A Prediction Method for the Index of the Time Scale Translation Ability of Electrical Energy Storage

Abstract

The present invention belongs to the field of energy storage power grids and relates to data analysis technology, and is used to solve the problem that the prior art cannot perform time-scale optimization analysis by combining multi-source data. Specifically, it is a method for predicting the time-scale translation ability index of electrical energy storage, which includes the following steps: performing time-scale tests and discharge environment tests on the electrical energy storage system: marking the electrical energy storage system as the test object, generating a number of time-scale test values and discharge environment temperature values, and obtaining a number of groups of test data groups by arbitrarily combining the time-scale test values and the discharge environment temperature values; analyzing the test data of the electrical energy storage system according to the test data groups and generating an analysis process; the present invention can analyze the discharge stability of the electrical energy storage system during the analysis process of the test data groups, evaluate the discharge stability of the test object through a stability coefficient, eliminate invalid data in the time-scale optimization analysis process, and improve the accuracy of the optimization analysis results.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage power grids and relates to data analysis technology, specifically an index prediction method for the time-scale translation ability of electrical energy storage. Background Art

[0002] The time-scale translation ability of electrical energy storage refers to the ability of an energy storage system to store and release energy on different time scales. Specifically, an electrical energy storage system can store and release electrical energy on different time scales to meet the different demands of a power system.

[0003] The invention patent with the publication number CN116822908B discloses a multi-time-scale energy storage planning method and device that can be quickly solved. The planning method takes the typical short-time energy storage operation scenarios extracted based on a clustering algorithm as input, and solves the objective function according to the constraint conditions to obtain the planning schemes for short-time energy storage components and long-time energy storage devices. However, this planning method cannot perform time-scale optimization analysis by combining multi-source data such as the time-scale characteristics, discharge environment, and discharge stability performance of the electrical energy storage system, resulting in the inability of the prior art to adapt to the applications of different electrical energy storage systems in different discharge environments.

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

[0005] The purpose of the present invention is to provide an index prediction method for the time-scale translation ability of electrical energy storage, which is used to solve the problem that the prior art cannot perform time-scale optimization analysis by combining multi-source data.

[0006] The technical problem to be solved by the present invention is: how to provide an index prediction method for the time-scale translation ability of electrical energy storage that can perform time-scale optimization analysis by combining multi-source data.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] An index prediction method for the time-scale translation ability of electrical energy storage includes the following steps:

[0009] Step 1: Perform time-scale testing and discharge environment testing on the electrical energy storage system: Mark the electrical energy storage system as the test object, generate a number of time-scale test values and discharge environment temperature values, and obtain a number of test data groups by arbitrarily combining the time-scale test values and discharge environment temperature values.

[0010] Step 2: Analyze the test data of the electrical energy storage system according to the test data groups and generate an analysis process.

[0011] Step 3: Analyze the discharge stability during the analysis of the electrical energy storage system for the test data set: Obtain the voltage stability data YW, current stability data LW, and conversion data ZH during the analysis of the test object, and perform numerical calculations to obtain the stability coefficient WD of the test object during the analysis;

[0012] Step 4: Evaluate the discharge stability during the analysis of the electrical energy storage system for the test data set, and mark the analysis process as a valid process or an invalid process through the stability coefficient WD;

[0013] Step 5: Conduct discharge optimization analysis on the electrical energy storage system: The maximum and minimum values of the discharge environment temperature values in all test data sets form the test temperature range. Divide the test temperature range into several test temperature intervals, and mark the time scale optimization values for the test temperature intervals; When conducting discharge control on the electrical energy storage system, obtain the temperature value of the discharge environment where the electrical energy storage system is located, retrieve the time scale optimization value of the test temperature interval corresponding to the temperature value, and set the discharge duration of the electrical energy storage system to the time scale optimization value;

[0014] Step 6: Conduct predictive analysis on the time scale translation ability of the electrical energy storage system.

[0015] Further, in Step 2, the generation process of the analysis process includes: Set the test environment temperature of the test object to the discharge environment temperature value in the test data set, set the discharge time of the test object to the time scale test value in the test data set. After setting, conduct a discharge test on the test object and mark the discharge test process as the analysis process of the test data set.

