CN112613661B - Judgment and selection system for applying multi-type batteries to energy storage - Google Patents
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- 238000004146 energy storage Methods 0.000 title claims abstract description 142
- 239000011159 matrix material Substances 0.000 claims abstract description 98
- 239000013598 vector Substances 0.000 claims description 30
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- 230000009286 beneficial effect Effects 0.000 abstract description 2
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- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 11
- QELJHCBNGDEXLD-UHFFFAOYSA-N nickel zinc Chemical compound [Ni].[Zn] QELJHCBNGDEXLD-UHFFFAOYSA-N 0.000 description 11
- 238000012983 electrochemical energy storage Methods 0.000 description 3
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- 230000004048 modification Effects 0.000 description 3
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Abstract
The invention relates to a judgment and selection system for applying multi-type batteries to energy storage, which comprises: the device comprises a battery type input module, an energy storage application parameter selection module, an energy storage application parameter importance degree sorting module, an energy storage application parameter importance degree matrix generation module, a quality degree sorting module, an economic factor selection module, a technical factor selection module, a comprehensive judgment module and an obtained battery type module; the battery type input module and the energy storage application type input module are both connected with the energy storage application parameter selection module, and the energy storage application parameter selection module is divided into two paths to be respectively connected with the energy storage application parameter importance degree sorting module and the quality degree sorting module. The invention has the beneficial effects that: the judgment and selection system for the energy storage of the multi-type batteries is provided, and the selected battery types are more reasonable by combining various technical characteristics and economic factors of the energy storage applied to the battery types.
Description
Technical Field
The invention belongs to the field of energy storage application of batteries, and particularly relates to a judgment and selection system for applying multiple types of batteries to energy storage.
Background
An electrochemical energy storage power station is one of important ways of energy storage in the field of new energy. The number of various electrochemical energy storage power stations is rapidly increased, and the number of energy storage batteries used by the corresponding electrochemical energy storage power stations is also remarkably increased. The energy storage battery types are numerous, and include a plurality of types such as lead-acid batteries, lithium ion batteries, flow batteries, nickel-hydrogen batteries, sodium-sulfur batteries, super capacitors and the like, and the various energy storage battery types can be subdivided, such as power type, energy type, high-temperature type and the like.
The energy storage application types can be divided into peak clipping and valley filling, peak and frequency modulation, household energy storage and backup application and the like due to different functions. For different applications, the performance requirements associated with energy storage batteries are different: if the peak clipping and valley filling requirements are long in continuous operation, the energy conversion efficiency is high, the cycle life is long, and the like; for example, the peak-shaving frequency modulation requires a high response speed; therefore, the appropriate battery type should be reasonably selected for the energy storage application type.
At present, two factors, namely price and service life, are mainly considered in selection, and a large number of energy storage power stations are more lead-acid batteries and lithium ion batteries.
In summary, it is very important to establish a selection system, and select the type of the energy storage battery by combining various technical features and economic factors of the energy storage application to the battery type, so that the energy storage battery is more reasonable to use.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a judgment and selection system for applying multiple types of batteries to energy storage.
The system for judging and selecting the energy storage of the multi-type batteries comprises: the device comprises a battery type input module, an energy storage application parameter selection module, an energy storage application parameter importance degree sorting module, an energy storage application parameter importance degree matrix generation module, a quality degree sorting module, an economic factor selection module, a technical factor selection module, a comprehensive judgment module and an obtained battery type module;
the battery type input module and the energy storage application type input module are both connected with the energy storage application parameter selection module, and the energy storage application parameter selection module is divided into two paths to be respectively connected with the energy storage application parameter importance degree sorting module and the quality degree sorting module; the parameter importance degree sorting module is connected with the energy storage application parameter importance degree matrix generating module, the goodness sorting module is connected with the goodness matrix generating module, and both the energy storage application parameter importance degree matrix generating module and the goodness matrix generating module are connected with the technical factor selecting module; the battery type input module is connected with the economic factor selection module, the economic factor selection module and the technical factor selection module are both connected with the comprehensive judgment module, and the comprehensive judgment module is connected with the battery type module.
