A highly elastic distribution network optimization algorithm for grid-based power capacity expansion in industrial parks

Abstract

The present invention discloses a highly elastic distribution network optimization algorithm for grid-based power capacity expansion in industrial parks. The algorithm comprises the following steps: obtaining a set of head-end distances SD and a set of straight-line distances ZD based on a given power grid diagram; obtaining a set of decision variables V; constructing a linear programming constraint set C; and constructing an objective function Z. The algorithm then seeks the minimum value of the objective function Z under the premise that the constraint conditions C are satisfied. The algorithm obtains a set of head-end distances and a set of straight-line distances based on a given power grid diagram; obtaining a set of decision variables based on pre-connected plants; constructing a linear programming constraint set based on the projected loads of the pre-connected plants, the current loads of the lines, the circuit limits, and the decision variable set; constructing an objective function Z based on the importance of construction costs and maintenance costs, assigning different weights to the levels; and solving for the minimum value of the objective function based on the constructed linear programming constraint set. The algorithm then plans an optimal distribution network optimization solution that considers both construction and maintenance costs in response to power grid expansion needs.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an industrial park grid-based power capacity-increasing and highly elastic distribution network optimization algorithm. Background Art

[0002] As a fundamental component of public life and production, smart grids play a vital role in ensuring quality of life and promoting economic development. Grid construction and investment must proactively consider the distance between pre-connection areas and circuit loads, and develop a distribution network plan that integrates both construction and maintenance costs.

[0003] Most existing distribution network technologies are planned based on the economic benefits of grid operation, the priority of power users, or the access of multiple energy sources. Few distribution network planning schemes take real-time power consumption and quotas into consideration.

[0004] For example, Chinese patent CN111463778A, published on July 28, 2020, discloses an active distribution network optimization and reconstruction method based on an improved coyote optimization algorithm. The method includes the following steps: establishing a multi-objective optimization and reconstruction model for a distribution network containing distributed generation (DGs) that considers voltage stability and active power losses. This model is a multi-constrained nonlinear mathematical model with the objectives of minimizing active power losses and optimizing voltage stability indicators. The network topology of the distribution network after the addition of DGs is identified, and the improved coyote algorithm is used to find the optimal solution for the active distribution network optimization and reconstruction. However, this reconstruction model does not consider distribution network planning issues such as real-time power consumption and quotas. Therefore, a distribution network optimization method is needed that can consider both grid construction costs and grid operation and maintenance costs. Summary of the Invention

[0005] The technical problem to be solved by this invention is: in response to the problems of existing distribution network planning methods that ignore maintenance cost planning and lack real-time power consumption and limits when constructing planning models, a highly elastic distribution network optimization algorithm for grid-based power capacity expansion in industrial parks is proposed, which can simultaneously consider construction and maintenance costs when constructing planning models. This algorithm establishes a linear programming model that includes factory access constraints and line load limit constraints. It innovatively associates the construction cost weight with the straight-line distance between the factory and the pole number, and the maintenance cost weight with the sum of the distance between the factory and the pole number and the distance from the pole number to the line head end, constructing a weighted objective function. This achieves dual optimization of construction and maintenance costs in response to grid expansion needs, ultimately obtaining an optimal distribution network solution that comprehensively considers both economy and reliability.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is: an industrial park grid-based power capacity expansion and high-elasticity distribution network optimization algorithm, comprising the following steps:

[0007] S1: According to the given power grid diagram, obtain the head-end distance set SD and the straight-line distance set ZD;

[0008] S2: Obtain the decision variable set V based on the pre-connected factory u;

[0009] S3: Construct a linear programming constraint set C;

[0010] S4: According to the different weights of the importance of construction cost and maintenance cost, construct the objective function Z, and solve the minimum value of the objective function Z according to the constructed linear programming constraint set to obtain the optimal distribution network solution.

[0011] In step S2, for the pre-connected factory u, a decision variable V is constructed based on whether it is connected to a certain line, where v j When it is 1, it means that factory u is connected to pole number j, otherwise it is not connected, and the 0-1 decision variable set V = {v j |v j ∈ {0, 1}, j=1, 2,..., m}.

