Energy storage power management system and method based on optimized allocation

Abstract

The application belongs to the field of energy storage power management, and relates to a data analysis technology, and is used for solving the problem that the numerical value of an operating parameter of an energy storage power cannot be optimally distributed in the prior art, and in particular relates to an energy storage power management system and method based on optimal distribution, which comprises a power management platform, the power management platform is communicatively connected with a distribution test module, a decay analysis module, an optimization analysis module, a performance evaluation module and a database; the distribution test module is used for testing and analyzing the operating parameter of the energy storage power; the energy storage power is marked as a test object, a test period is generated, and a test coefficient CS of the test object is obtained; the test coefficients CS of all test objects are sent to the decay analysis module through the power management platform; the operating parameter of the energy storage power can be optimally analyzed, the operating numerical value is distributed through the optimal distribution parameter, and the aging rate of the energy storage power during operation is reduced.

Description

Technical Field

[0001] This invention belongs to the field of energy storage power management and involves data analysis technology, specifically an energy storage power management system and method based on optimized allocation. Background Technology

[0002] Energy storage power sources are devices that can convert electrical energy into other forms of energy for storage and convert it back into electrical energy when needed. They can store electrical energy through various technologies, such as battery energy storage, supercapacitor energy storage, and mechanical energy storage. These technologies enable energy storage power sources to play an important role in the power system, especially in the field of renewable energy, such as solar and wind power generation.

[0003] The invention patent with announcement number CN115811116A discloses an adaptive power management system, method, and energy storage power supply. The power management system improves the scalability of energy storage power supply capacity, battery life, and output power, and can continuously output stable power, thereby improving user experience. However, the power management system cannot optimize the allocation of the operating parameters of the energy storage power supply, resulting in uncontrolled performance degradation of the energy storage power supply, faster aging, and shorter lifespan.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an energy storage power management system and method based on optimized allocation, which solves the problem that the existing technology cannot optimize the allocation of the operating parameters of energy storage power.

[0006] The technical problem to be solved by the present invention is: how to provide an energy storage power management system and method based on optimized allocation of the values ​​of operating parameters of energy storage power.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An energy storage power management system based on optimized allocation includes a power management platform, which is communicatively connected to an allocation test module, an attenuation analysis module, an optimization analysis module, a performance evaluation module, and a database.

[0009] The allocation test module is used to test and analyze the operating parameters of the energy storage power source: the energy storage power source is marked as the test object, and the operating parameters of the test object are marked as allocation parameters FPi, i = 1, 2, ..., n, where n is a positive integer. A test cycle is generated. At the beginning of the test cycle, an operating value YXi is assigned to the allocation parameter FPi of the test object. The allocation parameter FPi of the test object is set to the corresponding operating value YXi within the test cycle. Then, the test coefficient CS of the test object is obtained. The test coefficient CS of all test objects is sent to the attenuation analysis module through the power management platform.

[0010] The attenuation analysis module is used to analyze the attenuation state of the energy storage power source: the test object is marked as a normal object or an abnormal object by the test coefficient CS, and the normal set ZCi and the abnormal set YCi of the allocation parameter FPi are generated according to the marking.

[0011] The performance evaluation module is used to evaluate and analyze the overall performance of the energy storage power supply: the ratio of the number of abnormal objects to the number of test objects is marked as the evaluation coefficient, and the overall performance of the energy storage power supply is judged to meet the requirements by the evaluation coefficient;

[0012] The optimization analysis module is used to optimize and analyze the operating parameters of the energy storage power supply and obtain the optimization range YHi of the allocation parameter FPi, and send the optimization range YHi of all allocation parameters FPi to the database for storage.

[0013] Furthermore, the process of obtaining the test coefficient CS of the test object includes: performing charge and discharge tests on the test object during the test cycle, obtaining the attenuation coefficient SJ and capacity attenuation rate RS of the test object at the end of the test cycle, and obtaining the test coefficient CS of the test object by numerically calculating the attenuation coefficient SJ and capacity attenuation rate RS.

