Power grid project evaluation method and device

Abstract

The invention relates to the technical field of electric power, and provides a power grid project evaluation method and device. The method comprises the following steps: obtaining index values of a plurality of evaluation indexes of a to-be-evaluated power grid project; searching similar power grid projects from a preset historical project library according to the index values of the plurality of evaluation indexes, and calculating an average evaluation value of each similar power grid project; for each evaluation index, calculating the weight through at least two weight calculation methods; and determining an evaluation value of the to-be-evaluated power grid project closest to the average evaluation value as an evaluation result of the to-be-evaluated power grid project by taking the weighting proportion corresponding to each weight as a variable. The power grid project can be evaluated scientifically, accurately and objectively, and construction of the power grid project is guided.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a power grid project evaluation method and device. Background Art

[0002] In order to ensure the sustainable development of power supply enterprises, it is necessary to scientifically grasp the balance between investment demand and investment capacity, accurately evaluate the investment benefits of construction projects, rationally select investment projects, grasp the timing of project construction, and control the scale of investment.

[0003] The comprehensive evaluation theory system is an effective way to solve this problem, that is, by reasonably evaluating the benefits of different dimensions of project construction, comprehensively evaluating the investment and construction plan of the power grid, and guiding the production and investment decisions of power grid companies. For example, the comparative document CN202310766774.4 proposes a combined weighting method based on hierarchical analysis-entropy weight method, which combines subjective weights and objective weights, fully combining the advantages of both in qualitative and quantitative aspects, and obtaining a more reasonable evaluation result.

[0004] However, in the related art, the subjective weight and the objective weight are combined by presetting a certain ratio for weighted summation, which leads to inaccurate evaluation results. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for evaluating a power grid project, so as to perform a scientific, accurate and objective evaluation on the power grid project and guide the construction of the power grid project.

[0006] In a first aspect, an embodiment of the present invention provides a power grid project evaluation method, comprising: Obtaining index values ​​of multiple evaluation indexes of the power grid project to be evaluated; According to the index values ​​of multiple evaluation indicators, similar power grid projects are found from a preset historical project library, and the average evaluation value of each similar power grid project is calculated; For each evaluation indicator, the weight is calculated by at least two weight calculation methods; The weighted ratio corresponding to each weight is used as a variable to determine the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value, which is taken as the evaluation result of the power grid project to be evaluated.

[0007] In combination with the first aspect, in a possible implementation of the first aspect, searching for similar power grid projects from a preset historical project library according to the index values ​​of multiple evaluation indicators includes: Traverse the same type of power grid projects in the historical project library, calculate the difference in the index value of each evaluation index between each power grid project and the power grid project to be evaluated, and take the power grid projects whose index value differences of each evaluation index are not greater than the preset threshold value corresponding to each evaluation index as similar power grid projects.

[0008] In combination with the first aspect, in a possible implementation of the first aspect, the weighted ratio corresponding to each weight is used as a variable, and the formula for determining the evaluation value of the to-be-evaluated power grid project closest to the average evaluation value is: ; in, N is the number of evaluation indicators; For the i The index value of each evaluation index; , , ...is the first value calculated by different weight calculation methods i The weight of each evaluation indicator; is the weighted ratio corresponding to each weight, are greater than zero and ; is the evaluation value of the power grid project to be evaluated; is the average evaluation value.

[0009] In conjunction with the first aspect, in a possible implementation of the first aspect, the multiple evaluation indicators include: a technical benefit indicator, an economic benefit indicator, and a social and environmental benefit indicator; Technical benefit indicators include at least one of the following: N-1 pass rate, comprehensive line loss rate, capacity-load ratio, line connection rate, power supply reliability and voltage qualification rate; The economic benefit indicators include at least one of the following: power supply increase per unit investment, power loss reduction per unit investment, investment payback period and internal rate of return; Environmental benefit indicators include: electricity savings converted into carbon dioxide emission reductions.

[0010] In combination with the first aspect, in a possible implementation of the first aspect, the at least two weight calculation methods include a hierarchy analysis method and an entropy weight method; For each evaluation indicator, the weight is calculated by at least two weight calculation methods, including: For each evaluation index, the subjective weight of the evaluation index is determined by the hierarchical analysis method, and the objective weight of the evaluation index is determined by the entropy weight method.

