Ultrahigh pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception

Abstract

The invention discloses an ultrahigh pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception. The method comprises the following steps: 1) constructing a carbon emission reduction and carbon reduction income evaluation index of an ultrahigh pressure company based on a correlation analysis method; and 2) considering the influence of multiple factors and providing a carbon emission reduction contribution rate model of the ultrahigh pressure company. And 3) carrying out simulation analysis on the provided model, wherein the result proves the effectiveness of the model. And the relevance between daily work and carbon emission reduction of an ultrahigh pressure company can be effectively analyzed. Meanwhile, actual operation data of a company is considered on the basis of a traditional subjective index weight sensing method, a subjective and objective weight sensing method is established, and guidance is provided for the layout of a carbon asset market and the improvement of asset benefits of an enterprise. And finally, the fuzzy comprehensive evaluation method is applied to the modeling aspect of the evaluation method for the carbon emission reduction contribution rate of the ultrahigh-pressure company, and the carbon emission reduction and carbon reduction income of the enterprise is quantified by adopting the fuzzy comprehensive evaluation method, so that a foundation is laid for trading in the carbon market in the future, and guidance is provided for creating a new effective mode and analyzing investment income for the enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction management in the electric power industry, and specifically to an ultra-high voltage carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception. Background Art

[0002] As the world pays more and more attention to climate change, the power industry, as the main contributor to carbon emissions, faces tremendous pressure to reduce emissions. Therefore, it is a vital task to study carbon emission reduction technologies and management methods in the power industry to achieve efficient use of energy and sustainable development of the environment. This can not only effectively improve the environmental image and market competitiveness of enterprises, but also has important significance for the response to global climate change. However, how to achieve effective control of carbon emissions and maximize economic benefits based on the existing power system still needs further exploration.

[0003] In this context, how to combine the operating characteristics of ultra-high voltage companies to build an evaluation system that can reflect both carbon emission reduction effects and economic benefits, and achieve a win-win situation for carbon emission reduction and corporate development, has become an urgent problem to be solved. Traditional carbon emission reduction evaluation methods often lack consideration of the specific operating environment of the power industry, and it is difficult to fully reflect the actual contribution of ultra-high voltage companies in carbon emission reduction.

[0004] This invention fully combines the existing domestic problems and starts from the actual needs of the power industry, and proposes an evaluation method for the contribution rate of ultra-high voltage carbon emission reduction based on the weight perception of subjective and objective factors. This model not only takes into account the direct reduction of carbon emissions, but also integrates multiple dimensions such as technological innovation, management optimization and economic benefits, providing power companies with a comprehensive and scientific carbon emission reduction evaluation tool. Through this method, it can effectively guide enterprises to optimize resource allocation, improve economic benefits and promote the green transformation of the power industry while achieving carbon emission reduction goals. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes an ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception. The method described in the present invention can effectively evaluate the carbon emission reduction efficiency of enterprises, reduce greenhouse gas emissions, and promote the development of a low-carbon economy. In order to effectively guide enterprises to reduce carbon emissions, promote the development of a low-carbon economy, respond to climate change and achieve dual carbon goals.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception includes the following steps:

[0008] 1) Constructing carbon emission reduction and carbon reduction benefit evaluation indicators based on correlation analysis method;

[0009] 2) Considering the influence of multiple factors, a carbon emission reduction contribution rate model for ultra-high voltage companies was proposed.

[0010] As a further improvement of the present invention, the step 1) constructs the carbon emission reduction of the ultra-high voltage company based on the correlation analysis method Carbon reduction The benefit evaluation index is expressed as:

[0011]

[0012] Where r is the indicator correlation coefficient; They are the average values ​​of the two indicators respectively. The two indicators that failed the correlation test can be used as independent indicators. For the two indicators with relatively high correlation, they are selected according to the above principles and one of them is retained. The indicators are simplified according to this method respectively, and finally the indicators obtained by correlation analysis can be obtained.

[0013] As a further improvement of the present invention, in the carbon emission reduction and carbon reduction benefit evaluation index, an evaluation index system is preliminarily determined from three dimensions: economy, intelligence and management, as follows:

[0014] The first-level indicators are economic indicators, intelligence indicators, and management indicators;

[0015] The secondary indicators of the economic indicators include equipment operation and maintenance costs, equipment investment costs, labor costs and personnel training costs;

[0016] The secondary indicators of the smart indicators are smart application accuracy, equipment-added carbon emissions and clean energy consumption;

[0017] The secondary indicators of the management indicators include employee acceptance, equipment optimization management and equipment after-sales quality.

