Offshore wind farm intensive power collection system planning method based on cable parallel laying quantity

Abstract

The application discloses an offshore wind farm intensive power collection system planning method based on cable parallel laying quantification, and comprises the following steps: obtaining wind farm node coordinates and engineering parameters; obtaining an initial solution through clustering partition, screening candidate cables and identifying the crossing relationship thereof; constructing a mixed integer linear programming model taking cable parallel laying length as a quantification index, wherein an objective function of the model is integrated with cable construction cost, full life cycle power loss cost and an intensive incentive term for parallel laying, and integrated with engineering constraints such as parallel laying logic, power conservation, node degree and anti-crossing; and solving and iteratively optimizing the model by setting and dynamically adjusting an intensive incentive coefficient, so as to finally automatically generate a reliable layout scheme meeting the intensive use of sea policy and having more optimal total cost, and effectively overcome the shortcomings of traditional methods, such as dependence on manual work, low efficiency and difficulty in overall planning of economic and intensive targets.

Description

TECHNICAL FIELD

[0001] The present application relates to offshore wind farm power collection system planning, in particular to an offshore wind farm intensive power collection system planning method based on cable parallel laying quantity. BACKGROUND

[0002] In an offshore wind farm, the power collection system is a key link connecting wind turbines and offshore booster stations, and its investment cost usually accounts for 15%-30% of the total investment of the entire offshore wind farm. Reasonable power collection system planning not only directly affects the construction cost and life cycle economy of the wind farm, but also relates to the reliability and flexibility of subsequent operation and maintenance. Due to the high combination complexity and nonlinear characteristics of the power collection topology, the power collection system planning is widely regarded as a class of NP-hard problems: in a large wind farm, the number of candidate cables increases approximately quadratically with the number of wind turbines, and if not controlled, the decision variables and constraint scales of the optimization model will quickly expand, making it difficult to obtain a high-quality scheme within an acceptable time in engineering.

[0003] In terms of research methods, existing power collection system planning methods can be roughly divided into two categories. One is the heuristic method based on graph theory and meta-heuristic algorithm, which usually expands the topology based on the minimum spanning tree (MST) and other radial wiring frameworks through meta-heuristic search algorithms such as adaptive particle swarm optimization and genetic algorithm, and gradually extends to consider cable selection, cross-boosting station grid connection, wind farm expansion, and re-power configuration. This method has good computational efficiency and certain engineering operability in medium-scale examples, but the solution result is difficult to guarantee global optimality, and the quality of the solution usually depends on the algorithm parameters and initial solution settings. The other is the mathematical optimization method based on mixed integer linear programming (MILP) or mixed integer quadratic programming (MIQP), which can obtain a planning scheme with global optimality or a verifiable optimality bound by explicitly modeling cable investment cost, network loss cost, and various engineering constraints, given a set of candidate arcs. However, this method is highly sensitive to the linear structure and size of the model, and often has low convergence efficiency and long solution time in large-scale wind farm scenarios, requiring efficient modeling and acceleration strategies.

[0004] Under this background, intensive use of sea is a new direction in the field of power collection system planning. According to relevant regulations, the power collection cable of an offshore wind farm with a total sea area of more than 700 hectares must adopt an "intensive" parallel laying structure. On the one hand, this reduces the disturbance to the seabed space and the survey area, which is beneficial to marine environmental protection and multi-target three-dimensional utilization; on the other hand, it also puts forward new constraints and optimization objectives for existing power collection system planning methods.

[0005] However, the research on the collection system planning based on intensive sea use requirements is still relatively weak. First, the concept of "intensive" lacks unified and reviewable quantitative standards in engineering practice. Some existing indicators, such as "total cable length" and "sea area used", can only reflect the overall line scale and sea bed occupation range, and cannot depict whether the cable corridor is concentrated enough in space, and cannot directly correspond to specific requirements such as "laid in parallel in a corridor". Second, the evaluation indicators and index system of intensive sea use proposed in existing research still remain at the level of sea use evaluation and comparative analysis, and are not deeply coupled with the mathematical model of collection system planning, making it difficult to balance economy and intensity in a unified optimization framework. There is also a lack of effective "intensive" methods in the industry. The current intensive planning scheme often relies on a two-stage method: first, a traditional radial planning scheme is obtained by planning software, and then "bundling" and wiring are performed manually. This requires a huge manual cost in practice, and also faces problems such as unstable quality and poor reproducibility. Moreover, the intensive scheme based on manual work will pay a high economic cost due to the lack of economic consideration, and there is a significant space for cost reduction and efficiency improvement.

