CN106953319A - Method and device for optimal segmentation of distribution network lines - Google Patents

Abstract

The invention relates to an optimal segmentation method and device for a power distribution network line. The method comprises the following steps: acquiring a topological structure of a power distribution network line to be segmented and related parameters of the power distribution network line; inputting the topological structure and the related parameters into a preset optimal section model of the power distribution network line, and assigning values to variables of nodes which are not allowed to be provided with section switches or must be provided with section switches in the power distribution network line; and under the set constraint condition, calculating the number and the length of the sections of the power distribution network line by using the optimal section model of the power distribution network line. The invention can directly solve the variables of the segment number and the segment length at the same time, and the scheme is convenient and simple.

Description

Method and device for optimal segmentation of distribution network lines

technical field

The invention relates to the field of distribution network technology, in particular to a distribution network line optimal segmentation method and a distribution network line optimal segmentation device.

Background technique

The network frame of the urban medium-voltage distribution network is composed of medium-voltage main lines. With the development of the city, the load density continues to increase, and the medium-voltage main lines will be extended or new branches will appear as the load expands. The total failure rate of a single line increases with the increase of load and equipment in operation and the extension of the line. The increase of the failure rate will lead to the reduction of line reliability and endanger the safe and stable operation of the line. Therefore, it is very necessary to scientifically carry out the planning and transformation of urban medium-voltage distribution network.

An effective way to improve the reliability of the lines that have been put into operation is to segment them, that is, to install section switches on the original lines, especially to realize automatic section switches, the purpose of which is to enable the distribution network to carry out regional division of loads , narrow the fault and maintenance interval, reduce the range of user power outages caused by line faults, and cut off faulty sections to reduce power outage losses and improve line reliability when a fault occurs. However, since the section equipment itself needs to invest in cost, and with the increase of section switch equipment installed on the line, its maintenance cost and equipment failure rate will increase, so the number of section switches installed on the line is limited, how to balance Its reliability and economy are a very important issue. And this is also an important purpose of optimally segmenting the line.

Furthermore, since different users have different reliability requirements for power supply, it is not economical to provide all users with power with different reliability requirements at the same electricity price cost under the situation of power reform. It is very meaningful how to plan to improve the reliability of power supply in local areas and users by transforming lines with minimum equipment investment. Another goal of the optimal segmenting of the line is to make the line fully or partially meet the reliability requirements of specific users at the most economical cost and improve the quality of power supply.

In addition, segmenting lines and dividing load areas are prerequisites for distribution network reconfiguration. At this time, the optimal segmentation target under reconstruction can be the minimum line loss of the distribution network or the optimization of load distribution in each segment, that is, the optimal load balancing target.

When the model is used to optimally segment the distribution network lines, the previous models mainly perform two types of modeling: one is to solve the length of each segment when the number of segments is known, and the other is to calculate the length of each segment when the number of segments is known. When the number of segments is unknown, the number of segments is calculated, but it is necessary to assume that the segment lengths are the same, that is, the average segment is performed. These models generally need to be performed twice or even multiple times to obtain the number of segments and the length of each segment, and the scheme is more complicated.

Contents of the invention

Based on this, it is necessary to provide a method and device for the optimal segmentation of distribution network lines in order to solve the problem of complex schemes in the prior art for optimal segmentation of lines, which can simultaneously calculate the two variables of the number of segments and the length of segments The solution is convenient and simple.

A method for optimal segmentation of distribution network lines, comprising the steps of:

Obtain the topology structure of the distribution network lines to be segmented and the relevant parameters of the distribution network lines; the distribution network lines include trunk lines and several branch lines, and the relevant parameters include line parameters, reliability parameters and Economic parameters;

Inputting the topology and the relevant parameters into the preset distribution network line optimal segmentation model, and assigning values to the variables of the nodes in the distribution network line that are not allowed to set a section switch or must set a section switch The optimal segmentation model of the distribution network line represents the corresponding relationship between nodes and segments in the distribution network line with the first matrix, and represents the nodes of each branch line and the nodes with the branch line with the second matrix The association relationship of the nodes of the connected trunk line, with the topology structure and related parameters as input, and the segment number and segment length of the distribution network as output;

Under the set constraint conditions, the number of segments and the segment length of the distribution network lines are calculated from the optimal segment model of the distribution network lines.

