Offshore wind plant intensive current collection system planning method based on cable parallel laying quantification

Abstract

The invention discloses an offshore wind power plant intensive current collection system planning method based on cable parallel laying quantification. The method comprises the following steps: acquiring node coordinates and engineering parameters of a wind power plant; an initial solution is obtained through clustering partition, candidate cables are screened, and the cross relation of the candidate cables is identified; a mixed integer linear programming model with the cable parallel laying length as a quantitative index is constructed, a target function of the mixed integer linear programming model fuses cable construction cost, full life cycle power loss cost and intensive excitation terms for parallel laying, and engineering constraints such as parallel laying logic, power conservation, node degree and crossing prevention are integrated; model solution and iterative optimization are carried out by setting and dynamically adjusting intensive excitation coefficients, finally, a reliable layout scheme which meets the intensive sea use policy and is better in total cost is automatically generated, and the defects that a traditional method depends on manpower, efficiency is low, and economy and intensive targets are difficult to plan as a whole are effectively overcome.

Description

Technical Field

[0001] This invention relates to the planning of power collection systems for offshore wind farms, and in particular to a method for planning intensive power collection systems for offshore wind farms based on the quantification of parallel cable laying. Background Technology

[0002] In offshore wind farms, the power collection system, as a crucial link connecting wind turbines and offshore substations, typically accounts for 15% to 30% of the total investment. A well-planned power collection system directly impacts the construction cost and life-cycle economics of the wind farm, as well as the reliability and flexibility of subsequent operation and maintenance. Due to the high combinatorial complexity and nonlinear characteristics of power collection topologies, power collection system planning is widely considered an NP-hard problem: in large-scale wind farms, the number of candidate cables increases approximately quadratically with the number of wind turbines. Without control, the decision variables and constraints of the optimization model will rapidly expand, making it difficult to obtain a high-quality solution within an acceptable timeframe.

[0003] In terms of research methods, existing power collection system planning methods can be broadly divided into two categories. One category is heuristic methods based on graph theory and metaheuristic algorithms. These methods typically build upon radial cabling frameworks such as minimum spanning trees (MSTs) and use metaheuristics like adaptive particle swarm optimization and genetic algorithms to search for topologies. They then gradually extend to consider issues such as cable selection, grid connection across substations, wind farm expansion, and reconfiguration of power. These methods offer good computational efficiency and some engineering feasibility on medium-sized examples, but the solution is rarely globally optimal, and its quality often depends on algorithm parameters and initial solution settings. The other category is mathematical optimization methods based on mixed-integer linear programming (MILP) or mixed-integer quadratic programming (MIQP). By explicitly modeling cable investment costs, network loss costs, and various engineering constraints, given a set of candidate arcs, these methods can obtain planning schemes with global optimality or verifiable optimality bounds. However, these methods are highly sensitive to the linear structure and scale of the model, often exhibiting low convergence efficiency and long solution times in large-scale wind farm scenarios, requiring efficient modeling and acceleration strategies.

[0004] Against this backdrop, intensive use of the sea surface is a new direction in the field of power collection system planning. According to relevant regulations, the power collection submarine cables for offshore wind farms with a total sea area greater than 700 hectares must adopt an "intensive" parallel laying structure. On the one hand, this reduces disturbance to the seabed space and the scope of surveying through intensive cable corridors, which is conducive to marine environmental protection and multi-objective three-dimensional utilization; on the other hand, it also poses new constraints and optimization objectives to existing power collection system planning methods.

[0005] However, research on power collection system planning based on the requirements of intensive sea area use remains relatively weak. First, the concept of "intensive" lacks a unified and verifiable quantitative standard in engineering practice. Existing indicators, such as "total cable length" and "sea area used," only reflect the overall scale of the line and the extent of seabed occupation, making it difficult to characterize whether the cable corridor is spatially concentrated enough, and unable to directly correspond to specific requirements such as "parallel laying in corridors." Second, the intensive sea area use evaluation indicators and indicator systems proposed in existing studies remain at the level of sea area use evaluation and comparative analysis, and are not deeply coupled with the mathematical model of power collection system planning, making it difficult to simultaneously consider economy and the degree of intensiveness within a unified optimization framework. Currently, the industry also lacks effective "intensive" methods. Current intensive planning schemes often rely on a two-stage approach: first, planning software generates a traditional radial planning scheme, and then manual "bundling" and merging of lines. This requires huge labor costs in practice and also faces problems such as unstable quality and poor reproducibility. Furthermore, labor-intensive solutions often incur high economic costs due to a lack of economic considerations, leaving significant room for cost reduction and efficiency improvement.