[0006] In summary, the existing technology has the following deficiencies in the collection system planning of offshore wind farms facing intensive sea use:

[0007] 1. Lack of a systematic method to convert "cable spatial intensity" into a unified quantitative indicator and incorporate it into a mathematical optimization model, making it difficult to directly control the intensity level at the model level.

[0008] 2. Existing collection system planning models mostly target economy or reliability, and have not yet formed an integrated planning framework that explicitly guides intensive sea use while considering construction cost and life cycle network loss cost.

[0009] 3. The mainstream "traditional model + manual bundling" process in engineering practice is inefficient and highly subjective, making it difficult to stably output an intensive collection system layout that meets relevant regulation constraints and has good economy in large-scale deep-sea wind farm scenarios.

[0010] Under this background, it is necessary to propose an intensive collection system planning method that takes the amount of cable parallel laying as the core quantitative indicator, considers construction cost, network loss cost and intensity level in a unified mathematical optimization model, and explicitly constrains engineering conditions such as cable parallel laying, cable non-intersection and node degree, to achieve an effective balance between regulation requirements and economic goals.

[0011] It should be noted that the information disclosed in the above background technology section is only for understanding the background of the present application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0012] The main purpose of the present application is to overcome the defects existing in the background art, and provide a cable parallel laying quantization-based intensive power collection system planning method for offshore wind farms.

[0013] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0014] A cable parallel laying quantization-based intensive power collection system planning method for offshore wind farms, comprising the following steps:

[0015] S1, obtaining the wind turbine and substation node coordinates, cable parameters and engineering configuration parameters of the offshore wind farm;

[0016] S2, clustering and partitioning based on the node coordinates, obtaining a hot start initial solution, and determining a candidate cable set connecting each node according to distance screening;

[0017] S3, based on the geometric relationship of the candidate cable set, identifying and constructing a constraint set representing the intersection relationship between cables;

[0018] S4, constructing a mixed integer linear programming model taking cable parallel laying length as the intensive core quantization index, the objective function of the model fuses cable construction cost, full life cycle power loss cost and intensive incentive term for parallel laying length, and its constraint system at least includes: constraints representing the number of cable parallel laying and construction state, constraints ensuring power transmission not exceeding the limit and node power conservation, constraints describing power aggregation at the wind turbine node and cable routing selection, node degree constraints limiting the number of cables connecting wind turbine and substation nodes, and geometric constraints preventing cable spatial intersection based on the intersection relationship constraint set;

[0019] S5, setting an intensive incentive coefficient, solving the mixed integer linear programming model, and obtaining an intensive power collection system layout scheme meeting the requirements of intensive sea use and optimizing the total cost;

[0020] S6, evaluating the intensive degree of the layout scheme, if it does not meet the preset requirements, adjusting the intensive incentive coefficient and returning to step S5, otherwise outputting the final planning result.

[0021] A computer program product comprising a computer program, which, when executed by a processor, implements the cable parallel laying quantization-based intensive power collection system planning method for offshore wind farms.

[0022] The present application has the following beneficial effects:

[0023] The application provides an offshore wind farm intensive power collection system planning method based on cable parallel laying quantification, effectively solves the prominent problems of lack of unified quantitative standard, excessive dependence on manual experience and manual adjustment in traditional intensive sea use planning. The method innovatively introduces the cable parallel laying length as the core quantitative index of the intensification effect, and successfully integrates it into a unified mathematical optimization framework. In this framework, a multi-objective mixed integer linear programming model is constructed, which takes into account the cable construction cost, the whole life cycle power loss cost and the intensification incentive item, and a series of engineering constraints such as cable parallel laying, node power conservation, power aggregation routing, node degree limitation and cable anti-crossing are systematically integrated. Thus, the automatic and integrated balance of the intensification target and the project economic target at the model level is realized, and the traditional two-stage inefficient mode of “first conventional planning, then manual binding” is completely eliminated.

[0024] Based on the core innovation of the above integrated modeling, the application achieves significant technical effects. First, the application first systematically coordinates the economy and intensification in global optimization, overcoming the shortcomings of existing methods in considering economy in the manual parallel line process, so as to directly generate a layout scheme that meets the requirements of intensive sea use and has better economy. Secondly, the method completely replaces the complicated and unstable manual “binding” parallel process with automatic mathematical optimization, greatly saving the labor cost while ensuring the reliability and reproducibility of the solution quality. Finally, the constructed model has excellent solving efficiency and can directly and efficiently process super large scale wind farm planning scenarios containing hundreds of wind turbines, output high quality schemes within an acceptable time in engineering, and effectively adapt to the future trend of deep sea and large scale offshore wind power development.