[0014] Furthermore, the specific process of marking a test object as a normal object or an abnormal object includes: obtaining the test threshold CSmax from the database, comparing the test coefficient CS of the test object with the test threshold CSmax; if the test coefficient CS is less than the test threshold CSmax, the attenuation state of the test object is determined to meet the requirements, and the corresponding test object is marked as a normal object; if the test coefficient CS is greater than or equal to the test threshold CSmax, the attenuation state of the test object is determined to not meet the requirements, and the corresponding test object is marked as an abnormal object.

[0015] Furthermore, the process of generating the normal set ZCi and the abnormal set YCi of the allocation parameter FPi includes: the normal set ZCi of the allocation parameter FPi is composed of the running values ​​YXi of all normal objects, and the abnormal set YCi of the allocation parameter FPi is composed of the running values ​​YXi of all abnormal objects.

[0016] Furthermore, the specific process for determining whether the overall performance of the energy storage power supply meets the requirements includes: marking the ratio of the number of abnormal objects to the number of test objects as the evaluation coefficient; obtaining the evaluation threshold through the database; comparing the evaluation coefficient with the evaluation threshold; if the evaluation coefficient is less than the evaluation threshold, the overall performance of the energy storage power supply is determined to meet the requirements, and the normal set ZCi and abnormal set YCi of all allocated parameters FPi are sent to the optimization analysis module through the power management platform; if the evaluation coefficient is greater than or equal to the evaluation threshold, the overall performance of the energy storage power supply is determined to not meet the requirements, a performance optimization signal is generated, and the performance optimization signal is sent to the mobile terminal of the management personnel.

[0017] Furthermore, the specific process of the optimization analysis module to optimize the operating parameters of the energy storage power supply includes: calculating the variance of all elements in the anomaly set YCi of the allocation parameter FPi to obtain the anomaly impact value YYi of the allocation parameter FPi; obtaining the impact threshold YUmax from the database; and marking the allocation parameter FPi as an anomaly marker parameter YBi or a non-identifiable parameter FSi based on the anomaly impact value YYi; calculating the variance of all elements in the normal set ZCi of the allocation parameter FPi to obtain the normal impact value ZYi of the allocation parameter FPi; and obtaining the impact of the allocation parameter FPi using the formula YFi=m1×YUmax. The analysis threshold YFi is used, where m1 is the proportional coefficient. The normal influence value ZYi of the allocation parameter FPi is compared with the influence analysis threshold YFi: if the normal influence value ZYi is less than the influence analysis threshold YFi, then the optimization range YHi of the allocation parameter FPi is formed by the maximum and minimum elements in the normal set ZCi of the allocation parameter FPi; if the normal influence value ZYi is greater than or equal to the influence analysis threshold YFi, then the maximum and minimum elements in the normal set ZCi of the allocation parameter FPi are removed, and then the normal influence value ZYi is recalculated, and so on, until the normal influence value ZYi is less than the influence analysis threshold YFi.

[0018] Furthermore, the specific process of marking the allocation parameter FPi as the anomaly labeling parameter YBi or the non-identification parameter FSi includes: comparing the anomaly impact value YYi with the impact threshold YUmax; if the anomaly impact value YYi is less than the impact threshold YUmax, then it is determined that the allocation parameter FPi does not have anomaly impact characteristics, and the corresponding allocation parameter FPi is marked as the non-identification parameter FSi; if the anomaly impact value YYi is greater than or equal to the impact threshold YUmax, then it is determined that the allocation parameter FPi has anomaly impact characteristics, and the corresponding allocation parameter FPi is marked as the anomaly labeling parameter YBi.

[0019] Furthermore, the process for determining the value of m1 includes: if the allocation parameter FPi is marked as the abnormal marker parameter YBi, then 0.83≤m1≤0.92; if the allocation parameter FPi is marked as the non-identification parameter FSi, then m1=1.

[0020] The energy storage power management method based on optimized allocation includes the following steps:

[0021] Step 1: Test and analyze the operating parameters of the energy storage power source;

[0022] Step 2: Analyze the attenuation state of the energy storage power source;

[0023] Step 3: Evaluate and analyze the overall performance of the energy storage power supply;

[0024] Step 4: Optimize and analyze the operating parameters of the energy storage power source.