[0011] In combination with the first aspect, in a possible implementation of the first aspect, determining the subjective weight of the evaluation index by using a hierarchy analysis method includes: Establish a hierarchical model of evaluation indicators; Compare the evaluation indicators pairwise, determine the relative importance, and construct a judgment matrix; Perform hierarchical single sorting and consistency check on the judgment matrix to obtain the weight factor vector; The weight factor vector is sorted and checked for consistency to obtain the subjective weight of each evaluation indicator.

[0012] In combination with the first aspect, in a possible implementation of the first aspect, the objective weight of each evaluation indicator is determined by an entropy weight method, including: Establish an evaluation matrix of evaluation indicators; Standardize the evaluation matrix and calculate the information entropy of each evaluation index; According to the information entropy, the objective weight of each evaluation index is calculated.

[0013] In conjunction with the first aspect, in a possible implementation of the first aspect, after obtaining the evaluation value of the power grid project to be evaluated, the method further includes: The evaluation value of the power grid project to be evaluated and the index value of each evaluation index are added to the historical project library to expand and update the historical project library.

[0014] In a second aspect, an embodiment of the present invention provides a power grid project evaluation device, including: An acquisition module, used to acquire the index values ​​of multiple evaluation indicators of the power grid project to be evaluated; A screening module is used to search for similar power grid projects from a preset historical project library according to the index values ​​of multiple evaluation indicators, and calculate the average evaluation value of each similar power grid project; The calculation module is used to calculate the weight for each evaluation index by at least two weight calculation methods; taking the weighted ratio corresponding to each weight as a variable, determining the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value as the evaluation result of the power grid project to be evaluated.

[0015] In conjunction with the second aspect, in a possible implementation manner of the second aspect, the computing module is used to: according to Determine the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value; in, N is the number of evaluation indicators; For the i The index value of each evaluation index; , , ...is the first value calculated by different weight calculation methods i The weight of each evaluation indicator; is the weighted ratio corresponding to each weight, are greater than zero and ; is the evaluation value of the power grid project to be evaluated; is the average evaluation value.

[0016] It can be seen from the above technical scheme that the present invention has the following advantages: according to the index values ​​of multiple evaluation indicators of the power grid project to be evaluated, similar power grid projects are found from the historical project library, and the average evaluation value of each similar power grid project is calculated; then the average evaluation value is used as the optimal standard, the weighted ratio of weights calculated by different methods is optimized to obtain the optimal weighted ratio, and the weights calculated by different methods are integrated using the optimal weighted ratio to obtain a more reasonable comprehensive weight, thereby improving the accuracy of power grid project evaluation.

[0017] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 It is a schematic diagram of the implementation flow of the power grid project evaluation method provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a power grid project evaluation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0021] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0022] In order to ensure the high-quality development of the power grid and meet the new construction needs under the "three types and two networks" environment, the demand for power grid development is still strong and the investment demand is still large, mainly including the following aspects: First, the power grid development has entered a new stage of "three types and two networks" construction, and there are a large number of new investment and construction development needs such as electric vehicle supporting, integrated energy system, and clean energy delivery; second, some areas still have safety hazards and weak links, and provincial power transmission main grids such as ultra-high voltage still need to be further optimized and improved. At present, the operating situation of power grid companies is becoming increasingly severe. The practice of pursuing investment scale, ignoring investment capacity, and ignoring production efficiency can no longer meet the new development requirements. Therefore, how to make scientific and reasonable decisions on power grid investment and improve the accuracy of project arrangements is crucial to the sustainable development of enterprises.

[0023] The complex internal and external development situation and changes in the investment environment have put forward higher requirements for power grid investment management. Power grid companies must not only assume the social responsibilities that they should assume as public utility enterprises, but also ensure their own healthy and sustainable development. Under the pressure of increasingly tight investment capacity, they must not only try their best to meet various external rigid investment needs, but also try their best to achieve better investment benefits, maintain healthy business operations, and improve investment management efficiency. Therefore, this paper systematically studies the quantitative evaluation and optimization ranking methods of power grid reserve projects, and applies the results to guide power grid companies to make scientific and reasonable investment decisions, calculate the optimal construction quantity of power grid reserve projects, and maximize the benefits of limited funds.