[0006] In summary, existing technologies have the following main shortcomings in the planning of power collection systems for offshore wind farms designed for intensive marine use: 1. There is a lack of systematic methods to transform "cable space concentration" into a unified quantitative indicator and incorporate it into mathematical optimization models, making it impossible to directly control the concentration level at the model level; 2. Existing power collection system planning models are mostly aimed at economy or reliability, and have not yet formed an integrated planning framework that takes intensive use of the sea as a clear guide and takes into account construction costs and the cost of network loss throughout the entire life cycle; 3. In engineering practice, the mainstream "conventional model + manual bundling" process is inefficient and highly subjective, making it difficult to stably output an intensive power collection system layout that meets relevant regulatory constraints and has good economic efficiency in large-scale deep-sea wind farm scenarios.

[0007] Against this backdrop, it is necessary to propose an intensive power collection system planning method that uses the amount of parallel cable laying as the core quantitative indicator, considers construction costs, network loss costs and the degree of intensification in a unified mathematical optimization model, and explicitly constrains engineering conditions such as parallel cable laying, cable non-crossing and node degree, so as to achieve an effective balance between relevant regulatory requirements and economic goals.

[0008] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a planning method for an intensive power collection system of offshore wind farms based on the quantification of parallel cable laying.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: A planning method for an intensive power collection system in offshore wind farms based on parallel cable laying quantification includes the following steps: S1. Obtain the coordinates of wind turbine and substation nodes, cable parameters, and engineering configuration parameters of the offshore wind farm; S2. Based on the node coordinates, perform clustering and partitioning to obtain the initial solution for hot start, and determine the candidate cable set connecting each node by filtering according to distance; S3. Based on the geometric relationships of the candidate cable set, identify and construct a set of constraints representing the crossing relationships between cables; S4. Construct a mixed integer linear programming model with the parallel cable laying length as the core quantitative indicator of intensive optimization. The objective function of this model integrates cable construction cost, full life cycle power loss cost, and intensive optimization incentive term for parallel laying length. Its constraint system includes at least: constraints characterizing the number of cables laid in parallel and the construction status, constraints to ensure that power transmission does not exceed the limit and node power is conserved, constraints describing power aggregation and cable routing selection at wind turbine nodes, node degree constraints limiting the number of cables connecting wind turbine and substation nodes, and geometric constraints based on the above-mentioned cross-relationship constraint set to prevent cable spatial cross-section. S5. Set the intensive incentive coefficient, solve the mixed integer linear programming model, and obtain the power collection system layout scheme that meets the requirements of intensive sea use and optimizes the total cost; S6. Evaluate the degree of intensification of the layout scheme. If it does not meet the preset requirements, adjust the intensification incentive coefficient and return to step S5; otherwise, output the final planning result.

[0011] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned method for planning an intensive power collection system for offshore wind farms based on quantized parallel cable laying.

[0012] The present invention has the following beneficial effects: This invention provides a planning method for intensive power collection systems in offshore wind farms based on the quantification of parallel cable laying, effectively solving the prominent problems of traditional intensive marine use planning, such as the lack of unified quantitative standards and excessive reliance on manual experience and adjustments. This method innovatively introduces the parallel cable laying length as a core quantitative indicator of intensive performance and successfully integrates it into a unified mathematical optimization framework. Within this framework, a multi-objective mixed-integer linear programming model is constructed, simultaneously considering cable construction costs, total life-cycle power loss costs, and intensive incentive terms. It also systematically integrates a series of engineering constraints, including parallel cable laying, node power conservation, power aggregation routing, node degree constraints, and cable cross-crossing prevention. Thus, it achieves an automatic and integrated balance between intensive objectives and project economic objectives at the model level, completely eliminating the inefficient two-stage model of "conventional planning first, then manual bundling."

[0013] Based on the core innovation of the integrated modeling described above, this invention has achieved significant technical results. First, for the first time, this invention systematically integrates economy and efficiency in global optimization, overcoming the shortcomings of existing methods in considering economic factors during manual paralleling. This allows for the direct generation of layout schemes that meet both intensive marine use requirements and superior economic efficiency. Second, this method completely replaces the tedious and unstable manual "bundling" paralleling process with automated mathematical optimization, significantly saving labor costs while ensuring the reliability and reproducibility of the solution quality. Finally, the constructed model possesses excellent solution efficiency, capable of directly and efficiently handling ultra-large-scale wind farm planning scenarios involving hundreds of wind turbines, outputting high-quality solutions within an engineering-acceptable timeframe, and effectively adapting to the future trend of deep-sea and large-scale offshore wind power development.

[0014] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the overall process of the present invention for the planning method of intensive power collection system for offshore wind farms based on the quantification of parallel cable laying.

