Power grid company carbon asset development differentiated demand grading method

Abstract

The application discloses a power grid company carbon asset development differentiated demand grading method, system and storage medium, and comprises the following steps: step A, constructing a carbon asset development demand grading evaluation index system; step B, generating relevant information of qualitative and quantitative indexes; step C, calculating the index weight by using information entropy, and constructing an intuitionistic fuzzy decision matrix; step D, calculating the comprehensive score of the operation and construction scale dimension and the carbon asset development comprehensive evaluation dimension of the power grid company; step E, establishing a carbon asset development differentiated demand grading matrix, and completing the carbon asset development differentiated grading of the power grid company; and step F, constructing a power grid company carbon asset development differentiated demand grading virtual device. The method provided by the application effectively avoids problems such as unreasonable resource allocation and unclear work focus caused by the adoption of a universal scheme in carbon asset development work, and provides technical support for the design of customized carbon asset development service strategies of the power grid company.

Description

Technical Field

[0001] This invention relates to the field of carbon asset management in the power industry, specifically to a method, system, and storage medium for classifying the differentiated needs of power grid companies in carbon asset development. Background Technology

[0002] The development and management of carbon assets has become an important direction for the business development of power grid companies. Given the significant differences among power grid companies in terms of operational scale, carbon emission levels, carbon asset management capabilities, and social impact, there is an urgent need to establish a scientific and reasonable method for classifying the differentiated needs of power grid companies for carbon asset development. This method will provide technical support for the design and implementation of customized carbon asset development service strategies for power grid companies.

[0003] Current research on carbon asset development in power grid companies mainly focuses on carbon emission accounting and carbon asset valuation, lacking a tiered approach to address the differentiated carbon asset development needs of power grid companies. This leads to widespread problems such as inaccurate positioning, redundant processes, and low efficiency in the practice of carbon asset development, making it difficult to provide more targeted decision support for power grid companies of varying sizes and with different levels of carbon reduction efforts. Therefore, selecting appropriate evaluation indicators, establishing a scientific and reasonable tiered system for differentiated carbon asset development needs of power grid companies, and formulating customized service strategies for power grid companies are crucial for power grid enterprises. Summary of the Invention

[0004] Technical Problem: The technical problem to be solved by this invention is to provide a method for classifying the differentiated carbon asset development needs of power grid companies. By constructing a reasonable and feasible evaluation index system, and integrating the use of logical deduction, information entropy-based intuitionistic fuzzy TOPSIS, and risk matrix methods, the method can achieve accurate classification of the carbon asset development needs of power grid companies, providing reference and guidance for the design and implementation of customized carbon asset development service strategies for power grid companies.

[0005] Technical Solution: To address the aforementioned technical problems, this invention proposes a differentiated demand grading method, system, and storage medium for the development of carbon assets by power grid companies. The grading method includes the following steps:

[0006] Step A: Using logical deduction and combining the identification results of key influencing factors for the development of carbon assets by power grid companies, construct a graded evaluation index system for carbon asset development needs;

[0007] Step B: Based on the hierarchical evaluation index system established in Step A, on the one hand, data information on six quantitative indicators (including electricity sales, operating revenue, capacity of substation equipment above 110 kV, length of transmission lines above 110 kV, line loss rate, and total amount of green electricity consumed) is collected and organized; on the other hand, questionnaire surveys and expert consultations are used in combination to determine six qualitative indicators, including R&D investment level, carbon asset funding guarantee, management system certification, social responsibility, social recognition and social reputation.

[0008] Step C: Standardize the quantitative indicators, convert the semantic evaluation results of the qualitative indicators into intuitionistic fuzzy numbers, calculate the weights of each indicator using information entropy, and construct an intuitionistic fuzzy decision matrix.

[0009] Step D: Calculate the comprehensive scores of the power grid company in the two dimensions (i.e., the scale of operation and construction and the comprehensive assessment of carbon asset development);

[0010] Step E: Combining the statistical characteristics of the score distribution of each power grid company, the uniform quantile method is used to complete the interval division of the coordinates. The risk matrix method is used to establish a differentiated demand rating matrix for carbon asset development of power grid companies, and the carbon asset development needs of each power grid company are differentiated and graded, laying the foundation for the subsequent formulation of customized carbon asset development service strategies for power grid companies.

[0011] Step F involves sequentially constructing a carbon asset development demand grading evaluation index system construction module, a qualitative and quantitative index data collection module, a weight calculation and decision matrix generation module, and a differentiated demand rating module, thereby completing the deployment of the virtual device for grading differentiated carbon asset development demand of the power grid company.

[0012] As a preferred example, in step A, a graded evaluation index system for carbon asset development needs is constructed by using logical deduction and combining the identification results of key influencing factors for the development of carbon assets by the power grid company.