[0024] Commonly used weighting methods in power grid project evaluation include analytic hierarchy process, Delphi method, mean square error method, combined weighting method, etc. The combined weighting method can first obtain subjective weights, use the degree of membership of each indicator to obtain objective weights, and modify the obtained subjective weights to form variable comprehensive weights. However, the combined weighting methods in related technologies are relatively simple, such as the multiplication synthesis normalization method and the linear combination method, which have the following shortcomings: the use of the multiplication synthesis normalization method may result in a multiplication effect, that is, the larger of the various indicators will be larger and the smaller will be smaller; while the linear weighting method will not result in a multiplication effect, but it is difficult to determine the weighting coefficient. In view of the shortcomings of the existing methods, an embodiment of the present invention proposes a power grid project evaluation method to obtain a more scientific, reasonable and accurate evaluation result.

[0025] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] Figure 1Schematic diagram of the implementation process of the power grid project evaluation method provided by the embodiment of the present invention. Figure 1 As shown, the method includes: Step S101, obtaining index values ​​of multiple evaluation indexes of a power grid project to be evaluated.

[0027] In this embodiment, relying on big data statistical analysis, the internal and external influencing factors of distribution network investment are identified, the evaluation indicators are selected using the inductive and deductive method, and an evaluation indicator system is constructed.

[0028] The evaluation index system is a two-level index system, which fully considers the actual effects brought about by the construction of the distribution network and evaluates from different dimensions of technical benefits, economic benefits, and social and environmental benefits.

[0029] Technical benefit indicators may include, but are not limited to: N-1 pass rate, comprehensive line loss rate, capacity-load ratio, line connection rate, power supply reliability and voltage qualification rate. The N-1 pass rate means that when any line is disconnected, it can cause other lines in the system to be overloaded or the system to be disconnected, indicating that the network does not meet the N-1 test. The comprehensive line loss rate refers to the total line loss in an area. The capacity-load ratio is the ratio of the total capacity of the substation equipment in a certain power supply area to the maximum load (network supply load) of the power supply area. It indicates the relationship between the installed capacity and the maximum actual operating capacity of the area, the station or the transformer, and reflects the capacity reserve situation. The line connection rate is equal to the number of reasonable lines divided by the total number of lines and then multiplied by 100%. Power supply reliability can be measured by the average power outage time of users, the average number of power outages of users, and the equivalent hours of system power outages, which are not limited in this embodiment. The voltage qualification rate refers to the percentage of the cumulative operating time (minutes) of the actual operating voltage within the allowable voltage deviation range to the corresponding total operating statistical time (minutes).

[0030] The economic benefit index may include but is not limited to: power supply increase per unit investment, power loss reduction per unit investment, investment payback period and internal rate of return, etc. The calculation method of these indexes can be set by oneself or realized by using existing methods, and will not be described in detail in this embodiment.

[0031] Environmental benefit indicators may include but are not limited to: electricity saving converted to carbon dioxide emission reduction, etc. According to expert statistics: for every kilowatt-hour of electricity saved, 0.4 kg of standard coal is saved accordingly, and pollution emissions are reduced by 0.272 kg of carbon dust, 0.997 kg of carbon dioxide, 0.03 kg of sulfur dioxide, and 0.015 kg of nitrogen oxides. Therefore, carbon dioxide emission reduction can be calculated based on electricity saving.

[0032] After the scheme of the power grid project to be evaluated is determined, the index values ​​of these evaluation indicators can be calculated and predicted by existing methods, which will not be described in detail in this embodiment.

[0033] Step S102, searching similar power grid projects from a preset historical project library according to the index values ​​of the plurality of evaluation indexes, and calculating the average evaluation value of each similar power grid project.

[0034] In this embodiment, a historical project library is constructed, which stores a large amount of information on accurately evaluated power grid projects. By finding power grid projects similar to the power grid project to be evaluated, the evaluation values ​​of similar power grid projects can be used as evaluation references.

