Power distribution network morphological characteristic evaluation index weighting method based on combined weight

Abstract

The invention discloses a power distribution network morphological characteristic evaluation index weighting method based on a combined weight, and relates to the technical field of power distribution network morphological characteristic evaluation, and the method comprises the steps: building an evaluation index system, and defining a power distribution network efficiency index and a source network load storage interaction index framework; performing weight distribution on the evaluation indexes through subjective and objective weight calculation; and the subjective and objective combination weight is optimized according to the game theory, and the optimal index system combination weight is obtained. According to the method, through establishing an evaluation index system, defining a power distribution network efficiency index and a source network load storage interaction index framework and refining a multi-level evaluation standard, evaluation indexes can comprehensively reflect the operation state of the power distribution network, through subjective and objective weight calculation, weight distribution is carried out on the evaluation indexes, and the evaluation efficiency of the power distribution network is improved. The scientificity and balance of the evaluation result are ensured, and dynamic balance between different weight sources is realized by optimizing subjective and objective combination weights through the game theory.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network morphological characteristic evaluation, and in particular to a method for weighting distribution network morphological characteristic evaluation indicators based on combined weights. Background Art

[0002] There are several shortcomings in the existing weighting methods for distribution network morphological characteristic evaluation indicators, mainly including the following aspects. First, the existing technology generally adopts a single weight allocation method, such as the hierarchical analysis method and the entropy weight method. These methods have weak adaptability and flexibility in dealing with complex problems. When using the hierarchical analysis method, evaluators often face the problem of consistency of judgment and are easily affected by subjective judgment. The multi-dimensional comparison may lead to subjective bias and affect the credibility of the results. On the other hand, although the entropy weight method can consider the objectivity of the data to a certain extent, it relies too much on the original data when calculating the weights of different indicators and fails to truly reflect their importance. In addition, the existing methods are also irrational in information allocation, especially when comparing different indicators, they fail to fully consider the relationship and complementarity between the indicators, resulting in limited accuracy and reliability of the comprehensive weight. It is limited to the use of a single weight method, which makes the evaluation of important information such as distribution network efficiency and source-grid-load-storage interaction relatively one-sided and limited.

[0003] In addition, modern distribution networks are undergoing rapid changes, especially the changes in network structure and charge characteristics brought about by the access of distributed photovoltaic power sources and a large number of electric vehicles, which has brought new challenges to the evaluation methods. Current evaluation methods often lack dynamic adaptability and it is difficult to quickly adjust the indicator weights in a rapidly changing environment, resulting in the inability to fully cover all key links of the distribution network, which in turn affects its overall operating efficiency. This limitation is especially prominent in responding to emergencies, such as fluctuations in power supply and demand, and access to renewable energy, and it is difficult to meet actual needs. The existing evaluation methods have failed to introduce scientific optimization mechanisms in operation and lack systematicity, which has greatly reduced their effectiveness in practical applications. Therefore, a new method is urgently needed to overcome these shortcomings in order to adapt to the complex operating environment of modern distribution networks. Summary of the invention

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

[0005] Therefore, the technical problems solved by the present invention are: the existing distribution network indicator weighting method is single and one-sided, lacks dynamic adaptability and flexibility, lacks a scientific optimization mechanism, and how to achieve accurate and comprehensive weight allocation and evaluation in a rapidly changing distribution network operating environment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for weighting distribution network morphological characteristic evaluation indicators based on combined weights, including establishing an evaluation indicator system, defining distribution network efficiency indicators and a source-grid-load-storage interaction indicator framework; weighting the evaluation indicators through subjective and objective weight calculations; optimizing the subjective and objective combined weights according to game theory to obtain the optimal indicator system combined weights.

[0007] As a preferred scheme of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in the present invention, wherein: the establishment of the evaluation indicator system includes constructing a distribution network morphological characterization evaluation indicator system through distribution network efficiency indicators and source-grid-load-storage interaction indicators to evaluate the morphological changes brought about by the access of photovoltaic power sources and electric vehicles to the distribution network.

[0008] As a preferred scheme of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in the present invention, wherein: the definition of distribution network efficiency indicators and source-grid-load-storage interaction indicator framework includes constructing detailed multi-level evaluation standards through multi-dimensional data analysis; distribution network efficiency indicators include standard wiring rate, line overload rate, distribution transformer overload ratio, comprehensive voltage qualification rate, comprehensive line loss rate, power supply reliability rate, line N-1 pass rate, line segmentation rationality rate, intelligent switch standard coverage rate and distribution automation self-healing rate; source-grid-load-storage interaction indicators include distributed power supply penetration rate, charging pile penetration rate, off-grid or on-grid non-stop switching success rate, energy storage configuration rate, supply-storage ratio, typical daily load peak-to-valley difference rate, distribution transformer intelligent terminal coverage rate, comprehensive prediction accuracy rate, equivalent controllable rate and proportion of dynamic electricity price users.

