A frequency modulation service-oriented energy storage battery index analysis method and system

Abstract

A kind of energy storage battery index analysis method and system for service frequency modulation scene, including scoring obtains the overall score of each battery in frequency modulation scene application condition;Each evaluation index of each battery and overall score is made into a decision information system, the core index of each battery is screened using rough set method, and the core attribute set of each battery evaluation index is obtained;All core attribute sets are made intersection, difference set operation to obtain common core attribute set and the unique core attribute set of each battery;The Pearson correlation coefficient of each evaluation index in common core attribute set and unique core attribute set and overall score is calculated, and the influence degree of each evaluation index in common core attribute set and unique core attribute set is sorted based on Pearson correlation coefficient.The core index is screened using rough set method in the present application, and then the importance of each battery index is determined using Pearson correlation coefficient method, to provide convenience for battery selection.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of energy storage batteries, in particular to an energy storage battery index analysis method and system for service frequency modulation scenarios. BACKGROUND

[0002] In recent years, with the adjustment of energy structure, new energy represented by wind power and photovoltaic has developed rapidly. By the end of 2020, the installed capacity of photovoltaic power in China reached 253 million kilowatts, and the installed capacity of wind power reached 281 million kilowatts. The installed capacity of new energy in China is gradually rising, and the penetration rate of new energy in the power system will further improve, which will affect the safe operation of the power system. In order to improve the stability of the power system, the Technical Specification for Power System Network and Source Coordination stipulates that wind farms and photovoltaic power stations should have primary frequency modulation capability. The main ways for new energy stations to realize primary frequency modulation are to reserve active reserve and configure energy storage. The method of reserving active reserve will lead to curtailment of wind and light, while battery energy storage has the advantages of fast response speed and flexible configuration, and its participation in power grid frequency modulation has become a hot topic in the industry.

[0003] When studying the participation of energy storage in auxiliary services, how to achieve the optimization of technology and economy is the most practical problem currently faced. The performance parameters of battery such as charge and discharge depth and state of charge affect the depreciation cost of the system and directly affect the comprehensive benefit and risk assessment of the enterprise. At present, the selection of frequency modulation battery is usually targeted at technology and economy, but the influence of time cost and benefit factors is not considered, resulting in inaccurate selection of frequency modulation battery. SUMMARY

[0004] In order to solve the problem that the existing technology usually selects frequency modulation battery with technology and economy as the target, but does not consider the influence of time cost and benefit factors, resulting in inaccurate selection of frequency modulation battery, the present application proposes an energy storage battery index analysis method for service frequency modulation scenarios, which comprises:

[0005] Scoring the influence of each battery on frequency modulation performance to obtain the overall score of each battery in the frequency modulation scenario application;

[0006] Make each evaluation index of each battery and the overall score into a decision information system, use rough set method to screen the core index of each battery, and obtain the core attribute set of each battery evaluation index;

[0007] Make the intersection and difference set operation of the core attribute set of all kinds of battery evaluation indexes to obtain the common core attribute set and the unique core attribute set of each battery;

[0008] Determine the influence degree of each evaluation index based on the Pearson correlation coefficient of each evaluation index in the common core attribute set and the unique core attribute set and the overall score.

[0009] Optionally, the influence of each battery on the frequency modulation performance is scored to obtain an overall score of each battery in the frequency modulation scenario application.

[0010] Determine the energy storage battery type applied to the frequency modulation scenario.

[0011] Statistical evaluation indicators of each energy storage battery, and develop a questionnaire based on the evaluation indicators of each energy storage battery.

[0012] Score each battery applied to the frequency modulation scenario to obtain an overall score of each battery in the frequency modulation scenario application.

[0013] Optionally, the evaluation indicators of each battery and the overall score of the battery in the frequency modulation scenario are made into a decision information system, and the core indicators of each battery are screened using a rough set method to obtain a core attribute set of each battery evaluation indicator, including:

[0014] The data of each evaluation indicator of each battery is arranged into a knowledge expression system. The knowledge expression system of each battery is made into a decision information table with m expert information as rows, n battery indicators and overall scores as columns. M is the number of experts, and n is the number of battery indicators.

[0015] The evaluation indicators of each knowledge expression system are numbered, the data of the same indicator of different types of batteries are classified, and the classification number is substituted for the indicator data to obtain a decision table composed of classification numbers.

[0016] The set of each evaluation indicator of the battery is taken as the criterion set, and the set of the overall score is taken as the decision criterion set. The positive domain of the decision criterion set of the criterion set is obtained.

[0017] Based on the positive domain and the criterion set, a core attribute set of each battery with respect to the battery evaluation indicator is determined.

[0018] Optionally, the core attribute set of each battery with respect to the battery evaluation indicator is determined based on the positive domain and the criterion set, including:

[0019] Each battery data in the criterion set is sequentially removed to obtain a set containing different types of battery data, and the set containing different types of battery data is compared with the positive domain, respectively. If they are completely equal, the removed battery does not belong to the core attribute set, otherwise the removed battery belongs to the core attribute set, to obtain a core attribute set of each battery with respect to the battery evaluation indicator.

