Topological optimization method and system for current collection system of offshore wind plant for global optimization

Abstract

The invention relates to the technical field of power system network topology optimization, in particular to an offshore wind plant current collection system topology optimization method and system oriented to global optimum, and the method comprises the steps: collecting coordinate position data of all wind driven generators; carrying out site selection on the offshore transformer substation through a fuzzy C-means algorithm; connecting all candidate cables based on the coordinate position data of the wind driven generator; a topological optimization problem of a current collection system of an offshore wind plant is formulated based on an MILP model of power network extension planning, an optimized objective function is established, constraint conditions are set, and the objective function aims at minimizing the total cost of current collection system planning of the offshore wind plant; and optimizing and solving the MILP model to obtain an optimal topology design scheme. According to the method, various physical and economic constraints in the offshore wind power plant current collection system are simulated based on the MILP optimization model, the optimal current collection system layout can be generated, and the power transmission loss can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of power system network topology optimization, and in particular to a global optimal offshore wind farm collection system topology optimization method and system. Background Art

[0002] As an important part of renewable energy, offshore wind power plays a key role in achieving global emission reduction targets. Compared with land-based wind power, offshore wind power has the characteristics of high wind speed, short windless period and low greenhouse gas emissions. According to the report of the Global Wind Energy Council, with the reduction of the average energy cost of offshore wind power, it has become a highly competitive energy option and has played a significant role in promoting cross-industry cooperation and decarbonization. As of the end of 2020, the total installed capacity of offshore wind farms in the world was about 35 GW, which is expected to reach about 270 GW by 2030 and is expected to jump to about 2,000 GW by 2050, making an important contribution to the global net zero emission target. China is in a stage of rapid development of offshore wind power installation, and plans to add 40 million kilowatts of installed capacity within a period of time, thus becoming the world's largest market. At the same time, with the expansion of the scale of offshore wind farms and the increase in the distance of power transmission, higher requirements are placed on the economic and reliability of the power collection system.

[0003] In the past few decades, many scholars have conducted extensive and in-depth research in the field of topological optimization of offshore wind power collection systems. The collection system is one of the key building elements of offshore wind farms and occupies an important part of the power generation cost. Its optimization involves the selection and structural design of submarine cables to achieve economic optimization. Different topological structures, such as radial, ring and multi-ring structures, have been widely studied. The radial structure is the most common in practical applications due to its simplicity and low construction cost. Its design is easy to implement and relatively convenient to maintain, so it has significant advantages in initial investment and operation management. Especially in small or medium-sized wind farms, the radial structure can effectively reduce construction and material costs; while in the case of high cable failure rate and repair time, the multi-ring structure shows significant advantages in improving system reliability and proves its economic benefits.

[0004] Some studies regard the topology optimization of the power collection system as a path optimization problem in two-dimensional space, and use heuristic algorithms to improve the optimization efficiency and accuracy. These algorithms include ant colony optimization, genetic algorithm and particle swarm optimization, which have shown certain advantages in solving complex optimization problems. However, the performance of these methods often depends on the setting and randomness of the initial parameters, so they are prone to fall into the local optimal solution and may not be able to find the global optimal solution.

[0005] In addition to heuristic algorithms, graph theory methods have also been applied to the optimization of power collection systems. Methods such as minimum spanning tree and Monte Carlo tree search provide some basic theoretical support for system topology optimization. However, these methods are not good enough in dynamically changing environments, especially when faced with dynamic factors such as power demand and wind speed changes, their flexibility and adaptability are limited to a certain extent.

[0006] On the other hand, the expansion planning of power grids has been studied for decades, and the research in this field provides important inspiration for the planning of offshore wind farm collection systems. Both are committed to optimizing investment and operating costs, reducing power losses and improving reliability, while meeting constraints such as voltage and capacity. In practical applications, radial topology is generally adopted, and ring topology is considered to improve reliability. Therefore, it is particularly important to develop an optimization model for offshore wind power collection systems that integrates power grid planning technology, which provides a new path for achieving technological progress and industrial upgrading. Summary of the invention

