Multi-level urban power grid self-similarity quantification method and system considering power transmission function and source load distribution

Abstract

The invention provides a multi-level urban power grid self-similarity quantification method and system considering a power transmission function and source load distribution, and belongs to the technical field of power grid system optimization. According to an urban power grid self-similarity concept, considering a path transmission capacity, and modeling an urban power grid into a weighted network; according to the weighted network, calculating the weighted fractal dimension of the grid structure of the urban power grid; according to the source load self-similarity concept and index, the calculation of the source load quality dimension is carried out for different scales in the weighted fractal dimension calculation process. According to the method, two urban power grid physical attributes of power transmission capacity and source load distribution are considered, the multi-level self-similarity characteristics of the urban power grid can be effectively quantified, the urban power grid source load distribution characteristics and the difference of different levels of power transmission functions are reflected, and the characteristics and evolution mechanism of the urban power grid are described from the bottom layer mechanism level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid system optimization, and particularly relates to a multi-level urban power grid self-similarity quantification method and system considering power transmission function and source-load distribution. BACKGROUND

[0002] The urban power grid is the lifeline of economic development. For a long time, with the development of economy and society, the urban power grid has gradually evolved to the current form, which is a long and slow process. In recent years, with the collaborative evolution of source, network and load of urban power grid, a multi-level self-similar system has gradually emerged, that is, the topology of each voltage level power grid has a certain self-similarity, and all contains elements such as source, network and load. The structure still maintains similarity at different scales, and the self-similarity needs to be quantified to depict the characteristics and evolution mechanism of urban power grid from the bottom mechanism of form evolution. Therefore, by considering the power transmission function and the source-load distribution, the self-similarity of the urban power grid, which is an engineering network, is quantified, so as to provide a basis for the future development planning and reasonable evolution of the urban power grid.

[0003] The existing self-similarity quantification method is mainly aimed at geometric figures and complex networks in nature, and quantitatively analyzed by means of fractal theory, wherein the box covering method is mainly used for the topological self-similarity quantification, and the spectral analysis method is mainly used for the self-similarity quantification of time series. At the beginning of the birth of fractal theory, the focus is on the geometric figures in nature, such as the shape of the coastline, the shape of the leaf, the distribution of the galaxy and the like, and these figures have self-similarity in the statistical sense: that is, the structural characteristics are similar from different spatial scales. Self-similarity can be defined as (approximate) invariance under a certain scale transformation. And the fractal dimension is used to represent the self-similarity. With the development of complex network theory, scholars found that complex networks also have certain invariance, i.e. self-similarity, at different scales, and proposed a method for representing the self-similarity of complex networks. Network science believes that network structure determines network function; in addition, since the power supply and load are the supply and demand subjects in the power grid, the source and load distribution also greatly affects the function of the power grid. The voltage level of the urban power grid includes 500kV, 220kV, 110kV / 35kV, 10kV and 380V / 220V. From the 500kV level, the power grid is a graph formed by connecting several 500kV substations through lines, and some nodes are connected with power sources; and then to the 220kV substation power grid, each substation power grid is supplied by one or more 500kV substations, and some power sources are also distributed in the substation power grid; and then to the 110 / 35kV network, each network is also connected with one or more 220kV substations, and some power sources are also distributed. In this way, each level can be regarded as a graph or subsystem that is connected with the upper-level power grid and contains source, network and load elements. The urban power grid is slowly evolved to the current form with the development of the city, and the internal driving force of the form evolution is the increasing demand for power grid function due to the increasing power load. This is very similar to the natural growth or formation process in nature (such as trees and coastlines), which gradually emerges self-similar structures in the growth and evolution process. However, the existing research does not consider the physical properties of engineering networks for the quantification of the self-similarity of urban power grids. SUMMARY

[0004] The purpose of the present application is to analyze the underlying mechanism of the self-similarity of urban power grids, support the development planning of urban power grids and the reasonable evolution of the form, and provide a multi-level urban power grid self-similarity quantification method and system considering the power transmission function and source and load distribution, so as to solve at least one of the technical problems in the above background technology.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0006] In a first aspect, the present application provides a multi-level urban power grid self-similarity quantification method considering the power transmission function and source and load distribution, comprising:

[0007] According to the self-similarity concept of the urban power grid, the urban power grid is modeled into a weighted network considering the transmission capacity of the transmission path;

[0008] According to the weighted network, the weighted fractal dimension of the network structure of the urban power grid is calculated;

[0009] According to the self-similarity concept and index of the source and load, the quality dimension of the source and load is calculated for different scales in the calculation process of the weighted fractal dimension.

