Quantitative grading method for carbon emission in production stage of power transformer

Abstract

The invention discloses a quantitative grading method for carbon emission in a production stage of a power transformer. The method comprises the following specific steps: after data are collected, firstly calculating a production link carbon footprint and a product use link carbon footprint of a to-be-evaluated transformer product, and calculating a production carbon efficiency ratio of the to-be-evaluated transformer product on the basis of the production link carbon footprint and the product use link carbon footprint; secondly, through data standardization integration processing, calculating the information entropy and the information utility value of each evaluation index, thereby determining the index weight; and finally, calculating the square value of the weighted mahalanobis distance of the to-be-evaluated product, and substituting the square value into a centesimal system grading algorithm to obtain a product production carbon footprint evaluation score and a corresponding grade. According to the method, the transformer quality parameters, the production stage carbon footprint and the use stage carbon footprint are integrated and unified, a traditional carbon efficiency ratio concept is optimized, multi-dimensional and refined grading of the transformer production stage carbon footprint is achieved, and the defects of an existing method in the aspects of comprehensiveness and accuracy are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon assessment of power grid materials in a novel power system, and in particular to a quantitative grading method for carbon emissions during the production stage of power transformers. Background Art

[0002] Under the current background of global climate change and energy transformation, the green and low-carbon transformation of the power system, as an important area of ​​energy consumption and carbon emissions, is particularly important. As a key equipment in the power grid, the carbon emissions of power transformers in the production stage cannot be ignored. Accurately quantifying and grading the carbon emissions in the production stage of power transformers can not only help companies identify emission reduction potential and formulate scientific emission reduction strategies, but also provide data support for the government to formulate relevant environmental protection policies and promote the green transformation of the entire power industry. Therefore, it is necessary to develop a scientific, reasonable and easy-to-operate method for quantifying and grading carbon emissions in the production stage of power transformers.

[0003] At present, the carbon footprint of transformer production is calculated using the Life Cycle Assessment (LCA) method, which evaluates the carbon footprint of products by comprehensively considering carbon emissions during the entire life cycle, including raw material acquisition, manufacturing, and transportation. This method has the following disadvantages in practical applications: 1. Difficulty in data acquisition: The LCA method requires the collection of a large amount of detailed data, including the source of raw materials, production processes, transportation methods, etc. These data are often difficult to fully obtain in actual operations.

[0004] 2. High computational complexity: Since it is necessary to consider multiple links in the entire life cycle, the calculation process of the LCA method is relatively complex and requires professional knowledge and tools.

[0005] 3. Ignoring product functional differences: The LCA method focuses on the total amount of carbon emissions and ignores the functional differences between different transformers, which leads to biased evaluation results.

[0006] 4. Ignoring the direct correlation between operating efficiency and carbon footprint: The LCA method fails to directly correlate the operating efficiency of the transformer with its carbon footprint, and ignores the indirect emissions caused by energy efficiency differences during the operation phase, which is one of the core sources of carbon emissions from transformers. The carbon footprint involves the entire life cycle. Even when quantifying the carbon footprint in the manufacturing process, in addition to considering the energy consumption of its production materials, it is also necessary to consider the differences in carbon emissions in the operating process corresponding to different quality indicators. That is, the quality indicators of the product correspond to different carbon footprints during the operation process. Therefore, for the quantitative rating of the carbon footprint of the product, in addition to considering the carbon evaluation of the manufacturing itself, it is also necessary to consider the operating carbon emissions corresponding to its core related quality indicators.

[0007] For the carbon classification of transformers, existing methods are mostly based on energy efficiency standards, emission factors or simple carbon footprint calculation results. Although these methods are simple and easy to implement, they still have the following disadvantages: 1. Incompleteness of single indicator classification: Classification based on single indicators such as energy efficiency level and carbon emissions per unit of energy consumption ignores the multi-dimensional performance differences of transformers in actual operation and does not consider the interaction and coupling effects between multiple indicators.

