A power distribution network planning evaluation method and system

Abstract

The application discloses a novel power distribution network planning evaluation method and system, and relates to the technical field of power system optimization and energy management, which comprises the following steps: obtaining original data of each index in a novel power distribution network index system, and performing standardization and dimensionless pretreatment on the index data; performing subjective weighting through a DEMATEL method, performing objective weighting through a CRITIC method, and performing subjective and objective comprehensive weighting based on optimization of weight coefficients according to a game theory; and performing novel power distribution network planning evaluation sorting based on a VIKOR method. The method improves data pretreatment quality, lays a solid foundation for subsequent weight assignment and evaluation sorting, accurately depicts the beneficial effects of mutual interaction between indexes, improves the rationality and reliability of weight assignment, fully considers the difference and conflict of index data, ensures the scientificity of objective weight assignment, improves the accuracy of objective weight assignment, and provides an important basis for comprehensive weighting.

Description

Technical Field

[0001] This invention relates to the field of power system planning and energy management technology, specifically to a distribution network planning and evaluation method and system. Background Technology

[0002] With the rapid development of my country's economy, the distribution network has become an increasingly important part of the national economy. As the foundation of economic development, the planning and evaluation of the distribution network is particularly important. In the past few decades, the technology of distribution network planning and evaluation has made great progress. Researchers have proposed a variety of evaluation methods and models, such as the analytic hierarchy process, fuzzy comprehensive evaluation method, and data envelopment analysis method. These methods have improved the rationality and economy of distribution network planning to a certain extent. However, with the acceleration of the process of intelligentization and informatization, existing technologies still have many shortcomings in dealing with the complex and ever-changing needs of distribution network planning and evaluation.

[0003] However, existing technologies have significant shortcomings in the preprocessing of indicator data. In the process of power distribution network planning and evaluation, numerous indicators are involved, and the data sources are diverse. How to effectively standardize and dimensionless these data is crucial to ensuring the accuracy of the evaluation results. Existing technologies often overlook this point, resulting in a lack of reliability and comparability in the evaluation results. The choice of weighting method has a significant impact on the evaluation results. Existing weighting methods mainly include subjective weighting and objective weighting, but most studies have failed to effectively combine the two, resulting in certain subjective biases or objective distortions in the evaluation results. Existing evaluation methods do not consider comprehensive weighting strategies, especially lacking the application of game theory to optimize weight coefficients, resulting in low accuracy of the evaluation results. In the ranking process, existing evaluation methods often fail to fully consider the preferences and actual needs of decision-makers, making it difficult for the evaluation results to guide practical operations. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing power system optimization and energy management technologies suffer from insufficient preprocessing of indicator data, a loose combination of subjective and objective weighting methods, and inadequate consideration of comprehensive weighting strategies, as well as the problem of how to achieve efficient and accurate evaluation and ranking.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a distribution network planning and evaluation method, comprising acquiring the original data of each indicator in the distribution network indicator system, performing standardization and dimensionless preprocessing on the indicator data; performing subjective weighting using the DEMATEL method, objective weighting using the CRITIC method, and performing comprehensive subjective and objective weighting based on game theory optimization of weight coefficients; and ranking the distribution network planning and evaluation based on the VIKOR method.

[0007] As a preferred embodiment of the power distribution network planning and evaluation method described in this invention, the standardization and dimensionless preprocessing of the indicator data includes preprocessing benefit-type indicators, cost-type indicators, and centrality-type indicators to obtain an n*m dimensional standardized data matrix, where n is the number of indicators and m is the number of scheme samples. The indicators are then compared and analyzed. The benefit-type indicator processing formula is expressed as:

[0008]

[0009] Where, x ij Let represent the original value of the i-th indicator in the j-th scheme, min(x i ) represents the minimum value of the i-th index among all possible solutions, max(x) i () represents the maximum value of the i-th index among all possible solutions. This represents the standardized value of the i-th benefit-type indicator. The formula for processing cost-type indicators is expressed as follows:

[0010]

[0011] The centralized index processing formula is expressed as follows:

[0012]

[0013] m = min(x) i M = max(x) i )

[0014] As a preferred embodiment of the power distribution network planning and evaluation method described in this invention, the subjective weighting using the DEMATEL method includes: analyzing the logical and direct influence relationships between various elements in the system through expert analysis, revealing the inherent causal relationships of the system and identifying key factors, determining the degree of direct influence between various indicators, and constructing a standardized influence matrix X, expressed as:

[0015]

[0016] Among them, a ij Let represent the degree of influence of indicator i on indicator j, with values ​​{0, 1, 2, 3, 4}. Larger values ​​indicate greater influence. n represents the number of indicators, and max represents the maximum summation by rows or columns. A normalized influence matrix is ​​achieved. The comprehensive influence matrix Y is calculated through matrix transformation, where E is the identity matrix, represented as:

[0017]

[0018] Among them, b ijThis represents the overall influence of the i-th indicator on the j-th indicator. The influence degree D of each indicator is calculated from the overall influence matrix. i Influence level C i Centrality M i and causal degree R i , represented as:

[0019]

[0020] M i =D i +C i

[0021] R i =D i -C i

[0022] The Dematel subjective weight w for each indicator is calculated using centrality. si , represented as:

[0023]

[0024] Where n represents the number of indicators.

[0025] As a preferred embodiment of the power distribution network planning and evaluation method described in this invention, the calculation of objective weights using the CRITIC method includes calculating the standard deviation S of each indicator. i To reflect the differences in indicator data, it is expressed as:

[0026]

[0027] in, This represents the average score of all options after standardization for the i-th indicator, and also calculates the correlation coefficient R' between the indicators to reflect the conflict between them. i , represented as:

[0028]

[0029] Where, r ij This represents the correlation coefficient between indicator i and indicator j. and Let i and j represent the standardized scores of the i-th and j-th indicators, respectively. The information content C of the indicators is calculated through indicator differences and conflicts. i , represented as:

[0030]

[0031] Calculate the objective weight w of the indicator oi , represented as:

[0032]

[0033] in, This represents the standardized value of the i-th benefit-type indicator.

[0034] As a preferred embodiment of the power distribution network planning and evaluation method described in this invention, the subjective and objective comprehensive weighting based on game theory optimization weight coefficients includes combining the subjective dual weights and objective weights through a linear combination to obtain the final subjective and objective combined weights, expressed as:

[0035] w=λ1w s +λ2w o

[0036] Where λ1 and λ2 represent the weight coefficients of subjective weight and objective weight, respectively, and w is the combined weight. s For subjective weighting, w o To determine the objective weights, an objective function is constructed based on a game theory mathematical model. After differentiation, two weight coefficients can be obtained, and the weight coefficients are normalized, expressed as:

[0037]

[0038] Where T represents the transpose operation. These are the normalized weight coefficients. Substituting them into the weight coefficients used in the optimization solution yields the combined weight w, expressed as:

[0039]

[0040] Among them, w o For objective weighting.

[0041] As a preferred embodiment of the distribution network planning and evaluation method described in this invention, the distribution network planning and evaluation ranking based on the VIKOR method includes normalizing the original index data matrix using a data preprocessing formula to obtain a normalized matrix X. In the normalized matrix, the optimal and worst values ​​of the positive and negative ideal solution scheme indicators are the maximum and minimum values ​​of the index data, expressed as:

[0042]

[0043] Calculate the group utility value S' and individual regret value R” of the planning scheme. j , represented as:

[0044]

[0045] The compromise decision value Q is calculated, where u is the decision mechanism coefficient, ranging from [0-1]. This represents the balance between maximizing group utility and minimizing individual regret, expressed as:

[0046]

[0047] The optimal ranking of the selected schemes is determined by the compromise decision value Q.