[0035] Step S103: For each evaluation index, a weight is calculated using at least two weight calculation methods.

[0036] In this embodiment, at least two weight calculation methods can be the analytic hierarchy process, the Delphi method, the mean square error method, etc. It is usually impossible to accurately evaluate through a single weighting method. For example, from the establishment of the hierarchical model to the provision of the judgment matrix, the subjective factor has a great influence on the entire process of the analytic hierarchy process. The weight of each layer of factors is given subjectively, which will affect the generalization in some cases. The entropy weight method can avoid the drawbacks of subjective weighting and the problem of overlapping multi-indicator data, but the disadvantage is that it ignores the importance of the indicator itself. Sometimes the determined indicator weight will be far from the expected result. At the same time, the entropy value method cannot reduce the dimension of the evaluation indicator. It can be seen that combining multiple methods for combined weighting is a better choice.

[0037] Step S104, taking the weighted ratio corresponding to each weight as a variable, determining the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value as the evaluation result of the power grid project to be evaluated.

[0038] By averaging the evaluation values ​​of similar power grid projects, the average value can basically reflect the evaluation of similar projects. This average value is used as a benchmark to determine the weighted ratio closest to the average evaluation value. As a comprehensive weight, , The first two weights are calculated using two different weight calculation methods. i The weight of the evaluation index, is the weighted ratio, by adjusting The value of the power grid project to be evaluated is obtained by Evaluation value , as the evaluation value of the power grid project to be evaluated. That is: Taking the weighted ratio corresponding to each weight as a variable, the formula for determining the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value is: ; in, N is the number of evaluation indicators; For the iThe index value of each evaluation index; , , ...is the first value calculated by different weight calculation methods i The weight of each evaluation indicator; is the weighted ratio corresponding to each weight, are greater than zero and ; is the evaluation value of the power grid project to be evaluated; is the average evaluation value.

[0039] here, are all greater than zero in order to integrate the results of each weight calculation method. In practical applications, Set a range constraint, for example, all are between [0.1-0.9].

[0040] In this way, the setting of the weighted ratio can be optimized with an optimal benchmark, which is more reasonable and accurate than the setting of the same weighted ratio for each weight in the prior art.

[0041] It can be seen that the embodiment of the present invention searches for similar power grid projects from the historical project library according to the index values ​​of multiple evaluation indicators of the power grid project to be evaluated, and calculates the average evaluation value of each similar power grid project; then, the average evaluation value is used as the optimal standard to optimize the weighted ratio of weights calculated by different methods to obtain the optimal weighted ratio, and the weights calculated by different methods are integrated using the optimal weighted ratio to obtain a more reasonable comprehensive weight, thereby improving the accuracy of power grid project evaluation.

[0042] As a possible implementation method, based on the index values ​​of multiple evaluation indicators, similar power grid projects are searched from a preset historical project library, which may include: Traverse the same type of power grid projects in the historical project library, calculate the difference in the index value of each evaluation index between each power grid project and the power grid project to be evaluated, and take the power grid projects whose index value differences of each evaluation index are not greater than the preset threshold value corresponding to each evaluation index as similar power grid projects.

[0043] There are many types of power grid projects, including power grid infrastructure projects and technical transformation projects. Power grid projects of the same type are more valuable for reference. In this embodiment, different evaluation indicators can be set for different types of power grid projects, and power grid projects of the same type have the same evaluation indicators. For power grid projects of the same type, similar power grid projects are screened by calculating the difference between each evaluation indicator and the indicator value of the power grid project to be evaluated. Moreover, all evaluation indicators must meet the corresponding preset threshold requirements to be similar power grid projects. If any evaluation indicator is not met, it cannot be classified as a similar power grid project.

[0044] As a possible implementation, after obtaining the evaluation value of the power grid project to be evaluated in step S104, the following may also be included: The evaluation value of the power grid project to be evaluated and the index value of each evaluation index are added to the historical project library to expand and update the historical project library.

[0045] By expanding and updating the historical project library, the historical project library can be continuously enriched to improve the accuracy of subsequent evaluations. In addition, after the power grid project is implemented, a post-evaluation of the power grid project can be carried out to obtain more accurate evaluation results, thereby replacing the original evaluation results.

