A reserve project sequencing method for new power grid infrastructure

Abstract

This invention discloses a method for ranking reserve projects for new power grid infrastructure, comprising: constructing a ranking indicator system from five dimensions: power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development; collecting raw indicator data and distinguishing indicator types; obtaining objective weights for the indicators through a combination of two objective weighting methods; constructing a matrix by standardization transformation and weight combination; calculating relative proximity to form an objective ranking; subsequently integrating expert rankings; obtaining a subjective consensus ranking by constructing a pairwise preference matrix and an integer programming model; and finally, calculating standardized scores and weighted fusion of the subjective and objective ranking results using a weighted aggregation method to determine the ranking position of the reserve projects. This method balances data objectivity and expert experience, covers the core requirements of projects from multiple dimensions, has a rigorous ranking logic, and effectively improves the scientificity and accuracy of ranking reserve projects for new power grid infrastructure.

Description

Technical Field

[0001] This invention relates to the field of power grid infrastructure project sequencing technology, and in particular to a method for sequencing reserve projects for new power grid infrastructure. Background Technology

[0002] As a core carrier supporting energy transformation, ensuring a safe and stable power supply, and serving dual-carbon goals, the scientific prioritization of new power grid infrastructure projects plays a crucial role in optimizing resource allocation, improving investment efficiency, and promoting high-quality power grid development. Currently, power grid infrastructure projects are characterized by diverse types, wide coverage, and complex influencing factors, involving multiple dimensions of demands, including power grid development, safe supply, service quality, efficiency, and green and low-carbon development. Traditional prioritization methods struggle to comprehensively consider these multi-dimensional indicators. With the deepening of new power system construction, the prioritization of reserve projects needs to integrate objective data support with expert judgment, reflecting both data-driven objectivity and industry-specific subjectivity. This will adapt to the refined and standardized needs of power grid infrastructure project decision-making and address the issue of ambiguous project priority definitions under different dimensional objectives.

[0003] Existing technologies for ranking reserve projects in new power grid infrastructure have two significant drawbacks: First, the methods for determining indicator weights are too simplistic, either relying solely on objective weighting methods such as entropy weighting while ignoring key demands in industry practice, or simply depending on subjective expert scoring without data support. This makes it difficult to balance objectivity and practicality in weight allocation, and fails to fully reflect the comprehensive impact of multi-dimensional indicators on project ranking. Second, the aggregation logic of subjective and objective ranking results is imperfect. Most methods simply superimpose the two types of ranking results without establishing a scientific consensus formation mechanism and weighted aggregation model. This makes it difficult to resolve disagreements among experts in ranking, and also fails to adjust the weight ratio of subjective and objective results according to different scenario needs. Consequently, the final ranking results do not match the actual project implementation priorities well, and fail to meet the requirements of precise and rational decision-making for new power grid infrastructure projects. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method for sorting reserve projects for new power grid infrastructure.

[0005] The technical solution adopted in this invention is a method for ranking reserve projects for new power grid infrastructure, comprising the following steps: S1, selecting several indicators from five dimensions—power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development—to construct a ranking indicator system covering different types of power grid infrastructure projects; S2, collecting the original data of each indicator in the corresponding indicator system for each reserve project, determining the indicator type based on the characteristics of the indicator data, and distinguishing between positive, negative, and interval indicators; S3, calculating the weight of each indicator using the entropy weight method and the coefficient of variation method respectively, and obtaining the objective weight of each indicator by averaging the two types of weights; S4, standardizing the original indicator data. After transformation, a weighted normalized matrix is ​​constructed by combining it with objective weights. The relative proximity of each project to the positive and negative ideal solutions is obtained by calculating the distance between each project and the positive and negative ideal solutions, thus forming an objective ranking result. In S5, several experts are organized to rank the reserve projects. A pairwise preference matrix is ​​constructed based on Kendalltau distance. A subjective consensus ranking result that satisfies the requirements of preserving collective wisdom and axiomaticity is obtained by solving a 0-1 type integer programming problem. In S6, the weighted Borda counting method is used to calculate standardized Borda scores for both the objective ranking result and the subjective consensus ranking result. Weight coefficients are introduced to weight and aggregate the two types of scores. The ranking position of the reserve projects is determined based on the final weighted comprehensive score.

[0006] Furthermore, in step S4, the corresponding standardization transformation rule is adopted according to the indicator type, specifically as follows:

[0007] For positive indicators, the formula is used. Standardize;

[0008] For negative indicators, the formula is used. Standardize;

[0009] For interval indicators, if the indicator value falls within the preset optimal interval... If the value is within the range, the standardized value is 1; if it is outside the range, the standardized value between 0 and 1 is calculated according to the degree of deviation and a preset rule.

[0010] in, For the i-th item, the raw data for the j-th indicator. For standardized data, and These are the maximum and minimum values ​​of the j-th indicator across all items, respectively.

[0011] Furthermore, the indicators for the power grid development dimension include the indicator for improving the level of cross-provincial resource allocation, which is calculated using the following formula:

[0012] ;

[0013] in, The new inter-provincial power transmission capacity is measured in megawatts (MW). The inter-provincial power transmission capacity is given in the baseline year, and the unit is megawatts. The newly added inter-provincial power transmission volume is expressed in megawatt-hours (MWh). The electricity transmitted across provinces and regions in the baseline year is expressed in megawatt-hours. It represents the dimensionless value for improving the level of cross-provincial resource allocation.

[0014] Furthermore, the indicators for the security and supply guarantee dimension include the main grid security level improvement indicator, which is calculated using the following formula:

[0015] ;

[0016] in, This represents the percentage increase in throughput for N-1 transformers in a 1000 kV power grid. The percentage increase in throughput for N-1 transformers in a 750 kV power grid. This represents the percentage increase in throughput for N-1 transformers in a 500 kV power grid. This represents the percentage increase in throughput for N-1 transformers in a 330 kV power grid. This represents the percentage increase in the throughput of N-1 transformers in a 220 kV power grid. The percentage increase represents the improvement in the safety level of the main grid. Each pass rate increase is the difference between the pass rate of the N-1 safety verification of the grid at that voltage level before and after the project implementation.

[0017] Furthermore, the efficiency and effectiveness dimension indicators include the equipment load balance index, which is calculated using the following formula:

[0018] ;

[0019] in, The load factor of the i-th transformer put into operation under typical operating conditions is expressed as a percentage. It is the average load rate of N-2 adjacent transformers in the same power supply area, expressed as a percentage; The number of adjacent transformers, taken as an integer from 3 to 10; The transformer load balance is a dimensionless quantity.

