Collaborative planning method, device and equipment for power distribution network based on weighted Voronoi diagram

Abstract

The invention provides a weighted Voronoi diagram-based collaborative planning method, device and equipment for a power distribution network. The method comprises the following steps of: constructing a dynamic collaborative planning model of the power distribution network; solving the dynamic collaborative planning model of the power distribution network: configuring a high-voltage station model which comprises load information of a region and capacity information of a single high-voltage station; arranging at least two single high-voltage stations in the area according to the load information and the capacity information, and taking the position information of the single high-voltage stations as candidate positions; adding candidate positions to the weighted Voronoi diagram, and dividing power supply ranges of the candidate positions; and establishing a directed graph model for the power supply range to obtain the optimal position of the medium-voltage line. According to the technical scheme provided by the invention, the weighted Voronoi graph embedded with the directed graph model is used, the power supply range is divided in the plurality of candidate positions provided by the high-voltage station model, and the optimal position of the medium-voltage line is obtained, so that the topological structure of the medium-voltage line is kept consistent with the reality, and the reliability of collaborative planning of the power distribution network is improved.

Description

Technical Field

[0001] This application provides embodiments in the field of power distribution network technology, and particularly relates to a collaborative planning method, apparatus, and equipment for distribution networks based on weighted Voronoi diagrams. Background Technology

[0002] High-voltage and medium-voltage distribution networks are the core link in the power system connecting power sources and users. High-voltage substations are responsible for stepping down and distributing power from the upper-level grid, while medium-voltage lines undertake the functions of load access and power transmission. The coordinated planning of the two directly determines the reliability, economy, and flexibility of the power supply of the distribution network.

[0003] Traditional high and medium voltage power distribution network planning adopts a "step-by-step" approach: first, the location and capacity of high voltage substations are selected and the power supply range is divided based on load distribution and experience; then, the cost of medium voltage lines is roughly calculated by "load distance estimation" (i.e., the straight-line distance from the load point to the substation multiplied by the load size); and finally, a medium voltage wiring scheme is formulated.

[0004] However, the aforementioned high-point substation-medium-voltage distribution network layout also has certain shortcomings. First, the phased planning approach for high-voltage substations and medium-voltage distribution networks ignores the actual topology and construction costs of the medium-voltage distribution network, dividing the power supply range only by geometric distance. This leads to a surge in costs and unreasonable paths when subsequent medium-voltage lines need to detour through areas where substations cannot be built or through street topologies, also causing a deviation of more than 20% between the load distance and the actual situation. In addition, the planning of medium and high voltage distribution networks involves multiple coupled variables such as "number of substations - location - capacity" and "medium-voltage line path - model", making it impossible to achieve global dynamic adaptation and prone to getting trapped in local optima. The conventional weighted Voronoi diagram used to divide the power supply range to address these problems is prone to inconsistencies between the directed topology of medium-voltage lines and the actual situation; furthermore, this result lacks dynamic feedback of "power supply range - line cost - substation location" in subsequent iterations, making it difficult to adapt to load fluctuations and line cost changes at different times.

[0005] Therefore, an improved weighted Voronoi diagram is needed to overcome the inconsistencies between medium-voltage line topology and reality, as well as the inaccuracy of iteration, and to ensure the reliability of collaborative planning for the distribution network. Summary of the Invention

[0006] This application provides a collaborative planning method, apparatus, and equipment for distribution networks based on weighted Voronoi diagrams, which can improve the inconsistency between the medium-voltage line topology process and the actual situation, and enhance the reliability of collaborative planning for distribution networks.

[0007] In a first aspect, embodiments of this application provide a collaborative planning method for a distribution network based on a weighted Voronoi diagram, including:

[0008] A dynamic collaborative planning model for the distribution network is constructed, which includes a high-voltage substation model for high-voltage substations and a directed graph model for medium-voltage lines.

[0009] Solving the dynamic collaborative programming model of the distribution network includes:

[0010] Configure a high-voltage substation model, which includes regional load information and capacity information for individual high-voltage substations;

[0011] Based on load and capacity information, at least two single high-voltage substations will be deployed in the area, and their location information will be used as candidate locations.

[0012] Add candidate locations to the weighted Voronoi diagram to define the power supply range of the candidate locations;

[0013] A directed graph model is established for the power supply area, and the optimal location of the medium-voltage line is obtained by solving the problem.

