Battery energy storage power station risk evaluation method based on fuzzy comprehensive evaluation

Abstract

The invention discloses a battery energy storage power station risk evaluation method based on fuzzy comprehensive evaluation, which belongs to the field of battery energy storage power station safety risk evaluation, and comprises the following steps: comprehensively considering risk factors influencing the safety of a battery energy storage power station, carrying out induction and classification on the risk factors, and selecting representative index parameters; and establishing a battery energy storage power station risk evaluation index system. And determining subjective weights and objective weights of the evaluation indexes through an analytic hierarchy process and an entropy weight method according to the established evaluation indexes of the battery energy storage power station. And calculating the comprehensive weight of each evaluation index of the battery energy storage power station by combining the subjective weight and the objective weight of the evaluation index of the battery energy storage power station with a game theory. According to a fuzzy comprehensive evaluation method, a fuzzy relation matrix is established by using a trapezoidal membership function, and the safety risk evaluation level of the energy storage power station is determined according to a maximum membership principle. Guidance is provided for operation and maintenance of the battery energy storage power station, and the safety of system operation is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of safety risk assessment of battery energy storage power stations, and in particular relates to a battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation. Background Art

[0002] Battery energy storage power stations play a key role in promoting the grid connection of renewable energy power generation and the safe and stable operation of power systems. Although significant progress has been made in large-scale construction and development, their legal and regulatory systems and standard systems are still imperfect, resulting in insufficient experience in related safety risk management. During the construction and operation process, a variety of new safety hazards have emerged, which may cause fires and explosions, causing significant negative impacts on society. Therefore, in order to reduce the safety risks during the operation of battery energy storage power stations, it is necessary to evaluate the safety risks of battery energy storage power stations and provide guidance for the safe operation and maintenance of battery energy storage power stations. Therefore, it is necessary to design a risk assessment method for battery energy storage power stations based on fuzzy comprehensive evaluation to solve the above problems. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a risk assessment method for a battery energy storage power station based on fuzzy comprehensive evaluation. The subjective and objective weights are determined respectively by means of hierarchical analysis method and entropy weight method, and the comprehensive weight is determined by introducing game theory. The safety risk of the battery energy storage power station is evaluated in combination with the fuzzy comprehensive evaluation method, so as to provide guidance for the operation, maintenance and design of the battery energy storage power station.

[0004] In order to achieve the above technical effects, the technical solution adopted by the present invention is: A risk assessment method for a battery energy storage power station based on fuzzy comprehensive evaluation includes the following steps: S1, set the risk factors that affect the safety of battery energy storage power stations and classify them, select representative indicator parameters to establish a risk assessment indicator system for battery energy storage power stations; set the safety risk assessment of battery energy storage power stations as a first-level indicator A, establish multiple second-level indicators B under the first-level indicator A, and establish multiple third-level indicators C under the second-level indicator B to form an evaluation indicator system; S2, determining the subjective weights and objective weights of evaluating all first-level indicators A, second-level indicators B, and third-level indicators C respectively according to the risk assessment indicator system of the battery energy storage power station established in step S1; S3, based on the subjective weight and objective weight of each evaluation indicator of the battery energy storage power station risk evaluation indicator system determined in step S2, determine the comprehensive weight of each evaluation indicator of the battery energy storage power station through game theory; S4, determining the safety level of the battery energy storage power station according to the comprehensive weight of each evaluation index of the battery energy storage power station risk evaluation index system described in step S3 and in combination with the fuzzy comprehensive evaluation method.

[0005] Preferably, in step S1, the secondary index B includes the battery body, protection system, operating environment, staff and safety management system; the third-level index C included in each secondary index B is: The three-level indicators C of the battery body include: voltage extreme difference between battery cells, temperature extreme difference between battery cells, temperature standard deviation between battery cells, battery state of charge SOC and battery health state SOH; The three-level indicators C of the protection system include: battery management system, insulation monitoring system, fire protection system and environmental monitoring system; The three-level indicators C of the operating environment include: temperature, humidity, dust content and ventilation conditions; The three-level indicators C of staff include: physical quality, psychological quality, skill level and safety awareness; The three-level indicators C of the safety management system include: emergency plan designation and drills, accident handling and personnel training management.

