A topology optimization method, control device and medium for a large wind farm power collection system

Abstract

The present invention belongs to the technical field of wind power generation, and specifically provides a large-scale wind farm power collection system topology optimization method, control device and medium. The large-scale wind farm power collection system topology optimization method of the present invention includes: based on the MILP model of the wind farm, reducing the optional paths in the cable path planning of the wind farm; setting constraints, the constraints include: making each node in the wind farm have at least one cable connection, wherein the nodes include wind turbines and booster stations; making the power flow on the cable line of the system positive when there is a cable connection in the system; setting the dynamically generated cable non-crossing constraint. By improving the classic mixed linear integer model, the calculation complexity can be greatly reduced while ensuring the design requirements, and the calculation resources required for the optimization process can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and specifically provides a topology optimization method, a control device and a medium for a large-scale wind farm power collection system. Background Art

[0002] As the global demand for renewable energy continues to grow, wind power generation is gradually developing rapidly towards large-scale and deep sea. Offshore wind farms have gradually become an important part of the wind power generation field because of their richer and more stable available wind resources and their role in alleviating the limited land resources. In the construction of offshore wind farms, the collector line, as a bridge connecting wind turbines and land power grids, undertakes the vital task of transmitting electricity. Its function is not limited to gathering the power of dispersed wind turbines into one place, but more importantly, it ensures the efficient and stable transmission of power to meet the needs of the power grid.

[0003] Compared with the collection lines of land wind farms, the collection lines of offshore wind farms not only face more severe natural environments and higher construction difficulties, but also need to deal with longer power transmission distances and higher power transmission losses, so their construction costs are relatively high. The topology optimization of the collection system plays a key role in the design of collection lines in offshore wind farms, which directly affects the efficiency and cost of power transmission. Taking effective optimization measures and reducing the construction cost of the collection system are of great significance to improving the competitiveness and sustainability of offshore wind power.

[0004] At present, the path planning of offshore wind farm collection system can be divided into two categories: heuristic algorithms and accurate algorithms. Heuristic algorithms, such as ant colony algorithms, can find relatively good solutions in a short time when solving the path planning problem of offshore wind farm collection system. However, since the path planning problem of offshore wind farm collection system usually has complex constraints, heuristic algorithms can often only find local optimal solutions or approximate optimal solutions, and cannot guarantee the global optimal solution, which to a certain extent limits its reliability and accuracy in practical applications. Relatively speaking, accurate algorithms such as mixed linear integer programming (MILP) have outstanding advantages in mathematical provability and global optimality. They can find the global optimal solution under multiple complex constraints, providing strong support for optimizing the path design of the collection system. However, due to the large scale and high complexity of offshore wind farms, traditional mixed linear integer programming methods often face problems such as large computational complexity and long running time in the solution process. Especially when dealing with large-scale problems with more than 50 wind turbines, the computational complexity will increase sharply, limiting its feasibility and efficiency in practical applications.

[0005] Currently, there is still a lack of topology optimization methods suitable for large-scale wind farm collection systems. Summary of the invention

[0006] In order to overcome the above-mentioned defects, the present invention provides a topology optimization method, a control device and a medium for a large wind farm collection system. By improving the classic mixed linear integer model, the calculation complexity can be greatly reduced while ensuring the design requirements, and the computing resources required for the optimization process can be reduced.

[0007] In a first aspect, the present invention provides a method for topology optimization of a large wind farm power collection system, comprising:

[0008] Based on the MILP model of wind farm, reduce the optional paths in wind farm cable path planning;

[0009] Set constraints, including:

[0010] Each node in the wind farm is connected by at least one cable, wherein the node includes a wind turbine and a booster station;

[0011] When there is a cable connection in the system, the power flow on the cable line of the system is positive;

[0012] Set the dynamically generated cables to not cross constraints.

[0013] Further, the reducing of optional paths in the wind farm cable path planning includes:

[0014] Each wind turbine in the wind farm is connected to a wind turbine or a booster station adjacent to the wind turbine, wherein the distance between each wind turbine and the wind turbine connected thereto does not exceed a preset value.

[0015] Furthermore, the step of connecting each wind turbine in the wind farm to a wind turbine or a booster station adjacent to the wind turbine, wherein the distance between each wind turbine and the wind turbine connected thereto does not exceed a preset value, comprises:

[0016] Define a binary variable x t (i,j), as in formula (11), where:

[0017]

[0018] Where T represents a set of optional cable types; a directed graph G = (V, A) is pre-constructed, where the arc segment set A y A set of arc segments of all possible cable path planning when each wind turbine group is connected to a wind turbine group or a booster station adjacent to the wind turbine group and the distance between each wind turbine and the wind turbine group connected to it does not exceed a preset value.