An optimal segmenting device for distribution network lines, comprising:

The data acquisition module is used to obtain the topology structure of the distribution network line to be segmented and the relevant parameters of the distribution network line; the distribution network line includes a trunk line and several branch lines, and the relevant parameters include the line parameters, reliability parameters and economical parameters;

The data input module is used to input the topology structure and the related parameters into the preset distribution network line optimal segmentation model, and to set the section switch or must set the section switch in the distribution network line The variable assignment of the node of the switch; the optimal segmentation model of the distribution network line represents the corresponding relationship between nodes and segments in the distribution network line with the first matrix, and represents the nodes and the segments of each branch line with the second matrix The association relationship of the nodes of the main line connected with the nodes of the branch line, with the topology structure and related parameters as the input, and the segment number and segment length of the distribution network as the output;

The data calculation module is used to calculate the number of segments and the segment length of the distribution network line from the optimal segment model of the distribution network line under the set constraint conditions.

The optimal segmentation method and device for the above-mentioned distribution network line establishes the optimal segmentation model of the distribution network line in advance, the optimal segmentation model of the distribution network line corresponds to the line nodes and segments, and the branch line and the main line The association relationship of the distribution network is abstracted into a matrix form, and the segmentation characteristics and association relationship are mathematically described to form mathematical constraints that can be accurately analyzed. The section model can directly use mathematical optimization technology to solve the two variables of the number of sections and the length of the section at the same time. The frame structure optimizes the grid structure of the distribution network and improves the reliability of the distribution network operation. The scheme is convenient and simple.

Description of drawings

Fig. 1 is a schematic flow diagram of a distribution network line optimal segmentation method of an embodiment;

Fig. 2 is a schematic structural diagram of an optimal segmentation device for distribution network lines according to an embodiment.

detailed description

In order to further illustrate the technical means adopted by the present invention and the achieved effects, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and preferred embodiments.

As shown in Figure 1, a distribution network line optimal segmentation method, including steps:

S110. Obtain the topology structure of the distribution network line to be segmented and the relevant parameters of the distribution network line; the distribution network line includes a trunk line and several branch lines, and the relevant parameters include line parameters, reliability parameters and economic parameters;

S120. Input the topological structure and the relevant parameters into the preset distribution network line optimal segmentation model, and determine the nodes in the distribution network line that are not allowed to set a section switch or must be set with a section switch variable assignment; the optimal segmentation model of the distribution network line represents the corresponding relationship between nodes and segments in the distribution network line in the first matrix, and represents the nodes and the relationship between the nodes of each branch line and the branch line in the second matrix The relationship between the nodes of the main line connected to the nodes, and the topology structure and related parameters are input, and the number of segments and segment length of the distribution network are output;

S130. Under the set constraint conditions, calculate the number of segments and segment lengths of the distribution network lines from the optimal segment model of the distribution network lines.

The above method for optimal segmentation of distribution network lines can be realized by a corresponding program, and the program runs in a terminal, such as a computer or a smart phone. By establishing the optimal segmentation model of the distribution network, this method can directly use mathematical optimization technology to solve the two variables of the number of segments and the length of the segment at the same time, and the obtained results do not need to be corrected, and can be directly applied to the optimal segmentation scheme The implementation of the scheme is convenient and simple. Each step is described in detail below.

In step S110, the connection mode of the medium voltage distribution network generally includes single power source radial connection, dual power source ring network connection, three power source ring network connection, three-segment three-connection connection, and N-supply-one-standby (N-1) connection Wait. The so-called N-1 wiring mode means that N cable lines are connected to form a cable ring network. One of the lines is used as a public backup line to run at no-load during normal times, and other lines can run at full load. If one of the operating lines fails, Then the backup line can be put into operation through line switching. The present invention is not only suitable for the optimal segmentation of N-1 connection distribution network lines, but also suitable for the optimal segmentation of distribution network lines in connection mode.