[0009] As a preferred solution of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in the present invention, the subjective and objective weight calculation includes subjective weighting of indicators by optimizing the hierarchical analysis method through the ordered weighted average operator, and calculating the subjective weight of indicators; subjective weighting of indicators includes defining a criterion layer indicator system, and constructing a judgment matrix according to the comparison of different indicators under the same criterion by the Yth expert, which is expressed as:

[0010] Z Y =(z ij ) n×n

[0011] Among them, Z Y is the judgment matrix based on the Yth expert's suggestion, z ij is the importance of the i-th indicator relative to the j-th indicator, n is the number of indicators; based on the judgment matrix, the maximum eigenvalue λ max The eigenvector corresponding to the maximum eigenvalue is normalized and the subjective weight vector of the judgment matrix recommended by the Yth expert is output, which is expressed as:

[0012] W Y =(w1,w2,...,w n )

[0013] Among them, W Y is the subjective weight vector of the judgment matrix recommended by the Yth expert, w n is the weight of the mth indicator; the subjective weight vector of the judgment matrix recommended by the Yth expert is subjected to consistency check, which is expressed as:

[0014]

[0015] Where D is the consistency ratio; when the consistency ratio is less than or equal to 0.1, the judgment matrix satisfies the consistency; when the consistency ratio is greater than 0.1, the judgment matrix does not satisfy the consistency, and the element values ​​in the judgment matrix are adjusted until the consistency is satisfied; the subjective weight vectors of the judgment matrices suggested by different experts are calculated, and the subjective weight vector matrix of the judgment matrices suggested by different experts is constructed, which is expressed as:

[0016]

[0017] Among them, B is the subjective weight vector matrix of the judgment matrix recommended by different experts, y is the number of experts, and y is the transpose operation of the vector. is the transpose of the subjective weight vector of the judgment matrix recommended by the Yth expert; based on the subjective weight vector matrix B, the subjective weight vector is corrected by the ordered weighted average operator. For each indicator, the weight values ​​given by different experts are collected and arranged in descending order. The absolute weight of the jth indicator is calculated, which is expressed as:

[0018]

[0019] in, is the absolute weight of the jth indicator, v t is the order weight factor, p t is the t+1th weight value formed by arranging the y weight values ​​corresponding to the jth indicator in descending order, To select t items from y-1, 2 y-1 is the normalization base; the comprehensive weight of each indicator is normalized to obtain the corrected subjective weight vector.

[0020] As a preferred solution of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in the present invention, wherein: the subjective and objective weight calculation also includes objectively weighting the indicators by entropy weight method to calculate the objective weights of the indicators; objective weighting of indicators includes collecting specific values ​​of the object to be evaluated under each evaluation indicator to construct a data matrix, which is expressed as:

[0021] S=(sij ) m×q

[0022] Among them, S is the data matrix, m is the number of evaluation objects, q is the number of evaluation indicators, and s ij is the specific value of the i-th evaluation object under the j-th evaluation indicator; the data matrix S is standardized, including standardization according to different indicator types and standardization of extremely large indicators, expressed as:

[0023]

[0024] Among them, x ij is the specific value of the i-th evaluation object under the j-th evaluation index after standardization; for very small indicators, standardization is performed and expressed as:

[0025]

[0026] The interval indicators are standardized and expressed as:

[0027]

[0028] Among them, δ j is the optimal reference value of the interval of the appropriateness index; the standardized processing results of different index types are used to form a standardized data matrix, which is expressed as:

[0029] X=(x ij ) m×q

[0030] By using the entropy weight method, the entropy value of the jth indicator is calculated, which is expressed as:

[0031]

[0032] Among them, e j is the entropy value of the jth indicator, b ij is the normalized ratio, Inm is the natural logarithm of m; according to the entropy value of the jth indicator, the objective weight of the jth indicator is calculated, which is expressed as:

[0033]

[0034] Among them, h j is the objective weight of the jth indicator; calculate the objective weight of each indicator, and construct the objective weight vector matrix of the evaluation indicator system, which is expressed as:

[0035] h=(h1,h2,...,h q )

[0036] Among them, h is the objective weight vector matrix, h qis the objective weight vector of the qth indicator.

[0037] As a preferred solution of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in the present invention, wherein: the optimization of subjective and objective combined weights according to game theory includes coordinating and optimizing subjective and objective weights using the Nash equilibrium principle; optimizing subjective and objective weights includes obtaining basic weights through L methods and constructing a comprehensive weight vector set, which is expressed as:

[0038]

[0039] Among them, U is the comprehensive weight vector set, a is the basic weight vector, a k is the basic weight vector of the kth method, g is the linear combination weight coefficient, g k is the weight coefficient of the kth method; establish the objective function game model, minimize the total deviation between each basic weight vector and the comprehensive weight vector, and minimize the objective function, which is expressed as:

[0040] min||Ua k ||2,k=1,2,...,L

[0041] Among them, ||Ua k |2 is the objective function, min||Ua k ||2 is the comprehensive weight U and any basic weight a k The Euclidean distance between them is minimized, and ||·||2 is the bi-norm of the vector; based on matrix differentiation, the objective function is transformed into a linear equation system of linear combination weight coefficients, which is expressed as:

[0042]

[0043] By solving the linear equation, the linear combination weight coefficients are obtained.