[0007] Offshore wind farms are an important part of the development of renewable energy. The planning and optimization of their power collection systems play a key role in the overall efficiency and economy of wind farms. However, due to the particularity of the marine environment and the volatility of wind resources, the design and planning of power collection systems face multiple challenges. In response to these problems, the present invention provides a global optimal offshore wind farm power collection system topology optimization method and system. The specific technical solutions are as follows:

[0008] A global optimal offshore wind farm collection system topology optimization method includes the following steps:

[0009] Step S1, collecting coordinate position data of all wind turbines;

[0010] Step S2, selecting a site for an offshore substation using a fuzzy C-means algorithm;

[0011] Step S3, connecting all candidate cables based on the coordinate position data of the wind turbine, the cables being used to connect the adjacent wind turbines to the offshore substation, ensuring that the distance between the adjacent wind turbines and the offshore substation does not exceed a preset range;

[0012] Step S4, formulating the topology optimization problem of the offshore wind farm collection system based on the MILP model of the power network expansion planning, establishing the optimization objective function and setting the constraint conditions, wherein the objective function aims to minimize the total cost of the offshore wind farm collection system planning;

[0013] Step S5, optimizing and solving the MILP model to obtain the optimal topology design solution.

[0014] Preferably, the site selection of the offshore substation by the fuzzy C-means algorithm in step S2 specifically includes the following steps:

[0015] Step S21: Assume that the input data set generated by collecting n coordinate positions of all wind turbines is X={x1, x2, ..., x i ,...,x n}, and construct the objective function and constraint conditions based on the input data set initialization fuzzy partition matrix U, which is composed of membership degree u ij The cluster membership matrix is ​​constructed, and the specific objective function and constraints are as follows:

[0016]

[0017] Among them, v j is the coordinate of the jth cluster center, that is, the coordinate of the jth offshore substation, u ij is the membership degree of the wind turbine coordinates to the cluster of offshore substation coordinates; m is the exponential parameter that determines the fuzziness of the clustering results; k is the number of clusters;

[0018] Step S22, calculating the cluster center of the wind turbine generator, that is, the coordinates of the offshore substation, according to the current membership matrix U;

[0019]

[0020] Step S23, updating the membership matrix U according to the new cluster center, as follows:

[0021]

[0022] Step S24, calculate the objective function to evaluate the quality of the current clustering, and determine whether the iteration condition is met; if the iteration condition is met, output the clustering result, that is, the final clustering coordinates of the offshore substation; otherwise, return to step S23.

[0023] Preferably, the total cost of the offshore wind farm collection system planning includes the investment cost, the power loss cost during the life of the offshore wind farm and the total cost of reducing wind power; the optimization objective function in step S4 is expressed as follows:

[0024]

[0025] Where L is the collection of cables; c i,j is the cable cost between the starting wind turbine i and the ending wind turbine j, x i,j is the investment decision variable; η is the coefficient for transferring the power loss of one time slot to the entire life cycle of the offshore wind farm, r i,j and f i,jare the impedance and current between lines (i, j), M is a maximum value, is the wind power reduction of the wind turbine at node i, Ψ WT is a collection of wind turbine nodes.

[0026] Preferably, the constraint conditions in step S4 are as follows:

[0027]

[0028] S·f=gg Cur ; (7)

[0029]

[0030]

[0031] in, is the cable current limit between lines (i, j); S is the node-branch association matrix; f is the i,j The line current matrix composed of g i The wind power matrix composed of g Cur Reason The wind curtailment power matrix is ​​composed of: i is the voltage of node i; v j is the voltage at node j; z i,j is the impedance between lines (i, j); and v represent the upper and lower limits of voltage respectively; g i is the wind power generation power at node i; Ψ and Ψ SUB They are the total node set and the substation node set; is the connection state from node i to node j or the directionality of the parent-child relationship; is the connection state from node j to node i or the directionality of the parent-child relationship.

[0032] A global optimal offshore wind farm power collection system topology optimization system is applied to the method described, comprising:

[0033] A data acquisition module is used to collect coordinate position data of all wind turbines;

[0034] The site selection module is used to select the site of the offshore substation using the fuzzy C-means algorithm;

[0035] A cable connection module is used to connect all candidate cables based on the coordinate position data of the wind turbines. The cables are used to connect the adjacent wind turbines to the offshore substations to ensure that the distance between the adjacent wind turbines and the offshore substations does not exceed the preset range. An optimization modeling module is used to formulate the topology optimization problem of the offshore wind farm collection system based on the MILP model of the power network expansion planning, establish the optimization objective function and set the constraints. The objective function aims to minimize the total cost of the offshore wind farm collection system planning.

