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

Info

Publication number
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
Authority
CN
China
Prior art keywords
energy storage
evaluation
entropy weight
entropy
configuration
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011127826.6A
Other languages
Chinese (zh)
Other versions
CN112258024A (en
Inventor
俞晓冬
董霞
刘树林
臧宏志
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qilu University of Technology
Original Assignee
Qilu University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qilu University of Technology filed Critical Qilu University of Technology
Priority to CN202011127826.6A priority Critical patent/CN112258024B/en
Publication of CN112258024A publication Critical patent/CN112258024A/en
Application granted granted Critical
Publication of CN112258024B publication Critical patent/CN112258024B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/82—Energy audits or management systems therefor

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Data Mining & Analysis (AREA)
  • Economics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Software Systems (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Databases & Information Systems (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Algebra (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Computing Systems (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本发明提供一种基于熵权法的混合储能容量配置方法及系统，包括：根据各典型日的储能配置结果指定评价指标，并将典型日数作为评价指标的个数；将各典型日内未被混合储能平抑的功率剩余量作为评价对象；设置采样频率和研究周期，所述研究周期与采用频率的商作为评价对象的个数；根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置；本发明中熵权法进入混合储能配置中，有效地提升了储能容量最终配置结果的准确性和合理性。

Figure 202011127826

The invention provides a hybrid energy storage capacity configuration method and system based on an entropy weight method. The residual amount of power stabilized by the hybrid energy storage is used as the evaluation object; the sampling frequency and the research period are set, and the quotient of the research period and the adoption frequency is used as the number of evaluation objects; according to the evaluation index and its number and the evaluation objects and their number The energy storage capacity configuration is performed by the entropy weight method; in the present invention, the entropy weight method enters the hybrid energy storage configuration, which effectively improves the accuracy and rationality of the final configuration result of the energy storage capacity.

Figure 202011127826

Description

一种基于熵权法的混合储能容量配置方法及系统A hybrid energy storage capacity allocation method and system based on entropy weight method

技术领域technical field

本发明属于风电技术领域，具体涉及一种基于熵权法的混合储能配置方法及系统。The invention belongs to the technical field of wind power, and in particular relates to a hybrid energy storage configuration method and system based on an entropy weight method.

背景技术Background technique

在风电场中储能系统的容量配置过程中，一般用某个典型日的配置结果作为最终的储能配置结果，即典型日法，这样的方法得到的储能容量配置偶然性太大，无法准确反映全年真实的水平；也有用求各典型日配置结果的数学期望的方法，虽然有所改进，但是各典型日对最终结果的影响是均等的，也不够合理。实际上，各典型日的结果对最终结果的影响不一样，即权重不一样；如何合理地确定各典型日配置结果的权重，不但影响储能容量的最终配置结果，而且也影响风电场的并网，影响风电的消纳。During the capacity allocation process of the energy storage system in the wind farm, the configuration result of a typical day is generally used as the final energy storage configuration result, that is, the typical day method. The energy storage capacity configuration obtained by this method is too contingent to be accurate. It reflects the real level of the whole year; there is also a method for calculating the mathematical expectation of the allocation results of each typical day. Although it has been improved, the impact of each typical day on the final result is equal, and it is not reasonable enough. In fact, the results of each typical day have different effects on the final result, that is, the weights are different; how to reasonably determine the weight of the allocation results of each typical day not only affects the final allocation result of energy storage capacity, but also affects the wind farms network, affecting the consumption of wind power.

熵最早是热力学范畴的概念，物理意义上的定义是对混乱程度的度量，后来C.E.Shannon把熵了引入信息论，已经在工程技术、社会经济等领域得到了非常广泛的应用，称为信息熵。信息熵是对一个随机变量的信息和不确定性的度量，可以衡量随机变量的不确定性，当熵最大时，表示随机变量的不确定性最大，信息熵的定义见公式：

Figure BDA0002734184520000011
Entropy was first a concept in the category of thermodynamics. The definition in the physical sense is a measure of the degree of chaos. Later, CE Shannon introduced entropy into information theory, which has been widely used in engineering technology, social economy and other fields. It is called information entropy. Information entropy is a measure of the information and uncertainty of a random variable. It can measure the uncertainty of a random variable. When the entropy is the largest, it means that the uncertainty of the random variable is the largest. For the definition of information entropy, see the formula:
Figure BDA0002734184520000011

其中，p(xi)代表随机事件X为xi的概率；Among them, p( xi ) represents the probability that the random event X is xi ;

H(x)为信息熵；H(x) is the information entropy;

基于信息熵计算所得出的权重能够较为精确地反应不同指标间的差别。熵权法是根据指标信息熵的大小来判断该指标所提供的信息量。熵越小，则信息量越大，在综合评价体系中的权重就越大；反之熵越大，则信息量越小，在综合评价体系中的权重就越小。相对于其它评价模型来说，熵权法的依据是数据本身所含有的信息量，具有客观准确的优点。The weights calculated based on information entropy can more accurately reflect the differences between different indicators. The entropy weight method is to judge the amount of information provided by the index according to the size of the information entropy of the index. The smaller the entropy, the greater the amount of information, and the greater the weight in the comprehensive evaluation system; conversely, the greater the entropy, the smaller the amount of information, and the smaller the weight in the comprehensive evaluation system. Compared with other evaluation models, the entropy weight method is based on the amount of information contained in the data itself, which has the advantage of being objective and accurate.