[0006] Preferably, in step S2, the method of evaluating the subjective weights and objective weights of all indicators includes: The subjective weights are determined based on the analytic hierarchy process, and the objective weights are determined based on the entropy weight method.

[0007] Preferably, the method for determining the subjective weight according to the hierarchical analysis method is: The established evaluation index system is scored multiple times using the 1-9 point scale method, and the scoring results are used to construct a judgment matrix M; The consistency of the judgment matrix M is tested. If the test does not meet the requirements, the judgment matrix is ​​reconstructed and the subjective weight is calculated after passing the consistency test; The eigenvector corresponding to the maximum eigenvalue of the judgment matrix M is calculated, and the subjective weight of the evaluation index is obtained after the eigenvector is normalized.

[0008] Preferably, the consistency check method is: Calculate consistency index; Look up the table to find the corresponding average random consistency index; Calculate the agreement ratio; When the consistency ratio is less than 0.1, the evaluation index judgment matrix M is determined to pass the consistency test.

[0009] Preferably, the method for determining the objective weight according to the entropy weight method is: Normalize all data to get a standardized matrix; Calculate the sample feature proportion; Calculate the entropy value of the evaluation index; Determine the objective weight of each evaluation indicator.

[0010] Preferably, in step S3, the method of determining the comprehensive weight of each evaluation index of the battery energy storage power station by game theory includes: Introduce weight combination coefficients to perform linear combination of weights; Minimize the deviation between the comprehensive weight and each weight, and solve for the optimal weight coefficient; The optimal weight combination coefficient obtained is normalized; the optimized weight combination coefficient is obtained, and the weights are linearly combined to obtain the comprehensive weight.

[0011] Preferably, in step S4, the method for determining the safety level of the battery energy storage power station in combination with the fuzzy comprehensive evaluation method includes: The safety of battery energy storage power stations is divided into five levels, including excellent level ( )、Good Grade( ),intermediate( ),qualified( ) and unqualified ( ), forming the evaluation set ; For the evaluation set The fuzzy relationship matrix can be obtained by selecting different levels of thresholds, selecting the trapezoidal membership function, and substituting the scores of the battery energy storage power station evaluation indicators into the membership functions of different levels for calculation. ; Fuzzy Relationship Matrix , combined with the comprehensive weight ,according to Get the comprehensive evaluation vector ; Based on the principle of maximum membership, the safety level of the battery energy storage power station is determined.

[0012] The beneficial effects of the present invention are as follows: BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 It is a schematic diagram of a flow chart of the present invention; Figure 2 It is a structural hierarchy diagram of the evaluation index of the battery energy storage power station in the embodiment of the present invention. DETAILED DESCRIPTION

[0013] Embodiment 1: like Figure 1 As shown, a risk assessment method for a battery energy storage power station based on fuzzy comprehensive evaluation includes the following steps: S1, set the risk factors that affect the safety of battery energy storage power stations and classify them, select representative indicator parameters to establish a risk assessment indicator system for battery energy storage power stations; set the safety risk assessment of battery energy storage power stations as a first-level indicator A, establish multiple second-level indicators B under the first-level indicator A, and establish multiple third-level indicators C under the second-level indicator B to form an evaluation indicator system; S2, determining the subjective weights and objective weights of evaluating all first-level indicators A, second-level indicators B, and third-level indicators C respectively according to the risk assessment indicator system of the battery energy storage power station established in step S1; S3, based on the subjective weight and objective weight of each evaluation indicator of the battery energy storage power station risk evaluation indicator system determined in step S2, determine the comprehensive weight of each evaluation indicator of the battery energy storage power station through game theory; S4, determining the safety level of the battery energy storage power station according to the comprehensive weight of each evaluation index of the battery energy storage power station risk evaluation index system described in step S3 and in combination with the fuzzy comprehensive evaluation method.

[0014] Preferably, in step S1, the secondary index B includes the battery body, protection system, operating environment, staff and safety management system; the third-level index C included in each secondary index B is: The three-level indicators C of the battery body include: voltage extreme difference between battery cells, temperature extreme difference between battery cells, temperature standard deviation between battery cells, battery state of charge SOC and battery health state SOH; The three-level indicators C of the protection system include: battery management system, insulation monitoring system, fire protection system and environmental monitoring system; The three-level indicators C of the operating environment include: temperature, humidity, dust content and ventilation conditions; The three-level indicators C of staff include: physical quality, psychological quality, skill level and safety awareness; The three-level indicators C of the safety management system include: emergency plan designation and drills, accident handling and personnel training management.

