Method for risk assessment and emergency response of large-scale new electrochemical energy storage

Abstract

This invention provides a novel large-scale electrochemical energy storage risk assessment and emergency response method, relating to the field of electrochemical energy storage technology. The steps of this invention are as follows: S1, collect multi-source data and preprocess it; S2, determine risk factors and construct coupling relationships; S3, perform coupled risk quantification assessment; S4, conduct risk warning and predict its changing trend; S5, take emergency measures according to the risk level. The beneficial effects of this invention are as follows: quantifying risk through risk factors improves the accuracy of risk assessment; introducing coupled risk values ​​into the comprehensive risk value considers the impact of multiple risk factor coupling on the risk level; the weights of risk factors are determined by both subjective and objective weights, avoiding the one-sidedness of risk factor weight values; the adjustment coefficients of subjective and objective weights combine the similarity of sample sites, avoiding the impact of excessively low similarity on the accuracy of weights; the adjustment of risk levels considers the warning needs of emergency scenarios.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage technology, specifically to a method for risk assessment and emergency response of large-scale novel electrochemical energy storage. Background Technology

[0002] Large-scale electrochemical energy storage battery systems are prone to significant safety incidents, and rescue efforts are hampered by the vast terrain. Current operation and maintenance management regulations rely solely on post-incident handling and manual risk assessment for safety control, which has theoretical flaws and practical difficulties. Furthermore, existing fire-fighting linkage mechanisms often suffer from poor performance or even fail to activate due to irrational sensor distribution and unreliable thresholds. Therefore, there is an urgent need for real-time risk assessment and emergency response methods for electrochemical energy storage systems. This would help operation and maintenance personnel at electrochemical energy storage power stations to conduct macro-level risk control at the station / regional level, thereby effectively improving the timeliness of risk assessment, the rationality of emergency plan development, and providing more reliable fire-fighting linkage triggering conditions. Summary of the Invention

[0003] The main objective of this invention is to provide a novel large-scale electrochemical energy storage risk assessment and emergency response method to solve the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by this invention is: a novel large-scale electrochemical energy storage risk assessment and emergency response method, comprising the following steps: Includes the following steps: S1. Collect multi-source data and perform preprocessing; S2. Identify risk factors and establish coupling relationships; S3. Conduct a quantitative assessment of coupling risks; S4. Conduct risk warnings and predict their changing trends; S5. Take emergency measures according to the risk level.

[0005] Furthermore, the multi-source data is divided into: lithium battery data, sodium battery data, vanadium redox flow battery data, and public data; Preprocessing operations include: data cleaning, noise reduction, and normalization.

[0006] Furthermore, risk factors are categorized into: physical risk factors, chemical risk factors, electrical risk factors, and human management risk factors. The coupling relationship is represented by a risk coupling correlation matrix; risk factors are shared. The class, risk coupling correlation matrix is A square matrix; by arranging the risk factors in order, the first... Each risk factor is denoted as The expression for the risk coupling correlation matrix is: (1); in, The risk coupling correlation matrix is ​​the first Line 1 The elements of the column represent and The strength of the association.

[0007] Furthermore, the assignment rules for the elements of the risk coupling correlation matrix are as follows: For diagonal elements, simply assign the value 1 to the diagonal element; For off-diagonal elements, the association strength is calculated using the following expression: (2); in, For the first The first risk factor indicator One time series data, For the first The mean of each risk factor indicator, This represents the total number of time-series data. When there is a lack of data, set the element value to 0.

[0008] Furthermore, the coupling risk is quantified using a comprehensive risk value; the comprehensive risk value is obtained by weighted summation of risk factors, and its expression is as follows: (3); in, For the comprehensive risk value, The total number of risk factors. For the first The index values ​​of each risk factor, For the first The weights of each risk factor For coupling risk value, This is the coupling risk adjustment coefficient; Risks are classified according to their comprehensive risk values, and the classification process is as follows: When the comprehensive risk value is When the risk level is 1, it indicates safety; when the comprehensive risk value is... When the risk level is 2, it indicates low risk; when the comprehensive risk value is... When the risk level is 3, it indicates medium risk; when the comprehensive risk value is... When the risk level is 4, it indicates high risk; when the comprehensive risk value is greater than or equal to 0.8, the risk level is 5, indicating extremely high risk.