[0044] As a preferred solution of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in the present invention, wherein: the obtaining of the optimal indicator system combined weights includes normalizing the linear combination weight coefficients obtained by solving, and obtaining the indicator system combined weights determined by game theory, that is, the optimal indicator system combined weights, which are expressed as:

[0045]

[0046] Among them, a * is the combined weight of the optimal indicator system, g k* is the normalized linear combination weight coefficient.

[0047] Another object of the present invention is to provide a distribution network morphological characteristic evaluation index weighting system based on combined weights, which can solve the problem of the singleness of the current distribution network index weighting technology by establishing an evaluation index system, defining the distribution network efficiency index and the source-grid-load-storage interaction index framework.

[0048] As a preferred solution of the distribution network morphological characteristic evaluation index empowerment system based on combined weights described in the present invention, it includes: an index system construction module, a weight calculation module, and a weight optimization module; the index system construction module is used to establish an evaluation index system, define distribution network efficiency indicators and source-grid-load-storage interaction index framework; the weight calculation module is used to assign weights to evaluation indicators through subjective and objective weight calculations; the weight optimization module is used to optimize the subjective and objective combined weights according to game theory to obtain the optimal index system combined weights.

[0049] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for weighting distribution network morphological characteristic evaluation indicators based on combined weights.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for weighting distribution network morphological characteristic evaluation indicators based on combined weights.

[0051] Beneficial effects of the present invention: The distribution network morphology characteristic evaluation index empowerment method based on combined weights provided by the present invention establishes an evaluation index system, defines distribution network efficiency indicators and source-grid-load-storage interaction indicator framework, and constructs a future-oriented distribution network morphology characteristic evaluation index system. Through multi-dimensional data analysis, the multi-level evaluation standards are refined so that the evaluation indicators can comprehensively reflect the operating status of the distribution network. The evaluation indicators are weighted through subjective and objective weight calculations. In the process of weighting the evaluation indicators, subjective and objective weight calculations are applied, combined with the advantages of ordered weighted average and entropy weight method. This process ensures the scientificity and balance of the evaluation results. In the weight calculation stage, by constructing a judgment matrix and combining the opinions of multiple experts, the importance evaluation of each indicator is ensured to be reasonable and consistent. Through objective and subjective weights, the evaluation indicators are weighted. The combination of the two makes the weight allocation not only take into account the judgment of a single expert, but also reflects the comprehensive wisdom of the entire expert group. This method effectively reduces the risk caused by the bias of a single expert. In addition, through consistency verification and correction, the weight allocation process is made more rigorous and transparent, ensuring that the evaluation mechanism is more stable and reliable. When facing a complex distribution network operating environment, it can quickly provide accurate guidance and improve the scientific nature of decision-making. By optimizing the subjective and objective combined weights through game theory, the subjective and objective weights are effectively coordinated and optimized. The Nash equilibrium principle is used to ensure that the deviation between each basic weight and the comprehensive weight is minimized. This mechanism overcomes the problem of unreasonable information allocation that may occur in traditional methods and realizes a dynamic balance between different weight sources. The present invention achieves better results in terms of efficiency, flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 An overall flow chart of a method for weighting distribution network morphological characteristic evaluation indicators based on combined weights provided in the first embodiment of the present invention.

[0054] Figure 2 A distribution network morphology characterization evaluation index system diagram of a distribution network morphology characteristic evaluation index weighting method based on combined weights provided in the first embodiment of the present invention.

[0055] Figure 3 An OWA-AHP weighting flow chart of a distribution network morphological characteristic evaluation index weighting method based on combined weights provided in the first embodiment of the present invention.

[0056] Figure 4 A flow chart of objective weight calculation using an entropy weight method for a method of weighting distribution network morphological characteristic evaluation indicators based on combined weights is provided for the first embodiment of the present invention.

[0057] Figure 5 A game theory combined weight calculation flow chart of a distribution network morphological characteristic evaluation index weighting method based on combined weights is provided for the first embodiment of the present invention.

[0058] Figure 6 A schematic diagram of modules of a distribution network morphological characteristic evaluation index weighting system based on combined weights provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0060] Example 1, reference Figure 1-Figure 5 , is an embodiment of the present invention, and provides a method for weighting distribution network morphological characteristic evaluation indicators based on combined weights, comprising:

[0061] S1: Establish an evaluation index system and define the distribution network efficiency index and the source-grid-load-storage interaction index framework.

[0062] Furthermore, the establishment of an evaluation index system includes constructing a distribution network morphology characterization evaluation index system through distribution network efficiency indicators and source-grid-load-storage interaction indicators to evaluate the morphological changes brought about by the access of photovoltaic power sources and electric vehicles to the distribution network.