[0025] The present invention has the following beneficial effects:

[0026] 1. The allocation test module can test and analyze the operating parameters of the energy storage power supply, allocate numerical values ​​for the allocation parameters of the test object, and then conduct charge and discharge tests according to the allocated values. At the end of the test cycle, the aging state of the test object is evaluated according to the test coefficient. Combined with the attenuation analysis module, the test object is differentiated and marked, and normal set and abnormal set are generated according to the marking results, providing data support for performance evaluation analysis and parameter optimization analysis.

[0027] 2. The performance evaluation module can evaluate and analyze the overall performance of the energy storage power supply. It evaluates the overall performance based on the proportion of marked abnormal objects, optimizes the performance when the overall performance is abnormal, triggers allocation optimization analysis when the overall performance is normal, and optimizes the value range of the energy storage power supply allocation parameters by indentation.

[0028] 3. The optimization analysis module can optimize and analyze the operating parameters of the energy storage power supply. It can differentiate the allocated parameters based on the abnormal impact values, generate the evaluation criteria of the normal set based on the differentiation marking results, generate the optimization range based on the normal impact values, and allocate the operating values ​​of the allocated parameters based on the optimization range to reduce the aging rate of the energy storage power supply during operation. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0031] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0032] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: As Figure 1 As shown, the energy storage power management system based on optimized allocation includes a power management platform, which is communicatively connected to an allocation test module, an attenuation analysis module, an optimization analysis module, a performance evaluation module, and a database.

[0034] The allocation test module is used to test and analyze the operating parameters of the energy storage power source: The energy storage power source is marked as the test object, and its operating parameters are marked as allocation parameters FPi, i = 1, 2, ..., n, where n is a positive integer. Allocation parameters FPi include depth of discharge, maximum charging current, and maximum discharging current, etc. A test cycle is generated, and at the beginning of the test cycle, an operating value YXi is assigned to the allocation parameters FPi of the test object. The operating value YXi is a value randomly selected from the normal setting range of allocation parameters FPi. The allocation parameters FPi of the test object are then set to the corresponding operating value YXi within the test cycle. Xi, then charge and discharge tests are performed on the test object during the test cycle. At the end of the test cycle, the attenuation coefficient SJ and capacity attenuation rate RS of the test object are obtained. The attenuation coefficient SJ is an important indicator for measuring the degree of battery attenuation, usually expressed as the ratio of the output capacity of the energy storage power supply before and after attenuation; the capacity attenuation rate RS is a measure of the proportion of battery capacity reduction over time. The test coefficient CS of the test object is obtained by the formula CS=c1×SJ+c2×RS, where c1 and c2 are both proportional coefficients, and c1>c2>1; the test coefficient CS of all test objects is sent to the attenuation analysis module through the power management platform.

[0035] The attenuation analysis module is used to analyze the attenuation state of the energy storage power supply. It obtains the test threshold CSmax from the database and compares the test coefficient CS of the test object with the test threshold CSmax. If the test coefficient CS is less than the test threshold CSmax, the attenuation state of the test object is deemed to meet the requirements, and the corresponding test object is marked as a normal object. If the test coefficient CS is greater than or equal to the test threshold CSmax, the attenuation state of the test object is deemed not to meet the requirements, and the corresponding test object is marked as an abnormal object. The normal set ZCi of the allocation parameter FPi is formed by the operating values ​​YXi of all normal objects, and the abnormal set YCi of the allocation parameter FPi is formed by the operating values ​​YXi of all abnormal objects. The allocation parameters for the test objects are numerically allocated, and then charge-discharge tests are performed according to the allocated values. At the end of the test cycle, the aging state of the test object is evaluated based on the test coefficient. Combined with the attenuation analysis module, the test objects are differentiated and marked, and normal and abnormal sets are generated based on the marking results, providing data support for performance evaluation analysis and parameter optimization analysis.