In order to represent the actual line more accurately, in one embodiment, the step of obtaining the topology structure of the distribution network line to be segmented may include:

S1101. Describe the topology structure of the distribution network line to be segmented in a tree-like linked list; the nodes in the distribution network line include towers and switch cabinets;

S1102. Merge the nodes in the tree linked list whose load active power is zero, to obtain the topological structure of the distribution network line.

The data processing of the present invention is based on the actual line structure, using a tree-like linked list to represent the topological structure of the distribution network line, and using the positions of the actual towers and switch cabinets as line nodes, so that the obtained solution is closer to the actual line, that is All segmentation points correspond to the towers or switchgears that are determined to exist in the actual line, and each solution has practical significance, so that planners can directly apply the results to the planning of the actual distribution network line. Traversing the tree-like linked list and merging the nodes whose load active power is zero can obtain the topological structure of the distribution network.

It should be noted that the present invention does not limit the specific form of the topology of the distribution network, and those skilled in the art may describe the topology of the distribution network in other ways.

The relevant parameters of the distribution network line can be determined according to the parameters required by the established optimal segmentation model of the distribution network line, and the specific acquisition method can be realized according to the existing methods in the prior art. Since it is the main purpose of optimal segmentation to scientifically and rationally segment the distribution network with the minimum economic cost so that the operational reliability of the line meets the power supply reliability requirements of the load, in one embodiment, the correlation Parameters include line parameters, reliability parameters and economical parameters, etc. For example, the parameters of the line include: the node number of the line, the length of the line between towers or switch stations, the load carried by each tower or switch station, etc. For another example, the reliability parameters include: line failure rate parameters between nodes (including line failure rate, equipment failure rate and load failure rate), switching time, branch starting position and repair time and other parameters. For another example, the economical parameters include: electricity production ratio, revenue from comprehensive electricity sales, and switch unit price, etc. In order to facilitate subsequent calculations, the read data can be simply processed.

In step S120 and step S130, the optimal segmentation model of the distribution network line is established in advance. The objective function contained in the model can be set according to the goal to be achieved, for example, it is optimal to use the minimum economic cost to scientifically and rationally segment the distribution network so that the operational reliability of the line can meet the power supply reliability requirements of the load For segmental objectives, the objective function can be to minimize the total investment cost and power failure loss cost. It should be noted that the present invention does not limit the form of the objective function.

There are many ways to build the model. For example, in the process of modeling using the failure mode consequence analysis method, it is required that the number of segments and the length of the segments cannot be variables, otherwise the model cannot be solved. The previous models mainly carry out two types of construction: modulus; one is to solve the length of each segment when the number of segments is known, and the other is to solve the number of segments when the number of segments is unknown, but it needs to assume that the length of each segment is the same, that is, average segment. These models do not allow two variables to be solved at the same time, and generally need to be performed twice or even multiple times. However, this model abstracts the relationship between line nodes and segments into a matrix form, mathematically describes the segment features, forms mathematical constraints that can be accurately analyzed, and transforms the solution of multivariables into the solution of matrix variables, so that the model can be directly Mathematical optimization technology is used to solve the problem, and the obtained results can be directly applied to the implementation of the optimal segmentation scheme without correction.

The distribution network lines include trunk lines, so, in one embodiment, the first matrix includes a backbone matrix for representing the corresponding relationship between nodes and segments of the trunk line, that is, the optimal segment of the distribution network line The model describes the segment relationship of the main line nodes in the form of matrix. The matrix can be a 0-1 matrix A[n,k], the number of columns n is the total number of nodes of the line, and the number of rows k is the maximum number of segments allowed for the line.

Since each node must be assigned to a certain segment, that is, the column corresponding to the node in the matrix has one and only one element that is 1, and A[i][j]=1 means that node j is in the i-th segment, because the segment The continuity of , the elements of 1 in the matrix will be distributed in steps. Transforming the above-mentioned segmentation features into matrix structural constraints, in one embodiment, the constraints corresponding to the backbone matrix, that is, the matrix structure constraints of the segmentation features, include:

A[i][j]+A[i-1][j]≥A[i][j+1] i=0,1,..k-1 j=0,1,..m-2 ( 2)

A[i][j]∈{0,1} (3)

Among them, A is the backbone matrix; A[][] is the row and column of the backbone matrix; k is the maximum allowed segment number of the line; m is the number of nodes of the backbone line; since each node must be allocated to a certain segment, That is, the column corresponding to the node of the matrix has and only one element is 1, that is, A[i][j] is 1, which means that node j is in the i-th segment.