[0016] Further, in Step 3, the process of obtaining the voltage stability data YW includes: Calculate the average discharge voltage of the test object during the analysis and mark it as the voltage average value. Mark 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. Mark the maximum value of the voltage difference value during the analysis as the voltage stability value. Mark 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: Calculate the average discharge current of the test object during the analysis and mark it as the current average value. Mark 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. Mark the maximum value of the current difference value during the analysis as the current stability value. Mark the ratio of the current stability value to the current average value 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 stored energy at the start of the analysis process.

[0017] Further, 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 a 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.

[0018] Further, in step five, the specific process of marking the time scale optimization value of the test temperature range includes: marking the valid processes in the test data group where the discharge environment temperature value is within the test temperature range as matching processes of the test temperature range, marking the matching process with the smallest stability coefficient WD value within the test temperature range 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 range.

[0019] Further, in step six, the specific process of predicting and analyzing the time scale translation ability of the electrical 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 ability 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 ability of the test object does not meet the requirements, generating a translation ability index abnormal signal and sending the translation ability index abnormal signal to the mobile terminal of the management personnel.

[0020] Further, the process of obtaining the translation coefficient PY of the test object includes: calculating the variance of the stability coefficient WD of the optimization processes of all test temperature ranges 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 through numerical calculation of the stable distribution value WF and the invalid data WX.

[0021] Further, it is applied to an electrical energy storage time scale translation ability index prediction system, including 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. 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 for communication.

[0022] The test processing module is used to perform time scale tests and discharge environment tests on the electrical energy storage system and generate a test data group;

[0023] The described test analysis module is used to analyze the test data of the electrical energy storage system according to the test data group and generate an analysis process;

[0024] The described stability analysis module is used to analyze the discharge stability during the analysis process of the electrical energy storage system for the test data group and obtain the stability coefficient WD of the analysis process;

[0025] The described stability evaluation module is used to evaluate the discharge stability during the analysis process of the electrical energy storage system for the test data group and mark the analysis process as a valid process or an invalid process;

[0026] The described discharge optimization module is used to perform discharge optimization analysis on the electrical energy storage system;

[0027] The described translation analysis module is used to perform predictive analysis on the time-scale translation ability of the electrical energy storage system.

[0028] The present invention has the following beneficial effects:

[0029] Perform time-scale tests and discharge environment tests on the electrical energy storage system to generate several test data groups, perform discharge tests on the test object according to the test data group, and provide data support for the stability analysis and stability evaluation processes based on the process data of the discharge test;

[0030] Analyze the discharge stability during the analysis process of the electrical energy storage system for the test data group, statistically analyze and analyze multiple discharge stability parameters of the test object during the analysis process to obtain the stability coefficient, evaluate the discharge stability of the test object through the stability coefficient, and then differentially mark the analysis process, eliminate the invalid data in the time-scale optimization analysis process, and improve the accuracy of the optimization analysis result;

[0031] Perform discharge optimization analysis on the electrical energy storage system, generate a time-scale optimization value according to the stability coefficient of the test object in all valid processes and the corresponding test data group, and the time-scale optimization value is matched and set according to the test temperature range, so as to match the most suitable discharge time scale for each discharge temperature range and improve the overall operation stability of the system;

[0032] 4. Perform predictive analysis on the time-scale translation ability of the electrical energy storage system, comprehensively analyze the stability coefficient of the optimization process and the number of marked invalid processes in the test temperature range to obtain the translation coefficient, and feedback the time-scale translation ability of the electrical energy storage system in different discharge environments through the translation coefficient. Description of the Drawings

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is the flowchart of the method according to the first embodiment of the present invention;

[0035] Figure 2 It is the system block diagram according to the second embodiment of the present invention. Detailed implementation manners

[0036] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0037] Embodiment 1: As Figure 1 shown, a method for predicting the time-scale translation ability index of electrical energy storage includes the following steps:

[0038] Step 1: Conduct time-scale tests and discharge environment tests on the electrical energy storage system: Mark the electrical energy storage system as the test object, generate a number of time-scale test values and discharge environment temperature values, and obtain a number of test data groups by arbitrarily combining the time-scale test values and the discharge environment temperature values;

