CN111695623A - Large-scale battery energy storage system group modeling method, system and equipment based on fuzzy clustering and readable storage medium - Google Patents
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Abstract
The invention discloses a fuzzy clustering-based large-scale battery energy storage system group modeling method, which comprises the steps of dividing an energy storage power station into m energy storage modules, and acquiring r parameters (r < m) of each energy storage module; performing principal component analysis on the parameters, performing fuzzy clustering on the energy storage module according to p principal components with high contribution degrees, evaluating to obtain an optimal clustering result, and performing equivalent modeling on the battery pack; the method comprehensively considers the relation between the inner layer energy distribution mode of the energy storage system and the SOC of the energy storage module and the inconsistency of the energy storage module, provides a simulation method which accords with the external characteristics of the energy storage power station, simplifies the detailed model of the energy storage power station, shortens the simulation calculation time of the energy storage module in the power system simulation project, is beneficial to researching the influence of the control mode of the energy storage system on the operation mode of the battery module, is convenient for configuring the energy storage system with proper capacity according to actual needs, and the like.
Description
Technical Field
The invention belongs to the technical field of energy storage system simulation, and particularly relates to a large-scale battery energy storage system group modeling method, system, equipment and readable storage medium based on fuzzy clustering.
Background
In the operation process of the energy storage power station, because the operation temperature, the string current, the battery capacity consistency and the like are different, the internal parameters, the response mode and the operation state of each energy storage module are also different. The difference between the energy storage units needs to be considered when the energy storage system is subjected to simulation modeling, and for a large-scale energy storage power station, the number of the energy storage modules is large, and the simulation speed is seriously slowed down when all the energy storage modules are listed and calculated. In the actual operation process of each energy storage unit, certain energy storage modules are in similar operation states at a certain moment due to the control action, and therefore a large-scale battery energy storage system group modeling method based on fuzzy clustering is needed.
Disclosure of Invention
The invention provides a fuzzy clustering-based large-scale battery energy storage system group modeling method, a fuzzy clustering-based large-scale battery energy storage system group modeling system, a fuzzy clustering-based large-scale battery energy storage system group modeling device and a readable storage medium, and aims to solve the problem that modeling considering inconsistency of a battery pack in the prior art is less.
The invention relates to a large-scale battery energy storage system grouping modeling method based on fuzzy clustering, which comprises the following steps,
the method comprises the steps of firstly, dividing an energy storage power station into m energy storage modules, and acquiring r parameters of each energy storage module;
secondly, performing principal component analysis on the obtained r parameters to obtain p principal components; carrying out fuzzy clustering on the energy storage modules according to the main components to obtain a plurality of energy storage battery packs;
thirdly, performing clustering evaluation on the plurality of energy storage battery packs to obtain an optimal clustering result; and determining the number of the energy storage battery packs according to the optimal clustering result, performing equivalent modeling on the energy storage battery packs, calculating equivalent parameters based on the parameters of the energy storage modules to obtain grouping parameters, and completing the grouping modeling of the large-scale battery energy storage system.
Preferably, in the first step, the acquired R parameters of each energy storage module include a remaining power SOC value, an active power reference value of the energy storage module, and an ohmic internal resistance R of the battery moduleΩPolarization internal resistance Rp, and id, iq, ud and uq after dq decomposition is carried out on current I and voltage U of the energy storage module; wherein the conversion formula of dq decomposition is shown as formula (1),
in the formula, θ is a phase angle.
Preferably, in the second step, the obtained r parameters are subjected to principal component analysis, a parameter matrix is constructed and standardized; p principal components are obtained by determining a p value so that the information utilization rate of the principal components becomes 85% or more.