In order to give planners a more reasonable planning solution, the optimal segmentation model of the distribution network line also expresses the relationship between the nodes of each branch line and the nodes of the main line connected to the node of the branch line in a second matrix , used to divide the nodes belonging to the same segment. The optimal segmentation model of the distribution network line describes the relationship between each branch line node and the connected backbone node in the form of a matrix, which is used to divide the node part of each branch line node and the connected backbone node that belong to the same segment.

Transforming the associative features of branch lines and trunk lines into matrix structural constraints, in one embodiment, the constraints corresponding to the second matrix, that is, the matrix structural constraints of the associative features include:

A[i][g]≥A[i][j] i=0,1,..k-1 j∈M _h (4)

Among them, A is the backbone matrix; A[][] is the row and column of the backbone matrix; k is the maximum allowable segment number of the line; i is the i-th segment; g is the node connected to the backbone line in the h-th branch line j is the node j; M _h is the set of node numbers of the h branch line.

If the distribution network line also includes several branch lines, in one embodiment, the first matrix also includes a branch matrix used to represent the corresponding relationship between nodes and segments of each branch line, that is, the distribution network line most The optimal segmentation model describes the segmentation relationship of each branch line node of the N-1 line of the distribution network in the form of a matrix. The matrix can be a 0-1 matrix B _h [n,k], the number of columns n is the total number of nodes of the line (including the head-end node), and the number of rows k is the maximum number of segments allowed for the branch line.

Transforming the branch line segmentation features and the branch line and trunk relationship into matrix structural constraints, in one embodiment, the constraints corresponding to the branch matrix, that is, the matrix structure constraints of the branch line segmentation features, include :

B _h [i][j]+B _h [i-1][j]≥B _h [i][j+1] i=0,1,..k-1 j=0,1,..n -2 (7)

B _h [i][j]∈{0,1} (8)

Among them, B _h is the segmentation matrix of the h-th branch line; B _h [][] is the row and column of the h-th branch line; f is the first node number of the h-th branch line; M _h is the The set of node numbers of h branch lines; n is the number of nodes of the h branch line; since each node must be assigned to a certain segment, that is, the column corresponding to the node in the matrix has and only one element is 1, that is, B _h [i][j] is 1, indicating that node j is in the i-th segment.

The invention uses the same matrix to represent the branches, and increases the association constraints between the branches and the trunk, so that the model can solve the line structure with multiple branches, which is closer to the actual distribution network line, and can give planners more reasonable planning plan.

In one embodiment, the constraints may also include a power outage time constraint, and the power outage time constraint includes:

Among them, t[d] is the outage time of the load of the node numbered d; d is the node numbered d of the line; k is the kth node of the line; i is the i-th segment; A is the backbone matrix ; A[][] is the row and column of the backbone matrix; j is node j; n is the number of nodes of the h branch line; m is the number of nodes of the main line; n+m is the total number of nodes of the line; γ[ k] is the failure rate of the line where node k is located; γ[h] is the failure rate of the line where node h is located; t ₁ is the comprehensive operation time of the section switch; t ₂ is the comprehensive operation time of the tie switch; Load fault recovery time; B[][] is the row and column of the branch matrix; γ is the total failure rate of the line.

In an embodiment, the constraints may also include reliability constraints, and the reliability constraints include:

Among them, SAIDI is the average power outage duration suffered by each user powered by the system in a year; t[j] is the power outage time of node j load; P[j] is the load of node j; P is the total load; ASAI is the average power supply availability of the system; H ₁ is the lower limit of the average power supply availability of the system; ENSI is the system power shortage; L[j] is the average load of node j, which is equal to the product of the annual peak load and the load factor; H ₂ is The system power is below the upper limit.