Preferably, the quality degree matrix generating module is used for generating a quality degree matrix of each parameter of the battery type in the energy storage application; the quality degree sequencing module is used for obtaining the quality degree sequencing of each parameter of the battery type in the energy storage application.
The working method of the judgment and selection system for the energy storage of the multi-type batteries specifically comprises the following steps:
Step 4.1, the battery type input module inputs the battery type to be judged and selected, the battery type is transmitted into the quality degree sequencing module through the energy storage application parameter selection module, and the quality degree sequencing module obtains the quality degree sequencing of the energy storage application parameters of each battery type in the energy storage application;
4.2, the quality degree sorting module sorts the quality degrees of the energy storage application parameters of the battery types in the energy storage application and transmits the quality degrees to the quality degree matrix generating module, and the quality degree matrix generating module generates quality degree matrixes of the energy storage application parameters of the battery types in the energy storage application and respectively calculates to obtain maximum eigenvalue vectors of the quality degree matrixes;
step 4.3, the maximum eigenvalue vectors corresponding to each battery type are respectively used as a row of maximum eigenvalue vector rows, and all the maximum eigenvalue vector rows form a maximum eigenvalue vector matrix B of the goodness matrix;
C＝BA (1)
In the above formula, C is a sorting matrix of the battery type, B is a maximum eigenvalue vector matrix of the goodness matrix, and A is a maximum eigenvalue vector of the importance matrix of the energy storage application parameters;
and 6, the battery type input module transmits the battery type to the economic factor selection module, and unit price normalization data of each battery type are formed in the economic factor selection module:
normalized data per unit price for each battery type ═ 1/unit price per battery type/((1/unit price per battery type 1) + (1/unit price per battery type 2) + … + (1/unit price per battery type n)) (2)
In the above formula, n is the total number of the battery types;
factor of battery type ═ technical factor weight ═ maximum eigenvalue of battery type + unit price normalized data of battery type · economic factor weight (3)
In the above formula, the maximum eigenvalue of the battery type is the numerical value of the battery type sorting in the sorting matrix C of the battery type;
And 8, according to the factors of each battery type calculated in the step 7, the battery type corresponding to the maximum factor value of each battery type is selected by the battery type module to serve as the preferred battery for the energy storage application, and the priority of the preferred battery is in the descending order of the factors of each battery type.
Preferably, the energy storage application parameters in step 1 are two or more of duration, response speed, power density, energy density, cycle life and energy conversion efficiency.
Preferably, in the step 2, if the more important the energy storage application parameter is, the more important the energy storage application parameter is.
Preferably, in the step 4, if the energy storage application parameters of each battery type are more advantageous in the energy storage application, the better and worse the energy storage application parameters are.
Preferably, in step 3: the energy storage application parameter importance degree matrix generation module sets an n multiplied by n matrix according to n of each energy storage application parameter; the matrix is transversely arranged into a parameter 1, a parameter 2, …, a parameter n-1 and a parameter n, the matrix is arranged into a parameter 1, a parameter 2, …, a parameter n-1 and a parameter n in a column direction, if the number of rows of a row in which data is located in the matrix is i, and the number of columns of the column in which the data is located is j, the data is: importance of parameter i/importance of parameter j.
The beneficial effects of the invention are: the invention provides a judgment and selection system for applying a plurality of types of batteries to energy storage, which combines a plurality of technical characteristics and economic factors of applying energy storage to the types of the batteries to ensure that the selected types of the batteries are more reasonable.
Drawings
Fig. 1 is a block diagram of a system for determining and selecting the energy storage of multiple types of batteries;
fig. 2 is a block diagram of a parameter matrix generation module of a multi-type battery applied to an energy storage judgment and selection system.