[0008] Difficult to reflect the true carbon efficiency level: Due to the failure to comprehensively consider multiple key factors affecting carbon efficiency, the existing grading methods are difficult to fully and accurately reflect the true carbon efficiency level of the transformer, which may lead to inaccurate or misleading grading results. Summary of the invention

[0009] One of the purposes of the present invention is to provide a green production evaluation system for power transformers, integrating the transformer quality, carbon footprint in the production stage and carbon footprint in the use stage; the second purpose is to provide a method for graded evaluation of the carbon footprint of the transformer production stage through the system.

[0010] To achieve the above object, the present invention provides the following technical solutions: A quantitative classification method for carbon emissions in the production stage of power transformers comprises the following steps: Step 1: Collect the quality inspection data and production data of the transformer product to be evaluated. The quality inspection data includes the load loss and no-load loss values ​​of the transformer product. The production data includes the carbon emission factor of electricity produced by the transformer product, the consumption of transformer raw materials and the corresponding carbon emission factor, and the electric energy consumed in the transformer production process. When collecting production data, discard the raw materials or production links that account for less than 10% of the total; Step 2: Calculate the carbon footprint C of the production process of the transformer product to be evaluated based on the collected quality inspection data and production data P and the carbon footprint of product use U ; Step 3: Calculate the production carbon efficiency ratio (PCER) of the transformer product to be evaluated based on the carbon footprint of the production link and the carbon footprint of the product use link. The production carbon efficiency ratio (PCER) refers to the value obtained by dividing the weighted carbon emissions during the entire life cycle of the product by the total output electric energy of the transformer during the entire life cycle. The weighted carbon emissions during the entire life cycle of the product are obtained by multiplying the carbon footprint of the production link and the carbon footprint of the product use link by their respective carbon footprint weight coefficients and then adding them together; Step 4: The carbon footprint C of the production process calculated in steps 2 and 3 P , Carbon footprint of product use U and production carbon efficiency ratio PCER three evaluation indicators, assuming there are n samples in total, the original data matrix Y of sample i is obtained by sortingi , combine the sample matrices to obtain the original overall data matrix M, perform reverse normalization on the original overall data matrix M, and obtain the normalized overall data matrix G; Step 5: Calculate the information entropy and information utility value of the three evaluation indicators, and determine the indicator weight matrix W; Step 6: Calculate the sample data matrix X of the product to be evaluated based on the indicator weight matrix W i The square value D of the weighted Mahalanobis distance to the overall data matrix G 2 ; Step 7: The square value D of the weighted Mahalanobis distance of the sample to be evaluated x 2 Substitute the percentage conversion algorithm and grading standards to obtain the product production carbon footprint evaluation score and corresponding grade.

[0011] Furthermore, step 2 calculates the carbon footprint C of the production process of the transformer product to be evaluated based on the collected quality inspection data and production data. P and the carbon footprint of product use U , specifically including: Carbon footprint of production process P The calculation formula is as follows: In the formula, C E is the carbon footprint of transformer production and manufacturing, C M Carbon footprint of raw materials obtained for products, C PV The carbon sink generated by photovoltaic power generation, the calculation method of each parameter is as follows: (1) Carbon footprint of transformer product manufacturing process C E : In the formula, F i is the energy consumption of each production process of distribution transformer, E e is the carbon emission factor of electricity; (2) Carbon footprint of product raw material acquisition C M : In the formula, C T The carbon footprint of raw material transportation. B The carbon footprint of the raw material itself, M i is the consumption of the i-th raw material (kg); T i is the average transportation distance of the ith raw material (km), D i is the carbon footprint factor corresponding to the unit weight transportation distance under the transportation mode of the i-th raw material, E m-iis the carbon footprint factor corresponding to the raw material; (3) Carbon sinks generated by photovoltaic power generation C PV : Where M i,PV,ELEC Refers to the net photovoltaic energy in the i-th production process of 1 unit of product, in kW / h, and the calculation formula is as follows: Where: W i is the total power consumption in the ith core production process of one unit of product, P i is the proportion of photovoltaic electricity in the total electricity consumption in the ith core production process of one unit of product, and P refers to the proportion of photovoltaic electricity used for production by the enterprise in the total photovoltaic electricity generated in the previous fiscal year; Carbon footprint of product use U The calculation formula is as follows: Where: P0 is the no-load loss of the transformer during operation, k is the average load factor of the transformer, P k is the load loss of the transformer during operation, E e is the carbon emission factor of electricity.