Import the topology and related parameters into the optimal segmentation model of the distribution network line, set the reliability boundary conditions, and assign corresponding fixed values to the node variables that are not allowed to set the segment switch or must be set with the segment switch (for example, 0 means that the section switch is not allowed to be set, and 1 means that the section switch is allowed to be set), and the optimal section model of the distribution network line with multi-branch lines can be calculated to obtain the optimal distribution network line that meets the reliability requirements. Segmented solution, so as to help grid planners adjust the grid structure reasonably, optimize the grid structure of the distribution network, and improve the reliability of the distribution network operation. Among them, the nodes for which the section switch must be set and the nodes for which the section switch is not allowed to be set may be determined according to set conditions. In addition, the calculation of the optimal segmentation model of distribution network lines with multi-branch lines can not only obtain the number of segments and segment length, but also obtain the results of other related parameters, such as reliability parameters or economic parameters.

In order to better understand the present invention, a detailed introduction will be given below in conjunction with a specific embodiment.

Taking a 12-node distribution network line as an example, the system grid structure parameters and node loads are shown in Table 1, and the economic parameters and reliability parameters related to the distribution network are shown in Table 2.

Table 1 System grid structure parameters and node loads

node j Load (kW) Line length (m) 1 100 250 2 100 250 3 100 250 4 200 250 5 200 250 6 300 250 7 300 250 8 500 250 9 500 250 10 600 250 11 800 250 12 800 250

Table 2 reliability parameters and economic parameters

Import the above data and the topological structure of distribution network lines into the optimal segmentation model of distribution network lines for solution, and obtain the optimal optimal segmentation scheme of distribution network. The optimal segmentation model of distribution network lines is as follows:

In this embodiment, the total investment cost and the power outage loss cost are minimized as the objective function with NF:

Min NF＝Z+L (13)

In formula (13), Z is the total investment cost; L is the system power failure loss.

and have

Z＝C ₀ +C _d +C _j +(n-1)C _f (14)

Among them, C ₀ is the line investment; C _d is the investment of the outlet circuit breaker; n is the segment number of the line; C _f is the investment of the subsection switch. The section switch of the overhead line is a load switch, and the section switch of the cable line is the ring main unit on the main feeder; C _j is the sum of other expenses such as annual maintenance costs.

and have

L＝L ₁ +L ₂ (15)

ENS＝P SAIDI (16)

L ₁ =ENS·h·10 ⁻⁴ (17)

L ₂ =ENS·k·10 ⁻⁴ (18)

Among them, L ₁ is the direct economic loss (10,000 yuan); L ₂ is the indirect economic loss (10,000 yuan); L is the total power outage loss of the system (10,000 yuan); ENS is the power shortage of the line (kWh); SAIDI is the average power outage duration of the system (h); h is the comprehensive electricity sales income of the power company, that is, the power supply net profit of the power company (yuan/kWh), which is the power sales price and the power supply cost of the company The difference; k is the electricity production ratio.

The constraints are as follows:

(1) Backbone matrix constraints:

A[i][j]+A[i-1][j]≥A[i][j+1] i=0,1,..k-1 j=0,1,..m-2 ( 20)

A[i][j]∈{0,1} (21)

Among them, A is the backbone matrix; A[][] is the row and column of the backbone matrix; k is the maximum allowed segment number of the line; m is the number of nodes of the backbone line; since each node must be allocated to a certain segment, That is, the column corresponding to the node of the matrix has and only one element is 1, that is, A[i][j] is 1, which means that node j is in the i-th segment.

This example does not contain branches, so there is no need to establish branching matrices and corresponding correlation matrices.

(2) Power outage time constraints:

Among them, _t1 is the comprehensive operation time of the section switch; _t2 is the comprehensive operation time of the tie switch; T is the fault recovery time of the load at point i; γ[j] is the failure rate of the line where node j is located; failure rate.

(3) Reliability constraints:

In the formula, SAIDI refers to the average power outage duration suffered by each user powered by the system in a year; ASAI is the average power supply availability of the system; P[j] is the j node load; P is the total load; H ₁ is The lower limit of the average power supply availability of the system; ENSI is the insufficient power of the system; the average load of L[j] node j is equal to the product of the annual peak load and the load factor; H ₂ is the upper limit of the insufficient power of the system.