Description of reference numerals: the device comprises a battery type input module 1, an energy storage application type input module 2, an energy storage application parameter selection module 3, an energy storage application parameter importance degree sorting module 4, an energy storage application parameter importance degree matrix generation module 5, a goodness matrix generation module 6, a goodness sorting module 7, an economic factor selection module 8, a technical factor selection module 9, a comprehensive judgment module 10 and an obtaining battery type module 11.
Detailed Description
The present invention will be further described with reference to the following examples. The following examples are set forth merely to aid in the understanding of the invention. It should be noted that, for a person skilled in the art, several modifications can be made to the invention without departing from the principle of the invention, and these modifications and modifications also fall within the protection scope of the claims of the present invention.
As an embodiment, the components of the modules of the judgment and selection system for applying multiple types of batteries to energy storage are shown in fig. 1.
Taking a certain energy storage application as an example, five types of parameters are selected, which are a parameter 1 (duration), a parameter 2 (response speed), a parameter 3 (energy density), a parameter 4 (cycle life), and a parameter 5 (energy conversion efficiency). The importance degrees of all parameters are ranked as cycle life, energy conversion efficiency, continuous operation time, response speed and energy density, and the cycle life is set to be 5, the energy conversion efficiency is set to be 4, the continuous operation time is set to be 3, the response speed is set to be 2 and the energy density is set to be 1. If the degree of importance is the same, the case of 3, 2, 1 or 5, 3, 2, 1 may be formed.
Namely, the degree of the parameter 1 (continuous operation time period) is 3, the degree of the parameter 2 (response speed) is 2, the degree of the parameter 3 (energy density) is 1, the degree of the parameter 4 (cycle life) is 5, and the degree of the parameter 5 (energy conversion efficiency) is 4.
The energy storage application parameter importance degree matrix generation module is arranged according to n multiplied by n matrixes of the number of parameters shown in fig. 2, the matrixes are transversely arranged into a parameter 1, a parameter 2, a parameter n-1 and a parameter n, the matrixes are arranged into a parameter 1, a parameter 2, a parameter n-1 and a parameter n in a column direction, data in the matrixes are arranged according to the parameters, and the degree corresponding to each parameter is divided by the degree corresponding to other parameters. The energy storage application parameter importance degree matrix is obtained through the energy storage application parameter importance degree matrix generation module as follows:
The maximum eigenvalue vector a of the matrix is: [0.20000.13330.06670.33330.2667]T；
The battery types are lithium iron phosphate, lead-acid storage battery and nickel-zinc battery. The battery type input module obtains a good and bad program sorting module of each parameter of the battery type in the energy storage application through the energy storage application parameter selection module, and then obtains a matrix through the degree matrix generation module, wherein the good and bad program sorting module comprises the following modules:
after the lithium iron phosphate, the lead-acid storage battery and the nickel-zinc battery in the parameter 1 (continuous operation time length) are sequenced by the quality program, a matrix is obtained by a degree matrix generation module as follows:
the maximum eigenvalue vector of the matrix is: [0.500.250.25]T；
In the parameter 2 (response speed), the lithium iron phosphate, the lead-acid storage battery and the nickel-zinc battery are sequenced by a quality program and then pass through a degree matrix generation module to obtain a matrix as follows:
the maximum eigenvalue vector of the matrix is: [0.400.200.40]T；
In the parameter 3 (energy density), the lithium iron phosphate, the lead-acid storage battery and the nickel-zinc battery are sequenced by a quality program and then pass through a degree matrix generation module to obtain a matrix as follows:
the maximum eigenvalue vector of the matrix is: [0.500.16670.3333]T；
In the parameter 4 (cycle life), the lithium iron phosphate, the lead-acid storage battery and the nickel-zinc battery are sequenced by a quality program and then pass through a degree matrix generation module to obtain a matrix as follows:
The maximum eigenvalue vector of the matrix is: [0.500.250.25]T；
In the parameter 5 (energy conversion efficiency), after being sorted by the quality program, the lithium iron phosphate, the lead-acid storage battery and the nickel-zinc battery obtain a matrix through a degree matrix generation module, the matrix comprises the following components:
the maximum eigenvalue vector of the matrix is: [0.500.16670.3333]T；
Finally, the battery types of the formed battery are lithium iron phosphate, a lead-acid storage battery and a nickel-zinc battery, and the matrix B is obtained under the conditions of various parameters of energy storage application as follows:
after the technical factor selection module multiplies the maximum eigenvalue vector obtained by the calculation of the importance degree matrix of the energy storage application parameters by the maximum eigenvalue vector obtained by the goodness program matrix of each parameter of the battery type in the energy storage application, a formed ranking matrix C of each battery type is as follows:
C＝BA＝[0.4867 0.2156 0.2978]T
the battery type input module enters an economic factor selection module, the economic factor selection module is unit price normalization data of each battery type, and the normalization data is obtained by dividing the reciprocal of the unit price of each battery type by the reciprocal of the unit price of all the selected batteries.
