CN112258024B - Mixed energy storage capacity configuration method and system based on entropy weight method - Google Patents

Mixed energy storage capacity configuration method and system based on entropy weight method Download PDF

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CN112258024B
CN112258024B CN202011127826.6A CN202011127826A CN112258024B CN 112258024 B CN112258024 B CN 112258024B CN 202011127826 A CN202011127826 A CN 202011127826A CN 112258024 B CN112258024 B CN 112258024B
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俞晓冬
董霞
刘树林
臧宏志
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Abstract

The invention provides a mixed energy storage capacity configuration method and a system based on an entropy weight method, which comprises the following steps: appointing an evaluation index according to the energy storage configuration result of each typical day, and taking the number of the typical days as the number of the evaluation indexes; taking the power surplus which is not stabilized by the hybrid energy storage in each typical day as an evaluation object; setting sampling frequency and research period, wherein the quotient of the research period and the adopted frequency is used as the number of evaluation objects; performing energy storage capacity configuration by an entropy weight method according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof; the entropy weight method of the invention enters into the hybrid energy storage configuration, thus effectively improving the accuracy and the rationality of the final configuration result of the energy storage capacity.

Description

Mixed energy storage capacity configuration method and system based on entropy weight method
Technical Field
The invention belongs to the technical field of wind power, and particularly relates to a hybrid energy storage configuration method and system based on an entropy weight method.
Background
In the capacity configuration process of an energy storage system in a wind power plant, a configuration result of a certain typical day is generally used as a final energy storage configuration result, namely a typical day method, and the obtained energy storage capacity configuration by the method has too large contingency and cannot accurately reflect the true level of the whole year; there is also a mathematical expectation of the results of the placement on each typical day, but the effect of each typical day on the final result is not reasonable enough, although improved. In fact, the results of each typical day have different effects on the final result, i.e. different weights; how to reasonably determine the weight of the configuration result of each typical day not only influences the final configuration result of the energy storage capacity, but also influences the grid connection of the wind power plant and influences the consumption of the wind power.
Entropy is the concept of the thermodynamic category at first, the definition in the physical sense is the measurement of the chaos degree, and the entropy is introduced into the information theory by C.E.Shannon, and has been widely applied in the fields of engineering technology, social economy and the like, and is called as information entropy. Information entropy is a measure of the information and uncertainty of a random variable, which can be measuredThe uncertainty of the random variable represents that the uncertainty of the random variable is maximum when the entropy is maximum, and the information entropy is defined by the formula:
Figure BDA0002734184520000011
wherein, p (x)i) Represents a random event X being XiThe probability of (d);
h (x) is information entropy;
the weights obtained based on the information entropy calculation can reflect the difference between different indexes more accurately. The entropy weight method is to judge the information quantity provided by the index according to the size of the information entropy of the index. The smaller the entropy is, the larger the information quantity is, and the larger the weight in the comprehensive evaluation system is; conversely, the larger the entropy, the smaller the information amount, and the smaller the weight in the comprehensive evaluation system. Compared with other evaluation models, the entropy weight method is based on the information content contained in the data, and has the advantages of objectivity and accuracy.
Disclosure of Invention
In view of the above disadvantages in the prior art, the present invention provides a hybrid energy storage configuration method and system based on an entropy weight method to solve the above technical problems.
In a first aspect, the present invention provides a hybrid energy storage configuration method based on an entropy weight method, including:
appointing an evaluation index according to the energy storage configuration result of each typical day, and taking the number of the typical days as the number of the evaluation indexes;
taking the power surplus which is not stabilized by the hybrid energy storage in each typical day as an evaluation object;
setting sampling frequency and research period, wherein the quotient of the research period and the adopted frequency is used as the number of evaluation objects;
and performing energy storage capacity configuration by an entropy weight method according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof.