[0019] Further, each node in the wind farm is connected with at least one cable, wherein the node includes a wind turbine and a booster station, including:

[0020]

[0021] Further, when there is a cable connection in the system, the power flow on the cable line of the system is positive, including:

[0022]

[0023] Further, the setting of dynamically generating a cable non-crossing constraint includes:

[0024] According to the MILP model, solving an initial relaxed model without non-crossover constraints;

[0025] After obtaining the initial solution, determine whether the cable crosses and identify the cross position, add no-cross constraints to the cross position to form a new relaxation model;

[0026] The new relaxed model is solved iteratively until a cable layout with no crossings is produced.

[0027] Furthermore, adding a non-intersection constraint to the position where the intersection occurs includes:

[0028] Add a non-intersecting constraint

[0029]

[0030] ,{(i,j),(l,k)}∈χ and

[0031] Furthermore, the MILP model based on the wind farm includes:

[0032] The topology optimization objective of the power collection system is set to minimize the cable cost, as shown in formula (2):

[0033]

[0034] Where N is the number of all nodes in the wind farm, C unit is the cable unit price, t represents the cable type, and d(i,j) represents the Euclidean distance between nodes i and j;

[0035] The constraints satisfied by the model include:

[0036]

[0037]

[0038]

[0039]

[0040] x t (i,j)+x t (j,i)≤1,i≠j (7);

[0041]

[0042]

[0043] In a third aspect, a control device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the large wind farm collection system topology optimization method described in any one of the technical solutions of the first aspect.

[0044] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the large wind farm collection system topology optimization method described in any one of the technical solutions of the first aspect.

[0045] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0046] The present invention improves the constraints of the classical mixed linear integer model (traditional MILP model) and introduces optional path pre-reduction and dynamically generated cable non-crossing constraints to form a new collection system path planning method suitable for large wind farms. The method can effectively perform topology optimization on large wind farms. The improved model greatly reduces the storage space requirement and solution time, and greatly improves the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0048] Figure 1 This is a schematic flow chart of the main steps of a topology optimization method for an offshore wind farm power collection system according to an embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of a complete MILP model solution process after improvement according to an embodiment of the present invention;

[0050] Figure 3is a schematic diagram of the original layout of the Horns Rev 1 wind farm according to an embodiment of the present invention;

[0051] Figure 4 It is a schematic diagram of the optimized layout of the Horns Rev1 wind farm using the improved complete MILP model of the present invention according to an embodiment of the present invention;

[0052] Figure 5 is a schematic diagram of an optimized layout of a wind farm with 50 wind turbines according to an embodiment of the present invention;

[0053] Figure 6 is a schematic diagram of an optimized layout of a wind farm with 60 wind turbines according to an embodiment of the present invention;

[0054] Figure 7 It is a schematic diagram of the optimized layout of a wind farm with 70 wind turbines according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0056] In the description of the present invention, "processor" may include hardware, software or a combination of the two. The processor has data and / or signal processing functions. The processor can be implemented in software, hardware or a combination of the two. Non-transitory computer-readable storage media include any suitable medium that can store program code, such as a disk, hard disk, optical disk, flash memory, read-only memory, random access memory, etc.

[0057] Reference Figure 1 The present invention provides a topology optimization method for a large wind farm power collection system, comprising:

[0058] S1, based on the MILP model of wind farm, reduces the optional paths in the cable path planning of wind farm;

[0059] S2, setting constraints, the constraints include:

[0060] Each node in the wind farm is connected by at least one cable, wherein the node includes a wind turbine and a booster station;

[0061] When there is a cable connection in the system, the power flow on the cable line of the system is positive;

[0062] S3, set the dynamically generated cables to not cross constraints.

[0063] The present invention makes three improvements on the basis of the classical mixed linear integer model (that is, the traditional mixed linear integer model), namely, pre-reduction of optional paths, addition of constraint conditions, and dynamic generation of cable non-crossing constraints, thereby constituting the topology optimization method of the large wind farm collection system of the present invention, which is used for path planning of the wind farm collection system.

[0064] Steps S1 to S3 are all improvements based on the classic mixed linear integer model.

[0065] Step S1 is to pre-reduce the optional paths, step S2 is to add constraints, and step S3 is to dynamically generate a non-crossing constraint for the cables.