The backbone matrix A is obtained as follows:

The results obtained according to the optimal segmentation model of the distribution network line are shown in Table 3 below:

Table 3 calculation results

Use mathematical optimization technology to solve the model, and get the optimal segmented solution that does not need to be checked and corrected, so as to help grid planners adjust the grid structure reasonably, optimize the grid structure of the distribution network, and improve the operation efficiency of the distribution network reliability.

Based on the same inventive concept, the present invention also provides an optimal segmentation device for distribution network lines. The specific implementation of the device of the present invention will be briefly introduced below in conjunction with the accompanying drawings.

As shown in Figure 2, an optimal segmentation device for distribution network lines, including:

The data acquisition module 110 is used to obtain the topology structure of the distribution network line to be segmented and the relevant parameters of the distribution network line; the distribution network line includes a trunk line and several branch lines, and the relevant parameters include Line parameters, reliability parameters and economic parameters;

The data input module 120 is used to input the topological structure and the related parameters into the preset distribution network line optimal segmentation model, and set the section switch or must set the section switch in the distribution network line. The variable assignment of the node of the segment switch; the optimal segmentation model of the distribution network line represents the corresponding relationship between nodes and segments in the distribution network line with the first matrix, and represents the node of each branch line with the second matrix The association relationship with the nodes of the main line connected to the nodes of the branch line, and take the topology structure and related parameters as input, and take the number of segments and segment length of the distribution network as output;

The data calculation module 130 is used to calculate the number of segments and the segment length of the distribution network lines from the optimal segmentation model of the distribution network lines under the set constraint conditions.

In one embodiment, the data acquisition module 110 uses a tree-like linked list to describe the topology of the distribution network line to be segmented; nodes in the distribution network line include towers and switch cabinets, and the tree-like linked list The nodes with zero active power of the medium load are merged to obtain the topological structure of the distribution network.

In one embodiment, the first matrix and/or the second matrix is a 0-1 matrix.

In one embodiment, the first matrix includes a backbone matrix used to represent the corresponding relationship between nodes and segments of the backbone line; the constraints corresponding to the backbone matrix include:

A[i][j]+A[i-1][j]≥A[i][j+1] i=0,1,..k-1 j=0,1,..m-2

A[i][j]∈{0,1}

Among them, A is the backbone matrix; A[][] is the row and column of the backbone matrix; k is the maximum allowable segment number of the line; m is the number of nodes of the backbone line; Subsection i.

In one embodiment, the first matrix further includes a branch matrix for representing the correspondence between nodes and segments of each branch line; the constraints corresponding to the branch matrix include:

B _h [i][j]+B _h [i-1][j]≥B _h [i][j+1] i=0,1,..k-1 j=0,1,..n -2

B _h [i][j]∈{0,1}

Among them, B _h is the segmentation matrix of the h-th branch line; B _h [][] is the row and column of the h-th branch line; f is the first node number of the h-th branch line; M _h is the A set of node numbers of h branch lines; n is the number of nodes of the h branch line; B _h [i][j] is 1, indicating that node j is in the i segment.

In one embodiment, the constraints corresponding to the second matrix include:

A[i][g]≥A[i][j] i=0,1,..k-1 j∈M _h

Among them, A is the backbone matrix; A[][] is the row and column of the backbone matrix; k is the maximum allowable segment number of the line; i is the i-th segment; g is the node connected to the backbone line in the h-th branch line j is the node j; M _h is the set of node numbers of the h branch line.

In one embodiment, the constraints also include power outage time constraints, including:

Among them, t[d] is the outage time of the load of the node numbered d; d is the node numbered d of the line; k is the kth node of the line; i is the i-th segment; A is the backbone matrix ; A[][] is the row and column of the backbone matrix; j is node j; n is the number of nodes of the h branch line; m is the number of nodes of the main line; n+m is the total number of nodes of the line; γ[ k] is the failure rate of the line where node k is located; γ[h] is the failure rate of the line where node h is located; t ₁ is the comprehensive operation time of the section switch; t ₂ is the comprehensive operation time of the tie switch; Load fault recovery time; B[][] is the row and column of the branch matrix; γ is the total failure rate of the line.