[0010] As a further limitation of the first aspect of the application, the self-similarity concept of the urban power grid includes: the topologies of each voltage level power grid have a certain similarity, and each contains elements such as source, network and load, and has a certain power self-balancing ability and local self-optimization ability; the modeling principles of the urban power grid are: ① converting the power transmission path of the urban power grid into a graph with weighted edges, and the transmission capacity is the weight of the edge; ② converting the 500kV, 220kV and 110 / 35kV buses in the substation into nodes and setting the levels to 1, 2 and 3 respectively; ③ converting the multi-loop line between nodes into an edge, and adding the power transmission capacity of the multi-loop line as the weight.

[0011] As a further limitation of the first aspect of the application, the weighted fractal dimension calculation of the network structure includes: the node distance of the weighted network is the sum of the weights of the edges contained in the shortest path connecting two nodes; a box of a given size is used to cover the entire network, and the diameter of the local network covered by each box is not greater than the size of the box The entire network is covered with a plurality of "boxes", and the power-law relationship between the size of the box and the minimum number N of boxes covering the entire network under the size is observed, and the power exponent is the weighted fractal dimension, which represents the self-similarity of the network structure of the urban power grid.

[0012] As a further limitation of the first aspect of the application, the calculation method of the weighted fractal dimension is to obtain the negative value of the slope of the fitting straight line by linear fitting and ; wherein, the setting rule of the box size is: arranging the edge weights in ascending order, the smallest box size is the smallest edge weight value, and thereafter, the edge weight values are added one by one in ascending order as the next box size, and the largest box size is the network diameter.

[0013] As a further limitation of the first aspect of the application, the covering process is: according to the edge weight, the box size is set, a new graph is first created, for each box size, if the distance between nodes i and j in the original graph is greater than the current box size, the edge between nodes i and j in the new graph is retained, otherwise the edge between nodes i and j is deleted; secondly, the strength of the node​ According to the greedy coloring algorithm, according to Color the nodes in the new graph from high to low, and the colors of adjacent nodes are different, and the colors of isolated nodes are the same The highest node color is the same, and one color represents a box, numbered Nodes with the same color are in the same box, and the set of all boxes is The node set in each box is ; Record the number of boxes of this size Calculate the weighted fractal dimension under double log coordinates .

[0014] As a further limitation of the first aspect of the application, in the process of box covering, for each box size, the source load mass of the covered box is calculated, and the fluctuation of the source load mass dimension under all sizes is described according to the coefficient of variation, that is, the fluctuation of the source load distribution; Specifically: after covering the entire network, identify the number of all internal nodes under this size The highest voltage level appears in the box with a total load greater than 0, and the box containing the node with the highest voltage level is selected in these boxes, and the source load mass of the selected box is calculated, and only the node with the highest voltage level is considered during calculation.

[0015] Secondly, the present application provides a multi-level urban power grid self-similarity quantification system considering power transmission function and source load distribution, comprising:

[0016] The construction module is used to model the urban power grid into a weighted network according to the concept of urban power grid self-similarity and considering the transmission capacity of the path.

[0017] The first calculation module is used to calculate the weighted fractal dimension of the urban power grid network structure according to the weighted network.

[0018] The second calculation module is used to calculate the source load mass dimension for different scales in the process of weighted fractal dimension calculation according to the source load self-similarity concept and index.

[0019] Thirdly, the present application provides a non-transitory computer readable storage medium for storing computer instructions, which are executed by a processor to realize the multi-level urban power grid self-similarity quantification method considering power transmission function and source load distribution.