[0012] As a preferred method, the process of obtaining the head-end distance set SD and the straight-line distance set ZD is as follows: according to the given line power grid diagram, n line functions are fitted in a two-dimensional coordinate system, m pole numbers are fitted, and the line set L = {l i |i=1,2,3...,n};According to the relevant information of pre-access factory u, obtain the pre-access factory coordinates U=(u x ,u y );Calculate the distance between each pole number and the line, and obtain the distance set SD={sd ij |i=1,2,3...,n,j=1,2,3...,m};Calculate the shortest distance from the pre-access factory to the j-th pole number, and obtain the straight-line distance set ZD={zd j |j=1, 2, 3, ..., m}.

[0013] According to the given power grid diagram, n line functions are fitted in the two-dimensional coordinate system to obtain the line set L = {l i |i=1,2,3...,n}, fit m rod numbers and obtain the rod number set F j ={F j |j=1,2,3...,m}. The relationship between the line and the pole number is the inclusion and inclusion relationship, and the relationship is recorded as a set K={k ji |j=1,2,3...,m,i=1,2,3...,n}, if k ji =TRUE, indicating that there is an inclusion relationship between pole number j and line i. If k ji = FALSE, indicating that there is no inclusion relationship between pole number j and line i. Calculate the head-end distance between each pole number and line, and obtain the head-end distance set SD = {sd ij|i=1,2,3....,n,j=1,2,3...,m}. According to the relevant information of the pre-access factory u, the pre-access factory coordinates U=(u x ,u y ), calculate the shortest distance from the pre-access factory to the jth pole number, and obtain the straight-line distance set ZD = {zd j |j=1, 2, 3, ..., m};

[0014] Preferably, step S1 includes the following steps:

[0015] S11: According to the given line power grid diagram, fit n line functions in a two-dimensional coordinate system, l i =k i x+b i When there are nonlinear lines, use r-segment piecewise functions to obtain the line set l i ={l i-p |i=1,2,...,n,p=1,2,...,r i};

[0016] S12: According to the location information of the pre-access factory, the pre-access factory information is fitted in the same two-dimensional coordinate system as step S11 to obtain the pre-access factory coordinate set U = (u x ,u y );

[0017] S13: Calculate the straight-line distance from the pre-connected factory u to the m pole number, and record the factory coordinate as U = (u x ,u y ), the coordinates of rod number j are (x fj ,y fj ), the straight-line distance is Get set ZD;

[0018] S14: Calculate the distance from pole number j to the beginning of line i, and record the coordinates of pole number j as (x fj ,y fj ), the coordinates of the first and last endpoints of line i are (x si ,y si ), the first end distance is Get the set SD.

[0019] The calculation data such as the head-end distance set SD and the straight-line distance set ZD are obtained according to the given power grid diagram and the location information of the pre-connected factory.

[0020] As an optimal method, the process of constructing the linear programming constraint set is as follows: obtain the current load of line i and construct the set G = {g i |i=1,2,…,n},get the load limit construction set E={e i|i=1,2,…,n}; According to the expected load N of the pre-connected factory u, the current line load set G, the circuit limit set E and the decision variable set V={v j |j=1,2,…,m}construct the linear programming constraint set C.

[0021] Construct a linear programming constraint set C and obtain constraint condition C.

[0022] Preferably, step S3 includes the following steps:

[0023] S31: Get the pre-connected factory u and make the sum of its connected j benchmark-related decision variables less than or equal to 1, that is Construct the linear programming constraint set C1;

[0024] S32: Obtain the pre-connected factory u and line i, record the expected load of the pre-connected factory u as N, and the current load of line i as g i , the circuit limit is e i , construct m*n linear programming constraints, specifically N·v j +g i ≤e i , i=1,2,...,n, j=1,2,...,m, constituting the linear programming constraint set C2;

[0025] S33: Obtain a linear programming constraint set C, where the linear programming constraint set C=C1∪C2.

[0026] First construct two subsets C1 and C2 of the linear programming constraint set, and then obtain the linear programming constraint set C.

[0027] As an optimal solution, the process of finding the minimum value of the objective function Z is:

[0028] According to the importance of construction cost and maintenance cost, positive weights w1 and w2 are set to construct the objective function Z. The objective function Under the premise of satisfying the constraint condition C, solve the minimum value of the objective function Z and obtain the solution; when v j When the value of is 1, it means that the pre-connected factory u is connected to the benchmark j; when v j When the value of is not 1, it means that the pre-connected factory u is not connected to the benchmark j.