[0048] Another objective of this invention is to provide a power distribution network planning and evaluation system that can perform subjective weighting using the DEMATEL method, objective weighting using the CRITIC method, and comprehensive subjective and objective weighting based on game theory optimization of weight coefficients. This solves the problems of low accuracy in weighting, inaccurate understanding of evaluation models, and inaccurate identification of key factors in current power system planning and energy management technologies.

[0049] As a preferred embodiment of the distribution network planning and evaluation system described in this invention, it includes: a data preprocessing module, a comprehensive weighting module, and a scheme scoring and selection module; the data preprocessing module is used to acquire the original data of each indicator in the distribution network indicator system, and to perform standardization and dimensionless preprocessing on the indicator data; the comprehensive weighting module is used to perform subjective weighting using the DEMATEL method, objective weighting using the CRITIC method, and comprehensive subjective and objective weighting based on game theory optimization of weight coefficients; the scheme scoring and selection module is used for distribution network planning evaluation ranking based on the VIKOR method.

[0050] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a distribution network planning and evaluation method.

[0051] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a power distribution network planning and evaluation method.

[0052] The beneficial effects of this invention are as follows: The distribution network planning and evaluation method provided by this invention obtains the original data of each indicator in the distribution network indicator system and performs standardization and dimensionless preprocessing on these data. This achieves data consistency and comparability, eliminates the influence of dimensions and differences in numerical magnitude between different indicators, ensures the accuracy and scientific nature of the subsequent evaluation process, improves the quality of data preprocessing, and lays a solid foundation for subsequent weight assignment and evaluation ranking. Subjective weighting using the DEMATEL method enables in-depth analysis of the inherent causal relationships of each element in the indicator system. By constructing a comprehensive influence matrix and calculating influence degree, influenced degree, centrality, and causal degree, the method achieves the identification and quantification of key indicators. The beneficial effects of this method are that it not only improves the transparency of weight assignment, but also enhances the practical guiding significance of the evaluation results through expert system analysis. Objective weighting is performed using the CRITIC method, calculating the standard deviation and correlation coefficient of each indicator to obtain the information content of the indicators. It fully considers the differences and conflicts in indicator data, ensuring the scientific nature of objective weight assignment and improving its accuracy. This provides an important basis for comprehensive weighting. By optimizing the weight coefficients based on game theory and coordinating the relationship between subjective and objective factors, the evaluation results are made more accurate and authentic. This invention achieves better results in terms of objectivity in the evaluation process, rationality of weight allocation, and practicality of the evaluation results. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 The first embodiment of the present invention provides an overall flowchart of a power distribution network planning and evaluation method.

[0055] Figure 2 The following is a flowchart of a power distribution network planning and evaluation system provided for the third embodiment of the present invention. Detailed Implementation

[0056] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0057] Example 1, referring to Figure 1As an embodiment of the present invention, a distribution network planning and evaluation method is provided, comprising:

[0058] S1: Obtain the original data of each indicator in the power distribution network indicator system, and perform standardization and dimensionless preprocessing on the indicator data.

[0059] Furthermore, the standardization and dimensionless preprocessing of the indicator data includes preprocessing benefit-type indicators, cost-type indicators, and centralizing indicators to obtain an n*m dimensional standardized data matrix, where n is the number of indicators and m is the number of sample schemes. The indicators are then compared and analyzed. The processing formula for benefit-type indicators is expressed as:

[0060]

[0061] Where, x ij Let represent the original value of the i-th indicator in the j-th scheme, min(x i ) represents the minimum value of the i-th index among all possible solutions, max(x) i () represents the maximum value of the i-th index among all possible solutions. This represents the standardized value of the i-th benefit-type indicator. The formula for processing cost-type indicators is expressed as follows:

[0062]

[0063] The centralized index processing formula is expressed as follows:

[0064]

[0065] m = min(x) i M = max(x) i )

[0066] It should be noted that by acquiring the original data of each indicator in the power distribution network indicator system and performing standardization and dimensionless preprocessing on these data, the consistency and comparability of the data were achieved, the influence of dimensions and differences in numerical values ​​between different indicators were eliminated, the accuracy and scientific nature of the subsequent evaluation process were ensured, the quality of data preprocessing was improved, and a solid foundation was laid for subsequent weight assignment and evaluation ranking.