[0020] In a fourth aspect, the present application provides a computer device comprising a memory and a processor, the processor and the memory being in communication with each other, the memory storing program instructions executable by the processor, and the processor invoking the program instructions to execute the method for quantifying self-similarity of multi-level urban power grid considering power transmission function and source-load distribution as described in the first aspect.

[0021] In a fifth aspect, the present application provides an electronic device comprising a processor, a memory and a computer program, wherein the processor is connected with the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to make the electronic device execute instructions for implementing the method for quantifying self-similarity of multi-level urban power grid considering power transmission function and source-load distribution as described in the first aspect.

[0022] The present application has the advantages of taking into account both the power transmission capacity and the source-load distribution of urban power grid, effectively quantifying the multi-level self-similarity characteristics of urban power grid, reflecting the source-load distribution characteristics of urban power grid and the differences in power transmission function of different levels, and describing the characteristics and evolution mechanism of urban power grid from the bottom mechanism level.

[0023] The advantages of the additional aspects of the present application will be more apparent from the following description section or be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0025] Figure 1 The self-similarity structure diagram of urban power grid described in the embodiments of the present application.

[0026] Figure 2 The flowchart of the method for quantifying self-similarity of multi-level urban power grid considering power transmission function and source-load distribution described in the embodiments of the present application.

[0027] Figure 3 The source-load distribution mode diagram under the scenario 1 source-load complete self-similarity for the same urban power grid network structure setting described in the embodiments of the present application.

[0028] Figure 4 The source-load distribution mode diagram under the scenario 2 power source concentrated distribution in the upper layer for the same urban power grid network structure setting described in the embodiments of the present application.

[0029] Figure 5A source-load distribution mode diagram under random distribution of the scene 3 power source for the same urban power grid framework structure provided by the embodiment of the present application.

[0030] Figure 6 A diagram for participating source-load calculation when the entire quantization is performed under the source-load completely self-similar scenario according to the embodiment of the present application.

[0031] Figure 7 A diagram for calculation result of source-load quality dimension of the framework according to the embodiment of the present application.

[0032] Figure 8 A diagram for calculation result of weighted fractal dimension of the framework according to the embodiment of the present application. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described below in detail, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as a limitation of the present application.

[0034] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as that generally understood by those skilled in the art to which the present application belongs.

[0035] It should also be understood that terms such as those defined in a general dictionary should be understood to have meanings consistent with those in the context of the prior art, and unless defined as such, should not be interpreted in an idealized or overly formal sense.

[0036] Those skilled in the art can understand that, unless otherwise stated, the singular forms "a", "an" and "the" used herein also include the plural forms. It should be further understood that the phrase "comprising" used in the specification of the present application means that the features, integers, steps, operations, elements and / or groups exist, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0037] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. Those skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0038] In order to facilitate the understanding of the present application, the present application will be further explained and described below with specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute limitations on the embodiments of the present application.

[0039] Those skilled in the art should understand that the drawings are only schematic diagrams of the embodiments, and the components in the drawings are not necessarily necessary for the implementation of the present application.

[0040] Embodiment 1

[0041] In this embodiment 1, a multi-level urban power grid self-similarity quantification system considering power transmission function and source load distribution is first provided, comprising: a construction module for modeling the urban power grid into a weighted network according to the concept of urban power grid self-similarity, considering the transmission capacity of the path; a first calculation module for calculating the weighted fractal dimension of the urban power grid network structure according to the weighted network; a second calculation module for calculating the source load quality dimension for different scales in the weighted fractal dimension calculation process according to the concept and index of source load self-similarity.

[0042] In this embodiment, the multi-level urban power grid self-similarity quantification method considering power transmission function and source load distribution is realized by using the above system, comprising: modeling the urban power grid into a weighted network according to the concept of urban power grid self-similarity, considering the transmission capacity of the path; calculating the weighted fractal dimension of the urban power grid network structure according to the weighted network; calculating the source load quality dimension for different scales in the weighted fractal dimension calculation process according to the concept and index of source load self-similarity.

