Novel power distribution network planning scheme evaluation method, system, equipment and medium

Abstract

The invention discloses a novel power distribution network planning scheme evaluation method, system and device, and a medium, and relates to the technical field of power system optimization and energy management, and the method comprises the steps: obtaining the original data of each index of a novel power distribution network planning scheme, and carrying out the standardization and dimensionless preprocessing of the index data; subjective empowerment is carried out through an improved DEMATEL method, objective empowerment is carried out through an improved EWM method, and subjective and objective comprehensive empowerment is carried out based on a minimum identification information theory; and evaluating and sorting the novel power distribution network planning schemes through a matter element extension-radar map evaluation model. According to the method, subjective empowerment is carried out by improving a DEMATEL method, so that the transparency of weight assignment is improved, and the actual guiding significance of an evaluation result is enhanced; objective weight assignment is carried out through an improved EWM method, and the objective weight assignment accuracy is improved; and constructing a matter element extension-radar map evaluation model, and carrying out scientific evaluation and optimization suggestion on a planning scheme.

Description

Technical Field

[0001] This invention relates to the field of power system optimization and energy management technology, and in particular to a novel method, system, equipment and medium for evaluating power distribution network planning schemes. Background Technology

[0002] With the continuous development of society and the economy, energy consumption demand is gradually increasing, and energy shortages and environmental pollution problems are becoming increasingly prominent. Against the backdrop of the continuous development of green, low-carbon, safe, and intelligent new power systems, and the rise of elements such as diversified loads, intelligent digital technologies, and clean energy, the power grid, as a key hub on both the energy and load sides of the new power system, plays a crucial role in power grid planning and evaluation. A comprehensive and reasonable evaluation of power grid planning can provide reference value and guidance for the planning and construction of new power systems, reducing or avoiding decision-making errors and effectively assisting in the construction and development of new power systems. Over the past few decades, distribution network planning and evaluation technology has made significant progress. Researchers have proposed various evaluation methods and models, such as the Analytic Hierarchy Process (AHP), CRITIC method, TOPSIS method, 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 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] Existing technologies have significant shortcomings in the preprocessing of indicator data. In the process of power distribution network planning and evaluation, numerous indicators and diverse data sources are involved. Effective standardization and dimensionless processing of this data are crucial to ensuring the accuracy of the evaluation results. Existing technologies often neglect this aspect, leading to 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 and objective weighting, but most studies have failed to effectively combine the two, resulting in subjective bias or objective distortion in the evaluation results. Existing evaluation methods do not comprehensively consider the overall weighting strategy, leading to low accuracy in the evaluation results. Furthermore, in the ranking process, existing evaluation methods often only calculate the final evaluation result and cannot demonstrate the advantages and disadvantages of indicators at each level of the planning scheme, making it difficult for the evaluation results to provide guidance and improvement suggestions for the planning scheme. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a novel method for evaluating power distribution network planning schemes, addressing the problems of insufficient preprocessing of indicator data, weak integration of subjective and objective weighting methods, and inadequate consideration of comprehensive weighting strategies in existing power system optimization and energy management technologies, as well as the issue of how to achieve scientific and accurate evaluation, ranking, and optimization recommendations.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a novel method for evaluating power distribution network planning schemes, comprising: acquiring the original data of each indicator of the novel power distribution network planning scheme, and performing standardization and dimensionless preprocessing on the indicator data; performing subjective weighting through the improved DEMATEL method, objective weighting through the improved EWM method, and performing comprehensive subjective and objective weighting based on the minimum discriminant information theory; and evaluating and ranking the novel power distribution network planning schemes through the matter-element extension-radar chart evaluation model.

[0008] As a preferred embodiment of the novel power distribution network planning scheme evaluation method described in this invention, the standardization and dimensionless preprocessing includes obtaining an n*m dimensional standardized data matrix by preprocessing the benefit-type indicators and cost-type indicators; where n is the number of scheme samples and m is the number of evaluation indicators.

