CN113159650A - Optical charging storage system operation benefit evaluation method based on improved matter element extension model - Google Patents

Optical charging storage system operation benefit evaluation method based on improved matter element extension model Download PDF

Info

Publication number
CN113159650A
CN113159650A CN202110558591.4A CN202110558591A CN113159650A CN 113159650 A CN113159650 A CN 113159650A CN 202110558591 A CN202110558591 A CN 202110558591A CN 113159650 A CN113159650 A CN 113159650A
Authority
CN
China
Prior art keywords
index
evaluation
weight
storage system
extension model
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.)
Pending
Application number
CN202110558591.4A
Other languages
Chinese (zh)
Inventor
朱国荣
董丹煌
王洪良
文凡
胡嘉骅
杨侃
叶玲节
孙秋洁
朱瑶琪
杨云露
张一泓
冯也
彭莎
陆易凡
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.)
North China Electric Power University
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
North China Electric Power University
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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 North China Electric Power University, Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd filed Critical North China Electric Power University
Priority to CN202110558591.4A priority Critical patent/CN113159650A/en
Publication of CN113159650A publication Critical patent/CN113159650A/en
Pending legal-status Critical Current

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
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Accounting & Taxation (AREA)
  • Human Resources & Organizations (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Finance (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Tourism & Hospitality (AREA)
  • Data Mining & Analysis (AREA)
  • Educational Administration (AREA)
  • Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses an operation benefit evaluation method of an optical charging storage system based on an improved matter element extension model, and belongs to the technical field of power systems. The method comprises the following steps: step 1: preliminarily constructing an operation benefit evaluation index system, collecting sample data of index values of the indexes, and then performing normalization processing on a criterion layer judgment matrix of the sample; step 2: determining the weight of the comprehensive evaluation index of the operational benefit of the optical charge storage system by integrating subjective and objective factors by adopting an analytic hierarchy process and an entropy weight method; and step 3: based on the classical matter element extension model, an improved matter element extension model is formed by introducing a calculation method of determining the degree of association by being maximally close to the median of the classical domain, so that the operation benefits of the optical storage system are comprehensively evaluated. The invention can further improve the standardization and the effectiveness of the operation benefit analysis of the optical charging and storing system, more comprehensively reflect the operation condition of the current optical charging and storing system and more reliably support the future operation decision of an enterprise.