[0014] Secondly, embodiments of this application provide a collaborative planning device for a distribution network based on a weighted Voronoi diagram, comprising:

[0015] The model building module is used to build a dynamic collaborative planning model for the distribution network. The dynamic collaborative planning model for the distribution network includes a high-voltage substation model for high-voltage substations and a directed graph model for medium-voltage lines.

[0016] The model solving module is used to solve the dynamic collaborative planning model of the distribution network. The model solving module includes:

[0017] The high-voltage substation model configuration submodule is used to configure the high-voltage substation model, which includes the load information of the area and the capacity information of a single high-voltage substation.

[0018] The candidate location selection submodule is used to deploy at least two single high-voltage substations in the area based on load information and capacity information, and use their location information as candidate locations.

[0019] The power supply range division submodule is used to add candidate locations to the weighted Voronoi diagram and divide the power supply range of the candidate locations.

[0020] The optimal location selection submodule is used to establish the directed graph model of the power supply range and solve for the optimal location of the medium voltage line.

[0021] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method provided in embodiments of this application.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed in a computer, causes the computer to perform the method provided in embodiments of this application.

[0023] The technical solution provided in this application involves constructing and solving a dynamic collaborative planning model for the distribution network. This solution process includes configuring a high-voltage substation model, which includes regional load information and the capacity information of individual high-voltage substations. Based on the load and capacity information, at least two individual high-voltage substations are deployed within the region, and their locations are used as candidate locations. These candidate locations are then added to a weighted Voronoi diagram to delineate the power supply range of medium-voltage lines. Finally, a directed graph model is established to solve for the optimal location of the medium-voltage lines. In other words, this application uses a weighted Voronoi diagram with an embedded directed graph model to delineate the power supply range from multiple candidate locations provided by the high-voltage substation model and obtain the optimal location of the medium-voltage lines. This ensures that the topology of the medium-voltage lines remains consistent with reality, improving the reliability of the distribution network collaborative planning. Attached Figure Description

[0024] Figure 1 This is a flowchart of a collaborative planning method for a distribution network based on a weighted Voronoi diagram, provided in an embodiment of this application.

[0025] Figure 2 This is a flowchart illustrating the solution of a dynamic collaborative planning model for a distribution network based on a weighted Voronoi diagram, provided in an embodiment of this application.

[0026] Figure 3 This is a flowchart illustrating the establishment of a directed graph model in a collaborative planning method for a distribution network based on a weighted Voronoi diagram, as provided in an embodiment of this application.

[0027] Figure 4 This is a block diagram of a collaborative planning device for a power distribution network based on a weighted Voronoi diagram, provided in an embodiment of this application.

[0028] Figure 5 This is a structural block diagram of the model solving module in a collaborative planning device for a distribution network based on a weighted Voronoi diagram, provided in an embodiment of this application.

[0029] Figure 6 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0030] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Figure 1 This application provides a collaborative planning method for a distribution network based on a weighted Voronoi diagram. The method can be executed by a collaborative planning device for a distribution network based on a weighted Voronoi diagram. The device can be implemented by software and / or hardware and can be configured in electronic devices such as computers.

[0032] like Figure 1 and Figure 2 As shown, the technical solution provided in this application includes the following steps:

[0033] S10: Construct a dynamic collaborative planning model for the distribution network; the dynamic collaborative planning model for the distribution network includes a high-voltage substation model for high-voltage substations and a directed graph model for medium-voltage lines;

[0034] Current high- and medium-voltage distribution network planning involves selecting suitable locations for high-voltage substations based on load forecasting and the load distribution within the planned area in the target year, determining their capacity and power supply range. Based on the locations of the high-voltage substations, a wiring scheme for medium-voltage lines is then developed to meet the power supply needs of the high-voltage substations to the distribution transformers in the distribution areas. Traditional planning methods use load distance to estimate the planning cost of medium-voltage lines during high-voltage planning, neglecting the coupling relationship between high-, medium-, and high-voltage planning, leading to inaccurate planning and even unreasonable substation layouts.

[0035] To address the aforementioned issues, the objective function of the dynamic collaborative planning model for the power distribution network can be constructed as follows:

[0036]

[0037] In the objective function above, N represents the number of newly built high-voltage substations; C li N represents the construction cost of medium-voltage lines within the power supply range of the i-th high-voltage substation. s Represents the number of available high-voltage substation capacity types; r0 is the discount rate; ms is the substation's service life; CZ s x represents the investment cost of the s-th candidate substation type; is This is the second Boolean variable. When it is 1, it means that the i-th substation has selected the s-th capacity, and when it is 0, it means that the capacity has not been selected. ml is the service life of the medium-voltage line.