[0067] S2: Subjective weighting is performed using the DEMATEL method, objective weighting is performed using the CRITIC method, and a comprehensive subjective and objective weighting is performed based on game theory optimization of weight coefficients.

[0068] Furthermore, subjective weighting using the DEMATEL method involves expert analysis of the logical and direct influence relationships between various elements in the system, revealing the system's inherent causal relationships and identifying key factors. This determines the degree of direct influence between each indicator and constructs a normalized influence matrix X, represented as follows:

[0069]

[0070] Among them, a ij Let represent the degree of influence of indicator i on indicator j, with values ​​{0, 1, 2, 3, 4}. Larger values ​​indicate greater influence. n represents the number of indicators, and max represents the maximum summation by rows or columns. A normalized influence matrix is ​​achieved. The comprehensive influence matrix Y is calculated through matrix transformation, where E is the identity matrix, represented as:

[0071]

[0072] Among them, b ij This represents the overall influence of the i-th indicator on the j-th indicator. The influence degree D of each indicator is calculated from the overall influence matrix. i Influence level C i Centrality M i and causal degree R i , is represented as:

[0073]

[0074] M i =D i +C i

[0075] R i =D i -C i

[0076] The Dematel subjective weight w for each indicator is calculated using centrality. si , is represented as:

[0077]

[0078] Where n represents the number of indicators.

[0079] It should be noted that subjective weighting using the DEMATEL method achieves a systematic utilization of expert knowledge, reveals the logical relationships and direct influence relationships between various elements in the system, identifies key factors, constructs a standardized influence matrix, and thus determines the degree of direct influence between each indicator. This step achieves the beneficial effect of accurately characterizing the interactions between indicators, improving the rationality and reliability of weight assignment.

[0080] Furthermore, the objective weights are calculated using the CRITIC method, including calculating the standard deviation S of each indicator. i To reflect the differences in indicator data, it is expressed as:

[0081]

[0082] in, This represents the average score of all options after standardization for the i-th indicator, and also calculates the correlation coefficient R' between the indicators to reflect the conflict between them. i , is represented as:

[0083]

[0084] Where, r ij This represents the correlation coefficient between indicator i and indicator j. and Let i and j represent the standardized scores of the i-th and j-th indicators, respectively. The information content C of the indicators is calculated through indicator differences and conflicts. i , is represented as:

[0085]

[0086] Calculate the objective weight w of the indicator oi , is represented as:

[0087]

[0088] in, This represents the standardized value of the i-th benefit-type indicator.

[0089] It should also be noted that by using the CRITIC method for objective weighting, effective and reasonable comparison and analysis of benefit-type indicators, cost-type indicators, and neutral indicators are conducted. The standard deviation and correlation coefficient of each indicator are calculated to obtain the information content of the indicators. This fully considers the differences and conflicts in indicator data, ensures the scientific nature of objective weighting, improves the accuracy of objective weighting, and provides an important basis for comprehensive weighting.

[0090] Furthermore, the subjective and objective combined weighting based on game theory optimization coefficients includes combining the subjective and objective weights through linear combination to obtain the final subjective and objective combined weights, expressed as:

[0091] w=λ1w s +λ2w o

[0092] Where λ1 and λ2 represent the weight coefficients of subjective and objective weights, respectively, and w is the combined weight, W s For subjective weighting, w oTo determine the objective weights, an objective function is constructed based on a game theory mathematical model. After differentiation, two weight coefficients can be obtained, and the weight coefficients are normalized, expressed as:

[0093]

[0094] Where T represents the transpose operation. These are the normalized weight coefficients. Substituting them into the weight coefficients used in the optimization solution yields the combined weight w, expressed as:

[0095]

[0096] Among them, w o For objective weighting.

[0097] S3: Ranking of distribution network planning based on the VIKOR method.