[0020] Optionally, the core attribute sets of all types of battery evaluation indicators are intersected and differenced to obtain a common core attribute set and a unique core attribute set of each battery, including:

[0021] taking intersection of the core attribute sets of different kinds of batteries to obtain a common core attribute set;

[0022] performing set difference operation between the core attribute sets of various batteries and the common core attribute set to obtain a specific core attribute set which only affects the frequency modulation service scenario capability of the kind of battery.

[0023] Optionally, the influence degree of each evaluation index is determined based on the Pearson correlation coefficient between each evaluation index in the common core attribute set and the overall score, and the influence degree of each evaluation index is determined based on the Pearson correlation coefficient between each evaluation index in the specific core attribute set and the overall score.

[0024] Pearson correlation coefficients between each element in the common core attribute set affecting the frequency modulation capability and the overall score affecting the frequency modulation capability are respectively calculated;

[0025] Pearson correlation coefficients between each element in the common core attribute set affecting the frequency modulation capability and the overall score affecting the frequency modulation capability are respectively calculated;

[0026] Pearson correlation coefficients between each element in the common core attribute set affecting the frequency modulation capability and the overall score affecting the frequency modulation capability are respectively calculated;

[0027] The influence degree of the common index and the specific index is determined based on the Pearson correlation coefficient mean value and the Pearson correlation coefficient between each evaluation index and the overall score affecting the frequency modulation capability.

[0028] Optionally, the influence degree of the common index and the specific index is determined based on the Pearson correlation coefficient mean value and the Pearson correlation coefficient between each evaluation index and the overall score affecting the frequency modulation capability.

[0029] The Pearson correlation coefficient mean values of each index are sorted from large to small to obtain a common index sorting of the frequency modulation capability from large to small;

[0030] The Pearson correlation coefficients between each index and the overall score affecting the frequency modulation capability are arranged in descending order to obtain a specific index sorting of the frequency modulation service energy storage selection of different kinds of batteries.

[0031] In still another aspect, the application further provides a service frequency modulation scenario-oriented energy storage battery index analysis system, comprising:

[0032] The scoring module is configured to score the influence of each battery on the frequency modulation performance to obtain an overall score of each battery in the frequency modulation scenario application.

[0033] The screening module is configured to make each evaluation index of each battery and the overall score into a decision information system, and use a rough set method to screen core indexes of each battery to obtain a core attribute set of each battery evaluation index.

[0034] The operation module is configured to make intersection and difference set operations on the core attribute sets of all battery evaluation indexes to obtain a common core attribute set and a unique core attribute set of each battery.

[0035] The sorting module is configured to determine the influence degree of each evaluation index based on a Pearson correlation coefficient of each evaluation index in the common core attribute set and the unique core attribute set and the overall score.

[0036] Optionally, the scoring module is specifically configured to:

[0037] determine the types of energy storage batteries applied to the frequency modulation scene;

[0038] count the evaluation indexes of each energy storage battery, and develop a questionnaire based on the evaluation indexes of each energy storage battery;

[0039] score each battery applied to the frequency modulation scene to obtain an overall score of each battery in the frequency modulation scene.

[0040] Optionally, the screening module includes:

[0041] The making submodule is configured to arrange the data of each evaluation index of each battery into a knowledge expression system, and make a decision information table with m pieces of expert information as rows, n battery indexes and the overall score as columns, where m is the number of experts, and n is the number of battery indexes.

[0042] The conversion submodule is configured to number the evaluation indexes of each knowledge expression system, divide the data of the same index of different types of batteries into grades, and replace the index data with the grade numbers to obtain a decision table composed of grade numbers.

[0043] The calculation submodule is configured to take the set of each evaluation index of the battery as a criterion set, take the set of the overall score as a decision criterion set, and calculate the positive domain of the decision criterion set of the criterion set.

[0044] The core attribute determination submodule is configured to determine the core attribute set of each battery with respect to the battery evaluation index based on the positive domain and the criterion set.

[0045] Optionally, the core attribute determination submodule is specifically configured to:

[0046] Remove each battery data in the criterion set in turn to obtain a set containing different battery data, and compare the set containing different battery data with the positive domain respectively, if they are completely equal, the removed battery does not belong to the core attribute set, otherwise, the removed battery belongs to the core attribute set, and a core attribute set of each battery about the battery evaluation index is obtained.