[0013] A carbon asset development demand evaluation index system is constructed using logical deduction. Step A of the construction process consists of five steps, detailed in the appendix. Figure 1 .

[0014] As a preferred example, in step B, based on the hierarchical evaluation index system established in step A, on the one hand, data information on six quantitative indicators (including electricity sales, operating revenue, capacity of substation equipment above 110 kV, length of transmission lines above 110 kV, line loss rate, and total amount of green electricity consumed) is collected and organized; on the other hand, questionnaire surveys and expert consultations are used in combination to determine six qualitative indicators, including the degree of R&D investment, carbon asset funding guarantee, management system certification, social responsibility, social recognition and social reputation.

[0015] As mentioned above, the evaluation indicators for carbon asset development needs encompass both qualitative and quantitative indicators. Decision-makers subjectively assess the qualitative indicators based on the actual situation of the power grid company's carbon asset development. This patent uses an intuitive fuzzy coefficient to represent the decision-maker's subjective opinion; this coefficient reflects the fuzziness and uncertainty present in the decision-making process. Table 1 shows the linguistic terms used by decision-makers when qualitatively evaluating the assessment indicators and their corresponding intuitive fuzzy coefficients.

[0016] Table 1. Linguistic terms and corresponding intuitive fuzzy coefficients

[0017] Language items Intuitive fuzzy coefficients (membership degree, non-membership degree) Very bad (0.02，0.98) Difference (0.15，0.75) Lower than average (0.35，0.55) medium (0.50，0.35) Upper-middle (0.65，0.25) good (0.75，0.15) very good (0.98，0.02)

[0018] As a preferred example, in step C, the quantitative indicators are standardized, the semantic evaluation results of the qualitative indicators are converted into intuitionistic fuzzy numbers, and the weights of each indicator are calculated using information entropy to construct an intuitionistic fuzzy decision matrix. Specifically, step C includes:

[0019] Step C1: Construct the decision matrix D.

[0020] Define A = {A1, A2, ..., A} n Let} be the set of power grid companies, C = {C1, C2, ..., C} m Let} be the set of indicators, and let the decision matrix D be:

[0021] D = [k ij ] m×n (1a)

[0022] In the formula: k ij For intuitive fuzzy values, define k ij =(u ij ,v ij ), where u ij and v ij A respectively j Company's indicator C i The degree of satisfaction (membership) and the degree of dissatisfaction (non-membership).

[0023] Define the hesitation index π ij For decision-makers regarding A j Company C i The ambiguity in indicator judgment, and the calculation method of the intuitive hesitation index are as follows:

[0024] π ij =1-u ij -v ij (1b)

[0025] Step C2: Calculate the normative decision matrix T.

[0026] The quantitative indicator data is standardized, and the corresponding membership and non-membership degrees are calculated using the following formula:

[0027]

[0028] v ij =1-u ij (1d)

[0029] In the formula: x ij For quantitative indicators, u ij and v ij These represent the membership degree and non-membership degree of quantitative indicators, respectively; and the hesitation index π of quantitative indicators. ij It is 0.

[0030] Step C3: In multi-attribute decision-making problems, the information entropy of evaluation indicators can be determined by utilizing the inherent information of each evaluation object. The smaller the information entropy, the lower the degree of disorder and the higher the effectiveness of the information; therefore, a higher weight coefficient should be assigned to that indicator. Conversely, the larger the information entropy, the higher the disorder and the lower the information utility; the weight coefficient of the indicator should be correspondingly reduced. The process of determining indicator weights based on information entropy mainly includes the following three steps:

[0031] Step C301: Construct the membership matrix. Merge the decision matrix D and the normalized decision matrix T, and calculate the new membership contribution and non-membership contribution according to formulas (1e) and (1f). The calculation formulas are as follows:

[0032]

[0033] In the formula: p ij This represents the i-th attribute and the j-th company A. j Subordinate contribution; q ij This represents the i-th attribute and the j-th company A. j Non-affiliated contribution.

[0034] Step C302: Calculate information entropy. i For the i-th index C i Information entropy, representing the information entropy of n candidate companies A n For the i-th index C i The total contribution. The calculation formula is as follows:

[0035]

[0036] In conclusion, 0 ≤ E i ≤1. Specifically, when p ij =q ij When = 1 / n, E i=1. When, under a certain indicator, the affiliated and non-affiliated contributions of each candidate company tend to be the same, E i The value will be close to 1; if the membership contribution and non-membership contribution are exactly equal, the role of this indicator in decision-making can be ignored, and the weight of this indicator is 0.