[0012] Furthermore, the calculation formula for the production carbon efficiency ratio PCER in step 3 is as follows: Where PCER is the production carbon efficiency ratio, the unit is kgCO2e / kWh, k p and k u is the carbon footprint weight coefficient, which takes values ​​of 0.6 and 0.4 respectively; G is the total output electrical energy of the transformer throughout its life cycle.

[0013] Furthermore, step 4 specifically includes: Let parameter a ij is the value of the i-th row and j-th column of the original population data matrix M, i=1,2,3, and j=1,2,…,n, using b ij Indicates a ij The reverse normalization result of is as follows: The normalized overall data matrix G is obtained, and the normalized sample data matrix X is used i Represents the evaluation data of the i-th product sample to be evaluated: .

[0014] Furthermore, step 5 specifically includes: (1) Calculate the proportion of the i-th sample value under the j-th indicator to the indicator: (2) Calculate the information entropy e of the j-th indicator j : (3) Calculate the information utility value d of the jth indicator j : (4) Calculate the weight w of the jth indicator j : (5) Construct the indicator weight matrix W: .

[0015] Furthermore, step 6 specifically includes: in, Represents the square value of the Mahalanobis weighted distance corresponding to sample X; μ is the mean vector of the overall data matrix G, W is the indicator weight diagonal matrix, ∑ is the covariance matrix of the overall data matrix G, and the calculation methods of each parameter are as follows: .

[0016] Furthermore, the percentage conversion algorithm in step 7 is as follows: The grading standard is: products with scores between 60 and 70 are Grade IV, 70 to 80 are Grade III, 80 to 90 are Grade II, and 90 to 100 are Grade I.

[0017] The present invention has the following beneficial effects: 1. The traditional concept of carbon efficiency ratio is optimized, and the concept of Production Carbon Efficiency Ratio (PCER) is proposed. When calculating the carbon emissions of a product throughout its life cycle, the traditional carbon efficiency ratio simply adds up the carbon footprint of the product’s production and the carbon footprint of the use phase, and uses this as the carbon footprint of the entire life cycle. However, the data of the transformer’s production carbon footprint and use carbon footprint are quite different, and directly adding them together will dilute the data entropy of the production process. The present invention proposes the concept of production carbon efficiency ratio, which weights the carbon footprints of the two links, and can highlight the impact of the production stage on the overall carbon efficiency.

[0018] 2. The traditional life cycle carbon footprint calculation method (Life-Cycle Assessment, LCA) has been optimized. Combined with the relevant provisions of international standard documents such as ISO 14064, the present invention only includes raw materials and production links whose single carbon footprint accounts for more than 10% in the production process into the evaluation system, which can eliminate the cumbersome problems of data collection and verification in traditional calculation methods and improve the practicality of the present invention. At the same time, similar calculations mostly do not take into account the impact of green energy applications on carbon footprints when calculating carbon footprints. At present, most transformer manufacturers have introduced photovoltaic power generation to assist production. Therefore, the present invention proposes a method for quantifying photovoltaic electricity in the production process when calculating the carbon footprint.

[0019] 3. Combining the two indicators of carbon footprint in the production stage and carbon footprint in the product use stage, by adopting the Mahalanobis weighted distance algorithm, a multi-dimensional and refined classification of the carbon footprint in the transformer production stage is achieved, overcoming the shortcomings of existing methods in comprehensiveness and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a method for quantifying and grading carbon emissions during the production stage of power transformers provided by the present invention.