[0036] The performance evaluation module is used to evaluate and analyze the overall performance of the energy storage power supply. It marks the ratio of the number of abnormal objects to the number of test objects as an evaluation coefficient, obtains an evaluation threshold from the database, and compares the evaluation coefficient with the evaluation threshold. If the evaluation coefficient is less than the evaluation threshold, the overall performance of the energy storage power supply is deemed to meet the requirements, and the normal set ZCi and abnormal set YCi of all allocation parameters FPi are sent to the optimization analysis module through the power management platform. If the evaluation coefficient is greater than or equal to the evaluation threshold, the overall performance of the energy storage power supply is deemed to not meet the requirements, a performance optimization signal is generated, and the performance optimization signal is sent to the mobile terminal of the management personnel. The overall performance is evaluated based on the proportion of marked abnormal objects. Performance optimization is performed when the overall performance is abnormal, and allocation optimization analysis is triggered when the overall performance is normal. The value range of the energy storage power supply allocation parameters is optimized by indentation.

[0037] The optimization analysis module is used to optimize the operating parameters of the energy storage power supply: It calculates the variance of all elements in the anomaly set YCi of the allocated parameter FPi to obtain the anomaly impact value YYi of FPi. It then retrieves the impact threshold YUmax from the database and compares the anomaly impact value YYi with the impact threshold YUmax: if the anomaly impact value YYi is less than the impact threshold YUmax, the allocated parameter FPi is determined not to have anomaly impact characteristics, and the corresponding allocated parameter FPi is marked as a non-identifiable parameter FSi; if the anomaly impact value YYi is greater than or equal to the impact threshold YUmax, the allocated parameter FPi is determined to have anomaly impact characteristics, and the corresponding allocated parameter FPi is marked as an anomaly-marked parameter YBi. It also calculates the variance of all elements in the normal set ZCi of the allocated parameter FPi to obtain the normal impact value ZYi of FPi. The impact analysis threshold YFi of the allocated parameter FPi is obtained using the formula YFi = m1 × YUmax, where m1 is a proportionality coefficient. The determination process for the value of m1 includes: if the allocated parameter FPi is marked as an anomaly-marked parameter... If YBi is specified, then 0.83 ≤ m1 ≤ 0.92; if the allocation parameter FPi is marked as a non-identification parameter FSi, then m1 = 1; compare the normal influence value ZYi of the allocation parameter FPi with the influence analysis threshold YFi: if the normal influence value ZYi is less than the influence analysis threshold YFi, then the maximum and minimum elements in the normal set ZCi of the allocation parameter FPi constitute the optimization range YHi of the allocation parameter FPi; if the normal influence value ZYi is greater than or equal to the influence analysis threshold YFi, then the maximum and minimum elements in the normal set ZCi of the allocation parameter FPi are removed, and then the normal influence value ZYi is recalculated, and so on, until the normal influence value ZYi is less than the influence analysis threshold YFi; send the optimization range YHi of all allocation parameters FPi to the database for storage; differentiate the allocation parameters according to the abnormal influence values, generate the evaluation criteria of the normal set according to the differentiation marking results, and then generate the optimization range according to the normal influence values. The optimization range is used to allocate the operating values ​​of the allocation parameters to reduce the aging rate of the energy storage power supply during operation.

[0038] Example 2: Figure 2 As shown, the energy storage power management method based on optimized allocation includes the following steps:

[0039] Step 1: Test and analyze the operating parameters of the energy storage power supply: Mark the energy storage power supply as the test object, mark the operating parameters of the test object as the allocation parameter FPi, generate a test cycle, perform charge and discharge tests on the test object within the test cycle, and obtain the test coefficient CS of the test object at the end of the test cycle;

[0040] Step 2: Analyze the attenuation state of the energy storage power source: Mark the test object as a normal object or an abnormal object by using the test coefficient CS, and mark the normal set ZCi and the abnormal set YCi of the assigned parameter FPi;

[0041] Step 3: Evaluate and analyze the overall performance of the energy storage power supply: Mark the ratio of the number of abnormal objects to the number of test objects as the evaluation coefficient. Determine whether the overall performance of the energy storage power supply meets the requirements based on the evaluation coefficient. If the requirements are met, proceed to Step 4.