[0066] The present invention improves the constraint conditions of the classic mixed linear integer programming model and introduces a method of pre-reduction of optional paths and dynamic generation of cable non-crossing constraints, thereby reducing computational complexity and increasing feasibility.

[0067] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0068] The present invention is used to optimize the topology of wind farm power collection systems, and is particularly suitable for large wind farm power collection systems with high computational complexity in the prior art, which can effectively reduce computational complexity and increase feasibility. The large wind farm includes a wind farm with more than 50 wind turbines, and an offshore wind farm is a typical large wind farm.

[0069] First, the classic mixed linear integer model is explained

[0070] The present invention solves the cable path problem of wind farms through mixed integer linear programming. Assuming that the locations of wind turbines and offshore substations (OSS) have been determined, the high-voltage transmission cables between the substations and the power grid are ignored, and radial structure path planning is performed for the medium-voltage collection system between wind turbine arrays.

[0071] In the classic mixed linear integer model, the nodes of the wind farm are subdivided into the set of all wind turbine nodes V t And all booster node collection V o , V represents the union of the two. By constructing a complete directed graph G = (V, A), where the arc set A includes all possible connections between nodes, the problem is transformed into an optimization problem of path planning on this graph.

[0072] Binary variable x i(i,j) is defined as follows, where T represents the set of optional cable types:

[0073]

[0074] The topology optimization goal of the power collection system is to minimize the cable cost, which is expressed as follows:

[0075]

[0076] Where N is the number of all nodes in the wind farm, C unit is the cable unit price, t represents the cable type, and d(i,j) represents the Euclidean distance between nodes i and j.

[0077] Nodes i and j represent the nodes where the wind turbines and booster stations in the wind farm are located.

[0078] The wind farm collection system allows for the mixed laying of multiple different types of cables to meet the specific needs of the power system. It should be noted that although there are t types of cables to choose from, each type of cable selection is mutually exclusive. Only one type of cable can be selected for the same path. The constraints are as follows:

[0079]

[0080] In order to meet the design requirements of the radial wind farm collection system, the following constraints should be met:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Based on the structural requirements of the radial collection system, formula (4) ensures that each wind turbine has only one cable for energy output.

[0088] Formula (5) ensures that at least one cable is connected to the booster station.

[0089] Formula (6) is used to prevent the same section of cable from being connected to the same node at both ends.

[0090] Formula (7) is used to prevent energy loops from occurring between any two wind turbine nodes.

[0091] Formula (8) is to avoid any two cables from crossing, where the set χ stores the crossed cable pairs {(i,j),(l,k)}.

[0092] Formula (9) represents the power balance of each node, where f is a continuous non-negative variable representing the line power flow. Formula (10) ensures that the power flow on each cable is lower than the cable capacity, where k t The maximum number of wind turbines that can be mounted on the cable.

[0093] So far, formulas (1)-(10) constitute the basic mixed linear integer model for wind farm collector line topology optimization, which is the classic mixed linear integer programming model (traditional MILP model) mentioned above.

[0094] Next, the three improvements of the present invention to the classical mixed linear integer programming model are described respectively.

[0095] Due to the large scale and complexity of offshore wind farms, the classic or traditional hybrid linear integer programming model often faces problems such as large amount of calculation and long running time in the solution process. When dealing with large-scale problems, its computational complexity will increase sharply, limiting its feasibility and efficiency in practical applications. In particular, the scale of the cable non-crossing constraint in formula (8) is the fourth power of the number of nodes N.

[0096] In order to reduce the computational complexity and increase the feasibility, the classic mixed linear integer programming model (traditional MILP model) is improved in three aspects: pre-reduction of optional paths, addition of constraints, and dynamic generation of cable non-crossing constraints. This improves the mixed linear integer programming model for topological optimization of offshore wind farm collection systems.

[0097] First, optional path pre-reduction

[0098] The reducing of optional paths in the wind farm cable path planning in step S1 includes:

[0099] Each wind turbine in the offshore wind farm is connected to a wind turbine adjacent to the wind turbine or an offshore booster station, wherein the distance between each wind turbine and the wind turbine connected thereto does not exceed a preset value.

[0100] Specifically, in the cable path planning of a wind farm, each wind turbine is usually connected to other wind turbines in the adjacent area or directly connected to an offshore booster station. The present invention limits each turbine to be connected to wind turbines within y kilometers of its location or directly connected to an offshore booster station, which simplifies the model complexity and improves the solution capability for large offshore wind farms.