[0036] Model solving model is used to optimize and solve the MILP model to obtain the optimal topology design solution.

[0037] A computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the global optimal offshore wind farm collection system topology optimization method.

[0038] A processor is used to run a program, wherein the program executes the global optimal offshore wind farm collection system topology optimization method when running.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention uses an optimization model based on MILP to simulate various physical and economic constraints in the offshore wind farm collection system. The model can not only generate the optimal collection system layout, but also accurately evaluate the power transmission loss. Secondly, by adopting the existing mature distribution network expansion planning technology, the optimization scheme can adapt to various wind farm sizes and specific conditions, providing high flexibility and adaptability.

[0041] This invention innovatively combines mature technologies in the distribution network expansion planning (DNEP) to optimize the design of the collection system of offshore wind farms. By comprehensively analyzing the characteristics and requirements of offshore wind farms, an optimization scheme is developed that can effectively reduce the power loss of the collection system, thereby improving the economic benefits of wind farm construction.

[0042] Through detailed comparison with existing technologies, the present invention has significant advantages in reducing power loss, optimizing resource allocation and improving economic benefits. The application of this technology is expected to provide reliable technical support for the construction and operation of offshore wind farms and promote the widespread application of renewable energy around the world. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0044] Figure 1 The figure is a flow chart of the method of the present invention.

[0045] Figure 2 Schematic diagram of potential connecting cables between adjacent wind turbines and the substation.

[0046] Figure 3 Schematic diagram of the locations of candidate cables and offshore substations.

[0047] Figure 4 This is the topology planning solution result for Case 1-3, where Figure 4 (a), 4(b), and 4(c) are the topological planning solution results for Case 1, Case 2, and Case 3, respectively.

[0048] Figure 5 It is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0051] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0052] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0053] Embodiment 1:

[0054] like Figure 1 As shown, this embodiment provides a global optimal offshore wind farm collection system topology optimization method, comprising the following steps:

[0055] Step S1, collecting coordinate position data of all wind turbines. The coordinate positions are obtained by micro positioning.

[0056] Step S2, selecting the site of the offshore substation by using the fuzzy C-means algorithm. The fuzzy C-means clustering (FCM) algorithm is a soft clustering method. The idea of ​​this method is to use the membership degree to represent the relationship between each data point, so as to determine the cluster to which each data point belongs. At the same time, the FCM algorithm is also an algorithm based on the objective function.

[0057] The specific steps include:

[0058] Step S21: Assume that the input data set generated by collecting n coordinate positions of all wind turbines is X={x1, x2, ..., x i ,...,x n}, and construct the objective function and constraint conditions based on the input data set initialization fuzzy partition matrix U, which is composed of membership degree u ij The cluster membership matrix is ​​constructed, and the specific objective function and constraints are as follows:

[0059]

[0060] Among them, v j is the coordinate of the jth cluster center, that is, the coordinate of the jth offshore substation, where the value of j is 1; u ij is the membership degree of the clustering of the wind turbine coordinates to the offshore substation coordinates; m is an exponential parameter that determines the fuzziness of the clustering result. The larger its value, the more fuzzy the clustering result. The value of the present invention is 2; k is the number of clusters;

[0061] Step S22, calculating the cluster center of the wind turbine generator, that is, the coordinates of the offshore substation, according to the current membership matrix U;

[0062]

[0063] Step S23, updating the membership matrix U according to the new cluster center, as follows:

[0064]

[0065] Step S24, calculate the objective function to evaluate the quality of the current clustering, and determine whether the iteration condition is met, that is, the change in coordinates generated by the previous iteration clustering is less than the preset threshold value 0.001; if the iteration condition is met, output the clustering result, that is, the final clustering coordinates of the offshore substation; otherwise, return to step S23.