[0047] Optionally, the sorting module is specifically used for:

[0048] Pearson correlation coefficients between each element in the common core attribute set affecting frequency modulation capability and the overall score affecting frequency modulation capability are calculated respectively;

[0049] Based on the Pearson correlation coefficients between each element and the overall score affecting frequency modulation capability, the average of the Pearson correlation coefficients of each index under all kinds of batteries is calculated;

[0050] The average of the Pearson correlation coefficients of each index is sorted from large to small to obtain a common index sorting of the influence on frequency modulation capability from large to small;

[0051] Pearson correlation coefficients between each index in the specific core attribute set of each battery and the overall score affecting frequency modulation capability are calculated respectively;

[0052] The Pearson correlation coefficients between each index and the overall score affecting frequency modulation capability are arranged in descending order to obtain a specific index sorting of the influence of frequency modulation service energy storage selection of different types of batteries.

[0053] Compared with the prior art, the beneficial effects of the present application are:

[0054] The present application provides a service-oriented frequency modulation scene energy storage battery index analysis method, which includes scoring the influence of each battery on frequency modulation performance to obtain the overall score of each battery in the frequency modulation scene application; each evaluation index of each battery and the overall score are made into a decision information system, and the core index of each battery is screened by using a rough set method to obtain a core attribute set of each battery evaluation index; the core attribute sets of all types of battery evaluation indexes are subjected to intersection and difference set operations to obtain a common core attribute set and a specific core attribute set of each battery; and the influence degree of each evaluation index is determined based on the Pearson correlation coefficients of each evaluation index in the common core attribute set and the specific core attribute set and the overall score. The present application uses a rough set method to screen the core index of each battery, and then uses a Pearson correlation coefficient method to determine the importance of each battery index, thereby providing convenience for battery selection. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 It is a flow chart of a service-oriented frequency modulation scene energy storage battery index analysis method of the present application.

[0056] Figure 2 The application is specifically applied to the index analysis method of the energy storage battery of embodiment 2. DETAILED DESCRIPTION

[0057] The energy conversion efficiency, response time, economic benefit, service life and other factors of various energy storages are closely related to the frequency modulation strategy method, and the application comprehensively analyzes the energy storage batteries participating in frequency modulation by investigating all kinds of batteries participating in frequency modulation service, comprehensively considering all indexes affecting the frequency modulation capability, and first screening the core indexes of various batteries by using rough set, simplifying the index set, then determining the common core index set of all batteries by using intersection operation, and determining the characteristic index set of each battery by using difference set operation, so that the common core index and specific core index of each battery can be comprehensively sorted, and the correlation degree between the core index of each battery and the selection influence is calculated by using Pearson correlation coefficient method, and the importance of each battery index is further clarified. The application not only provides a new idea for battery analysis and selection, but also has important significance and application value for the development of energy storage system.

[0058] Embodiment 1

[0059] A kind of energy storage battery index analysis method for service frequency modulation scene, as shown in Figure 1 , comprising:

[0060] S1: scoring the influence of each battery on frequency modulation performance to obtain the overall score of each battery in the frequency modulation scene application;

[0061] S2: making each evaluation index of each battery and the overall score into a decision information system, using rough set method to screen the core index of each battery to obtain the core attribute set of each battery evaluation index;

[0062] S3: making intersection and difference set operation on the core attribute set of all kinds of battery evaluation indexes to obtain the common core attribute set and the unique core attribute set of each battery;

[0063] S4: determining the influence degree of each evaluation index based on the Pearson correlation coefficient of each evaluation index in the common core attribute set and the unique core attribute set and the overall score.

[0064] The application will be described in detail as follows:

[0065] In S1, the expert scoring method is used to score the influence of each battery on frequency modulation performance to obtain the overall score of each battery in the frequency modulation scene application, which specifically includes:

[0066] Step 1: Select multiple batteries for frequency modulation services, develop a questionnaire based on the indicators of the batteries, distribute the prepared questionnaire to experts for scoring, collect and analyze the questionnaire;

[0067] Step 1.1 Determine the types of energy storage batteries commonly used in frequency modulation scenarios through research , set the battery set , determine the indicators of the batteries affecting the frequency modulation performance, and set the battery indicator set ;

[0068] Step 1.2 Create a survey questionnaire table, with rows representing energy storage battery types , columns representing indicators affecting the frequency modulation performance , and the last column representing the overall evaluation score of the battery applied in the frequency modulation scenario, i.e., the overall score;

[0069] Step 1.3 Distribute the survey questionnaire table to experts , and experts score each indicator of each battery for frequency modulation services based on their experience, and finally score the overall application of the battery in the frequency modulation scenario to obtain the overall score;

[0070] In S2, each evaluation indicator of each battery and the overall score are made into a decision information system, and the rough set method is used to screen the core indicators of each battery to obtain the core attribute set of each battery evaluation indicator, which includes:

[0071] Step 2: Based on the evaluation data obtained from the survey, the overall score of each battery evaluation indicator is made into a decision information system, and the rough set method is used to process the data to obtain the core attribute set of each battery evaluation indicator;

[0072] Step 2.1 Organize the evaluation data of p battery into p knowledge representation systems, with expert information as rows and indicators as columns, and the overall score in the last column as the decision column. The evaluation results of the evaluation indicators are made into a decision information table;