[0021] Figure 2 Schematic diagram of the evaluation index system of the present invention. DETAILED DESCRIPTION

[0022] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0023] Figure 1 The present invention provides a flow chart of the quantitative classification method for carbon emissions in the production stage of power transformers. The method is divided into seven steps. Steps 1 to 4 calculate and organize the three indicators of carbon footprint in the production stage, carbon footprint in the use stage, and carbon efficiency ratio of production (such as Figure 2 As shown); Steps 5 and 6 use the entropy weight-weighted Mahalanobis distance method to analyze the evaluation index set and calculate the square value of the weighted Mahalanobis distance of each sample to be tested; Step 7 converts the obtained mathematical parameters into evaluation scores and grading results. The specific steps are as follows: Step 1: Collect the quality inspection data and production data of the transformer product to be evaluated. The quality inspection data includes the load loss P0 and no-load loss value P0 of the transformer product. k The production data are detailed as follows: 1. Carbon emission factor of electricity produced by transformer products.

[0024] 2. The consumption of transformer raw materials and the corresponding carbon emission factor. The carbon footprint of each raw material should be higher than 10% of the total. For example, for oil-immersed transformers, this item includes the mass of silicon steel sheets, copper materials, transformer oil, and oil tanks and the corresponding carbon emission factors.

[0025] 3. Electric energy consumed in the transformer production process. The energy consumption of each link here should be higher than 10% of the total. For example, for oil-immersed transformers, this item includes three production links: coil manufacturing, body drying, and vacuum oil filling.

[0026] Step 2: Calculate the carbon footprint of the production link and the carbon footprint of the product use link of the transformer product to be evaluated based on the collected quality inspection data and production data. The calculation method is as follows.

[0027] 1. Carbon footprint of production process C P : In the formula, C E It is the carbon footprint of transformer production and manufacturing. M The carbon footprint of raw materials obtained for the product. PV is the carbon sink generated by photovoltaic power generation. The calculation methods of each parameter are described as follows.

[0028] (1) Carbon footprint of transformer product manufacturing process C E In the formula, F i is the energy consumption of each production process of distribution transformer, E e is the carbon emission factor of electricity.

[0029] (2) Carbon footprint of product raw material acquisition C M In the formula, C T The carbon footprint of raw material transportation. B The carbon footprint of the raw material itself. i is the consumption of the i-th raw material (kg); T i is the average transportation distance of the ith raw material (km), D i is the carbon footprint factor corresponding to the unit weight transportation distance under the transportation mode of the i-th raw material. m-i is the carbon footprint factor corresponding to this raw material.

[0030] (3) Carbon sinks generated by photovoltaic power generation C PV Where Mi,PV,ELEC It refers to the net photovoltaic energy in the i-th production process of 1 unit of product, with the unit of kW / h. The calculation method is shown in the formula.

[0031] Where: W i is the total power consumption in the ith core production process of one unit of product. i is the proportion of photovoltaic electricity in the total electricity consumption in the ith core production process of one unit of product, and P refers to the proportion of photovoltaic power generation used for production by the enterprise in the total photovoltaic power generation in the previous fiscal year.

[0032] 2. Carbon footprint of product use U The carbon footprint of the use process is related to the operating life of the transformer. Generally, the life of a distribution transformer is 30 years, calculated as 365 days a year and 24 hours a day.