[0066] Step S3, connecting all candidate cables based on the coordinate position data of the wind turbines, the cables being used to connect the adjacent wind turbines to the offshore substation, ensuring that the distance between the adjacent wind turbines and the offshore substation does not exceed a preset range.

[0067] like Figure 2 The present invention assumes that in an ideal cable layout, there is no need to use long cables connecting distant wind turbines to the offshore substation, such as connecting lines spanning more than three turbines. This is because longer cables will cause greater power losses and may not be allowed in certain specific situations.

[0068] Figure 2 The figure shows the process of listing all possible connecting cables between wind turbines and offshore substations within the determined maximum range. In this process, it is assumed that the optimal cable layout does not require the use of long cables between wind turbines and offshore substations that are farther away, for example, cables spanning more than 3 wind turbines. Because longer cables mean greater power losses, they are not even allowed in some cases. Taking wind turbine T1 as an example, the figure shows possible connecting cables, including T1-T2, T1-T3, T1-T5, T1-T6, T1-T7, T1-T9 and T1-Sub (Sub represents offshore substation), while there are no candidate cables between T1-T4 and T1-T8 because they exceed the maximum range. The same rule is applied to other wind turbines to list all candidate cables.

[0069] Step S4, based on the MILP model of power network expansion planning, formulate the topology optimization problem of the offshore wind farm collection system, including operation constraints, flow constraints, voltage constraints and other topology constraints, to ensure the rationality of the final planning result. Establish the optimization objective function and set the constraint conditions, the objective function is to minimize the total cost of the offshore wind farm collection system planning.

[0070] The total cost of the offshore wind farm collection system planning includes the investment cost, the power loss cost during the life of the offshore wind farm and the total cost of reducing wind power; the optimization objective function in step S4 is expressed as follows:

[0071]

[0072] Where L is the set of cables; the investment cost is calculated by dividing the cost of each cable ci,j Multiply its investment decision x i,j To minimize the cost of building the collection system cables, the cost of power loss during the life of the offshore wind farm is The total cost of curtailing wind power is

[0073] c i,j is the cable cost between the starting wind turbine i and the ending wind turbine j, x i,j is the investment decision variable. When it is 1, it means investment, and when it is 0, it means no investment;

[0074] η is the coefficient that transfers the power loss of one time slot to the entire life cycle of the offshore wind farm, r i,j and f i,j are the impedance and current between lines (i, j), M is a maximum value, is the wind power reduction of the wind turbine at node i, Ψ WT is a collection of wind turbine nodes.

[0075] The constraints are as follows:

[0076]

[0077] S·f=gg Cur ; (7)

[0078]

[0079] in, is the cable current limit between lines (i, j); S is the node-branch association matrix; f is the i,j The line current matrix composed of g i The wind power matrix composed of g Cur Reason The wind curtailment power matrix is ​​composed of: i is the voltage of node i; v j is the voltage at node j; z i,j is the impedance between lines (i, j); and v represent the upper and lower limits of voltage respectively; g i is the wind power generation power at node i; Ψ and Ψ SUB They are the total node set and the substation node set; is the connection state from node i to node j or the directionality of the parent-child relationship; is the connection state from node j to node i or the directionality of the parent-child relationship.

[0080] Among them, constraints (6)-(11) are operational constraints. Constraint (6) indicates that the usage of the cable is limited by its investment decision, and it indicates the power limit of the cable ( represents the cable capacity limit between lines (i, j)), that is, the flow of the cable cannot exceed its capacity, and this constraint is also used to represent the power limit of the cable. Constraint (7) is achieved by introducing the node-branch association matrix S and the power injection vector gg of each node Cur , represents power balance, which means that the total generation in the system must equal the total load plus losses,. Constraint (8) is a large-M formula that enforces Kirchhoff’s voltage law (KVL), ensuring that the sum of the voltage drops in any closed loop in the grid is zero. Constraint (9) represents the voltage limit ( and v represent the upper and lower limits of voltage respectively), that is, the voltage of the node must be within the specified range and cannot be too high or too low. Constraint (10) means that the unsatisfied load (i.e., lost wind power) of each node should not exceed the demand, which means that the loss or curtailment of wind power in the system should not exceed a certain large value M. Constraint (11) means that the final topology should be composed of a tree, where Ψ and Ψ SUB are the total node set and the substation node set respectively. The root node of each tree is the substation node, and the number of cables in operation should be equal to the number of leaf nodes (load nodes); constraints (12)-(14) belong to the spanning tree constraints, which are used to ensure the correctness of the network topology. Among them, constraint (12) ensures that the parent-child relationship between nodes i and j can only be realized when the line (i, j) is connected ( Variables representing the parent-child relationship between nodes i and j); constraint (13) ensures that the substation node is always the parent node of other nodes; constraint (14) means that each wind turbine node can only have one parent node.