Further, the configuring the energy storage capacity according to the evaluation indexes and the number thereof, the evaluation objects and the number thereof by an entropy weight method includes:
(1) according to the evaluation indexes and the number thereof, and the evaluation objects and the number thereofCounting, establishing an original evaluation matrix, and recording as A [ a ]ij]m×nM is the number of evaluation targets, n is the number of evaluation indexes, aijIs an evaluation index;
(2) the matrix a is subjected to a data normalization process,
Figure BDA0002734184520000021
Pwobtaining a normalized matrix A ' ═ a ' for the installed capacity of the wind power plant 'ij)m×n，
Further calculating and processing to obtain
Figure BDA0002734184520000022
(3) And (3) solving the information entropy of the jth typical daily energy storage configuration result:
Figure BDA0002734184520000023
wherein: e.g. of the typejInformation entropy of the jth typical day result;
Figure BDA0002734184520000031
if p isijWhen 0, ln p is definedij＝0；
(4) Determining the weight w of the jth typical daily energy storage configuration resultj；
Figure BDA0002734184520000032
(5) Weighting the result of each typical day through the entropy weight of each typical day, thereby obtaining an entropy weight method energy storage configuration result: s ═ w1S1+w2S2+w3S3+w4S4S is the entropy weight method configuration result of the energy storage capacity, and S1, S2, S3, and S4 are the energy storage capacity configuration results for each typical day, respectively.
Further, the method further comprises:
calculating the average capacity error and the power error of the energy storage configuration results of a typical daily method, an expected value method and an entropy weight method;
and the accuracy of the energy storage capacity is configured according to the average capacity error and the power error evaluation entropy weight method.
In a second aspect, the present invention provides a hybrid energy storage configuration system based on an entropy weight method, including:
the evaluation preparation unit is configured for specifying evaluation indexes according to the energy storage configuration result of each typical day and taking the number of the typical days as the number of the evaluation indexes;
an object setting unit configured to set, as an evaluation object, a power remaining amount that is not stabilized by hybrid energy storage on each typical day;
the device comprises a sampling setting unit, a data processing unit and a data processing unit, wherein the sampling setting unit is configured to set sampling frequency and research period, and the quotient of the research period and the adopted frequency is used as the number of evaluation objects;
and the energy storage configuration unit is configured to perform energy storage capacity configuration by an entropy weight method according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof.
Further, the energy storage configuration unit includes:
a matrix establishing module configured to establish an original evaluation matrix A [ a ] according to the evaluation indexes and the evaluation indexes, and the evaluation objectsij]m×nM is the number of evaluation targets, n is the number of evaluation indexes, aijIs an evaluation index;
a matrix normalization module configured to perform data normalization on the matrix A,
Figure BDA0002734184520000041
Pwobtaining a normalized matrix A ' ═ a ' for the installed capacity of the wind power plant 'ij)m×n，
Further calculating and processing to obtain
Figure BDA0002734184520000042
The information entropy calculation module is configured to calculate the information entropy of the jth typical daily energy storage configuration result:
wherein: e.g. of the typejEntropy of information for jth typical day result
Figure BDA0002734184520000043
And is
Figure BDA0002734184520000044
If p isijWhen 0, ln p is definedij＝0；
A weight calculation module for determining the weight w of the jth typical daily energy storage configuration resultj：
Figure BDA0002734184520000045
And the result output unit is configured to perform weighting processing on the result of each typical day through the entropy weight of each typical day, so as to obtain an entropy weight method energy storage configuration result: s ═ w1S1+w2S2+w3S3+w4S4S is the entropy and weight method configuration result of the energy storage capacity, and S1, S2, S3, and S4 are the energy storage capacity configuration results of each typical day, respectively.
Further, the system further comprises:
the error calculation unit is configured for calculating the average capacity error and the power error of the energy storage configuration results of the typical daily method, the expected value method and the entropy weight method;
and the result evaluation unit is configured to evaluate the accuracy of the energy storage capacity according to the average capacity error and the power error.
The beneficial effect of the invention is that,
according to the mixed energy storage configuration method and system based on the entropy weight method, the weight of the energy storage result of the typical daily method is analyzed, the residual power quantity of which the energy storage is not stabilized is taken as an evaluation object, and the information content and uncertainty contained in the residual power quantity of which the energy storage is not stabilized are analyzed, so that the weight of each typical daily result in the final result is determined, and the accuracy and the reasonability of the final configuration result of the energy storage are effectively improved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a diagram showing the result of energy storage capacity allocation on each typical day in the embodiment of the present application.