Further, the specific steps of performing principal component analysis on the acquired r parameters are as follows,
step 2.1, set the r parameters to vector x ═ x (x)1,x2,......xr) Then energy storage module xi＝(xi1,xi2,......xir)，i＝1,2，……m；
Step 2.2, according to the setting in the step 2.1, constructing a parameter matrix and carrying out standardization transformation to obtain a standardization matrix A as the formula (2),
wherein,
step 2.3, solving a correlation coefficient matrix of the normalized matrix A, as shown in formula (4):
step 2.4, solving the characteristic root lambda of the coefficient matrix HkAnd corresponding feature vector bkDetermining a p value according to the following discrimination formula (5);
step 2.5, p principal component vectors Z are obtainedk＝Abk,k＝1,2,......p。
Further, in the second step, the principal component vectors obtained by a principal component analysis method are used as classification bases, fuzzy clustering is carried out on the energy storage modules, the optimal clustering result is selected, and the m energy storage modules are divided into n groups of energy storage battery packs; the method specifically comprises the following steps of,
a. constructing a principal component parameter matrix W ═ (Z) of m energy storage modules1,Z2,......Zp) Setting the classification into n classes, n being taken
Setting the clustering center as V: { v1,v2,...vn}, set uijA probability of dividing the jth object into the ith class;
b. the given objective function is as follows (6):
wherein,
α is the fuzzy index, WjIs the jth column vector, v, of the principal component parameter matrixjkJ column vector, u, for the k cluster centerijA probability of dividing the jth object into the ith class;
c. and repeatedly iterating and solving to minimize the objective function J, and selecting the optimal clustering result to obtain the group n of the energy storage battery pack.
Thirdly, fuzzy clustering is carried out on the energy storage module according to the p main components with high contribution degrees, and the value of n is determined to obtain the optimal clustering result; the optimal clustering result is the clustering result with the minimum Xie-Beni index; wherein, the Xie-Beni index formula is as shown in formula (7):
wherein, WjIs the jth column vector of the principal component parameter matrix, and v is the clustering center corresponding to the subscript.
Preferably, in the third step, the energy storage battery pack after fuzzy clustering is subjected to equivalent modeling of external characteristics by adopting a parallel connection mode, and the formula of the equivalent parameters is shown as the formula (8):
in the formula, PrefThe method comprises the steps of calculating a grouping parameter of n groups of battery packs, wherein the grouping parameter is an active power reference value of an energy storage module, SOC is residual capacity, U and I are respectively corresponding values of voltage U and current I of the energy storage module, subscript I is 1,2, … … m, and k is the number of battery modules contained in a certain group of energy storage battery packs.
A large-scale battery energy storage system group modeling system based on fuzzy clustering comprises,
the energy storage module dividing and collecting module is used for dividing the energy storage power station into m energy storage modules and acquiring r parameters of each energy storage module;
the energy storage battery group forming module is used for carrying out principal component analysis on the obtained r parameters to obtain p principal components; carrying out fuzzy clustering on the energy storage modules according to the main components to obtain a plurality of energy storage battery packs;
and the grouping modeling module is used for carrying out clustering evaluation on the plurality of energy storage battery packs to obtain the optimal clustering result, carrying out equivalent modeling on the energy storage battery packs, calculating equivalent parameters based on the parameters of the energy storage module to obtain grouping parameters, and completing the grouping modeling of the large-scale battery energy storage system.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the fuzzy clustering based large-scale battery energy storage system grouping modeling method according to any one of the above aspects when executing the computer program.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method for modeling a large-scale battery energy storage system group based on fuzzy clustering according to any one of the above aspects.