[0009] As a preferred embodiment of the novel power distribution network planning scheme evaluation method described in this invention, the subjective weighting includes: revealing the inherent causal relationship of the system and identifying key factors through expert analysis of the logical and direct influence relationships between various elements in the system; determining the degree of direct influence between various indicators; constructing a standardized influence matrix; calculating the comprehensive influence matrix through matrix transformation; calculating the influence degree, affected degree, centrality, and causal degree of each indicator through the comprehensive influence matrix; calculating the subjective weight of the indicator through the indicator centrality; and introducing an indicator quantity correction calculation formula to optimize the indicator weight and avoid influence bias due to different numbers of subordinate indicators.

[0010] As a preferred embodiment of the novel power distribution network planning scheme evaluation method described in this invention, the method includes: the objective weighting includes calculating information entropy and further calculating the information entropy value of the indicators through the indicator probability matrix; calculating the objective weight of the indicators through the improved EWM method; the improved EWM method includes adding 0.5 times the average entropy value to the traditional EWM method.

[0011] As a preferred embodiment of the novel power distribution network planning scheme evaluation method described in this invention, the subjective and objective comprehensive weighting includes: using subjective weights and objective weights as prior distributions, establishing a combined weight as a target distribution, and constructing a Lagrange function to calculate the subjective and objective combined weight when the identification information between the prior distribution and the target distribution is minimized.

[0012] As a preferred embodiment of the novel power distribution network planning scheme evaluation method described in this invention, the evaluation and ranking of the novel power distribution network planning scheme using the matter-element extension-radar chart evaluation model includes: setting four evaluation levels according to the power distribution network evaluation guidelines; the four evaluation levels are represented by classical domain matter-element, section domain matter-element, and matter-to-be-evaluated respectively; and calculating the correlation value between the index and each level using a correlation function.

[0013] As a preferred embodiment of the novel power distribution network planning scheme evaluation method described in this invention, the evaluation and ranking of novel power distribution network planning schemes using the matter-element extension-radar chart evaluation model further includes: combining the improved DEMATEL-EWM combined weights with the correlation degree of each planning scheme indicator to calculate the comprehensive correlation degree; determining the planning scheme evaluation level as the level with the highest comprehensive correlation degree based on the principle of maximum membership correlation; and calculating the level characteristic evaluation value when the power distribution network planning schemes have the same evaluation level, and evaluating and ranking each scheme.

[0014] Secondly, this invention provides a novel power distribution network planning scheme evaluation system, comprising: a data preprocessing module, a comprehensive weighting model module, and a scheme evaluation and result analysis module; the data preprocessing module is used to acquire the original data of each indicator of the novel power distribution network planning scheme, and to perform standardization and dimensionless preprocessing on the indicator data; the comprehensive weighting model module is used to perform subjective weighting through the improved DEMATEL method, objective weighting through the improved EWM method, and comprehensive subjective and objective weighting based on the minimum discriminant information theory; the scheme evaluation and result analysis module is used to evaluate and rank the novel power distribution network planning schemes through the matter-element extension-radar chart evaluation model.

[0015] Thirdly, the present invention provides an electronic device, comprising:

[0016] Memory and processor;

[0017] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the evaluation method for the new power distribution network planning scheme.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the novel power distribution network planning scheme evaluation method.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: The novel power distribution network planning scheme evaluation method provided by this invention achieves in-depth analysis of the inherent causal relationships among various elements in the indicator system through subjective weighting using an improved DEMATEL method; by improving the subjective weighting of DEMATEL, it avoids the subjective bias caused by traditional methods, not only improving the transparency of weight assignment but also enhancing the practical guiding significance of the evaluation results through expert system analysis; by improving the EWM method for objective weighting, it calculates the information content of each indicator, fully considering the differences in indicator data; by introducing a 0.5 times average entropy value into the EWM method, it ensures the scientific nature of objective weight assignment, improves the accuracy of objective weight assignment, and provides an important basis for comprehensive weighting; by constructing a matter-element extension-radar chart evaluation model, it provides scientific evaluation and optimization suggestions for planning schemes. This invention achieves better results in terms of objectivity in the evaluation process, rationality of weight allocation, and practicality of evaluation results. Attached Figure Description

[0020] 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.

[0021] Figure 1 This is a schematic diagram of the overall process of the novel power distribution network planning scheme evaluation method according to an embodiment of the present invention.