Description

Optical charging storage system operation benefit evaluation method based on improved matter element extension model
Technical Field
The invention relates to the technical field of power systems, in particular to an operation benefit evaluation method of a light charging storage system based on an improved matter element extension model.
Background
The energy storage technology is the key for improving the utilization rate of renewable energy, and is continuously developed and innovated at present when the renewable energy is developed at a high speed. Through development for many years, the photovoltaic power generation industry has become a strategic emerging industry in China. However, with the wide construction and grid connection of the photovoltaic power station, problems such as light abandonment and electricity limitation, photovoltaic power consumption, difficulty in predicting photovoltaic output and the like are increasingly prominent, which causes great influence on the safe and stable operation of the power system, and leads to further popularization and grid connection of the photovoltaic power station to be increasingly difficult.
The energy storage equipment can well deal with the characteristics of strong fluctuation of photovoltaic power generation in time and nonuniform space, and the establishment and application of an energy storage system can ensure that photovoltaic electric quantity is continuously and stably connected into a power grid in a large scale. In recent years, the energy storage technology is rapidly developed, and with the reduction of the cost of the energy storage battery, the increase of the life cycle and the improvement of the use efficiency, the energy storage is developed to a large capacity and a large scale from small-capacity and small-scale research and demonstration. The energy storage system developed in a large scale is combined with the photovoltaic power generation system, so that the formed light storage and charging system can absorb off-peak electricity by using the battery energy storage system, supports a quick charging load in a peak period, is supplemented by the photovoltaic power generation system, effectively reduces the power grid load of a charging station in the peak period, improves the operation efficiency of the system and provides an auxiliary service function for a power grid.
Whether the optical charging and storing system can be put into use or not, firstly, the operation benefit must be considered. Many researchers start to research the economy of the optical storage system from the perspective of energy storage optimization configuration, but the research of comprehensive evaluation and analysis of the operation benefits of the optical storage and energy storage combined system is less, and the research of some comprehensive evaluation of the benefits also has the problem that an index system is less in construction or a proper algorithm model is not adopted for development and evaluation.
The method comprises the steps of analyzing the economical efficiency of the light charging and storing combined system, developing evaluation on the operation benefit of the light charging and storing system, establishing a relatively complete and comprehensive operation benefit index evaluation system from the aspects of profitability, debt paying capacity, operation capacity and income level, determining the weight of the comprehensive operation benefit evaluation index of the light charging and storing system by adopting a combined Analytic Hierarchy Process (AHP) and an entropy weight method, and introducing a calculation method for determining the relevance degree by maximally sticking to the median in a classical domain on the basis of a classical matter-element model to realize the comprehensive evaluation on the operation benefit of the light charging and storing system.
Disclosure of Invention
The invention aims to provide an optical charging and storing system operation benefit evaluation method based on an improved matter element extension model, which is characterized by comprising the following steps of:
step 1: data acquisition and pretreatment; according to the technical characteristics and functions of the optical charging and storing system, selecting indexes from the four aspects of profitability, debt paying capacity, operation capacity and income level, preliminarily constructing an operation benefit evaluation index system, collecting sample data of the index values of the indexes, and then carrying out normalization processing on a criterion layer judgment matrix of the sample;
step 2: determining the combined weight of the evaluation indexes; determining the weight of the comprehensive evaluation index of the operational benefit of the optical charge storage system by integrating subjective and objective factors by adopting an analytic hierarchy process and an entropy weight method;
and step 3: performing operation benefit evaluation on the optical charging and storing system based on the matter element extension model; based on the classical matter element extension model, an improved matter element extension model is formed by introducing a calculation method of determining the degree of association by being maximally close to the median of the classical domain, so that the operation benefits of the optical storage system are comprehensively evaluated.