发明内容SUMMARY OF THE INVENTION

针对现有技术的上述不足，本发明提供一种基于熵权法的混合储能配置方法及系统，以解决上述技术问题。In view of the above shortcomings of 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.

第一方面，本发明提供一种基于熵权法的混合储能配置方法，包括：In a first aspect, the present invention provides a hybrid energy storage configuration method based on an entropy weight method, including:

根据各典型日的储能配置结果指定评价指标，并将典型日数作为评价指标的个数；Specify evaluation indicators according to the energy storage configuration results of each typical day, and take the number of typical days as the number of evaluation indicators;

将各典型日内未被混合储能平抑的功率剩余量作为评价对象；The remaining power that has not been stabilized by the hybrid energy storage in each typical day is taken as the evaluation object;

设置采样频率和研究周期，所述研究周期与采用频率的商作为评价对象的个数；Set the sampling frequency and the research period, and the quotient of the research period and the adoption frequency is used as the number of evaluation objects;

根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置。According to the evaluation index and its number, and the evaluation object and its number, the energy storage capacity is allocated by the entropy weight method.

进一步的，所述根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置，包括：Further, according to the evaluation index and its number and the evaluation object and its number, the energy storage capacity configuration is carried out by the entropy weight method, including:

(1)根据评价指标及其个数和评价对象及其个数，建立原始评价矩阵，记为A[aij]m×n，m为评价对象个数，n为评价指标的个数，aij为评价指标；(1) According to the evaluation index and its number and the evaluation object and its number, establish the original evaluation matrix, denoted as A[a ij ] m×n , where m is the number of evaluation objects, n is the number of evaluation indicators, a ij is the evaluation index;

(2)对矩阵A进行数据归一化处理，

Figure BDA0002734184520000021
Pw为风电场装机容量，得到标准化矩阵A'＝(a'ij)m×n，(2) Perform data normalization processing on matrix A,
Figure BDA0002734184520000021
P w is the installed capacity of the wind farm, and the standardized matrix A'=(a' ij ) m×n is obtained,

进一步计算处理得

Figure BDA0002734184520000022
After further calculation, the
Figure BDA0002734184520000022

(3)求第j个典型日储能配置结果的信息熵：(3) Find the information entropy of the jth typical daily energy storage configuration result:

Figure BDA0002734184520000023
Figure BDA0002734184520000023

其中：ej为第j个典型日结果的信息熵；Where: e j is the information entropy of the jth typical day result;

Figure BDA0002734184520000031
如果pij＝0，则定义ln pij＝0；
Figure BDA0002734184520000031
If p ij =0, then define ln p ij =0;

(4)确定第j个典型日储能配置结果的权重wj；(4) Determine the weight w j of the jth typical daily energy storage configuration result;

Figure BDA0002734184520000032
Figure BDA0002734184520000032

(5)通过各典型日的熵权，对各典型日结果进行加权处理，从而得到熵权法储能配置结果：S＝w1S1+w2S2+w3S3+w4S4，S为储能容量的熵权法配置结果，S1、S2、S3、S4分别为各典型日的储能容量配置结果。(5) Through the entropy weight of each typical day, the results of each typical day are weighted to obtain the energy storage configuration result of the entropy weight method: S=w 1 S 1 +w 2 S 2 +w 3 S 3 +w 4 S 4. S is the configuration result of the entropy weight method of the energy storage capacity, S1, S2, S3, and S4 are the configuration results of the energy storage capacity on each typical day, respectively.

进一步的，所述方法还包括：Further, the method also includes:

计算典型日法、期望值法以及熵权法的储能配置结果的平均容量误差、功率误差；Calculate the average capacity error and power error of the energy storage configuration results of the typical daily method, expected value method and entropy weight method;

根据所述平均容量误差、功率误差评价熵权法配置储能容量的准确性。According to the average capacity error and power error, the accuracy of energy storage capacity configuration by the entropy weight method is evaluated.

第二方面，本发明提供一种基于熵权法的混合储能配置系统，包括：In a second aspect, the present invention provides a hybrid energy storage configuration system based on an entropy weight method, including:

评价准备单元，配置用于根据各典型日的储能配置结果指定评价指标，并将典型日数作为评价指标的个数；The evaluation preparation unit is configured to designate evaluation indicators according to the energy storage configuration results of each typical day, and take the number of typical days as the number of evaluation indicators;

对象设置单元，配置用于将各典型日内未被混合储能平抑的功率剩余量作为评价对象；The object setting unit is configured to use the remaining power that is not stabilized by the hybrid energy storage in each typical day as the evaluation object;

采样设置单元，配置用于设置采样频率和研究周期，所述研究周期与采用频率的商作为评价对象的个数；a sampling setting unit, configured to set a sampling frequency and a research period, and the quotient of the research period and the adoption frequency is used as the number of evaluation objects;

储能配置单元，配置用于根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置。The energy storage configuration unit is configured to configure the energy storage capacity by the entropy weight method according to the evaluation index and its number, and the evaluation object and its number.