[0073] Step 2.2 Number the evaluation indicators of each knowledge representation system, with different experts numbered as ( m is the number of experts), and the battery indicators and overall score are numbered as ( nThis represents the number of battery indicators. Then, based on the specific data, each column of data—the evaluation data for different battery indicators—is categorized into levels. The data processing method is the same for each type of battery. From this, we can obtain... p A knowledge representation system composed of raw evaluation data is transformed into a decision table composed of level numbers, which converts the evaluation results of the evaluation indicators into a... m OK( n +1) Decision information table;

[0074] Step 2.3 Based on the decision table obtained in Step 2.2, the overall score is placed in the last column as the decision set. Using rough set theory and data analysis methods, the following is obtained: p The set of core properties of three different types of batteries, denoted as follows: A 1, A 2,... A p The specific implementation process is as follows:

[0075] The decision table is set with two criteria: a set of battery indicators and a set of overall scores. Battery metrics set C ,set up The overall scoring set is D Find C of D Positive domain, denoted as: pos C D Remove them one by one For each type of battery data in the battery set, calculate pos. C-r1 D ,pos C-r2 D , …,pos C-rn D And respectively with pos C D By comparison, if the two sets are completely equal, then this type of battery does not belong to the kernel attribute set; if the two sets are not equal, then the removed battery belongs to the kernel attribute set. Repeat the above steps for each decision table, and thus obtain... p The set of kernel properties relating to battery performance for each type of battery, denoted as follows: A 1, A 2,... A p ;

[0076] In S3, the intersection and difference operations of the kernel attribute sets of all types of battery evaluation indicators are performed to obtain a common kernel attribute set and a unique kernel attribute set for each type of battery. Specifically, these include:

[0077] Step 3: The results obtained in step 2.3 pThe set of core attributes of different types of batteries is intersected to obtain the set of common core attributes. A q Next, the set of core attributes for each type of battery and the set of common core attributes are combined. A q By taking the difference set, we obtain the set of unique core properties for each type of battery;

[0078] Step 3.1 The results obtained in Step 2.3 p The set of core properties of different types of batteries, and taking the intersection of these sets, yields the set of common core properties. A q ;

[0079] Step 3.2 Then, combine the core attribute sets of various batteries with the common core attribute set. A q Performing a difference operation yields a set of unique core attributes that only affect the frequency modulation capabilities of this type of battery service. A 1-q , A 2-q ,... A p-q ;

[0080] In S4, the influence of each evaluation indicator is determined based on the Pearson correlation coefficient between each evaluation indicator in the common kernel attribute set and the unique kernel attribute set and the overall score. Specifically, this includes:

[0081] The fourth step is to calculate the common kernel attribute set obtained in step 3.1. A q and the set of unique kernel properties obtained in step 3.2 A 1-q , A 2-q ,... A p-q The Pearson correlation coefficients of each indicator with the overall score were calculated, and the indicators were sorted from largest to smallest according to the value obtained. The commonalities affecting the selection of energy storage for frequency regulation services and the degree of influence of specific indicators were summarized.

[0082] Step 4.1 Calculate the set of common core attributes that affect frequency modulation capability. A q The Pearson correlation coefficients of each indicator with the overall score were calculated and ranked from largest to smallest.

[0083] Step 4.1.1 Calculate the set of common indicators affecting frequency modulation capability for each type of battery. A q Inner t The Pearson correlation coefficient between the indicators and the final score affecting FM capability;

[0084] Step 4.1.2 After the calculation according to step 4.1, the common index set of each battery is obtained A q The Pearson correlation coefficient between the first t The Pearson correlation coefficient between the first The Pearson correlation coefficient between the first

[0085] Step 4.1.3 Repeat step 4.1.1 and step 4.1.2 to calculate the common index set A q The Pearson correlation coefficient between the first

[0086] Step 4.2 Calculate the specific nuclear attribute set of each battery affecting the FM ability The Pearson correlation coefficient between the first

[0087] Step 4.2.1 According to step 4.1.1, calculate the specific index set of different types of batteries affecting FM ability A p-q The Pearson correlation coefficient between the first

[0088] Step 4.2.2 Sort the Pearson correlation coefficients of each index in the specific index set of different types of batteries from large to small, and the battery index with the largest Pearson correlation coefficient has a greater impact on the FM ability of this type of battery.

[0089] Step 4.3 According to the calculation results of step 4.1 and step 4.2, summarize the common index impact degree ranking and the specific index impact degree ranking for each different battery, and sort the energy storage selection indexes affecting the FM service.