The method for performing normalization processing on the criterion layer judgment matrix of the sample in the step1 specifically comprises the following steps:
assuming that n evaluation indexes and m evaluation objects are provided, an index matrix X ═ X is first constructedij)m×nThen, carrying out non-dimensionalization on the index matrix:
Figure BDA0003078063500000021
wherein, x'ijA value representing the j-th evaluation index of the i-th evaluation object after the index matrix standardization; x is the number ofijA numerical value indicating a j-th evaluation index of an i-th evaluation object; min (x)j) Represents the minimum value in the jth evaluation index; max (x)j) Represents the maximum value of the j-th evaluation index.
In the step2, the specific steps of subjectively weighting by adopting an analytic hierarchy process are as follows:
step C1: establishing a judgment matrix; quantifying the judgment by adopting a nine-level scale method, and constructing a judgment matrix U-U (U-U) according to the importance of the indexes of the same level relative to the indexes of the upper layerij)n×n；
Step C2: sorting the hierarchical lists, and carrying out consistency check; judging the matrix U ═ U by solvingij)n×nThe feature root problem of (a) is to obtain a feature vector W and normalize the feature vector W to obtain a standard matrix a ═ aij)n×nObtaining the relative importance of the hierarchical element to the corresponding upper hierarchical element; calculating a consistency index
Figure BDA0003078063500000022
Wherein λmaxFor the maximum eigenvalue, the consistency of the judgment matrix is checked, and when the check coefficient is satisfied
Figure BDA0003078063500000031
Then, wherein RI is an average random consistency index, and the judgment matrix is considered to have satisfactory consistency;
step C3: overall hierarchical sorting; sequentially calculating from top to bottom layer by layer along the hierarchical structure, and calculating the synthetic weight W of the bottommost layer relative to the highest layer of the target layer1＝[ν1,ν2,…,νm]T。
In the step2, the entropy weight method is adopted to carry out objective weighting, and the specific steps are as follows:
step S1: acquiring an information entropy; information entropy EjThe calculation formula is as follows:
Figure BDA0003078063500000032
Figure BDA0003078063500000033
in the formula, PijThe j index accounts for the specific gravity of the j index in the ith evaluation object;
step S2: calculating the entropy values of the profitability index, the repayment ability index and the operation ability index and the objective weight of the evaluation factors of each target layer; the information entropy normalization processing is carried out to obtain index weight, and the calculation formula is as follows:
Figure BDA0003078063500000034
in the formula, vjRepresents the weight of the j-th index.
In the step2, the weight for determining the comprehensive evaluation index of the operation benefit of the optical charging and storage system by synthesizing the subjective and objective factors is specifically as follows:
ri＝αWi+βVi (5)
in the formula, riWeights to weight the combinations; wiSubjective weights determined for the analytic hierarchy process; viAn objective weight determined for the entropy weight method; α and β represent the importance of the objective weight, and α is 0.5 and β is 0.5.
In the step3, the specific steps of comprehensively evaluating the operation benefits of the optical charging and storage system by adopting the improved matter element extension model are as follows:
step D1: determining classical domains
Figure BDA0003078063500000041
In the formula: u shapejJ divided evaluation levels; c1,C2,…,CnTo evaluate the index system; xjnTo evaluate the grade UjEvaluation index CnA defined magnitude range, Xjn＝(ajn,bjn)，ajnAnd bjnMinimum and maximum values of the magnitude range, respectively;
step D2: determining section domains
Figure BDA0003078063500000042
In the formula: u shapepThe whole evaluation grade is obtained; interval Xpn＝(apn,bpn) Is UpWith respect to CnThe specified magnitude range, i.e., section domain;
step D3: determining a degree of association
Figure BDA0003078063500000043
In the formula, Kj(xi) The correlation function value of the ith index for the jth grade; ρ (x)i,xin) The distance between the element value to be evaluated of the ith index and the classical domain; | ρinL is the distance of the ith index with respect to the jth level classical domain;
when x isi∈X1And ask for K1(xn) The method comprises the following steps:
Figure BDA0003078063500000044
when x isi∈XmAnd ask for Km(xn) The method comprises the following steps:
Figure BDA0003078063500000045
in other cases:
Figure BDA0003078063500000046
step D4: determining a comprehensive degree of association
Comprehensive relevance K of object to be evaluated to each evaluation gradej(pij) The calculation formula is as follows:
Kj(pij)＝∑σiKj(xi) (12)
in the formula, pijIs an object to be evaluated; sigmaiIs the weight of the ith evaluation index.
The invention has the beneficial effects that:
by providing the comprehensive evaluation method for the operation benefit of the optical charging and storing system for improving the matter element extension model, the standardization and the effectiveness of the operation benefit analysis of the optical charging and storing system can be further improved, the operation condition of the current optical charging and storing system can be more comprehensively reflected, and the future operation decision of an enterprise can be more reliably supported.
Drawings
Fig. 1 is a general flowchart of an operation benefit evaluation method of an optical charging and storage system based on an improved matter element extension model.
Detailed Description
The invention provides an improved matter element extension model-based operation benefit evaluation method for an optical charging and storing system, which is further described with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a general flowchart of an operation benefit evaluation method of an optical charging and storage system based on an improved matter element extension model. The method provided by the invention is applied to comprehensively evaluate the light charging, storage and transportation benefits in Zhejiang province, an index system for evaluating the light charging, storage and transportation benefits is established by collecting relevant data, and the comprehensive evaluation on the economic benefits of the light charging, storage and transportation system is carried out based on an improved matter element extension model.
1. Construction of comprehensive evaluation index system for operation benefit of optical charging and storage system
The method is used for objectively, comprehensively and scientifically reflecting the operation benefits of the optical charging and storing system, providing a reliable decision basis for a decision maker, selecting relevant indexes from the four aspects of profitability, repayment ability, operation ability, income level and the like, and initially constructing an economic activity evaluation index system. Meanwhile, a Delphi method is adopted to invite related industry experts, the opinions of the industry experts on the primary selection indexes are requested anonymously, the opinions are summarized and fed back, and finally, an optical charge storage system operation benefit comprehensive evaluation index system shown in the table 1 is determined.
TABLE 1
Figure BDA0003078063500000061
(1) Profitability
The profitability guarantees the long-term healthy development of enterprises, and 5 corresponding three-level indexes are further constructed, namely total investment earning rate, financial net present value, investment recovery period, capital fund net earning rate and internal earning rate.
(2) Ability to pay off debt
Whether the enterprise has the cash payment capability and the debt payment capability is the key for the healthy survival and development of the enterprise, and 3 corresponding three-level indexes, namely a borrowing and repayment period, an asset liability rate and an interest reserve rate, should be further constructed.
(3) Operational capacity
The operation capacity represents the consumption level and the stability level of photovoltaic power generation, 4 corresponding three-level indexes are further constructed, namely the reduction of the photovoltaic light rejection rate, the photovoltaic grid connection and consumption, the power supply reliability of a light storage system and the reduction of the power deviation punishment cost.
(4) Profit level
The income level is the fundamental power of economic activities carried out by power grid enterprises, and 6 corresponding three-level indexes are further constructed, namely low storage and high discharge income, government subsidy income, system photovoltaic electric quantity income, retired power battery recycling benefit, capacity and electricity charge income reduction and holiday photovoltaic surplus internet access income.
Data acquisition:
each index data of a certain optical charging and storage system operation benefit evaluation index system is shown in table 2:
TABLE 2
Index (I) Index data Unit of
Total throwRate of return 5.08 ％
Net present financial value 13.25 Wan Yuan
Period of investment recovery 6.56 Wan Yuan
Net profit margin of capital fund 10.92 ％
Internal rate of return 6.90 ％
Loan repayment period 14 Year of year
Rate of assets and liabilities 79.86 ％
Rate of interest reserve 1.50 ％
Reduction of photovoltaic rejection 1.90 ％
Photovoltaic grid connection and absorption 719 Thousands of kilowatts
Power supply reliability of optical storage system 9 Wan Yuan
Reducing power offset penalty cost 0.5 Element/kw
Low reserve high gain 13.4 Wan Yuan
Government subsidy revenue 50 Universal seat
System photovoltaic power yield 18.1 Wan Yuan
Recycling benefit of retired power battery 48 Ten thousand yuan/festival
Reducing capacity charges for electricity revenue 75.6 Wan Yuan
Photovoltaic surplus internet access income in holidays 5.3 Wan Yuan
2. Operation benefit evaluation index empowerment
(1) Standardization of evaluation index
Because different evaluation indexes have differences in measurement units and economic meanings and have different evaluation effects on the total target, the index values are subjected to standardized treatment and then comprehensively evaluated, so that the adverse effect of the index differences is weakened, and the objectivity of the evaluation process and the results is enhanced. The overall index was treated as follows:
STEP 1: and (6) standardizing data. Assuming that n evaluation indexes and m evaluation objects are provided, an index matrix X ═ X is first constructedij)m×nAnd then, carrying out non-dimensionalization processing on the index matrix. The treatment method is as follows:
Figure BDA0003078063500000081
(2) determining evaluation index combination weights
And determining the operation benefit of the light charging and storing system by integrating subjective and objective factors through an Analytic Hierarchy Process (AHP) and an entropy weight method.
The method adopts an analytic hierarchy process to subjectively weight the designed three-layer index system, and comprises the following specific processes:
step 1: comparing two by two to establish a judgment matrix
Consulting expert opinions, quantifying judgment by adopting a nine-level scale method, constructing a judgment matrix, and constructing the judgment matrix U (U) by adopting the nine-level scale method according to the importance of the indexes of the same level relative to the indexes of the upper layerij)n×n。
Step 2: hierarchical single ordering and consistency checking
Judging the matrix U ═ U by solvingij)n×nAnd (4) obtaining a feature vector W by the feature root problem, and normalizing to obtain a ranking weight of the hierarchical element relative to the corresponding upper hierarchical element.
Calculating a consistency index
Figure BDA0003078063500000082
Performing consistency check of the judgment matrix when the judgment matrix is satisfied
Figure BDA0003078063500000083
The decision matrix is considered to have satisfactory consistency.
Step 3: total ordering of layers
Sequentially calculating from top to bottom layer by layer along the hierarchical structure, and calculating the synthetic weight W of the bottommost layer relative to the target layer (the highest layer)1＝[ν1,ν2,…,νm]T
Secondly, an entropy weight method is adopted, known data are utilized to objectively weight the index system, and the method comprises the following specific steps:
STEP 1: and acquiring the information entropy. Information entropy EjThe calculation formula is as follows:
Figure BDA0003078063500000084
Figure BDA0003078063500000085
STEP 2: and calculating the entropy values of the profit storage capacity index, the repayment capacity index and the operation capacity index and the objective weight of the evaluation factors of each target layer. The information entropy normalization processing is carried out to obtain index weight, and the calculation formula is as follows:
Figure BDA0003078063500000091
combining weighting, and combining a subjective weighting method and an objective weighting method according to different preference coefficients to determine the index weight. The calculation formula is as follows:
ri＝αWi+βVi (5)
in the formula, WiSubjective weights determined for the analytic hierarchy process; viThe index weight determined for the entropy weight method (here, α ═ 0.5, β ═ 0.5) yields W ═ ω ═ ω1,ω2,…,ωn]TNamely, the evaluation index combined weight vector is obtained.
Through an analytic hierarchy process and an entropy weight process, the main weight and the objective weight of the economic activity comprehensive evaluation index are respectively determined, the combined weight of the operation benefit comprehensive evaluation index of the optical storage system can be obtained, and the result of the combined weight is shown in table 3.
TABLE 3 evaluation index combination weights
Figure BDA0003078063500000092
3. Improved matter element extension model
Based on the classical matter element model, when the association degree of the matter elements is calculated through the association function, the higher the association degree of the indexes is, the more obvious the membership relationship is when the matter elements are closer to the highest or lowest grade, so that the operation benefit of the light charging and storage system is comprehensively evaluated, and the change rule among the matter elements is more effectively analyzed.
The improved matter element combination evaluation steps are as follows:
step 1: determining classical domains
Figure BDA0003078063500000101
In the formula: u shapejJ divided evaluation levels; c1,C2,…,CnTo evaluate the index system; xjiTo evaluate the grade UjEvaluation index CnA defined magnitude range, Xjn＝(ajn,bjn)。
Step 2: determining section domains
Figure BDA0003078063500000102
In the formula: u shapepThe whole evaluation grade is obtained; interval Xpn＝(apn,bpn) Is UpWith respect to CnThe specified magnitude range, the section domain.
Step 3: determining a degree of association
The correlation function can embody the operation benefit level of the index, embody the correlation degree between the evaluated unit and the evaluation grade thereof, and describe the benefit degree of each index through the grade level.
Figure BDA0003078063500000103
When x isi∈X1And ask for K1(xn) The method comprises the following steps:
Figure BDA0003078063500000104
when x isi∈XmAnd ask for Km(xn) The method comprises the following steps:
Figure BDA0003078063500000105
in other cases:
Figure BDA0003078063500000106