Fig. 2 is a graph of the remaining power of the energy storage on each typical day in the example of the present application.
FIG. 3 is an entropy weight graph of typical daily results in an embodiment of the present application.
Fig. 4 is a diagram comparing the final results of various methods of energy storage configurations in the embodiments of the present application.
Fig. 5 is a diagram illustrating an examination of energy storage configuration results of various methods according to an embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a first aspect, an embodiment of the present application provides a hybrid energy storage configuration method based on an entropy weight method, including:
1. specifying an evaluation index according to the energy storage configuration result of each typical day, wherein the energy storage capacity configuration result of each typical day is shown in figure 1, and the number of the typical days is taken as the number of the evaluation indexes; the remaining power amount which is not stabilized by the hybrid energy storage in each typical day is taken as an evaluation object, and the remaining power amount which is not stabilized in each typical day is shown in fig. 2; setting sampling frequency and research period, wherein the quotient of the research period and the adopted frequency is used as the number of evaluation objects; the essence of the entropy weight method is to "estimate" the information entropy of the index by using a limited number of samples, and when the number of samples is too small, the weight calculated based on the entropy weight method may have a large error, so the number of samples must be greater than or equal to the number of the index, the sampling frequency in the embodiment used in the method is 20 seconds, the study period is one day, the number of samples obtained by calculation per day is 4320, and therefore, the number m of the evaluation objects in the embodiment is 4320;
2. and performing energy storage capacity configuration by an entropy weight method according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof.
(1) Taking the power surplus of 4320 sampling points of each typical working day as an evaluation object, taking the configuration results of 4 typical days as evaluation indexes, establishing an original evaluation matrix, and recording the original evaluation matrix as A [ a ]ij]m×nM is the number of evaluation targets, n is the number of evaluation indexes, aijIs an evaluation index;
(2) establishing an original evaluation matrix according to the evaluation indexes and the number thereof, and the evaluation objects and the number thereof, and marking as A [ a ]ij]m×nM is the number of evaluation targets, n is the number of evaluation indexes, aijIs an evaluation index;
(3) the matrix a is subjected to a data normalization process,
Figure BDA0002734184520000061
Pwobtaining a normalized matrix A ' ═ a ' for the installed capacity of the wind power plant 'ij)m×n；
Further calculating and processing to obtain
Figure BDA0002734184520000062
(4) And (3) solving the information entropy of the jth typical daily energy storage configuration result:
Figure BDA0002734184520000063
wherein: e.g. of the typejInformation entropy of the jth typical day result;
Figure BDA0002734184520000064
if p isijWhen 0, ln p is definedij＝0；
(5) Determining the weight w of the jth typical daily energy storage configuration resultjThe results are shown in FIG. 3 as entropy weights for each typical day;
Figure BDA0002734184520000065
(6) weighting the result of each typical day through the entropy weight of each typical day, thereby obtaining an entropy weight method energy storage configuration result: s ═ w1S1+w2S2+w3S3+w4S4S is the entropy weight method configuration result of the energy storage capacity, and S1, S2, S3, and S4 are the energy storage capacity configuration results of each typical day, respectively, as shown in fig. 4, and are compared with the expected value method and the typical day method.
3. In order to verify the effectiveness of the entropy weight method, data of 12 days in total are randomly selected from 1 day in each month of the year as test data, and the average capacity error and the power error of various energy storage configuration methods are calculated, as shown in fig. 5, and the following conclusions can be drawn from fig. 5:
(1) the error of the final energy storage configuration scheme which is the result of the random typical day is large, because the contingency of the typical day data is too strong, the fluctuation characteristic of the wind power all the year around is difficult to reflect;
(2) compared with a typical daily method, the expectation method is improved to a certain extent on capacity errors and power errors, but has a certain difference from the entropy weight method, so that the expectation method is too simple in weight determination and is not scientific enough;
(3) compared with an expected value method, the capacity error and the power error of the configuration scheme determined by the entropy weight method are both the lowest, and the entropy weight method determines the weight according to the information content and the uncertainty degree of each typical day result, so that the difference degree and the importance level of each typical day result can be objectively and effectively reflected, and the method is more accurate and reasonable.