[0018] As a further improvement of the present invention, the subjective weight of the evaluation index is calculated according to the following method using the ordinal relationship analysis method:

[0019] Step 1: Determine the indicator sequence, select evaluation indicators to conduct a comprehensive evaluation of the object to be evaluated, and the evaluator selects the most important indicator from the indicators, and the corresponding weight is recorded as; then continue to select the most important indicator from the remaining indicators, and so on; until all indicators are selected, a sequence of indicators ranked by relative importance is formed, and the corresponding indicator weight is;

[0020] Step 2: Determine the relative importance ratio of the indicators. The relative importance ratio of the indicators determines the indicator weight. It is obtained after discussion by all evaluators. The calculation formula of the relative importance ratio is:

[0021]

[0022] Step 3: Calculate the weight of the nth indicator. The calculation formula is:

[0023]

[0024] Step 4: Calculate the weights of all indicators according to the relative importance ratio r j , determine the weights of other n-1 indicators;

[0025] α j-1 =r j α j (4)

[0026] Step 5: Determine the weight coefficient of each indicator to the total target layer, set α pq is the weight coefficient of the qth indicator under the pth criterion layer in the indicator system in the total target layer, α p is the weight coefficient of the pth criterion layer in the total target layer; α (p) q is the weight coefficient of the qth indicator in the pth criterion layer, then:

[0027] α pq =α p ×α (p) q (5).

[0028] As a further improvement of the present invention, based on the subjective and objective factor weight perception method, the objective weight of the evaluation index is calculated by the grey correlation analysis method according to the following method:

[0029] Step 1: Determine the system main behavior reference sequence X0 and the comparison sequence X1 of system related factors:

[0030] X0(k)=(x0(1),x0(2),…,x0(n)) (6)

[0031] X i (k)=(x i (1),x i (2),…,x i (n))(i=1,2,…,m) (7)

[0032] Step 2: Normalization of the sequence: normalize the data (dimensionless) and convert the data into a pure digital sequence:

[0033]

[0034] Step 3: Calculate the correlation coefficient:

[0035]

[0036] Step 4: Calculate the correlation:

[0037]

[0038] Step 5: Sort by the degree of correlation between factors.

[0039] As a further improvement of the present invention, considering the actual operation data of the company, a perception method based on the combined weight of subjective and objective factors is established, which can be expressed as:

[0040]

[0041] Among them, θ1 is the expert preference coefficient of the ordinal relation analysis method, θ2 is the expert preference coefficient of the grey relation analysis method; α j is the subjective weight of the evaluation index by ordinal relationship analysis method; β j It is the objective weight of the evaluation indexes by grey relational analysis method.

[0042] As a further improvement of the present invention, the step 2) considers the influence of multiple factors and proposes an evaluation model for the carbon emission reduction contribution rate of ultra-high voltage companies. The specific steps are as follows:

[0043] Step 1: Establish a corresponding first-level evaluation indicator set, which means that there are three evaluation indicators in the first-level evaluation indicators, which correspond to energy efficiency indicators, economic indicators and environmental indicators respectively. On the basis of the first-level indicator set, establish a second-level indicator set, that is, each first-level indicator corresponds to several second-level indicators. On the basis of the second-level indicator set, establish a third-level indicator set, that is, each second-level indicator corresponds to several third-level indicators.

[0044] Step 2: Establish a comment level set V = (V1, V2, V3, V4) = (excellent, good, average, poor). The invited experts make judgments on the questions corresponding to each indicator in the questionnaire based on the actual situation of each evaluated project, and then select appropriate comments in the comment level;

[0045] Step 3: Establish an evaluation index weight set based on the combined weight;

[0046] Step 4: Establish a fuzzy judgment matrix, the specific form is shown in R below:

[0047]

[0048] Among them, r ij =μ R (U i ,V j ), r∈[1,n] is the membership of the i-th factor U to the j-th comment level V. For each three-level factor U ij You can use an R i , then the secondary factor U i The fuzzy relation matrix on the V domain is R i =(B1,B2,B3,…,B i ) T ;

[0049] Relationship Matrix R i The element B in i Yes ij and r ij pass After fuzzy transformation, the first-level evaluation vector is obtained. After processing, the fuzzy evaluation result B=W of the first-level factor is obtained. i ·R i =(b1,b2,L,b i );

[0050] Step 5: Repeat the above calculation process, take the first-level judgment result as the second-level indicator membership, calculate the comprehensive evaluation result vector of the previous level indicator, and after determining the fuzzy relationship matrix and weight set of each layer, perform fuzzy evaluation in layers. Through the hierarchical evaluation and then comprehensive method, finally get the fuzzy comprehensive evaluation result of project U. Then, according to the maximum membership principle, select the comment level corresponding to the maximum value in the set as the final project evaluation result;

[0051] Step 6: Calculate the final project evaluation results through normalization Get the carbon emission reduction contribution rate of each project.