[0022] Figure 2 This is a radar chart showing the evaluation results of a novel power distribution network planning scheme evaluation method according to an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of the overall process of a novel power distribution network planning scheme evaluation system according to an embodiment of the present invention. Detailed Implementation

[0024] 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.

[0025] Example 1, referring to Figure 1 As an embodiment of the present invention, a novel method for evaluating power distribution network planning schemes is provided, comprising:

[0026] S1: Obtain the original data of each indicator of the new power distribution network planning scheme, and perform standardization and dimensionless preprocessing on the indicator data;

[0027] S2: Subjective weighting is performed using the improved DEMATEL method, objective weighting is performed using the improved EWM method, and comprehensive subjective and objective weighting is performed based on the theory of least discriminative information.

[0028] S3: Evaluation and ranking of new power distribution network planning schemes using the matter-element extension-radar chart evaluation model.

[0029] It should be noted that since the distribution network is directly connected to users, power outages caused by its faults account for a relatively high proportion. Correctly evaluating the distribution network planning scheme can ensure that the power grid can operate stably under both normal and fault conditions, reducing the impact on users' lives and production. At the same time, reasonable and accurate evaluation can avoid decision-making errors when balancing various indicators in the planning scheme, and avoid resource waste or insufficient power supply.

[0030] Therefore, to address the aforementioned problems in evaluating distribution network planning schemes, this paper proposes a method through steps S1-S3. This method employs a modified DEMATEL method for subjective weighting, a modified EWM method for objective weighting, a comprehensive subjective and objective weighting based on the minimum discriminant information theory, and a matter-element extension-radar chart evaluation model to rank the planning schemes. This approach achieves scientific evaluation, accurate ranking, and weak link diagnosis of distribution network planning schemes, effectively solving the shortcomings of traditional methods in terms of data comparability, weight bias, and result interpretability. It provides key technical support for the low-carbon and intelligent planning of new power systems.

[0031] Example 2, refer to Figure 1 This is one embodiment of the present invention. Based on the above embodiment, a novel method for evaluating power distribution network planning schemes is provided.

[0032] In the embodiments of this application, the standardization and dimensionless preprocessing in step S1 includes obtaining an n*m dimensional standardized data matrix by preprocessing the benefit-type indicators and cost-type indicators.

[0033] Where n is the number of sample schemes and m is the number of evaluation indicators.

[0034] Specifically, the formula for processing benefit-type indicators is expressed as follows:

[0035]

[0036] The formula for processing cost-type indicators is expressed as follows:

[0037]

[0038] Where, x ijLet m represent the original value of the i-th indicator in the j-th scheme. j Let M represent the minimum value of the j-th index among all possible solutions. j This represents the maximum value of the j-th index among all possible solutions. This represents the standardized value of the j-th benefit-type indicator.

[0039] It should be noted that by obtaining the original data of each indicator in the indicator system of the new power distribution network planning scheme 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.

[0040] In this application embodiment, the subjective weighting in step S2 includes the following steps A1-A5:

[0041] A1: By analyzing the logical relationships and direct influence relationships between the elements in the system through expert analysis, the inherent causal relationships of the system are revealed and key factors are identified. The degree of direct influence between each indicator is determined and a standardized influence matrix is ​​constructed.

[0042] A2: Calculate the comprehensive influence matrix through matrix transformation;

[0043] A3: Calculate the influence, affectedness, centrality, and causation of each indicator by using the comprehensive influence matrix;

[0044] A4: Calculate the subjective weight of the indicator using the indicator centrality;

[0045] A5: Introduce a formula to correct the number of indicators, optimize indicator weights, and avoid the bias caused by different numbers of subordinate indicators.

[0046] Specifically, in step A1, a normalized influence matrix is ​​constructed, represented as follows:

[0047]

[0048] Among them, a ij ∈{0,1,2,3,4} represents the degree of influence of index i on index j. The larger the value, the more important it is. max is the maximum value of the sum of the rows and columns of matrix X.

[0049] In step A2, the comprehensive influence matrix is ​​represented as follows:

[0050] Y = X(EX) -1 =[b ij ] n×n

[0051] Here, E represents the identity matrix.