[0081] Together, these constraints ensure that the planning and operation of offshore wind farm collection systems are both economical and reliable, while meeting technical specifications and safety requirements.

[0082] Step S5, optimizing and solving the MILP model to obtain the optimal topology design solution.

[0083] The proposed method and traditional planning method are compared and analyzed using existing wind farm cases:

[0084] The present invention adopts an offshore wind farm including 63 wind turbine coordinate positions as the research object (specific data are shown in Table 1) and assumes that the full-load operation hours of the wind farm are 2500 hours, which is lower than the conventional full-load hours of a general open wind farm (usually about 3000-4000 hours).

[0085] Table 1. Specific parameters of 63 wind turbine examples

[0086]

[0087] Case 1( Figure 4 (a)) Traditional collection system planning scheme. In this scheme, wind turbines are arranged in rows, and the wind turbines in each row are connected to the offshore substation in series. The fixed blue line in the figure represents the newly built cable, while the other dotted lines represent the unselected lines.

[0088] Case 2( Figure 4 (b)): Proposed collector system planning approach without radial constraints. In this scenario, the location of the offshore substation is optimized by fuzzy C-means clustering method and is located in the center of the wind turbines. Such a layout reduces the total cable length, thereby reducing cable investment and power losses. The network topology is radial, but the details are different compared to Case 1.

[0089] Case 3( Figure 4 (c)): Proposed collector system planning method with radioactivity constraints. This scheme is similar to Case 2, but with the addition of radioactivity constraints, which makes the network topology more symmetrical and computationally more efficient.

[0090] In case 1, the present invention assumes that the offshore substation has a fixed location, located in the southeast direction of the offshore wind farm. For cases 2 and 3, the location of the offshore substation is determined by step 2, that is, the coordinate location optimization strategy based on the fuzzy C-Means clustering method is adopted. After steps 1 and 2, the locations of the candidate cables and substations are as follows Figure 3 In this figure, blue square nodes represent offshore substations, yellow circular nodes indicate all offshore wind turbines, and dotted lines mark potential candidate cable routes.

[0091] Figure 3 The figure shows the locations of candidate cables and offshore substations determined after steps S1-S3 in the case of 63 wind turbines. Specifically, the fuzzy C-means clustering method is used to determine the optimal location of the offshore substation (the location of the offshore substation is indicated by a blue marker in the figure); then all potential connecting cables between adjacent wind turbines and offshore substations within the preset maximum range are listed (these candidate cables are indicated by dashed line segments in the figure).

[0092] In this paper, three specific cases are analyzed to explore the problem of offshore wind farm collection system planning. The paper uses the advanced optimization tool Gurobi with the MATLAB programming environment to quantitatively plan and analyze the optimal connection between wind turbines and their corresponding offshore substations. In order to better understand the benefits of different planning schemes, Figure 4 The planning results of the three cases are shown in Table 2. Figure 4 In the example, established cable connections are represented by fixed blue lines, while other alternative but unselected cable connections are shown as dotted lines. This visual distinction helps participants understand the selection logic and final results of the project more intuitively. Case 1 adopts the traditional series connection method, that is, the wind turbines in each row are connected in series and finally converged to the substation. This layout is effective for simple wind farms, but as the scale of wind farms increases, it may lead to an increase in cable length and corresponding cost and energy loss. In order to optimize this traditional layout, Cases 2 and 3 introduce a "central layout" strategy, arranging the offshore substation at the center of the wind turbine. This change significantly reduces the total length of the required cable, thereby reducing the investment and operating costs related to the cable, and also helps to reduce power losses. In these two cases, the network topology of the wind farm adopts a radial structure, which helps to achieve higher system stability and reliability. In particular, Case 3 adopts a symmetrical topology layout, which shows more efficient cable utilization and more balanced load distribution compared to Case 2. This symmetrical layout not only optimizes the overall length of the cables, but also brings additional benefits in terms of visual and technical maintenance, further improving the efficiency and effectiveness of the entire system.