[0052] Beneficial effects:

[0053] The present invention proposes an evaluation method for the contribution rate of ultra-high voltage carbon emission reduction based on the perception of subjective and objective factors weights. Ultra-high voltage companies are the core link of the power industry chain and are also important carriers for achieving energy conservation and emission reduction benefits on the power generation and power consumption sides. As the main body of the transmission and distribution link, ultra-high voltage companies should take corresponding measures to create significant contributions to energy conservation and emission reduction based on the operation strategies of the upstream and downstream industrial chains. Therefore, analyzing and studying the logical relationship between the production factors of ultra-high voltage companies and energy conservation and emission reduction, and quantitatively evaluating the contribution rate of enterprises to energy conservation and emission reduction are issues that need to be considered at present.

[0054] In response to the above problems, the present invention proposes an ultra-high pressure carbon emission reduction contribution rate evaluation method based on the weight perception of subjective and objective factors, and constructs a carbon emission reduction and carbon reduction benefit evaluation system from the economic, intelligent and management levels.

[0055] By introducing this ultra-high voltage carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception, we can better quantify and evaluate the contribution rate of enterprises to energy conservation and emission reduction, optimize power transmission and distribution equipment, improve power transmission efficiency, and reduce energy loss; at the same time, strengthen power load management, reasonably dispatch power supply, and reduce power waste. This will help increase the carbon reduction benefits of enterprises, and more importantly, contribute to saving energy, reducing pollution, and achieving sustainable development for society. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a research framework diagram of the enterprise carbon emission reduction contribution rate assessment model and its application method based on the operation characteristics of ultra-high voltage companies of the present invention;

[0057] Figure 2 It is the carbon emission reduction contribution rate of Shanghai substation r1, digital operation and maintenance monitoring center r2 and logistics service center green technology r3. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0059] On the one hand, the present invention constructs an evaluation index system including three dimensions: economy, intelligence and management, the main contents of which are as follows:

[0060] 1) Economic level

[0061] ① Equipment operation and maintenance costs

[0062] Intelligent equipment operation and maintenance is an effective means for enterprises to reduce costs and increase efficiency. Equipment operation and maintenance requires costs. Usually, in different contexts, equipment maintenance strategies are divided into two types: one is responsive maintenance, which is characterized by repair and maintenance when a failure occurs; the other is preventive maintenance, which is divided into two situations: one is regular maintenance, which is carried out at fixed intervals, and the other is predictive maintenance based on the real-time status of the equipment.

[0063] ② Equipment investment cost

[0064] In just a few years, smart grid has developed rapidly in my country. Among them, smart inspection, as a key link in the construction of smart grid, has also ushered in the spring of development. At the same time, the application of smart inspection has also brought new development opportunities to the drone and UAV industries. Power inspection, as a new application of drones, will play an increasingly important role.

[0065] ③ Human cost

[0066] After the equipment was put into use, the labor cost of the company was reduced compared with the previous one. The digital operation and maintenance monitoring center and the line patrol drones and substation drones replaced manpower, reducing the manpower cost of station operation, saving more than half of the manpower resources. Investing resources in the maintenance of equipment within the station can effectively improve the quality of equipment operation and maintenance, with significant results.

[0067] ④ Personnel training costs

[0068] Digital transformation will instill new and powerful vitality into traditional enterprises, help them get rid of inherent problems, and optimize and reshape processes. In order to promote the digital process of enterprises and further enhance the digital thinking and technical means of enterprise employees, the deployment of drones needs to provide detailed, clear, and easy-to-understand teaching to participating employees, and ensure that employees can fully master the knowledge in the form of "theory + practice".