[0029] According to the head end distance set SD={sd ij |i=1,2,3...,n,j=1,2,3...,m}, straight-line distance set ZD={zd j |j=1,2,3...,m}, the expected load of the new connected factory u is N, the decision variable set V={v j|j=1,2,…,m} construct the objective function Z. Under the premise of satisfying the constraint condition C, the minimum value of the objective function Z is solved to obtain the optimal distribution network solution.

[0030] As a preference, the relationship between lines and pole numbers is recorded as a set K = {k ji |j=1, 2, 3..., m, i=1, 2, 3..., n}.

[0031] The relationship between lines and pole numbers is inclusion and inclusion, and the relationship is recorded as a set K = {k ji |j=1,2,3...,m,i=1,2,3...,n}, if k ji =TRUE, indicating that there is an inclusion relationship between pole number j and line i. If k ji =FALSE, indicating that there is no containment relationship between pole number j and line i.

[0032] The substantial effects of the present invention are as follows: the present invention obtains a head-end distance set SD and a straight-line distance set ZD according to a given power grid diagram; obtains a decision variable set V according to a pre-access factory u; constructs a linear programming constraint set C according to the expected load N of the pre-access factory u, the current line load set G, the circuit limit set E, and the decision variable set V; constructs an objective function Z according to different weights of the importance of construction cost and maintenance cost, solves the minimum value of the objective function Z based on the constructed linear programming constraint set C, and plans a distribution network optimization plan that takes both construction cost and maintenance cost into consideration according to the power grid expansion demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of the main implementation steps of this embodiment;

[0034] Figure 2 This is the initial diagram for algorithm visualization in this embodiment;

[0035] Figure 3 This is a visualization diagram after inputting relevant data for Example 1;

[0036] Figure 4 This is a visualization diagram of the calculation results of Example 1;

[0037] Figure 5 This is a visualization diagram after inputting relevant data for Example 2;

[0038] Figure 6 This is a visualization diagram of the calculation results of Example 2. DETAILED DESCRIPTION

[0039] The specific implementation of the present invention will be further described below with reference to specific embodiments and in conjunction with the accompanying drawings.

[0040] A highly elastic distribution network optimization algorithm for grid-based power capacity expansion in industrial parks, such as Figure 1 As shown, the following steps are included:

[0041] S1: According to the given power grid diagram, obtain the head-end distance set SD and the straight-line distance set ZD;

[0042] S2: Obtain the decision variable set V based on the pre-connected factory u;

[0043] S3: Construct a linear programming constraint set C;

[0044] S4: According to the different weights of the importance of construction cost and maintenance cost, construct the objective function Z, and solve the minimum value of the objective function Z according to the constructed linear programming constraint set C to obtain the optimal distribution network solution.

[0045] The specific implementation steps are as follows:

[0046] Step 1: According to the given power grid diagram, fit n line functions in the two-dimensional coordinate system to obtain the line set L = {l i |i=1,2,3...,n}, fit m rod numbers and obtain the rod number set F j ={F j |j=1,2,3...,m}. The relationship between the line and the pole number is the inclusion and inclusion relationship, and the relationship is recorded as a set K={k ji |j=1,2,3...,m,i=1,2,3...,n}, if k ji =TRUE, indicating that there is an inclusion relationship between pole number j and line i. If k ji = FALSE, indicating that there is no inclusion relationship between pole number j and line i. Calculate the head-end distance between each pole number and line, and obtain the head-end distance set SD = {sd ij |i=1,2,3...,n,j=1,2,3...,m}. According to the relevant information of the pre-access factory u, the pre-access factory coordinates U=(u x ,u y ), calculate the shortest distance from the pre-access factory to the jth pole number, and obtain the straight-line distance set ZD = {zd j |j=1, 2, 3, ..., m};

[0047] Step 1-1, according to the given line power grid diagram, fit n line functions in a two-dimensional coordinate system, l i =k i x+b i In particular, if a line is nonlinear, then use r-segment piecewise functions to obtain the line set l i ={l i-p|i=1,2,...,n,p=1,2,...,r i};

[0048] Step 1-2: According to the location information of the pre-access factory, the pre-access factory information is fitted in the same two-dimensional coordinate system as step 1-1 to obtain the pre-access factory coordinate set U = (u x ,u y );

[0049] Step 1-3, calculate the straight-line distance from the pre-connected factory u to the m pole number, and record the factory coordinate as U = (u x ,u y ), the coordinates of rod number j are (x fj ,y fj ), the straight-line distance is Get set ZD;