[0046] As a possible implementation, at least two weight calculation methods include a hierarchy analysis method and an entropy weight method; for each evaluation indicator, the weight is calculated by at least two weight calculation methods, including: For each evaluation index, the subjective weight of the evaluation index is determined by the hierarchical analysis method, and the objective weight of the evaluation index is determined by the entropy weight method.

[0047] This embodiment considers combining the hierarchical analysis method with the entropy weight method to determine the weight of the evaluation index. The hierarchical analysis method decomposes the problem into different components according to the nature of the problem and the overall goal to be achieved, and clusters and combines the factors at different levels according to the mutual correlation and affiliation between the factors to form a multi-level analysis structure model, so that the problem is ultimately reduced to the determination of the relative importance weight of the lowest level relative to the highest level or the ranking of the relative order of superiority and inferiority.

[0048] The subjective weights of the evaluation indicators are determined by the hierarchical analysis method, including: (1) Establish a hierarchical model of evaluation indicators. In this hierarchical evaluation model, we need to confirm the goal layer, criterion layer, and solution layer of the entire decision-making event.

[0049] (2) Compare the evaluation indicators in pairs, determine the relative importance, and construct a judgment matrix. When determining the weights between factors at each level, if the results are only qualitative, they are often not easily accepted by others. The consistent matrix method is: do not compare all factors together, but compare them two by two. When comparing, use a relative scale to minimize the difficulty of comparing factors of different natures and improve accuracy. The importance can be expressed by 1-9, and the scale table is shown in Table 1: Table 1

[0050] use Representation and indicators compared to, The importance of is obtained, and the judgment matrix is ​​shown in Table 2.

[0051] Table 2

[0052] (3) Perform hierarchical single sorting and consistency check on the judgment matrix to obtain the weight factor vector.

[0053] Simply put, hierarchical single sorting is to solve the weight of each evaluation index based on the judgment matrix. That is, calculate the mth power of the product of each row to obtain an m-dimensional vector:

[0054] Normalizing the vector into a weight vector gives us the weight:

[0055] After obtaining the weight matrix, the maximum characteristic root can be calculated, and the formula is:

[0056] After the maximum eigenvalue is solved, the CI value can be calculated, CI = (λmax - n) / (n-1), and then the corresponding average random consistency index RI is found, and then the consistency ratio CR = CI / RI is calculated. If CR < 0.1, it can be considered that the consistency of the judgment matrix is ​​acceptable.

[0057] (4) Perform hierarchical total sorting and consistency test on the weight factor vector to obtain the subjective weight of each evaluation indicator.

[0058] Calculating the relative importance of all factors at a certain level to the highest level (target level) is called the total ranking of the level. This process is carried out from the highest level to the lowest level.

[0059] The basic idea of ​​the entropy weight method is to determine the objective weight according to the variability of the indicator. Generally speaking, if the information entropy of an indicator is smaller, it means that the variability of the indicator is greater, the amount of information provided is more, the role it can play in the comprehensive evaluation is greater, and its weight is greater. On the contrary, the greater the information entropy of an indicator, the smaller the variability of the indicator is, the less information provided is, the smaller the role it plays in the comprehensive evaluation is, and the smaller its weight is.

[0060] The objective weight of each evaluation index is determined by the entropy weight method, including: Establish an evaluation matrix of evaluation indicators; Standardize the evaluation matrix and calculate the information entropy of each evaluation index; According to the information entropy, the objective weight of each evaluation index is calculated.

[0061] Here, each indicator is first de-dimensionalized. Assuming that m evaluation indicators are given, the standardized value of each indicator data is ,but: Positive indicators: .

[0062] Negative indicators: .

[0063] Find the ratio of each indicator under each scheme: , establish an evaluation matrix.

[0064] Calculate the information entropy of each indicator: .

[0065] The weight of each indicator is calculated by information entropy: , where k refers to the number of indicators, that is, k=m.

[0066] The weight is calculated by calculating the information redundancy: .

[0067] Then calculate the indicator weights: .

[0068] Finally, calculate the comprehensive score of each solution: .