[0033] The losses of distribution transformers during operation are divided into no-load losses and load losses. No-load losses always exist during the operation of the transformer, and are independent of the actual load size of the transformer. They are represented by P0. Load losses are proportional to the square of the load rate during operation, and the average load factor k of the transformer is 0.75. The load loss is large when the load is high, and small when the load is low. There is no load loss when the transformer is running at no load. The load loss is generally converted to the reference temperature. The reference temperature of the oil-immersed distribution transformer is 75°C, and the reference temperature of the dry-type distribution transformer varies depending on the insulation heat resistance grade. The reference temperature of H-class insulation is 145°C, and the reference temperature of F-class insulation is 120°C. Load loss is represented by P k Indicated. Step 3: Calculate the production carbon efficiency of the transformer product to be evaluated based on the carbon footprint of the production stage and the carbon footprint of the product use stage. The production carbon efficiency refers to the value obtained by dividing the weighted carbon emissions (kgCO2e) during the entire life cycle of the product by the electrical energy (kWh) output during the entire life cycle of the product. The unit is kgCO2e / kWh, expressed as PCER. This indicator is used as one of the main evaluation indicators of the low-carbon performance of distribution transformers. The calculation method is described as follows.

[0034] Where PCER is the production carbon efficiency ratio, in kgCO2e / kWh. p and k u are carbon footprint weight coefficients, and their values ​​are 0.6 and 0.4 respectively.

[0035] G is the total output electrical energy of the transformer over its entire life cycle, and the calculation method is as follows.

[0036] The rated life cycle of a distribution transformer is generally 30 years, 365 days a year, and 24 hours a day. Where S is the rated capacity of the transformer in kVA; k is the average load factor of the transformer, which is 0.75.

[0037] Step 4: The carbon footprint C of the production process calculated in steps 2 to 3 P , Carbon footprint of product use U and production carbon efficiency ratio PCER three evaluation indicators. Assuming there are n samples in total, the original data matrix Y of sample i is obtained by sorting i . Combine the sample matrices to obtain the original overall data matrix M. Perform inverse normalization on the matrix M to obtain the normalized overall data matrix G. The purpose of this step is to eliminate the dimensional effects between different indicators so that they can be compared and analyzed on the same scale. At the same time, inverse normalization can also help highlight the differences between indicators. The specific principle is to convert the original data into a value between 0 and 1, but unlike conventional normalization, inverse normalization uses a specific algorithm to ensure that the processed data can reflect the relative size relationship of the original data, but in the opposite direction. That is, larger values ​​in the original data will become smaller after inverse normalization, and smaller values ​​will become larger.

[0038] Let parameter a ij is the value of the i-th row and j-th column of the original population data matrix M. Obviously, i=1,2,3, and j=1,2,…,n. Use b ij Indicates a ij The reverse normalization result of . The algorithm is described as follows.

[0039] Thus, the normalized overall data matrix G is obtained. Using the normalized sample data matrix X i Represents the evaluation data of the i-th product sample to be evaluated.

[0040] Step 5: Calculate the information entropy and information utility value of the above three evaluation indicators, and determine the indicator weights. The calculation method is as follows.

[0041] (1) Calculate the proportion of the i-th sample value under the j-th indicator to the indicator: (2) Calculate the information entropy e of the j-th indicator j : (3) Calculate the information utility value d of the jth indicator j : (4) Calculate the weight w of the jth indicator j : (5) Construct the indicator weight matrix W: Step 6: Calculate the sample data matrix X of the product to be evaluated based on the indicator weight matrix W i The square value D of the weighted Mahalanobis distance to the overall data matrix G 2 The calculation method is as follows.

[0042] in, represents the square value of the Mahalanobis weighted distance corresponding to sample X; μ is the mean vector of the overall data matrix G, W is the indicator weight diagonal matrix, and ∑ is the covariance matrix of the overall data matrix G. The parameter calculation method is as follows.

[0043] Step 7: The square value D of the weighted Mahalanobis distance of the sample to be evaluated X x 2 Substitute the percentage conversion algorithm and grading standards to obtain the product production carbon footprint evaluation score and corresponding grade.

[0044] 1. Percentage conversion algorithm This algorithm ensures that the converted scores are between 60 and 100, while also making their distribution more even.

[0045] 2. Hierarchy Products with scores between 60 and 70 are classified as Level IV, 70 to 80 are classified as Level III, 80 to 90 are classified as Level II, and 90 to 100 are classified as Level I.