[0069] 2) Intelligent level

[0070] ① Intelligent application accuracy

[0071] The precision of intelligent application refers to the accuracy and correctness of the inspection of high-voltage transmission lines by line inspection drones, whether difficult-to-identify faults can be discovered in time, whether the clarity of the video transmitted by the drone is high enough, and whether the application of inspection drones is safe and efficient; for substation inspection drones, when facing long-distance inspections and angle problems, whether the accuracy of fault identification can be guaranteed; for monitoring centers, its ability to predict prone faults accurately, distinguish equipment faults, and detect and perceive in real time. The higher the accuracy of equipment application, the more efficient the system will be.

[0072] ②Additional carbon emissions from equipment

[0073] When the equipment is in operation, carbon emissions may be generated. Drones need to be charged to operate. Since the main electricity production comes from fossil energy such as coal, the power source of drone charging piles is the possible additional carbon emissions of drones. The number of drone charging times and electricity consumption are related to carbon emissions. The monitoring center also needs electricity when working. Similarly, the monitoring center's electricity consumption also involves the source of electricity. When its electricity comes from clean energy, its carbon emissions are lower. When repairing these equipment, maintenance personnel will also generate carbon emissions. When purchasing and installing these equipment, the purchasers and installers will also generate carbon emissions. The more equipment there are, the more these additional carbon emissions will be.

[0074] ③Clean energy consumption

[0075] Since clean energy is highly intermittent, unstable and random, the power grid needs to face the problem of insufficient adaptability of smart power devices to the connection and absorption of large-scale high-proportion new energy. Therefore, the clean energy absorption capacity of smart devices needs to be improved to avoid the dilemma of large-scale abandonment of wind and electricity in the development and utilization of new energy.

[0076] 3) Management level

[0077] ①Employee acceptance

[0078] The introduction of new equipment requires employees’ “acceptance”. This means that if the equipment is easy to operate, the financial and time costs of training employees will be much lower; if the equipment is difficult to operate and old employees cannot adapt to it, the company will face personnel issues such as layoffs, salary cuts, and recruiting new employees. When old employees resist these new equipment, the time and financial costs of training will increase, and more carbon emissions will be generated invisibly. Even if the new equipment is introduced by force, old employees will still work in the traditional way. Even if the equipment is fully utilized, it will still produce higher carbon emissions.

[0079] ②Equipment optimization management

[0080] Obviously, the more patrol drones, substation inspection drones and digital operation and maintenance monitoring centers are deployed, the better. Excessive deployment of these smart devices will also generate carbon emissions, so the equipment needs to be managed optimally. Managers need to calculate the optimal number of deployed devices, as well as the specific route planning of patrol drones, the deployment location and jurisdiction of the monitoring center.

[0081] ③ Equipment after-sales quality

[0082] Undoubtedly, companies also need to purchase these devices, and it is also important to choose reliable merchants when selecting equipment. Since these devices need to be used for a long time and are highly consumable, they also need regular maintenance and inspection, which requires professional maintenance personnel. When equipment fails, a series of chain reactions will also occur. For example, the line where the line inspection drone works needs to be temporarily assigned manpower for line inspection. For another example, if the monitoring center fails, it will have an avalanche of consequences, greatly reducing the efficiency of power grid operation.

[0083] Since there are many factors to be considered in the construction of evaluation indicators, the relationship between evaluation indicators is relatively complex, and there is inevitably a certain correlation between the preliminary indicators. If these related indicators are directly used in the evaluation indicator system for the necessity of power grid project investment, it will cause the amplification of some benefits and affect the rationality of the comprehensive evaluation results. Therefore, it is necessary to simplify the preliminary indicators and eliminate highly correlated indicators. The correlation analysis method does not reconstruct the indicators, which can ensure that the significance of the indicators does not change, and is suitable for simplifying the investment benefit evaluation indicator system of power supply enterprises. The correlation analysis method requires a correlation analysis matrix. The closer the absolute value of the correlation coefficient of the two indicator data is to 1, the stronger the correlation is.

[0084] Assume there are S evaluation indicators S1, S2, ..., S m These m indicators are divided into k categories:

[0085] Each row above represents a type of indicator. For the indicators in each row, a correlation analysis matrix is ​​made. The closer the absolute value of the correlation coefficient of two indicator data is to 1, the stronger the correlation is. The range of the correlation coefficient r is used to judge the degree of correlation between the two indicators: r = 0, indicating no correlation at all; 0 < |r| < 0.3, indicating basically no correlation; 0.3 < |r| < 0.5, indicating low correlation; 0.5 < |r| < 0.8, indicating significant correlation; 0.8 < |r| < 1, indicating high correlation; r = 1, indicating complete correlation.