For example, the unit prices of cell types such as lithium iron phosphate, lead-acid battery, and nickel-zinc battery are 1.0 yuan/Wh, 0.8 yuan/Wh, and 1.2 yuan/Wh, respectively.
The unit price normalization of the battery type lithium iron phosphate, the lead-acid storage battery and the nickel-zinc battery is respectively 0.324, 0.405 and 0.270.
And the technical factor selection module and the economic factor selection module finally generate and obtain a battery type module through the comprehensive judgment module. The technical factor weight is 0.65, and the economic factor weight is 0.35.
The factor of the lithium iron phosphate is 0.4867 multiplied by 0.65+0.324 multiplied by 0.35-0.430; the lead-acid storage battery factor is 0.2156 multiplied by 0.65+0.405 multiplied by 0.35 to 0.282; the nickel-zinc cell factor is 0.2978 × 0.65+0.270 × 0.35-0.288; the data retains three decimal places.
Finally, the type of the battery to be selected in the energy storage application is lithium iron phosphate, and the battery is selected to be a nickel-zinc battery.
Claims (1)
1. A working method of a judging and selecting system for applying multi-type batteries to energy storage is characterized in that the judging and selecting system for applying the multi-type batteries to the energy storage comprises the following steps: the device comprises a battery type input module (1), an energy storage application type input module (2), an energy storage application parameter selection module (3), an energy storage application parameter importance degree sorting module (4), an energy storage application parameter importance degree matrix generation module (5), a goodness degree matrix generation module (6), a goodness degree sorting module (7), an economic factor selection module (8), a technical factor selection module (9), a comprehensive judgment module (10) and an obtaining battery type module (11);
The battery type input module (1) and the energy storage application type input module (2) are both connected with the energy storage application parameter selection module (3), and the energy storage application parameter selection module (3) is divided into two paths to be respectively connected with the energy storage application parameter importance degree sorting module (4) and the quality degree sorting module (7); the energy storage application parameter importance degree sorting module (4) is connected with the energy storage application parameter importance degree matrix generating module (5), the goodness sorting module (7) is connected with the goodness matrix generating module (6), and both the energy storage application parameter importance degree matrix generating module (5) and the goodness matrix generating module (6) are connected with the technical factor selecting module (9); the battery type input module (1) is connected with the economic factor selection module (8), the economic factor selection module (8) and the technical factor selection module (9) are both connected with the comprehensive judgment module (10), and the comprehensive judgment module (10) is connected with the battery type module (11);
the quality degree matrix generating module (6) is used for generating a quality degree matrix of each parameter of the battery type in the energy storage application; the quality degree sequencing module (7) is used for obtaining quality degree sequencing of each parameter of the battery type in the energy storage application;
The working method specifically comprises the following steps:
step 1, inputting the type of energy storage application through an energy storage application type input module (2), and selecting the type of energy storage application parameters through an energy storage application parameter selection module (3); the energy storage application parameters are more than two of continuous operation duration, response speed, power density, energy density, cycle life and energy conversion efficiency;
step 2, sorting the importance degrees of the selected energy storage application parameter types through an energy storage application parameter importance degree sorting module (4); if the importance degrees of the multiple energy storage application parameters are the same, arranging the energy storage application parameters with the same importance degrees in the same sequence; if the more important the energy storage application parameter is, the more important the energy storage application parameter is;