[0063] It should be noted that the definition of distribution network performance indicators and source-grid-load-storage interaction indicator framework includes building detailed multi-level evaluation standards through multi-dimensional data analysis; Figure 2 As shown in Figure 1, the distribution network performance indicators include standard connection rate, line overload rate, distribution transformer overload ratio, comprehensive voltage qualification rate, comprehensive line loss rate, power supply reliability rate, line N-1 pass rate, line segmentation rationality rate, intelligent switch standard coverage rate and distribution automation self-healing rate; Figure 2 As shown, the source-grid-load-storage interaction indicators include the penetration rate of distributed power sources, the penetration rate of charging piles, the success rate of switching off or on the grid without power outages, the energy storage configuration rate, the supply-storage ratio, the peak-to-valley difference rate of typical daily load, the coverage rate of distribution transformer intelligent terminals, the comprehensive prediction accuracy, the equivalent controllable rate and the proportion of users with dynamic electricity prices.

[0064] It should also be noted that the specific definitions and calculation methods of each indicator in the distribution network morphology characterization evaluation index system are shown in Tables 1 and 2.

[0065] Table 1 Definition and calculation method of distribution network efficiency indicators

[0066]

[0067]

[0068] Table 2 Definition and calculation method of source-grid-load-storage interaction index

[0069]

[0070]

[0071] It should also be noted that the distribution network morphology characteristic evaluation index system is constructed from two aspects: distribution network efficiency indicators and source-grid-load-storage interaction indicators. It aims to comprehensively reflect the current situation of diversified distribution network entities and enriched business forms after the access of distributed photovoltaic power sources and a large number of electric vehicles. The system not only includes traditional efficiency indicators, such as standard wiring rate and power supply reliability, but also fully considers the impact of emerging technologies on distribution networks, such as the penetration rate of electric vehicle charging piles and the integration of distributed power sources. This comprehensiveness enables the distribution network to better grasp its morphological changes when facing the complexity brought by the access of photovoltaic power sources and electric vehicles, and provide a scientific basis for the optimization and improvement of the power grid. Through this multi-dimensional and multi-level indicator system, decision makers can intuitively identify the current strengths and weaknesses of the distribution network and formulate targeted improvement measures, which not only improves It improves the operating efficiency and reliability of the distribution network, promotes the effective use of clean energy, helps achieve the goal of low-carbon sustainable development, and provides strong support for the intelligent transformation and optimized management of the distribution network. The system is based on the standards and technical guidelines of the state, the power industry and power grid companies. The high interaction index focuses on the flexible resource utilization of the four sides of source, grid, load and storage, while the high efficiency index focuses on the comprehensive benefits and energy consumption levels of all links of the system. Through multi-dimensional analysis such as clean energy development, energy storage configuration and interactive resources on the source side, grid standardization and intelligence on the grid side, load stability and demand-side management space on the load side, and development potential and interaction level on the energy storage side, a multi-level, clear-structured and highly aggregated evaluation index system is constructed, which can comprehensively evaluate the characteristic differences in distribution network morphology and has good applicability and integrity.

[0072] S2: Assign weights to evaluation indicators through subjective and objective weight calculation.

[0073] Furthermore, the subjective and objective weight calculation includes the subjective weighting of indicators by optimizing the analytic hierarchy process (AHP) through the ordered weighted average (OWA) operator. Figure 3 The OWA-AHP weighting flow chart is shown.

[0074] (1) Define the criterion-level indicator system: input the evaluation criteria and the number of specific indicators n under the target.

[0075] (2) Constructing a judgment matrix: Invite the Yth expert to compare different indicators under the same criterion and generate a judgment matrix, which is expressed as:

[0076] Z Y =(z ij ) n×n

[0077] Among them, Z Y is the judgment matrix based on the Yth expert's suggestion, z ij is the importance of the i-th indicator relative to the j-th indicator, and a 1-9 scale is used to express the importance of one indicator relative to another.

[0078] (3) Calculate the subjective weight vector: Based on the judgment matrix, solve the maximum eigenvalue λ max And its corresponding eigenvector, normalized, to obtain the subjective weight vector of this level, expressed as:

[0079] W Y =(w1,w2,...,w n )

[0080] Among them, W Y is the subjective weight vector of the judgment matrix recommended by the Yth expert, w n is the weight of the mth indicator.

[0081] (4) Perform consistency check to ensure the consistency of the experts’ judgments. The judgment matrix needs to be tested for consistency ratio (D). If the D value is less than or equal to 0.1, the judgment matrix is ​​considered to have satisfactory consistency. Otherwise, the element values ​​in the judgment matrix need to be readjusted until the consistency requirements are met. The test formula is expressed as:

[0082]

[0083] (5) Combining the opinions of multiple experts: Multiple experts participate in the evaluation, repeat steps 2 to 4, calculate the weight vector corresponding to the judgment matrix given by each expert, and construct the subjective weight vector matrix of the judgment matrix suggested by different experts, which is expressed as:

[0084]

[0085] Among them, B is the subjective weight vector matrix of the judgment matrix recommended by different experts, y is the number of experts, and y is the transpose operation of the vector. is the transpose of the subjective weight vector of the judgment matrix recommended by the Yth expert.