进一步的，所述储能配置单元包括：Further, the energy storage configuration unit includes:

矩阵建立模块，配置用于根据评价指标及其个数和评价对象及其个数，建立原始评价矩阵A[aij]m×n，m为评价对象个数，n为评价指标的个数，aij为评价指标；The matrix establishment module is configured to establish the original evaluation matrix A[a ij ] m×n according to the evaluation indicators and their number, and the evaluation objects and their number, where m is the number of evaluation objects, n is the number of evaluation indicators, a ij is the evaluation index;

矩阵标准化模块，配置用于对矩阵A进行数据归一化处理，

Figure BDA0002734184520000041
Pw为风电场装机容量，得到标准化矩阵A'＝(a'ij)m×n，a matrix normalization module, configured to perform data normalization processing on matrix A,
Figure BDA0002734184520000041
P w is the installed capacity of the wind farm, and the standardized matrix A'=(a' ij ) m×n is obtained,

进一步计算处理得

Figure BDA0002734184520000042
After further calculation, the
Figure BDA0002734184520000042

信息熵计算模块，配置用于求第j个典型日储能配置结果的信息熵：The information entropy calculation module is configured to calculate the information entropy of the jth typical daily energy storage configuration result:

其中：ej为第j个典型日结果的信息熵where: e j is the information entropy of the jth typical day result

Figure BDA0002734184520000043
Figure BDA0002734184520000043

且

Figure BDA0002734184520000044
如果pij＝0，则定义ln pij＝0；and
Figure BDA0002734184520000044
If p ij =0, then define ln p ij =0;

权重计算模块，确定第j个典型日储能配置结果的权重wj：The weight calculation module determines the weight w j of the jth typical daily energy storage configuration result:

Figure BDA0002734184520000045
Figure BDA0002734184520000045

结果输出单元，配置用于通过各典型日的熵权，对各典型日结果进行加权处理，从而得到熵权法储能配置结果：S＝w1S1+w2S2+w3S3+w4S4，S为储能容量的熵权法配置结果，S1、S2、S3、S4分别为各典型日的储能容量配置结果。The result output unit is configured to weight the results of each typical day through the entropy weight of each typical day, so as to obtain the energy storage configuration result of the entropy weight method: S=w 1 S 1 +w 2 S 2 +w 3 S 3 +w 4 S 4 , S is the configuration result of energy storage capacity by entropy weight method, and S1, S2, S3, and S4 are the configuration results of energy storage capacity on each typical day, respectively.

进一步的，所述系统还包括：Further, the system also includes:

误差计算单元，配置用于计算典型日法、期望值法以及熵权法的储能配置结果的平均容量误差、功率误差；The error calculation unit is configured to calculate the average capacity error and power error of the energy storage configuration results of the typical daily method, the expected value method and the entropy weight method;

结果评价单元，配置用于根据所述平均容量误差、功率误差评价熵权法配置储能容量的准确性。The result evaluation unit is configured to evaluate the accuracy of the energy storage capacity configuration by the entropy weight method according to the average capacity error and the power error.

本发明的有益效果在于，The beneficial effect of the present invention is that,

本发明提供的一种基于熵权法的混合储能配置方法及系统，分析典型日法储能结果的权重，把储能未能平抑的功率剩余量作为评价对象，分析这些未平抑功率剩余量中所蕴含的信息量和不确定性，以此来确定各典型日结果在最终结果中所占的权重，有效地提升了储能最终配置结果的准确性和合理性。The invention provides a hybrid energy storage configuration method and system based on the entropy weight method, which analyzes the weight of typical daily energy storage results, takes the residual power that cannot be stabilized by the energy storage as the evaluation object, and analyzes the remaining power that cannot be stabilized. In order to determine the weight of each typical daily result in the final result, it can effectively improve the accuracy and rationality of the final energy storage configuration result.

此外，本发明设计原理可靠，结构简单，具有非常广泛的应用前景。In addition, the present invention has reliable design principle and simple structure, and has a very wide application prospect.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. In other words, other drawings can also be obtained based on these drawings without creative labor.

图1为本申请实施例中各典型日的储能容量配置结果图。FIG. 1 is a diagram showing the configuration result of energy storage capacity on each typical day in the embodiment of the application.

图2为本申请实施例中各典型日储能未平抑的功率剩余量图。FIG. 2 is a diagram of the unsuppressed power remaining amount of each typical daily energy storage in the embodiment of the present application.

图3为本申请实施例中各典型日结果的熵权图。FIG. 3 is an entropy weight diagram of each typical daily result in the embodiment of the present application.

图4为本申请实施例中各种方法储能配置最终结果比较图。FIG. 4 is a comparison diagram of final results of energy storage configuration of various methods in the embodiment of the present application.

图5为本申请实施例中各种方法储能配置结果的检验图。FIG. 5 is a verification diagram of the energy storage configuration results of various methods in the embodiment of the present application.