[0093] Table 2 provides more details of the computational results. It can be observed that the radial constraint shows a significant effect in improving computational efficiency. In particular, in Case 3, the computational time is significantly reduced compared to Case 1 and Case 2. More importantly, Case 2 and Case 3 achieve a significant reduction in total cost (by 26.15% to 26.85%, respectively) compared to Case 1, which is mainly due to the reduction in cable length, resulting in a reduction in cable investment (by 23.76% to 24.31%) and operating cost (by 42.61% to 44.96% in power loss). It is worth noting that Case 3 is superior to Case 2 in terms of the quality of the optimal solution (optimality gap: 2.66% < 6.20%) and computational efficiency (consuming less time and obtaining a better quality solution). This result reflects the effectiveness of the radial constraint in the proposed mixed integer linear programming (MILP) model.

[0094] Table 2 Comparison of planning results: Example of 63 wind turbines

[0095]

[0096]

[0097] Therefore, through the specific example test of 63 units, we can conclude that the offshore wind farm collection system optimization design method based on mixed integer linear programming proposed in this invention is superior to the traditional planning scheme in terms of solution investment cost, operation cost, power loss and calculation time. In addition, in order to further improve the performance, the optimality of the model can be further improved by introducing radial constraints.

[0098] By analyzing and comparing three different scenarios, Case 1 (traditional planning method), Case 2 (collection system planning method without radial constraints), and Case 3 (collection system planning method with radial constraints), the present invention explores the improvement effects of key indicators including investment cost, operating cost (including 20-year power loss), total cost, cable length, loss rate, and calculation time. These comparison results highlight the advantages of the method proposed in the present invention in terms of technical implementation and economic benefits, thereby providing a more effective and economical planning scheme for the design of offshore wind farm collection systems.

[0099] Embodiment 2:

[0100] like Figure 5 As shown, based on the same inventive concept as that of Example 1, this embodiment provides a global optimal offshore wind farm collection system topology optimization system, which is applied to the method described, including:

[0101] A data acquisition module is used to collect coordinate position data of all wind turbines;

[0102] The site selection module is used to select the site of the offshore substation using the fuzzy C-means algorithm;

[0103] A cable connection module is used to connect all candidate cables based on the coordinate position data of the wind turbines. The cables are used to connect the adjacent wind turbines to the offshore substations to ensure that the distance between the adjacent wind turbines and the offshore substations does not exceed the preset range. An optimization modeling module is used to formulate the topology optimization problem of the offshore wind farm collection system based on the MILP model of the power network expansion planning, establish the optimization objective function and set the constraints. The objective function aims to minimize the total cost of the offshore wind farm collection system planning.

[0104] Model solving model is used to optimize and solve the MILP model to obtain the optimal topology design solution.

[0105] Embodiment 3:

[0106] Based on the same inventive concept as Example 1, this embodiment provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the global optimal offshore wind farm collection system topology optimization method described in any one of claims 1 to 4.

[0107] Embodiment 4:

[0108] Based on the same inventive concept as Example 1, this embodiment provides a processor, characterized in that the processor is used to run a program, wherein the program, when running, executes the global optimal offshore wind farm collection system topology optimization method described in any one of claims 1 to 4.

[0109] Those of ordinary skill in the art will appreciate that the modules of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0110] In the embodiments provided by the present invention, it should be understood that the division of modules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple modules can be combined into one module, one module can be split into multiple modules, or some features can be ignored, etc.