[0086] Then, the subjective weight vector is corrected by the ordered weighted average operator. For each indicator, the weight values ​​given by all experts are collected and arranged in descending order. The absolute weight of the jth indicator is calculated, which is expressed as:

[0087]

[0088] in, is the absolute weight of the jth indicator, v t is the order weight factor, p t is the t+1th weight value formed by arranging the y weight values ​​corresponding to the jth indicator in descending order, To select t items from y-1, 2 y-1 is the normalized base.

[0089] (6) Normalized result output: The comprehensive weight of each indicator is normalized to ensure that the sum of all weights is equal to 1, and a corrected subjective weight vector is obtained to guide the decision-making process.

[0090] It should be noted that the subjective and objective weight calculation also includes objectively assigning weights to indicators through the entropy weight method to calculate the objective weights of indicators. The specific operation process is as follows: Figure 4 The objective weight calculation flow chart of the entropy weight method is shown in the figure.

[0091] (1) Constructing a data matrix: Collect the specific values ​​of all objects to be evaluated (such as different distribution networks) under various evaluation indicators to form a data matrix with m rows and q columns, expressed as:

[0092] S=(s ij ) m×q

[0093] Among them, S is the data matrix, m is the number of evaluation objects, q is the number of evaluation indicators, and s ij is the specific value of the i-th evaluation object under the j-th evaluation indicator.

[0094] (2) Data standardization: Since the dimensions and numerical ranges of different indicators may be different, the data matrix S needs to be standardized first to eliminate the dimension effect and make different indicators comparable. The standardization method varies depending on the type of indicator. The standardized data matrix is ​​obtained, which is expressed as:

[0095] X=(xij ) m×q

[0096] Among them, x ij is the specific value of the i-th evaluation object under the j-th evaluation indicator after standardization.

[0097] There are different processing methods for different types of indicators. The specific processing methods are as follows:

[0098] For extremely large indicators (i.e. indicators with larger values, the better), standardization is performed and expressed as:

[0099]

[0100] The extremely small index (i.e. the smaller the value, the better the index) is standardized and expressed as:

[0101]

[0102] For interval indicators (i.e. indicators with optimal interval range), standardization is performed and expressed as:

[0103]

[0104] Among them, δ j It is the optimal benchmark value of the interval for the appropriateness indicator.

[0105] (3) Calculate the entropy value: The entropy value reflects the uncertainty of information. The smaller the entropy value of an indicator, the greater the amount of information provided by the indicator and the more important its role in the evaluation.

[0106] By using the entropy weight method, the entropy value of the jth indicator is calculated, which is expressed as:

[0107]

[0108] Among them, e j is the entropy value of the jth indicator, b ij is the normalized ratio, Inm is the natural logarithm of m;

[0109] (4) Calculate objective weight: Calculate the objective weight of each indicator based on the entropy value. The weight value reflects the relative importance of each indicator in the overall evaluation system. Calculate the objective weight of the jth indicator, expressed as:

[0110]

[0111] Among them, h j is the objective weight of the jth indicator.

[0112] (5) Obtaining the objective weight vector: The objective weights of all indicators are combined to form a q-dimensional vector, which is the objective weight vector of the evaluation indicator system.

[0113] Calculate the objective weight of each indicator and construct the objective weight vector matrix of the evaluation indicator system, which is expressed as:

[0114] h=(h1,h2,...,h q )

[0115] Among them, h is the objective weight vector matrix, h q is the objective weight vector of the qth indicator.

[0116] It should also be noted that determining the indicator weights is the core link in evaluating the morphological characteristics of the distribution network. It reflects the importance of each indicator to the overall evaluation target and directly affects the rationality of the evaluation conclusion. The present invention comprehensively considers the role of subjective and objective weights. In order to reduce the random influence caused by a single expert scoring the indicator separately at each level in the traditional method, an improved hierarchical analysis method is used to construct a judgment matrix to determine the subjective weights of the indicators at each level, and a consistency test is implemented. The ordered weighted average operator is used to adjust the preliminary weight values ​​obtained by AHP, which not only reduces the influence of extreme value deviations on the accuracy of AHP weights, but also enhances the stability and accuracy of subjective weights. For objective weights, the entropy weight method is used for objective weighting to determine the relative importance of each indicator in the evaluation index system. After the collected original indicator data is standardized, it is determined according to the weight method. In this way, the existing information can be fully utilized, the deviation caused by personal subjective judgment can be effectively reduced, and the weight distribution can be ensured to be more fair.