具体实施方式Detailed ways

为了使本技术领域的人员更好地理解本发明中的技术方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

第一方面，本申请实施例提供一种基于熵权法的混合储能配置方法，包括：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、根据各典型日的储能配置结果指定评价指标,各典型日的储能容量配置结果如图1所示，并将典型日数作为评价指标的个数；将各典型日内未被混合储能平抑的功率剩余量作为评价对象，各典型日未平抑的功率剩余量如图2所示；设置采样频率和研究周期，所述研究周期与采用频率的商作为评价对象的个数；由于熵权法的本质是用有限个样本去“估计”指标的信息熵，在样本量过少的情况下，基于熵权法所计算得出的权重则有可能出现较大误差，故样本数必须大于等于指标数，本方法所用本实施例中的采样频率为20秒，研究周期为一天，则计算可得每天的样本数为4320个，因此在本实施例中评价对象的个数m＝4320；1. Specify the evaluation index according to the energy storage configuration results of each typical day. The energy storage capacity configuration results of each typical day are shown in Figure 1, and the number of typical days is used as the number of evaluation indicators; The stabilized power remaining amount is used as the evaluation object, and the unsuppressed power remaining amount on each typical day is shown in Figure 2; the sampling frequency and the research period are set, and the quotient of the research period and the adopted frequency is used as the number of evaluation objects; due to the entropy weight The essence of the method is to use a limited number of samples to "estimate" the information entropy of the indicator. When the sample size 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 indicators, the sampling frequency in this example used in this method is 20 seconds, and the research period is one day, then the number of samples per day that can be calculated is 4320, so in this example, the number of evaluation objects m=4320;

2、根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置。2. According to the evaluation index and its number, and the evaluation object and its number, the energy storage capacity is allocated by the entropy weight method.

(1)以每个典型工作日的4320个采样点的的功率剩余量作为评价对象，以4个典型日的配置结果作为评价指标，建立原始评价矩阵，建立原始评价矩阵，记为A[aij]m×n，m为评价对象个数，n为评价指标的个数，aij为评价指标；(1) Taking the residual power of 4320 sampling points in each typical working day as the evaluation object, and using the configuration results of 4 typical days as the evaluation index, the original evaluation matrix is established, and the original evaluation matrix is established, denoted as A[a ij ] m×n , m is the number of evaluation objects, n is the number of evaluation indicators, and a ij is the evaluation index;

(2)根据评价指标及其个数和评价对象及其个数，建立原始评价矩阵，记为A[aij]m×n，m为评价对象个数，n为评价指标的个数，aij为评价指标；(2) According to the evaluation index and its number and the evaluation object and its number, establish the original evaluation matrix, denoted as A[a ij ] m×n , where m is the number of evaluation objects, n is the number of evaluation indicators, a ij is the evaluation index;

(3)对矩阵A进行数据归一化处理，

Figure BDA0002734184520000061
Pw为风电场装机容量，得到标准化矩阵A'＝(a'ij)m×n；(3) Perform data normalization processing on matrix A,
Figure BDA0002734184520000061
P w is the installed capacity of the wind farm, and the standardized matrix A'=(a' ij ) m×n is obtained;

进一步计算处理得

Figure BDA0002734184520000062
After further calculation, the
Figure BDA0002734184520000062

(4)求第j个典型日储能配置结果的信息熵：(4) Find the information entropy of the jth typical daily energy storage configuration result:

Figure BDA0002734184520000063
Figure BDA0002734184520000063

其中：ej为第j个典型日结果的信息熵；Where: e j is the information entropy of the jth typical day result;

Figure BDA0002734184520000064
如果pij＝0，则定义ln pij＝0；
Figure BDA0002734184520000064
If p ij =0, then define ln p ij =0;

(5)确定第j个典型日储能配置结果的权重wj，作为各典型日的熵权，结果如图3所示；

Figure BDA0002734184520000065
(5) Determine the weight w j of the energy storage configuration result of the jth typical day as the entropy weight of each typical day, and the result is shown in Figure 3;
Figure BDA0002734184520000065

(6)通过各典型日的熵权，对各典型日结果进行加权处理，从而得到熵权法储能配置结果：S＝w1S1+w2S2+w3S3+w4S4，S为储能容量的熵权法配置结果，S1、S2、S3、S4分别为各典型日的储能容量配置结果，如图4所示，并与期望值法以及典型日法进行比较。(6) Through the entropy weight of each typical day, the results of each typical day are weighted to obtain the energy storage configuration result of the entropy weight method: S=w 1 S 1 +w 2 S 2 +w 3 S 3 +w 4 S 4 , S is the configuration result of the entropy weight method of energy storage capacity, S1, S2, S3, S4 are the configuration results of the energy storage capacity of each typical day, respectively, as shown in Figure 4, and compared with the expected value method and the typical day method.

