: S22. Use fuzzy decision making and laboratory evaluation method to determine the impact relationship of evaluation indicators: S23. Normalize the centrality of the evaluation index to obtain the modified weight, including the initial comprehensive weight and the comprehensive weight calculated by the centrality; Preferably, in step S21, the method of calculating the initial weights of the evaluation indicators using the fuzzy analytic hierarchy process includes the following sub-steps: S211. Combining the nine-level scaling method and triangular fuzzy numbers, a nine-level fuzzy scaling table is established to evaluate the importance of evaluation indicators and establish a fuzzy judgment matrix; S212. Calculate the triangular fuzzy number of the evaluation index weights of each level using the modified formula; S213. Calculate the total ranking weight of the evaluation index; S214. Defuzzify the result of the total ranking weight.

[0008] Further, in step S22, the method of determining the index influence relationship by using fuzzy decision making and laboratory evaluation method includes the following sub-steps: S221. Establishing the mapping relationship between triangular fuzzy numbers and expert terms; S222. Using the mapping relationship to establish the initial direct impact matrix of the evaluation index; S223. Convert the fuzzy data in the initial direct impact matrix into clear scores to obtain standardized triangular fuzzy numbers; S224. Calculate the left and right standard values, the standardized total value, the precise value, and the precise value of the expert comments of the standardized initial direct impact matrix to obtain a direct relationship matrix; S225. Convert the direct relationship matrix into an equivalent standard type of evidence, and obtain the comprehensive influence matrix by calculation; S226. Calculate the influence, influence and centrality of the indicator factors in the comprehensive influence matrix.

[0009] S227. Calculate the comprehensive weight of the evaluation index through the initial comprehensive weight and centrality.

[0010] Furthermore, in step S3, the following sub-steps are included: S31 rank the data of evaluation objects and evaluation indicators; S32 calculates the weighted rank sum ratio by combining the evaluation index weights and the rank matrix; Preferably, in step S31, ranking includes the following sub-steps: S311 constructs the data of the evaluation object and the evaluation index into a data matrix, and ranks the evaluation objects of the evaluation index in the matrix to obtain an initial rank matrix; In the S312 rank matrix, economic indicators are ranked from small to large, and cost indicators are ranked from large to small. When the data are the same under the same indicator, the average rank is calculated to obtain the rank matrix; Furthermore, in step S32, the weighted rank sum ratio is calculated by combining the indicator weights and the rank matrix, including the following sub-steps: S321 determines the probability unit of the evaluation object; S322 calculates a linear regression equation of the evaluation object; S323 is sorted in 5 grades, where grades 1 to 5 correspond to the deterioration, attention, medium, good and normal states of the battery energy storage power station respectively. The probability unit critical value is used as the independent variable, and the corresponding weighted rank sum ratio is calculated through the regression equation, and the weighted rank sum ratio is sorted according to the grades.

[0011] Compared with the prior art, the beneficial effects of the present invention include: 1) The present invention considers the impact of various factors on the safety assessment of battery energy storage power stations, establishes evaluation indicators including battery modules, electrical protection systems, safety protection systems, environmental monitoring and staff evaluation, and uses fuzzy analytic hierarchy process-decision-making and laboratory evaluation method to calculate the weights of evaluation indicators, and then establishes a safety status evaluation model of battery energy storage power stations based on weighted rank sum ratio, and constructs a comprehensive evaluation method for battery energy storage power stations.

[0012] 2) The present invention uses the fuzzy hierarchical analysis method to determine the initial weights, and uses decision-making and laboratory evaluation to correct the initial weights to obtain comprehensive weights. It takes into account the mutual influence of indicators at the same level, evaluates the safety status of a single battery energy storage power station, and comprehensively evaluates and ranks the operating status of multiple battery energy storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0014] Figure 1 This is a flow chart of evaluation indicators for a battery energy storage power station according to an embodiment of the present invention.