[0046] The present invention is further described below by taking the sampling inspection data of power material procurement of a local power grid company as an example.

[0047] Step 1: There are 8 samples from 8 manufacturers to be tested in this power transformer material inspection. The sample model is S20-400kVA three-dimensional coil oil-immersed transformer. The data collection and arrangement are shown in the following table.

[0048] Table 1 S20-400kVA three-dimensional coil oil-immersed transformer sample test index data table Table 2 Carbon footprint factor data table Table 3 S20-400kVA three-dimensional coil oil-immersed transformer production index data table Step 2: Calculate the carbon footprint of the production link and the carbon footprint of the product use link of the transformer product to be evaluated. The calculation results are shown in Table 4.

[0049] Table 4 Carbon footprint indicator calculation table for S20-400kVA three-dimensional coil oil-immersed transformer sample Step 3: Calculate the production carbon efficiency ratio PREC of the transformer sample to be evaluated. The calculation results are shown in Table 5.

[0050] Table 5 S20-400kVA three-dimensional coil oil-immersed transformer sample production carbon efficiency calculation table Step 4: The results of normalization of indicators are shown in Table 6.

[0051] Table 6 Normalized data table of evaluation indicators The overall data matrix G: Step 5: Using the data in step 4, the entropy weight method of the present invention is used to calculate the weight of each indicator. The calculation results are shown in Table 7.

[0052] Table 7 Evaluation index weight table Step 6: Calculate the square value of the Mahalanobis weighted distance of each manufacturer's products as shown in Table 8.

[0053] Table 8 Sample weighted Mahalanobis distance square data table Step 7: Calculate scores and conduct grading assessment. The calculation results are shown in Table 9.

[0054] Covariance matrix: Table 9 Sample grading evaluation results Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A quantitative classification method for carbon emissions during the production stage of power transformers, characterized in that: The following steps are involved: Step 1: Collect the quality inspection data and production data of the transformer product to be evaluated. The quality inspection data includes the load loss and no-load loss values ​​of the transformer product. The production data includes the carbon emission factor of electricity produced by the transformer product, the consumption of transformer raw materials and the corresponding carbon emission factor, and the electric energy consumed in the transformer production process. When collecting production data, discard the raw materials or production links that account for less than 10% of the total; Step 2: Calculate the carbon footprint C of the production process of the transformer product to be evaluated based on the collected quality inspection data and production data P and the carbon footprint of product use U ; Step 3: Calculate the production carbon efficiency ratio (PCER) of the transformer product to be evaluated based on the carbon footprint of the production link and the carbon footprint of the product use link. The production carbon efficiency ratio (PCER) refers to the value obtained by dividing the weighted carbon emissions during the entire life cycle of the product by the total output electric energy of the transformer during the entire life cycle. The weighted carbon emissions during the entire life cycle of the product are obtained by multiplying the carbon footprint of the production link and the carbon footprint of the product use link by their respective carbon footprint weight coefficients and then adding them together; Step 4: The carbon footprint C of the production process calculated in steps 2 and 3 P , Carbon footprint of product use U and production carbon efficiency ratio PCER three evaluation indicators, assuming there are n samples in total, the original data matrix Y of sample i is obtained by sorting i , combine the sample matrices to obtain the original overall data matrix M, perform reverse normalization on the original overall data matrix M, and obtain the normalized overall data matrix G; Step 5: Calculate the information entropy and information utility value of the three evaluation indicators, and determine the indicator weight matrix W; Step 6: Calculate the sample data matrix X of the product to be evaluated based on the indicator weight matrix W i The square value D of the weighted Mahalanobis distance to the overall data matrix G 2 ; Step 7: The square value D of the weighted Mahalanobis distance of the sample to be evaluated x 2 Substitute the percentage conversion algorithm and grading standards to obtain the product production carbon footprint evaluation score and corresponding grade.