[0090] The application comprehensively considers all indexes applied to frequency modulation service batteries, screens them by using a rough set, obtains a core index set of each battery, analyzes common core indexes and characteristic core indexes of all batteries by using intersection operation and difference set operation, classifies all batteries participating in energy storage frequency modulation service in a more refined manner, meanwhile, considering that the influence of each index in each set on the importance of frequency modulation service is different, the importance is estimated and evaluated by using a Pearson correlation coefficient method, so that the evaluation process is more accurate and reasonable, the technical scheme of the application can accurately find the core evaluation indexes of each battery, is easier to select suitable batteries applied to frequency modulation service based on the core evaluation indexes, provides a new idea for battery evaluation and classification, and has important significance and application value. Compared with the existing evaluation technology, the application innovatively considers the common indexes and characteristic indexes of each battery, can refine battery classification, has strong innovation, and can be more accurate.

[0091] Embodiment 2

[0092] The application provides a core index difference analysis method for energy storage batteries in a service frequency modulation scene, Figure 2 The implementation process of the method in the embodiment is shown, and includes the following steps:

[0093] Step 1: investigating energy storage batteries currently serving the frequency modulation scene, formulating an investigation questionnaire according to various indexes of the batteries, distributing the prepared investigation questionnaire to experts for scoring, and collecting and counting the investigation questionnaire;

[0094] Step 1.1 determining several types of energy storage batteries currently commonly applied to the frequency modulation scene through field engineering investigation ， such as lithium ion batteries, lead-acid batteries and the like, and the battery numbers are respectively v 1 v p , setting a battery set , determining various indexes of the batteries affecting frequency modulation performance, and numbering the various indexes r 1 r n , setting a battery index set ;

[0095] Step 1.2 preparing an investigation questionnaire table according to the data obtained through steps 1.1 and 1.2, for example, Table 1, the several types of energy storage batteries commonly applied to the frequency modulation scene are listed in the table , various indexes of the batteries affecting frequency modulation performance are listed , wherein the last column is a total evaluation score of the energy storage batteries applied to the frequency modulation scene, in the table, represents the mThe score of the first p index of the battery is obtained by the first n expert, The score of the first m battery is obtained by the first p expert;

[0096] Step 1.3: The questionnaire table prepared in step 1.2 is sent to each expert , and each expert scores the influence of each index of each battery on the FM performance according to experience, and finally scores the overall application of the battery in the FM scene;

[0097] Step 2: The questionnaire is collected, and according to the evaluation data obtained from the questionnaire, the evaluation results of each battery evaluation index are made into a decision information system, and the rough set method is used to process the data to obtain the core attribute set of each battery evaluation index;

[0098] Step 2.1: Process the data of the evaluated batteries, and arrange the data of each evaluated battery into a knowledge expression system. Since p batteries participate in evaluation, a total of p knowledge expression systems of different types of batteries are obtained, and the knowledge expression system of each battery is arranged with m expert information as rows, n battery indexes and overall evaluation scores as columns, and the evaluation results of the evaluation indexes are made into a decision information table with m rows n +1) columns;

[0099] Step 2.2: Number the evaluation indexes of each knowledge expression system, as shown in Table 1, number the different experts as ( m , where n represents the number of experts), number the battery indexes and overall scores in turn as ( n , where m represents the number of battery indexes), and then according to the specific data, grade the evaluation data of each column, i.e. the evaluation data of different battery indexes. For example, the data range of a certain group is 0-100, which can be divided into five grades: 0-20, 21-40, 41-60, 61-80 and 81-100, and the five grades are numbered as 0, 1, 2, 3 and 4 respectively. The number of intervals and the length of intervals can be different, but the numbering must start from 0, and the data processing method of each battery is the same. Thus, a decision table composed of grade numbers can be obtained from p knowledge expression systems composed of original evaluation data;

[0100]

[0101] Step 2.3 The criterion set of the decision table consists of the battery index set and the overall score set, where The battery indicator set is a set of conditional criteria. The overall scoring set is the decision criterion set, let it be... Battery metrics set C ,set up The overall scoring set is D Find C of D Positive domain, denoted as: pos C D The positive domain is a set. D The lower approximation is removed sequentially. For each type of battery data in the battery set, calculate pos. C-r1 D ,pos C-r2 D , …,pos C-rn D And respectively with pos C D By comparison, if the two sets are completely equal, then the removed battery type does not belong to the kernel attribute set; if the two sets are not equal, then the removed battery belongs to the kernel attribute set. Repeat the above steps for each decision table, thus obtaining... p The set of kernel properties relating to battery performance for each type of battery, denoted as follows: A 1, A 2,... A p ;

[0102] Step 3: The results obtained in Step 2 p The set of core properties of different types of batteries is used to perform an intersection operation to obtain the set of common core properties. A q Next, the set of core attributes for each type of battery and the set of common core attributes are combined. A q By taking the difference set, we obtain the set of unique core properties for each type of battery;

[0103] Step 3.1 The results obtained in Step 2 p The set of core properties of different types of batteries is obtained by taking the intersection of these sets using Formula 3.1. A q ;

[0104] (3.1)

[0105] Step 3.2 Using Formula 3.2, combine the core property sets of various batteries with the common core property set. A qPerforming a difference operation yields a specific set of indicators that only affect the frequency modulation capabilities of this type of battery service. A 1-q , A 2-q ,... A p-q ;

[0106] (3.2)

[0107] Step 4: Calculate the set of common core attributes affecting frequency modulation capability obtained in Step 3. A q And the unique core properties of various types of batteries A 1-q , A 2-q ,... A p-q The Pearson correlation coefficients of each indicator with the overall score were calculated, and the indicators were sorted from largest to smallest according to the value. The ranking of the common indicators affecting the selection of energy storage for frequency regulation services and the ranking of the specific indicators for each type of battery were summarized.