[0020] Furthermore, the service quality dimension indicators include the power distribution network supply capacity improvement indicator, which is calculated using the following formula:

[0021] ;

[0022] in, The degree of fit between the 110 kV power grid capacity ratio and the preset reasonable capacity ratio range [1.5, 2.0] is considered when the actual capacity ratio falls within this range. The value is 1. When the actual load ratio deviates from this range, a value between 0 and 1 is calculated according to the degree of deviation and a preset rule. To determine the matching degree between the average transformer capacity per household and the preset reasonable value, when the actual average transformer capacity per household reaches the preset reasonable value... The value is 1; otherwise, the value between 0 and 1 is calculated proportionally. This is a dimensionless value representing the improvement in the power supply capacity of the distribution network.

[0023] The indicators for the green and low-carbon dimension include the contribution indicator to the dual-carbon goals, which is calculated using the following formula:

[0024]

[0025] in, Carbon emission reductions for non-fossil energy power generation To reduce carbon emissions by replacing electricity, To improve energy efficiency and reduce carbon emissions, This refers to the carbon emission reduction due to line loss. Carbon emission reductions for electricity generation rights trading Carbon emission reductions in response to electricity demand For supply chain management of carbon emission reductions, the unit for each carbon emission reduction is ton of carbon dioxide equivalent, and the accounting period for each carbon emission reduction is one calendar year after the project is put into operation. The accounting benchmark for each carbon emission reduction is the carbon emission level of the corresponding field before the project is implemented.

[0026] Furthermore, step S3 includes the following sub-steps:

[0027] S31, based on the information entropy theory, calculates the information entropy of each indicator to reflect the amount of information contained in the original data of the indicator, and then derives the weight of each indicator under the entropy weight method.

[0028] S32, the coefficient of variation is obtained by calculating the ratio of the standard deviation to the mean of each indicator. The magnitude of the coefficient of variation reflects the degree of dispersion of the indicator distribution, and then the weight of each indicator under the coefficient of variation method is determined.

[0029] S33. The weights obtained by the entropy weight method and the weights obtained by the coefficient of variation method are arithmetically averaged to obtain the objective weights of each indicator.

[0030] Furthermore, step S4 includes the following sub-steps:

[0031] S41, the indicator data after standardization according to the indicator type is multiplied with the objective weights obtained in S3 to construct a weighted normalized matrix that matches the dimension and the number of items.

[0032] S42, in the weighted normalization matrix, the maximum value is extracted for positive indicators and the minimum value is extracted for negative indicators to form a positive ideal solution, the minimum value is extracted for positive indicators and the maximum value is extracted for negative indicators to form a negative ideal solution, and the ideal solution for interval indicators is determined according to the standardization rules;

[0033] S43, calculate the distance between the weighted normalized matrix elements corresponding to each reserve project and the positive ideal solution and negative ideal solution respectively using the Euclidean distance formula;

[0034] S44. Divide the distance of each item from the negative ideal solution by the sum of the distances of the item from the positive ideal solution and the negative ideal solution to obtain the relative proximity, and sort them by size to form an objective ranking result.

[0035] Furthermore, the 0-1 type integer programming model constructed in S5 has an objective function of minimizing the inconsistency between the consensus ranking and all expert rankings. This inconsistency is measured by Kendalltau distance. The constraints include that any two items have a mutually exclusive order relationship in the ranking and the transitivity requirement of the ranking. By solving this model, the consensus ranking result with the minimum sum of Kendalltau distances with all expert rankings is obtained.

[0036] This invention also discloses a project ranking system for new power grid infrastructure, characterized in that it uses the project ranking method for new power grid infrastructure as described in any one of claims 1-9 as its operating logic, including:

[0037] The multi-dimensional indicator system construction unit is used to select indicators from five dimensions—power grid development, power supply security, service quality, efficiency and effectiveness, and green and low-carbon development—and integrate them to form a ranking indicator system.

[0038] The indicator data acquisition unit, connected to the multi-dimensional indicator system construction unit, is used to collect the original indicator data corresponding to each reserve project and distinguish the indicator types.

[0039] An objective weight calculation unit is connected to the indicator data acquisition unit and calculates the objective weight of the indicator using a combination of entropy weight method and coefficient of variation method.

[0040] An objective ranking generation unit is connected to the objective weight calculation unit. It calculates the relative proximity based on the weighted normalization matrix and the TOPSIS method and forms an objective ranking.

[0041] The subjective ranking generation unit is used to form a subjective consensus ranking by statistical expert opinions and solving an integer programming model.

[0042] The sorting result aggregation unit is connected to the objective sorting generation unit and the subjective sorting generation unit respectively. It uses the weighted Borda counting method to perform standardized score calculation and weighted aggregation on the objective and subjective sorting results, and outputs the final ranking of the reserve items.

[0043] Beneficial Effects: This invention proposes a ranking method for reserve projects of new power grid infrastructure. Through the construction of a multi-dimensional indicator system, the integration of subjective and objective weights, the formation of scientific consensus, and weighted aggregation logic, it achieves comprehensiveness, objectivity, and rationality in the ranking of reserve projects for new power grid infrastructure. In terms of determining indicator weights, it abandons the single weighting mode and combines the advantages of two objective weighting methods and performs average processing. This not only utilizes data characteristics to ensure the objectivity of weights, but also avoids the limitations of single weighting through the complementarity of multiple methods, balancing the matching degree between data support and actual needs. In terms of the integration of subjective and objective ranking, it forms an expert consensus ranking by constructing a pairwise preference matrix and an integer programming model, and then integrates the two types of results by using a weighted aggregation method. This establishes a standardized consensus formation mechanism and a flexible weight adjustment logic, solving the problems of irreconcilable differences of opinion and insufficient scenario adaptability caused by the simple superposition of traditional methods. Meanwhile, the multi-dimensional indicator system comprehensively covers the core development needs of the power grid, and the standardized conversion and distance calculation ensure the accuracy of objective ranking. The overall process takes into account both data-driven and expert experience, so that the ranking results not only conform to the quantitative analysis conclusions, but also meet the needs of industry practice. This significantly improves the scientificity and operability of the ranking of reserve projects, and provides strong support for the optimal allocation and accurate decision-making of new power grid infrastructure resources. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0045] Figure 2 This is a flowchart of method step S3 of the present invention;

[0046] Figure 3 This is a flowchart of method step S4 of the present invention;

[0047] Figure 4 This is a flowchart of step S5 of the method of the present invention;

[0048] Figure 5 This is a diagram showing the unit composition for implementing the method of the present invention. Detailed Implementation

[0049] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] like Figure 1 As shown, a method for prioritizing reserve projects for new power grid infrastructure includes the following steps:

[0051] S1 selects several indicators from five dimensions: power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development, to construct a ranking indicator system covering different types of power grid infrastructure projects;