3、为了验证熵权法的有效性，在全年每个月中随机选择1天共计12天的数据作为检验数据，计算各种储能配置方法的平均容量误差、功率误差，如图5所示，从图5中可以得出以下结论：3. In order to verify the effectiveness of the entropy weight method, randomly select 1 day of data for a total of 12 days in each month of the year as the test data, and calculate the average capacity error and power error of various energy storage configuration methods, as shown in Figure 5. The following conclusions can be drawn from Figure 5:

(1)以随机典型日结果作为储能最终配置方案的误差较大，这是由于典型日数据的偶然性太强，难以反映出全年风电功率的波动特征；(1) The error of using random typical daily results as the final configuration plan of energy storage is large, because the contingency of typical daily data is too strong, and it is difficult to reflect the fluctuation characteristics of wind power throughout the year;

(2)期望值法比典型日法在容量误差和功率误差上有一定程度的改进，但比熵权法仍有一定差距，故期望法在权重的确定上过于简单，不够科学；(2) Compared with the typical daily method, the expected value method has a certain degree of improvement in capacity error and power error, but there is still a certain gap compared with the entropy weight method, so the weight determination of the expected value method is too simple and not scientific enough;

(3)相比期望值法，熵权法确定的配置方案的容量误差和功率误差均为最低，由于熵权法是根据各典型日结果中所包含信息量多少和不确定程度大小来确定权重，能客观有效地反映各典型日结果的差异程度和重要水平，所以更加准确合理。(3) Compared with the expected value method, the capacity error and power error of the configuration scheme determined by the entropy weight method are the lowest. Since the entropy weight method determines the weight according to the amount of information and the degree of uncertainty contained in the typical daily results, It can objectively and effectively reflect the degree of difference and importance level of the results of each typical day, so it is more accurate and reasonable.

综上所述，在本实施例中熵权法是根据各典型日结果中所包含信息量多少和不确定程度大小来确定权重，能客观有效地反映各典型日结果的差异程度和重要水平，所以更加准确合理，检验结果表明用熵权法得到的储能容量最终配置，其容量误差和功率误差均为最低。因此基于熵权法的储能容量配置方法，能够客观评价各典型日结果的权重，有效地提升了储能最终配置结果的准确性和合理性。To sum up, in this embodiment, the entropy weight method determines the weight according to the amount of information and the degree of uncertainty contained in the results of each typical day, which can objectively and effectively reflect the degree of difference and the level of importance of the results of each typical day. Therefore, it is more accurate and reasonable. The test results show that the final configuration of energy storage capacity obtained by the entropy weight method has the lowest capacity error and power error. Therefore, the energy storage capacity allocation method based on the entropy weight method can objectively evaluate the weight of each typical daily result, and effectively improve the accuracy and rationality of the final energy storage allocation result.

第二方面，本申请实施例提供一种基于熵权法的混合储能配置系统，包括：In a second aspect, an embodiment of the present application provides a hybrid energy storage configuration system based on an entropy weight method, including:

评价准备单元，配置用于根据各典型日的储能配置结果指定评价指标，并将典型日数作为评价指标的个数；The evaluation preparation unit is configured to designate evaluation indicators according to the energy storage configuration results of each typical day, and take the number of typical days as the number of evaluation indicators;

对象设置单元，配置用于将各典型日内未被混合储能平抑的功率剩余量作为评价对象；The object setting unit is configured to use the remaining power that is not stabilized by the hybrid energy storage in each typical day as the evaluation object;

采样设置单元，配置用于设置采样频率和研究周期，所述研究周期与采用频率的商作为评价对象的个数；a sampling setting unit, configured to set a sampling frequency and a research period, and the quotient of the research period and the adoption frequency is used as the number of evaluation objects;

储能配置单元，配置用于根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置。The energy storage configuration unit is configured to configure the energy storage capacity by the entropy weight method according to the evaluation index and its number, and the evaluation object and its number.

可选的，作为本申请的一种实施例，所述储能配置单元包括：Optionally, as an embodiment of the present application, the energy storage configuration unit includes:

矩阵建立模块，配置用于根据评价指标及其个数和评价对象及其个数，建立原始评价矩阵A[aij]m×n，m为评价对象个数，n为评价指标的个数，aij为评价指标；The matrix establishment module is configured to establish the original evaluation matrix A[a ij ] m×n according to the evaluation indicators and their number, and the evaluation objects and their number, where m is the number of evaluation objects, n is the number of evaluation indicators, a ij is the evaluation index;

矩阵标准化模块，配置用于对矩阵A进行数据归一化处理，

Figure BDA0002734184520000081
Pw为风电场装机容量，得到标准化矩阵A'＝(a'ij)m×n，a matrix normalization module, configured to perform data normalization processing on matrix A,
Figure BDA0002734184520000081
P w is the installed capacity of the wind farm, and the standardized matrix A'=(a' ij ) m×n is obtained,

进一步计算处理得

Figure BDA0002734184520000082
After further calculation, the
Figure BDA0002734184520000082

信息熵计算模块，配置用于求第j个典型日储能配置结果的信息熵：The information entropy calculation module is configured to calculate the information entropy of the jth typical daily energy storage configuration result:

其中：ej为第j个典型日结果的信息熵，where: e j is the information entropy of the jth typical day result,

Figure BDA0002734184520000083
Figure BDA0002734184520000083

且

Figure BDA0002734184520000084
如果pij＝0，则定义ln pij＝0；and
Figure BDA0002734184520000084
If p ij =0, then define ln p ij =0;

权重计算模块，确定第j个典型日储能配置结果的权重wj：The weight calculation module determines the weight w j of the jth typical daily energy storage configuration result:

Figure BDA0002734184520000085
Figure BDA0002734184520000085

结果输出单元，配置用于通过各典型日的熵权，对各典型日结果进行加权处理，从而得到熵权法储能配置结果：S＝w1S1+w2S2+w3S3+w4S4，S为储能容量的熵权法配置结果，S1、S2、S3、S4分别为各典型日的储能容量配置结果。The result output unit is configured to weight the results of each typical day through the entropy weight of each typical day, so as to obtain the energy storage configuration result of the entropy weight method: S=w 1 S 1 +w 2 S 2 +w 3 S 3 +w 4 S 4 , S is the configuration result of energy storage capacity by entropy weight method, and S1, S2, S3, and S4 are the configuration results of energy storage capacity on each typical day, respectively.