[0016] Figure 2 This is a flowchart illustrating the overall planning scheme of an embodiment of the present invention.

[0017] Figure 3 The coordinates of the wind turbines and substations, and a schematic diagram of the candidate submarine cables for Experiment 1 (125 wind turbine wind farm).

[0018] Figure 4 The diagram shows the planning results of the power collection system for Comparative Examples 1, 2, and 3 of Experiment 1 and Example 2.

[0019] Figure 5The coordinates of the wind turbines and substations, and a schematic diagram of the candidate submarine cables for Experiment 2 (300 wind turbine wind farm).

[0020] Figure 6 The diagram shows the planning results of the power collection system for Comparative Examples 1, 2, and 3 of Experiment 2 and Example 2. Detailed Implementation

[0021] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0023] See Figure 1 and Figure 2 This invention provides a method for planning an intensive power collection system for offshore wind farms based on the quantification of parallel cable laying, comprising the following steps: Step S1: Obtain the coordinates of wind turbine and substation nodes, cable parameters, and engineering configuration parameters of the offshore wind farm.

[0024] Step S2: Perform clustering and partitioning based on the node coordinates to obtain the initial solution for hot start, and determine the set of candidate cables connecting each node based on distance filtering.

[0025] In some embodiments, obtaining the initial solution for hot start based on clustering partitions in step S2 refers to performing clustering analysis based on the spatial distribution of wind turbine nodes to form several partitions, and pre-constructing a collector network that meets the basic connection and power transmission requirements in each partition, which serves as the initial feasible solution for the entire optimization model, thereby accelerating the solution process of the subsequent mathematical programming model.

[0026] Step S3: Based on the geometric relationships of the candidate cable set, identify and construct a set of constraints representing the crossing relationships between cables.

[0027] In some embodiments, the geometric relationship identification and construction of the cross-relationship constraint set based on the candidate cable set in step S3 specifically includes: S31. Crossing Relationship Judgment: Traverse all candidate cable combinations. For each pair of two candidate cable segments consisting of four nodes, identify the spatial crossing relationship by calculating the vector cross product and performing sign judgment. Specifically, it is determined to be a crossing if and only if the two segments cross each other, and all crossing cable pairs are recorded in the crossing relationship set. S32. Simplified Constraint Construction: To reduce the problem of excessive constraint size caused by directly prohibiting all crossing cable pairs, a constraint construction method based on mutual exclusion triples is adopted. For any two non-overlapping nodes and a third distinct node in the network, a mutual exclusion triple is constructed. This triple contains candidate arcs in both positive and negative directions connecting the first two nodes, as well as all candidate arcs starting from the third node and intersecting with the line segment connecting the first two nodes. S33. Apply mutual exclusion constraints: For each constructed mutual exclusion triplet, apply a linear constraint to restrict that at most one of the candidate cable arcs contained in the triplet can be selected for construction, thereby effectively preventing cable paths from crossing in space.

[0028] Step S4: Construct a mixed-integer linear programming model with the parallel cable laying length as the core quantitative indicator of intensive optimization. The objective function of this model integrates cable construction cost, full life cycle power loss cost, and intensive optimization incentive term for parallel laying length. Its constraint system includes: constraints characterizing the number of cables laid in parallel and the construction status, constraints ensuring that power transmission does not exceed the limit and node power conservation, constraints describing power aggregation and cable routing selection at wind turbine nodes, node degree constraints limiting the number of connecting cables between wind turbine and substation nodes, and geometric constraints based on the aforementioned cross-relationship constraint set to prevent cable spatial crossover.

[0029] In some embodiments, the objective function of constructing the mixed integer linear programming model in step S4 is specifically: minimizing the sum of cable construction cost and total life-cycle power loss cost, and subtracting an intensive incentive term proportional to the total length of parallel cable laying; wherein, the parallel laying length is defined as the cumulative length of all cables laid on the same path starting from the second cable, and the intensive incentive term controls the incentive intensity for the degree of intensive laying through an adjustable incentive coefficient.

[0030] In some embodiments, the constraints characterizing the number of cables laid in parallel and the construction status in step S4 are implemented by introducing a binary variable representing whether a cable is under construction, an integer variable representing the number of cables carrying different numbers of wind turbines, and an intermediate variable representing the total number of cables laid and the number of overlapping cables. Specifically, this includes defining the total number of cables on each candidate path as the sum of the number of cables carrying different numbers of wind turbines, and constraining the total number of cables to be between a preset minimum and maximum value when the path is selected for construction, and the number of overlapping cables equal to the total number of cables laid minus the path construction status variable.