[0009] Furthermore, the risk factor weights are calculated based on subjective and objective weights; Subjective weights were obtained through joint evaluation by multiple experts; objective weights were calculated based on the dispersion of historical data, and their expression is as follows: (4); in, For the first The objective weights of each risk factor For the first The difference coefficients of the risk factors are expressed as follows: (5); in, For the first The entropy value of each risk factor is expressed as follows: (6); The expression for the risk factor weights is as follows: (7); in, For the first Subjective weights of each risk factor, , These are the percentage coefficients for subjective weight and objective weight, respectively.

[0010] Furthermore, the expression for the coupling risk adjustment coefficient is as follows: (8).

[0011] Furthermore, in the process of calculating objective weights, when historical data is lacking, a cold start strategy is adopted as follows: Select operational energy storage sites with the same chemical system, capacity level, and structural layout as sample sites, migrate the samples of the sample sites, and calculate the similarity between the two. Set a migration threshold. When the similarity is greater than the migration threshold, the sample data from the sample station will be migrated to the newly established station as historical data and used as historical data for objective weight calculation.

[0012] Furthermore, the percentage coefficient is set as follows: when the energy storage station itself has historical data, the percentage coefficient takes the base value; When using sample data from sample sites as historical data, the expression for the proportion coefficient is as follows: (9); in, For similarity, This is the base value for the subjective weighting ratio coefficient.

[0013] Furthermore, the detailed process of step S4 is as follows: S401. Implement a tiered early warning strategy based on risk levels; S402. Conduct real-time risk tracking; set the tracking frequency, update the comprehensive risk value and risk level in real time according to the set tracking frequency, and issue real-time warnings based on the risk level; The risk level used for real-time early warning is also adjusted based on the predicted values ​​of risk factor indicators, which can be calculated from the indicator values ​​at a certain moment. Based on the predicted values ​​of the risk factors, the predicted comprehensive risk value for future time periods is calculated. A mutation determination is then made based on this predicted value. The mutation determination process is as follows: The risk change rate is calculated based on the comprehensive risk value prediction. A mutation threshold is set. When the risk change rate is greater than the mutation threshold, it is determined to be in a risk mutation state. At this time, the risk level is upgraded by one level, and an early warning is issued based on the upgraded risk level.

[0014] Beneficial effects: (1) By quantifying risk through risk factors, a risk assessment system can be established, thereby improving the accuracy of risk assessment; (2) A coupled risk value is introduced into the comprehensive risk value to take into account the impact of the coupling of multiple risk factors on the degree of risk; (3) The weights of risk factors are determined by both subjective and objective weights, thus avoiding the one-sidedness of the risk factor weight values; (4) Considering the cold start situation where there is a lack of historical data, sample stations are selected based on similarity, and the data of the sample stations are used as historical data to achieve cold start; (5) The adjustment coefficients for subjective and objective weights take into account the similarity of the sample stations to avoid the accuracy of the weights being affected by excessively low similarity. (6) The risk level will be adjusted according to the predicted values ​​of the risk factors, taking into account the early warning needs of emergency scenarios. Attached Figure Description

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the steps of the method of the present invention. Detailed Implementation

[0016] Example 1 like Figure 1 As shown, the method for risk assessment and emergency response of large-scale novel electrochemical energy storage includes the following steps: S1. Collect multi-source data and perform preprocessing; Multi-source data is divided into: lithium battery data, sodium battery data, vanadium redox flow battery data, and public data; Lithium battery data includes: cell voltage, module voltage, charge and discharge current, cell temperature at a single point, module average temperature, temperature difference amplitude, voltage deviation, SOC, SOH, DC internal resistance, polarization voltage, lithium plating characteristic voltage, gas production rate, and combustible gas concentration, etc. Sodium battery data includes: cell voltage, module voltage, charge and discharge current, cell temperature, temperature difference, voltage imbalance, SOC, SOH, internal resistance increment, side reaction gas generation rate, electrode expansion, combustible gas concentration, electrolyte evaporation concentration, and related characteristic parameters of hard carbon or soft carbon negative electrode, etc. Electrolyte potential at positive and negative electrodes, electrolyte flow rate, pump operating current, pipeline pressure, vanadium ion concentration, electrolyte level, electrolyte temperature, stack voltage consistency, electrolyte leakage, ion cross-linking rate, electrolyte acidity, leakage point monitoring data, inert protective gas pressure, waste liquid collection status, etc. Public data includes: DC bus voltage, AC side voltage, insulation resistance, leakage current, harmonic distortion rate, charging and discharging power, conversion efficiency, contactor status, safety management system operating status, ambient temperature and humidity of the energy storage compartment or station, smoke concentration, oxygen concentration, internal pressure, cooling system start / stop status, heat dissipation efficiency, fire protection system readiness status, daily operation and maintenance records, periodic inspection records, historical fault alarm records, power grid voltage fluctuations, frequency deviation, environmental meteorological data, lightning strike monitoring, grounding resistance value, etc. Preprocessing operations include: data cleaning, noise reduction and normalization, which remove outliers and erroneous values ​​from the data, fill in missing values, reduce noise in the data, map the data to the same interval, and eliminate the influence of units. Add a timestamp to the preprocessed data.