[0052] In step A3, the comprehensive influence matrix is ​​used to calculate each indicator, which is expressed as follows:

[0053]

[0054] Among them, D j Indicates the degree of influence, C j M represents the degree of influence. j R represents centrality. j Indicates degree of causation;

[0055] In step A4, the subjective weights of the indicators are calculated and expressed as follows:

[0056]

[0057] Among them, w sj For subjective weighting, M j The centrality of the indicator.

[0058] In step 5, the number of indicators is introduced to optimize the indicator weights, expressed as:

[0059]

[0060] Among them, W sj For the corrected weights, k j The number of indicators under indicator j;

[0061] Finally, the subjective weights of the indicators after correction and optimization at each level can be recalculated sequentially.

[0062] It should be noted that by improving the DEMATEL method for subjective weighting, a systematic utilization of expert knowledge was achieved, revealing the logical relationships and direct influence relationships between various elements in the system. Furthermore, the introduction of a modification method to optimize and improve the subjective weights achieved the beneficial effect of accurately depicting the interaction between indicators, thereby improving the rationality and reliability of weight assignment.

[0063] In an optional implementation, step S2 can also perform subjective weighting through order relation analysis. Specifically, experts rank the importance of indicators at the same level to form a complete order relation; experts assign values ​​to the importance ratio of adjacent indicators and calculate subjective weights.

[0064] In another alternative implementation, step S2 can also be subjectively weighted by direct weighting. Specifically, each expert assigns a score of 0-10 to each indicator, with higher scores indicating greater importance. The average score of all experts is then taken and normalized to obtain the final weight.

[0065] In this embodiment of the application, the objective weighting in step S2 includes the following steps B1-B2:

[0066] B1: Calculate information entropy, and further calculate the information entropy value of the indicator through the indicator probability matrix;

[0067] B2: Calculate the objective weights of the indicators by improving the EWM method.

[0068] The improved EWM method includes adding a 0.5 times average entropy value to the traditional EWM method.

[0069] Specifically, in step B1, the information entropy is calculated. The information entropy value is further calculated using the indicator probability matrix, and is expressed as:

[0070]

[0071] Where, p ij Let e ​​be the probability value of the j-th index of the i-th scheme. j Let be the entropy value of the j-th index;

[0072] When the traditional EWM method calculates entropy weights, the objective weights may deviate too much when the entropy value approaches 1. To avoid this problem, the average entropy value is increased by 0.5 times, and the original calculation formula is optimized and improved to obtain more scientific and accurate objective weights.

[0073] In step B2, the objective weight W of the indicator is calculated. oj , represented as:

[0074]

[0075] It should be noted that by improving the EWM method for objective weighting, effective and reasonable comparison and analysis of benefit-type and cost-type indicators are conducted, the information content of each indicator is calculated, the differences in indicator data are fully considered, and the introduction of 0.5 times the average entropy value avoids excessive deviation of the weights in the entropy weight method, ensuring the scientific nature of objective weighting, improving the accuracy of objective weighting, and providing an important basis for comprehensive weighting.

[0076] In an alternative implementation, step S2 can also be objectively weighted using the entropy weighting method. Specifically, by calculating the entropy value for each indicator and then calculating the difference coefficient based on the entropy value, the objective weight is obtained.

[0077] In another alternative implementation, step S2 can be objectively weighted using the CRITIC method. Specifically, the original indicators are processed by Z-score standardization to calculate the degree of influence and the degree of causation. Based on the calculation results, the information content and weights are calculated.

[0078] In this embodiment of the application, the subjective and objective combined weighting in step S2 includes: taking the subjective weight and objective weight as prior distributions, establishing the combined weight as the target distribution, and constructing a Lagrangian function when the identification information between the prior distribution and the target distribution is minimized, and calculating the subjective and objective combined weight.