[0098] Furthermore, the distribution network planning evaluation ranking based on the VIKOR method involves normalizing the original index data matrix using a data preprocessing formula to obtain a normalized matrix X. In the normalized matrix, the optimal and worst values ​​of the positive and negative ideal solution indicators are the maximum and minimum values ​​of the index data, expressed as:

[0099]

[0100] Calculate the group utility value S' and individual regret value R” of the planning scheme. j , is represented as:

[0101]

[0102] The compromise decision value Q is calculated, where u is the decision mechanism coefficient, ranging from [0-1]. This represents the balance between maximizing group utility and minimizing individual regret, expressed as:

[0103]

[0104] The optimal ranking of the selected schemes is determined by the compromise decision value Q.

[0105] Example 2, one embodiment of the present invention, provides a method for evaluating power distribution network planning. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0106] First, in order to verify the beneficial effects of the present invention, a comparative experiment was conducted for scientific demonstration. Taking four power distribution network planning schemes as examples, the data of each indicator are shown in Table 1.

[0107] Table 1 Indicators for Distribution Network Planning Scheme

[0108]

[0109]

[0110] Data preprocessing involves standardizing and dimensionless processing of the indicator data in Table 1. Standardized data can be found in Table 2.

[0111] Table 2 Indicators for Standardized Distribution Network Planning Scheme

[0112]

[0113]

[0114] Based on the established power system planning evaluation index system, the four planning schemes were evaluated using a subjective and objective weighting method. The weight results of each index are shown in Table 3, and the overall score results are shown in Table 4.

[0115] Table 3: Weight Results of Each Indicator

[0116]

[0117]

[0118] Table 4 Overall Scoring Table for Power System Planning Evaluation

[0119]

[0120] As shown in Table 4, the evaluation and ranking results of the method constructed in this invention show that Scheme 3 and Scheme 4 have better overall scores, while Scheme 1 and Scheme 2 have poorer scores. When decision experts use the VIKOR method for ranking, the ranking results fluctuate slightly depending on the different planning scenarios and the different values ​​of the decision mechanism coefficient u. The overall ranking results of Scheme 3 and Scheme 4 show greater variation. In terms of indicators, Scheme 3 is slightly lacking in traditional indicators, such as the proportion of heavy-load lines and the N-1 pass rate, which need to be improved, but the indicator levels are all very high. Scheme 4, on the other hand, focuses on being the best in traditional indicators. In general, Scheme 3 and Scheme 4 are both excellent evaluation schemes that can be promoted, with different focuses. Decision experts can select the optimal and appropriate scheme in different planning scenarios. The conclusion shows that the game theory-VIKOR evaluation model constructed in this invention can reasonably and accurately evaluate and rank the distribution network.

[0121] In summary, the power distribution network planning evaluation method described in this invention can evaluate different schemes to a certain extent, obtain the overall differences between the schemes, and assess the advantages and disadvantages of various aspects of the planning schemes. This can provide a basis for the construction of power distribution networks, continuously improve system performance, and achieve the goal of improving system architecture. It can provide effective support and guarantee for the planning and development of power distribution networks.

[0122] Example 3, referring to Figure 2 As an embodiment of the present invention, a power distribution network planning and evaluation system is provided, including a data preprocessing module, a comprehensive weighting model module, and a scheme scoring and selection module.

[0123] The data preprocessing module is used to obtain the original data of each indicator in the distribution network indicator system and to perform standardization and dimensionless preprocessing on the indicator data; the comprehensive weighting model module is used to perform subjective weighting through the DEMATEL method, objective weighting through the CRITIC method, and comprehensive subjective and objective weighting based on game theory optimization of weight coefficients; the scheme scoring and selection module is used to rank the distribution network planning evaluation based on the VIKOR method.