可选的，作为本申请的一种实施例，所述系统还包括：Optionally, as an embodiment of the present application, the system further includes:

误差计算单元，配置用于计算典型日法、期望值法以及熵权法的储能配置结果的平均容量误差、功率误差；The error calculation unit is configured to calculate the average capacity error and power error of the energy storage configuration results of the typical daily method, the expected value method and the entropy weight method;

结果评价单元，配置用于根据所述平均容量误差、功率误差评价熵权法配置储能容量的准确性。The result evaluation unit is configured to evaluate the accuracy of the energy storage capacity configuration by the entropy weight method according to the average capacity error and the power error.

尽管通过参考附图并结合优选实施例的方式对本发明进行了详细描述，但本发明并不限于此。在不脱离本发明的精神和实质的前提下，本领域普通技术人员可以对本发明的实施例进行各种等效的修改或替换，而这些修改或替换都应在本发明的涵盖范围内/任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应所述以权利要求的保护范围为准。Although the present invention has been described in detail in conjunction with the preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all fall within the scope of the present invention/any Those skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (7)

1.一种基于熵权法的混合储能配置方法，其特征在于，包括：1. a hybrid energy storage configuration method based on entropy weight method, is characterized in that, comprises: 根据各典型日的储能配置结果指定评价指标，并将典型日数作为评价指标的个数；Specify evaluation indicators according to the energy storage configuration results of each typical day, and take the number of typical days as the number of evaluation indicators; 将各典型日内未被混合储能平抑的功率剩余量作为评价对象；The remaining power that has not been stabilized by the hybrid energy storage in each typical day is taken as the evaluation object; 设置采样频率和研究周期，所述研究周期与采用频率的商作为评价对象的个数；Set the sampling frequency and the research period, and the quotient of the research period and the adoption frequency is used as the number of evaluation objects; 根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置。According to the evaluation index and its number, and the evaluation object and its number, the energy storage capacity is allocated by the entropy weight method. 2.根据权利要求1所述的一种基于熵权法的混合储能配置方法，其特征在于，所述根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置，包括：2. A hybrid energy storage configuration method based on an entropy weight method according to claim 1, wherein the energy storage capacity is carried out by the entropy weight method according to the evaluation index and the number thereof and the evaluation objects and the number thereof. configuration, including: (1)根据评价指标及其个数和评价对象及其个数，建立原始评价矩阵A[aij]m×n，m为评价对象个数，n为评价指标的个数，aij为评价指标；(1) Establish an original evaluation matrix A[a ij ] m×n according to the evaluation indicators and their number and the evaluation objects and their number, where m is the number of evaluation objects, n is the number of evaluation indicators, and a ij is the evaluation index; (2)对矩阵A进行数据归一化处理，
Figure FDA0003613620670000011
Pw为风电场装机容量，得到标准化矩阵A'＝(a'ij)m×n，(2) Perform data normalization processing on matrix A,
Figure FDA0003613620670000011
P w is the installed capacity of the wind farm, and the standardized matrix A'=(a' ij ) m×n is obtained, 进一步计算处理得
Figure FDA0003613620670000012
After further calculation, the
Figure FDA0003613620670000012
 (3)求第j个典型日储能配置结果的信息熵ej：(3) Find the information entropy e j of the jth typical daily energy storage configuration result: 其中：ej为第j个典型日结果的信息熵where: e j is the information entropy of the jth typical day result
Figure FDA0003613620670000013
Figure FDA0003613620670000013
 且
Figure FDA0003613620670000014
如果pij＝0，则定义ln pij＝0；and
Figure FDA0003613620670000014
If p ij =0, then define ln p ij =0; (4)确定第j个典型日储能配置结果的权重wj：(4) Determine the weight w j of the jth typical daily energy storage configuration result:
Figure FDA0003613620670000015
Figure FDA0003613620670000015
 (5)通过各典型日的熵权，对各典型日结果进行加权处理，从而得到熵权法储能配置结果：S＝w1S1+w2S2+w3S3+w4S4，S为储能容量的熵权法配置结果，S1、S2、S3、S4分别为各典型日的储能容量配置结果。(5) Through the entropy weight of each typical day, the results of each typical day are weighted to obtain the energy storage configuration result of the entropy weight method: S=w 1 S 1 +w 2 S 2 +w 3 S 3 +w 4 S 4 , S is the configuration result of the entropy weight method of the energy storage capacity, S1, S2, S3, S4 are the configuration results of the energy storage capacity on each typical day, respectively. 3.根据权利要求1所述的一种基于熵权法的混合储能配置方法，其特征在于，所述方法还包括：3. A hybrid energy storage configuration method based on an entropy weight method according to claim 1, wherein the method further comprises: 计算典型日法、期望值法以及熵权法的储能配置结果的平均容量误差、功率误差；Calculate the average capacity error and power error of the energy storage configuration results of the typical daily method, expected value method and entropy weight method; 根据所述平均容量误差、功率误差评价熵权法配置储能容量的准确性。According to the average capacity error and power error, the accuracy of energy storage capacity configuration by the entropy weight method is evaluated. 4.