[0015] Preferably, in step S2, the method of evaluating the subjective weights and objective weights of all indicators includes: The subjective weights are determined based on the analytic hierarchy process, and the objective weights are determined based on the entropy weight method.

[0016] Preferably, the method for determining the subjective weight according to the hierarchical analysis method is: The established evaluation index system is scored multiple times using the 1-9 point scale method, and the scoring results are used to construct a judgment matrix M; The consistency of the judgment matrix M is tested. If the test does not meet the requirements, the judgment matrix is ​​reconstructed and the subjective weight is calculated after passing the consistency test; The eigenvector corresponding to the maximum eigenvalue of the judgment matrix M is calculated, and the subjective weight of the evaluation index is obtained after the eigenvector is normalized.

[0017] Preferably, the consistency check method is: Calculate consistency index; Look up the table to find the corresponding average random consistency index; Calculate the agreement ratio; When the consistency ratio is less than 0.1, the evaluation index judgment matrix M is determined to pass the consistency test.

[0018] Preferably, the method for determining the objective weight according to the entropy weight method is: Normalize all data to get a standardized matrix; Calculate the sample feature proportion; Calculate the entropy value of the evaluation index; Determine the objective weight of each evaluation indicator.

[0019] Preferably, in step S3, the method of determining the comprehensive weight of each evaluation index of the battery energy storage power station by game theory includes: Introduce weight combination coefficients to perform linear combination of weights; Minimize the deviation between the comprehensive weight and each weight, and solve for the optimal weight coefficient; The optimal weight combination coefficient obtained is normalized; the optimized weight combination coefficient is obtained, and the weights are linearly combined to obtain the comprehensive weight.

[0020] Preferably, in step S4, the method for determining the safety level of the battery energy storage power station in combination with the fuzzy comprehensive evaluation method includes: The safety of battery energy storage power stations is divided into five levels, including excellent level ( )、Good Grade( ),intermediate( ),qualified( ) and unqualified ( ), forming the evaluation set ; For the evaluation set The fuzzy relationship matrix can be obtained by selecting different levels of thresholds, selecting the trapezoidal membership function, and substituting the scores of the battery energy storage power station evaluation indicators into the membership functions of different levels for calculation. ; Fuzzy Relationship Matrix , combined with the comprehensive weight ,according to Get the comprehensive evaluation vector ; Based on the principle of maximum membership, the safety level of the battery energy storage power station is determined.

[0021] Embodiment 2: like Figure 2 As shown, this embodiment provides a practical application of a battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation in a battery energy storage power station, specifically including: Step 1: Comprehensively consider the risk factors that affect the safety of battery energy storage power stations, summarize and classify them, select representative indicator parameters, and establish a risk assessment system for battery energy storage power stations; set the safety risk assessment of battery energy storage power stations as the first-level indicator A, establish multiple second-level indicators B under the A-level indicator, and establish multiple third-level indicators C under the second-level indicator B to form an evaluation indicator system; The first level is the safety risk assessment of battery energy storage power stations; Secondary indicators include battery body, protection system, operating environment, staff, and safety management system; The third-level indicators contained in each second-level indicator are as follows: The three-level indicators of the battery body are: voltage extreme difference between battery cells, temperature extreme difference between battery cells, temperature standard deviation between battery cells, battery state of charge SOC, and battery health state SOH; The protection system includes three levels of indicators: battery management system, insulation monitoring system, fire protection system, and environmental monitoring system; The three-level indicators of the operating environment include: temperature, humidity, dust content, and ventilation conditions; The three-level indicators for staff members include: physical quality, psychological quality, skill level, and safety awareness; The three-level indicators of the safety management system include: emergency plan designation and drills, accident handling, and personnel training management.