[0101] In an application scenario, Y is 3.

[0102] The complete directed graph of path planning G = (V, A) is reduced to G y =(V,A y ), where A y represents the reduced set of optional cable arc segments. Formula (1) is modified as follows:

[0103]

[0104] Second, add constraints

[0105] The present invention removes the basic constraint condition of the classical mixed linear integer model (formula (8)) and adds two constraints.

[0106] The new constraints are as follows:

[0107]

[0108]

[0109] Among them, formula (12) is a further enhancement of formula (4) to ensure that each node has at least one cable connection.

[0110] Formula (13) is enhanced by t The relationship between the two independent variables, f, constrains the power flow on the line in equation (10) so that the power flow is positive when there is a cable connection.

[0111] In summary, formula (8) is removed from the classic mixed linear integer model (traditional MILP model), and formulas (11), (12) and (13) are added to achieve the first and second improvements, forming an improved MILP initial relaxation model.

[0112] Third, dynamically generated cables do not cross constraints

[0113] Formula (8) imposes a non-crossing restriction on cables. For a wind farm with N nodes, directly introducing this constraint will increase N 4 The variable equation greatly increases the difficulty of solving. The present invention proposes a new solution method, which first solves the initial relaxation model (excluding formula (8)) that does not contain the no-crossing constraint. After obtaining the initial solution, it is determined whether the cable crosses and identifies the cross position. The no-crossing constraint is added to the cross position, see formula (14), to form a new relaxation model. The new relaxation model is repeatedly solved iteratively until a cable layout without crossover is generated.

[0114]

[0115] In summary, formula (8) is removed from the classic mixed linear integer model (traditional MILP model), and formulas (11), (12), (13) and (14) are added to achieve the first, second and third improvements, forming an improved complete MILP model.

[0116] The detailed constraints of each model are shown in Table 1.

[0117] Table 1 Constraint list of three MILP models

[0118]

[0119] Formula (14) is used to identify the intersection position and add a no-intersection constraint at that specific position. t ' is a newly introduced binary variable used to record the crossover position that occurs during the iteration. t The initial value of ' is 0, and it is changed by x during the repeated iterations to solve the relaxation model. t Assignment. Formula (14) only imposes non-crossing constraints on the crossing arc pairs that have appeared in the iterative solution process, while the crossing arc pairs that have not appeared in the iterative process are ignored. This greatly reduces the number of model constraints and the complexity of the solution. The specific solution process of the improved complete MILP model is as follows: Figure 2 As shown, Figure 2 In the above, the improved MILP initial relaxation model includes formula (11), formula (2)-(7), and formula (9)-(13).

[0120] The present invention proposes a path planning method suitable for the collection system of a large wind farm. Based on the classical mixed linear integer programming model (traditional MILP model), the model constraints are further enhanced, and a method of pre-reduction of optional paths and dynamic generation of cable non-crossing constraints is introduced, which can effectively perform topology optimization on large wind farms.

[0121] The CPLEX solver is used to perform topology optimization on the HornsRev 1 wind farm using the improved complete MILP model and the traditional MILP model in Table 1. The maximum solution time is set to 36,000 seconds, and the current solution is output if the time limit is exceeded. The wind farm contains 80 Vestas V80-2.0MW. Figure 3 The original detailed layout of the Horns Rev 1 wind farm is shown in Table 2. The three optional cable types are shown in Table 2, where the total cable cost consists of the fixed installation cost and the cable unit price.

[0122] Table 2 Optional cable parameters for wind farms

[0123]

[0124] The wind farm's collection system topology optimization was performed using the improved complete MILP model of the present invention. The results are as follows: Figure 4 . Figure 4 The original layout uses a regular columnar line arrangement, while the optimized layout is more inclined to a tree structure. The cable 3 with the highest unit price and the largest number of cables that can be mounted is mainly used for the main confluence near the booster station. The cable 1 with a small capacity and low price is used far away from the booster station. Figure 3 The original layout required a total cable cost of 21.46M€, while the optimized layout only required a total cable cost of 19.90M€, saving 7.27% of the cable cost.

[0125] The improved complete MILP model of the present invention can not only effectively optimize the topology of large wind farms, but also greatly reduce the required storage space and solution time. The WT1-WT80 in the Horns Rev1 wind farm is split and combined into rows and columns to form 7 wind farms with different numbers of wind turbines. These 7 wind farms are tested, and the solution results and required computing resources are shown in Table 3. Figure 5-7 The optimized topology layout of the wind farm collector lines with different numbers of units is provided. Specifically, Figure 5 This is a schematic diagram of the optimized layout of a wind farm with 50 wind turbines. Figure 6 This is a schematic diagram of the optimized layout of a wind farm with 60 wind turbines. Figure 7 This is a schematic diagram of the optimized layout of a wind farm with 70 wind turbines.