In one embodiment, the constraints also include reliability constraints, including:

Among them, SAIDI is the average power outage duration suffered by each user powered by the system in a year; t[j] is the power outage time of node j load; P[j] is the load of node j; P is the total load; ASAI is the average power supply availability of the system; H ₁ is the lower limit of the average power supply availability of the system; ENSI is the system power shortage; L[j] is the average load of node j, which is equal to the product of the annual peak load and the load factor; H ₂ is The system power is below the upper limit.

The optimal segmentation method and device for the above-mentioned distribution network line respectively establishes the matrix of the relationship between the nodes and the segments of the backbone and branches of the distribution network line and the correlation matrix of the backbone and the branches, and converts the segmentation characteristics and correlation relationships into a matrix structure Description, based on the fault mode consequence analysis method, the optimal segmentation model of the distribution network line containing multi-branch lines is established. Compared with the optimal segmentation scheme of the traditional distribution network, the present invention has the following advantages:

1. The data processing is based on the actual line structure, and the tree-like linked list is used to represent the topological structure of the distribution network line, so that the solution obtained by the scheme is closer to the actual line, that is, all the segmentation points correspond to the actual line to determine the existence tower or switchgear;

2. Mathematical optimization technology can be directly used to solve the two variables of segment number and segment length at the same time, that is, while solving the optimal segment number, the optimal position of the segment is also solved, and the obtained results do not need to be corrected. Can be directly applied to the implementation of the optimal segmentation scheme;

3. Use the same matrix to represent the branches, and increase the association constraints between the branches and the trunk, so that the model can solve the line structure with multiple branches, which is closer to the actual distribution network line, and can give planners more reasonable planning plan;

4. It allows simulation calculations on lines with different load density distributions and large branches.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware, and the programs can be stored in a computer-readable storage medium. During execution, it may include the processes of the embodiments of the above-mentioned methods. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random Access Memory, RAM) and the like.

The technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, should be considered as within the scope of this specification.

The above-mentioned embodiments only express several implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but should not be construed as limiting the patent scope of the invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (10)