[0043] The concept of urban power grid self-similarity includes: the topologies of each voltage level power grid have certain similarity, and each contains source network load elements, and has certain power self-balancing ability and local self-optimization ability; the urban power grid modeling principle: ① converting the power transmission path of the urban power grid into an edge with weight in the graph, and the transmission capacity is the weight of the edge; ② converting the 500kV, 220kV, 110 / 35kV bus in the substation into nodes and setting the levels to be 1, 2, 3 respectively; ③ converting the multi-loop line between nodes into an edge, and adding the power transmission capacity of the multi-loop line as the weight.

[0044] The weighted fractal dimension of the network structure is calculated as follows: the node distance in the weighted network is the sum of the weights of the edges contained in the shortest path connecting two nodes; the entire network is covered by boxes of a given size, and the diameter of the local network covered by each box is no larger than the size of the box. The entire network is covered by several "boxes". The power-law relationship between different box sizes and the minimum number of boxes N that can cover the entire network under a given size is observed. The power exponent is the weighted fractal dimension, which is used to characterize the self-similarity of the urban power grid structure.

[0045] The weighted fractal dimension is calculated by linear fitting. and The negative value of the slope of the fitted line is... The rules for setting the box size are as follows: arrange the edge weights in ascending order, with the smallest box size being the smallest edge weight value. Then, in ascending order of edge weights, accumulate the edge weight values ​​to obtain the next box size, with the largest box size being the network diameter.

[0046] The covering process is as follows: First, a new graph is created based on the box size set according to the edge weights. For each box size, if the distance between nodes i and j in the original graph is larger than the current box size, then the edge between nodes i and j in the new graph is retained; otherwise, the edge between i and j is deleted. Second, the strength of the nodes is calculated. According to the greedy coloring algorithm, according to Color the nodes in the new graph from highest to lowest color, with adjacent nodes having different colors and isolated nodes having the same color. The highest nodes are all the same color, each color represents one box, and they are numbered as follows: Nodes of the same color belong to the same box. The set of all such boxes is denoted as . The set of nodes within each box is Record the number of boxes of this size. Calculate the weighted fractal dimension in double log coordinates. .

[0047] During the box coverage process, for each box size, the source payload mass is calculated for the covered boxes, and the fluctuation of the source payload mass dimension across all sizes is described using the coefficient of variation, i.e., the fluctuation of the source payload distribution; specifically, after covering the entire network, the number of all internal nodes at that size is identified. The highest voltage level appears in the boxes with a total load greater than 0. The boxes containing the highest voltage level are selected from these boxes. The source load quality is calculated for the selected boxes, and only the nodes with the highest voltage level are considered in the calculation.

[0048] Embodiment 2

[0049] The physical properties of the urban power grid, which are different from general complex networks, mainly include: the network contains different voltage levels, the transmission capacity of the transmission channel is different, and the source and load distribution exists in different scales / levels. As shown in the self-similar structure of the urban power grid, the network frame has similarity at different scales, and contains elements such as sources and loads, and has certain self-balancing ability of power and local self-optimization ability. Figure 1

[0050] As shown in the self-similar structure of the urban power grid, the network frame has similarity at different scales, and contains elements such as sources and loads, and has certain self-balancing ability of power and local self-optimization ability. Figure 2 As shown in the self-similar structure of the urban power grid, the network frame has similarity at different scales, and contains elements such as sources and loads, and has certain self-balancing ability of power and local self-optimization ability.

[0051] The concept of self-similarity of urban power grid: the topologies of each voltage level power grid have certain similarity, and contain elements such as sources and loads, and have certain power self-balancing ability and local self-optimization ability. The modeling principle of urban power grid: ① Convert the transmission path of urban power grid into an edge with weight in the graph, and the transmission capacity is the weight of the edge; ② Convert the 500kV, 220kV and 110 / 35kV buses in the substation into nodes respectively and set the levels to 1, 2 and 3 respectively; ③ Convert the multi-loop line between nodes into an edge, and add the transmission capacity of the multi-loop line as the weight.