[0042] Step 4: Optimize and analyze the operating parameters of the energy storage power source and obtain the optimization range YHi of the allocation parameter FPi. Send the optimization range YHi of all allocation parameters FPi to the database for storage.

[0043] The energy storage power management system and method based on optimized allocation are as follows: During operation, the energy storage power is marked as a test object, and the operating parameters of the test object are marked as allocation parameters FPi. A test cycle is generated, and the test object is subjected to charge and discharge tests within the test cycle. At the end of the test cycle, the test coefficient CS of the test object is obtained. The test object is marked as a normal object or an abnormal object based on the test coefficient CS. The normal set ZCi and the abnormal set YCi of allocation parameters FPi are marked. The ratio of the number of abnormal objects to the number of test objects is marked as the evaluation coefficient. The overall performance of the energy storage power is judged based on the evaluation coefficient. If the requirements are met, step four is executed. The operating parameters of the energy storage power are optimized and analyzed to obtain the optimization range YHi of allocation parameters FPi. The optimization ranges YHi of all allocation parameters FPi are sent to the database for storage.

[0044] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0045] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the true values. The coefficients in the formulas are set by those skilled in the art according to the actual situation; for example, the formula CS=c1×SJ+c2×RS; those skilled in the art collect multiple sets of sample data and set corresponding test coefficients for each set of sample data; substitute the set test coefficients and the collected sample data into the formulas, any two formulas form a system of two linear equations in two variables, filter the calculated coefficients and take the average value, and obtain the values ​​of c1 and c2 as 4.32 and 2.19 respectively;

[0046] The magnitude of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The magnitude of the coefficient depends on the amount of sample data and the test coefficient initially set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantized value, such as the test coefficient being proportional to the attenuation coefficient.

[0047] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0048] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An energy storage power management system based on optimized allocation, characterized in that, It includes a power management platform, which is communicatively connected to a distribution test module, an attenuation analysis module, an optimization analysis module, a performance evaluation module, and a database; The allocation test module is used to test and analyze the operating parameters of the energy storage power source: the energy storage power source is marked as the test object, and the operating parameters of the test object are marked as allocation parameters FPi, i=1,2,…,n, where n is a positive integer. A test cycle is generated. At the beginning of the test cycle, an operating value YXi is assigned to the allocation parameter FPi of the test object. The allocation parameter FPi of the test object is set to the corresponding operating value YXi within the test cycle. Then, the test coefficient CS of the test object is obtained. The test coefficient CS of all test objects is sent to the attenuation analysis module through the power management platform. The attenuation analysis module is used to analyze the attenuation state of the energy storage power source: the test object is marked as a normal object or an abnormal object by the test coefficient CS, and the normal set ZCi and the abnormal set YCi of the allocation parameter FPi are generated according to the marking. The performance evaluation module is used to evaluate and analyze the overall performance of the energy storage power supply: the ratio of the number of abnormal objects to the number of test objects is marked as the evaluation coefficient, and the overall performance of the energy storage power supply is judged to meet the requirements by the evaluation coefficient; The optimization analysis module is used to optimize and analyze the operating parameters of the energy storage power supply and obtain the optimization range YHi of the allocation parameter FPi, and send the optimization range YHi of all allocation parameters FPi to the database for storage.

2. The energy storage power management system based on optimized allocation according to claim 1, characterized in that, The process of obtaining the test coefficient CS of the test object includes: performing charge and discharge tests on the test object during the test cycle, obtaining the attenuation coefficient SJ and capacity attenuation rate RS of the test object at the end of the test cycle, and obtaining the test coefficient CS of the test object by numerical calculation of the attenuation coefficient SJ and capacity attenuation rate RS.

3. The energy storage power management system based on optimized allocation according to claim 2, characterized in that, The specific process of marking a test object as a normal or abnormal object includes: obtaining the test threshold CSmax from the database, comparing the test coefficient CS of the test object with the test threshold CSmax; if the test coefficient CS is less than the test threshold CSmax, the attenuation state of the test object is determined to meet the requirements, and the corresponding test object is marked as a normal object; if the test coefficient CS is greater than or equal to the test threshold CSmax, the attenuation state of the test object is determined to not meet the requirements, and the corresponding test object is marked as an abnormal object.