[0039] Step 2: Analyze the test data of the electrical energy storage system according to the test data groups: 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, and after setting, conduct a discharge test on the test object and mark the discharge test process as the analysis process of the test data group; Conduct time-scale tests and discharge environment tests on the electrical energy storage system to generate a number of test data groups, conduct a discharge test on the test object according to the test data groups, and provide data support for the stable analysis and stable evaluation process based on the process data of the discharge test;

[0040] Step 3: Analyze the discharge stability of the electrical energy storage system during the analysis of the test data set: 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 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 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 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 value 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 stored energy at the start 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 proportionality coefficients, and k1 > k2 > k3 > 1;

[0041] Step 4: Evaluate the discharge stability of the electrical energy storage system during the analysis of the test data set: Compare 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 an effective 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; Analyze the discharge stability of the electrical energy storage system during the analysis of the test data set, statistically analyze multiple discharge stability parameters of the test object during the analysis process to obtain the stability coefficient, evaluate the discharge stability of the test object through the stability coefficient, and then differentially mark the analysis process, eliminate the invalid data of the time-scale optimization analysis process, and improve the accuracy of the optimization analysis result;

[0042] Step 5: Conduct discharge optimization analysis on the electrical energy storage system: The maximum and minimum values of the discharge ambient temperature values in all test data groups form the test temperature range. The test temperature range is divided into several test temperature intervals. The valid processes in the test data groups where the discharge ambient temperature values are within the test temperature intervals are marked as the matching processes of the test temperature intervals. The matching process with the minimum value of the stability coefficient WD within the test temperature interval is marked as the optimization process. The time scale test value in the test data group corresponding to the optimization process is marked as the time scale optimization value of the test temperature interval. When the electrical energy storage system conducts discharge control, obtain the temperature value of the discharge environment where the electrical energy storage system is located, retrieve the time scale optimization value of the test temperature interval corresponding to the temperature value, and set the discharge duration of the electrical energy storage system as the time scale optimization value. Generate the time scale optimization value according to the stability coefficient of the test object in all valid processes and the corresponding test data groups. The time scale optimization value is matched and set according to the test temperature interval, so as to match the most suitable discharge time scale for each discharge temperature interval and improve the overall operation stability of the system;

[0043] Step 6: Conduct predictive analysis on the time scale translation ability of the electrical energy storage system: Calculate the variance of the stability coefficient WD of the optimization processes in 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 the invalid data WX. Obtain the translation coefficient PY of the test object through the formula PY = c1×WX - c2×WF, where both c1 and c2 are proportionality 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 ability 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 ability of the test object does not meet the requirements, generate a translation ability index abnormal signal and send the translation ability index abnormal signal to the mobile terminal of the management personnel. Conduct comprehensive analysis by combining the stability coefficient of the optimization process in the test temperature interval and the marked number of invalid processes to obtain the translation coefficient, and feedback the time scale translation ability of the electrical energy storage system in different discharge environments through the translation coefficient.

[0044] Embodiment 2: As Figure 2 shown, an electrical energy storage time scale translation ability index prediction system includes 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. 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 for communication.

[0045] The test processing module is used to conduct time scale tests and discharge environment tests on the electrical energy storage system and generate test data groups;

[0046] The test analysis module is used to analyze the test data of the electrical energy storage system according to the test data set and generate an analysis process;

[0047] The stability analysis module is used to analyze the discharge stability during the analysis process of the electrical energy storage system with respect to the test data set and obtain the stability coefficient WD of the analysis process;

[0048] The stability evaluation module is used to evaluate the discharge stability during the analysis process of the electrical energy storage system with respect to the test data set and mark the analysis process as a valid process or an invalid process;

[0049] The discharge optimization module is used to perform discharge optimization analysis on the electrical energy storage system;

[0050] The translation analysis module is used to perform predictive analysis on the time-scale translation ability of the electrical energy storage system.