[0015] Figure 2 This is a structural hierarchy diagram of evaluation indicators for a battery energy storage power station according to an embodiment of the present invention; DETAILED DESCRIPTION like Figure 1 As shown in FIG. 1 , the comprehensive evaluation method of the battery energy storage power station based on the improved AHP-WRSR includes the following steps: S1: Obtain evaluation indicators based on historical data, accident investigation reports, consulting risk management personnel and the logical relationship between risk accidents, and preliminarily establish an evaluation indicator system with comprehensive evaluation of battery energy storage power stations as the target indicator, which includes a criterion layer under the target indicator layer; S2: Calculate the weights of evaluation indicators using fuzzy analytic hierarchy process and decision and laboratory evaluation method; S3: Calculate the weighted rank sum ratio for the evaluation index using the evaluation index weight; S4: Use the probability unit result of step S3 as the independent variable to substitute into the regression equation to obtain the evaluation level, forming a complete comprehensive evaluation of the battery energy storage power station.

[0016] In step S1, the criterion layer contains evaluation indicators, which include battery modules, electrical protection systems, safety protection systems, environmental monitoring and staff; the evaluation indicators under the battery module are: battery module status, battery cell temperature consistency, battery overcharge status and battery overdischarge status; the evaluation indicators of the electrical protection system are: charge and discharge management system, battery management system, lightning protection and grounding protection system and insulation monitoring system; the evaluation indicators under the safety protection system are: gas monitoring system, fire safety system and safety linkage system; the evaluation indicators under environmental monitoring are: external fire source, battery compartment temperature, humidity and poor ventilation; the evaluation indicators of staff are: misoperation, technical level, psychological condition and physical quality.

[0017] In step S2, a weighting method is determined based on the fuzzy analytic hierarchy process-decision making and laboratory evaluation method, including the following sub-steps: S21. Calculate the initial weights of evaluation indicators using fuzzy analytic hierarchy process: S22. Use fuzzy decision making and laboratory evaluation method to determine the impact relationship of evaluation indicators: S23. Normalize the centrality of the evaluation index to obtain the modified weight, including the initial comprehensive weight and the comprehensive weight calculated by the centrality; In step S21, the initial weights of the evaluation indicators are calculated using the fuzzy analytic hierarchy process, which includes the following sub-steps: S211. Combining the nine-level scaling method and triangular fuzzy numbers, a nine-level fuzzy scaling table is established, as shown in Table 1, the importance of the evaluation indicators is evaluated, and a fuzzy judgment matrix is ​​established: Table 1 Conversion relationship between expert judgment terms and fuzzy numbers

[0018] According to Table 1, the evaluation opinions of K experts on the relative importance of each evaluation index are collected, and the fuzzy judgment matrix of the index layer is established. The calculation formula is: ; In the formula, For the Experts judged The factor The relative importance of the factors .

[0019] S212. Calculate the triangular fuzzy number of the evaluation index weights of each level using the modified formula; The modified formula is used to calculate the triangular fuzzy number of the weight of each level of evaluation indicators, and the triangular fuzzy number of the single ranking weight of the evaluation indicators determined by experts is calculated as follows: ; In the formula, Indicates The experts judged The triangular fuzzy number of the evaluation index with a single ranking weight, .

[0020] S213. Calculate the total ranking weight of the evaluation index; Assume that the indicator layers are Layer, according to the hierarchical relationship of fuzzy analytic hierarchy process, the total ranking weight of each evaluation index relative to the target layer It is expressed as: ; In the formula, Indicates the index layer number; Indicates Experts on The weight of the layer indicator; Indicates the total number of indicator layers, .

[0021] S214. Defuzzify the result of the total ranking weight.

[0022] right Defuzzify and then take the average value to get the initial comprehensive weight ; The defuzzification formula is: ; In the formula, represents a triangular fuzzy number, The initial weight is obtained according to the above calculation; In step S22, the fuzzy decision making and laboratory evaluation method are used to determine the index influence relationship, including the following sub-steps: S221. Establish a mapping relationship between triangular fuzzy numbers and expert terms, as shown in Table 2; Table 2 Expert terms and fuzzy conversion

[0023] S222. Using the mapping relationship to establish the initial direct impact matrix of the evaluation index; Invite experts to judge the degree of influence between the evaluation indicators and obtain the initial direct influence matrix; ; In the formula, For the The judgment result of an expert.

[0024] S223. Convert the fuzzy data in the initial direct impact matrix into clear scores to obtain standardized triangular fuzzy numbers; Convert the fuzzy number to a fraction and compute the normalized triangular fuzzy number: ; In the formula, is the difference between the rightmost value and the leftmost value of the initial triangular fuzzy number; S224. Calculate the left and right standard values, the standardized total value, the precise value and the precise value of the expert comments of the standardized initial direct impact matrix to obtain a direct relationship matrix; The calculation formula for the left and right standard values ​​is: ; In the formula, represents the left standard value, Indicates the right standard value.