[0126] Table 3 Solution results and required computing resources for different wind farms

[0127]

[0128] As can be seen from Table 3, as the number of wind turbines increases, the storage space required for the solution increases rapidly. The improved model greatly reduces the demand for storage space. When the number of wind turbines is 80, the storage space is reduced to one thousandth of the traditional model. At the same time, the increase in the number of wind turbines also increases the model complexity and solution time. When the number of wind turbines is greater than 70, the traditional model fails to obtain the optimal solution within the maximum solution time limit of 36,000 seconds. The improved model can obtain the optimal line layout of a large offshore wind farm with 80 turbines within 40 minutes. When the number of wind turbines is 60, the solution time of the improved model is only 11% of the traditional model.

[0129] The present invention improves the constraints of the classic mixed linear integer model (traditional MILP model) and introduces optional path pre-reduction and dynamically generated cable non-crossing constraints to form a new collection system path planning method suitable for large offshore wind farms. It can effectively optimize the topology of large wind farms. Test results show that the cost of wind farm collection lines after optimization is reduced by 7.27%. The improved model greatly reduces the storage space requirements and solution time, and greatly improves the calculation efficiency.

[0130] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0131] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the offshore wind farm collection system topology optimization method described in the above method embodiment. The processor can be configured to execute the program in the storage device, which includes but is not limited to executing the program for the offshore wind farm collection system topology optimization method described in the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0132] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the offshore wind farm collection system topology optimization method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned offshore wind farm collection system topology optimization method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0133] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

[0134] Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0135] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for topological optimization of a large wind farm power collection system, characterized in that: include: Based on the MILP model of wind farm, reduce the optional paths in wind farm cable path planning; Set constraints, including: Each node in the wind farm is connected by at least one cable, wherein the node includes a wind turbine and a booster station; When there is a cable connection in the system, the power flow on the cable line of the system is positive; Set the dynamically generated cables to not cross constraints; The reducing of optional paths in the wind farm cable path planning includes: Define a binary variable x t (i,j), as in formula (11), where: Where T represents a set of optional cable types; a directed graph G = (V, A) is pre-constructed, and the nodes of the wind farm are divided into the set of all wind turbine nodes V t And all booster node collection V o , V represents the union of the two; the arc set A includes all possible connections between nodes; the arc set A y The arc segment set of all possible cable path planning when each wind turbine is connected to the wind turbine or offshore booster station adjacent to the wind turbine and the distance between each wind turbine and the wind turbine connected to it does not exceed the preset value; nodes i and j represent the nodes where the wind turbine and booster station are located in the wind farm; t represents the cable type; The method of making each node in the wind farm connected with at least one cable, wherein the node includes a wind turbine and a booster station, includes: Where N is the number of all nodes in the wind farm; The step of making the power flow on the cable line of the system positive when there is a cable connection in the system comprises: Where, f is a continuous non-negative variable, representing the line power flow; The setting for dynamically generating a cable non-crossing constraint includes: According to the MILP model, solving an initial relaxed model without non-crossover constraints; After obtaining the initial solution, determine whether the cable crosses and identify the cross position, add no-cross constraints to the cross position to form a new relaxation model; The new relaxation model is solved iteratively until a cable layout without crossing is produced; The non-intersection constraints added to the locations where the intersection will occur include: Add a non-intersecting constraint and x t ′ is a binary variable used to record the crossover position that occurs during the iteration; x t The initial value of ′ is 0, and it is replaced by x t Assignment; Set χ stores cross-cable pairs {(i, j), (l, k)}.

2. The method according to claim 1, characterized in that The wind farm-based MILP model includes: The topology optimization objective of the power collection system is set to minimize the cable cost, as shown in formula (2): Where N is the number of all nodes in the wind farm, C unit is the cable unit price, t represents the cable type, and d(i, j) represents the Euclidean distance between nodes i and j; The constraints satisfied by the model include: x t (i,j)+x t (j,i)≤1,i≠j (7); 3. A control device, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by the processor to execute the large-scale wind farm collection system topology optimization method according to any one of claims 1 to 2.

4. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the large-scale wind farm collection system topology optimization method according to any one of claims 1 to 2.

Images