[0038] The decision variables for the distribution network dynamic collaborative planning model on the high-voltage substation side include the number of substations N and the high-voltage substation capacity selection variable x. is and the coordinates of the newly built high-voltage substation (x i ,y i ).

[0039] Then, step S20 is executed: solving the dynamic collaborative planning model of the distribution network, specifically including:

[0040] S21: Configure the high-voltage substation model, which includes regional load information and individual high-voltage substation capacity information; the regional load information is the total load within the planned area, and the individual high-voltage substation capacity information is the upper and lower limits of the individual high-voltage substation capacity. These two factors determine the number of high-voltage substations N∈[n...]. min ,n max Then, for any number of high-voltage substations N, a corresponding capacity combination S = {S1, S2, ..., Sn} is generated, which is used to provide the basic capacity boundary for subsequent dynamic iterations.

[0041] S22: Based on load and capacity information, deploy at least two single high-voltage substations within the area and use their location information as candidate locations. It should be noted that the high-voltage substation model is a combination of multiple single high-voltage substations. The number N of any high-voltage substation is obtained from the aforementioned load and capacity information to generate a corresponding capacity combination S = {S1, S2, ..., Sn}. Deploy n initial candidate locations that match the capacity combination scheme S as candidate locations for the high-voltage substations.

[0042] S23: Add candidate locations to the weighted Voronoi diagram to define the power supply range of the candidate locations; the weighted Voronoi diagram requires pre-setting weight factors, which include the load density of the power supply range, the source load weight coefficient, and the cost weight coefficient; it should be noted that the weighted Voronoi diagram has an embedded directed graph model to construct a directed graph model for medium-voltage lines.

[0043] The cost weighting coefficient mainly considers the line cost output by the directed graph model. This is because the decision variable path set and line type of the distribution network dynamic collaborative planning model on the medium-voltage line side are considered. The line cost output by the directed graph model is quantified by the street length, and the initial line cost coefficient is 1. The power supply range that can be obtained needs to satisfy the topological connectivity of the directed graph at the range boundary, that is, the line at the boundary can be connected to the corresponding high-voltage substation in the directed graph.

[0044] If the load density, source load weighting coefficient, and cost weighting coefficient of the power supply area satisfy the following formula, the weighting factor corresponding to the following formula shall be determined as the weighting factor of the weighted Voronoi diagram:

[0045]

[0046] Where, ω i ρ is the weighting factor for substation i in the next iteration; i This represents the load density after the power supply area is divided; 'a' is the source load weighting coefficient, and 'b' is the cost weighting coefficient. Initially, ω... i The value is 1; when the load density is greater, ρ iThe smaller the substation capacity, the more load the high-voltage substation is allocated in this load calculation. The weight ω i The larger the load, the less likely it is that surrounding loads will be connected to the high-voltage substation in the next allocation; when the construction cost b of the planned line accounts for a large proportion, it indicates that the power supply area allocated in this allocation is large, and the weight ω i The larger the load, the less likely it is that the surrounding loads will be to be connected to the high-voltage substation in the next allocation.

[0047] S24: Establish a directed graph model of the power supply area and solve for the optimal location of the medium-voltage line. Specifically, the process of establishing a directed graph model refers to establishing a directed graph model of the medium-voltage line, such as... Figure 3 As shown, it specifically includes:

[0048] S241: Construct a directed graph model of medium-voltage lines; the directed graph model includes a set of nodes and a set of edges, where the node set includes substation locations, load point power, and street intersections; specifically, it includes candidate substation locations q. i Load point power p j Street intersection c j Node attributes include coordinates and load values ​​(capacity for substations);

[0049] Edge sets consist of street segments between adjacent nodes in a node set; their properties primarily relate to length L. e .

[0050] S242: Obtain the optimized model of the high-voltage station model and the directed graph model.

[0051] The decision variables of the optimization model include the first Boolean variable x. sm and the first variable parameter C vk The third Boolean variable z ve This indicates whether the e-th edge is selected as line type v, and the maximum active power allowed to pass through line type v is P. vk The fourth Boolean variable y si This indicates whether to select the s-th node as the substation location.

[0052] If the first Boolean variable x sm and the first variable parameter C vk If the following formula is satisfied, the objective function corresponding to the following formula shall be determined as the objective function of the optimization model:

[0053]

[0054] Where the first Boolean variable x sm The first variable parameter C indicates whether a path needs to be constructed between nodes s and m. vk This indicates the corresponding line cost.