[0025] The formula for calculating the standardized total value is: ; In the formula, Represents the normalized total value.

[0026] The exact value is calculated as: ; In the formula, Indicates an exact value.

[0027] The exact value of expert comments is calculated as: ; In the formula, Indicates the exact value of the expert's comment.

[0028] The direct relationship matrix calculation formula is: ; S225. Convert the direct relationship matrix into an equivalent standard type of proof and obtain the comprehensive influence matrix through calculation.

[0029] Equivalent standardization, the calculation formula is: ; In the formula, Indicates equivalent normalized results.

[0030] The comprehensive impact matrix is ​​calculated as follows: ; In the formula, Represents the comprehensive impact matrix.

[0031] S226. Calculate the influence, influence and centrality of the indicator factors in the comprehensive influence matrix.

[0032] Calculate the influence and impact of the index factors, the calculation formula is: ; In the formula, Indicates the influence of the indicator factor, Indicates the degree of influence of the indicator factor.

[0033] Calculate the centrality, the calculation formula is: ; In the formula, Indicates The centrality of the evaluation index.

[0034] The centrality is normalized and the calculation formula is: ; In the formula, Indicates The normalized centrality of the evaluation index.

[0035] S227. The comprehensive weight of the evaluation index is calculated by the initial comprehensive weight and the centrality. The calculation formula is: ; In the formula, Indicates The initial weights of the evaluation indicators, Indicates The comprehensive weight of the evaluation indicators.

[0036] In step S3, the following sub-steps are included: S31 rank the data of evaluation objects and evaluation indicators; S32 calculates the weighted rank sum ratio by combining the evaluation index weights and the rank matrix; 7. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1, characterized in that: in step S31, ranking includes the following sub-steps: S311 constructs the data of the evaluation object and the evaluation index into a data matrix, and ranks the evaluation objects of the evaluation index in the matrix to obtain an initial rank matrix; The economic indicators in the S312 rank matrix are ranked from small to large, and the cost indicators are ranked from large to small. When the data are the same under the same indicator, the average rank is calculated to obtain the rank matrix ; 8. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: in step S32, the weighted rank sum ratio is calculated by combining the index weight and the rank matrix, comprising the following sub-steps: S321 determines the probability unit of the evaluation object; Calculate the estimated value of the weighted rank sum ratio. When the weights of the evaluation indicators are different, the calculation formula of the weighted rank sum ratio is: ; In the formula For the Line The rank of the column elements, For the The weight of each evaluation indicator; When the weights of the evaluation indicators are the same, the calculation formula of the weighted rank sum ratio is: ; In the formula, Represents the result of weighted rank sum ratio.

[0037] Arrange the calculated weighted rank sum ratio estimates from small to large in a row, group the values ​​with the same rank, and compile a frequency distribution table of the weighted rank sum ratio, listing the frequency of each group. and the cumulative frequency of each group , Determine the rank range of each group's weighted rank sum ratio and the average rank ; The expression for calculating the percentage is: ; In the formula, represents the average rank.

[0038] Then Convert to Probability unit of evaluation object ; ; In the formula, Indicates percentage The corresponding standard normal deviation. S322 calculates a linear regression equation of the evaluation object; In probability units As the independent variable, the weighted rank sum ratio estimate is used as the dependent variable. With the help of the software, the regression equation can be determined as follows: ; In the formula, represents the weighted rank sum ratio, are the coefficients of the constant term and the linear term of the regression equation respectively.

[0039] S323 is sorted by grading in 5 grades, where grades 1 to 5 correspond to the deterioration, attention, medium, good and normal states of the battery energy storage power station respectively. The probability unit critical value under common grading conditions can be obtained by looking up the table, and the probability unit critical value is used as the independent variable. The corresponding weighted rank sum ratio is calculated through the regression equation. The larger the weighted rank sum ratio, the higher the grade, and the grading of the evaluation object is performed.