[0086]

[0087] in, are the average values ​​of the two indicators.

[0088] The correlation matrix is ​​obtained as:

[0089]

[0090] The correlation coefficient r ij In the above example, the two indicators that failed the correlation test can be used as independent indicators. For the two indicators with high correlation, one of them can be selected according to the above principle. The k types of indicators are simplified according to this method, and finally the indicators obtained by correlation analysis can be obtained.

[0091] On the other hand, the principle adopted by the present invention is to encourage enterprises to adopt more environmentally friendly production methods, promote the consumption of renewable energy and the application of clean energy technology by comprehensively considering the carbon emissions of enterprises, the implementation effect of energy-saving and emission reduction measures, and economic benefits, so as to promote the green transformation of the power industry. The subjective and objective weights of the indicators are determined by the G1 method and the gray correlation analysis method. The main process of the weight method calculation is as follows:

[0092] 1) G1 method to determine subjective weight

[0093] Assume that n evaluation indicators are selected for comprehensive evaluation of the object to be evaluated. The evaluator selects the most important indicator X1 from the n indicators, and the corresponding weight is recorded as α1; then the most important indicator is selected from the remaining n-1 indicators, and so on; until all indicators are selected, a sequence of indicators (X1, X2, ..., X1) ranked by relative importance is formed. n )The corresponding indicator weights are (α1,α2,...,α n ).

[0094] The relative importance ratio of indicators j Determines the weight of the indicator, which is determined by discussion among all evaluators. j The calculation is shown in formula (1).

[0095]

[0096] The calculation of the nth indicator weight is shown in formula (2).

[0097]

[0098] According to the relative importance ratio r j , determine the weights of the other n-1 indicators, see formula (3).

[0099] α j-1 =r j α j (4)

[0100] Let α pq is the weight coefficient of the qth indicator under the pth criterion layer in the indicator system in the total target layer, α p is the weight coefficient of the pth criterion layer in the total target layer; α (p) q is the weight coefficient of the qth indicator in the pth criterion layer, then:

[0101] α pq =α p ×α (p) q (5)

[0102] The ordinal relationship analysis method uses experts to rank the importance of indicators and rationally weight the importance of adjacent indicators, which is highly subjective and arbitrary.

[0103] 2) Grey correlation analysis method to determine objective weights

[0104] Determine the system main behavior reference sequence X0 and the comparison sequence X1 of system related factors.

[0105] X0(k)=(x0(1),x0(2),…,x0(n)) (6)

[0106] X i (k)=(x i (1),x i (2),…,x i (n)) (i=1,2,…m) (7)

[0107] Since the physical meanings of different factors in the system are different, the dimensions of their data are also different, making it difficult to draw correct conclusions when comparing. Therefore, it is necessary to normalize (dimensionless) the data and convert the data into a pure digital sequence for calculation and comparative analysis. The data of the sequence is classified into a range.

[0108]

[0109] Calculate the correlation coefficient:

[0110]

[0111] Where ρ is the resolution coefficient, which ranges from [0,1] and is generally 0.5.

[0112] Calculate the correlation:

[0113]

[0114] Sorting is performed according to the magnitude of the factor correlation, reflecting the strength of the relationship between the comparison sequence and the reference sequence.

[0115] 3) Comprehensive weight acquisition method

[0116] Determination of combination weighting: (1) Use the G1 method to subjectively weight the evaluation indicators. (2) Use the grey correlation analysis method to objectively weight the indicators. (3) Combine the weights of (1) and (2) to determine the combination weight. (4) Select the expert preference coefficient to perform dynamic combination weighting and determine the linear combination weight.

[0117]

[0118] Combining the above two aspects, Figure 1 The above process, based on the fuzzy comprehensive evaluation method, can quantify the company's contribution to carbon emission reduction.

[0119] The fuzzy comprehensive evaluation method is a method that uses the principle of fuzzy transformation to analyze the object to be evaluated and then obtain its comprehensive evaluation. It is mainly divided into two steps: the first step is to judge according to the influence of a single factor, and the second step is to consider the influence of all factors for a comprehensive evaluation.

[0120] First, determine the evaluation index factor set, factor set (U = (u1, u2, ..., u i )) refers to various factors that can affect the evaluation object (u i ).

[0121] Generally speaking, each factor has different importance. In order to express their importance, for each factor u i Assign the corresponding weight w i , then the weight set w i It can be expressed as: W = (w1, w2, ..., w i ).