根据权利要求1所述的一种基于熵权法的混合储能配置方法，其特征在于，所述方法还包括：4. A hybrid energy storage configuration method based on an entropy weight method according to claim 1, wherein the method further comprises: 所述评价对象个数大于等于所述评价指标的个数。The number of the evaluation objects is greater than or equal to the number of the evaluation indicators. 5.一种基于熵权法的混合储能配置系统，其特征在于，包括：5. A hybrid energy storage configuration system based on entropy weight method, characterized in that, comprising: 评价准备单元，配置用于根据各典型日的储能配置结果指定评价指标，并将典型日数作为评价指标的个数；The evaluation preparation unit is configured to designate evaluation indicators according to the energy storage configuration results of each typical day, and take the number of typical days as the number of evaluation indicators; 对象设置单元，配置用于将各典型日内未被混合储能平抑的功率剩余量作为评价对象；The object setting unit is configured to use the remaining power that is not stabilized by the hybrid energy storage in each typical day as the evaluation object; 采样设置单元，配置用于设置采样频率和研究周期，所述研究周期与采用频率的商作为评价对象的个数；a sampling setting unit, configured to set a sampling frequency and a research period, and the quotient of the research period and the adoption frequency is used as the number of evaluation objects; 储能配置单元，配置用于根据评价指标及其个数和评价对象及其个数以熵权法进行储能容量配置。The energy storage configuration unit is configured to configure the energy storage capacity by the entropy weight method according to the evaluation index and its number, and the evaluation object and its number. 6.根据权利要求5所述的一种基于熵权法的混合储能配置系统，其特征在于，所述储能配置单元包括：6. A hybrid energy storage configuration system based on an entropy weight method according to claim 5, wherein the energy storage configuration unit comprises: 矩阵建立模块，配置用于根据评价指标及其个数和评价对象及其个数，建立原始评价矩阵A[aij]m×n，m为评价对象个数，n为评价指标的个数，aij为评价指标；The matrix establishment module is configured to establish the original evaluation matrix A[a ij ] m×n according to the evaluation indicators and their number, and the evaluation objects and their number, where m is the number of evaluation objects, n is the number of evaluation indicators, a ij is the evaluation index; 矩阵标准化模块，配置用于对矩阵A进行数据归一化处理，
Figure FDA0003613620670000031
Pw为风电场装机容量，得到标准化矩阵A'＝(a'ij)m×n，a matrix normalization module, configured to perform data normalization processing on matrix A,
Figure FDA0003613620670000031
P w is the installed capacity of the wind farm, and the standardized matrix A'=(a' ij ) m×n is obtained, 进一步计算处理得
Figure FDA0003613620670000032
After further calculation, the
Figure FDA0003613620670000032
 信息熵计算模块，配置用于求第j个典型日储能配置结果的信息熵：The information entropy calculation module is configured to calculate the information entropy of the jth typical daily energy storage configuration result: 其中：ej为第j个典型日结果的信息熵where: e j is the information entropy of the jth typical day result
Figure FDA0003613620670000033
Figure FDA0003613620670000033
 且
Figure FDA0003613620670000034
如果pij＝0，则定义ln pij＝0；and
Figure FDA0003613620670000034
If p ij =0, then define ln p ij =0; 权重计算模块，确定第j个典型日储能配置结果的权重wj：The weight calculation module determines the weight w j of the jth typical daily energy storage configuration result:
Figure FDA0003613620670000035
Figure FDA0003613620670000035
 结果输出模块，配置用于通过各典型日的熵权，对各典型日结果进行加权处理，从而得到熵权法储能配置结果：S＝w1S1+w2S2+w3S3+w4S4，S为储能容量的熵权法配置结果，S1、S2、S3、S4分别为各典型日的储能容量配置结果。The result output module is configured to weight the results of each typical day through the entropy weight of each typical day, so as to obtain the energy storage configuration result of the entropy weight method: S=w 1 S 1 +w 2 S 2 +w 3 S 3 +w 4 S 4 , S is the configuration result of energy storage capacity by entropy weight method, and S1, S2, S3, and S4 are the configuration results of energy storage capacity on each typical day, respectively. 7.根据权利要求5所述的一种基于熵权法的混合储能配置系统，其特征在于，所述系统还包括：7. A hybrid energy storage configuration system based on an entropy weight method according to claim 5, wherein the system further comprises: 误差计算单元，配置用于计算典型日法、期望值法以及熵权法的储能配置结果的平均容量误差、功率误差；The error calculation unit is configured to calculate the average capacity error and power error of the energy storage configuration results of the typical daily method, the expected value method and the entropy weight method; 结果评价单元，配置用于根据所述平均容量误差、功率误差评价熵权法配置储能容量的准确性。The result evaluation unit is configured to evaluate the accuracy of the energy storage capacity configuration by the entropy weight method according to the average capacity error and the power error.
CN202011127826.6A 2020-10-20 2020-10-20 Mixed energy storage capacity configuration method and system based on entropy weight method Active CN112258024B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011127826.6A CN112258024B (en) 2020-10-20 2020-10-20 Mixed energy storage capacity configuration method and system based on entropy weight method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011127826.6A CN112258024B (en) 2020-10-20 2020-10-20 Mixed energy storage capacity configuration method and system based on entropy weight method