In summary, in the present embodiment, the entropy weight method determines the weight according to the amount of information included in each typical day result and the size of the degree of uncertainty, and can objectively and effectively reflect the difference degree and the importance level of each typical day result, so that the method is more accurate and reasonable, and the test result indicates that the capacity error and the power error of the energy storage capacity final configuration obtained by the entropy weight method are both the lowest. Therefore, the energy storage capacity configuration method based on the entropy weight method can objectively evaluate the weight of each typical daily result, and effectively improves the accuracy and the reasonability of the final configuration result of energy storage.
In a second aspect, an embodiment of the present application provides an entropy weight method-based hybrid energy storage configuration system, including:
the evaluation preparation unit is configured for specifying evaluation indexes according to the energy storage configuration result of each typical day and taking the number of the typical days as the number of the evaluation indexes;
an object setting unit configured to set, as an evaluation object, a power remaining amount that is not stabilized by hybrid energy storage on each typical day;
the device comprises a sampling setting unit, a data processing unit and a data processing unit, wherein the sampling setting unit is configured to set sampling frequency and research period, and the quotient of the research period and the adopted frequency is used as the number of evaluation objects;
and the energy storage configuration unit is configured for performing energy storage capacity configuration by an entropy weight method according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof.
Optionally, as an embodiment of the present application, the energy storage configuration unit includes:
a matrix establishing module configured to establish an original evaluation matrix A [ a ] according to the evaluation indexes and the number thereof and the evaluation objects and the number thereofij]m×nM is the number of evaluation targets, n is the number of evaluation indexes, aijIs an evaluation index;
matrix normalizationA module configured to perform data normalization processing on the matrix A,
Figure BDA0002734184520000081
Pwobtaining a normalized matrix A ' ═ a ' for the installed capacity of the wind power plant 'ij)m×n，
Further calculating and processing to obtain
Figure BDA0002734184520000082
The information entropy calculation module is configured to calculate the information entropy of the jth typical daily energy storage configuration result:
wherein: e.g. of the typejThe entropy of the information for the jth typical day result,
Figure BDA0002734184520000083
and is
Figure BDA0002734184520000084
If p isijWhen equal to 0, ln p is definedij＝0；
A weight calculation module for determining the weight w of the jth typical daily energy storage configuration resultj：
Figure BDA0002734184520000085
And the result output unit is configured to perform weighting processing on the result of each typical day through the entropy weight of each typical day, so as to obtain an entropy weight method energy storage configuration result: s ═ w1S1+w2S2+w3S3+w4S4S is the entropy weight method configuration result of the energy storage capacity, and S1, S2, S3, and S4 are the energy storage capacity configuration results for each typical day, respectively.
Optionally, as an embodiment of the present application, the system further includes:
the error calculation unit is configured for calculating the average capacity error and the power error of the energy storage configuration results of the typical daily method, the expected value method and the entropy weight method;
and the result evaluation unit is configured to evaluate the accuracy of the energy storage capacity according to the average capacity error and the power error.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (7)

1. A hybrid energy storage configuration method based on an entropy weight method is characterized by comprising the following steps:
appointing an evaluation index according to the energy storage configuration result of each typical day, and taking the number of the typical days as the number of the evaluation indexes;
taking the power surplus which is not stabilized by the hybrid energy storage in each typical day as an evaluation object;
setting sampling frequency and research period, wherein the quotient of the research period and the adopted frequency is used as the number of evaluation objects;
and performing energy storage capacity configuration by an entropy weight method according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof.