[0037] Step C303: Determine the indicator weights. Let d i =1-E i For the i-th index C i If the importance of the indicator is such that the i-th indicator C is... i The formula for calculating the indicator weights is as follows:

[0038]

[0039] Step C4: Generate a weighted intuitionistic fuzzy decision matrix. Weighted intuitionistic fuzzy values. ω i The index C determined using the information entropy method i The weights;

[0040] Step C5: Determine the intuitive fuzzy positive ideal solution A + And intuitive fuzzy negative ideal solution A - .

[0041]

[0042] In the formula: I is the set of benefit-type attributes, and J is the set of cost-type attributes.

[0043] As a preferred example, the comprehensive scores of the power grid company in two dimensions (i.e., the scale of operation and construction and the comprehensive assessment of carbon asset development) are calculated respectively. Step D specifically includes:

[0044] Step D1: Calculate the separation degree between the company's index value and the positive and negative ideal solutions.

[0045]

[0046] Step D2: Calculate the relative closeness between the index value of the formula and the positive ideal solution.

[0047]

[0048] As a preferred example, in step E, the coordinate intervals are divided using the uniform quantile method, taking into account the statistical characteristics of the score distribution of each power grid company. A risk matrix method is then used to establish a differentiated carbon asset development demand rating matrix for each power grid company, classifying their carbon asset development needs accordingly. This lays the foundation for subsequent customized carbon asset development service strategies for the power grid companies.

[0049] The risk matrix method categorizes the two factors that determine the risk of a hazardous event (i.e., the severity of all types of harm and the probability of causing harm) into corresponding levels according to their characteristics, forming a risk matrix, or multidimensional table, to measure the risk value. This method is simple and easy to implement, and can effectively quantify and assess the risk level and implementation hierarchy of a project, thus it has also been widely used in the field of hierarchical system construction.

[0050] In a common risk matrix diagram (see appendix) Figure 2 The X and Y axes represent the key influencing factors for demand tier classification. The diagram defines three regions: green, yellow, and red. The green region represents low development demand, corresponding to a relatively low carbon asset development demand rating for the power grid company. The yellow region represents medium development demand, indicating that the power grid company's carbon emissions have already impacted its normal operations and social reputation, corresponding to a higher carbon asset development rating. The red region represents high development demand, indicating that carbon emissions have exerted a significant impact on the power grid company, and that the company has a good foundation for carbon asset development, making it an important target for formulating targeted carbon reduction strategies and cultivating emerging carbon market businesses.

[0051] As a preferred example, in step F, a carbon asset development demand grading evaluation index system construction module, a qualitative and quantitative index data collection module, a weight calculation and decision matrix generation module, and a differentiated demand rating module are constructed sequentially to complete the deployment of the virtual device for grading the differentiated demand of the power grid company's carbon asset development.

[0052] Step F1: Construction of the carbon asset development demand grading evaluation index system module: Through logical deduction and combined with the identification results of key influencing factors, establish an index system covering dimensions such as technology, environment, economy, society and risk.

[0053] Step F2: Construction of the qualitative and quantitative indicator data collection module: Collect data on six quantitative indicators (such as electricity sales and operating revenue) and six qualitative indicators (such as R&D investment and social responsibility) to provide comprehensive data support;

[0054] Step F3, Construction of Weight Calculation and Decision Matrix Generation Module: Standardize the quantitative indicators, convert the qualitative indicators into intuitionistic fuzzy numbers, calculate the weights of each indicator, and construct the intuitionistic fuzzy decision matrix;

[0055] Step F4: Construction of Differentiated Demand Rating Module: Calculate the comprehensive score, divide the intervals using the uniform quantile method, and complete the differentiated classification of carbon asset development demand using the risk matrix method.

[0056] Beneficial Effects: Compared with existing technologies, the embodiments of this invention have the following advantages: This invention proposes a differentiated demand classification method, system, and storage medium for carbon asset development in power grid companies. Based on the first use of the intuitionistic fuzzy TOPSIS method based on information entropy to comprehensively assess the operational and construction scale and overall level of carbon asset development of power grid companies, it innovatively applies the risk matrix method to classify the carbon asset development needs of the five major power grid companies. This method can assist higher-level units and decision-making departments in accurately grasping the characteristics and development potential of carbon asset development needs of different power grid companies, effectively avoiding problems such as unreasonable resource allocation, unclear work priorities, low efficiency, and poor results caused by adopting universal solutions in carbon asset development work. It provides scientific and technical support and decision-making basis for formulating differentiated carbon asset development strategies for power grid companies with different demand levels, promoting the green and low-carbon transformation of power grid enterprises and improving economic and social benefits. Attached Figure Description

[0057] Figure 1 This is a flowchart of the construction process of the indicator system based on logical deduction proposed in this invention.

[0058] Figure 2 This is a common risk matrix diagram in the embodiments of the present invention.

