Electric energy storage time scale translation capability index prediction method

Abstract

The invention belongs to the field of energy storage power grids, relates to a data analysis technology, is used for solving the problem that time scale optimization analysis cannot be carried out by combining multi-source data in the prior art, and particularly relates to an electric energy storage time scale translation capability index prediction method. Comprising the following steps: performing time scale test and discharge environment test on the electric energy storage system: marking the electric energy storage system as a test object, generating a plurality of time scale test values and discharge environment temperature values, and randomly combining the time scale test values and the discharge environment temperature values to obtain a plurality of test data groups; analyzing the test data of the electric energy storage system according to the test data group and generating an analysis process; according to the method, the discharge stability of the electric energy storage system in the analysis process of the test data set can be analyzed, the discharge stability of the test object is evaluated through the stability coefficient, invalid data in the time scale optimization analysis process is eliminated, and the accuracy of the optimization analysis result is improved.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage power grids and relates to data analysis technology, in particular to a method for predicting an electric energy storage time scale translation capability index. Background Art

[0002] The time scale shift capability of electric energy storage refers to the ability of the energy storage system to store and release energy on different time scales. Specifically, the electric energy storage system can store and release electric energy on different time scales to meet the different needs of the power system.

[0003] The invention patent with announcement number CN116822908B discloses a multi-time scale energy storage planning method and device that can be solved quickly. The planning method uses typical short-term energy storage operation scenarios extracted based on a clustering algorithm as input, solves the objective function according to the constraints, and obtains planning schemes for short-term energy storage components and long-term energy storage devices; however, the planning method cannot combine multi-source data such as the time scale characteristics, discharge environment, and discharge stability performance of the electric energy storage system to perform time scale optimization analysis, resulting in the inability of the existing technology to adapt to the application of different electric energy storage systems under different discharge environments.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting the time scale translation capability index of electric energy storage, which is used to solve the problem that the prior art cannot combine multi-source data for time scale optimization analysis; The technical problem to be solved by the present invention is: how to provide a method for predicting the time scale translation capability index of electric energy storage that can combine multi-source data for time scale optimization analysis.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A method for predicting the time scale translation capability index of electric energy storage comprises the following steps: Step 1: Perform time scale test and discharge environment test on the electric energy storage system: mark the electric energy storage system as a test object, generate a number of time scale test values ​​and discharge environment temperature values, and obtain a number of test data groups by any combination of the time scale test values ​​and the discharge environment temperature values; Step 2: Analyze the test data of the electric energy storage system according to the test data group and generate an analysis process; Step 3: Analyze the discharge stability of the electric energy storage system during the analysis of the test data group: obtain the voltage stability data YW, current stability data LW and conversion data ZH of the test object during the analysis process and perform numerical calculations to obtain the stability coefficient WD of the test object during the analysis process; Step 4: Evaluate the discharge stability of the electric energy storage system during the analysis process of the test data set and mark the analysis process as a valid process or an invalid process through the stability factor WD; Step 5: Perform discharge optimization analysis on the electric energy storage system: The maximum and minimum values ​​of the discharge environment temperature values ​​in all test data groups constitute a test temperature range, divide the test temperature range into several test temperature intervals, and mark the time scale optimization value of the test temperature interval; when the electric energy storage system performs discharge control, obtain the temperature value of the discharge environment in which the electric energy storage system is located, call the time scale optimization value of the test temperature interval corresponding to the temperature value, and set the discharge duration of the electric energy storage system to the time scale optimization value; Step 6: Predict and analyze the time scale shifting capability of the electric energy storage system.

[0007] Furthermore, in step two, the generation process of the analysis process includes: setting the test environment temperature of the test object to the discharge environment temperature value in the test data group, setting the discharge time of the test object to the time scale test value in the test data group, and after the setting is completed, performing a discharge test on the test object and marking the discharge test process as the analysis process of the test data group.

[0008] Further, in step three, the process of acquiring the voltage stability data YW includes: calculating the average discharge voltage of the test object during the analysis process and marking it as the voltage average value, marking the absolute value of the difference between the discharge voltage value of the test object and the voltage average value as the voltage difference value, marking the maximum value of the voltage difference value during the analysis process as the voltage stability value, and marking the ratio of the voltage stability value to the voltage average value as the voltage stability data YW; the process of acquiring the current stability data LW includes: calculating the average discharge current of the test object during the analysis process and marking it as the current average value, marking the absolute value of the difference between the discharge current value of the test object and the current average value as the current difference value, marking the maximum value of the current difference value during the analysis process as the current stability value, and marking the ratio of the current stability value to the current average as the current stability data LW; the conversion data ZH is the ratio of the discharge amount at the end of the analysis process to the energy storage amount at the beginning of the analysis process.