[0108] Step 4.1 Calculate the set of common core attributes that affect frequency modulation capability. A q The Pearson correlation coefficients of each indicator and the final score were calculated, and the indicators were arranged from largest to smallest according to the calculated Pearson correlation coefficient values. The common indicators affecting the selection of energy storage for frequency regulation services were then ranked according to their degree of influence.

[0109] Step 4.1.1 Calculate the set of common core attributes that affect frequency modulation capability. A q The Pearson correlation coefficient between each element and the final score affecting frequency modulation capability is first calculated using Formula 4.1 to obtain the Pearson correlation coefficient between each battery indicator and the overall score.

[0110] (4.1)

[0111] In the formula: This represents the Pearson correlation coefficient between the t-th indicator of the p-th battery and the overall score of that battery in the set of common indicators. Indicates the first i The experts scored this type of battery for this specific indicator. This represents the average score given by all experts for this particular indicator of the battery. This represents the overall score given by the i-th expert for this type of battery. This represents the average score given to this type of battery by all experts. m Indicates the number of experts;

[0112] Step 4.1.2 Calculate the common attribute set of each battery after step 4.1 A q The Pearson correlation coefficient between the first t The Pearson correlation coefficient between the first The Pearson correlation coefficient between the first

[0113] (4.2)

[0114] In the formula, p is the number of battery types, The Pearson correlation coefficient between the first

[0115] Step 4.1.3 Repeat step 4.1.1 and step 4.1.2 to calculate the common attribute set A q The Pearson correlation coefficient between the first

[0116] Step 4.2 Calculate the specific attribute set of each battery affecting the FM service The Pearson correlation coefficient between the first

[0117] Step 4.2.1 Calculate the specific attribute set of each battery affecting the FM service according to step 4.1.1 A p-q The Pearson correlation coefficient between the first

[0118] Step 4.2.2 Sort the Pearson correlation coefficients of each attribute in the specific attribute set of each battery from large to small, and the battery attribute with the largest Pearson correlation coefficient has a greater impact on the FM service of this battery;

[0119] Step 4.3 According to the calculation results of step 4.1 and step 4.2, summarize the common attribute impact degree ranking and the specific attribute impact degree ranking for each different battery, and sort the general and special attributes affecting the FM service of the battery;

[0120] In this example, through the analysis of the above steps, the common core indicators and characteristic core indicators of various energy storage batteries applied in the frequency modulation scene can be finally obtained, and they can be scientifically sorted according to the degree of correlation with the frequency modulation capability. The method proposes to use intersection operation and difference set operation to further classify each battery core indicator, and also uses the Pearson correlation coefficient method for evaluation, and the result is more objective. Finally, the classification and evaluation of energy storage batteries applied in the frequency modulation scene can be realized. The common indicators and special indicators of all energy storage batteries that can be used for frequency modulation service are innovatively analyzed, which can contribute to the application and development of energy storage.

[0121] Embodiment 3

[0122] The application based on the same inventive concept also provides an energy storage battery index analysis system for a service frequency modulation scene, including:

[0123] A scoring module is configured to score the influence of each battery on the frequency modulation performance to obtain an overall score of each battery in the frequency modulation scene application.

[0124] A screening module is configured to make each evaluation index and the overall score of each battery into a decision information system, and use a rough set method to screen the core indicators of each battery to obtain a core attribute set of each battery evaluation index.

[0125] An operation module is configured to perform intersection and difference set operations on the core attribute sets of all kinds of battery evaluation indexes to obtain a common core attribute set and a unique core attribute set of each battery.

[0126] A sorting module is configured to determine the influence degree of each evaluation index based on the Pearson correlation coefficient of each evaluation index in the common core attribute set and the unique core attribute set and the overall score.

[0127] Optionally, the scoring module is specifically configured to:

[0128] Determine the types of energy storage batteries applied in the frequency modulation scene.

[0129] Statistically analyze the evaluation indexes of each energy storage battery, and develop a questionnaire based on the evaluation indexes of each energy storage battery.

[0130] Score each battery applied in the frequency modulation scene by using an expert scoring method to obtain an overall score of each battery in the frequency modulation scene application.