[0031] In some embodiments, the constraints describing power aggregation and cable routing at wind turbine nodes described in step S4 are modeled using power layer discretization and inter-layer promotion mechanisms. Specifically, this includes: assigning a unique power layer identifier variable to the power output cable of each wind turbine; defining the number of cables flowing out and into each power layer at the node; establishing a hierarchical conservation relationship such that, for non-highest power layers, the difference between the number of cables flowing out and into is equal to the difference between the number of cables promoted from this layer (i.e., the power identifier variable of this layer) and the number of cables promoted from the previous layer to this layer (i.e., the power identifier variable of the previous layer); and simultaneously constraining that only when a cable merges into a certain power layer is it allowed to promote a cable of that layer to a higher layer.

[0032] In some embodiments, the constraints in step S4 to ensure that power transmission does not exceed limits and that node power is conserved include: the total power transmitted on each candidate path is equal to the sum of the power carried by each cable on that path, wherein the power of a single cable is proportional to the number of wind turbines it carries; for each wind turbine node, the difference between the total power of all outflow paths and the total power of all inflow paths is equal to the rated power of that wind turbine.

[0033] In some embodiments, the node degree constraint in step S4 that limits the number of cables connecting the wind turbine and the substation node includes: constraining each wind turbine node to have one and only one outgoing cable path; constraining the substation node to have no outgoing cable path; and constraining the total number of cables connected to each substation node to be within a preset lower and upper limit range.

[0034] In some embodiments, the process of constructing the geometric constraints to prevent cable spatial crossings in step S4 includes: traversing all candidate cable pairs, determining whether their corresponding line segments cross in space through vector cross product operation, and recording all crossing cable pairs as a set of crossing relationships; to reduce the constraint size, for any two non-overlapping node pairs and a third node, constructing a mutually exclusive triplet, which includes candidate arcs in both positive and negative directions connecting the node pair and all candidate arcs connected to the third node and crossing the line segments of the aforementioned node pair; applying constraints such that in each such mutually exclusive triplet, at most one candidate arc can be selected for construction.

[0035] Step S5: Set the intensive incentive coefficients, solve the mixed integer linear programming model, and obtain a power collection system layout scheme that meets the requirements of intensive sea use and optimizes the total cost.

[0036] In some embodiments, the solution to the mixed-integer linear programming model in step S5 is performed using a commercial mathematical programming solver, which can directly handle offshore wind farm planning scenarios involving hundreds of wind turbines and obtain high-quality feasible solutions within an acceptable engineering timeframe.

[0037] Step S6: Evaluate the degree of intensification of the layout scheme. If it does not meet the preset requirements, adjust the intensification incentive coefficient and return to step S5; otherwise, output the final planning result.

[0038] In some embodiments, the step S6 of evaluating the compactness of the layout scheme and adjusting the coefficients specifically includes: S61. Scheme Evaluation: For the power collection system layout scheme obtained in step S5, calculate the total length of parallel cable laying as the core indicator for quantitative evaluation of the degree of intensification. S62. Judgment and Feedback: Based on the calculated total parallel laying length, determine whether the degree of intensification meets the standard. If the length does not meet the standard, increase the intensification incentive coefficient in the objective function of the mixed integer linear programming model described in step S4. If it is determined that the degree of intensification is too high, decrease the intensification incentive coefficient. S63. Iterative optimization: Substitute the adjusted intensive incentive coefficients into the model, return to step S5 to solve again, thus forming an iterative optimization loop of "solving-evaluating-adjusting" until the final planning scheme that simultaneously meets the intensive requirements and economic objectives is obtained.

[0039] The proposed method for planning intensive power collection systems for offshore wind farms based on the quantification of parallel cable laying has the following main technical advantages: by innovatively establishing the parallel cable laying length as a quantifiable core optimization index, and constructing a unified mixed integer programming model that integrates construction costs, operating losses, and intensive incentive terms, it fundamentally overcomes the shortcomings of traditional methods, such as reliance on manual experience, lack of quantitative standards, and difficulty in balancing economy and intensiveness. Thus, while ensuring the engineering feasibility of the planning scheme, it achieves an efficient balance between policy requirements and economic goals. This not only significantly improves the solution efficiency and scheme quality of large-scale wind farm power collection system layout, but also greatly reduces the cost of manual intervention, providing a stable, reliable, and economical planning means for the intensive development of deep-sea wind power.

[0040] The following further describes specific embodiments of the present invention, algorithm examples, and experimental verification.