[0009] Furthermore, in step four, the specific process of marking the analysis process as a valid process or an invalid process includes: comparing the stability coefficient WD of the test object during the analysis process with the preset stability threshold WDmax: if the stability coefficient WD is less than the stability threshold WDmax, it is determined that the discharge stability of the test object during the analysis process meets the requirements, and the corresponding analysis process is marked as a valid process; if the stability coefficient WD is greater than or equal to the stability threshold WDmax, it is determined that the discharge stability of the test object during the analysis process does not meet the requirements, and the corresponding analysis process is marked as an invalid process.

[0010] Furthermore, in step five, the specific process of marking the time scale optimization value of the test temperature interval includes: marking the effective process in the test data group whose discharge environment temperature value is within the test temperature interval as the matching process of the test temperature interval, marking the matching process with the smallest stability coefficient WD value in the test temperature interval as the optimization process, and marking the time scale test value in the test data group corresponding to the optimization process as the time scale optimization value of the test temperature interval.

[0011] Furthermore, in step six, the specific process of predicting and analyzing the time-scale translation capability of the electric energy storage system includes: obtaining the translation coefficient PY of the test object, and comparing the translation coefficient PY of the test object with a preset translation threshold PYmax: if the translation coefficient PY is less than the translation threshold PYmax, it is determined that the time-scale translation capability of the test object meets the requirements; if the translation coefficient PY is greater than or equal to the translation threshold PYmax, it is determined that the time-scale translation capability of the test object does not meet the requirements, generating an abnormal translation capability index signal, and sending the abnormal translation capability index signal to the mobile phone terminal of the manager.

[0012] Furthermore, the process of obtaining the translation coefficient PY of the test object includes: performing variance calculation on the stability coefficient WD of the optimization process in all test temperature intervals to obtain a stable distribution value WF, marking the ratio of the number of invalid processes to the number of analysis processes as invalid data WX, and obtaining the translation coefficient PY of the test object by performing numerical calculation on the stable distribution value WF and the invalid data WX.

[0013] Furthermore, it is applied to an electric energy storage time scale translation capability index prediction system, including a test processing module, a test analysis module, a stability analysis module, a stability assessment module, a discharge optimization module and a translation analysis module, and the test processing module, the test analysis module, the stability analysis module, the stability assessment module, the discharge optimization module and the translation analysis module are communicatively connected in sequence.

[0014] The test processing module is used to perform time scale test and discharge environment test on the electric energy storage system and generate a test data group; The test analysis module is used to analyze the test data of the electric energy storage system according to the test data group and generate an analysis process; The stability analysis module is used to analyze the discharge stability of the electric energy storage system during the analysis of the test data group and obtain the stability coefficient WD of the analysis process; The stability evaluation module is used to evaluate the discharge stability of the electric energy storage system during the analysis process of the test data group and mark the analysis process as a valid process or an invalid process; The discharge optimization module is used to perform discharge optimization analysis on the electric energy storage system; The translation analysis module is used to predict and analyze the time scale translation capability of the electric energy storage system.