[0131] Optionally, the screening module includes:

[0132] The making submodule is used for arranging data of each evaluation index of each battery into a knowledge expression system, and the knowledge expression system of each battery is made into a decision information table with m pieces of expert information as rows, n battery indexes and the overall score as columns, where m is the number of experts, and n is the number of battery indexes;

[0133] The converting submodule is used for numbering evaluation indexes of each knowledge expression system, grading data of the same index of different types of batteries, and replacing the index data with the grade number to obtain a decision table composed of the grade number;

[0134] The calculating submodule is used for taking a set of each evaluation index of the battery as a criterion set, taking a set of the overall score as a decision criterion set, and calculating a positive domain of the criterion set and the decision criterion set;

[0135] The core attribute determining submodule is used for determining a core attribute set of each battery with respect to the battery evaluation index based on the positive domain and the criterion set.

[0136] Optionally, the core attribute determining submodule is specifically used for:

[0137] Each battery data in the criterion set is sequentially removed to obtain a set containing different types of battery data, and the set containing different types of battery data is compared with the positive domain respectively, if they are completely equal, the removed battery does not belong to the core attribute set, otherwise, the removed battery belongs to the core attribute set, and a core attribute set of each battery with respect to the battery evaluation index is obtained.

[0138] Optionally, the sorting module is specifically used for:

[0139] Pearson correlation coefficients between each element in the common core attribute set affecting the frequency modulation capability and the overall score affecting the frequency modulation capability are calculated respectively;

[0140] Pearson correlation coefficients of each index under all types of batteries are calculated based on the Pearson correlation coefficients between the elements and the overall score affecting the frequency modulation capability;

[0141] The Pearson correlation coefficients of each index are sorted from large to small to obtain a common index sorting of the frequency modulation capability from large to small;

[0142] Pearson correlation coefficients between each index in the specific core attribute set of each battery and the overall score affecting the frequency modulation capability are calculated respectively;

[0143] Pearson correlation coefficients between each index and the overall score affecting the frequency modulation capability are arranged in descending order to obtain a specific index sorting of the frequency modulation service energy storage selection of different types of batteries.

[0144] Optionally, the operation module is specifically configured to:

[0145] The common core attribute set is obtained by taking the intersection of the core attribute sets of the different types of batteries.

[0146] The unique core attribute set that only affects the service frequency modulation scene capability of the type of battery is obtained by performing a difference set operation between the core attribute set of the type of battery and the common core attribute set.

[0147] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer usable program code.

[0148] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions that are executed by the processor of the computer or other programmable data processing apparatus generate an apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams.

[0149] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flowcharts and / or block diagrams.

[0151] The above merely illustrates the embodiments of the present application, but should not be taken as limitations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall fall into the protection scope of the present application.

Claims

1. A method for analyzing energy storage battery indicators in a service-oriented frequency modulation scenario, characterized in that, The application comprises the following steps: grading the influence of each battery on the frequency modulation performance to obtain the overall score of each battery in the frequency modulation scenario application; making a decision information system of each evaluation index of each battery and the overall score, screening the core index of each battery by using the rough set method, and obtaining the core attribute set of the evaluation index of each battery; performing intersection and difference set operations on the core attribute sets of all kinds of battery evaluation indexes to obtain the common core attribute set and the unique core attribute set of each battery; determining the influence degree of each evaluation index based on the Pearson correlation coefficient of each evaluation index in the common core attribute set and the unique core attribute set and the overall score; wherein the step of making a decision information system of each evaluation index of each battery and the overall score, screening the core index of each battery by using the rough set method, and obtaining the core attribute set of the evaluation index of each battery comprises the following steps: arranging the data of each evaluation index of each battery into a knowledge expression system, making a decision information table with m pieces of expert information as rows, n battery indexes and the overall score as columns, m being the number of experts and n being the number of battery indexes, numbering the evaluation indexes of each knowledge expression system, grading the data of the same index of different kinds of batteries, replacing the index data with the grade number, and obtaining a decision table composed of grade numbers; taking the set of each evaluation index of the battery as the criterion set, taking the set of the overall score as the decision criterion set, and obtaining the positive domain of the decision criterion set of the criterion set; and determining the core attribute set of each battery with respect to the battery evaluation index based on the positive domain and the criterion set; the step of determining the influence degree of each evaluation index based on the Pearson correlation coefficient of each evaluation index in the common core attribute set and the unique core attribute set and the overall score comprises the following steps: calculating the Pearson correlation coefficient between each element in the common core attribute set influencing the frequency modulation capability and the overall score influencing the frequency modulation capability; calculating the average Pearson correlation coefficient of each index under all kinds of batteries based on the Pearson correlation coefficient between each element and the overall score influencing the frequency modulation capability; calculating the Pearson correlation coefficient between each index in the unique core attribute set of each battery and the overall score influencing the frequency modulation capability; and determining the influence degree of the common index and the specific index based on the average Pearson correlation coefficient and the Pearson correlation coefficient between each index and the overall score influencing the frequency modulation capability.