[0124] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0126] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0127] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating power distribution network planning, characterized in that, include: Obtain the original data of each indicator in the distribution network planning and evaluation indicator system, and perform standardization and dimensionless preprocessing on the indicator data; Subjective weighting is performed using the DEMATEL method, objective weighting is performed using the CRITIC method, and a combination of subjective and objective weighting is performed based on game theory optimization of weight coefficients. Ranking of power distribution network planning using the VIKOR method; The objective weights are calculated using the CRITIC method, and the standard deviation of each indicator is calculated. To reflect the differences in indicator data, it is expressed as: in, Indicates the first The average score of each scheme after standardization of each indicator is calculated, and the correlation coefficient between the indicators is also calculated to reflect the conflict between them. , represented as: in, Indicators With indicators The correlation coefficient between them and They represent the first and the The standardized scores of each indicator are used to calculate the information content of the indicator through indicator variability and conflict. , represented as: Calculate the objective weight of the indicator , represented as: in, Indicates the first The standardized values ​​of the benefit-oriented indicators; The aforementioned subjective and objective comprehensive weighting based on game theory optimization weight coefficients includes: By combining the primary and objective weights using a linear combination method, the final primary-objective combined weight is obtained, expressed as: in, and These represent the weight coefficients for subjective weights and objective weights, respectively. For combined weights, Subjective weighting, To determine the objective weights, an objective function is constructed based on a game theory mathematical model. After differentiation, two weight coefficients can be obtained, and the weight coefficients are normalized, expressed as: in, This indicates the transpose operation. These are the normalized weight coefficients. Substituting them into the weight coefficients used in the optimization solution yields the combined weighted weights. , represented as: in, For objective weighting.

2. The distribution network planning and evaluation method as described in claim 1, characterized in that: The standardization and dimensionless preprocessing of the indicator data includes: By preprocessing the data of benefit-type indicators, cost-type indicators, and center-type indicators, a result is obtained. A dimensional standardized data matrix For the number of indicators, Given the sample size of the scheme, the indicators are compared and analyzed. The formula for processing benefit-type indicators is expressed as follows: in, Indicates the first The first indicator in the The original values ​​in each scheme, Indicates the first The minimum value of each indicator among all options. Indicates the first The maximum value of each indicator across all options. Indicates the first The standardized values ​​of the benefit-type indicators and the processing formulas for the cost-type indicators are expressed as follows: The centralized indicator processing formula is expressed as follows: 。 3. The distribution network planning and evaluation method as described in claim 2, characterized in that: The subjective weighting using the DEMATEL method includes: By analyzing the logical relationships and direct influence relationships among the elements in the system through expert analysis, the inherent causal relationships of the system are revealed, key factors are identified, the degree of direct influence between various indicators is determined, and a standardized influence matrix is ​​constructed. , represented as: in, Indicators For indicators The degree of influence is denoted by a value of {0, 1, 2, 3, 4}, with larger values ​​indicating greater influence. Indicates the number of indicators. This represents the maximum value of the summation by row or column, achieving a normalized influence matrix. The comprehensive influence matrix is ​​then calculated through matrix transformations. , The identity matrix is ​​represented as: in, Indicates the first Individual indicators for indicators The overall impact is determined by calculating the impact of each indicator using the overall impact matrix. Influence Centrality and causal degree , represented as: Calculate the subjective weight of each indicator using the Dematel method based on centrality. , represented as: in, Indicates the number of indicators.

4. A system employing the distribution network planning and evaluation method as described in any one of claims 1 to 3, characterized in that: It includes a data preprocessing module, a comprehensive weighting module, and a scheme scoring and selection module; The data preprocessing module is used to obtain the original data of each indicator in the power distribution network indicator system and to perform standardization and dimensionless preprocessing on the indicator data. The comprehensive weighting module is used to perform subjective weighting through the DEMATEL method, objective weighting through the CRITIC method, and comprehensive subjective and objective weighting based on game theory optimization weight coefficients. The scheme scoring and selection module is used to evaluate and rank distribution network planning using the VIKOR method.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power distribution network planning and evaluation method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power distribution network planning and evaluation method according to any one of claims 1 to 3.

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