[0122] The evaluation level set (V) is composed of various evaluation result elements (v i), that is, V=(v1,v2,…,v i ).

[0123] The fuzzy relationship matrix is ​​an evaluation of each independent factor in the factor set U, and its purpose is to calculate the degree of membership of each evaluation object to each element in the evaluation conclusion. First, the fuzzy evaluation matrix R is used to represent the fuzzy relationship of each indicator factor from the evaluation indicator factor set U to the comment level set V. The evaluation expert group is organized to evaluate and score each indicator factor, and the fuzzy relationship matrix is ​​constructed as follows:

[0124]

[0125] The R of the i-th row i It represents the membership of the evaluated factor i to each comment in the comment set, and the jth column represents the degree to which the evaluated factor takes the jth comment in the comment set.

[0126] The fuzzy relationship matrix can only reflect the impact of a single factor on the evaluation object. If you want to accurately judge an object, you also need to make a comprehensive analysis based on the weight set and the influence of various factors to make an accurate judgment. After calculating the weight set W and the fuzzy relationship matrix R, you can use the fuzzy comprehensive evaluation method to conduct the evaluation.

[0127]

[0128] Where: B is the fuzzy comprehensive evaluation result, b j is the membership degree of the evaluation object to the jth level in the comment set.

[0129] Taking the existing statistical data of Shanghai Ultra High Voltage Company’s carbon emission reduction contribution rate as an example, the carbon asset contribution rate of Shanghai substation, digital operation and maintenance monitoring center and logistics service center is evaluated, and then the carbon assets and carbon reduction benefits of each case are calculated and evaluated. Figure 1 As shown, a method for evaluating the contribution rate of ultra-high pressure carbon emission reduction based on the weight perception of subjective and objective factors includes the following steps:

[0130] Step 1: Use the correlation coefficient method to simplify the carbon emission reduction contribution rate evaluation index system with the characteristics of ultra-high voltage companies and eliminate the coupling relationship between indicators. Calculate the correlation coefficients between the secondary indicators, and judge the correlation between the indicators based on the correlation coefficients between the indicators in the table. For two indicators with smaller absolute values ​​of correlation coefficients, they can be used as independent indicators; for indicators with larger absolute values ​​of correlation coefficients, only one of them needs to be retained. Set the threshold of the absolute value of the correlation coefficient to 0.9.

[0131] Step 2: Use the G1 method to calculate the subjective weight of the index system. Comprehensively evaluate the three evaluation indicators of the first level, and have four evaluators select the most important indicator X1 from the three indicators, with the corresponding weight recorded as α1; then continue to select the most important indicator from the remaining two indicators until all indicators are selected, forming an indicator sequence (X1, X2, X3) ranked by relative importance, with corresponding indicator weights (α1, α2, α3). The indicator sequence (X1, X2, X3) ranked by relative importance corresponds to the indicator weights (A1, A2, A3) of (α1, α2, α3).

[0132] Step 3: Determine the relative importance ratio of the indicators, which is obtained after discussion among the four evaluators.

[0133] R2=α2(A1) / α1(A2)=1.5

[0134] R3=α3(A3) / α2(A1)=1.3

[0135] Step 4: Calculate the weight of the third indicator, α3(A3) = 1 / (1+1.3+1.3*1.5) = 0.235

[0136] Step 5: Calculate the weights of all indicators according to the relative importance ratio r j , determine the weights of the other two indicators as shown below.

[0137] α2(A1)=α3*R3=0.306

[0138] α1(A2)=α2*R2=0.459

[0139] Step 6: Similarly, calculate the secondary indicators under the primary indicators.

[0140] Step 7: Determine the relative importance of all factors at a certain level to the overall goal, starting from the highest level to the lowest level. pq =α p ×α (p) q, the weight of the first-level indicator is multiplied by the weight of the second-level indicator below it to obtain the subjective weight of each indicator.

[0141] Step 8: Obtain the objective weight of carbon emission reduction contribution rate assessment index based on grey correlation analysis method.

[0142] Step 9: Based on the calculation results of subjective weights and objective weights, the combined weights of carbon emission reduction contribution rate assessment indicators with the characteristics of ultra-high voltage companies are obtained according to the formula.

[0143] Step 10: Based on the combined weight values, the fuzzy comprehensive evaluation method is used to calculate the carbon emission reduction contribution rates of Shanghai substations, digital operation and maintenance monitoring centers, and logistics service centers.