[0015] The present invention has the following beneficial effects: Conduct time scale tests and discharge environment tests on the electric energy storage system to generate several test data groups, conduct discharge tests on the test objects based on the test data groups, and provide data support for the stability analysis and stability assessment process based on the process data of the discharge test; Analyze the discharge stability of the electric energy storage system during the analysis of the test data group, and obtain the stability coefficient by statistically analyzing the multiple discharge stability parameters of the test object during the analysis. Evaluate the discharge stability of the test object through the stability coefficient, and then differentiate and mark the analysis process, eliminate invalid data in the time scale optimization analysis process, and improve the accuracy of the optimization analysis results; Conduct discharge optimization analysis on the electric energy storage system, generate time scale optimization values ​​according to the stability coefficient of the test object in all effective processes and the corresponding test data group, and match the time scale optimization value according to the test temperature range, so as to match the most appropriate discharge time scale for each discharge temperature range, and improve the overall operation stability of the system; 4. Predict and analyze the time scale translation capability of the electric energy storage system, and conduct a comprehensive analysis of the stability coefficient of the optimization process in the test temperature range and the number of marks of the invalid process to obtain the translation coefficient. The translation coefficient is used to provide feedback on the time scale translation capability of the electric energy storage system in different discharge environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is a flow chart of a method according to Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1: Figure 1 As shown, a method for predicting the time scale translation capability index of electric energy storage comprises the following steps: Step 1: Perform time scale test and discharge environment test on the electric energy storage system: mark the electric energy storage system as a test object, generate a number of time scale test values ​​and discharge environment temperature values, and obtain a number of test data groups by any combination of the time scale test values ​​and the discharge environment temperature values; Step 2: Analyze the test data of the electric energy storage system according to the test data group: set the test environment temperature of the test object to the discharge environment temperature value in the test data group, set the discharge time of the test object to the time scale test value in the test data group, perform a discharge test on the test object after the setting is completed, and mark the discharge test process as the analysis process of the test data group; perform a time scale test and a discharge environment test on the electric energy storage system to generate several test data groups, perform a discharge test on the test object according to the test data group, and provide data support for the stability analysis and stability evaluation process according to the process data of the discharge test; Step 3: Analyze the discharge stability of the electric energy storage system during the analysis of the test data group: obtain the voltage stability data YW, current stability data LW and conversion data ZH of the test object during the analysis process. The process of obtaining the voltage stability data YW includes: calculating the average discharge voltage of the test object during the analysis process and marking it as the voltage average value, marking the absolute value of the difference between the discharge voltage value and the voltage average value of the test object as the voltage difference value, marking the maximum value of the voltage difference value during the analysis process as the voltage stability value, and marking the ratio of the voltage stability value to the voltage average value as the voltage stability data YW; the process of obtaining the current stability data LW includes: calculating Calculate the average discharge current of the test object during the analysis process and mark it as the current average value, mark the absolute value of the difference between the discharge current value and the current average value of the test object as the current difference value, mark the maximum value of the current difference value during the analysis process as the current steady value, and mark the ratio of the current steady value to the current average value as the current stability data LW; convert the data ZH into the ratio of the discharge amount at the end of the analysis process to the energy storage at the beginning of the analysis process; obtain the stability coefficient WD of the test object during the analysis process through the formula WD=k1×YW+k2×LW-k3×ZH, where k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1; Step 4: Evaluate the discharge stability of the electric energy storage system during the analysis of the test data group: compare the stability coefficient WD of the test object during the analysis with the preset stability threshold WDmax: if the stability coefficient WD is less than the stability threshold WDmax, it is determined that the discharge stability of the test object during the analysis meets the requirements, and the corresponding analysis process is marked as a valid process; if the stability coefficient WD is greater than or equal to the stability threshold WDmax, it is determined that the discharge stability of the test object during the analysis does not meet the requirements, and the corresponding analysis process is marked as an invalid process; analyze the discharge stability of the electric energy storage system during the analysis of the test data group, and statistically analyze multiple discharge stability parameters of the test object during the analysis to obtain the stability coefficient. The discharge stability of the test object is evaluated by the stability coefficient, and then the analysis process is differentially marked, and invalid data of the time scale optimization analysis process is eliminated to improve the accuracy of the optimization analysis results; Step 5: Perform discharge optimization analysis on the electric energy storage system: The maximum and minimum values ​​of the discharge environment temperature values ​​in all test data groups constitute the test temperature range, divide the test temperature range into several test temperature intervals, mark the effective process in the test data group where the discharge environment temperature value is within the test temperature interval as the matching process of the test temperature interval, mark the matching process with the smallest stability coefficient WD value in the test temperature interval as the optimization process, and mark the time scale test value in the test data group corresponding to the optimization process as the time scale optimization value of the test temperature interval; when the electric energy storage system performs discharge control, obtain the temperature value of the discharge environment in which the electric energy storage system is located, call the time scale optimization value of the test temperature interval corresponding to the temperature value, and set the discharge duration of the electric energy storage system to the time scale optimization value; generate the time scale optimization value according to the stability coefficient of the test object in all effective processes and the corresponding test data group, and match and set the time scale optimization value according to the test temperature interval, so as to match the most suitable discharge time scale for each discharge temperature interval, thereby improving the overall operation stability of the system; Step 6: Predict and analyze the time scale translation capability of the electric energy storage system: perform variance calculation on the stability coefficient WD of the optimization process of all test temperature intervals to obtain the stable distribution value WF, mark the ratio of the number of invalid processes to the number of analysis processes as invalid data WX, and obtain the translation coefficient PY of the test object through the formula PY=c1×WX-c2×WF, where c1 and c2 are both proportional coefficients, and c1>c2>1; compare the translation coefficient PY of the test object with the preset translation threshold PYmax: if the translation coefficient PY is less than the translation threshold PYmax, it is determined that the time scale translation capability of the test object meets the requirements; if the translation coefficient PY is greater than or equal to the translation threshold PYmax, it is determined that the time scale translation capability of the test object does not meet the requirements, generate a translation capability index abnormal signal and send the translation capability index abnormal signal to the mobile terminal of the manager; combine the stability coefficient of the optimization process of the test temperature interval and the number of invalid process marks to obtain the translation coefficient, and use the translation coefficient to provide feedback on the time scale translation capability of the electric energy storage system in different discharge environments.