[0052] Specifically, step S1 involves constructing an indicator system for ranking new power grid infrastructure reserve projects. Starting from five core dimensions—power grid development, power supply security, service quality, efficiency and effectiveness, and green and low-carbon development—8 to 10 quantifiable and representative indicators are selected for each dimension, ultimately forming a complete ranking system comprising 40 to 50 indicators. The power grid development dimension includes indicators such as improved inter-provincial transmission capacity and power grid coverage density; the power supply security dimension includes parameters related to the throughput improvement of N-1 transformers at different voltage levels (1000 kV, 750 kV, 500 kV, etc.); the service quality dimension includes indicators such as distribution network capacity-to-load ratio and average transformer capacity per household; the efficiency and effectiveness dimension involves parameters such as equipment load rate and return on investment; and the green and low-carbon dimension focuses on various carbon emission reduction-related indicators. When implementing the system, it is necessary to refer to the State Grid industry standards, regional energy development plans, and project construction technical specifications. By sorting out the core evaluation elements of the industry and investigating the actual influencing factors of the project, the indicator list should be determined to ensure that the indicator system covers the key characteristics of the entire life cycle of the project, and has data availability and evaluation operability. The subsequent ranking will provide a comprehensive and systematic evaluation basis, avoid the one-sidedness of the evaluation caused by a single dimension or a small number of indicators, and ensure that the ranking results can truly reflect the comprehensive value and priority of the project.

[0053] S2, collect the original data of each indicator in the corresponding indicator system of each reserve project, determine the indicator type based on the characteristics of the indicator data, and distinguish between positive, negative and interval indicators;

[0054] Specifically, step S2 involves collecting and classifying the raw data for the indicators. During implementation, based on the indicator system established in S1, raw data for 40 to 50 indicators corresponding to each reserve project are comprehensively collected through various channels, including reviewing power grid dispatch operation records, project feasibility study reports, equipment testing reports, data published by statistical departments, and industry-specific surveys. The data collection period covers key time nodes in the project planning phase, ensuring the authenticity, completeness, and timeliness of the data. After data collection, based on the changing patterns and evaluation logic of the indicator data, all indicators are clearly divided into three categories: positive, negative, and interval-type. Positive indicators are those where a larger index value indicates better project performance, such as inter-provincial power transmission and carbon emission reduction. Negative indicators are those where a smaller index value indicates better project performance, such as line loss-related derivative indicators. Interval-type indicators are those where the project performs best when the index value is within a specific range, such as indicators where equipment load rate is controlled between 60% and 80% or power grid capacity ratio is controlled between 1.5 and 2.0. The classification of indicators should be based on the connotation of the indicators, industry evaluation standards and actual project operation requirements. The attributes should be determined by analyzing the relationship between indicator data and the comprehensive benefits of the project. This lays the foundation for subsequent data standardization, weighting and ranking calculations, ensuring that the evaluation logic of different types of indicators is consistent, avoiding ranking errors caused by confusion of indicator types, and ensuring the scientificity and accuracy of the ranking process.

[0055] S3. The entropy weight method and the coefficient of variation method are used to calculate the weight of each indicator respectively. The objective weight of each indicator is obtained by averaging the two types of weights.

[0056] Specifically, step S3 calculates the objective weights of the indicators using a dual weighting model combining entropy weighting and the coefficient of variation method. First, based on the raw data collected in step S2, the weights of each indicator are calculated using both methods. In the entropy weighting method, the information entropy value of the raw data for each indicator is quantified. A smaller information entropy value indicates greater data variation and richer information content, resulting in a higher impact on the ranking results. Based on this, the entropy weight of each indicator is derived. In the coefficient of variation method, the ratio of the standard deviation to the mean of each indicator is calculated to obtain the coefficient of variation. A larger coefficient of variation indicates a more dispersed and discrete distribution of indicator data, resulting in stronger distinguishing ability of the indicator. This determines the coefficient of variation weight of each indicator. Since the entropy weighting method focuses on reflecting the information content of the data itself, while the coefficient of variation method focuses on reflecting the distribution characteristics of the data, and there is no primary or secondary distinction between the two methods in weight determination, the arithmetic mean method is used to fuse the two types of weights. That is, the objective weight of each indicator is equal to half the sum of its entropy weight and its coefficient of variation weight. This step requires the use of data processing tools to calculate entropy, coefficient of variation, and average weight, ensuring accurate calculations and avoiding weight bias caused by a single weighting method. This ensures that the definition of indicator importance utilizes both objective data characteristics and the comprehensiveness of different evaluation perspectives, providing reliable weight support for the subsequent construction of the weighted normalization matrix and guaranteeing the objectivity and rationality of the ranking results.

[0057] S4. After standardizing and transforming the original data of the indicators, the weighted normalization matrix is ​​constructed by combining it with the objective weights. The relative proximity is obtained by calculating the distance between each item and the positive ideal solution and the negative ideal solution, thus forming an objective ranking result.

[0058] Specifically, in step S4, the corresponding standardization transformation rule is adopted according to the indicator type, as follows:

[0059] For positive indicators, the formula is used. Standardize;

[0060] For negative indicators, the formula is used. Standardize;

[0061] For interval indicators, if the indicator value falls within the preset optimal interval... If the value is within the range, the standardized value is 1; if it is outside the range, the standardized value between 0 and 1 is calculated according to the degree of deviation and a preset rule.

[0062] in, For the i-th item, the raw data for the j-th indicator. For standardized data, and The maximum and minimum values ​​of the j-th indicator across all projects are given, respectively. A corresponding standardization process is applied to ensure that the transformed data values ​​are uniformly controlled between 0 and 1, accurately reflecting the relative performance of each project across all indicators. Subsequently, the standardized indicator data is multiplied one-by-one with the objective weights obtained from S3, constructing a weighted normalized matrix whose dimensions perfectly match the number of reserve projects. The number of rows in the matrix corresponds to the number of reserve projects, and the number of columns corresponds to 40 to 50 indicators. Each element represents the weighted normalized value of the corresponding project under its corresponding indicator. Based on this matrix, the maximum value under each indicator is extracted to form the positive ideal solution, representing the optimal performance of each indicator dimension; the minimum value under each indicator is extracted to form the negative ideal solution, representing the worst performance of each indicator dimension. Then, a distance calculation method is used to quantify the difference between the matrix elements corresponding to each reserve project and the positive and negative ideal solutions. The distance calculation process strictly follows the correspondence of matrix elements to ensure accurate quantification results. Finally, the distance between each item and the negative ideal solution is divided by the sum of the distances between the item and the positive and negative ideal solutions to obtain the relative proximity score. The value is controlled between 0 and 1. The items are sorted from largest to smallest according to this score to form an objective ranking result. The implementation requires the use of professional data processing tools to complete matrix construction and distance calculation. Data quantification and model calculation ensure the objectivity of the ranking and provide accurate quantitative basis for the subsequent integration of subjective and objective results, thereby improving the scientificity and credibility of the ranking result.