The invention has the beneficial effects that:
aiming at the problem that modeling considering the inconsistency of the battery pack is less in the prior art, the energy storage power station is divided into m energy storage modules, r parameters (r < m) of each energy storage module are obtained, principal component analysis is carried out on the parameters, fuzzy clustering is carried out on the energy storage modules according to p principal components with high contribution degree, evaluation is carried out on the energy storage modules to obtain the optimal clustering result, and equivalent modeling is carried out on the battery pack; the battery pack modeling scheme under the PQ control mode is provided, the operation difference among the energy storage units is considered, and the simulation accuracy is improved; the method comprehensively considers the relation between the inner layer energy distribution mode of the energy storage system and the SOC of the energy storage module and the inconsistency of the energy storage module, provides a simulation method which accords with the external characteristics of the energy storage power station, simplifies the detailed model of the energy storage power station, shortens the simulation calculation time of the energy storage module in the power system simulation project, is beneficial to researching the influence of the control mode of the energy storage system on the operation mode of the battery module, and is convenient for configuring the energy storage system with proper capacity according to actual needs and other multi-aspect research works.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of the large-scale battery energy storage system grouping modeling method based on fuzzy clustering.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The invention relates to a group modeling method of a large-scale battery energy storage system based on fuzzy clustering, which is shown in figure 1 and comprises the following steps,
firstly, dividing an energy storage power station into m energy storage modules, and acquiring an SOC value of each module and an active power reference value P of each energy storage modulerefVoltage U and current I of energy storage module and ohmic internal resistance R of battery moduleΩThe quantity of the polarization internal resistance Rp is less than that of the energy storage modules;
wherein SOC is residual electric quantity, and the common percentage indicates the active power reference value P of the energy storage modulerefIs a main factor influencing the charge and discharge power of energy storage;
secondly, performing abc-dq transformation on each energy storage module U, I to obtain id, iq, ud and uq so as to obtain r different parameters for distinguishing the energy storage modules in the first step, and dividing m different operating states of the energy storage modules; using the following abc-dq transformation formula,
third, for SOC and Pref、id、iq、ud、uq、RΩR parameters such as Rp, r<m; acquiring principal components to enable the information utilization rate of the principal components to be more than 85%, reducing the dimension of r parameters of the energy storage module, and reducing the number of variables in clustering analysis to reduce the operation amount; wherein:
(1) let r-dimensional parameter vector x ═ x1,x2,......xr) (ii) a m energy storage modules xi＝(xi1,xi2,......xir)，i＝1,2，……m。
(2) Constructing a parameter matrix and carrying out standardization transformation to obtain a standardization matrix A,
(3) a matrix of correlation coefficients is solved for the normalized matrix a,
(4) solving the characteristic root lambda of the coefficient matrix HkAnd corresponding feature vector bkAccording to
The p value is determined.
(5) P principal component vectors Z are obtainedk＝Abk,k＝1,2,......p；
And fourthly, taking principal component vectors obtained by a principal component analysis method as a classification basis, carrying out fuzzy clustering on the energy storage modules, and dividing the m energy storage modules into N groups of energy storage battery packs, wherein the method comprises the following steps:
(1) constructing a principal component parameter matrix W ═ (Z) of m energy storage modules1,Z2,......Zp) Setting and dividing the cluster center into n types, and setting the cluster center as V: { v1,v2,...vn}，UijA probability of dividing the jth object into the ith class;
wherein n may be taken
(2) Given an objective function of
Wherein,
α is fuzzy index, generally 2, WjIs the jth column vector, v, of the principal component parameter matrixjkJ column vector, u, for the k cluster centerijA probability of dividing the jth object into the ith class;
(3) repeatedly iterating to obtain a target function J which is the minimum; and selecting the optimal clustering result to obtain a group n of the energy storage battery pack.