[0041] A planning method for intensive power collection systems in offshore wind farms based on the quantification of parallel cable laying is proposed. This method overcomes the limitations of traditional intensive sea use methods that lack unified quantitative indicators and rely on manual adjustments. It achieves a balance between intensive sea use requirements and economic goals through integrated optimization of quantitative definitions and mathematical models. Specifically, it can use the parallel cable laying length as the core quantitative indicator for intensive use, construct a multi-objective mixed integer linear programming model that integrates cable construction costs, life cycle power loss costs, and intensive incentives, and design engineering constraints such as the number of parallel cables, cable anti-crossing, and wind turbine node degree. This generates a power collection system layout that meets the requirements of intensive sea use and is more economical, breaking through the inefficient traditional model of "conventional planning first, then manual bundling".

[0042] The mathematical model for planning a power collection system considering intensive marine use is as follows: objective function Establish a complete weighted directed graph in the wind farm. ,in For including substation nodes Wind turbine node The set of nodes, This represents the set of all candidate cables connecting the nodes. The objective function of the model can be expressed as: (1) in, This represents the sum of the construction cost and the total life-cycle network loss cost of an offshore wind farm. Indicates from node To the node The cable, It refers to the number of fans supported by the cable. This indicates the maximum number of wind turbines that the currently used cable can support; Indicates bearing The sum of construction cost per unit length of cable for typhoon generators and network loss cost; Indicates cable The length of the project; Indicates in The number of cables that support k wind turbines arranged in parallel at a location. The parallel reward coefficient represents the reward obtained per unit length of overlapping cable in the objective function. The magnitude of this coefficient can indicate the "aggressiveness" of the given scheme's intensiveness. This refers to the number of overlapping cables. In order to be in The cable length at that location, Indicates in The total length of the second and subsequent cables laid in parallel.

[0043] Constraints Constraints on Parallel Laying of Submarine Cables (2) (3) (4) (5) In constraint (2) In order to be in The number of cables laid at this location represents the number of cables supporting various types of wind turbines. The sum; constraints (3) and (4) indicate that in The range of the number of cables, among which For the line Construction variables, express The cable was either installed or not. The maximum number of cables allowed to be laid in parallel; in equation (5) Indicates in The number of overlapping cables (starting from the second one).

[0044] submarine cable capacity and power conservation constraints (6) (7) In constraint (6) Indicates in The total power at a given location is the sum of the power of all cables at that location; constraint (7) is a node power conservation constraint, indicating that for each wind turbine node, the difference between the outflow power and the inflow power of that node is the node's rated power. .

[0045] Power layer balancing and boosting selection constraints For ease of description, the load-bearing capacity of each cable is discretized into several power layers: the load capacity of a certain cable... The typhoon machine stated that it was in the [number]th [location]. Layer. (Note) For nodes The place sent out at the first Number of cables per layer For nodes The place where the flow is in Number of cables per layer, variable Indicates actual from the wind turbine Was the submarine cable that was sent out at the [number]th [location]? There are multiple power layers, and for each wind turbine node, the cable actually transmitting power from that node can only be in one of the power layers. Therefore: (8) And for each wind turbine node There are only two possible "connection choices": either start a new submarine cable with a power layer of 1 (…). ), or upgrade the power layer of one of the incoming submarine cables by one level and continue to transmit power outwards ( This indicates that the submarine cable was actually connected to the wind turbine and supplied its power. The corresponding hierarchical conservation relationship is: (9) (10) Constraints (9) and (10) can be intuitively understood as: at each level, leaving the node The number of cables is equal to the number of cables that arrive, plus the number of cables "lifted up from the previous level", and minus the number of cables "lifted to a higher level and leaving the current level". To prevent cables from "lifting from an empty level", the following feasibility constraints are added: (12) That is, only when the first Only when there are incoming cables at a layer can one of them be promoted to the next layer. Layer. The above constraint formula utilizes the concept of power layer to fully characterize the power distribution of submarine cables laid in parallel at the wind turbine.

[0046] Conservation constraint on the number of submarine cables (13) (14) Constraints (13) and (14) are the cable number conservation constraints expressed using the Big M method, where For the inflow node The total number of cables. Constraint expression in the line. If it is being constructed, if at the node If a new cable is installed, then The total number of cables at the node is The total number of incoming cables is increased by 1; otherwise, the number of incoming cables equals the number of nodes. Total number of incoming cables.

[0047] Wind turbine and substation node degree constraints (15) (16) (17) (18) When represented using graph theory, the wind turbine and the substation are considered as nodes, and the number of branches they connect to is defined as the degree, specifically out-degree / in-degree in a directed graph, which is equivalent to the number of outgoing lines. Equation (15) restricts the wind turbine's outgoing cables to be on only one edge, and Equation (16) stipulates that the substation cannot have an edge with outgoing cables; Equations (17) and (18) use the actual number of cables laid on the edge. The cable entry depth range for substation nodes is specified, among which... and These represent the lower and upper bounds of the substation's in-degree, respectively, and are usually given by the actual requirements of the project.