[0020] Embodiment 2: Figure 2 As shown, a system for predicting the time scale translation capability index of electric energy storage includes a test processing module, a test analysis module, a stability analysis module, a stability assessment module, a discharge optimization module and a translation analysis module. The test processing module, the test analysis module, the stability analysis module, the stability assessment module, the discharge optimization module and the translation analysis module are communicatively connected in sequence.

[0021] The test processing module is used to perform time scale test and discharge environment test on the electric energy storage system and generate a test data group; The test analysis module is used to analyze the test data of the electric energy storage system according to the test data group and generate an analysis process; The stability analysis module is used to analyze the discharge stability of the electric energy storage system during the analysis of the test data group and obtain the stability coefficient WD of the analysis process; The stability evaluation module is used to evaluate the discharge stability of the electric energy storage system during the analysis process of the test data group and mark the analysis process as a valid process or an invalid process; The discharge optimization module is used to perform discharge optimization analysis on the electric energy storage system; The translation analysis module is used to predict and analyze the time scale translation capability of the electric energy storage system.

[0022] A method for predicting the time scale translation capability index of an electric energy storage system comprises the following steps: performing a time scale test and a discharge environment test on an electric energy storage system and generating a test data group; analyzing the test data of the electric energy storage system according to the test data group and generating an analysis process; analyzing the discharge stability of the electric energy storage system during the analysis process of the test data group and obtaining a stability coefficient WD of the analysis process; evaluating the discharge stability of the electric energy storage system during the analysis process of the test data group and marking the analysis process as a valid process or an invalid process; performing a discharge optimization analysis on the electric energy storage system; and predicting and analyzing the time scale translation capability of the electric energy storage system.

[0023] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0024] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula WD=k1×YW+k2×LW-k3×ZH; technicians in this field collect multiple groups of sample data and set corresponding stability coefficients for each group of sample data; substitute the set stability coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values ​​of k1, k2 and k3 as 3.82, 2.74 and 2.51 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding stability coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the stability coefficient is proportional to the value of the voltage stability data YW.

[0025] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0026] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for predicting the time scale translation capability index of electric energy storage, characterized in that: The following steps are involved: Step 1: Perform time scale test and discharge environment test on the electric energy storage system: mark the electric energy storage system as a test object, generate a number of time scale test values ​​and discharge environment temperature values, and obtain a number of test data groups by any combination of the time scale test values ​​and the discharge environment temperature values; Step 2: Analyze the test data of the electric energy storage system according to the test data group and generate an analysis process; Step 3: Analyze the discharge stability of the electric energy storage system during the analysis of the test data group: obtain the voltage stability data YW, current stability data LW and conversion data ZH of the test object during the analysis process and perform numerical calculations to obtain the stability coefficient WD of the test object during the analysis process; Step 4: Evaluate the discharge stability of the electric energy storage system during the analysis process of the test data set and mark the analysis process as a valid process or an invalid process through the stability factor WD; Step 5: Perform discharge optimization analysis on the electric energy storage system: The maximum and minimum values ​​of the discharge environment temperature values ​​in all test data groups constitute a test temperature range, divide the test temperature range into several test temperature intervals, and mark the time scale optimization value of the test temperature interval; when the electric energy storage system performs discharge control, obtain the temperature value of the discharge environment in which the electric energy storage system is located, call the time scale optimization value of the test temperature interval corresponding to the temperature value, and set the discharge duration of the electric energy storage system to the time scale optimization value; Step 6: Predict and analyze the time scale shifting capability of the electric energy storage system.

2. The method for predicting the time scale translation capability index of electric energy storage according to claim 1, characterized in that: In step two, the generation process of the analysis process includes: setting the test environment temperature of the test object to the discharge environment temperature value in the test data group, setting the discharge time of the test object to the time scale test value in the test data group, and after the setting is completed, performing a discharge test on the test object and marking the discharge test process as the analysis process of the test data group.