step 3, generating an importance degree matrix of the energy storage application parameters through an energy storage application parameter importance degree matrix generating module (5), and calculating to obtain a maximum eigenvalue vector A of the importance degree matrix; the energy storage application parameter importance degree matrix generation module (5) sets an n multiplied by n matrix according to n of each energy storage application parameter; the matrix is transversely arranged into a parameter 1, a parameter 2, …, a parameter n-1 and a parameter n, the matrix is arranged into a parameter 1, a parameter 2, …, a parameter n-1 and a parameter n in a column direction, if the number of rows of a row in which data is located in the matrix is i, and the number of columns of the column in which the data is located is j, the data is: importance of parameter i/importance of parameter j;
Step 4, obtaining the ranking of the goodness and the badness of the energy storage application parameters of each battery type in the energy storage application, generating a goodness matrix of the energy storage application parameters of each battery type in the energy storage application, and forming a maximum eigenvalue vector matrix B of the goodness matrix; if the energy storage application parameters of each battery type are more advantageous in the energy storage application, the quality degree of the energy storage application parameters is higher;
step 4.1, the battery type input module (1) inputs the battery type to be judged and selected, the battery type is transmitted into the quality degree sorting module (7) through the energy storage application parameter selection module (3), and the quality degree sorting module (7) obtains the quality degree sorting of the energy storage application parameters of each battery type in the energy storage application;
4.2, sequencing the goodness and the badness of the energy storage application parameters of each battery type in the energy storage application by a goodness degree sequencing module (7) and transmitting the goodness degree sequencing of the energy storage application parameters of each battery type in the energy storage application to a goodness degree matrix generating module (6), generating a goodness degree matrix of the energy storage application parameters of each battery type in the energy storage application by the goodness degree matrix generating module (6), and respectively calculating to obtain a maximum eigenvalue vector of the goodness degree matrix;
step 4.3, the maximum eigenvalue vectors corresponding to each battery type are respectively used as a row of maximum eigenvalue vector rows, and all the maximum eigenvalue vector rows form a maximum eigenvalue vector matrix B of the goodness matrix;
Step 5, the goodness degree matrix generating module (6) transmits the maximum eigenvalue vector B of the goodness degree matrix into the technical factor selecting module (9), and the energy storage application parameter importance degree matrix generating module (5) also transmits the maximum eigenvalue vector A of the importance degree matrix of the energy storage application parameters into the technical factor selecting module (9); calculating to obtain a sequencing matrix C of the battery types:
C=BA （1）
in the above formula, C is a sorting matrix of the battery type, B is a maximum eigenvalue vector matrix of the goodness matrix, and A is a maximum eigenvalue vector of the importance matrix of the energy storage application parameters;
step 6, the battery type input module (1) transmits the battery type into the economic factor selection module (8), and unit price normalization data of each battery type is formed in the economic factor selection module (8):
normalized data per unit price for each battery type = (1/per unit price for battery type)/((1/1 unit price for battery type) + (1/2 unit price for battery type) + … + (1/n unit price for battery type)) (2)
In the above formula, n is the total number of the battery types;
7, a technical factor selection module (9) transmits the sequencing matrix C of the battery types into a comprehensive judgment module (10), and an economic factor selection module (8) transmits unit price normalization data of each battery type into the comprehensive judgment module (10); factors for each battery type were calculated:
Factor of battery type = technical factor weight maximum eigenvalue of battery type + unit price normalized data of battery type economic factor weight (3)
In the above formula, the maximum eigenvalue of the battery type is the numerical value of the battery type sorting in the sorting matrix C of the battery type;
and 8, according to the factors of each battery type calculated in the step 7, the battery type module (11) is obtained to select the battery type corresponding to the maximum factor of each battery type as the preferred battery for the energy storage application, and the priority of the preferred battery is in the descending order of the factors of each battery type.
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