Claims

1. A comprehensive evaluation method for battery energy storage power stations based on improved AHP-WRSR is proposed to ensure safe and stable operation of battery energy storage power stations. The method is characterized by: The method comprises the following steps: S1: Obtain evaluation indicators based on historical data, accident investigation reports, consulting risk management personnel and the logical relationship between risk accidents, and preliminarily establish an evaluation indicator system with comprehensive evaluation of battery energy storage power stations as the target indicator, which includes a criterion layer under the target indicator layer; S2: Calculate the weights of evaluation indicators using fuzzy analytic hierarchy process and laboratory evaluation method; S3: Calculate the weighted rank sum ratio of the evaluation index using the evaluation index weight; S4: Substitute the probability unit as the independent variable into the regression equation to obtain the evaluation level and form a complete comprehensive evaluation of the battery energy storage power station.

2. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: In step S1, the criterion layer includes evaluation indicators, including battery module, electrical protection system, safety protection system, environmental monitoring and staff; the evaluation indicators under the battery module are: battery module status, battery cell temperature consistency, battery overcharge status and battery overdischarge status; the evaluation indicators of the electrical protection system are: charge and discharge management system, battery management system, lightning protection and grounding protection system and insulation monitoring system; the evaluation indicators under the safety protection system are: gas monitoring system, fire safety system and safety linkage system; the evaluation indicators under environmental monitoring are: external fire source, battery compartment temperature, humidity and poor ventilation; the evaluation indicators of staff are: misoperation, technical level, psychological condition and physical quality.

3. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: In step S2, the weight determination method based on fuzzy analytic hierarchy process-decision making and laboratory evaluation method includes the following sub-steps: S21. Calculate the initial weights of evaluation indicators using fuzzy analytic hierarchy process: S22. Use fuzzy decision making and laboratory evaluation method to determine the impact relationship of evaluation indicators: S23. Normalize the centrality of the evaluation index to obtain the revised weight, including the initial comprehensive weight and the comprehensive weight calculated by the centrality.

4. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: In step S21, the method of calculating the initial weights of the evaluation indicators using the fuzzy analytic hierarchy process includes the following sub-steps: S211. Combining the nine-level scaling method and triangular fuzzy numbers, a nine-level fuzzy scaling table is established to evaluate the importance of evaluation indicators and establish a fuzzy judgment matrix; S212. Calculate the triangular fuzzy number of the evaluation index weights of each level using the modified formula; S213. Calculate the total ranking weight of the evaluation index; S214. Defuzzify the result of the total ranking weight.

5. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: In step S22, the method of determining the index influence relationship by using fuzzy decision making and laboratory evaluation method includes the following sub-steps: S221. Establishing the mapping relationship between triangular fuzzy numbers and expert terms; S222. Using the mapping relationship to establish the initial direct impact matrix of the evaluation index; S223. Convert the fuzzy data in the initial direct impact matrix into clear scores to obtain standardized triangular fuzzy numbers; S224. Calculate the left and right standard values, the standardized total value, the precise value, and the precise value of the expert comments of the standardized initial direct impact matrix to obtain a direct relationship matrix; S225. Convert the direct relationship matrix into an equivalent standard type of evidence, and obtain the comprehensive influence matrix by calculation; S226. Calculate the influence, influence and centrality of the indicator factors in the comprehensive influence matrix; S227. Calculate the comprehensive weight of the evaluation index through the initial comprehensive weight and centrality.

6. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: In step S3, the weighted rank sum ratio is calculated using the evaluation index weights for the evaluation index, including the following sub-steps: S31. Rank the data of evaluation objects and evaluation indicators; S32. Calculate the weighted rank sum ratio by combining the evaluation index weights and the rank matrix.

7. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: In step S31, ranking includes the following sub-steps: S311. The data of the evaluation object and the evaluation index are constructed into a data matrix, and the evaluation object of the evaluation index in the matrix is ​​ranked to obtain an initial rank matrix; S312. The economic indicators in the rank matrix are ranked from small to large, and the cost indicators are ranked from large to small. When the data are the same under the same indicator, the average rank is calculated to obtain the rank matrix.

8. The comprehensive evaluation method of battery energy storage power station based on improved AHP-WRSR according to claim 1 is characterized in that: In step S32, the weighted rank sum ratio is calculated by combining the index weights and the rank matrix, including the following sub-steps: S321. Determine the probability unit of the evaluation object; S322. Calculate the linear regression equation of the evaluation object; S323. Sorting by tiers: adopt the 5-tier tiering method, take the probability unit critical value as the independent variable, calculate the corresponding weighted rank sum ratio through the regression equation, and sort the weighted rank sum ratio by tiers.

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