[0144] Step 11: Based on the above information and in combination with industry standards, 20 experts are invited to score each evaluation indicator based on the actual data of the project. This project sets four levels of comments for each indicator, namely V = (V1, V2, V3, V4) = (excellent, good, general, poor). 20 experienced personnel will evaluate the indicator system, and each expert will individually score each indicator in the indicator layer. Due to the fuzziness of the indicator, the number of times each person scores the indicator can be combined to obtain the degree of membership of the indicator to a certain comment level. The proportion of the comment levels of the 20 people who agree with each indicator is taken as the degree of membership, so as to establish a single-factor fuzzy comprehensive evaluation matrix.

[0145] Step 12: Based on the calculation of the membership model, the membership matrix of indicators at all levels can be obtained.

[0146] Step 13: Through fuzzy comprehensive evaluation Hi=Ci×V(i=1,2,3) can get the final score.

[0147] Step 14: Calculate by normalization Obtain the carbon emission reduction contribution rate of green technologies in Shanghai substations, digital operation and maintenance monitoring centers, and logistics service centers.

[0148] Step 15: Shanghai Substation is a substation under the jurisdiction of State Grid Shanghai Ultra High Voltage Company, and is also a super-large hub substation with the most complete voltage levels in China. In the example given in the present invention, Figure 2 As shown in the figure, the carbon emission reduction contribution rate of Shanghai substation is 43%, and the carbon emission reduction of Shanghai substation is its contribution rate multiplied by the total carbon emission reduction, and the annual carbon emission reduction is 9,755.6t; operation and maintenance monitoring has become crucial in today's increasingly digitalized world. The integrated operation and maintenance monitoring management platform has emerged as a well-known data collection and operation and maintenance insight solution provider in the industry, bringing a new experience of "seeing everything" to the ultra-high voltage company. In this project, the carbon emission reduction contribution rate of the digital operation and maintenance monitoring center is 47%, and the carbon emission reduction of the digital operation and maintenance monitoring center is calculated to be 10,663t; energy enterprise logistics services, as an important support for energy enterprises, play an important role in promoting the high-quality development of enterprises and achieving strength, optimization and expansion. Only by continuously improving the level of modern logistics services can we relieve the worries of energy enterprises and ensure high-quality development. In this project, the carbon emission reduction contribution rate of the logistics service center is calculated to be 10%, and its annual carbon emission reduction is calculated to be 2,268.7t. The carbon reduction benefit of the green technology in the example of the present invention is 1.475 million yuan; the carbon reduction benefit generated by the hidden dangers warned by the visualization system is 3.32 million yuan, and the total carbon reduction benefit is 4.795 million yuan.

[0149] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception is characterized by: The following steps are involved: 1) Constructing carbon emission reduction and carbon reduction benefit evaluation indicators based on correlation analysis method; 2) Considering the influence of multiple factors, a carbon emission reduction contribution rate model for ultra-high voltage companies was proposed.

2. The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception according to claim 1 is characterized in that: The step 1) constructs the carbon emission reduction and carbon reduction benefit evaluation index of the ultra-high voltage company based on the correlation analysis method, which is expressed as: Where r is the indicator correlation coefficient; They are the average values ​​of the two indicators respectively. The two indicators that failed the correlation test can be used as independent indicators. For the two indicators with relatively high correlation, they are selected according to the above principles and one of them is retained. The indicators are simplified according to this method respectively, and finally the indicators obtained by correlation analysis can be obtained.

3. The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception according to claim 2 is characterized in that: Among the carbon emission reduction and carbon benefit evaluation indicators, the evaluation indicator system is initially determined from three dimensions: economy, intelligence and management, as follows: The first-level indicators are economic indicators, intelligence indicators, and management indicators; The secondary indicators of the economic indicators include equipment operation and maintenance costs, equipment investment costs, labor costs and personnel training costs; The secondary indicators of the smart indicators are smart application accuracy, equipment-added carbon emissions and clean energy consumption; The secondary indicators of the management indicators include employee acceptance, equipment optimization management and equipment after-sales quality.