[0048] Line crossing constraint First, based on the candidate cable set, establish a set representing cable pairs with intersecting relationships. The specific method is as follows: if and only if simultaneously satisfying and When determining line segments and A crossing occurs. Therefore, iterate through all cable combinations and determine if they cross. If they cross, add the current cable combination. ,in This represents the set of all candidate cable combinations where crossings occur. This considers the candidate cable set in large-scale scenarios. It is very large in scale and directly targets Applying non-crossing constraints to pairwise arc pairs will result in an order of magnitude of [missing information]. Here, a triplet approach is used to apply non-crossing constraints: for any pair of non-crossing points... and any Agreement Indicates from Depart and with All candidate directed arcs that are strictly intersecting, i.e., satisfy... and The arcs, and based on this, the set of mutually exclusive triples is defined as follows: (19) This imposes a non-crossing constraint on up to one cable: (20) Therefore, a complete power collection system planning model based on intensive marine utilization is established as follows: (1) (2)-(20) Therefore, the overall planning scheme flowchart is as follows: Figure 2 As shown.

[0049] Experimental verification The site for Experiment 1 contains 125 wind turbines with a single unit capacity of 16MW and a voltage level of 66KV; the site for Experiment 2 contains 300 wind turbines with a single unit capacity of 16MW and a voltage level of 110KV. The comparative calculation schemes used are derived from the planning software of the design institute and the parallel connection results of professional engineers. The solutions are obtained using MATLAB (2024a) and the Gurobi solver (12.0.3).

[0050] Experiment 1: A wind farm with 125 wind turbines, from an actual offshore wind farm in China. The coordinates of the wind turbines and substations of the wind farm, and a schematic diagram of the candidate submarine cables are shown below. Figure 3 As shown.

[0051] Comparative Example 1 shows the non-intensive power collection system planning results obtained from the design institute's planning software. Example 1 shows the ECS planning results without using an intensive mathematical optimization algorithm, i.e., by maximizing the parallelism of the model proposed in this invention. Set to 1 to obtain; Comparative Example 2 is the result of manual bundling and merging based on Comparative Example 1, which is also the conventional path for obtaining intensive planning schemes in current engineering; Example 2 is the intensive ECS planning result based on the method of the present invention. Figure 4 The planning results of Comparative Examples 1 to 3 of Experiment 1 and Example 2 are shown from top to bottom.

[0052] The cost comparison results (cost unit: RMB 100 million, length unit: kilometers) are shown in Table 1: Table 1

[0053] The results of Experiment 1 show that, without considering intensive use of the sea, the method of the present invention (Example 1) reduces costs by 6.3% compared to Comparative Example 1. Considering intensive use of the sea, the method of the present invention (Example 2) reduces costs by 9.8% compared to Comparative Example 2, and the degree of intensive use in Example 2 (parallel submarine cable length 141.80 km) is higher than that in Comparative Example 2 (parallel submarine cable length 128.50 km). Experiment 2: 300-ton offshore wind farm The coordinates of the wind turbines and substations of the wind farm, and a schematic diagram of the candidate submarine cables are shown below. Figure 5 As shown.

[0054] The same calculation example as Experiment 1 is used in this experiment. Since the design institute software cannot directly solve the problem in the case of ultra-large scale, a partitioned solution + symmetric solution method is adopted. The model proposed in this invention can directly solve offshore wind farms with up to 300 wind turbines and can provide a high-quality feasible solution within the project time. Figure 6 The planning results of Comparative Examples 1 to 3 of Experiment 2 and Example 2 are shown from top to bottom.

[0055] The cost comparison results (cost unit: RMB 100 million, length unit: kilometers) are shown in Table 2: Table 2

[0056] The results of Experiment 2 show that, without considering intensive use of the sea, the method of the present invention (Example 1) reduces costs by 6.8% compared to Comparative Example 1. Considering intensive use of the sea, the method of the present invention (Example 2) reduces costs by 5.8% compared to Comparative Example 2, and the degree of intensive use in Example 2 (parallel submarine cable length 301.17 km) is higher than that in Comparative Example 2 (parallel submarine cable length 276.21 km).