[0079] Specifically, when the discriminative information between the prior distribution and the target distribution is minimized, the combined weight obtained by integrating subjective and objective weights is the most reasonable and scientific, with the smallest bias. The formula for minimizing the discriminative information of the combined weight is expressed as:

[0080]

[0081] Construct the Lagrangian function and calculate the subjective-objective combination weight W. j , represented as:

[0082]

[0083] In this embodiment of the application, step S3, which evaluates and ranks new power distribution network planning schemes using the matter-element extension-radar chart evaluation model, includes the following steps C1-C5:

[0084] C1: According to the distribution network evaluation guidelines, four evaluation levels are set, which are represented by classical domain matter-element, node domain matter-element, and matter-evaluation matter-element, respectively.

[0085] C2: Calculate the correlation between the index and each level using the correlation function;

[0086] C3: Combine the improved DEMATEL-EWM combined weights with the correlation degree of each planning scheme indicator to calculate the comprehensive correlation degree;

[0087] C4: Based on the principle of maximum subordinate association, the evaluation level of the planning scheme is determined as the level with the highest comprehensive correlation.

[0088] C5: When the evaluation levels of distribution network planning schemes are the same, calculate the evaluation value of the level characteristics and rank the schemes.

[0089] Specifically, the matter-element theory unifies the representation of multiple characteristics of a thing and the information value of its characteristics. The matter-element R = (N, C, V), where N represents the thing, C represents the characteristic, and V represents the information value of the characteristic. In the evaluation model of the new power distribution network planning scheme, N represents the planning scheme to be evaluated, C represents the index set, and V represents the corresponding index data of the characteristic.

[0090] In step C1, the four evaluation levels, from worst to best, are (1, 2, 3, 4);

[0091] Classical Domain Element R q , represented as:

[0092]

[0093] Where q∈(1,2,3,4), R q Let a represent the classical field element of the q-th order. jq b jq These represent the upper and lower bounds of the index corresponding to the q level for the j-th index;

[0094] Domain element R t The range of values ​​for the classical domain across all levels is represented as:

[0095]

[0096] Among them, a jt b jt These represent the upper and lower bounds of all levels of the j-th indicator;

[0097] The object element R to be evaluated n This refers to the distribution network planning scheme to be evaluated, expressed as:

[0098]

[0099] Among them, v ij It is the data of the j-th indicator under the i-th scheme.

[0100] In step C2, the correlation degree between the index and each level is calculated using a correlation function, expressed as:

[0101]

[0102] Where, k ijq Let represent the correlation value of the j-th indicator with respect to the q-th level in the i-th planning scheme.

[0103] In step C3, the overall correlation degree is calculated and expressed as:

[0104]

[0105] Among them, K iq This represents the comprehensive correlation between the i-th planning scheme and the q-th level;

[0106] In step C4, the evaluation level of the planning scheme is determined by the level with the highest comprehensive correlation, denoted as:

[0107] q = max(K) i )

[0108] In step C5, to facilitate decision-makers' judgment when distribution network planning schemes have the same evaluation level, this paper further calculates the evaluation value of the level characteristics to rank the schemes, as shown below:

[0109]

[0110] Among them, T i This is the evaluation value of the ranking characteristics of the i-th planning scheme. The larger the value, the better the ranking of the scheme.

[0111] Further incorporating the "step-by-step, bottom-up" approach of the Analytic Hierarchy Process (AHP), the system evaluates the level of each indicator layer by layer. Through radar charts, it fully demonstrates the advantages and disadvantages of power distribution network planning schemes, helping decision-makers to more comprehensively and intuitively evaluate, rank, and analyze the results of planning schemes.

[0112] Example 3, referring to Figure 2 In one embodiment of the present invention, a novel method for evaluating power distribution network planning schemes is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0113] First, in order to verify the beneficial effects of the present invention, a comparative experiment was conducted for scientific demonstration. Taking four new power distribution network planning schemes as examples, the data of each indicator are shown in Table 1.

[0114] Table 1. Indicator Data for New Distribution Network Planning Scheme

[0115] Indicator Name Option 1 Option 2 Option 3 Option 4 Clean energy emission reduction 130 322 229 199 Clean energy installed capacity ratio 95.1 88.1 82.9 73.8 Clean energy utilization rate 94 91 98 94 …… …… …… …… …… Power distribution communication coverage 87 89 94 97 Smart meter coverage 98 96 98.5 99.5 intelligent measurement terminal coverage 93 98 95 96

[0116] Data preprocessing involves standardizing and dimensionless processing of the indicator data in Table 1. Standardized data can be found in Table 2.