[0022] Step 2: Quantify the indicators of the battery energy storage power station to be evaluated through the evaluation indicators established in step 1, and invite relevant experts and industry operation and maintenance personnel to score these quantified data. The value range is 0~100. The higher the score, the better the indicator of the battery energy storage power station to be evaluated. Step 3: Obtain subjective weights through the hierarchical analysis method. According to the established battery energy storage power station risk assessment index system, use the 1-9 point scale method to score each level of the established index system multiple times and mark them. The meaning of the markings is shown in Table 1. The scoring results construct the judgment matrix M. Table 1: Meaning of the nine-scale method

[0023] The judgment matrix established for each layer of indicators is subjected to consistency check to determine whether the constructed judgment matrix is ​​reasonable. The consistency check steps are as follows: Calculate consistency index : ; In the formula, represents the maximum value of the eigenvector, is the number of indicators, through Find the corresponding average random consistency index in Table 2 ; Table 2: Average random consistency index

[0024] Calculate the consistency ratio : ; if Then the consistency of the evaluation index judgment matrix can be considered acceptable, otherwise the evaluation index judgment matrix needs to be revised; Obtain the eigenvector of the maximum eigenroot of the judgment matrix corresponding to each layer in the evaluation index system, perform a sleight of hand on the feature correlation to obtain the weight of the corresponding level indicator, multiply the weight of the third-level indicator by the weight of the second-level indicator, and finally obtain the subjective weight of the bottom-level indicator to the top-level indicator; The objective weight is obtained by entropy weight method, and all data in the evaluation matrix are normalized; among the 20 indicators of the third-level indicator C, there are economic and cost indicators; the normalization formulas of economic and cost indicators are: ; ; In the formula, is the sample data of evaluation index, is the normalized data; It is the maximum value of sample data under the same indicator; It is the minimum value of sample data under the same indicator; After normalizing all data, a standardized matrix can be obtained.

[0025] Assumption Selection evaluation objects, each of which has the same indicators, Indicates The sample evaluation indicators, . Under the indicator, The characteristic proportion of samples for: ; Calculate the entropy value, The calculation formula of the entropy value of each indicator is: ; Utilization Determine the objective weight of each evaluation indicator .

[0026] Step 4: Determine the comprehensive weight through game theory, find a compromise among the evaluation index weights obtained from multiple weight determination methods, minimize the deviation between possible weights and each basic weight, and select the optimal solution. The detailed calculation process is as follows: Two different weight calculation methods are used to obtain the evaluation index weights of battery energy storage power stations, and two evaluation index weight vector sets are obtained. ; Linearly combine the subjective weight and objective weight vectors to obtain possible weight vectors; ; Based on the optimal strategy idea of ​​game theory, find the optimal solution among all possible weight vectors , the coefficients of the two linear combinations , Optimize processing so that and , The minimum deviation is: ; The optimal first-order derivative condition of the above formula is transformed as follows: ; According to the above formula, the weight coefficient is obtained , and then normalize the weight coefficient, that is: ; Finally, the comprehensive weight is obtained; .

[0027] Step 5: divide the safety risk level of the battery energy storage power station into five levels: excellent, good, medium, qualified, and unqualified to form an evaluation set; According to the opinions of professionals, on-site operation management personnel and corresponding regulations, these five levels are scored, and the corresponding scores are [95, 100], [85, 90], [75, 80], [65, 70], and [0, 60]; According to the thresholds of different levels in the evaluation set, the trapezoidal membership function is selected, and the scores of the evaluation indicators of the 20 battery energy storage power stations in the factor set are substituted into the membership functions of different levels for calculation, and the fuzzy relationship matrix can be obtained. The membership function calculation formula is as follows: ; ; ; ; ; In the formula, To score the evaluation index, the fuzzy relationship matrix is ​​obtained according to the above process , combined with the comprehensive weight , the comprehensive evaluation vector is obtained as follows ; ; In the formula, Corresponding to the five levels of membership in the battery energy storage power station evaluation indicators, based on the maximum membership principle, the safety level of the battery energy storage power station can be determined to complete the evaluation of the battery energy storage power station.