[0017] S2. Identify risk factors and establish coupling relationships; Risk factors are categorized into: physical risk factors, chemical risk factors, electrical risk factors, and human management risk factors. Physical risk factors include: temperature exceedance rate, temperature difference amplitude, pressure change rate, and heat dissipation efficiency; Chemical risk factors include: flammable gas concentration, electrolyte leakage, oxygen concentration, cell gas generation rate, and toxic gas concentration; Electrical risk factors include: voltage deviation rate, current overload factor, insulation resistance value, short circuit probability SOC imbalance, and internal resistance deviation rate. Human-managed risk factors include: maintenance frequency compliance rate, repair qualification rate, personnel operation standardization, emergency plan completeness, and hazard rectification timeliness rate; Coupling relationships are represented by risk coupling correlation matrices; types of risk factors There are 20, and the risk coupling correlation matrix is: A square matrix; by arranging the risk factors in order, the first... Each risk factor is denoted as The expression for the risk coupling correlation matrix is: (1); in, The risk coupling correlation matrix is ​​the first Line 1 The elements of the column represent and The strength of the association; The assignment rules for the elements of the risk coupling correlation matrix are as follows: For diagonal elements, i.e. Because it represents Based on the strength of its association with itself, the diagonal element is directly assigned the value 1; For off-diagonal elements, i.e. The association strength is calculated using the absolute value of the Pearson correlation coefficient. The expression for association strength is as follows: (2); in, For the first The first risk factor indicator One time series data, For the first The mean of each risk factor indicator, This represents the total number of time-series data. When there is a lack of data, set the element value to 0.

[0018] S3. Conduct a quantitative assessment of coupling risks; Coupling risk is quantified using a comprehensive risk value; the comprehensive risk value is obtained by weighted summation of risk factors, and its expression is as follows: (3); in, For the comprehensive risk value, The total number of risk factors. For the first The index values ​​of each risk factor, For the first The weights of each risk factor For coupling risk value, This is the coupling risk adjustment coefficient; In a preferred approach, the weights of risk factors are calculated based on subjective and objective weights. Subjective weights were obtained through joint evaluation by multiple experts; objective weights were calculated based on the dispersion of historical data, and their expression is as follows: (4); in, For the first The objective weights of each risk factor For the first The difference coefficient of each risk factor is determined by the entropy value of that risk factor's index, and the expression is as follows: (5); in, For the first The entropy value of each risk factor is expressed as follows: (6); Now that we have obtained the subjective and objective weights, the expression for the risk factor weights is as follows: (7); in, For the first Subjective weights of each risk factor, , These are the percentage coefficients for subjective weight and objective weight, respectively; Since objective weighting requires a large amount of historical data, newly built facilities face a cold start problem with no historical data. In a preferred solution of this implementation, a cold start strategy based on data migration from similar facilities plus factory standard calibration is provided to solve the problem of no historical data. The cold start strategy is as follows: Select operational energy storage sites with the same chemical system, capacity level, and structural layout as sample sites, migrate the samples from the sample sites, and calculate the similarity expression between the two as follows: (8); in, To determine the similarity between two energy storage sites, , The first and second sites, respectively, are newly built sites and sample sites. Individual station characteristic index values, The total number of station features. It is a maximum value function, that is, it extracts the maximum value from the independent variables; Set a migration threshold. When the similarity is greater than the migration threshold, the sample data of the sample station will be migrated to the newly built station as historical data and used as historical data for objective weight calculation. Sample similarity affects the accuracy of the comprehensive weight. Therefore, in a preferred embodiment, the proportion coefficients of subjective weight and objective weight are adjusted according to sample similarity. Specifically, the proportion coefficients are set as follows: when the energy storage station has its own historical data, the proportion coefficient is taken as the base value; the sum of the two proportion coefficients is 1, and in general, they are all taken as 0.5. When using sample data from sample sites as historical data, the expression for the proportion coefficient is as follows: (9); in, For similarity, The base value of the subjective weighting ratio coefficient for Its value ranges from 0 to 1, and is obtained by normalizing the value of the risk factor. Taking combustible gas concentration as an example, its value is simply the concentration. Upper and lower limits for combustible gas concentration are set. When the combustible gas concentration is greater than or equal to the combustible gas concentration threshold, its value is 1; when the combustible gas concentration is greater than or less than the combustible gas concentration threshold, its expression is as follows: (10); in, This is an index value for the concentration of combustible gases. For the value of combustible gas concentration, , These are the upper and lower thresholds for combustible gas concentration, with the lower limit being 0. The coupling risk adjustment coefficient reflects the impact of the coupling strength between two risk factors on risk, and its expression is as follows: (11); Risks are classified according to their comprehensive risk values, and the classification process is as follows: When the comprehensive risk value is When the risk level is 1, it indicates safety; when the comprehensive risk value is... When the risk level is 2, it indicates low risk; when the comprehensive risk value is... When the risk level is 3, it indicates medium risk; when the comprehensive risk value is... When the risk level is 4, it indicates high risk; when the comprehensive risk value is greater than or equal to 0.8, the risk level is 5, indicating extremely high risk.