2. The entropy-weight-method-based hybrid energy storage configuration method according to claim 1, wherein the energy storage capacity configuration according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof by the entropy weight method comprises:
(1) establishing an original evaluation matrix A [ a ] according to the evaluation indexes and the number thereof and the evaluation objects and the number thereofij]m×nM is the number of evaluation targets, n is the scoreNumber of price indices, aijIs an evaluation index;
(2) the matrix a is subjected to a data normalization process,
Figure FDA0003613620670000011
Pwobtaining a normalized matrix A ═ a 'for installed capacity of the wind farm'ij)m×n，
Further calculating and processing to obtain
Figure FDA0003613620670000012
(3) Solving the information entropy e of the jth typical daily energy storage configuration resultj：
Wherein: e.g. of the typejEntropy of information for jth typical day result
Figure FDA0003613620670000013
And is
Figure FDA0003613620670000014
If p isijWhen 0, ln p is definedij＝0；
(4) Determining the weight w of the jth typical daily energy storage configuration resultj：
Figure FDA0003613620670000015
(5) Weighting the result of each typical day through the entropy weight of each typical day, thereby obtaining an entropy weight method energy storage configuration result: s ═ w1S1+w2S2+w3S3+w4S4S is the entropy weight method configuration result of the energy storage capacity, and S1, S2, S3, and S4 are the energy storage capacity configuration results for each typical day, respectively.
3. The entropy weight method-based hybrid energy storage configuration method according to claim 1, further comprising:
calculating the average capacity error and the power error of the energy storage configuration results of a typical daily method, an expected value method and an entropy weight method;
and the accuracy of the energy storage capacity is configured according to the average capacity error and the power error evaluation entropy weight method.
4. The entropy weight method-based hybrid energy storage configuration method according to claim 1, further comprising:
the number of the evaluation objects is equal to or greater than the number of the evaluation indexes.
5. A hybrid energy storage configuration system based on an entropy weight method is characterized by comprising:
the evaluation preparation unit is configured for specifying evaluation indexes according to the energy storage configuration result of each typical day and taking the number of the typical days as the number of the evaluation indexes;
an object setting unit configured to set, as an evaluation object, a power remaining amount that is not stabilized by hybrid energy storage on each typical day;
the device comprises a sampling setting unit, a data processing unit and a data processing unit, wherein the sampling setting unit is configured to set sampling frequency and research period, and the quotient of the research period and the adopted frequency is used as the number of evaluation objects;
and the energy storage configuration unit is configured for performing energy storage capacity configuration by an entropy weight method according to the evaluation indexes and the number thereof and the evaluation objects and the number thereof.
6. The entropy-weight-method-based hybrid energy storage configuration system according to claim 5, wherein the energy storage configuration unit comprises:
a matrix establishing module configured to establish an original evaluation matrix A [ a ] according to the evaluation indexes and the number thereof and the evaluation objects and the number thereofij]m×nM is the number of evaluation targets, n is the number of evaluation indexes, aijIs an evaluation index;
a matrix normalization module configured to perform on the matrix AThe data is processed by the normalization method,
Figure FDA0003613620670000031
Pwobtaining a normalized matrix A ' ═ a ' for the installed capacity of the wind power plant 'ij)m×n，
Further calculating and processing to obtain
Figure FDA0003613620670000032
The information entropy calculation module is configured to calculate the information entropy of the jth typical daily energy storage configuration result:
wherein: e.g. of the typejEntropy of information for jth typical day result
Figure FDA0003613620670000033
And is
Figure FDA0003613620670000034
If p isijWhen equal to 0, ln p is definedij＝0；
A weight calculation module for determining the weight w of the jth typical daily energy storage configuration resultj：
Figure FDA0003613620670000035
And the result output module is configured to perform weighting processing on the result of each typical day through the entropy weight of each typical day, so as to obtain an entropy weight method energy storage configuration result: s ═ w1S1+w2S2+w3S3+w4S4S is the entropy weight method configuration result of the energy storage capacity, and S1, S2, S3, and S4 are the energy storage capacity configuration results for each typical day, respectively.
7. The entropy weight method-based hybrid energy storage configuration system of claim 5, further comprising:
the error calculation unit is configured for calculating the average capacity error and the power error of the energy storage configuration results of the typical daily method, the expected value method and the entropy weight method;
and the result evaluation unit is configured to evaluate the accuracy of the energy storage capacity according to the average capacity error and the power error.
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