[0063] S5. Organize a number of experts to rank the reserve projects, construct a pairwise preference matrix based on Kendalltau distance, and obtain the subjective consensus ranking result that satisfies the requirements of collective wisdom preservation and axiomaticity by solving a 0-1 type integer programming problem.

[0064] Specifically, step S5 integrates expert opinions to form a subjective consensus ranking result. During implementation, a team of 10 to 15 professionals with over 5 years of experience in planning, constructing, or evaluating new power grid infrastructure projects is first selected. This team includes specialists in multiple fields such as power grid dispatching, planning and design, safety management, economic benefit analysis, and green energy technology, ensuring the professionalism and representativeness of the expert team's opinions. Next, experts independently prioritize all reserve projects based on the indicator system constructed in S1 and the raw data collected in S2, generating 10 to 15 independent expert ranking results. Then, the number of experts who support the ranking of any two projects where the preceding project precedes the following project is counted. Based on this statistical result, an n×n pairwise preference matrix (where n is the total number of reserve projects) is constructed. The matrix elements intuitively reflect the degree of consensus among the expert group regarding the priority relationship between different projects. Finally, a 0-1 type integer programming model is constructed with the objective of minimizing the total inconsistency between the consensus ranking and all expert rankings. Mutual exclusion constraints and transitivity constraints are set for project ranking to ensure that the ranking result meets logical consistency requirements. Finally, the integer programming model is solved using professional planning and solving tools to obtain the optimal consensus ranking result that satisfies the constraints, which is the subjective ranking result. This fully leverages the advantages of experts in industry practice experience, technical judgment, and macro-decision-making, and makes up for the shortcomings of pure data-driven ranking in terms of practical adaptability. By integrating scattered opinions through the consensus mechanism, a scientific and unified subjective ranking basis is formed, providing decision support that is in line with industry realities for the final comprehensive ranking.

[0065] S6 uses the weighted Borda counting method to calculate standardized Borda scores for both the objective ranking results and the subjective consensus ranking results. Weighting coefficients are introduced to aggregate the two types of scores, and the ranking of the reserve projects is determined based on the final weighted composite score.

[0066] Specifically, step S6, as the final aggregation step of the ranking method, uses a weighted Borda count method to merge the objective and subjective ranking results. In implementation, firstly, for the objective ranking results obtained in S4 and the subjective consensus ranking results obtained in S5, a standardized Borda score is calculated for each reserve project. For a ranking scenario including m reserve projects, the project ranked 1st in the objective ranking receives m points, the 2nd receives m-1 points, and so on, decreasing to 1 point for the mth project. The subjective ranking is scored according to the same rules. Then, both scores are divided by the full score (m points) of the corresponding ranking system to obtain the standardized objective and subjective Borda scores, with standardized scores ranging from 0 to 1. Next, weighting coefficients are introduced. The objective ranking weight and subjective ranking weight are determined according to the actual needs of the ranking scenario, with the sum of the weighting coefficients being 1. Typically, the objective ranking weight is set to 0.5 to 0.7, and the subjective ranking weight to 0.3 to 0.5, which can be flexibly adjusted according to the emphasis placed on data objectivity or expert experience in project decision-making. The standardized objective Borda score is then multiplied by its corresponding weighting coefficient, and the standardized subjective Borda score is also multiplied by its corresponding weighting coefficient. The two products are then summed to obtain the final weighted composite score for each project. Finally, all reserve projects are ranked from largest to smallest according to their final weighted composite scores to determine their final ranking. Through a scientific weighted aggregation logic, data-driven objective ranking and experience-supported subjective ranking are organically integrated. This approach retains the accuracy of quantitative analysis while also considering the rationality of industry practices, avoiding the limitations of a single ranking result. The final ranking result comprehensively and accurately reflects the overall priority of the projects, providing a reliable basis for resource allocation and investment decisions for new power grid infrastructure reserve projects.

[0067] Preferably, the calculation of the improvement of cross-provincial resource allocation level in the power grid development dimension adopts the following formula: ,in, To increase inter-provincial power transmission capacity, For inter-provincial power transmission capacity in 2020, To increase the amount of electricity transmitted across provinces and regions, This represents the improvement in the level of cross-provincial and regional resource allocation.

[0068] Specifically, the quantitative calculation of the improvement in inter-provincial resource allocation level in the power grid development dimension first clarifies the collection standards for two core technical parameters. One parameter is the newly added inter-provincial transmission capacity, which is determined through power grid planning and design documents, equipment technical parameter calculations, and transmission line operation limit assessments. The other parameter is the newly added inter-provincial transmission volume, which is based on accurate data obtained from regional energy supply and demand forecasts, inter-provincial power trading plans, and power grid operation load data statistics. Simultaneously, a fixed benchmark parameter is the 2020 inter-provincial transmission capacity, which is determined by consulting official documents such as the annual statistical report of the power grid industry and the special assessment report on transmission capacity for that year, ensuring the authority and comparability of the parameters. The calculation process employs a weighted summation logic, assigning a 50% weight to the ratio of the newly added inter-provincial transmission capacity to the 2020 benchmark value, and directly assigning a 50% weight to the newly added inter-provincial transmission volume. The sum of the two weighted results yields the final value of the improvement in inter-provincial resource allocation level. By utilizing tools such as the power grid planning data management system and the power trading data platform, parameter collection and calculation are completed. Through clear parameter definitions and fixed weight allocation, the effectiveness of cross-provincial resource optimization in the power grid development dimension can be accurately quantified, avoiding subjective arbitrariness in the evaluation process. This provides an objective and operable quantitative basis for the ranking indicator system, ensuring that the performance of different reserve projects in this dimension can be scientifically measured, and supporting the rationality and accuracy of the subsequent overall ranking.

[0069] Preferably, the calculation of the improvement in power grid security level in the dimension of power supply security adopts the following formula: ,in, 1000 kV Pass rate improvement value 750 kV Pass rate improvement value 500 kV Pass rate improvement value 330 kV Pass rate improvement value 220 kV Pass rate improvement value This represents the improvement in the security level of the main power grid.