[0069] Combining subjective weights with objective weights can absorb the advantages of both methods while overcoming their respective shortcomings, achieving complementary advantages and obtaining more reasonable and scientific evaluation results. And by optimizing the weight ratio of the two methods, the accuracy can be further improved.

[0070] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0071] Figure 2 is a schematic diagram of the structure of a power grid project evaluation device 20 provided in an embodiment of the present invention, see Figure 2 As shown, the device comprises: The acquisition module 21 is used to acquire the index values ​​of multiple evaluation indicators of the power grid project to be evaluated.

[0072] The screening module 22 is used to search for similar power grid projects from a preset historical project library according to the index values ​​of multiple evaluation indicators, and calculate the average evaluation value of each similar power grid project.

[0073] The calculation module 23 is used to calculate the weight for each evaluation index by at least two weight calculation methods; taking the weighted ratio corresponding to each weight as a variable, determine the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value as the evaluation result of the power grid project to be evaluated.

[0074] As a possible implementation, the screening module 22 is specifically used for: Traverse the same type of power grid projects in the historical project library, calculate the difference in the index value of each evaluation index between each power grid project and the power grid project to be evaluated, and take the power grid projects whose index value differences of each evaluation index are not greater than the preset threshold value corresponding to each evaluation index as similar power grid projects.

[0075] As a possible implementation method, the computing module is specifically used for: according to Determine the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value; in, N is the number of evaluation indicators; For the i The index value of each evaluation index; , , ...is the first value calculated by different weight calculation methods i The weight of each evaluation indicator; is the weighted ratio corresponding to each weight, are greater than zero and ; is the evaluation value of the power grid project to be evaluated; is the average evaluation value.

[0076] As a possible implementation method, multiple evaluation indicators include: technical benefit indicators, economic benefit indicators, and social and environmental benefit indicators; Technical benefit indicators include at least one of the following: N-1 pass rate, comprehensive line loss rate, capacity-load ratio, line connection rate, power supply reliability and voltage qualification rate; The economic benefit indicators include at least one of the following: power supply increase per unit investment, power loss reduction per unit investment, investment payback period and internal rate of return; Environmental benefit indicators include: electricity savings converted into carbon dioxide emission reductions.

[0077] As a possible implementation, at least two weight calculation methods include a hierarchical analysis method and an entropy weight method; the calculation module 23 is specifically used for: For each evaluation index, the subjective weight of the evaluation index is determined by the hierarchical analysis method, and the objective weight of the evaluation index is determined by the entropy weight method.

[0078] As a possible implementation manner, the calculation module 23 is specifically used for: Establish a hierarchical model of evaluation indicators; Compare the evaluation indicators pairwise, determine the relative importance, and construct a judgment matrix; Perform hierarchical single sorting and consistency check on the judgment matrix to obtain the weight factor vector; The weight factor vector is sorted and checked for consistency to obtain the subjective weight of each evaluation indicator.

[0079] As a possible implementation manner, the calculation module 23 is specifically used for: Establish an evaluation matrix of evaluation indicators; Standardize the evaluation matrix and calculate the information entropy of each evaluation index; According to the information entropy, the objective weight of each evaluation index is calculated.

[0080] As a possible implementation, after obtaining the evaluation value of the power grid project to be evaluated, the calculation module 23 is further used to: The evaluation value of the power grid project to be evaluated and the index value of each evaluation index are added to the historical project library to expand and update the historical project library.

[0081] The embodiment of the present invention searches for similar power grid projects from a historical project library based on the index values ​​of multiple evaluation indicators of the power grid project to be evaluated, and calculates the average evaluation value of each similar power grid project; then, the average evaluation value is used as the optimal standard to optimize the weighted ratio of weights calculated by different methods to obtain the optimal weighted ratio, and the weights calculated by different methods are integrated using the optimal weighted ratio to obtain a more reasonable comprehensive weight, thereby improving the accuracy of power grid project evaluation.

[0082] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0084] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A power grid project evaluation method, characterized in that: The steps include: Obtaining index values ​​of multiple evaluation indexes of the power grid project to be evaluated; According to the index values ​​of the plurality of evaluation indexes, similar power grid projects are searched from a preset historical project library, and an average evaluation value of each similar power grid project is calculated; For each evaluation indicator, the weight is calculated by at least two weight calculation methods; The weighted ratio corresponding to each weight is used as a variable to determine the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value as the evaluation result of the power grid project to be evaluated.