2. The method of claim 1, wherein, The step of grading the influence of each battery on the frequency modulation performance to obtain the overall score of each battery in the frequency modulation scenario application comprises the following steps: determining the types of energy storage batteries applied in the frequency modulation scenario; counting the evaluation indexes of each energy storage battery and formulating a questionnaire based on the evaluation indexes of each energy storage battery; grading each battery applied in the frequency modulation scenario to obtain the overall score of each battery in the frequency modulation scenario application.

3. The method of claim 1, wherein, The step of determining the core attribute set of each battery with respect to the battery evaluation index based on the positive domain and the criterion set comprises the following steps: The core attribute set of each battery evaluation index is obtained by sequentially removing each battery data in the criterion set, obtaining a set containing different battery data, and comparing the set containing different battery data with the positive domain respectively, if they are completely equal, the removed battery does not belong to the core attribute set, otherwise the removed battery belongs to the core attribute set, and the core attribute set of each battery about the battery evaluation index is obtained.

4. The method of claim 1, wherein, The common core attribute set and the unique core attribute set of each battery are obtained by performing intersection and difference set operations on the core attribute sets of all kinds of battery evaluation indexes, including: The common core attribute set is obtained by taking the intersection of the core attribute sets of different kinds of batteries; The unique core attribute set that only affects the service frequency modulation scene capability of the kind of battery is obtained by performing difference set operation on the core attribute set of each kind of battery and the common core attribute set.

5. The method of claim 1, wherein, The influence degree of the common index and the specific index is determined based on the mean of the Pearson correlation coefficients and the Pearson correlation coefficients between each index and the overall score affecting the frequency modulation capability, including: The common index affecting the frequency modulation capability from large to small is obtained by sorting the mean of the Pearson correlation coefficients of each index from large to small; The specific index affecting the frequency modulation service energy selection of different kinds of batteries is obtained by arranging the Pearson correlation coefficients between each index and the overall score affecting the frequency modulation capability in order from large to small.

6. A service-oriented frequency modulation scene energy storage battery index analysis system, characterized in that, It includes: The scoring module is used to score the influence of each battery on the frequency modulation performance to obtain the overall score of each battery in the frequency modulation scene application; The screening module is used to make a decision information system of each evaluation index and the overall score of each battery, and use rough set method to screen the core index of each battery to obtain the core attribute set of each battery evaluation index; The operation module is used to perform intersection and difference set operations on the core attribute sets of all kinds of battery evaluation indexes to obtain the common core attribute set and the unique core attribute set of each battery; The sorting module is used to determine the influence degree of each evaluation index based on the Pearson correlation coefficients between each evaluation index in the common core attribute set and the unique core attribute set and the overall score; The screening module includes a making submodule, a making submodule, a calculating submodule and a core attribute determination submodule; The making submodule is used to arrange the data of each evaluation index of each battery into a knowledge expression system, and make a decision information table with m expert information as rows, n battery indexes and the overall score as columns, where m is the number of experts and n is the number of battery indexes; The making submodule is used to number the evaluation indexes of each knowledge expression system, grade the data of the same index of different kinds of batteries, and replace the index data with grade numbers to obtain a decision table composed of grade numbers; The calculating submodule is used to take the set of each evaluation index of the battery as the criterion set, take the set of the overall score as the decision criterion set, and calculate the positive domain of the criterion set and the decision criterion set; The core attribute determination submodule is used to determine the core attribute set of each battery about the battery evaluation index based on the positive domain and the criterion set; ​ The sorting module is specifically configured to: calculate a Pearson correlation coefficient between each element in a common core attribute set affecting frequency modulation capability and a total score affecting frequency modulation capability respectively; calculate a mean value of the Pearson correlation coefficient of each index under all kinds of batteries based on the Pearson correlation coefficient between the element and the total score affecting frequency modulation capability; sort the mean values of the Pearson correlation coefficients of the indexes from large to small to obtain a common index sorting affecting frequency modulation capability from large to small; calculate a Pearson correlation coefficient between each index in a specific core attribute set of each battery and the total score affecting frequency modulation capability respectively; and arrange the Pearson correlation coefficients between the indexes and the total score affecting frequency modulation capability in descending order to obtain a specific index sorting affecting frequency modulation service energy storage selection of different kinds of batteries.

7. The system of claim 6, wherein, The scoring module is specifically configured to: determine the energy storage battery type applied to the frequency modulation scene; count the evaluation indexes of each energy storage battery and develop a questionnaire based on the evaluation indexes of each energy storage battery; score the application of each battery to the frequency modulation scene to obtain a total score of the application of each battery to the frequency modulation scene.

8. The system of claim 6, wherein, The core attribute determination submodule is specifically configured to: remove each battery data in the criterion set in turn to obtain a set containing different kinds of battery data, and compare the set containing different kinds of battery data with the positive domain respectively, if they are completely equal, the removed battery does not belong to the core attribute set, otherwise, the removed battery belongs to the core attribute set, to obtain the core attribute set of each battery with respect to the battery evaluation index.

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