[0117] Table 2. Standardized New Distribution Network Planning Scheme Indicator Data

[0118] Indicator Name Option 1 Option 2 Option 3 Option 4 Clean energy emission reduction 0.0000 1.0000 0.5156 0.3594 Clean energy installed capacity ratio 1.0000 0.6714 0.4272 0.0000 Clean energy utilization rate 0.4286 0.0000 1.0000 0.4286 …… …… …… …… …… Power distribution communication coverage 0.0000 0.2000 0.7000 1.0000 Smart meter coverage 0.5714 0.0000 0.7143 1.0000 intelligent measurement terminal coverage 0.0000 1.0000 0.4000 0.6000

[0119] Based on the established new power system planning evaluation index system, the four planning schemes were evaluated using a combined weighting-matter-extension evaluation method. The weights of each index are shown in Table 3, and the overall scoring results are shown in Table 4. The index evaluation results are for reference only. Figure 2 .

[0120] Table 3: Weight Results of Each Indicator

[0121] Third-level indicator name Subjective weight Objective weight Overall weighting of subjective and objective factors Clean energy emission reduction 0.02833 0.02972 0.02920 Clean energy installed capacity ratio 0.03315 0.02853 0.03096 Clean energy utilization rate 0.03621 0.02961 0.03296 …… …… …… …… Power distribution communication coverage 0.01312 0.03190 0.02109 Smart meter coverage 0.03342 0.02752 0.03126 intelligent measurement terminal coverage 0.01312 0.02894 0.02009

[0122] Table 4 Overall Scoring Table for New Power System Planning Schemes

[0123]

[0124]

[0125] Table 4 shows the evaluation and ranking results of the method constructed in this invention. Based on the comprehensive correlation, schemes 1 and 3 belong to level 3, indicating that the overall scheme indicator level needs further improvement; scheme 2 belongs to level 2, with the worst overall evaluation; scheme 4 has the best evaluation and belongs to level 4. Furthermore, based on the level characteristic evaluation values, it can be seen that scheme 4 (3.3543) > scheme 3 (2.9846) > scheme 1 (2.9103) > scheme 2 (2.6797). Figure 2 As can be seen, radar chart analysis can effectively analyze the advantages and disadvantages of various indicators of the evaluation scheme, so as to facilitate decision-makers to optimize and improve the distribution network, thus verifying the feasibility and practicality of the evaluation model constructed in this paper in distribution network planning evaluation.

[0126] In summary, the novel power distribution network planning scheme evaluation method described in this invention can evaluate different schemes to a certain extent, obtain the overall differences between different schemes, and assess the advantages and disadvantages of various aspects of the planning scheme. This can provide a basis for the construction of new 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 new power distribution networks.

[0127] Example 4, refer to Figure 3 The above is a schematic diagram of a novel distribution network planning scheme evaluation method. It should be noted that the technical solution of this novel distribution network planning scheme evaluation system and the technical solution of the aforementioned novel distribution network planning scheme evaluation method belong to the same concept. Details not described in detail in this embodiment of the technical solution of the novel distribution network planning scheme evaluation system can be found in the description of the technical solution of the aforementioned novel distribution network planning scheme evaluation method.

[0128] This embodiment also provides a novel power distribution network planning scheme evaluation system, including: a data preprocessing module, a comprehensive weighting model module, and a scheme evaluation and result analysis module.

[0129] The data preprocessing module is used to acquire the original data of each indicator of the new distribution network planning scheme and to perform standardization and dimensionless preprocessing on the indicator data; the comprehensive weighting model module is used to perform subjective weighting through the improved DEMATEL method, objective weighting through the improved EWM method, and comprehensive subjective and objective weighting based on the minimum discriminant information theory; the scheme evaluation and result analysis module is used to evaluate and rank the new distribution network planning schemes through the matter-element extension-radar chart evaluation model.

[0130] This embodiment also provides an electronic device suitable for evaluating new power distribution network planning schemes, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the evaluation method for new power distribution network planning schemes proposed in the above embodiment.

[0131] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the evaluation method for a novel power distribution network planning scheme as proposed in the above embodiments.