step 4: determining a comprehensive degree of association
Comprehensive relevance K of object to be evaluated to each evaluation gradej(pij) The calculation formula is as follows:
Kj(pij)＝∑σiKj(xi) (12)
in the formula, σiIs the weight of the ith evaluation index.
The profitability index, the repayment ability index, the operation ability and the income level index of the operation benefit of the optical charging and storage system are divided into five grades of 'poor', 'normal', 'good' and 'good', an improved matter element extension model is established, comprehensive evaluation is carried out on the comprehensive evaluation index and evaluation grade data of economic activities, and finally the maximum comprehensive relevance degree and evaluation grade result of the comprehensive evaluation of the optical charging and storage system are calculated and shown in table 4.
TABLE 4 maximum comprehensive degree of association and evaluation grade of evaluation indexes of each target layer
Operational benefits Maximum integrated degree of association Comprehensive evaluation grade
Profitability 0.1989 In general
Ability to pay off debt 0.7898 Is preferably used
Operational capacity 0.0724 Good taste
Profit level 0.1197 Good taste
From the above, the comprehensive benefits of a certain optical charging and storage system are rated according to the maximum comprehensive relevance, so that the profit capacity of the optical charging and storage system can be evaluated as general, the repayment capacity can be evaluated as good, and the operation capacity and the income level can be evaluated as good.
In conclusion, the improved object element extension model provided by the invention can realize scientific and effective evaluation on the comprehensive benefits of the optical charge storage system. Firstly, respectively calculating subjective and objective weights of 18 economic activity evaluation indexes by an analytic hierarchy process and an entropy weight process, and performing weight combination; and secondly, comprehensively evaluating the benefits of the optical storage system by adopting an improved matter element extension model, calculating the corresponding maximum comprehensive relevance based on the comprehensive evaluation, and dividing five grades of 'poor', 'common', 'good' and 'good' according to the comprehensive benefit level of the optical storage system so as to further reflect the trend of the economic activity rating. According to example verification, the result obtained by the comprehensive evaluation of the model is consistent with the actual situation.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. An optical charging storage system operation benefit evaluation method based on an improved matter element extension model is characterized by comprising the following steps:
step 1: data acquisition and pretreatment; according to the technical characteristics and functions of the optical charging and storing system, selecting indexes from the four aspects of profitability, debt paying capacity, operation capacity and income level, preliminarily constructing an operation benefit evaluation index system, collecting sample data of the index values of the indexes, and then carrying out normalization processing on a criterion layer judgment matrix of the sample;
step 2: determining the combined weight of the evaluation indexes; determining the weight of the comprehensive evaluation index of the operational benefit of the optical charge storage system by integrating subjective and objective factors by adopting an analytic hierarchy process and an entropy weight method;
and step 3: performing operation benefit evaluation on the optical charging and storing system based on the matter element extension model; based on the classical matter element extension model, an improved matter element extension model is formed by introducing a calculation method of determining the degree of association by being maximally close to the median of the classical domain, so that the operation benefits of the optical storage system are comprehensively evaluated.
2. The method for evaluating the operation benefit of the optical charging and storage system based on the improved matter element extension model according to claim 1, wherein the method for performing normalization processing on the criterion layer judgment matrix of the sample in the step1 specifically comprises the following steps:
assuming that n evaluation indexes and m evaluation objects are provided, an index matrix X ═ X is first constructedij)m×nThen, carrying out non-dimensionalization on the index matrix:
Figure FDA0003078063490000011
wherein, x'ijA value representing the j-th evaluation index of the i-th evaluation object after the index matrix standardization; x is the number ofijA numerical value indicating a j-th evaluation index of an i-th evaluation object; min (x)j) Represents the minimum value in the jth evaluation index; max (x)j) Represents the maximum value of the j-th evaluation index.
3. The method for evaluating the operation benefit of the optical charging and storing system based on the improved matter element extension model according to claim 1, wherein in the step2, the specific steps of subjectively weighting by adopting an analytic hierarchy process are as follows:
step C1: establishing a judgment matrix; quantifying the judgment by adopting a nine-level scale method, and constructing a judgment matrix U-U (U-U) according to the importance of the indexes of the same level relative to the indexes of the upper layerij)n×n；
Step C2: sorting the hierarchical lists, and carrying out consistency check; judging the matrix U ═ U by solvingij)n×nGet the feature vector W, and proceedNormalizing to obtain a standard matrix A ═ aij)n×nObtaining the relative importance of the hierarchical element to the corresponding upper hierarchical element; calculating a consistency index
Figure FDA0003078063490000021
Wherein λmaxFor the maximum eigenvalue, the consistency of the judgment matrix is checked, and when the check coefficient is satisfied
Figure FDA0003078063490000022
Then, wherein RI is an average random consistency index, and the judgment matrix is considered to have satisfactory consistency;
step C3: overall hierarchical sorting; sequentially calculating from top to bottom layer by layer along the hierarchical structure, and calculating the synthetic weight W of the bottommost layer relative to the highest layer of the target layer1＝[ν1,ν2,…,νm]T。
4. The method for evaluating the operational benefits of the optical charging and storage system based on the improved matter element extension model according to claim 1, wherein in the step2, the specific steps of objectively weighting by using an entropy weight method are as follows:
step S1: acquiring an information entropy; information entropy EjThe calculation formula is as follows:
Figure FDA0003078063490000023
Figure FDA0003078063490000024
in the formula, PijThe j index accounts for the specific gravity of the j index in the ith evaluation object;
step S2: calculating the entropy values of the profitability index, the repayment ability index and the operation ability index and the objective weight of the evaluation factors of each target layer; the information entropy normalization processing is carried out to obtain index weight, and the calculation formula is as follows:
Figure FDA0003078063490000025
in the formula, vjRepresents the weight of the j-th index.
5. The method for evaluating the operation benefit of the optical charging and storage system based on the improved matter element extension model according to claim 1, wherein in the step2, the weight for determining the comprehensive evaluation index of the operation benefit of the optical charging and storage system by integrating subjective and objective factors is specifically as follows:
ri＝αWi+βVi (5)
in the formula, riWeights to weight the combinations; wiSubjective weights determined for the analytic hierarchy process; viAn objective weight determined for the entropy weight method; α and β represent the importance of the objective weight, and α is 0.5 and β is 0.5.
6. The method for evaluating the operation benefit of the optical charging and storage system based on the improved matter element extension model according to claim 1, wherein in the step3, the specific steps of comprehensively evaluating the operation benefit of the optical charging and storage system by using the improved matter element extension model are as follows:
step D1: determining classical domains
Figure FDA0003078063490000031
In the formula: u shapejJ divided evaluation levels; c1,C2,…,CnTo evaluate the index system; xjnTo evaluate the grade UjEvaluation index CnA defined magnitude range, Xjn＝(ajn,bjn)，ajnAnd bjnMinimum and maximum values of the magnitude range, respectively;
step D2: determining section domains
Figure FDA0003078063490000032
In the formula: u shapepThe whole evaluation grade is obtained; interval Xpn＝(apn,bpn) Is UpWith respect to CnThe specified magnitude range, i.e., section domain;
step D3: determining a degree of association
Figure FDA0003078063490000033
In the formula, Kj(xi) The correlation function value of the ith index for the jth grade; ρ (x)i,xin) The distance between the element value to be evaluated of the ith index and the classical domain; | ρinL is the distance of the ith index with respect to the jth level classical domain;
when x isi∈X1And ask for K1(xn) The method comprises the following steps:
Figure FDA0003078063490000041
when x isi∈XmAnd ask for Km(xn) The method comprises the following steps:
Figure FDA0003078063490000042
in other cases:
Figure FDA0003078063490000043
step D4: determining a comprehensive degree of association
Comprehensive relevance K of object to be evaluated to each evaluation gradej(pij) MeterThe calculation formula is as follows:
Kj(pij)＝∑σiKj(xi) (12)
in the formula, pijIs an object to be evaluated; sigmaiIs the weight of the ith evaluation index.
CN202110558591.4A 2021-05-21 2021-05-21 Optical charging storage system operation benefit evaluation method based on improved matter element extension model Pending CN113159650A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110558591.4A CN113159650A (en) 2021-05-21 2021-05-21 Optical charging storage system operation benefit evaluation method based on improved matter element extension model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110558591.4A CN113159650A (en) 2021-05-21 2021-05-21 Optical charging storage system operation benefit evaluation method based on improved matter element extension model