2. A quantitative classification method for carbon emissions during the production stage of power transformers according to claim 1, characterized in that: Step 2: Calculate the carbon footprint C of the production process of the transformer product to be evaluated based on the collected quality inspection data and production data. P and the carbon footprint of product use U , specifically including: Carbon footprint of production process P The calculation formula is as follows: ； In the formula, C E is the carbon footprint of transformer production and manufacturing, C M Carbon footprint of raw materials obtained for products, C PV The carbon sink generated by photovoltaic power generation, the calculation method of each parameter is as follows: (1) Carbon footprint of transformer product manufacturing process C E : ； In the formula, F i is the energy consumption of each production process of distribution transformer, E e is the carbon emission factor of electricity; (2) Carbon footprint of product raw material acquisition C M : ； In the formula, C T is the carbon footprint of raw material transportation, C B The carbon footprint of the raw material itself, M i is the consumption of the i-th raw material (kg); T i is the average transportation distance of the ith raw material (km), D i is the carbon footprint factor corresponding to the unit weight transportation distance under the transportation mode of the i-th raw material, E m-i is the carbon footprint factor corresponding to the raw material; (3) Carbon sinks generated by photovoltaic power generation C PV : ； Where M i,PV,ELEC Refers to the net photovoltaic energy in the i-th production process of 1 unit of product, in kW / h, and the calculation formula is as follows: ； Where: W i is the total power consumption in the ith core production process of one unit of product, P i is the proportion of photovoltaic electricity in the total electricity consumption in the ith core production process of one unit of product, and P refers to the proportion of photovoltaic electricity used for production by the enterprise in the total photovoltaic electricity generated in the previous fiscal year; Carbon footprint of product use U The calculation formula is as follows: ； Where: P0 is the no-load loss of the transformer during operation, k is the average load factor of the transformer, P k is the load loss of the transformer during operation, E e is the carbon emission factor of electricity.

3. A quantitative classification method for carbon emissions during the production stage of power transformers according to claim 2, characterized in that: The calculation formula for production carbon efficiency ratio PCER in step 3 is as follows: ； Where PCER is the production carbon efficiency ratio, the unit is kgCO2e / kWh, k p and k u is the carbon footprint weight coefficient, which takes values ​​of 0.6 and 0.4 respectively; G is the total output electrical energy of the transformer throughout its life cycle.

4. A quantitative classification method for carbon emissions during the production stage of power transformers according to claim 3, characterized in that: Step 4 specifically includes: ； Let parameter a ij is the value of the i-th row and j-th column of the original population data matrix M, i=1,2,3, and j=1,2,…,n, using b ij Indicates a ij The reverse normalization result of is as follows: ； The normalized overall data matrix G is obtained, and the normalized sample data matrix X is used i Represents the evaluation data of the i-th product sample to be evaluated: 。 5. The method for quantitatively grading carbon emissions during the production phase of power transformers according to claim 1 is characterized in that: Step 5 specifically includes: (1) Calculate the proportion of the i-th sample value under the j-th indicator to the indicator: ； (2) Calculate the information entropy e of the jth indicator j : ； (3) Calculate the information utility value d of the jth indicator j : ； (4) Calculate the weight w of the jth indicator j : ； (5) Construct the indicator weight matrix W: 。 6. The method for quantitatively grading carbon emissions during the production phase of power transformers according to claim 1 is characterized by: Step 6 specifically includes: ； in, Represents the square value of the Mahalanobis weighted distance corresponding to sample X; μ is the mean vector of the overall data matrix G, W is the indicator weight diagonal matrix, ∑ is the covariance matrix of the overall data matrix G, and the calculation methods of each parameter are as follows: 。 7. A quantitative classification method for carbon emissions during the production phase of power transformers according to claim 6, characterized in that: The percent conversion algorithm in step 7 is as follows: ； The grading standard is: products with scores between 60 and 70 are Grade IV, 70 to 80 are Grade III, 80 to 90 are Grade II, and 90 to 100 are Grade I.

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