[0117] S3: Optimize the subjective and objective combination weights according to game theory to obtain the optimal indicator system combination weights.

[0118] Furthermore, optimizing the subjective and objective weights according to game theory includes using the Nash equilibrium principle to coordinate and optimize the subjective and objective weights. Figure 5 The game theory combination weight calculation flow chart is shown in the figure.

[0119] (1) Constructing a comprehensive weight vector set: Assume that there are L different methods to obtain basic weights. The weight vector generated by each method is denoted as a = (a1, a2, ..., a L ), set the linear combination weight coefficient of each method to g = (g1, g2, ..., g L ), then the comprehensive weight vector set can be expressed as the weighted average of all basic weight vectors, and the comprehensive weight vector set is constructed as:

[0120]

[0121] Among them, U is the comprehensive weight vector set, a is the basic weight vector, a k is the basic weight vector of the kth method, g is the linear combination weight coefficient, g k is the weight coefficient of the kth method.

[0122] (2) Establishing an objective function game model: The goal is to minimize the total deviation between each basic weight vector and the comprehensive weight vector, which can be achieved by minimizing the following objective function, expressed as:

[0123] min||Ua k ||2,k=1,2,...,L

[0124] Among them, ‖Ua k ‖2 is the objective function, min‖Ua k ‖2 is the comprehensive weight U and any basic weight a k The Euclidean distance between them is minimized, ‖·‖2 is the bi-norm of the vector.

[0125] (3) Model solution: By using the matrix differential properties, the objective function can be transformed into a linear equation system of linear combination weight coefficients. By solving this set of linear equations, the linear combination weight coefficients are determined. The linear equation system of linear combination weight coefficients is expressed as:

[0126]

[0127] (4) Normalization: To ensure the rationality and comparability of the weight coefficients, the linear combination weight coefficients obtained are normalized to obtain the combined weight of the indicator system determined by game theory, that is, the optimal combined weight of the indicator system, which is expressed as:

[0128]

[0129] Among them, a * is the combined weight of the optimal indicator system, g k* is the normalized linear combination weight coefficient, g k* Will satisfy

[0130] It should be noted that game theory optimizes the subjective and objective combined weights, aiming to achieve the Nash equilibrium state between the subjective and objective weights, that is, to ensure that the deviation between the basic weights and the final comprehensive weight is minimized, and to overcome problems such as unreasonable information distribution that may arise in traditional weight combination methods. In practical applications, different distribution network operating conditions and renewable energy access conditions will change rapidly. Through the optimization of game theory, the weight distribution of each evaluation indicator can be adjusted dynamically in real time, ensuring the rationality and effectiveness of the evaluation.

[0131] Embodiment 2 is an embodiment of the present invention, which provides a method for weighting distribution network morphological characteristic evaluation indicators based on combined weights. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0132] In this embodiment, an experiment on the evaluation of distribution network morphological characteristics was conducted to verify the effectiveness and superiority of the indicator weighting method based on subjective and objective weight calculation and game theory optimization. The experiment selected a certain city's distribution network as the research object. The corresponding indicator system has been established by relevant experts, covering distribution network efficiency indicators and source-grid-load-storage interaction indicators. These indicators specifically include standard connection rate, line overload rate, distributed power supply penetration rate, etc., so as to comprehensively reflect the operating status and morphological changes of the distribution network.

[0133] To ensure the reliability of the experimental results, the experimental process is divided into several stages. First, determine the data indicators that need to be collected, use EMS (energy management system) to obtain data of each indicator in real time, and collect the operating parameters of several distribution networks in the specified area. These data are recorded in digital form to form an original data set to be analyzed. Subsequently, the indicators are standardized to eliminate the dimensional differences between the indicators and effectively achieve relative comparison of the indicators.

[0134] Next, we invited several experts in the field to independently evaluate each indicator and construct a judgment matrix using the analytic hierarchy process. We generated subjective weights through expert opinion data and implemented consistency checks to ensure the reliability of expert evaluations. At the same time, we applied the entropy weight method to objectively weight each indicator. This step calculated the entropy value of each indicator based on the collected data matrix, obtained the objective weight, and finally combined the subjective weight with the objective weight.

[0135] In the weight optimization phase, the game theory principle is applied to find the Nash equilibrium between each basic weight and the final comprehensive weight. Through multiple iterations of optimizing the combined weights, a set of overall optimal indicator weights is finally obtained.

[0136] Refer to Table 3 for a comparative analysis of the experimental data.

[0137] Table 3 Experimental data record table

[0138]

[0139]

[0140] Through the analysis of the experimental data in Table 3, we can clearly observe the performance of each distribution network in terms of efficiency indicators and source-grid-load-storage interaction indicators. From the data, we can see that the standard connection rate of distribution network A reaches 95%, while distribution network D is the lowest, only 87%. This shows that distribution network A has a clear advantage in the number of lines that meet the design specifications. In terms of line overload rate, distribution network B is the worst performer, with a line overload rate of up to 25%, exceeding the acceptable load standard of 80%, which may lead to an increase in distribution safety hazards.