[0055] The objective function then includes load point power balance constraints, single radiation constraints, edge node energy flow constraints, high-voltage substation energy constraints, and line current carrying capacity constraints.

[0056] The load point power balance constraint requires that the active power of node s be the same as the power flowing through the line between its adjacent node m:

[0057]

[0058] p sm p represents the active power flowing between node s and node m. j F represents the maximum load power of node s; out,s Let be the set of neighboring nodes of node s;

[0059] A single-radiation constraint ensures that the power flow direction at the load point is unique.

[0060]

[0061] The energy flow constraint for edge nodes is that the energy flow into and out of the nodes is consistent:

[0062]

[0063] Where F in,s is a neighboring node of node s, and the power direction is towards node s;

[0064] The energy constraint condition for high-voltage substations is that the energy level must not exceed the energy threshold.

[0065]

[0066] Where S i The substation capacity within this power supply area; λ i This is the maximum allowable load rate of the substation; The load power factor within the power supply range;

[0067] The line current carrying capacity constraint is that the active power flowing through the line is less than the maximum active power flowing through the line: 0 ≤ P sm ≤P vk .

[0068] In the objective function, since x sm With C vk Multiplication leads to nonlinearity, therefore adjustments need to be made using initial variables, such as adjusting the line cost C. vk Allowing the use of line models with zero power and cost means that no new lines need to be built, thus omitting the decision variable x. sm This transforms the objective function into an integer linear optimization model, which is then solved using a quotient solver.

[0069] It's worth noting that, since the objective function includes multiple constraints, a preferred approach is to construct an association matrix A for the "high-voltage substation-medium-voltage line" relationship to accommodate the directed graph model and dynamic iteration. For example, A can be an n×m matrix, where A... i,k =1 indicates that the k-th line belongs to the i-th substation and satisfies:

[0070] (1) Uniqueness constraint: The sum of the elements in each column is 1, ensuring that each line belongs to only one substation.

[0071]

[0072] (2) Coverage constraint: The sum of each row of elements is greater than or equal to 1, ensuring that each substation is responsible for at least one line.

[0073]

[0074] (3) Capacity matching constraint:

[0075]

[0076] In the formula, p k This represents the maximum load of the k-th line.

[0077] It should be added here that after performing step S24, the following steps also need to be performed:

[0078] S25: Iterate to the optimal position and obtain the change; in an optional implementation, the change can be obtained by performing a difference operation based on the optimal positions obtained from two adjacent iterations.

[0079] S26: If the change is less than the change threshold, determine the optimal position;

[0080] S27: Calculate the path planning cost between the location information of a single station and the optimal location to obtain the path planning cost within the power supply range of the single station location. Then, calculate the total path planning cost corresponding to all high-voltage power supply stations and select the scheme with the minimum total cost as the final planning result.

[0081] The technical solution provided in this application involves constructing and solving a dynamic collaborative planning model for the distribution network. This solution process includes configuring a high-voltage substation model, which includes regional load information and the capacity information of individual high-voltage substations. Based on the load and capacity information, at least two individual high-voltage substations are deployed within the region, and their locations are used as candidate locations. These candidate locations are then added to a weighted Voronoi diagram to delineate the power supply range of medium-voltage lines. Finally, a directed graph model is established to solve for the optimal location of the medium-voltage lines. In other words, this application uses a weighted Voronoi diagram with an embedded directed graph model to delineate the power supply range from multiple candidate locations provided by the high-voltage substation model and obtain the optimal location of the medium-voltage lines. This ensures that the topology of the medium-voltage lines remains consistent with reality, improving the reliability of the distribution network collaborative planning.

[0082] Figure 4 This is a structural block diagram of a collaborative planning device for a distribution network based on a weighted Voronoi diagram, as provided in an embodiment of this application. Figure 4 and Figure 5 As shown, the device includes:

[0083] Model building module 01 is used to build a dynamic collaborative planning model for the distribution network. The dynamic collaborative planning model for the distribution network includes a high-voltage substation model for high-voltage substations and a directed graph model for medium-voltage lines.

[0084] Model Solver Module 02 is used to solve the dynamic collaborative planning model of the distribution network. The model solver module includes:

[0085] The high-voltage substation model configuration submodule 021 is used to configure the high-voltage substation model, which includes the load information of the area and the capacity information of a single high-voltage substation.