[0057] In summary, this invention proposes a planning method for an intensive power collection system for offshore wind farms based on the quantification of parallel cable laying. Key innovative contributions and design features of this invention include: optimizing the power collection system of offshore wind farms and offshore substations using a node-arc graphical model; creatively establishing the parallel cable laying length (i.e., overlap length) as a core quantitative indicator for intensive use of marine resources, and directly incorporating this indicator as an intensive reward (or equivalent penalty) into the optimization objective function, rather than merely using it for post-evaluation or simple constraints; the constructed objective function integrates cable construction costs and life-cycle power loss costs, thereby achieving an automatic trade-off between economy and intensiveness within a unified mathematical optimization model; and establishing a complete engineering constraint system to ensure the feasibility of the scheme. This system encompasses logical constraints characterizing the number of parallel cables and construction status, electrical constraints ensuring safe power transmission and node power conservation, routing constraints describing the power aggregation process using power layer layering and inter-layer boosting mechanisms, node degree constraints limiting the number of connecting cables between wind turbines and substations, and physical constraints based on geometric relationships to prevent cable spatial crossings.

[0058] Compared with existing technologies, the significant technical advantages of this invention are reflected in the following aspects: For the first time, it comprehensively considers both global economic efficiency and intensive requirements within a unified mathematical optimization framework, fundamentally overcoming the subjectivity and inefficiency of traditional two-stage methods that rely on manual "bundling." This not only saves significant labor costs but also consistently outputs high-quality planning schemes. Furthermore, the constructed mixed-integer linear programming model possesses efficient solution capabilities, directly handling large-scale deep-sea wind farm planning scenarios involving hundreds of wind turbines, obtaining reliable layout schemes that meet intensive marine use requirements and offer better overall costs within an engineering-acceptable timeframe.

[0059] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0060] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.

[0061] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0062] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc or CD-ROM; magnetic surface memory can be disk storage or magnetic tape storage. The storage media described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0063] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0064] The units described above 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 units may be selected to achieve the purpose of this embodiment according to actual needs.

[0065] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0066] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0068] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0069] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0070] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0071] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A planning method for an intensive power collection system of an offshore wind farm based on the quantification of parallel cable laying, characterized in that, Includes the following steps: S1. Obtain the coordinates of wind turbine and substation nodes, cable parameters, and engineering configuration parameters of the offshore wind farm; S2. Based on the node coordinates, perform clustering and partitioning to obtain the initial solution for hot start, and determine the candidate cable set connecting each node by filtering according to distance; S3. Based on the geometric relationships of the candidate cable set, identify and construct a set of constraints representing the crossing relationships between cables; S4. Construct a mixed integer linear programming model with the parallel cable laying length as the core quantitative indicator of intensive optimization. The objective function of this model integrates cable construction cost, full life cycle power loss cost, and intensive optimization incentive term for parallel laying length. Its constraint system includes: constraints characterizing the number of cables laid in parallel and construction status, constraints ensuring that power transmission does not exceed the limit and node power conservation, constraints describing power aggregation and cable routing selection at wind turbine nodes, node degree constraints limiting the number of cables connecting wind turbine and substation nodes, and geometric constraints based on the above cross-relationship constraint set to prevent cable spatial cross-section. S5. Set the intensive incentive coefficient, solve the mixed integer linear programming model, and obtain the power collection system layout scheme that meets the requirements of intensive sea use and optimizes the total cost. S6. Evaluate the degree of intensification of the layout scheme. If it does not meet the preset requirements, adjust the intensification incentive coefficient and return to step S5; otherwise, output the final planning result.

2. The method for planning an intensive power collection system for offshore wind farms based on parallel cable laying quantification as described in claim 1, characterized in that, The step S2, which involves obtaining the initial solution for hot start based on clustering partitions, refers to performing clustering analysis based on the spatial distribution of wind turbine nodes to form several partitions. Within each partition, a collector network that meets the requirements for basic connection and power transmission is pre-constructed, which serves as the initial feasible solution for the entire optimization model, thereby accelerating the solution process of the subsequent mathematical programming model.

3. The method for planning an intensive power collection system for offshore wind farms based on quantified parallel cable laying as described in claim 1, characterized in that, Step S3, which involves identifying geometric relationships based on candidate cable sets and constructing a set of cross-relationship constraints, specifically includes: Traverse all candidate cable combinations. For each pair of candidate cable segments consisting of four nodes, identify spatial crossing relationships by calculating the cross product of vectors and performing sign judgment. Specifically, a crossing is determined if and only if the two segments cross each other, and all crossing cable pairs are recorded in the crossing relationship set. To mitigate the problem of excessive constraint size caused by directly prohibiting all crossing cable pairs, a constraint construction method based on mutual exclusion triples is adopted. For any two non-overlapping nodes and a third distinct node in the network, a mutual exclusion triple is constructed. This triple contains candidate arcs in both positive and negative directions connecting the first two nodes, as well as all candidate arcs starting from the third node and intersecting with the line segment connecting the first two nodes. For each constructed mutually exclusive triplet, a linear constraint is applied, restricting that at most one of the candidate cable arcs contained in the triplet can be selected for construction, thereby effectively preventing cable paths from crossing in space.