1. A distribution network line optimal segmentation method, is characterized in that, comprises the steps: Obtain the topology structure of the distribution network lines to be segmented and the relevant parameters of the distribution network lines; the distribution network lines include trunk lines and several branch lines, and the relevant parameters include line parameters, reliability parameters and Economic parameters; Inputting the topology and the relevant parameters into the preset distribution network line optimal segmentation model, and assigning values to the variables of the nodes in the distribution network line that are not allowed to set a section switch or must set a section switch The optimal segmentation model of the distribution network line represents the corresponding relationship between nodes and segments in the distribution network line with the first matrix, and represents the nodes of each branch line and the nodes with the branch line with the second matrix The association relationship of the nodes of the connected trunk line, with the topology structure and related parameters as input, and the segment number and segment length of the distribution network as output; Under the set constraint conditions, the number of segments and the segment length of the distribution network lines are calculated from the optimal segment model of the distribution network lines. 2. The distribution network line optimal segmentation method according to claim 1, wherein the step of obtaining the topological structure of the distribution network line to be segmented comprises: Describe the topology structure of the distribution network line to be segmented in a tree linked list; the nodes in the distribution network line include towers and switch cabinets; The nodes in the tree linked list whose load active power is zero are combined to obtain the topological structure of the distribution network line. 3. The distribution network line optimal segmentation method according to claim 1, wherein the first matrix comprises a backbone matrix for representing the correspondence between nodes and segments of the trunk line; The constraints corresponding to the backbone matrix include: $\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; ..</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}$ A[i][j]+A[i-1][j]≥A[i][j+1]i=0,1,..k-1 j=0,1,..m-2 A[i][j]∈{0,1} Among them, A is the backbone matrix; A[][] is the row and column of the backbone matrix; k is the maximum allowable segment number of the line; m is the number of nodes of the backbone line; Subsection i. 4. The distribution network line optimal segmentation method according to claim 3, characterized in that, the first matrix also includes a branch matrix for representing the node of each branch line and the corresponding relationship of the segment; The constraints corresponding to the branching matrix include: $\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}$ $\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; ..</span>}\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}$ B _h [i][j]+B _h [i-1][j]≥B _h [i][j+1]i=0,1,..k-1 j=0,1,..n -2 B _h [i][j]∈{0,1} Among them, B _h is the segmentation matrix of the h-th branch line; B _h [][] is the row and column of the h-th branch line; f is the first node number of the h-th branch line; M _h is the A set of node numbers of h branch lines; n is the number of nodes of the h branch line; B _h [i][j] is 1, indicating that node j is in the i segment. 5. The distribution network line optimal segmentation method according to claim 1, wherein the constraints corresponding to the second matrix include: A[i][g]≥A[i][j]i=0,1,..k-1j∈M _h Among them, A is the backbone matrix; A[][] is the row and column of the backbone matrix; k is the maximum allowable segment number of the line; i is the i-th segment; g is the node connected to the backbone line in the h-th branch line j is the node j; M _h is the set of node numbers of the h branch line. 6. The method for optimal segmentation of distribution network lines according to any one of claims 1 to 5, wherein the constraints also include power outage time constraints, including: $\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; \&rsqb;</span>\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}\\ <span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\underset{\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\\ <span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\underset{\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}\right\}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; i</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; \&rsqb;</span>\mathrm{<span\; class="notranslate">\; B</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; \}</span>$ Among them, t[d] is the outage time of the load of the node numbered d; d is the node numbered d of the line; k is the kth node of the line; i is the i-th segment; A is the backbone matrix ; A[][] is the row and column of the backbone matrix; j is node j; n is the number of nodes of the h branch line; m is the number of nodes of the main line; n+m is the total number of nodes of the line; γ[ k] is the failure rate of the line where node k is located; γ[h] is the failure rate of the line where node h is located; t ₁ is the comprehensive operation time of the section switch; t ₂ is the comprehensive operation time of the tie switch; Load fault recovery time; B[][] is the row and column of the branch matrix; γ is the total failure rate of the line. 7. The method for optimal segmentation of distribution network lines according to any one of claims 1 to 5, wherein the constraints also include reliability constraints, including: $\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; P</span>}$ $\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 8760</span>}}\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}$ $\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; \&lsqb;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}$ Among them, SAIDI is the average power outage duration suffered by each user powered by the system in a year; t[j] is the power outage time of node j load; P[j] is the load of node j; P is the total load; ASAI is the average power supply availability of the system; H ₁ is the lower limit of the average power supply availability of the system; ENSI is the system power shortage; L[j] is the average load of node j, which is equal to the product of the annual peak load and the load factor; H ₂ is The system power is below the upper limit. 8. An optimal segmentation device for distribution network lines, characterized in that it comprises: The data acquisition module is used to obtain the topology structure of the distribution network line to be segmented and the relevant parameters of the distribution network line; the distribution network line includes a trunk line and several branch lines, and the relevant parameters include the line parameters, reliability parameters and economic parameters; The data input module is used to input the topology structure and the related parameters into the preset distribution network line optimal segmentation model, and to set the section switch or must set the section switch in the distribution network line The variable assignment of the node of the switch; the optimal segmentation model of the distribution network line represents the corresponding relationship between nodes and segments in the distribution network line with the first matrix, and represents the nodes and the segments of each branch line with the second matrix The association relationship of the nodes of the main line connected with the nodes of the branch line, with the topology structure and related parameters as the input, and the segment number and segment length of the distribution network as the output; The data calculation module is used to calculate the number of segments and the segment length of the distribution network line from the optimal segment model of the distribution network line under the set constraint conditions. 9. The distribution network line optimal segmentation device according to claim 8, characterized in that, the data acquisition module describes the topological structure of the distribution network line to be segmented with a tree linked list; The nodes in the network line include towers and switch cabinets, and the nodes with zero load active power in the tree linked list are combined to obtain the topological structure of the distribution network line. 10. The distribution network line optimal segmentation device according to claim 8 or 9, characterized in that, the first matrix and/or the second matrix is a 0-1 matrix.