[0052] The weighted fractal dimension of the network structure is calculated. Specifically, the node distance of the weighted network is the sum of the weights of the edges contained in the shortest path connecting two nodes, as shown in formula (1). The weight of the edge is the weight of the edge, and the node distance is the maximum value of the network diameter. The entire network is covered by boxes of a given size (the local network diameter covered by each box is not greater than the box size ), and the entire network is covered by a number of "boxes". The relationship between the different box sizes and the minimum number of boxes covering the entire network at this size satisfies the power law relationship of formula (2), and the power exponent is the weighted fractal dimension , which represents the self-similarity of the urban power grid network frame. The calculation method of the weighted fractal dimension is to obtain the negative value of the slope of the fitting straight line and by linear fitting ​The box size setting rule is that the edge weight is arranged from small to large, the smallest box size is the smallest edge weight value, and then the edge weight value is added one by one as the next box size in the order of small to large, and the largest box size is the network diameter.

[0053] (1)

[0054] (2)

[0055] The covering process is specifically: according to the edge weight setting box size, first create a new graph, for each box size, if the distance between nodes i and j in the original graph is greater than the current box size, the edge between nodes i and j in the new graph is retained, otherwise the edge between nodes i and j is deleted. Secondly, according to formula (3), the strength of the node is calculated , according to the greedy coloring algorithm, according to from high to low coloring the nodes in the new graph, adjacent nodes have different colors, isolated nodes have the same color as the highest node color, one color represents a box, and the number is , the nodes with the same color are in the same box, and the set composed of all boxes is denoted as , the node set in each box is . Record the number of boxes under this size , and calculate the weighted fractal dimension under the double log coordinate according to formula (2) .

[0056] (3)

[0057] The source load self-similarity concept and index are proposed, and the source load quality dimension is calculated for different scales in the weighted fractal dimension calculation process. First, the index of the relevant source load distribution is defined, and the source load quality of the weighted network under a given box size is defined as , as shown in formula (4). It is used to represent the source load distribution of the highest voltage level node under this size. The quality of the box participating in the source load calculation under the given box size of the weighted network is defined as , as shown in formula (5), which is used to represent the source load distribution of the highest voltage level node existing in the box. And the coefficient of variation (CV) is used to describe the similarity of source load distribution under different scales, as shown in formula (6).

[0058] (4)

[0059] (5)

[0060] (6)

[0061] where, is the number of boxes involved in the calculation of source-load mass; and are the total power supply capacity and total load demand of the highest voltage level in the box, respectively, and are the total power supply capacity and total load demand of the lower level nodes connected to the node, respectively, is the set of nodes of the highest voltage level in the box, is the set of nodes of the lower voltage level connected to the node of the highest voltage level; is the standard deviation, is the mean.

[0062] In the process of box covering, for each box size, the source-load mass of the covered box is calculated, and the fluctuation of the source-load mass dimension under all sizes is described according to the coefficient of variation, i.e. the fluctuation of the source-load distribution. Specifically, after covering the entire network, the highest voltage level appearing in all internal nodes of the size and the total load greater than 0, the boxes containing the highest voltage level nodes are selected from these boxes, and the source-load mass of the selected boxes is calculated according to formula (5), and only the nodes of the highest voltage level are considered during calculation (if there is only one voltage level node inside, it is considered as the highest voltage level node).

[0063] For the same city grid structure, three source-load distribution modes are set: source-load completely self-similar, power supply concentrated distribution in the upper layer, and power supply random distribution, as shown in Figures 3 to 5 , the weighted fractal dimension of the grid and the source-load mass are calculated, and the detailed process of the entire quantitative method is shown according to the first source-load distribution mode, i.e. source-load completely self-similar, as shown in Figure 6 , the box size represents different grid scale, and the gradually increasing process represents the process of observing the city grid at a larger scale, which can be equivalent to the process of gradually merging the source-load of the lower grid to the upper grid, gradually increasing the coverage range and voltage level, and then observing the source-load distribution and grid at different scales.