[0050] Step 1-4, calculate the distance from pole number j to the beginning of line i, and record the coordinates of pole number j as (x fj ,y fj ), the coordinates of the first and last endpoints of line i are (x si ,y si ), the first end distance is Get the set SD;

[0051] Step 2: For the pre-connected factory u, construct a decision variable V based on whether it is connected to a certain line, where v j When it is 1, it means that factory u is connected to pole number j, otherwise it is not connected, and the 0-1 decision variable set V = {v j |v j ∈{0, 1}, j=1, 2,..., m};

[0052] Step 3: Construct a set G = {g i |i=1,2,…,n},for line i, the load limit set E={e i |i=1,2,…,n},according to the expected load N of the pre-connected factory u, the current line load set G, the circuit limit set E, the decision variable set V={v j |j=1,2,…,m} construct linear programming constraint set C;

[0053] Step 3-1: For pre-connected factory u, make the sum of its connected j benchmark-related decision variables less than or equal to 1, that is, Construct the linear programming constraint set C1;

[0054] Step 3-2: For pre-connected factory u and line i, the expected load of pre-connected factory u is N, and the current load of line i is g i, the circuit limit is e i , construct m*n linear programming constraints, specifically N·v j +g i ≤e i , i=1,2,...,n, j=1,2,...,m,, constitute the linear programming constraint set C2;

[0055] Step 3-3, linear programming constraint set C = C1∪C2;

[0056] Step 4: According to the head end distance set SD = {sd ij |i=1,2,3....,n,j=1,2,3...,m}, straight-line distance set ZD={zd j |j=1,2,3...,m}, the expected load of the new connected factory u is N, the decision variable set V={v j |j=1,2,…,m} construct the objective function Z. Under the premise of satisfying the constraint condition C, solve the minimum value of the objective function Z to obtain the optimal distribution network solution;

[0057] Step 4-1: Set positive weights w1 and w2 according to the importance of construction cost and maintenance cost, and construct the objective function Z, specifically:

[0058] Step 4-2: Under the premise of satisfying the constraint condition C, solve the minimum value of the objective function Z to obtain the solution. Specifically, when v j When the value of is 1, it means that the pre-connected factory u is connected to the benchmark j, and vice versa.

[0059] In the initial visualization interface, you can view the initial visualization graph of the algorithm, such as Figure 2 As shown, the specific embodiments are as follows.

[0060] Example 1:

[0061] Input the factory x coordinate as 20, y coordinate as 30, and the factory load condition as 30. The visualization diagram after inputting the relevant data is as follows Figure 3 As shown; the visualization of the operation results is as follows Figure 4 shown.

[0062] Example 2:

[0063] Input the factory x coordinate as 50, y coordinate as 20, and the factory load condition as 100. The visualization diagram after inputting the relevant data is as follows Figure 5 As shown; the visualization of the operation results is as follows Figure 6 shown.

[0064] This embodiment obtains a head-end distance set SD and a straight-line distance set ZD based on a given power grid diagram; obtains a decision variable set V based on the pre-connected plant u; constructs a linear programming constraint set C based on the expected load N of the pre-connected plant u, the current line load set G, the circuit limit set E, and the decision variable set V; constructs an objective function Z based on the different weights of the importance of construction cost and maintenance cost, and solves the minimum value of the objective function Z based on the constructed linear programming constraint set C, so as to plan a distribution network optimization plan that takes into account both construction cost and maintenance cost in response to the power grid expansion demand.

[0065] The above embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A highly elastic distribution network optimization algorithm for grid-based power capacity expansion in industrial parks, characterized by: The steps include: S1: Based on a given power grid diagram, fit n line functions and m pole numbers, calculate the head-end distance between each pole number and the line, and calculate the straight-line distance from the pre-access factory to the m pole numbers based on the location information of the pre-access factory. Obtain the head-end distance set SD and the straight-line distance set ZD. The head-end distance is the straight-line distance from the pole number coordinate to the line head-end coordinate. S2: Obtain the decision variable set V based on the pre-connected factory u; S3: Construct a linear programming constraint set C, wherein the linear programming constraint set C includes an access constraint that ensures that the sum of the decision variables related to the factory access j benchmarks is less than or equal to 1 and a load constraint that ensures that the line load does not exceed the limit; S4: Set positive weights according to the importance of construction cost and maintenance cost respectively. Associate the weight of construction cost with the straight-line distance between the factory and the pole number, and associate the weight of maintenance cost with the sum of the straight-line distance between the factory and the pole number and the distance from the pole number coordinate to the head end of the line. Construct a weighted objective function. Under the premise of satisfying the constraint condition C, solve the minimum value of the objective function to obtain the optimal distribution network solution.