2. The power grid project evaluation method according to claim 1, characterized in that: According to the index values ​​of the multiple evaluation indexes, searching for similar power grid projects from a preset historical project library includes: Traverse the same type of power grid projects in the historical project library, calculate the difference in index value of each evaluation index between each power grid project and the power grid project to be evaluated, and take the power grid projects whose index value differences of each evaluation index are not greater than the preset threshold values ​​corresponding to each evaluation index as similar power grid projects.

3. The power grid project evaluation method according to claim 2, characterized in that: Taking the weighted ratio corresponding to each weight as a variable, the formula for determining the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value is: ； in, N is the number of evaluation indicators; For the i The index value of each evaluation index; , , ...is the first value calculated by different weight calculation methods i The weight of each evaluation indicator; is the weighted ratio corresponding to each weight, are greater than zero and ; is the evaluation value of the power grid project to be evaluated; is the average evaluation value.

4. The power grid project evaluation method according to claim 3, characterized in that: Multiple evaluation indicators include: technical benefit indicators, economic benefit indicators and social and environmental benefit indicators; The technical benefit indicators include at least one of the following: N-1 pass rate, comprehensive line loss rate, capacity-load ratio, line connection rate, power supply reliability and voltage qualification rate; The economic benefit index includes at least one of the following: power supply increase per unit investment, power loss reduction per unit investment, investment payback period and internal rate of return; The environmental benefit indicators include: electricity saving converted into carbon dioxide emission reduction.

5. The power grid project evaluation method according to any one of claims 1 to 4, characterized in that: At least two weight calculation methods include the analytic hierarchy process and the entropy weight method; For each evaluation indicator, the weight is calculated by at least two weight calculation methods, including: For each evaluation index, the subjective weight of the evaluation index is determined by the hierarchical analysis method, and the objective weight of the evaluation index is determined by the entropy weight method.

6. The power grid project evaluation method according to claim 5, characterized in that: The subjective weights of the evaluation indicators are determined by the hierarchical analysis method, including: Establish a hierarchical model of evaluation indicators; Compare the evaluation indicators pairwise, determine the relative importance, and construct a judgment matrix; Performing hierarchical single sorting and consistency check on the judgment matrix to obtain a weight factor vector; The weight factor vector is subjected to hierarchical total sorting and consistency check to obtain the subjective weight of each evaluation index.

7. The power grid project evaluation method according to claim 6, characterized in that: The objective weight of each evaluation index is determined by the entropy weight method, including: Establish an evaluation matrix of evaluation indicators; Standardizing the evaluation matrix and calculating the information entropy of each evaluation index; According to the information entropy, the objective weight of each evaluation index is calculated.

8. The power grid project evaluation method according to any one of claims 1 to 4, characterized in that: After obtaining the evaluation value of the power grid project to be evaluated, the method further includes: The evaluation value of the power grid project to be evaluated and the index value of each evaluation index are added to the historical project library to expand and update the historical project library.

9. A power grid project evaluation device, characterized in that: include: An acquisition module, used to acquire the index values ​​of multiple evaluation indicators of the power grid project to be evaluated; A screening module, used to search for similar power grid projects from a preset historical project library according to the index values ​​of the multiple evaluation indicators, and calculate the average evaluation value of each similar power grid project; The calculation module is used to calculate the weight for each evaluation indicator by at least two weight calculation methods; taking the weighted ratio corresponding to each weight as a variable, determining the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value as the evaluation result of the power grid project to be evaluated.

10. The power grid project evaluation device according to claim 9, characterized in that: The calculation module is specifically used for: according to Determine the evaluation value of the power grid project to be evaluated that is closest to the average evaluation value; in, N is the number of evaluation indicators; For the i The index value of each evaluation index; , , ...is the first value calculated by different weight calculation methods i The weight of each evaluation indicator; is the weighted ratio corresponding to each weight, are greater than zero and ; is the evaluation value of the power grid project to be evaluated; is the average evaluation value.

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