[0086] The candidate location selection submodule 022 is used to deploy at least two single high-voltage substations in the area based on load information and capacity information, and use their location information as candidate locations.

[0087] The power supply range division submodule 023 is used to add candidate locations to the weighted Voronoi diagram and divide the power supply range of the candidate locations.

[0088] The optimal location selection submodule 024 is used to establish a directed graph model of the power supply range and solve for the optimal location of the medium voltage line.

[0089] In one alternative implementation, after obtaining the optimal location of the medium-voltage line, the method further includes:

[0090] The change acquisition submodule 025 is used to iterate the optimal position and acquire the change.

[0091] The optimal position confirmation submodule 026 is used to determine the optimal position when the change is less than the change threshold.

[0092] The cost calculation submodule 027 is used to calculate the path planning cost between the single station location information and the optimal location.

[0093] In one alternative implementation, a dynamic collaborative planning model for the distribution network is constructed based on the following formula:

[0094]

[0095] Where N represents the number of newly built high-voltage substations, C li N represents the construction cost of medium-voltage lines within the power supply range of the i-th high-voltage substation. s Represents the number of available high-voltage substation capacity types; r0 is the discount rate; ms is the substation's service life; CZ s x represents the investment cost of the s-th candidate substation type; is This is the second Boolean variable. When it is 1, it means that the i-th substation has selected the s-th capacity, and when it is 0, it means that the capacity has not been selected. ml is the service life of the medium-voltage line.

[0096] In one optional implementation, a directed graph model is established for the power supply range, specifically including:

[0097] Construct a directed graph model of the medium-voltage lines;

[0098] Obtain optimized models of the high-voltage substation model and the directed graph model.

[0099] In one alternative implementation, the optimization model includes a node set and an edge set;

[0100] The node set includes substation locations, load point power, and street intersections;

[0101] An edge set consists of street segments between adjacent nodes in a node set.

[0102] In one alternative implementation, the decision variables of the optimization model include a first Boolean variable x. sm and the first variable parameter C vk ;

[0103] If the first Boolean variable x sm and the first variable parameter C vk If the following formula is satisfied, the objective function corresponding to the following formula shall be determined as the objective function of the optimization model:

[0104]

[0105] Where the first Boolean variable x smThe first variable parameter C indicates whether a path needs to be constructed between nodes s and m. vk This indicates the corresponding line cost.

[0106] In one alternative implementation, the objective function includes load point power balance constraints, single-radiation constraints, edge node energy flow constraints, high-voltage substation energy constraints, and line current carrying capacity constraints.

[0107] The power balance constraint at the load point requires that the active power of node s be the same as the power flowing through the edge between it and its adjacent node m.

[0108]

[0109] Where, p sm p represents the active power flowing between node s and node m. j F represents the maximum load power of node s; out,s Let be the set of neighboring nodes of node s;

[0110] A single-radiation constraint ensures that the power flow direction at the load point is unique.

[0111]

[0112] The energy flow constraint for edge nodes is that the energy flow into and out of the nodes is consistent:

[0113]

[0114] Where F in,s is a neighboring node of node s, and the power direction is towards node s;

[0115] The energy constraint condition for high-voltage substations is that the energy level must not exceed the energy threshold.

[0116]

[0117] Where S i The substation capacity within this power supply area; λ i Cosφ is the maximum allowable load factor of the substation. i The load power factor within the power supply range;

[0118] The line current carrying capacity constraint is that the active power flowing through the line is less than the maximum active power of this type of line: 0 ≤ P sm ≤P vk .

[0119] like Figure 6As shown in the figure, this application provides an electronic device, including a processor 611, a communication interface 612, a memory 613, and a communication bus 614, wherein the processor 611, the communication interface 612, and the memory 613 communicate with each other through the communication bus 614.

[0120] Memory 613 is used to store computer programs;

[0121] In one embodiment of this application, when the processor 611 executes a program stored in the memory 613, it implements the method provided in any of the foregoing method embodiments, including:

[0122] A dynamic collaborative planning model for the distribution network is constructed, which includes a high-voltage substation model for high-voltage substations and a directed graph model for medium-voltage lines.

[0123] Solving the dynamic collaborative programming model of the distribution network includes:

[0124] Configure a high-voltage substation model, which includes regional load information and capacity information for individual high-voltage substations;

[0125] Based on load and capacity information, at least two single high-voltage substations will be deployed in the area, and their location information will be used as candidate locations.