[0070] Specifically, the quantitative calculation of the improvement of the main grid security level in the dimension of safe supply involves collecting core security parameters of five different voltage levels of the grid during implementation. These parameters correspond to key main grid levels such as the highest voltage level, ultra-high voltage level, and high voltage level. The parameter for each voltage level is the pass rate improvement value of N-1 transformers. This value is determined by comparing the N-1 safety verification results of the corresponding voltage level grid before and after the project implementation. The specific data collection process requires the use of the grid security and stability analysis system to simulate and calculate the grid operation mode before and after the project is put into operation. The proportion of safe pass times when a single fault occurs in a line or equipment at each voltage level is counted to the total number of verification times. The difference between the two is the pass rate improvement value. The calculation process employs a differentiated weighting allocation. The highest voltage level's pass rate improvement is assigned a 20% weight, the next two highest high-voltage levels are each assigned a 30% weight, and the other two medium-high voltage levels are each assigned a 10% weight. This weighting allocation is determined based on the core importance of each voltage level in the safe and stable operation of the main grid. The highest voltage level, as the backbone of the power grid, has a relatively low weighting but is fundamentally essential. The next two highest high-voltage levels, as the mainstay of the main grid, have the highest weighting, while the medium-high voltage levels, as the connection between the main grid and the distribution network, have the lowest weighting. The improvement in the main grid's security level is obtained by multiplying each parameter by its corresponding weight and summing the results. Simulation calculations and parameter statistics are then performed using power grid security analysis software, comprehensively covering the security improvement effectiveness of key voltage levels in the main grid. This scientific weighting allocation reflects the differences in the security importance of different voltage levels, providing a precise method for quantitative evaluation of security supply dimensions and ensuring that the contribution of reserve projects to improving main grid security can be objectively measured.

[0071] Preferably, the calculation of equipment load balance in the efficiency dimension uses the following formula: ,in, This represents the load factor of a transformer put into operation under typical conditions. This represents the average load rate of adjacent transformers. This represents the number of adjacent transformer stands. For transformer load balance.

[0072] Specifically, the quantitative calculation of equipment load rate balance in the efficiency dimension first clarifies that the calculation object is adjacent transformers put into operation under typical operating conditions. The definition of adjacent transformers is a group of equipment within the same power supply area, at the same voltage level, and with overlapping or connected power supply ranges. The number range is determined based on the scale of the power supply area and the power grid topology, typically consisting of 3 to 10 units forming an evaluation unit. Core parameters include the load rate of each transformer in operation. Actual load data and rated capacity data under typical operating conditions are collected through a transformer operation monitoring system, and the ratio of these two data points is calculated to obtain the load rate of a single unit. Then, the average load rate of the adjacent transformers in the group is calculated as the benchmark parameter for load balance evaluation. The calculation process first squares the difference between the load rate of each transformer and the average value to eliminate the mutual cancellation of positive and negative deviations. Then, all squared differences are summed and divided by the number of adjacent transformers to obtain the mean squared difference. Next, the square root of the mean squared difference is taken to obtain the standard deviation. Finally, the standard deviation is divided by the average value to obtain the final transformer load balance. By utilizing an online monitoring platform for power equipment and a power grid GIS system to group equipment, collect and calculate data, and through multi-step mathematical operations, the balance of load distribution among adjacent transformers can be accurately quantified. This avoids the one-sidedness of evaluating the load rate of a single device and can objectively reflect the project's effectiveness in improving equipment utilization efficiency and reducing overload risk. It provides a scientific method for quantifying efficiency and benefit indicators and supports the accurate evaluation of project benefits based on the ranking results.

[0073] Preferably, the calculation of the improvement in power supply capacity of the distribution network in the service quality dimension adopts the following formula: ,in, For a 110 kV power grid capacity-to-load ratio, To match the average transformer capacity per household, This represents the improvement in the power supply capacity of the distribution network.

[0074] Specifically, in the quantitative calculation of the power supply capacity improvement of the distribution network in the service quality dimension, two core technical parameters are collected during the implementation process. The first parameter is the 110 (66) kV power grid capacity ratio. By calculating the ratio of the total power supply capacity of the power grid at this voltage level to the maximum load, and then comparing it with the reasonable capacity ratio range specified by the industry standard, the degree of fit between the actual capacity ratio and the reasonable range is quantified. The reasonable range of capacity ratio is determined according to the regional distribution network planning guidelines, load growth characteristics and power supply reliability requirements, and is usually set to 1.5 to 2.0. The second parameter is the matching degree of the average distribution transformer capacity per household. By statistically analyzing the total distribution transformer capacity and the total number of users in the power supply area, the average distribution transformer capacity per household is calculated. Then, combined with the regional user load characteristics, electricity demand growth forecast and distribution network construction standards, the reasonable value of the average distribution transformer capacity per household is determined, and the degree of matching between the actual average capacity per household and the reasonable value is quantified. The calculation process adopts the equal weight allocation logic, with each of the two parameters assigned a weight of 50%, and the weighted sum is used to obtain the value of the power supply capacity improvement of the distribution network. By utilizing the power distribution network planning and management system and the user electricity consumption information collection system, parameter calculation and comparison are completed. From the two core perspectives of the rationality of power grid capacity configuration and the matching degree of user demand, the effectiveness of the improvement of power distribution network power supply capacity is comprehensively quantified. By fixing the voltage level range, clarifying the parameter calculation standards and equal weighting, the objectivity and consistency of the evaluation are ensured, providing accurate quantitative indicators for the service quality dimension, so that the performance of different reserve projects in improving power supply service capacity can be scientifically measured.

[0075] Preferably, the contribution of green and low-carbon services to the dual-carbon goals is calculated using the following formula: ,in, Carbon emission reductions for non-fossil energy power generation To reduce carbon emissions by replacing electricity, To improve energy efficiency and reduce carbon emissions, This refers to the carbon emission reduction due to line loss. Carbon emission reductions for electricity generation rights trading Carbon emission reductions in response to electricity demand Carbon emission reductions for supply chain management Contribution value to serving dual carbon goals.

[0076] Specifically, the quantitative calculation of the contribution of green and low-carbon services to the dual-carbon goals requires the systematic collection of seven core carbon emission reduction parameters during implementation. These parameters cover seven key areas: non-fossil energy power generation, electricity substitution, energy efficiency improvement, line loss reduction, power generation rights trading, electricity demand response, and supply chain management. The collection of each parameter must adhere to different technical standards. Carbon emission reduction from non-fossil energy power generation is calculated based on non-fossil energy power generation and the carbon emission factor per unit of power generation. Carbon emission reduction from electricity substitution is calculated based on the type and quantity of fossil energy substituted and the corresponding carbon emission factor. Carbon emission reduction from energy efficiency improvement is determined by multiplying the energy consumption difference before and after project implementation by the carbon emission factor. Carbon emission reduction from line loss is calculated based on the reduction in line loss rate, power supply, and the carbon emission factor per unit of electricity. Carbon emission reduction from power generation rights trading is calculated based on the traded electricity volume and the difference in carbon emission intensity of the substituted power source. Carbon emission reduction from electricity demand response is determined based on the response load scale, response duration, and the corresponding fossil energy power generation carbon emission factor. Carbon emission reduction from supply chain management is calculated using parameters such as the reduction in supplier carbon emission intensity and procurement scale. The calculation process employs a direct summation logic, directly adding the seven carbon emission reduction parameters to obtain the final contribution value to the "dual carbon" target, without the need for additional weighting, ensuring comprehensive coverage of the emission reduction effectiveness of various low-carbon measures. Parameter collection and aggregation calculations are completed through multiple channels, including carbon emission accounting systems, power energy data platforms, and supply chain management information systems, constructing a comprehensive and systematic low-carbon contribution quantification system. By clarifying the accounting standards for each emission reduction parameter, the system achieves accurate measurement of the green and low-carbon benefits of reserve projects, avoiding the limitations of evaluation based on a single emission reduction dimension. This provides scientific and comprehensive methodological support for the quantification of green and low-carbon indicators, helping the ranking results fully reflect the low-carbon value of the projects.