Claims

1. A risk assessment method for a battery energy storage power station based on fuzzy comprehensive evaluation, characterized in that: The following steps are involved: S1, set the risk factors that affect the safety of battery energy storage power stations and classify them, select representative indicator parameters to establish a risk assessment indicator system for battery energy storage power stations; set the safety risk assessment of battery energy storage power stations as a first-level indicator A, establish multiple second-level indicators B under the first-level indicator A, and establish multiple third-level indicators C under the second-level indicator B to form an evaluation indicator system; S2, determining the subjective weights and objective weights of evaluating all first-level indicators A, second-level indicators B, and third-level indicators C respectively according to the risk assessment indicator system of the battery energy storage power station established in step S1; S3, based on the subjective weight and objective weight of each evaluation indicator of the battery energy storage power station risk evaluation indicator system determined in step S2, determine the comprehensive weight of each evaluation indicator of the battery energy storage power station through game theory; S4, determining the safety level of the battery energy storage power station according to the comprehensive weight of each evaluation index of the battery energy storage power station risk evaluation index system described in step S3 and in combination with the fuzzy comprehensive evaluation method.

2. A battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation according to claim 1, characterized in that: In step S1, the secondary index B includes the battery body, protection system, operating environment, staff and safety management system; the third-level index C contained in each secondary index B is: The three-level indicators C of the battery body include: voltage extreme difference between battery cells, temperature extreme difference between battery cells, temperature standard deviation between battery cells, battery state of charge SOC and battery health state SOH; The three-level indicators C of the protection system include: battery management system, insulation monitoring system, fire protection system and environmental monitoring system; The three-level indicators C of the operating environment include: temperature, humidity, dust content and ventilation conditions; The three-level indicators C of staff include: physical quality, psychological quality, skill level and safety awareness; The three-level indicators C of the safety management system include: emergency plan designation and drills, accident handling and personnel training management.

3. A battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation according to claim 1, characterized in that: In step S2, the method of evaluating the subjective weight and objective weight of all indicators includes: The subjective weights are determined based on the analytic hierarchy process, and the objective weights are determined based on the entropy weight method.

4. A battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation according to claim 3, characterized in that: The method to determine subjective weights based on the hierarchical analysis method is: The established evaluation index system is scored multiple times using the 1-9 point scale method, and the scoring results are used to construct a judgment matrix M; The consistency of the judgment matrix M is tested. If the test does not meet the requirements, the judgment matrix is ​​reconstructed and the subjective weight is calculated after passing the consistency test; The eigenvector corresponding to the maximum eigenvalue of the judgment matrix M is calculated, and the subjective weight of the evaluation index is obtained after the eigenvector is normalized.

5. A battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation according to claim 4, characterized in that: The consistency test method is: Calculate consistency index; Look up the table to find the corresponding average random consistency index; Calculate the consistency ratio; When the consistency ratio is less than 0.1, the evaluation index judgment matrix M is determined to pass the consistency test.

6. A battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation according to claim 3, characterized in that: The method for determining objective weights based on the entropy weight method is: Normalize all data to get a standardized matrix; Calculate the sample feature proportion; Calculate the entropy value of the evaluation index; Determine the objective weight of each evaluation indicator.

7. A battery energy storage power station risk assessment method based on fuzzy comprehensive evaluation according to claim 1, characterized in that: In step S3, the method of determining the comprehensive weight of each evaluation index of the battery energy storage power station through game theory includes: Introduce weight combination coefficients to perform linear combination of weights; Minimize the deviation between the comprehensive weight and each weight, and solve for the optimal weight coefficient; The optimal weight combination coefficient obtained is normalized; the optimized weight combination coefficient is obtained, and the weights are linearly combined to obtain the comprehensive weight.

8. The risk assessment method for a battery energy storage power station based on fuzzy comprehensive evaluation according to claim 1 is characterized in that: In step S4, the method for determining the safety level of the battery energy storage power station by combining the fuzzy comprehensive evaluation method includes: The safety of battery energy storage power stations is divided into five levels, including excellent level ( )、Good Grade( ),intermediate( ),qualified( ) and unqualified ( ), forming the evaluation set ; For the evaluation set The fuzzy relationship matrix can be obtained by selecting different levels of thresholds, selecting the trapezoidal membership function, and substituting the scores of the battery energy storage power station evaluation indicators into the membership functions of different levels for calculation. ; Fuzzy Relationship Matrix , combined with the comprehensive weight ,according to Get the comprehensive evaluation vector ; Based on the principle of maximum membership, the safety level of the battery energy storage power station is determined.

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