[0141] In terms of the comprehensive voltage qualification rate, distribution network C performed the best, achieving a qualification rate of 92%, indicating that the distribution network is more effective in maintaining voltage stability. By comparing the penetration rate of distributed power sources and the penetration rate of charging piles, the penetration rate of charging piles in distribution network E is 29%, demonstrating its support for the access of electric vehicles.

[0142] In terms of subjective and objective weight calculation, through the standardization of the above data and weight integration, game theory can be used to optimize these weights to achieve a Nash equilibrium. This method effectively coordinates the difference between the subjective evaluation of experts and objective data, and reduces the shortcomings of the traditional weight allocation method that may affect the overall evaluation results due to the judgment of a single expert.

[0143] By comparing the data of the above different distribution networks, it can be found that the method based on subjective and objective weight calculation combined with game theory optimization can more comprehensively and objectively reflect the actual operating status of each distribution network when analyzing the morphological characteristics of the distribution network, thereby providing a scientific basis for the future optimization and integration of the distribution network. This method improves the accuracy and authority of the evaluation, lays the foundation for the widespread application of smart grids and renewable energy, and promotes the modern management of power systems. Therefore, the present invention is creative.

[0144] Example 3, reference Figure 6 , which is an embodiment of the present invention, provides a distribution network morphological characteristic evaluation index weighting system based on combined weights, including an index system construction module, a weight calculation module, and a weight optimization module.

[0145] The indicator system construction module is used to establish an evaluation indicator system and define the distribution network efficiency indicators and the source-grid-load-storage interaction indicator framework; the weight calculation module is used to assign weights to evaluation indicators through subjective and objective weight calculations; the weight optimization module is used to optimize the subjective and objective combined weights according to game theory to obtain the optimal indicator system combination weights.

[0146] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0147] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0148] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0149] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for weighting distribution network morphological characteristic evaluation indicators based on combined weights, characterized in that: include: Establish an evaluation index system and define distribution network efficiency indicators and the source-grid-load-storage interaction index framework; The evaluation indicators are weighted by subjective and objective weight calculation; According to game theory, the subjective and objective combination weights are optimized to obtain the optimal indicator system combination weights.

2. The method for weighting distribution network morphological characteristic evaluation indicators based on combined weights according to claim 1, characterized in that: The establishment of the evaluation index system includes constructing a distribution network morphology characterization evaluation index system through distribution network efficiency indicators and source-grid-load-storage interaction indicators to evaluate the morphological changes brought about by the access of photovoltaic power sources and electric vehicles to the distribution network.

3. The method for weighting distribution network morphological characteristic evaluation indicators based on combined weights according to claim 2, characterized in that: The definition of distribution network performance indicators and source-grid-load-storage interaction indicator framework includes building detailed multi-level evaluation standards through multi-dimensional data analysis; The distribution network performance indicators include standard connection rate, line overload rate, distribution transformer overload ratio, comprehensive voltage qualification rate, comprehensive line loss rate, power supply reliability rate, line N-1 pass rate, line segmentation rationality rate, intelligent switch standard coverage rate and distribution automation self-healing rate; The source-grid-load-storage interaction indicators include the penetration rate of distributed power sources, the penetration rate of charging piles, the success rate of switching off or on the grid without power outages, the energy storage configuration rate, the supply-storage ratio, the peak-to-valley difference rate of typical daily load, the coverage rate of distribution transformer intelligent terminals, the comprehensive prediction accuracy, the equivalent controllable rate, and the proportion of users with dynamic electricity prices.

4. The method for weighting distribution network morphological characteristic evaluation indicators based on combined weights according to claim 3, characterized in that: The subjective and objective weight calculation includes optimizing the analytic hierarchy process by using the ordered weighted average operator to subjectively weight the indicators and calculate the subjective weight of the indicators; The subjective weighting of indicators includes defining the indicator system at the criterion level, and constructing a judgment matrix based on the pairwise comparison of different indicators under the same criterion by the Yth expert, which can be expressed as: WITH Y =(from ij ) n×n Among them, Z Y is the judgment matrix based on the Yth expert's suggestion, z ij is the importance of the i-th indicator relative to the j-th indicator, and n is the number of indicators; Based on the judgment matrix, the maximum eigenvalue λ max The eigenvector corresponding to the maximum eigenvalue is normalized and the subjective weight vector of the judgment matrix recommended by the Yth expert is output, which is expressed as: IN Y =(w1,w2,...,w n ) Among them, W Y is the subjective weight vector of the judgment matrix recommended by the Yth expert, w n is the weight of the nth indicator; The consistency test is performed on the subjective weight vector of the judgment matrix recommended by the Yth expert, which is expressed as: Where D is the consistency ratio; When the consistency ratio is less than or equal to 0.1, the judgment matrix satisfies consistency; When the consistency ratio is greater than 0.1, the judgment matrix does not meet the consistency, and the element values ​​in the judgment matrix are adjusted until the consistency is met; Calculate the subjective weight vectors of the judgment matrices suggested by different experts, and construct the subjective weight vector matrix of the judgment matrices suggested by different experts, which can be expressed as: Among them, B is the subjective weight vector matrix of the judgment matrix recommended by different experts, y is the number of experts, and y is the transpose operation of the vector. is the transpose of the subjective weight vector of the judgment matrix recommended by the Yth expert; Based on the subjective weight vector matrix B, the subjective weight vector is corrected by the ordered weighted average operator. For each indicator, the weight values ​​given by different experts are collected and arranged in descending order. The absolute weight of the jth indicator is calculated, which is expressed as: in, is the absolute weight of the jth index, v t is the order weight factor, p t is the t+1th weight value formed by arranging the y weight values ​​corresponding to the jth indicator in descending order, To select t items from y-1, 2 y-1 is the normalized base; The comprehensive weight of each indicator is normalized to obtain the modified subjective weight vector.