[0059] Figure 3 This is a hierarchical diagram of the carbon asset development needs of the five major power grid companies in this embodiment of the invention. Detailed Implementation

[0060] To more clearly and explicitly demonstrate the technical content of the present invention, the specific implementation methods of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0061] This invention relates to a method for classifying the differentiated carbon asset development needs of power grid companies, belonging to the field of carbon asset management in the power industry. Taking five power grid companies in Guangdong, Guangxi, Jiangsu, Sichuan, and Zhejiang as research subjects, the method combines on-site surveys, document review, and expert consultation, and comprehensively utilizes logical deduction, the information entropy-based intuitionistic fuzzy TOPSIS method, and the risk matrix method to classify the carbon asset development needs of the five major power grid companies.

[0062] This invention proposes a method, system, and storage medium for classifying differentiated needs in the development of carbon assets by power grid companies. The method includes the following steps:

[0063] Step A: Using logical deduction and combining the identification results of key influencing factors in the development of carbon assets by the power grid company, construct a graded evaluation index system for carbon asset development demand. Step A specifically includes:

[0064] To ensure that the constructed indicator system is purposeful, comprehensive, feasible, stable, and coordinated, this project plans to consult relevant experts and technical personnel and use logical deduction to construct a comprehensive evaluation indicator system for carbon asset development needs, covering the company's key influencing factors on carbon emissions, carbon asset planning and management, and social impact. This will enable a gradual deepening, refinement, improvement, and systematization of the understanding of the essential characteristics of the power grid company's carbon emissions and management.

[0065] The indicator system construction process based on logical deduction fully considers the relevant data requirements for subsequent use of the risk matrix method to complete the carbon asset demand classification. The operation and construction scale dimension and the comprehensive assessment dimension of carbon asset development are pre-set as the horizontal and vertical axes of the risk matrix, respectively. The operation and construction scale dimension includes four types of indicators: electricity sales, operating revenue, capacity of 110 kV and above transformer equipment, and length of 110 kV and above transformer equipment. The comprehensive assessment dimension of carbon asset development includes three aspects: carbon asset planning, carbon asset management, and comprehensive social impact. The specific indicators are eight types of indicators: line loss rate, R&D investment level, total amount of green electricity consumed, carbon asset funding guarantee, management system certification, social responsibility, social recognition, and social reputation. Table 1 shows the final constructed demand classification evaluation indicator system.

[0066] Table 1. Comprehensive Evaluation Index System for Differentiated Demands in Carbon Asset Development

[0067]

[0068] Step B, based on the hierarchical evaluation index system established in Step A, involves two aspects: firstly, collecting and organizing data on six quantitative indicators (including electricity sales, operating revenue, capacity of 110 kV and above transformer equipment, length of 110 kV and above transmission lines, line loss rate, and total green energy consumption); secondly, using a combination of questionnaire surveys and expert consultations, determining six qualitative indicators, including R&D investment level, carbon asset funding guarantee, management system certification, social responsibility, social recognition, and social reputation. Step B specifically includes:

[0069] Based on the promotion and practical experience of the power grid companies' carbon asset development projects, and after consulting relevant experts and technicians, a qualitative assessment of six categories of indicators was conducted. The evaluation indicator scores for the five power grid companies are shown in Table 2.

[0070] Table 2: Evaluation Indicator Scores of the Five Power Grid Companies

[0071]

[0072] Step C: Standardize the quantitative indicators, convert the semantic evaluation results of the qualitative indicators into intuitionistic fuzzy numbers, calculate the weights of each indicator using information entropy, and construct an intuitionistic fuzzy decision matrix. Step C specifically includes:

[0073] Step C1: Construct the decision matrix D. Based on the quantitative indicator data (such as electricity sales volume, line loss rate, etc.) and the intuitive fuzzy numbers of qualitative indicators (such as the semantic evaluation transformation results of R&D investment level) collected in Step B, construct the decision matrix D = {k ij}. Where, k ij =(u ij ,v ij ) represents the membership degree u of the j-th power grid company on the i-th indicator. ij non-membership degree v ij This reflects their level of satisfaction and dissatisfaction.

[0074] Step C2: Calculate the normalized decision matrix T. Normalize the quantitative indicator data and calculate the membership degree u using the linear normalization method. ij =(x ij -min(x ij )) / (max(x ij )-min(x ij )) and non-membership degree v ij =1-u ij , where x ij The original data is used; the intuitive fuzzy numbers of the qualitative indicators remain unchanged and are combined to form the normalized decision matrix T.