[0132] The storage medium proposed in this embodiment and the evaluation method for implementing a new power distribution network planning scheme proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0133] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not 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 novel method for evaluating power distribution network planning schemes, characterized in that, include: Obtain the original data of each indicator of the new power distribution network planning scheme, and perform standardization and dimensionless preprocessing on the indicator data; Subjective weighting is performed using the improved DEMATEL method, objective weighting is performed using the improved EWM method, and a combination of subjective and objective weighting is performed based on the theory of least discriminative information. The evaluation and ranking of new power distribution network planning schemes are carried out using the matter-element extension-radar chart evaluation model.

2. The novel power distribution network planning scheme evaluation method as described in claim 1, characterized in that, The standardization and dimensionless preprocessing includes preprocessing the benefit-type indicators and cost-type indicators to obtain an n*m dimensional standardized data matrix. Where n is the number of sample schemes and m is the number of evaluation indicators.

3. The evaluation method for the novel power distribution network planning scheme as described in claim 2, characterized in that, The subjective empowerment includes, through expert analysis of the logical and direct influence relationships between the elements in the system, revealing the inherent causal relationships of the system and identifying key factors, determining the degree of direct influence between the various indicators, and constructing a standardized influence matrix; The comprehensive influence matrix is ​​calculated through matrix transformation; By integrating the influence matrix, the influence degree, the degree of being influenced, the centrality, and the causal degree of each indicator are calculated. Calculate the subjective weight of the indicator using indicator centrality; A formula for adjusting the number of indicators is introduced to optimize indicator weights and avoid bias caused by different numbers of subordinate indicators.

4. The novel power distribution network planning scheme evaluation method as described in claim 3, characterized in that, The objective weighting includes calculating information entropy and further calculating the information entropy value of the indicator through the indicator probability matrix; By improving the EWM method, the objective weights of the indicators are calculated; The improved EWM method includes adding a 0.5 times average entropy value to the traditional EWM method.

5. The novel power distribution network planning scheme evaluation method as described in claim 4, characterized in that, The subjective and objective combined weighting includes using subjective weights and objective weights as prior distributions, establishing a combined weight as the target distribution, and constructing a Lagrangian function to calculate the subjective and objective combined weight when the discriminative information between the prior distribution and the target distribution is minimized.

6. The novel power distribution network planning scheme evaluation method as described in claim 5, characterized in that, The evaluation and ranking of new power distribution network planning schemes using the matter-element extension-radar chart evaluation model includes setting four evaluation levels according to the power distribution network evaluation guidelines. The four evaluation levels are represented by classical domain matter-element, node domain matter-element, and matter-evaluation to be evaluated, respectively. The correlation function is used to calculate the correlation value between the index and each level.

7. The novel power distribution network planning scheme evaluation method as described in claim 6, characterized in that, The evaluation and ranking of new power distribution network planning schemes using the matter-element extension-radar chart evaluation model also includes combining the improved DEMATEL-EWM combined weights with the correlation degree of each planning scheme indicator to calculate the comprehensive correlation degree. Based on the principle of maximum subordinate relationship, the evaluation level of the planning scheme is determined by the level with the highest comprehensive correlation. When the evaluation levels of distribution network planning schemes are the same, the evaluation values ​​of the level characteristics are calculated, and the schemes are evaluated and ranked.

8. A novel power distribution network planning scheme evaluation system, using the method described in any one of claims 1-7, characterized in that, include: Data preprocessing module, comprehensive weighting model module, scheme evaluation and result analysis module; The data preprocessing module is used to obtain the original data of each indicator of the new power distribution network planning scheme, and to perform standardization and dimensionless preprocessing on the indicator data. The comprehensive weighting model module is used to perform subjective weighting through the improved DEMATEL method, objective weighting through the improved EWM method, and comprehensive subjective and objective weighting based on the principle of minimum discriminative information. The scheme evaluation and result analysis module is used to evaluate and rank new power distribution network planning schemes using the matter-element extension-radar chart evaluation model.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the novel power distribution network planning scheme evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the novel power distribution network planning scheme evaluation method according to any one of claims 1 to 7.

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