Publications (1)

Publication Number Publication Date
CN113159650A true CN113159650A (en) 2021-07-23

Family

ID=76877161

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110558591.4A Pending CN113159650A (en) 2021-05-21 2021-05-21 Optical charging storage system operation benefit evaluation method based on improved matter element extension model

Country Status (1)

Country Link
CN (1) CN113159650A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114240037A (en) * 2021-11-10 2022-03-25 中国民航科学技术研究院 Plateau airport emergency support capability evaluation method and system
CN114595945A (en) * 2022-02-17 2022-06-07 成都飞机工业（集团）有限责任公司 Manufacturing process-oriented health degree evaluation method for MBOM (moving object model)
CN114897278A (en) * 2021-09-29 2022-08-12 国网(苏州)城市能源研究院有限责任公司 Regional energy Internet comprehensive evaluation method based on improved matter element extension model
CN115545440A (en) * 2022-09-23 2022-12-30 国网冀北电力有限公司经济技术研究院 Differential evaluation method for bidding project quantity and project settlement quantity construction cost of power transmission and transformation project

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114897278A (en) * 2021-09-29 2022-08-12 国网(苏州)城市能源研究院有限责任公司 Regional energy Internet comprehensive evaluation method based on improved matter element extension model
CN114240037A (en) * 2021-11-10 2022-03-25 中国民航科学技术研究院 Plateau airport emergency support capability evaluation method and system
CN114595945A (en) * 2022-02-17 2022-06-07 成都飞机工业（集团）有限责任公司 Manufacturing process-oriented health degree evaluation method for MBOM (moving object model)
CN114595945B (en) * 2022-02-17 2023-11-10 成都飞机工业（集团）有限责任公司 Manufacturing process-oriented aviation product MBOM health evaluation method
CN115545440A (en) * 2022-09-23 2022-12-30 国网冀北电力有限公司经济技术研究院 Differential evaluation method for bidding project quantity and project settlement quantity construction cost of power transmission and transformation project

Similar Documents

Publication Publication Date Title
CN113159650A (en) Optical charging storage system operation benefit evaluation method based on improved matter element extension model
Öğüt et al. Prediction of bank financial strength ratings: The case of Turkey
CN111695718A (en) Power grid development aid decision-making method considering investment demand and planning target
CN111695807A (en) Regional power grid energy efficiency evaluation method and system considering power generation and power utilization side energy efficiency
CN111798057B (en) Charging station site selection method based on fuzzy-hierarchy profit analysis
CN112465286A (en) Method for evaluating service capability of electric vehicle charging network
CN111222755A (en) Effectiveness state evaluation method for intelligent power distribution network
CN112884359A (en) Electric power spot market risk assessment method
CN115660170A (en) Multidimensional index weight collaborative optimization data asset management effect differentiation evaluation method and system
CN111460378A (en) Power distribution network accurate investment project optimization method considering risk measure
CN115907539A (en) Method, system and device for constructing new energy utilization evaluation index system in power market
Tan et al. Benefit allocation model of distributed photovoltaic power generation vehicle shed and energy storage charging pile based on integrated weighting-Shapley method
Chen [Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm
Ji et al. A multi-criteria decision-making framework for distributed generation projects investment considering the risk of electricity market trading
Li et al. Evaluating the regulatory environment of overseas electric power market based on a hybrid evaluation model
CN113095696A (en) Comprehensive energy project full life cycle evaluation method and system
CN112734274A (en) Mining and comprehensive evaluation method for low-carbon power grid operation leading influence factors
CN111428938A (en) Power transmission network scheme optimization method based on function difference and full life cycle
CN115860552A (en) Rural energy Internet comprehensive benefit evaluation method
CN113592292A (en) Power grid primary equipment technical improvement overhaul project admission evaluation method
CN110782369A (en) Method for determining operation risk of multi-energy complementary new energy power generation system and evaluation system
Wu et al. Research on Utility Evaluation of Grid Investment considering Risk Preference of Decision‐Makers
Haoran et al. Research on credit risk assessment of online network credit based on GBDT
Zhang et al. Analysis of Assessment of Power Grid Corp Asset Management Based on Set Pair Analysis Model
Wang Fuzzy Comprehensive Evaluation Model of Project Investment Risk Based on Computer Vision Technology

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
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20210723

WD01 Invention patent application deemed withdrawn after publication