[0077] Preferred, such as Figure 2 As shown, step S3 includes four sub-steps: S31, based on the information entropy theory, the information entropy of each indicator is calculated to reflect the amount of information contained in the original data of the indicator, and then the weight of each indicator under the entropy weight method is derived; S32, the coefficient of variation is obtained by calculating the ratio of the standard deviation to the mean of each indicator, and the magnitude of the coefficient of variation reflects the degree of dispersion of the indicator distribution, and then the weight of each indicator under the coefficient of variation method is determined; S33, the weighting logic of the entropy weight method and the coefficient of variation method is analyzed, and it is clarified that the two methods assign weights from the perspectives of information content and data distribution, respectively, and there is no primary or secondary relationship; S34, the weights obtained by the entropy weight method and the weights obtained by the coefficient of variation method are calculated by arithmetic average to finally obtain the objective weight of each indicator.

[0078] Specifically, the objective weight calculation process in step S3 includes four sub-steps, S31 to S34. S31, based on information entropy theory, statistically analyzes the 40 to 50 raw data points collected in S2, obtaining the information entropy value for each indicator through quantitative calculation. The entropy value is strictly controlled between 0 and 1; a smaller entropy value indicates more significant differences in the indicator data and a more sufficient amount of effective evaluation information. Based on this, the weight values ​​for each indicator under the entropy weight method are derived in reverse. This process requires the use of data processing tools to accurately convert between entropy values ​​and weights. S32 obtains the standard deviation and mean of each indicator through statistical calculation, calculating the ratio of the two to obtain the coefficient of variation. A larger coefficient of variation indicates a higher degree of dispersion in the indicator data distribution and a stronger ability to distinguish differences between different reserve projects. Based on the magnitude of the coefficient of variation, the weight results for each indicator under the coefficient of variation method are determined. During the calculation, the accuracy of the standard deviation and mean calculations must be ensured to avoid data errors affecting the weight results. S33 delves into the core logic of the two weighting methods, clarifying that the entropy weight method focuses on the information inherent in the data itself, while the coefficient of variation method emphasizes the differences in the distribution characteristics of the data. Both methods are equal in weight determination, with no primary or secondary distinction. Through logical analysis, the necessity and rationality of their integration are clarified, providing theoretical support for subsequent weight integration. S34 employs the arithmetic mean method to integrate the entropy weight and the coefficient of variation weight. That is, the objective weight of each indicator is equal to half the sum of the two single weight values. The calculation process utilizes professional data processing software for batch calculations, ensuring the objectivity, stability, and accuracy of the weight results. Through a multi-step, dual-method weighting logic, the limitations of a single weighting method are avoided. This ensures that the indicator weights utilize both the objective characteristics of the data and the comprehensiveness of different evaluation perspectives, providing reliable support for the subsequent construction of the weighted normalization matrix and guaranteeing the scientific and rational nature of the ranking results. Its implementation requires complete and accurate original indicator data as a foundation, strictly adhering to the calculation rules and logical order of each step.

[0079] Preferred, such as Figure 3 As shown, step S4 includes four sub-steps: S41, multiplying the standardized indicator data with the objective weights obtained in S3 to construct a weighted normalized matrix with dimensions matching the number of items; S42, extracting the maximum value under each indicator to form a positive ideal solution and extracting the minimum value under each indicator to form a negative ideal solution from the weighted normalized matrix; S43, calculating the distance between the weighted normalized matrix element corresponding to each reserve item and the positive and negative ideal solutions using the Euclidean distance formula; S44, dividing the distance between each item and the negative ideal solution by the sum of the distances between the item and the positive and negative ideal solutions to obtain the relative closeness and sorting them by size to form an objective ranking result.

[0080] Specifically, the objective ranking generation process in step S4 includes four sub-steps, S41 to S44. S41 first standardizes the original data of the positive, negative, and interval indicators divided in S2, eliminating differences in the dimensions of different indicators. After transformation, the data values ​​are uniformly controlled between 0 and 1. Then, the standardized data is aligned and multiplied one by one with the objective weights obtained in S3 to construct a weighted normalized matrix with rows corresponding to the number of reserve projects and columns corresponding to 40 to 50 indicators. Each element in the matrix accurately reflects the weighted normalized performance of the corresponding project under the corresponding indicator. The construction process must ensure that the data matching is unbiased. S42, based on the constructed weighted normalized matrix, extracts the maximum value under each indicator dimension, integrating them to form a positive ideal solution representing the best performance of each indicator. Simultaneously, it extracts the minimum value under each indicator, integrating it to form a negative ideal solution representing the worst performance of each indicator. The extraction process must traverse all elements of the matrix to ensure the comprehensiveness and accuracy of the ideal solution. S43 employs a distance calculation method to quantify the gap between the matrix elements corresponding to each reserve project and the positive and negative ideal solutions. The calculation strictly adheres to the correspondence between matrix elements and ideal solutions, obtaining two distance values ​​through comprehensive calculation of multi-dimensional data. Smaller distance values ​​indicate a higher degree of fit between the project and its corresponding ideal solution. Professional tools are used to ensure the accuracy of the quantification process. S44 divides the distance of each project to the negative ideal solution by the sum of its distances to the positive and negative ideal solutions to obtain a relative closeness score, controlled between 0 and 1. All reserve projects are then sorted in descending order of relative closeness score to form the final objective ranking result. This process strictly follows a step-by-step logic, based on standardized data and objective weights. Through ideal solution construction and distance quantification, it achieves accurate ranking of the projects' overall performance. The data-driven quantification method ensures the objectivity and fairness of the ranking results, avoiding interference from subjective factors and providing accurate quantitative evidence for the subsequent integration of subjective and objective results, supporting the scientific nature of the overall ranking.