4. The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception according to claim 2 is characterized in that: The subjective weight of the evaluation index using the ordinal relationship analysis method should be calculated according to the following method: Step 1: Determine the indicator sequence, select evaluation indicators to conduct a comprehensive evaluation of the object to be evaluated, and the evaluator selects the most important indicator from the indicators, and the corresponding weight is recorded as; then continue to select the most important indicator from the remaining indicators, and so on; until all indicators are selected, a sequence of indicators ranked by relative importance is formed, and the corresponding indicator weight is; Step 2: Determine the relative importance ratio of the indicators. The relative importance ratio of the indicators determines the indicator weight. It is obtained after discussion by all evaluators. The calculation formula of the relative importance ratio is: Step 3: Calculate the weight of the nth indicator. The calculation formula is: Step 4: Calculate the weights of all indicators according to the relative importance ratio r j , determine the weights of other n-1 indicators; α j-1 ＝r j α j (4) Step 5: Determine the weight coefficient of each indicator to the total target layer, set α pq is the weight coefficient of the qth indicator under the pth criterion layer in the indicator system in the total target layer, α p is the weight coefficient of the pth criterion layer in the total target layer; α (p) q is the weight coefficient of the qth indicator in the pth criterion layer, then: α pq ＝α p ×α (p) q (5)。 5. The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception according to claim 4 is characterized in that: In the method based on subjective and objective factor weight perception, the objective weight of the evaluation index in the grey correlation analysis method needs to be calculated according to the following method: Step 1: Determine the system main behavior reference sequence X0 and the comparison sequence X1 of system related factors: X0(k)=(x0(1),x0(2),…,x0(n)) (6) X i (k)=(x i (1),x i (2),…,x i (n))(i=1,2,…,m) (7) Step 2: Normalization of the sequence: normalize the data (dimensionless) and convert the data into a pure digital sequence: Step 3: Calculate the correlation coefficient: Step 4: Calculate the correlation: Step 5: Sort by the degree of correlation between factors.

6. The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception according to claim 5 is characterized in that: Considering the actual operating data of the company, a weighted perception method based on the combination of subjective and objective factors is established, which can be expressed as: Among them, θ1 is the expert preference coefficient of the ordinal relation analysis method, θ2 is the expert preference coefficient of the grey relation analysis method; α j is the subjective weight of the evaluation index by ordinal relationship analysis method; β j It is the objective weight of the evaluation indexes by grey relational analysis method.

7. The ultra-high pressure carbon emission reduction contribution rate evaluation method based on subjective and objective factor weight perception according to claim 5 is characterized in that: The step 2) considers the influence of multiple factors and proposes an evaluation model for the carbon emission reduction contribution rate of ultra-high voltage companies. The specific steps are as follows: Step 1: Establish a corresponding first-level evaluation indicator set, which means that there are three evaluation indicators in the first-level evaluation indicators, which correspond to energy efficiency indicators, economic indicators and environmental indicators respectively. On the basis of the first-level indicator set, establish a second-level indicator set, that is, each first-level indicator corresponds to several second-level indicators. On the basis of the second-level indicator set, establish a third-level indicator set, that is, each second-level indicator corresponds to several third-level indicators. Step 2: Establish a comment level set V = (V1, V2, V3, V4) = (excellent, good, average, poor). The invited experts make judgments on the questions corresponding to each indicator in the questionnaire based on the actual situation of each evaluated project, and then select appropriate comments in the comment level; Step 3: Establish an evaluation index weight set based on the combined weight; Step 4: Establish a fuzzy judgment matrix, the specific form is shown in R below: Among them, r ij =μ R (U i ,V j ), r∈[1,n] is the membership of the i-th factor U to the j-th comment level V. For each three-level factor U ij You can use an R i , then the secondary factor U i The fuzzy relation matrix on the V domain is R i =(B1,B2,B3,…,B i ) T ; Relationship Matrix R i The element B in i Yes ij and r ij pass After fuzzy transformation, the first-level evaluation vector is obtained. After processing, the fuzzy evaluation result B=W of the first-level factor is obtained. i ·R i =(b1,b2,L,b i ); Step 5: Repeat the above calculation process, take the first-level judgment result as the second-level indicator membership, calculate the comprehensive evaluation result vector of the previous level indicator, and after determining the fuzzy relationship matrix and weight set of each layer, perform fuzzy evaluation in layers. Through the hierarchical evaluation and then comprehensive method, finally get the fuzzy comprehensive evaluation result of project U. Then, according to the maximum membership principle, select the comment level corresponding to the maximum value in the set as the final project evaluation result; Step 6: Calculate the final project evaluation results through normalization i=1,2,3 to obtain the carbon emission reduction contribution rate of each project.

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