[0064] Finally, Figure 7 , Figure 8 shows the calculation results of the weighted fractal dimension of the grid and the source-load mass dimension, from Figure 7It can be seen that when the box size is 120 (log(box size) is 2.08), the lowest voltage level, i.e., 110kV level source load, is observed; when the box size is 320 and 920 (log(box size) is 2.51 and 2.96), the 220kV level source load is observed; and when the box size is 2920, 5920 and 8840 (log(box size) is 3.47, 3.77 and 3.95), the 500kV level source load is observed. In scenario 1, the source load distribution mode is completely self-similar, and it can be seen that the power grid source load ratio under different box sizes (different scales) is the same. In scenario 2, the log(box size) is 3.47, and there is a non-zero source load mass value, which corresponds to the power distribution at the 500kV high voltage level. The smaller size box mainly covers the low voltage level area, and since the low voltage level area in scenario 2 is not distributed with power, the source load mass is 0. As the box size increases, at log(box size) of 3.47, the box can cover the power nodes in the 500kV level, so that the source load mass has a value, and the 500kV level node source load distribution is balanced, so the curve remains horizontal. In this scenario, since there is no power distribution at the low voltage level, the source load mass is 0, so the coefficient of variation is calculated from the point before the first non-zero point. In scenario 3, the power is randomly distributed at each level, so the source load mass is not completely self-similar, and the curve fluctuation amplitude (i.e., CV value) also reflects the degree of deviation of the entire urban power grid at each scale from the completely self-similar. By comparing scenarios 2 and 3, it can be seen that, compared to the power concentrated distribution in scenario 2, the CV value of the power randomly distributed at each level in scenario 3 is smaller, the source load self-similarity is higher, and the difference in the self-balancing ability (function) of the urban power grid at each scale is reflected. Figure 8 The weighted fractal dimension of the net frame is shown in the table, which indicates that the complexity of the structure is low, and most of the structures are repeated similar structures.

[0065] Embodiment 3

[0066] The embodiment 3 provides a non-transitory computer readable storage medium for storing computer instructions, which, when executed by a processor, implements a multi-level urban power grid self-similarity quantification method considering power transmission function and source load distribution as described above. The method comprises: modeling the urban power grid into a weighted network according to the concept of urban power grid self-similarity and considering the transmission capacity; calculating the weighted fractal dimension of the net frame structure of the urban power grid according to the weighted network; and calculating the source load mass dimension for different scales in the weighted fractal dimension calculation process according to the concept and index of source load self-similarity.

[0067] Embodiment 4

[0068] The embodiment 4 provides a computer device, comprising a memory and a processor, the processor and the memory are in communication with each other, the memory stores program instructions which can be executed by the processor, and the processor calls the program instructions to execute the method for quantifying self-similarity of multi-level urban power grid considering power transmission function and source load distribution as described above, the method comprises the following steps: according to the concept of self-similarity of urban power grid, the urban power grid is modeled into a weighted network by considering the transmission capacity of the path; according to the weighted network, the weighted fractal dimension of the network structure of the urban power grid is calculated; and according to the concept and index of source load self-similarity, the source load quality dimension is calculated for different scales in the process of weighted fractal dimension calculation.

[0069] Embodiment 5

[0070] The embodiment 5 provides an electronic device, comprising a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the instructions for realizing the method for quantifying self-similarity of multi-level urban power grid considering power transmission function and source load distribution as described above, the method comprises the following steps: according to the concept of self-similarity of urban power grid, the urban power grid is modeled into a weighted network by considering the transmission capacity of the path; according to the weighted network, the weighted fractal dimension of the network structure of the urban power grid is calculated; and according to the concept and index of source load self-similarity, the source load quality dimension is calculated for different scales in the process of weighted fractal dimension calculation.

[0071] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0072] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks

[0073] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 The flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0075] The above description is only a specific implementation of the present application, and is not intended to limit the protection scope of the present application. It should be understood by those skilled in the art that various modifications or changes can be made to the disclosed technical solutions without inventive labor, and all these modifications or changes should be covered within the protection scope of the present application.

Claims

1. A method for quantifying the self-similarity of a multi-level urban power grid considering power transmission function and source-load distribution, characterized in that, include: Based on the concept of self-similarity of urban power grids and considering the transmission capacity of the pathways, urban power grids are modeled as weighted networks. Based on the weighted network, the weighted fractal dimension of the urban power grid structure is calculated. Based on the concept and index of source-charge self-similarity, the source-charge mass dimension is calculated at different scales in the weighted fractal dimension calculation process.