[0081] Preferred, such as Figure 4 As shown, S5 includes four sub-steps: S51, collecting independent ranking results given by several experts for all reserve projects, and clarifying the ranking relationship of each expert for different projects; S52, counting the number of experts who support the ranking of the first project before the second project in any two projects, and constructing an n×n pairwise preference matrix based on the statistical results, where n is the total number of reserve projects; S53, constructing a 0-1 type integer programming model with the objective of minimizing the total inconsistency between the consensus ranking and the ranking of all experts, and setting mutual exclusion constraints and transitivity constraints for project ranking; S54, solving the integer programming model to obtain the optimal consensus ranking result that satisfies the constraints, which is the subjective ranking result.

[0082] Specifically, the subjective consensus ranking process in step S5 includes four sub-steps, S51 to S54. S51 first involves forming an expert team of 10 to 15 professionals with over 5 years of experience in new power grid infrastructure construction, covering multiple fields such as power grid dispatching, planning and design, and safety management, to ensure representativeness of opinions. Then, based on the indicator system of S1 and the raw data of S2, the experts independently prioritize all reserve projects, collecting 10 to 15 independent expert ranking results. The ranking process requires experts to clearly define the ranking relationships between projects to avoid ambiguous evaluations. S52 involves calculating the number of experts who support ranking any two projects higher than the previous one. Based on this statistical data, an n×n pairwise preference matrix is ​​constructed (n being the total number of reserve projects). The matrix elements directly reflect the degree of consensus among the expert group regarding the priority relationships between different projects. The statistical process requires verifying the ranking opinions of each expert one by one to ensure the accuracy of the matrix data. S53 constructs an integer programming model with the goal of minimizing the total inconsistency between the consensus ranking and all expert rankings. It sets mutually exclusive and transitive constraints on item ranking, clarifying that the positional relationship between any two items is mutually exclusive and conforms to logical transitivity rules. The constraints must ensure the rationality and consistency of the ranking results, avoiding logical contradictions. S54 solves the integer programming model using professional programming tools. Based on expert ranking data and constraints, it obtains the optimal consensus ranking result that satisfies the minimum inconsistency requirement, i.e., the subjective ranking result. The solution process must ensure computational accuracy and efficiency, guarantee the reliability of the results, and fully leverage the industry experience and technical judgment advantages of experts. Through the step-by-step process from S51 to S54, scattered expert opinions are transformed into a unified and scientific consensus ranking. This avoids the influence of subjective bias from a single expert and compensates for the lack of practical adaptability of pure data ranking, providing a subjective basis that fits industry reality for the final comprehensive ranking, and improving the practicality and acceptance of the overall ranking result.

[0083] like Figure 5As shown, a method for ranking reserve projects for new power grid infrastructure is implemented through different units, including: a multi-dimensional indicator system construction unit, which selects and integrates indicators from five dimensions—power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development—to form a ranking indicator system, and establishes a data transmission connection with the indicator data acquisition unit; an indicator data acquisition unit, which collects the original indicator data corresponding to each reserve project and distinguishes the indicator types, and transmits the data to the objective weight calculation unit; an objective weight calculation unit, which calculates the objective weights of the indicators using a combination of entropy weight method and coefficient of variation method, and outputs the results to the objective ranking generation unit; an objective ranking generation unit, which calculates the relative proximity based on the weighted normalized matrix and TOPSIS method to form an objective ranking, and connects to the ranking result aggregation unit along with the subjective ranking generation unit; a subjective ranking generation unit, which integrates expert rankings to form a subjective consensus ranking using the Kemeny-Young method, and transmits the results to the ranking result aggregation unit; and a ranking result aggregation unit, which uses the weighted Borda counting method to perform standardized score calculation and weighted aggregation on the subjective and objective ranking results, and outputs the final ranking position of the reserve projects.

[0084] A ranking method for reserve projects in new power grid infrastructure is proposed. This method possesses core advantages such as multi-dimensional coverage, integration of subjective and objective factors, and rigorous logic, fully adapting to the complex ranking requirements of new power grid infrastructure projects. By constructing a complete indicator system encompassing power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development, it achieves a comprehensive consideration of the overall value of projects, avoiding the one-sidedness of single-dimensional evaluation. In the weight determination stage, it integrates the advantages of two objective weighting methods, utilizing data characteristics to ensure the objectivity of weight allocation while reducing the bias of single weighting through methodological complementarity, making the definition of indicator importance more realistic. During the ranking generation process, subjective and objective ranking results are formed through quantitative models and expert consensus, respectively, and then scientifically aggregated and logically integrated, balancing the accuracy of data-driven approaches with the practicality of industry experience, thus enhancing the credibility and persuasiveness of the ranking results.

[0085] This method addresses the issue of the single weighting method in traditional approaches by combining two objective weighting methods. This avoids the limitations of relying solely on data while ignoring practical needs, and also eliminates the drawbacks of subjective weighting lacking data support, thus achieving a balance between objectivity and practicality. To address the issue of imperfect aggregation logic between subjective and objective results, it first establishes an expert consensus ranking by constructing a pairwise preference matrix and an integer programming model, resolving the challenge of reconciling differing expert opinions. Then, a weighted aggregation method is adopted, which can flexibly adjust the weight ratio of subjective and objective results according to different scenarios, replacing the crude method of simple superposition. This significantly improves the matching degree between the final ranking result and the actual implementation priority of the project, meeting the precision requirements of decision-making in new power grid infrastructure projects.

[0086] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for prioritizing reserve projects for new power grid infrastructure, characterized in that, Includes the following steps: S1 selects several indicators from five dimensions: power grid development, security and supply, service quality, efficiency and effectiveness, and green and low-carbon development, to construct a ranking indicator system covering different types of power grid infrastructure projects; S2, collect the original data of each indicator in the indicator system corresponding to each reserve project, determine the indicator type based on the characteristics of the indicator data, and classify the indicators into positive, negative and interval indicators; S3. The entropy weight method and the coefficient of variation method are used to calculate the weight of each indicator respectively. The objective weight of each indicator is obtained by averaging the two types of weights. S4. Based on the indicator types distinguished in S2, the original data of each indicator are standardized using the corresponding standardization transformation rules. After eliminating the difference in dimensions, the data is combined with the objective weights obtained in S3 to construct a weighted normalization matrix. The relative proximity is obtained by calculating the distance between each item and the positive ideal solution and the negative ideal solution, thus forming an objective ranking result. S5. Organize a number of experts to rank the reserve projects, count the number of experts who support the first project being ranked before the second project in any two projects, construct an n×n pairwise preference matrix based on the statistical results, where n is the total number of reserve projects, construct a 0-1 type integer programming model with the objective of minimizing the total inconsistency between the consensus ranking and the ranking of all experts, and solve it to obtain the subjective consensus ranking result. S6. Using the weighted Borda counting method, standardized Borda scores are calculated for the objective ranking results obtained in S4 and the subjective consensus ranking results obtained in S5, respectively. Weighting coefficients are introduced to aggregate the two types of scores, and the ranking position of the reserve projects is determined based on the final weighted composite score.

2. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, In step S4, the corresponding standardization transformation rule is adopted according to the indicator type, specifically as follows: For positive indicators, the formula is used. Standardize; For negative indicators, the formula is used. Standardize; For interval indicators, if the indicator value falls within the preset optimal interval... If the value is within the range, the standardized value is 1; if it is outside the range, the standardized value between 0 and 1 is calculated according to the degree of deviation and a preset rule. in, For the i-th item, the raw data for the j-th indicator. For standardized data, and These are the maximum and minimum values ​​of the j-th indicator across all items, respectively.

3. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, The indicators for the power grid development dimension include the indicator for improving the level of cross-provincial resource allocation, which is calculated using the following formula: ； in, The new inter-provincial power transmission capacity is measured in megawatts (MW). The inter-provincial power transmission capacity is given in the baseline year, and the unit is megawatts. The newly added inter-provincial power transmission volume is expressed in megawatt-hours (MWh). The electricity transmitted across provinces and regions in the baseline year is expressed in megawatt-hours. It represents the dimensionless value for improving the level of cross-provincial resource allocation.

4. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, The indicators for ensuring safe power supply include the main power grid security level improvement indicator, which is calculated using the following formula: ； in, This represents the percentage increase in throughput for N-1 transformers in a 1000 kV power grid. The percentage increase in throughput for N-1 transformers in a 750 kV power grid. This represents the percentage increase in throughput for N-1 transformers in a 500 kV power grid. This represents the percentage increase in throughput for N-1 transformers in a 330 kV power grid. This represents the percentage increase in the throughput of N-1 transformers in a 220 kV power grid. The percentage increase represents the improvement in the safety level of the main grid. Each pass rate increase is the difference between the pass rate of the N-1 safety verification of the grid at that voltage level before and after the project implementation.

5. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, The efficiency and effectiveness dimension indicators include the equipment load balance index, which is calculated using the following formula: ； in, The load factor of the i-th transformer put into operation under typical operating conditions is expressed as a percentage. It is the average load rate of N-2 adjacent transformers in the same power supply area, expressed as a percentage; The number of adjacent transformers, taken as an integer from 3 to 10; The transformer load balance is a dimensionless quantity.

6. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, The service quality dimension indicators include the power distribution network supply capacity improvement indicator, which is calculated using the following formula: ； in, The degree of fit between the 110 kV power grid capacity ratio and the preset reasonable capacity ratio range [1.5, 2.0] is considered when the actual capacity ratio falls within this range. The value is 1. When the actual load ratio deviates from this range, a value between 0 and 1 is calculated according to the degree of deviation and a preset rule. To determine the matching degree between the average transformer capacity per household and the preset reasonable value, when the actual average transformer capacity per household reaches the preset reasonable value... The value is 1; otherwise, the value between 0 and 1 is calculated proportionally. This is a dimensionless value representing the improvement in the power supply capacity of the distribution network. The indicators for the green and low-carbon dimension include the contribution indicator to the dual-carbon goals, which is calculated using the following formula: ； in, Carbon emission reductions for non-fossil energy power generation To reduce carbon emissions by replacing electricity, To improve energy efficiency and reduce carbon emissions, This refers to the carbon emission reduction due to line loss. Carbon emission reductions for electricity generation rights trading Carbon emission reductions in response to electricity demand For supply chain management of carbon emission reductions, the unit for each carbon emission reduction is ton of carbon dioxide equivalent, and the accounting period for each carbon emission reduction is one calendar year after the project is put into operation. The accounting benchmark for each carbon emission reduction is the carbon emission level of the corresponding field before the project is implemented.

7. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, S3 includes the following steps: S31, based on the information entropy theory, calculates the information entropy of each indicator to reflect the amount of information contained in the original data of the indicator, and then derives the weight of each indicator under the entropy weight method. S32, the coefficient of variation is obtained by calculating the ratio of the standard deviation to the mean of each indicator. The magnitude of the coefficient of variation reflects the degree of dispersion of the indicator distribution, and then the weight of each indicator under the coefficient of variation method is determined. S33. The weights obtained by the entropy weight method and the weights obtained by the coefficient of variation method are arithmetically averaged to obtain the objective weights of each indicator.

8. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, S4 includes the following steps: S41, the indicator data after standardization according to the indicator type is multiplied with the objective weights obtained in S3 to construct a weighted normalized matrix that matches the dimension and the number of items. S42, in the weighted normalization matrix, the maximum value is extracted for positive indicators and the minimum value is extracted for negative indicators to form a positive ideal solution, the minimum value is extracted for positive indicators and the maximum value is extracted for negative indicators to form a negative ideal solution, and the ideal solution for interval indicators is determined according to the standardization rules; S43, calculate the distance between the weighted normalized matrix elements corresponding to each reserve project and the positive ideal solution and negative ideal solution respectively using the Euclidean distance formula; S44. Divide the distance of each item from the negative ideal solution by the sum of the distances of the item from the positive ideal solution and the negative ideal solution to obtain the relative proximity, and sort them by size to form an objective ranking result.

9. The method for ranking reserve projects for new power grid infrastructure according to claim 1, characterized in that, The 0-1 type integer programming model constructed in S5 has an objective function that minimizes the inconsistency between the consensus ranking and all expert rankings. This inconsistency is measured by Kendalltau distance. The constraints include that any two items have a mutually exclusive order relationship in the ranking and the transitivity requirement of the ranking. By solving this model, the consensus ranking result with the minimum sum of Kendalltau distances with all expert rankings is obtained.

10. A project ranking system for new power grid infrastructure, characterized in that, Using the project ranking method for new power grid infrastructure as described in any one of claims 1-9 as the operating logic, the method includes: The multi-dimensional indicator system construction unit is used to select indicators from five dimensions—power grid development, power supply security, service quality, efficiency and effectiveness, and green and low-carbon development—and integrate them to form a ranking indicator system. The indicator data acquisition unit, connected to the multi-dimensional indicator system construction unit, is used to collect the original indicator data corresponding to each reserve project and distinguish the indicator types. An objective weight calculation unit is connected to the indicator data acquisition unit and calculates the objective weight of the indicator using a combination of entropy weight method and coefficient of variation method. An objective ranking generation unit is connected to the objective weight calculation unit. It calculates the relative proximity based on the weighted normalization matrix and the TOPSIS method and forms an objective ranking. The subjective ranking generation unit is used to form a subjective consensus ranking by statistical expert opinions and solving an integer programming model. The sorting result aggregation unit is connected to the objective sorting generation unit and the subjective sorting generation unit respectively. It uses the weighted Borda counting method to perform standardized score calculation and weighted aggregation on the objective and subjective sorting results, and outputs the final ranking of the reserve items.

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