[0126] Add candidate locations to the weighted Voronoi diagram to define the power supply range of the candidate locations;

[0127] A directed graph model is established for the power supply area, and the optimal location of the medium-voltage line is obtained by solving the problem.

[0128] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method provided in any of the foregoing method embodiments.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0131] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A collaborative planning method for a distribution network based on a weighted Voronoi diagram, characterized in that, include: A dynamic collaborative planning model for the distribution network is constructed, which includes a high-voltage substation model for high-voltage substations and a directed graph model for medium-voltage lines. Solving the dynamic collaborative planning model of the distribution network includes: Configure the high-voltage substation model, which includes regional load information and capacity information of a single high-voltage substation; Based on the load information and the capacity information, at least two single high-voltage substations are deployed in the area, and their location information is used as candidate locations. Add the candidate locations to the weighted Voronoi diagram to define the power supply range of the candidate locations; A directed graph model is established for the power supply range, and the optimal location of the medium-voltage line is obtained by solving the problem.

2. The method according to claim 1, characterized in that, After obtaining the optimal location of the medium-voltage line, the solution further includes: Iterate through the optimal position to obtain the change. If the change is less than the change threshold, the optimal position is determined; Calculate the path planning cost between the single-station location information and the optimal location.

3. The method according to claim 1, characterized in that, A dynamic collaborative planning model for the power distribution network is constructed based on the following formula: Where N represents the number of newly built high-voltage substations, C li N represents the construction cost of medium-voltage lines within the power supply range of the i-th high-voltage substation. s This represents the number of available high-voltage substation capacity types; r0 is the discount rate. ms represents the service life of the substation; CZ s x represents the investment cost of the s-th candidate substation type; is This is the second Boolean variable. When it is 1, it means that the i-th substation has selected the s-th capacity, and when it is 0, it means that the capacity has not been selected. ml is the service life of the medium-voltage line.

4. The method according to claim 1, characterized in that, The establishment of the directed graph model for the power supply range specifically includes: Construct the directed graph model of the medium-voltage line; Obtain an optimized model of the high-voltage station model and the directed graph model.

5. The method according to claim 4, characterized in that, The directed graph model includes a node set and an edge set; The node set includes substation locations, load point power, and street intersections; The edge set includes street segments between adjacent nodes in the node set.

6. The method according to claim 5, characterized in that, The decision variables of the optimization model include a first Boolean variable x. sm and the first variable parameter C vk ; If the first Boolean variable x sm and the first variable parameter C vk The objective function corresponding to the following formula is determined as the objective function of the optimization model: Where the first Boolean variable x sm The first variable parameter C indicates whether a path needs to be constructed between nodes s and m. vk This indicates the corresponding line cost.

7. The method according to claim 6, characterized in that, The objective function includes load point power balance constraints, single radiation constraints, edge node energy flow constraints, high-voltage station energy constraints, and line current carrying capacity constraints. The load point power balance constraint requires that the active power of node s be the same as the power flowing along the edge between its adjacent node m: Where, p sm p represents the active power flowing between node s and node m. j F represents the maximum load power of node s; out,s Let be the set of neighboring nodes of node s; The single-radiative constraint ensures that the power flow direction at the load point is unique: The energy flow constraint of the edge node is that the energy flow into the node and the energy flow out of the node are consistent: Where F in,s is a neighboring node of node s, and the power direction is towards node s; The energy constraint condition for the high-voltage station is that it must not exceed the energy threshold. The load power factor within the power supply range; The current carrying capacity constraint condition for the line is that the active power flowing through the line is less than the maximum active power of this type of line: 0≤P sm ≤P vk .

8. A collaborative planning device for a distribution network based on a weighted Voronoi diagram, characterized in that, include: The model building module is used to build a dynamic collaborative planning model for the distribution network. The dynamic collaborative planning model for the distribution network includes a high-voltage substation model for high-voltage substations and a directed graph model for medium-voltage lines. The model solving module is used to solve the dynamic collaborative planning model of the distribution network. The model solving module includes: The high-voltage station model configuration submodule is used to configure the high-voltage station model, which includes regional load information and capacity information of a single high-voltage station. The candidate location selection submodule is used to deploy at least two of the single high-voltage substations in the area based on the load information and the capacity information, and use their location information as candidate locations. The power supply range division submodule is used to add the candidate positions to the weighted Voronoi diagram and divide the power supply range of the candidate positions. The optimal location selection submodule is used to establish the directed graph model of the power supply range and solve for the optimal location of the medium voltage line.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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