4. The method for planning an intensive power collection system for offshore wind farms based on parallel cable laying quantification as described in claim 1, characterized in that, The objective function of constructing the mixed integer linear programming model in step S4 is specifically: minimizing the sum of cable construction cost and total life-cycle power loss cost, and subtracting an intensive incentive term that is proportional to the total length of parallel cable laying; wherein, the parallel laying length is defined as the cumulative length of all cables laid on the same path starting from the second cable, and the intensive incentive term controls the incentive intensity for the degree of intensive laying through an adjustable incentive coefficient.

5. The method for planning an intensive power collection system for offshore wind farms based on parallel cable laying quantification as described in claim 1, characterized in that, The constraints characterizing the number of cables laid in parallel and the construction status described in step S4 are implemented by introducing a binary variable representing whether a cable is under construction, an integer variable representing the number of cables carrying different numbers of wind turbines, and an intermediate variable representing the total number of cables laid and the number of overlapping cables. Specifically, the total number of cables on each candidate path is defined as the sum of the number of cables carrying different numbers of wind turbines, and the total number of cables is constrained to be between a preset minimum and maximum value when the path is selected for construction, and the number of overlapping cables is equal to the total number of cables laid minus the path construction status variable.

6. The method for planning an intensive power collection system for offshore wind farms based on parallel cable laying quantification as described in claim 1, characterized in that, The constraints describing power aggregation and cable routing at wind turbine nodes in step S4 are modeled using power layer discretization and inter-layer boosting mechanisms. Specifically, this includes: assigning a unique power layer identifier variable to the power output cable of each wind turbine; defining the number of cables flowing out and into each power layer at the node; establishing a hierarchical conservation relationship such that, for non-highest power layers, the difference between the number of cables flowing out and into is equal to the difference between the number of cables boosted from this layer and the number of cables boosted from the previous layer to this layer; and constraining that only when a cable merges into a power layer is it allowed to boost a cable from that layer to a higher layer.

7. The method for planning an intensive power collection system for offshore wind farms based on quantified parallel cable laying as described in claim 1, characterized in that, The constraints in step S4 to ensure that power transmission does not exceed the limit and that node power is conserved include: the total power transmitted on each candidate path is equal to the sum of the power carried by each cable on that path, wherein the power of a single cable is proportional to the number of wind turbines it carries; for each wind turbine node, the difference between the total power of all outflow paths and the total power of all inflow paths is equal to the rated power of that wind turbine. The node degree constraint in step S4, which limits the number of cables connecting the wind turbine and the substation node, includes: constraining each wind turbine node to have one and only one outgoing cable path; constraining the substation node to have no outgoing cable path; and constraining the total number of cables connected to each substation node to be within a preset lower and upper limit.

8. The method for planning an intensive power collection system for offshore wind farms based on quantified parallel cable laying as described in claim 1, characterized in that, The process of constructing the geometric constraints to prevent cable spatial crossings in step S4 includes: traversing all candidate cable pairs, determining whether their corresponding line segments cross in space through vector cross product operation, and recording all crossing cable pairs as a set of crossing relationships; to reduce the constraint scale, for any two non-overlapping node pairs and a third node, constructing a mutually exclusive triplet, which contains candidate arcs in both positive and negative directions connecting the node pair and all candidate arcs connected to the third node and crossing the line segments of the aforementioned node pair; applying constraints such that in each such mutually exclusive triplet, at most one candidate arc can be selected for construction.

9. The method for planning an intensive power collection system for offshore wind farms based on quantified parallel cable laying as described in claim 1, characterized in that, The mixed-integer linear programming model described in step S5 is solved using a commercial mathematical programming solver, which can directly handle offshore wind farm planning scenarios involving hundreds of wind turbines and obtain high-quality feasible solutions within an acceptable engineering timeframe.

10. The method for planning an intensive power collection system for offshore wind farms based on parallel cable laying quantification according to claim 1, characterized in that, Step S6, which involves evaluating the degree of intensification of the layout scheme and adjusting the coefficients, specifically includes: For the power collection system layout scheme obtained in step S5, calculate the total length of parallel cable laying as the core indicator for quantitatively evaluating the degree of intensification. The degree of intensification is determined based on the calculated total length of parallel laying. If the length does not meet the standard, the intensification incentive coefficient in the objective function of the mixed integer linear programming model described in step S4 is increased. If the degree of intensification is determined to be too high, the intensification incentive coefficient is decreased. Substitute the adjusted intensive incentive coefficients into the model and return to step S5 to solve again, thus forming an iterative optimization loop of "solving-evaluating-adjusting" until a final planning scheme that simultaneously meets the requirements of intensive development and economic objectives is obtained.

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