4. The energy storage power management system based on optimized allocation according to claim 3, characterized in that, The process of generating the normal set ZCi and the abnormal set YCi of the allocation parameter FPi includes: the normal set ZCi of the allocation parameter FPi is composed of the running values ​​YXi of all normal objects, and the abnormal set YCi of the allocation parameter FPi is composed of the running values ​​YXi of all abnormal objects.

5. The energy storage power management system based on optimized allocation according to claim 4, characterized in that, The specific process for determining whether the overall performance of the energy storage power supply meets the requirements includes: marking the ratio of the number of abnormal objects to the number of test objects as the evaluation coefficient; obtaining the evaluation threshold from the database; comparing the evaluation coefficient with the evaluation threshold; if the evaluation coefficient is less than the evaluation threshold, the overall performance of the energy storage power supply is determined to meet the requirements, and the normal set ZCi and abnormal set YCi of all allocated parameters FPi are sent to the optimization analysis module through the power management platform; if the evaluation coefficient is greater than or equal to the evaluation threshold, the overall performance of the energy storage power supply is determined to not meet the requirements, a performance optimization signal is generated, and the performance optimization signal is sent to the mobile terminal of the management personnel.

6. The energy storage power management system based on optimized allocation according to claim 5, characterized in that, The optimization analysis module performs the following process for optimizing the operating parameters of the energy storage power supply: First, it calculates the variance of all elements within the anomaly set YCi of the allocation parameter FPi to obtain the anomaly impact value YYi of FPi. Then, it retrieves the impact threshold YUmax from the database and marks the allocation parameter FPi as either an anomaly marker parameter YBi or a non-identifiable parameter FSi based on the anomaly impact value YYi. Second, it calculates the variance of all elements within the normal set ZCi of the allocation parameter FPi to obtain the normal impact value ZYi of FPi. Finally, it obtains the impact analysis threshold of the allocation parameter FPi using the formula YFi=m1×YUmax. The value YFi is used, where m1 is the proportionality coefficient. The normal influence value ZYi of the allocation parameter FPi is compared with the influence analysis threshold YFi: if the normal influence value ZYi is less than the influence analysis threshold YFi, then the optimization range YHi of the allocation parameter FPi is formed by the maximum and minimum elements in the normal set ZCi of the allocation parameter FPi; if the normal influence value ZYi is greater than or equal to the influence analysis threshold YFi, then the maximum and minimum elements in the normal set ZCi of the allocation parameter FPi are removed, and then the normal influence value ZYi is recalculated, and so on, until the normal influence value ZYi is less than the influence analysis threshold YFi.

7. The energy storage power management system based on optimized allocation according to claim 6, characterized in that, The specific process of marking the allocation parameter FPi as the anomaly labeling parameter YBi or the non-identification parameter FSi includes: comparing the anomaly impact value YYi with the impact threshold YUmax; if the anomaly impact value YYi is less than the impact threshold YUmax, it is determined that the allocation parameter FPi does not have anomaly impact characteristics, and the corresponding allocation parameter FPi is marked as the non-identification parameter FSi; if the anomaly impact value YYi is greater than or equal to the impact threshold YUmax, it is determined that the allocation parameter FPi has anomaly impact characteristics, and the corresponding allocation parameter FPi is marked as the anomaly labeling parameter YBi.

8. The energy storage power management system based on optimized allocation according to claim 7, characterized in that, The process of determining the value of m1 includes: if the allocation parameter FPi is marked as the abnormal marker parameter YBi, then 0.83≤m1≤0.92; if the allocation parameter FPi is marked as the non-identification parameter FSi, then m1=1.

9. A method for managing energy storage power based on optimized allocation, applied to the energy storage power management system based on optimized allocation as described in any one of claims 1-8, characterized in that, Energy storage power management methods include the following steps: Step 1: Test and analyze the operating parameters of the energy storage power source; Step 2: Analyze the attenuation state of the energy storage power source; Step 3: Evaluate and analyze the overall performance of the energy storage power supply; Step 4: Optimize and analyze the operating parameters of the energy storage power source.

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