[0075] Step C3: After standardizing the quantitative indicator data and converting the semantic terms of the qualitative indicators into intuitionistic fuzzy coefficients, the weight of each indicator is calculated using information entropy. On the horizontal axis, the information entropy of the capacity of substation equipment above 110 kV (C3) is the lowest, indicating the lowest degree of information disorder and the highest information utility value; therefore, this indicator has the highest weight of 0.3808. Conversely, the information entropy of the length of transmission lines above 110 kV (C4) is the highest, while the information utility value is the lowest; therefore, this indicator has the lowest weight of 0.1990. Similarly, on the vertical axis, the information entropy of R&D investment level (D3) is the lowest, corresponding to the highest weight of the indicator at 0.2932; the information entropy of line loss rate (D1) is the highest, therefore, the indicator has the lowest weight of only 0.0953.

[0076] Step C301: Table 3 provides the membership matrix.

[0077] Table 3 Membership Matrix

[0078]

[0079] Step C302: Calculate the information entropy. Based on the membership matrix, use the formula Ei=-∑(p ij ·ln(p ij )+q ij ·ln(q ij The information entropy E of each indicator is calculated using the formula )) / ln(n). i , where p ij and q ij These represent the contribution of membership degree and non-membership degree, respectively.

[0080] Step C303: Determine the indicator weights. Based on the information entropy E i Calculate the importance of the indicator d i =1-E i Then normalize to weight w i =d i / ∑d i The results are shown in Table 4.

[0081] Table 4 Information Entropy and Indicator Weights

[0082]

[0083]

[0084] Step C4: Based on the calculated index weights, construct a weighted intuitionistic fuzzy decision matrix (see Table 5), where C3, C4 and D1 are cost-type indicators, and C1, C2, D2, D3, D4, D5, D6, D7 and D8 are benefit-type indicators.

[0085] Table 5 Weighted Intuitive Fuzzy Decision Matrix

[0086]

[0087] Step C5: Determine the intuitionistic fuzzy positive ideal solution and the intuitionistic fuzzy negative ideal solution of the index, as shown in Table 6.

[0088] Table 6. Positive and Negative Ideal Solutions of Intuitive Fuzzy Concepts

[0089]

[0090] Step D involves calculating the power grid company's comprehensive score across two dimensions: operational and construction scale and comprehensive assessment of carbon asset development. Step D specifically includes:

[0091] Step D1: Based on the weighted intuitionistic fuzzy decision matrix, calculate the separation degrees d+ and d- between the operation and construction scale dimension (C1-C4) and the comprehensive assessment dimension of carbon asset development (D1-D8) of each power grid company and the positive ideal solution A+ and the negative ideal solution A-, respectively.

[0092] Step D2: Calculate the relative proximity r = d- / (d++d-), where the r value for the operation and construction scale dimension is the score on the horizontal axis, and the r value for the comprehensive assessment dimension of carbon asset development is the score on the vertical axis. See Table 7 and Table 8 for the specific calculation.

[0093] Table 7: Relative proximity and sorting of the horizontal axis.

[0094] Power Grid Company d+ d- r Guangdong A1 0.1350 0.1723 0.5607 Guangxi A2 0.1495 0.1579 0.5137 Jiangsu A3 0.0187 0.2887 0.9392 Sichuan A4 0.2266 0.0809 0.2631 Zhejiang A5 0.1539 0.1535 0.4994

[0095] Table 8. Relative proximity and sorting of the vertical axis.

[0096] Power Grid Company d+ d- r Guangdong A1 0.0366 0.2794 0.8843 Guangxi A2 0.1607 0.1551 0.4910 Jiangsu A3 0.1796 0.1363 0.4315 Sichuan A4 0.1701 0.1457 0.4614 Zhejiang A5 0.1132 0.2031 0.6421

[0097] Step E: Combining the statistical characteristics of the score distribution of each power grid company, the uniform quantile method is used to divide the coordinate intervals. A risk matrix method is then used to establish a differentiated carbon asset development demand rating matrix for each power grid company. This differentiates and classifies the carbon asset development needs of each power grid company, laying the foundation for subsequent customized carbon asset development service strategies for power grid companies. Step E specifically includes:

[0098] First, based on the operational construction scale scores and comprehensive carbon asset development assessment scores of the five power grid companies, a differentiated demand rating matrix for carbon asset development was constructed (see Table 9). Then, the horizontal axis was divided into five intervals: 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, and 0.8-1; and the vertical axis was divided into ten intervals: 0-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, 0.4-0.5, 0.5-0.6, 0.6-0.7, 0.7-0.8, 0.8-0.9, and 0.9-1. Finally, considering the specific scores of the power grid companies, a carbon asset development demand classification diagram for the five major power grid companies was drawn (see Appendix). Figure 3 ).