2. The industrial park grid-based power capacity expansion and high-elasticity distribution network optimization algorithm according to claim 1 is characterized in that: The process of obtaining the head-end distance set SD and the straight-line distance set ZD is as follows: according to the given line power grid diagram, n line functions are fitted in the two-dimensional coordinate system, m pole numbers are fitted, and the line set L = {l i |i=1,2,3...,n};According to the relevant information of pre-access factory u, obtain the pre-access factory coordinates U=(u x ,u y );Calculate the distance between each pole number and the line, and obtain the distance set SD={sd ij |i=1,2,3...,n,j=1,2,3...m},;Calculate the shortest distance from the pre-access factory to the jth pole number, and obtain the straight-line distance set ZD={zd j |j=1, 2, 3, ..., m}.

3. The industrial park grid-based power capacity expansion and high-elasticity distribution network optimization algorithm according to claim 1 or 2 is characterized in that: The step S1 includes the following steps: S11: According to the given line power grid diagram, fit n line functions in a two-dimensional coordinate system, l i =k i x+b i When there are nonlinear lines, use r-segment piecewise functions to obtain the line set l i ={l i-p |i=1,2,...,n,p=1,2,...,r i }; S12: According to the location information of the pre-access factory, the pre-access factory information is fitted in the same two-dimensional coordinate system as step S11 to obtain the pre-access factory coordinate set U = (u x ,u y ); S13: Calculate the straight-line distance from the pre-connected factory u to the m pole number, and record the factory coordinate as U = (u x ,u y ), the coordinates of rod number j are (x fj ,y fj ), the straight-line distance is Get set ZD; S14: Calculate the distance from pole number j to the beginning of line i, and record the coordinates of pole number j as (x fj ,y fj ), the coordinates of the first and last endpoints of line i are (x si ,y si ), the first end distance is Get the set SD.

4. The industrial park grid-based power capacity expansion and high-elasticity distribution network optimization algorithm according to claim 1 is characterized in that: The process of constructing the linear programming constraint set C is as follows: obtain the current load of line i and construct the set G = {g i |i=1,2,…,n},get the load limit construction set E={e i |i=1,2,…,n}; According to the expected load N of the pre-connected factory u, the current line load set G, the circuit limit set E and the decision variable set V={v j |j=1,2,…,m}construct the linear programming constraint set C.

5. The industrial park grid-based power capacity expansion and high-elasticity distribution network optimization algorithm according to claim 1 or 4, characterized in that: The step S3 comprises the following steps: S31: Get the pre-connected factory u and make the sum of its connected j benchmark-related decision variables less than or equal to 1, that is Construct the linear programming constraint set C1; S32: Obtain the pre-connected factory u and line i, record the expected load of the pre-connected factory u as N, and the current load of line i as g i , the circuit limit is e i , construct m*n linear programming constraints, specifically N·v j +g i ≤e i , i=1,2,...,n, j=1,2,...,m, constituting the linear programming constraint set C2; S33: Obtain a linear programming constraint set C, where the linear programming constraint set C=C1∪C2.

6. An industrial park grid-based power capacity expansion and high-elasticity distribution network optimization algorithm according to claim 1 or 2, characterized in that: The process of solving the minimum value of the objective function Z is: According to the importance of construction cost and maintenance cost, positive weights w1 and w2 are set to construct the objective function Z. The objective function Under the premise of satisfying the constraint condition C, solve the minimum value of the objective function Z and obtain the solution; when v j When the value of is 1, it means that the pre-connected factory u is connected to the benchmark j; when v j When the value of is not 1, it means that the pre-connected factory u is not connected to the benchmark j.

7. The industrial park grid-based power capacity expansion and high-elasticity distribution network optimization algorithm according to claim 2 is characterized in that: The relationship between lines and pole numbers is recorded as a set K = {k ji |j=1, 2, 3..., m, i=1, 2, 3..., n}.

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