5. The method for weighting distribution network morphological characteristic evaluation indicators based on combined weights according to claim 4, characterized in that: The subjective and objective weight calculation also includes objectively assigning weights to the indicators through the entropy weight method to calculate the objective weights of the indicators; The objective weighting of indicators includes collecting the specific values ​​of the object to be evaluated under each evaluation indicator and constructing a data matrix, which can be expressed as: S=(s ij ) m×q Among them, S is the data matrix, m is the number of evaluation objects, q is the number of evaluation indicators, and s ij is the specific value of the i-th evaluation object under the j-th evaluation indicator; The data matrix S is standardized, including standardization according to different indicator types and standardization of extremely large indicators, which can be expressed as: Among them, x ij is the specific value of the i-th evaluation object under the j-th evaluation indicator after standardization; For extremely small indicators, standardization is performed and expressed as: The interval indicators are standardized and expressed as: Among them, δ j It is the optimal benchmark value of the interval for the appropriateness index; The standardized processing results of different indicator types form a standardized data matrix, which is expressed as: X=(x ij ) m×q By using the entropy weight method, the entropy value of the jth indicator is calculated, which is expressed as: Among them, e j is the entropy value of the jth indicator, b ij is the normalized ratio, Inm is the natural logarithm of m; According to the entropy value of the jth indicator, the objective weight of the jth indicator is calculated, which is expressed as: Among them, h j is the objective weight of the jth indicator; Calculate the objective weight of each indicator and construct the objective weight vector matrix of the evaluation indicator system, which is expressed as: h=(h1,h2,...,h q ) Among them, h is the objective weight vector matrix, h q is the objective weight vector of the qth indicator.

6. The method for weighting distribution network morphological characteristic evaluation indicators based on combined weights according to claim 5, characterized in that: Optimizing the subjective and objective combined weights according to game theory includes using the Nash equilibrium principle to coordinate and optimize the subjective and objective weights; Optimizing subjective and objective weights involves obtaining basic weights through L methods and constructing a comprehensive weight vector set, which is expressed as: Among them, U is the comprehensive weight vector set, a is the basic weight vector, a k is the basic weight vector of the kth method, g is the linear combination weight coefficient, g k is the weight coefficient of the kth method; The objective function game model is established to minimize the total deviation between each basic weight vector and the comprehensive weight vector, and minimize the objective function, which is expressed as: min||Ua k ||2,k=1,2,...,L Among them, ||Ua k ||2 is the objective function, min||Ua k ||2 is the comprehensive weight U and any basic weight a k Minimize the Euclidean distance between them, ||·||2 is the vector norm; Based on matrix differentiation, the objective function is transformed into a linear equation system of linear combination weight coefficients, expressed as: By solving the linear equation, the linear combination weight coefficients are obtained.

7. The method for weighting distribution network morphological characteristic evaluation indicators based on combined weights according to claim 6, characterized in that: The obtaining of the optimal index system combination weight includes normalizing the linear combination weight coefficients obtained by solving to obtain the index system combination weight determined by game theory, that is, the optimal index system combination weight, which is expressed as: Among them, a* is the combined weight of the optimal indicator system, g k* is the normalized linear combination weight coefficient.

8. A system using the method for weighting distribution network morphological characteristic evaluation indexes based on combined weights as claimed in any one of claims 1 to 7, characterized in that: Including indicator system construction module, weight calculation module, and weight optimization module; The indicator system construction module is used to establish an evaluation indicator system, define distribution network efficiency indicators and source-grid-load-storage interaction indicator framework; The weight calculation module is used to assign weights to the evaluation indicators through subjective and objective weight calculation; The weight optimization module is used to optimize the subjective and objective combined weights according to game theory to obtain the optimal indicator system combined weights.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for weighting distribution network morphological characteristic evaluation indicators based on combined weights described in any one of claims 1 to 7 are implemented.

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