And fifthly, carrying out clustering evaluation on the clustered energy storage battery pack by using an Xie-Beni index to obtain an optimal clustering result, wherein the Xie-Beni index formula is as follows:
sixthly, performing equivalent modeling on the n groups of energy storage battery packs in a parallel connection mode, calculating group parameters on the basis of the parameters of the energy storage modules, and calculating the parameters according to the following formula,
the large-scale battery energy storage system group modeling method based on fuzzy clustering of the embodiment includes the steps of firstly dividing an energy storage power station into m energy storage modules, and acquiring an SOC (state of charge, commonly-used percentage) value and an active power reference value P of each moduleref(main factors influencing energy storage charge and discharge power), voltage U and current I of the energy storage module, and ohmic internal resistance R of the battery moduleΩPolarization internal resistance Rp and other parameters (r)<m); then, each energy storage module U, I is subjected to abc-dq transformation to obtain id, iq, ud and uq; for SOC and P againref、id、iq、ud、uq、RΩPerforming principal component analysis on r parameters such as Rp to obtain p principal components, so that the information utilization rate is over 85 percent; then, taking principal components obtained by a principal component analysis method as classification bases, carrying out fuzzy clustering on the energy storage modules, and dividing m energy storage modules into n groups of energy storage battery packs; clustering evaluation is carried out on the clustered energy storage battery pack by using an Xie-Beni index; finally, performing equivalent modeling on the n groups of energy storage battery packs in a parallel connection mode, and calculating group parameters on the basis of the parameters of the energy storage modules; the method comprehensively considers the relation between the inner layer energy distribution mode of the energy storage system and the SOC of the energy storage module and the inconsistency of the energy storage module, provides a simulation method conforming to the external characteristics of the energy storage power station, simplifies the detailed model of the energy storage power station, and shortens the simulation time of the power systemThe simulation calculation time of the energy storage module in the real engineering is also beneficial to researching the influence of the control mode of the energy storage system on the operation mode of the battery module, and the energy storage system with proper capacity can be configured according to actual needs, and the like.
In a preferred embodiment of the present invention, there is also provided a large-scale battery energy storage system group modeling system based on fuzzy clustering, which corresponds to the method, including,
the energy storage module dividing and collecting module is used for dividing the energy storage power station into m energy storage modules and acquiring r parameters of each energy storage module;
the energy storage battery group forming module is used for carrying out principal component analysis on the obtained r parameters to obtain p principal components; carrying out fuzzy clustering on the energy storage modules according to the main components to obtain a plurality of energy storage battery packs;
and the grouping modeling module is used for carrying out clustering evaluation on the plurality of energy storage battery packs to obtain the optimal clustering result, carrying out equivalent modeling on the energy storage battery packs, calculating equivalent parameters based on the parameters of the energy storage module to obtain grouping parameters, and completing the grouping modeling of the large-scale battery energy storage system.
On the basis of the method and the system, the invention also provides computer equipment which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the group modeling method of the large-scale battery energy storage system based on fuzzy clustering.
A computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of the above fuzzy clustering-based large-scale battery energy storage system group modeling method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A large-scale battery energy storage system group modeling method based on fuzzy clustering is characterized by comprising the following steps,
the method comprises the steps of firstly, dividing an energy storage power station into m energy storage modules, and acquiring r parameters of each energy storage module;
secondly, performing principal component analysis on the obtained r parameters to obtain p principal components; carrying out fuzzy clustering on the energy storage modules according to the main components to obtain a plurality of energy storage battery packs;
thirdly, performing clustering evaluation on the plurality of energy storage battery packs to obtain an optimal clustering result; and determining the number of the energy storage battery packs according to the optimal clustering result, performing equivalent modeling on the energy storage battery packs, calculating equivalent parameters based on the parameters of the energy storage modules to obtain grouping parameters, and completing the grouping modeling of the large-scale battery energy storage system.
2. The fuzzy clustering-based group modeling method for the large-scale battery energy storage system according to claim 1, wherein in the first step, the acquired R parameters of each energy storage module include a remaining power SOC value, an active power reference value of the energy storage module, and an ohmic internal resistance R of the battery moduleΩPolarization internal resistance Rp, and id, iq, ud and uq after dq decomposition is carried out on current I and voltage U of the energy storage module; wherein the conversion formula of dq decomposition is shown as formula (1),
in the formula, θ is a phase angle.
3. The large-scale battery energy storage system group modeling method based on fuzzy clustering of claim 1, characterized in that in the second step, the principal component analysis is performed on the obtained r parameters, a parameter matrix is constructed and standardized; p principal components are obtained by determining a p value so that the information utilization rate of the principal components becomes 85% or more.