Publications (2)

Publication Number Publication Date
CN112258024A CN112258024A (en) 2021-01-22
CN112258024B true CN112258024B (en) 2022-06-10

Family

ID=74245224

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011127826.6A Active CN112258024B (en) 2020-10-20 2020-10-20 Mixed energy storage capacity configuration method and system based on entropy weight method

Country Status (1)

Country Link
CN (1) CN112258024B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106712064A (en) * 2017-02-16 2017-05-24 湖南省德沃普储能有限公司 Economic configuration method for participating in power gird real-time deep peak shaving through collaboration of battery energy storage system and thermal power plant
CN109685287A (en) * 2019-01-14 2019-04-26 浙江大学 Increment power distribution network power supply capacity multiple-objection optimization configuration method
CN109755967A (en) * 2019-03-26 2019-05-14 安徽工程大学 An optimal configuration method of photovoltaic storage system in distribution network
CN110659814A (en) * 2019-09-12 2020-01-07 国网山东省电力公司寿光市供电公司 A method and system for risk assessment of power grid operations based on entropy weight method
CN111725826A (en) * 2020-07-02 2020-09-29 国网青海省电力公司 A comprehensive method for constant capacity of energy storage based on high-proportion photovoltaic access to power system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106712064A (en) * 2017-02-16 2017-05-24 湖南省德沃普储能有限公司 Economic configuration method for participating in power gird real-time deep peak shaving through collaboration of battery energy storage system and thermal power plant
CN109685287A (en) * 2019-01-14 2019-04-26 浙江大学 Increment power distribution network power supply capacity multiple-objection optimization configuration method
CN109755967A (en) * 2019-03-26 2019-05-14 安徽工程大学 An optimal configuration method of photovoltaic storage system in distribution network
CN110659814A (en) * 2019-09-12 2020-01-07 国网山东省电力公司寿光市供电公司 A method and system for risk assessment of power grid operations based on entropy weight method
CN111725826A (en) * 2020-07-02 2020-09-29 国网青海省电力公司 A comprehensive method for constant capacity of energy storage based on high-proportion photovoltaic access to power system

Also Published As

Publication number Publication date
CN112258024A (en) 2021-01-22

Similar Documents

Publication Publication Date Title
CN108593990B (en) Electricity stealing detection method based on electricity consumption behavior mode of electric energy user and application
WO2018157691A1 (en) Active distribution network safety quantifying method
CN105260803B (en) A system power consumption forecast method
CN106780117A (en) A kind of power distribution network project financial leasing methods of risk assessment
CN105225000B (en) A kind of wind power probability model nonparametric probability method based on Fuzzy Ordered optimization
CN105005711A (en) Method and device for acquiring statistical line loss
CN114357670A (en) Power distribution network power consumption data abnormity early warning method based on BLS and self-encoder
CN104834975A (en) Power network load factor prediction method based on intelligent algorithm optimization combination
CN118016180A (en) System and method for predicting and analyzing time sequence of atmospheric methane based on ARIMA-LSTM hybrid model
CN111047079B (en) Wind power plant wind speed time series prediction method and system
CN112348281A (en) Power data processing method and device
CN112258024B (en) Mixed energy storage capacity configuration method and system based on entropy weight method
CN114779154A (en) Intelligent ammeter data time scale calibration method and device based on temporal analysis
CN109559046A (en) A kind of natural gas development influence factor appraisal procedure and assessment system
CN117972485A (en) A method for evaluating the health of a fan based on dynamic fuzzy theory
CN111310121A (en) New energy output probability prediction method and system
CN108599147B (en) A Combined Interval Forecasting Method Based on Normal Exponential Smoothing and Kernel Density Estimation
CN109871998A (en) A method and device for predicting line loss rate of distribution network based on expert sample library
CN116186950A (en) Processing method, system, device and storage medium for gas pipe network simulation
CN111552911A (en) A Quantitative Analysis Method of Technical Line Loss Influencing Factors Based on Multi-scenario Generation
CN113837520B (en) Power quality evaluation method and device, server, and computer-readable storage medium
CN118134295B (en) Demand response user credit evaluation method, system, storage medium and equipment
CN119294918B (en) Production-sales difference rate abnormality detection method and system, electronic device, and readable storage medium
CN119151296B (en) Dam body detection control method and system for intelligent hydropower station
CN115907293A (en) Distributed photovoltaic power quality evaluation method and system based on time probability distribution

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CP03 Change of name, title or address

Address after: 250353 University Road, Changqing District, Ji'nan, Shandong Province, No. 3501

Patentee after: Qilu University of Technology (Shandong Academy of Sciences)

Country or region after: China

Address before: 250353 University Road, Changqing District, Ji'nan, Shandong Province, No. 3501

Patentee before: Qilu University of Technology

Country or region before: China

CP03 Change of name, title or address