[0051] An electrical energy storage time-scale translation ability index prediction method, when operating, performs time-scale tests and discharge environment tests on the electrical energy storage system and generates a test data set; analyzes the test data of the electrical energy storage system according to the test data set and generates an analysis process; analyzes the discharge stability during the analysis process of the electrical energy storage system with respect to the test data set and obtains the stability coefficient WD of the analysis process; evaluates the discharge stability during the analysis process of the electrical energy storage system with respect to the test data set and marks the analysis process as a valid process or an invalid process; performs discharge optimization analysis on the electrical energy storage system; performs predictive analysis on the time-scale translation ability of the electrical energy storage system.

[0052] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

[0053] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation; for example: the formula WD = k1×YW + k2×LW - k3×ZH; those skilled in the art 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, and any three formulas form a system of linear equations with three variables. Screen the calculated coefficients and take the average value to obtain the values of k1, k2, and k3 as 3.82, 2.74, and 2.51 respectively;

[0054] The magnitude of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the magnitude of the coefficient, it depends on the amount of sample data and the initial setting of the corresponding stability coefficient for each group of sample data by those skilled in the art; as long as the proportional relationship between the parameter and the quantified value is not affected, for example, the stability coefficient is directly proportional to the value of the voltage stability data YW.

[0055] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0056] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations 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 well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A method for predicting the index of the time-scale translation ability of electrical energy storage, characterized in that, It includes the following steps: Step 1: Conduct time-scale testing and discharge environment testing on the electrical energy storage system: Mark the electrical energy storage system as the test object, generate several time-scale test values and discharge environment temperature values, and obtain several groups of test data sets by arbitrarily combining the time-scale test values and discharge environment temperature values; Step 2: Analyze the test data of the electrical energy storage system according to the test data sets and generate an analysis process; Step 3: Analyze the discharge stability of the electrical energy storage system during the analysis process of the test data sets: 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 electrical energy storage system during the analysis process of the test data sets and mark the analysis process as a valid process or an invalid process through the stability coefficient WD; Step 5: Conduct discharge optimization analysis on the electrical energy storage system: The maximum and minimum values of the discharge environment temperature values in all test data sets form the test temperature range. Divide the test temperature range into several test temperature intervals and mark the time-scale optimization values of the test temperature intervals; When discharging the electrical energy storage system, obtain the temperature value of the discharge environment where the electrical energy storage system is located, retrieve the time-scale optimization value of the test temperature interval corresponding to the temperature value, and set the discharge duration of the electrical energy storage system as the time-scale optimization value; Step 6: Conduct predictive analysis on the time-scale translation ability of the electrical energy storage system. The time-scale translation ability of the electrical energy storage system refers to the ability of the energy storage system to store and release energy on different time scales; In Step 3, the process of obtaining the conversion data ZH includes: The conversion data ZH is the ratio of the discharged amount at the end of the analysis process to the stored energy at the beginning of the analysis process; In Step 5, the specific process of marking the time-scale optimization values of the test temperature intervals includes: Mark the valid processes in the test data sets where the discharge environment temperature values are within the test temperature intervals as the matching processes of the test temperature intervals, mark the matching process with the smallest stability coefficient WD value within the test temperature interval as the optimization process, and mark the time-scale test value in the test data set corresponding to the optimization process as the time-scale optimization value of the test temperature interval; In Step 6, the specific process of conducting predictive analysis on the time-scale translation ability of the electrical energy storage system includes: Obtain the translation coefficient PY of the test object, and 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 ability 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 ability of the test object does not meet the requirements, generate a translation ability index abnormal signal and send the translation ability index abnormal signal to the mobile terminal of the management personnel; The process of obtaining the translation coefficient PY of the test object includes: calculating the variance of the stability coefficient WD in the optimization process for 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 the invalid data WX, and obtaining the translation coefficient PY of the test object through numerical calculation of the stable distribution value WF and the invalid data WX.

2. The method for predicting the electric energy storage time-scale translation ability index according to claim 1, wherein 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. A method for predicting the electric energy storage time-scale translation ability index 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 discharge in 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 in 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. A method for predicting the electric energy storage time-scale translation ability index 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 discharge in 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 in the analysis process as the current stability value, and marking the ratio of the current stability value to the current average value as the current stability data LW.

5. A method for predicting the index of the time-scale translation ability of electrical energy storage, according to claim 4, 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 in 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 in 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 in the analysis process does not meet the requirements, and the corresponding analysis process is marked as an invalid process.

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