4. The large-scale battery energy storage system group modeling method based on fuzzy clustering of claim 3, wherein the specific steps of performing principal component analysis on the obtained r parameters are as follows,
step 2.1, set the r parameters to vector x ═ x (x)1,x2,......xr) Then energy storage module xi＝(xi1,xi2,......xir)，i＝1,2，……m；
Step 2.2, according to the setting in the step 2.1, constructing a parameter matrix and carrying out standardization transformation to obtain a standardization matrix A as the formula (2),
wherein,
step 2.3, solving a correlation coefficient matrix of the normalized matrix A, as shown in formula (4):
step 2.4, solving the characteristic root lambda of the coefficient matrix HkAnd corresponding feature vector bkDetermining a p value according to the following discrimination formula (5);
step 2.5, p principal component vectors Z are obtainedk＝Abk,k＝1,2,......p。
5. The large-scale battery energy storage system grouping modeling method based on fuzzy clustering of claim 4, characterized in that in the second step, the principal component vectors obtained by the principal component analysis method are used as a classification basis to perform fuzzy clustering on the energy storage modules, so as to select the best clustering result, and divide m energy storage modules into n groups of energy storage battery packs; the method specifically comprises the following steps of,
a. constructing a principal component parameter matrix W ═ (Z) of m energy storage modules1,Z2,......Zp) Setting the classification into n classes, n being taken
Setting the clustering center as V: { v1,v2,...vn}, set uijA probability of dividing the jth object into the ith class;
b. the given objective function is as follows (6):
wherein,
α is the fuzzy index, WjIs the jth column vector, v, of the principal component parameter matrixjkJ column vector, u, for the k cluster centerijA probability of dividing the jth object into the ith class;
c. and repeatedly iterating and solving to minimize the objective function J, and selecting the optimal clustering result to obtain the group n of the energy storage battery pack.
6. The large-scale battery energy storage system group modeling method based on fuzzy clustering of claim 5, wherein in the third step, fuzzy clustering is performed on the energy storage module according to p principal components with high contribution degree, and the value of n is determined to obtain the best clustering result; the optimal clustering result is the clustering result with the minimum Xie-Beni index; wherein, the Xie-Beni index formula is as shown in formula (7):
wherein, WjIs the jth column vector of the principal component parameter matrix, and v is the clustering center corresponding to the subscript.
7. The fuzzy clustering-based group modeling method for the large-scale battery energy storage system according to claim 1, wherein in the third step, the energy storage battery pack after fuzzy clustering is subjected to equivalent modeling of external characteristics in a parallel connection mode, and the formula of the equivalent parameter is as follows (8):
in the formula, PrefThe method comprises the steps of calculating a grouping parameter of n groups of battery packs, wherein the grouping parameter is an active power reference value of an energy storage module, SOC is residual capacity, U and I are respectively corresponding values of voltage U and current I of the energy storage module, subscript I is 1,2, … … m, and k is the number of battery modules contained in a certain group of energy storage battery packs.
8. A large-scale battery energy storage system group modeling system based on fuzzy clustering is characterized by comprising,
the energy storage module dividing and collecting module is used for dividing the energy storage power station into m energy storage modules and acquiring r parameters of each energy storage module;
the energy storage battery group forming module is used for carrying out principal component analysis on the obtained r parameters to obtain p principal components; carrying out fuzzy clustering on the energy storage modules according to the main components to obtain a plurality of energy storage battery packs;
and the grouping modeling module is used for carrying out clustering evaluation on the plurality of energy storage battery packs to obtain the optimal clustering result, carrying out equivalent modeling on the energy storage battery packs, calculating equivalent parameters based on the parameters of the energy storage module to obtain grouping parameters, and completing the grouping modeling of the large-scale battery energy storage system.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the fuzzy clustering based large scale battery energy storage system group modeling method of any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for large-scale battery energy storage system group modeling based on fuzzy clustering according to any one of claims 1 to 7.
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