2. The method for quantifying the self-similarity of multi-level urban power grids considering power transmission function and source-load distribution according to claim 1, characterized in that, The concept of self-similarity in urban power grids includes: the topology of power grids at various voltage levels has a certain degree of similarity, and all of them contain elements such as source, grid, and load, and have a certain power self-balancing capability and local self-optimization capability; the modeling principles of urban power grids are: ① Convert the power transmission paths of the urban power grid into weighted edges in the graph, with the transmission capacity as the edge weight; ② Convert the 500kV, 220kV, and 110 / 35kV buses in the substation into nodes and set their levels as 1, 2, and 3 respectively; ③ Convert the multiple circuits between nodes into a single edge, and sum the power transmission capacities of the multiple circuits as the weight.

3. The method for quantifying the self-similarity of multi-level urban power grids considering power transmission function and source-load distribution according to claim 1, characterized in that, The weighted fractal dimension of the network structure is calculated as follows: the node distance in the weighted network is the sum of the weights of the edges contained in the shortest path connecting two nodes; the entire network is covered by boxes of a given size, and the diameter of the local network covered by each box is no larger than the size of the box. The entire network is covered by several "boxes". The power-law relationship between different box sizes and the minimum number of boxes N that can cover the entire network under a given size is observed. The power exponent is the weighted fractal dimension, which is used to characterize the self-similarity of the urban power grid structure.

4. The method for quantifying the self-similarity of multi-level urban power grids considering power transmission function and source-load distribution according to claim 3, characterized in that, The weighted fractal dimension is calculated by linear fitting. and The negative value of the slope of the fitted line is... The rules for setting the box size are as follows: arrange the edge weights in ascending order, with the smallest box size being the smallest edge weight value. Then, in ascending order of edge weights, accumulate the edge weight values ​​to obtain the next box size, with the largest box size being the network diameter.

5. The method for quantifying the self-similarity of multi-level urban power grids considering power transmission function and source-load distribution according to claim 4, characterized in that, The covering process is as follows: Set the box size according to the edge weight. First, create a new graph. For each box size, if the distance between nodes i and j in the original graph is larger than the current box size, then the edge between nodes i and j in the new graph is retained; otherwise, the edge between i and j is deleted. Secondly, calculate the strength of the nodes. According to the greedy coloring algorithm, according to Color the nodes in the new graph from highest to lowest color, with adjacent nodes having different colors and isolated nodes having the same color. The highest nodes are all the same color, each color represents one box, and they are numbered as follows: Nodes of the same color belong to the same box. The set of all such boxes is denoted as . The set of nodes within each box is ; Record the number of boxes of this size. Calculate the weighted fractal dimension in double log coordinates. .

6. The method for quantifying the self-similarity of multi-level urban power grids considering power transmission function and source-load distribution according to claim 5, characterized in that, During the box coverage process, for each box size, the source payload mass is calculated for the covered boxes, and the fluctuation of the source payload mass dimension across all sizes is described using the coefficient of variation, i.e., the fluctuation of the source payload distribution; specifically, after covering the entire network, the number of all internal nodes at that size is identified. The highest voltage level appears in the boxes with a total load greater than 0. The boxes containing the highest voltage level are selected from these boxes. The source load quality is calculated for the selected boxes, and only the nodes with the highest voltage level are considered in the calculation.

7. A self-similarity quantification system for a multi-level urban power grid considering power transmission function and source-load distribution, characterized in that, include: The module is used to model the urban power grid as a weighted network based on the concept of urban power grid self-similarity and considering the transmission capacity of the pathway; The first calculation module is used to calculate the weighted fractal dimension of the urban power grid structure based on the weighted network. The second calculation module is used to calculate the source load mass dimension at different scales in the weighted fractal dimension calculation process, based on the source load self-similarity concept and index.

8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the self-similarity quantification method for multi-level urban power grids considering power transmission function and source-load distribution as described in any one of claims 1-6.

9. A computer device, characterized in that, The system includes a memory and a processor, which communicate with each other. The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute the self-similarity quantification method for multi-level urban power grids that considers power transmission function and source-load distribution as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the self-similarity quantification method for multi-level urban power grids considering power transmission function and source-load distribution as described in any one of claims 1-6.

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