[0019] S4. Conduct risk warnings and predict their changing trends. The detailed process is as follows: S401. A graded early warning strategy is adopted according to the risk level, specifically: For Level 2 risk, there will be audible and visual alerts within the station and a push notification from the operation and maintenance platform; for Level 3 risk, there will be audible and visual alarms throughout the station and SMS notifications to operation and maintenance personnel; for Level 4 risk, there will be emergency alarms throughout the station, SMS notifications to operation and maintenance personnel, and notifications to management personnel via telephone and SMS; for Level 5 risk, there will be audible and visual alarms throughout the station plus fire alarm linkage, and notifications to all personnel in the power station via telephone and SMS. S402. Conduct real-time risk tracking; set the tracking frequency, update the comprehensive risk value and risk level in real time according to the set tracking frequency, and issue real-time warnings based on the risk level; In a preferred embodiment, the risk level used for real-time early warning is further adjusted based on the predicted values ​​of risk factor indicators. These predicted values ​​can be calculated from the indicator values ​​at a given time. Specifically, the prediction interval for the predicted values ​​of the risk factor indicators is set to... That is, according to the first Data at time 1 can predict the next time. The data at time t is expressed as follows: (12); in, For the first The first moment The index values ​​of each risk factor, For the first The first moment The predicted values ​​of the indicators for each risk factor, For development coefficient, The gray action amount and these two values ​​are obtained by fitting historical data; the above prediction interval can be obtained based on the tracking frequency. Based on the predicted values ​​of the risk factors, the first... The comprehensive risk value is predicted at a given time. Based on this prediction value, a mutation determination is made. The mutation determination process is as follows: The rate of change of risk is calculated based on the predicted comprehensive risk value, as shown in the following expression: (13); in, For the first Risk change rate at any given time For the first The overall risk value at any given moment. For the first The predicted comprehensive risk value at any given time; Set a mutation threshold. When the risk change rate is greater than the mutation threshold, it is determined to be in a risk mutation state. At this time, the risk level is upgraded by one level, and an early warning is issued based on the upgraded risk level.

[0020] S5. Take emergency measures according to the risk level; the specific emergency measures are as follows: For Level 1 risks, maintenance personnel perform routine maintenance, including: routine inspections, data monitoring, and equipment maintenance; For Level 2 risks, maintenance personnel are required to rectify the hazards within 24 hours. For Level 3 risks, maintenance personnel and technical leaders will take specific measures, including: reducing power, isolating the faulty module, and handling the faulty module. For Level 4 risks, maintenance and emergency management personnel should disconnect the power supply to the faulty cluster and activate inert gas protection. For Level 5 risks, the emergency command center and fire department will cut off the main power supply to the site, activate the dedicated fire protection system, and organize the evacuation of personnel.

[0021] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A novel large-scale electrochemical energy storage risk assessment and emergency response method, characterized in that, Includes the following steps: S1. Collect multi-source data and perform preprocessing; S2. Identify risk factors and establish coupling relationships; S3. Conduct a quantitative assessment of coupling risks; S4. Conduct risk warnings and predict their changing trends; S5. Take emergency measures according to the risk level.

2. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 1, characterized in that, Multi-source data is divided into: lithium battery data, sodium battery data, vanadium redox flow battery data, and public data; Preprocessing operations include: data cleaning, noise reduction, and normalization.

3. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 1, characterized in that, Risk factors are categorized into: physical risk factors, chemical risk factors, electrical risk factors, and human management risk factors. The coupling relationship is represented by a risk coupling correlation matrix; risk factors are shared. The class, risk coupling correlation matrix is A square matrix; by arranging the risk factors in order, the first... Each risk factor is denoted as The expression for the risk coupling correlation matrix is: （1）； in, The risk coupling correlation matrix is ​​the first Line 1 The elements of the column represent and The strength of the association.

4. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 3, characterized in that, The assignment rules for the elements of the risk coupling correlation matrix are as follows: For diagonal elements, simply assign the value 1 to the diagonal element; For off-diagonal elements, the association strength is calculated using the following expression: （2）； in, For the first The first risk factor indicator One time series data, For the first The mean of each risk factor indicator, This represents the total number of time-series data. When there is a lack of data, set the element value to 0.

5. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 4, characterized in that, Coupling risk is quantified using a comprehensive risk value; the comprehensive risk value is obtained by weighted summation of risk factors, and its expression is as follows: （3）； in, For the comprehensive risk value, The total number of risk factors. For the first The index values ​​of each risk factor, For the first The weights of each risk factor For coupling risk value, This is the coupling risk adjustment coefficient; Risks are classified according to their comprehensive risk values, and the classification process is as follows: When the comprehensive risk value is When the risk level is 1, it indicates safety; when the comprehensive risk value is... When the risk level is 2, it indicates low risk; when the comprehensive risk value is... When the risk level is 3, it indicates medium risk; when the comprehensive risk value is... When the risk level is 4, it indicates high risk; when the comprehensive risk value is greater than or equal to 0.8, the risk level is 5, indicating extremely high risk.

6. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 5, characterized in that, Risk factor weights are calculated based on subjective and objective weights. Subjective weights were obtained through joint evaluation by multiple experts; objective weights were calculated based on the dispersion of historical data, and their expression is as follows: （4）； in, For the first The objective weights of each risk factor For the first The difference coefficients of the risk factors are expressed as follows: （5）； in, For the first The entropy value of each risk factor is expressed as follows: （6）； The expression for the risk factor weights is as follows: （7）； in, For the first Subjective weights of each risk factor, , These are the percentage coefficients for subjective weight and objective weight, respectively.

7. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 5, characterized in that, The expression for the coupling risk adjustment coefficient is as follows: （8）。 8. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 6, characterized in that, In the process of calculating objective weights, when historical data is lacking, a cold start strategy is adopted as follows: Select operational energy storage sites with the same chemical system, capacity level, and structural layout as sample sites, migrate the samples of the sample sites, and calculate the similarity between the two. Set a migration threshold. When the similarity is greater than the migration threshold, the sample data from the sample station will be migrated to the newly established station as historical data and used as historical data for objective weight calculation.

9. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 8, characterized in that, The percentage coefficient is set as follows: when the energy storage station itself has historical data, the percentage coefficient is taken as the base value; When using sample data from sample sites as historical data, the expression for the proportion coefficient is as follows: （9）； in, For similarity, This is the base value for the subjective weighting ratio coefficient.

10. The method for risk assessment and emergency response of large-scale novel electrochemical energy storage according to claim 5, characterized in that, The detailed process of step S4 is as follows: S401. Implement a tiered early warning strategy based on risk levels; S402. Conduct real-time risk tracking; Set the tracking frequency, update the comprehensive risk value and risk level in real time according to the set tracking frequency, and issue real-time warnings based on the risk level; The risk level used for real-time early warning is also adjusted based on the predicted values ​​of risk factor indicators, which can be calculated from the indicator values ​​at a certain moment. Based on the predicted values ​​of the risk factors, the predicted comprehensive risk value for future time periods is calculated. A mutation determination is then made based on this predicted value. The mutation determination process is as follows: The risk change rate is calculated based on the comprehensive risk value prediction. A mutation threshold is set. When the risk change rate is greater than the mutation threshold, it is determined to be in a risk mutation state. At this time, the risk level is upgraded by one level, and an early warning is issued based on the upgraded risk level.

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