[0099] Table 9. Differentiated Demand Rating Matrix for Carbon Asset Development by the Five Major Power Grid Companies

[0100] Power grid company's operation and construction scale score Comprehensive assessment score for carbon asset development Guangdong Power Grid Company 0.5607 0.8843 Guangxi Power Grid Company 0.5137 0.4910 Jiangsu Power Grid Company 0.9392 0.4315 Sichuan Power Grid Company 0.2631 0.4614 Zhejiang Power Grid Company 0.4994 0.6421

[0101] Based on the carbon asset development demand classification map, the carbon asset development needs of various power grid companies are classified in a differentiated manner, laying the foundation for the subsequent formulation of customized carbon asset development service strategies for power grid companies.

[0102] According to the appendix Figure 3As shown, (1) Low development demand level: Sichuan Power Grid Company and Guangxi Power Grid Company have relatively low operating income and construction scale, relatively limited carbon emissions, and low comprehensive carbon asset development scores. The basic conditions for energy conservation and emission reduction are not yet mature. Therefore, the carbon asset development demand rating of the above two companies is low. (2) Medium development demand level: Zhejiang Power Grid Company has the second highest comprehensive carbon asset development score and a large operating income and construction scale. It is rated as medium development demand level. Jiangsu Power Grid Company has the highest operation and construction score. Although its comprehensive carbon asset development assessment score is low, the carbon emissions generated by its large-scale operation and construction have a significant impact on production and society. Therefore, the carbon asset development demand rating is medium. (3) High development demand level: Guangdong Power Grid Corporation. Since the company's carbon emissions are not only subject to the national carbon market, but also the two pilot carbon markets in Guangzhou and Shenzhen, the region has good basic conditions for carbon asset development. The company has the highest comprehensive evaluation score for carbon asset development. In addition, the company's operation and construction scale is relatively large. This indicates that under this situation, the carbon asset development of Guangdong Power Grid Corporation not only has a positive incentive effect on production income, but also brings positive social impact. Therefore, it is identified as a high development demand level and is an important target for the subsequent formulation of carbon reduction "targeted service" strategy and the cultivation of new carbon market businesses.

[0103] Step F involves sequentially constructing a carbon asset development demand grading evaluation index system construction module, a qualitative and quantitative index data collection module, a weight calculation and decision matrix generation module, and a differentiated demand rating module, thereby completing the deployment of the virtual device for grading differentiated carbon asset development demand of the power grid company.

[0104] Step F1: Construction of the carbon asset development demand grading evaluation index system module: Through logical deduction and combined with the identification results of key influencing factors, establish an index system covering dimensions such as technology, environment, economy, society and risk.

[0105] Step F2: Construction of the qualitative and quantitative indicator data collection module: Collect data on six quantitative indicators (such as electricity sales and operating revenue) and six qualitative indicators (such as R&D investment and social responsibility) to provide comprehensive data support;

[0106] Step F3, Construction of Weight Calculation and Decision Matrix Generation Module: Standardize the quantitative indicators, convert the qualitative indicators into intuitionistic fuzzy numbers, calculate the weights of each indicator, and construct the intuitionistic fuzzy decision matrix;

[0107] Step F4: Construction of Differentiated Demand Rating Module: Calculate the comprehensive score, divide the intervals using the uniform quantile method, and complete the differentiated classification of carbon asset development demand using the risk matrix method.

[0108] This invention proposes a differentiated demand classification method, system, and storage medium for the development of carbon assets by power grid companies, which falls under the scope of carbon asset management in the power industry. First, by analyzing the operational characteristics, carbon emission reduction needs, and carbon asset development status of power grid companies, and through logical deduction, an evaluation index system is constructed, encompassing two dimensions: operational scale (electricity sales, operating revenue, substation capacity, and transmission line length) and comprehensive assessment of carbon asset development (planning, management, and social impact). Second, taking five power grid companies as research subjects, six specific data points, including electricity sales, line loss rate, and total green electricity consumption, are collected and organized. Semantic representations of six qualitative indicators, such as R&D investment, funding, and social reputation, are also employed. Third, the quantitative data is standardized, and the qualitative indicators are transformed into intuitive fuzzy numbers. Information entropy is then used to calculate the weights of each indicator, establishing a decision matrix. Next, the scores of each power grid company in the operational scale and comprehensive assessment dimensions are calculated. Using the risk matrix method, the two dimensions are selected as the horizontal and vertical axes, respectively. The uniform quantile method is used to divide the intervals, constructing a differentiated demand rating matrix. Finally, the carbon asset development needs of each power grid company are differentiated and graded, providing technical support for the formulation of customized carbon asset development service strategies for power grid companies with different demand levels. This grading method overcomes the shortcomings of traditional assessment methods, which are limited by a single dimension and imprecise positioning. It can effectively improve resource allocation efficiency and promote the low-carbon transformation and economic and social benefits of power grid companies.