[0111] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0112] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program codes.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A global optimal offshore wind farm collection system topology optimization method, characterized in that: The following steps are involved: Step S1, collecting coordinate position data of all wind turbines; Step S2, selecting a site for an offshore substation using a fuzzy C-means algorithm; Step S3, connecting all candidate cables based on the coordinate position data of the wind turbine, the cables being used to connect the adjacent wind turbines to the offshore substation, ensuring that the distance between the adjacent wind turbines and the offshore substation does not exceed a preset range; Step S4, formulating the topology optimization problem of the offshore wind farm collection system based on the MILP model of the power network expansion planning, establishing the optimization objective function and setting constraints, wherein the objective function aims to minimize the total cost of the offshore wind farm collection system planning; Step S5, optimizing and solving the MILP model to obtain the optimal topology design solution.

2. The method for topology optimization of offshore wind farm power collection system for global optimization according to claim 1, characterized in that: The site selection of the offshore substation by the fuzzy C-means algorithm in step S2 specifically includes the following steps: Step S21: Assume that the input data set generated by collecting n coordinate positions of all wind turbines is X={x1, x2, ..., x i ,...,x n }, and construct the objective function and constraint conditions based on the input data set initialization fuzzy partition matrix U, which is composed of membership degree u ij The cluster membership matrix is ​​constructed, and the specific objective function and constraints are as follows: Among them, v j is the coordinate of the jth cluster center, that is, the coordinate of the jth offshore substation, u ij is the membership degree of the wind turbine coordinates to the cluster of offshore substation coordinates; m is the exponential parameter that determines the fuzziness of the clustering results; k is the number of clusters; Step S22, calculating the cluster center of the wind turbine generator, that is, the coordinates of the offshore substation, according to the current membership matrix U; Step S23, updating the membership matrix U according to the new cluster center, as follows: Step S24, calculate the objective function to evaluate the quality of the current clustering, and determine whether the iteration condition is met; if the iteration condition is met, output the clustering result, that is, the final clustering coordinates of the offshore substation; otherwise, return to step S23.

3. The method for topology optimization of offshore wind farm power collection system for global optimization according to claim 1, characterized in that: The total cost of the offshore wind farm collection system planning includes the investment cost, the power loss cost during the life of the offshore wind farm and the total cost of reducing wind power; the optimization objective function in step S4 is expressed as follows: Where L is the collection of cables; c i,j is the cable cost between the starting wind turbine i and the ending wind turbine j, x i,j is the investment decision variable; η is the coefficient for transferring the power loss of one time slot to the entire life cycle of the offshore wind farm, r i,j and f i,j are the impedance and current between lines (i, j), M is a maximum value, is the wind power reduction of the wind turbine at node i, Ψ WT is a collection of wind turbine nodes.

4. The method for topology optimization of offshore wind farm power collection system for global optimization according to claim 3, characterized in that: The constraints in step S4 are as follows: S·f=g-g Cur ;(7) in, is the cable current limit between lines (i, j); S is the node-branch association matrix; f is the i,j The line current matrix composed of g i The wind power matrix composed of g Cur Reason The wind curtailment power matrix is ​​composed of: i is the voltage of node i; v j is the voltage at node j; z i,j is the impedance between lines (i, j); and v represent the upper and lower limits of voltage respectively; g i is the wind power generation power at node i; Ψ and Ψ SUB They are the total node set and the substation node set; is the connection state from node i to node j or the directionality of the parent-child relationship; is the connection state from node j to node i or the directionality of the parent-child relationship.

5. A global optimal offshore wind farm power collection system topology optimization system, characterized in that: The method applied to any one of claims 1 to 4, comprising: A data acquisition module is used to collect coordinate position data of all wind turbines; The site selection module is used to select the site of the offshore substation using the fuzzy C-means algorithm; A cable connection module is used to connect all candidate cables based on the coordinate position data of the wind turbines. The cables are used to connect the adjacent wind turbines to the offshore substations to ensure that the distance between the adjacent wind turbines and the offshore substations does not exceed the preset range. An optimization modeling module is used to formulate the topology optimization problem of the offshore wind farm collection system based on the MILP model of the power network expansion planning, establish the optimization objective function and set the constraints. The objective function aims to minimize the total cost of the offshore wind farm collection system planning. Model solving model is used to optimize and solve the MILP model to obtain the optimal topology design solution.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the global optimal offshore wind farm collection system topology optimization method described in any one of claims 1 to 4.

7. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the global optimal offshore wind farm collection system topology optimization method described in any one of claims 1 to 4.

Images