3. The method for predicting the time scale translation capability index of electric energy storage according to claim 2, characterized in that: In step three, the process of obtaining the voltage stability data YW includes: calculating the average discharge voltage of the test object during the analysis process and marking it as the voltage average value, marking the absolute value of the difference between the discharge voltage value of the test object and the voltage average value as the voltage difference value, marking the maximum value of the voltage difference value during the analysis process as the voltage stability value, and marking the ratio of the voltage stability value to the voltage average value as the voltage stability data YW.

4. The method for predicting the time scale translation capability index of electric energy storage according to claim 3, characterized in that: In step three, the process of obtaining the current stability data LW includes: calculating the average discharge current of the test object during the analysis process and marking it as the current mean value, marking the absolute value of the difference between the discharge current value and the current mean value of the test object as the current difference value, marking the maximum value of the current difference value during the analysis process as the current stability value, and marking the ratio of the current stability value to the current mean value as the current stability data LW.

5. A method for predicting the time scale translation capability index of electric energy storage according to claim 4, characterized in that: In step three, the process of acquiring the conversion data ZH includes: the conversion data ZH is the ratio of the discharge amount at the end of the analysis process to the stored energy at the beginning of the analysis process.

6. A method for predicting the time scale translation capability index of electric energy storage according to claim 5, characterized in that: In step four, the specific process of marking the analysis process as a valid process or an invalid process includes: comparing the stability coefficient WD of the test object during the analysis process with the preset stability threshold WDmax: if the stability coefficient WD is less than the stability threshold WDmax, it is determined that the discharge stability of the test object during the analysis process meets the requirements, and the corresponding analysis process is marked as a valid process; if the stability coefficient WD is greater than or equal to the stability threshold WDmax, it is determined that the discharge stability of the test object during the analysis process does not meet the requirements, and the corresponding analysis process is marked as an invalid process.

7. A method for predicting the time scale translation capability index of electric energy storage according to claim 6, characterized in that: In step five, the specific process of marking the time scale optimization value of the test temperature interval includes: marking the effective process in the test data group whose discharge environment temperature value is within the test temperature interval as the matching process of the test temperature interval, marking the matching process with the smallest stability coefficient WD value in the test temperature interval as the optimization process, and marking the time scale test value in the test data group corresponding to the optimization process as the time scale optimization value of the test temperature interval.

8. The method for predicting the time scale translation capability index of electric energy storage according to claim 7, characterized in that: In step six, the specific process of predicting and analyzing the time-scale translation capability of the electric energy storage system includes: obtaining the translation coefficient PY of the test object, and comparing the translation coefficient PY of the test object with the preset translation threshold PYmax: if the translation coefficient PY is less than the translation threshold PYmax, it is determined that the time-scale translation capability of the test object meets the requirements; if the translation coefficient PY is greater than or equal to the translation threshold PYmax, it is determined that the time-scale translation capability of the test object does not meet the requirements, and a translation capability index abnormality signal is generated and sent to the mobile phone terminal of the manager.

9. A method for predicting the time scale translation capability index of electric energy storage according to claim 8, characterized in that: The process of obtaining the translation coefficient PY of the test object includes: performing variance calculation on the stability coefficient WD of the optimization process in all test temperature ranges to obtain the stable distribution value WF, marking the ratio of the number of invalid processes to the number of analysis processes as invalid data WX, and obtaining the translation coefficient PY of the test object by numerically calculating the stable distribution value WF and the invalid data WX.

10. A method for predicting the time scale translation capability index of electric energy storage according to any one of claims 1 to 9, characterized in that: The invention is applied to a prediction system of time scale translation capability index of electric energy storage, comprising a test processing module, a test analysis module, a stability analysis module, a stability evaluation module, a discharge optimization module and a translation analysis module, wherein the test processing module, the test analysis module, the stability analysis module, the stability evaluation module, the discharge optimization module and the translation analysis module are sequentially connected in communication; the test processing module is used to perform time scale test and discharge environment test on the electric energy storage system and generate a test data group; The test analysis module is used to analyze the test data of the electric energy storage system according to the test data group and generate an analysis process; The stability analysis module is used to analyze the discharge stability of the electric energy storage system during the analysis of the test data group and obtain the stability coefficient WD of the analysis process; The stability evaluation module is used to evaluate the discharge stability of the electric energy storage system during the analysis process of the test data group and mark the analysis process as a valid process or an invalid process; The discharge optimization module is used to perform discharge optimization analysis on the electric energy storage system; The translation analysis module is used to predict and analyze the time scale translation capability of the electric energy storage system.

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