Claims

1. A method, system, and storage medium for classifying differentiated needs in the development of carbon assets by a power grid company, the method comprising the following steps: Step A: Using logical deduction and combining the identification results of key influencing factors for the development of carbon assets by power grid companies, construct a graded evaluation index system for carbon asset development needs; Step B: Based on the hierarchical evaluation index system established in Step A, on the one hand, data information on six quantitative indicators (including electricity sales, operating revenue, capacity of substation equipment above 110 kV, length of transmission lines above 110 kV, line loss rate, and total amount of green electricity consumed) is collected and organized; on the other hand, questionnaire surveys and expert consultations are used in combination to determine six qualitative indicators, including R&D investment level, carbon asset funding guarantee, management system certification, social responsibility, social recognition and social reputation. Step C: Standardize the quantitative indicators, convert the semantic evaluation results of the qualitative indicators into intuitionistic fuzzy numbers, calculate the weights of each indicator using information entropy, and construct an intuitionistic fuzzy decision matrix. Step D: Calculate the comprehensive scores of the power grid company in the two dimensions (i.e., the scale of operation and construction and the comprehensive assessment of carbon asset development); Step E: Establish a differentiated demand rating matrix for carbon asset development and complete the differentiated classification of carbon asset development needs of power grid companies; Step F involves sequentially constructing a carbon asset development demand grading evaluation index system construction module, a qualitative and quantitative index data collection module, a weight calculation and decision matrix generation module, and a differentiated demand rating module, thereby completing the deployment of the virtual device for grading differentiated carbon asset development demand of the power grid company.

2. A method, system, and storage medium for classifying differentiated needs in the development of carbon assets for power grid companies, as described in claim 1, characterized in that: In step A, a graded evaluation index system for carbon asset development needs is constructed by using logical deduction and combining the identification results of key influencing factors for the development of carbon assets by the power grid company. A carbon asset development demand evaluation index system is constructed using logical deduction. The construction process of step A consists of five steps, as shown in Figure 1.

3. A method, system, and storage medium for classifying differentiated needs in the development of carbon assets for power grid companies, as described in claim 1, characterized in that: In step B, based on the hierarchical evaluation index system established in step A, on the one hand, data information on six quantitative indicators (including electricity sales, operating revenue, capacity of substation equipment above 110 kV, length of transmission lines above 110 kV, line loss rate, and total amount of green electricity consumed) is collected and organized; on the other hand, questionnaire surveys and expert consultations are used in combination to determine six qualitative indicators, including the degree of R&D investment, carbon asset funding guarantee, management system certification, social responsibility, social recognition and social reputation. As mentioned above, the evaluation indicators for carbon asset development needs encompass both qualitative and quantitative indicators. Decision-makers subjectively assess the qualitative indicators based on the actual situation of the power grid company's carbon asset development. This patent uses an intuitive fuzzy coefficient to represent the decision-maker's subjective opinion; this coefficient reflects the fuzziness and uncertainty present in the decision-making process. Table 1 shows the linguistic terms used by decision-makers when qualitatively evaluating the assessment indicators and their corresponding intuitive fuzzy coefficients. Table 1. Linguistic terms and corresponding intuitive fuzzy coefficients 4. A method, system, and storage medium for classifying differentiated needs in the development of carbon assets for power grid companies, as described in claim 1, characterized in that: In step C, the quantitative indicators are standardized, the semantic evaluation results of the qualitative indicators are converted into intuitionistic fuzzy numbers, and the weights of each indicator are calculated using information entropy to construct an intuitionistic fuzzy decision matrix. Specifically, step C includes: Step C1: Construct the decision matrix D. Define A = {A1, A2, ..., A} n Let} be the set of power grid companies, C = {C1, C2, ..., C} m Let} be the set of indicators, and let the decision matrix D be: D=[k ij ] m×n (1a) In the formula: k ij For intuitive fuzzy values, define k ij =(u ij ,v ij ), where u ij and v ij A respectively j Company's indicator C i The degree of satisfaction (membership) and the degree of dissatisfaction (non-membership). Define the hesitation index π ij For decision-makers regarding A j Company C i The ambiguity in indicator judgment, and the calculation method of the intuitive hesitation index are as follows: π ij =1-u ij -v ij (1b) Step C2: Calculate the normative decision matrix T. The quantitative indicator data is standardized, and the corresponding membership and non-membership degrees are calculated using the following formula: v ij =1-u ij (1d) In the formula: x ij For quantitative indicators, u ij and v ij These represent the membership degree and non-membership degree of quantitative indicators, respectively; and the hesitation index π of quantitative indicators. ij It is 0. Step C3: In multi-attribute decision-making problems, the information entropy of evaluation indicators can be determined by utilizing the inherent information of each evaluation object. The smaller the information entropy, the lower the degree of disorder and the higher the effectiveness of the information; therefore, a higher weight coefficient should be assigned to that indicator. Conversely, the larger the information entropy, the higher the disorder and the lower the information utility; the weight coefficient of the indicator should be correspondingly reduced. The process of determining indicator weights based on information entropy mainly includes the following three steps: Step C301: Construct the membership matrix. Merge the decision matrix D and the normalized decision matrix T, and calculate the new membership contribution and non-membership contribution according to formulas (1e) and (1f). The calculation formulas are as follows: In the formula: p ij This represents the i-th attribute and the j-th company A. j Subordinate contribution degree; q ij This represents the i-th attribute and the j-th company A. j Non-affiliated contribution. Step C302: Calculate information entropy. i For the i-th index C i Information entropy, representing the information entropy of n candidate companies A n For the i-th index C i The total contribution. The calculation formula is as follows: In conclusion, 0 ≤ E i ≤1. Specifically, when p ij =q ij When = 1 / n, E i =1. When, under a certain indicator, the affiliated and non-affiliated contributions of each candidate company tend to be the same, E i The value will be close to 1; if the membership contribution and non-membership contribution are exactly equal, the role of this indicator in decision-making can be ignored, and the weight of this indicator is 0. Step C303: Determine the indicator weights. Let d i =1-E i For the i-th index C i If the importance of the indicator is determined by the value of the i-th indicator C, then the i-th indicator C i The formula for calculating the indicator weights is as follows: Step C4: Calculate the weighted intuitionistic fuzzy decision matrix. Weighted intuitionistic fuzzy values. ω i The index C determined using the information entropy method i The weights; Step C5: Determine the intuitive fuzzy positive ideal solution A + And intuitive fuzzy negative ideal solution A - . In the formula: I is the set of benefit-type attributes, and J is the set of cost-type attributes.

5. A method, system, and storage medium for classifying differentiated needs in the development of carbon assets for power grid companies, as described in claim 1, characterized in that: The comprehensive scores of the power grid company in two dimensions (i.e., the scale of operation and construction and the comprehensive assessment of carbon asset development) are calculated separately. Step D specifically includes: Step D1: Calculate the separation degree between the company's index value and the positive and negative ideal solutions. Step D2: Calculate the relative closeness between the index value of the formula and the positive ideal solution.

6. A method, system, and storage medium for classifying differentiated needs in the development of carbon assets for power grid companies, as described in claim 1, characterized in that: In step E, based on the statistical characteristics of the score distribution of each power grid company, the uniform quantile method is used to divide the coordinate intervals. A risk matrix method is then used to establish a differentiated carbon asset development demand rating matrix for each power grid company. This differentiates and grades the carbon asset development needs of each power grid company, laying the foundation for subsequent customized carbon asset development service strategies for the power grid companies. Step E specifically includes: The risk matrix method categorizes the two factors that determine the risk of a hazardous event (i.e., the severity of all types of harm and the probability of causing harm) into corresponding levels according to their characteristics, forming a risk matrix, or multidimensional table, to measure the risk value. This method is simple and easy to implement, and can effectively quantify and assess the risk level and implementation hierarchy of a project, thus it has also been widely used in the field of hierarchical system construction. A common risk matrix diagram (see Appendix 2) shows the hierarchical classification of major influencing factors on the X and Y axes. The diagram defines three areas: green, yellow, and red. The green area represents low development demand, corresponding to a relatively low carbon asset development demand rating for the power grid company. The yellow area represents medium development demand, indicating that the power grid company's carbon emissions have already impacted its normal operations and social reputation, corresponding to a higher carbon asset development rating. The red area represents high development demand, indicating that carbon emissions have exerted a significant impact on the power grid company, and that the company has a good foundation for carbon asset development, making it an important target for developing targeted carbon reduction strategies and cultivating emerging carbon market businesses.

7. A method, system, and storage medium for classifying differentiated carbon asset development needs of power grid companies according to claim 1, characterized in that: Step F specifically includes: Step F1: Construction of the carbon asset development demand grading evaluation index system module: Through logical deduction and combined with the identification results of key influencing factors, establish an index system covering dimensions such as technology, environment, economy, society and risk. Step F2: Construction of the qualitative and quantitative indicator data collection module: Collect data on six quantitative indicators (such as electricity sales and operating revenue) and six qualitative indicators (such as R&D investment and social responsibility) to provide comprehensive data support; Step F3, Construction of Weight Calculation and Decision Matrix Generation Module: Standardize the quantitative indicators, convert the qualitative indicators into intuitionistic fuzzy numbers, calculate the weights of each indicator, and construct the intuitionistic fuzzy decision matrix; Step F4: Construction of Differentiated Demand Rating Module: Calculate the comprehensive score, divide the intervals using the uniform quantile method, and complete the differentiated classification of carbon asset development demand using the risk matrix method.

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