[0025] Other beneficial effects of the embodiments of the application will be further described below. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The overall flowchart of the offshore wind farm intensive power collection system planning method based on cable parallel laying quantification of the application.

[0027] Figure 2 The overall planning scheme flowchart of the embodiment of the application.

[0028] Figure 3 The wind turbine and substation coordinates and candidate sea cable schematic diagram of experiment one (125 wind turbine wind farms).

[0029] Figure 4 The power collection system planning result diagram of comparative example 1, comparative example 2, comparative example 3 and embodiment 2 of experiment one.

[0030] Figure 5The wind turbine and substation coordinates of the second experiment (300 wind turbine wind power plants) and the candidate sea cable schematic diagram.

[0031] Figure 6 The power collection system planning result diagram of the comparative example 1, the comparative example 2, the comparative example 3 and the example 2 of the second experiment. DETAILED DESCRIPTION

[0032] The embodiments of the present application will be described in detail below. It should be emphasized that the following description is only exemplary and is not intended to limit the scope of the present application and its applications.

[0033] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0034] Referring to Figure 1 and Figure 2 , the embodiments of the present application provide an intensive power collection system planning method for offshore wind power plants based on cable parallel laying quantification, comprising the following steps:

[0035] Step S1, obtaining the wind turbine and substation node coordinates, cable parameters and engineering configuration parameters of the offshore wind power plant.

[0036] Step S2, clustering partitioning based on the node coordinates, obtaining a hot start initial solution, and determining a candidate cable set connecting each node according to distance screening.

[0037] In some embodiments, the hot start initial solution obtained based on clustering partitioning in step S2 refers to clustering analysis according to the spatial position distribution of the wind turbine nodes to form several partitions, and pre-constructing a power collection sub-network in each partition to meet the basic connection and power transmission requirements, which is used as the initial feasible solution of the entire optimization model to accelerate the subsequent mathematical programming model solving process.

[0038] Step S3, identifying and constructing a constraint set representing the cross relationship between cables based on the geometric relationship of the candidate cable set.

[0039] In some embodiments, the cross relationship constraint set is identified and constructed based on the geometric relationship of the candidate cable set in step S3, specifically including:

[0040] S31, intersection relationship judgment: traverse all candidate cable combinations, for each pair of two candidate cable line segments composed of four nodes, identify the spatial intersection relationship by calculating the vector cross product and making a sign judgment, specifically, if and only if the two line segments are mutually perpendicular, it is determined to be intersected, and all intersected cable pairs are recorded to the intersection relationship set;

[0041] S32, constraint simplification construction: to reduce the problem of too large constraint scale caused by directly prohibiting all intersected cable pairs, a constraint construction method based on mutual exclusion triplets is adopted; for any two non-overlapping nodes and a third different node in the network, a mutual exclusion triplet is constructed, which contains two positive and negative direction candidate arcs connecting the first two nodes, and all candidate arcs that cross the line segment connecting the first two nodes from the third node;

[0042] S33, apply mutual exclusion constraints: for each constructed mutual exclusion triplet, apply linear constraints to limit the number of selected candidate cable arcs in the triplet to at most one, thereby effectively preventing cable paths from intersecting in space.

[0043] Step S4, construct a mixed integer linear programming model with cable parallel laying length as the intensive core quantitative index. The objective function of the model integrates cable construction cost, full life cycle power loss cost and intensive incentive term for parallel laying length. The constraint system includes: constraints representing the number of cable parallel laying and construction state, constraints ensuring power transmission not exceeding the limit and node power conservation, constraints describing power aggregation at the fan node and cable routing selection, node degree constraints limiting the number of cable connecting wind turbines and substations, and geometric constraints preventing cable spatial intersection based on the intersection relationship constraint set.

[0044] In some embodiments, the objective function of the mixed integer linear programming model constructed in step S4 is specifically: minimizing the sum of cable construction cost and full life cycle power loss cost, and subtracting an intensive reward term proportional to the total length of cable parallel laying; wherein the parallel laying length is defined as the cumulative value of the length of all cables laid on the same path from the second root, and the intensive reward term controls the incentive intensity of the degree of intensification through an adjustable reward coefficient.

[0045] In some embodiments, the constraint of characterizing the number of parallel cable laying and construction state in step S4 is achieved by introducing a binary variable representing whether the cable is constructed, an integer variable representing the number of cables carrying different numbers of wind turbines, and intermediate variables representing the total number of laid cables and the number of overlapping cables; specifically including: 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 value and a maximum value when the path is selected to be constructed, and the number of overlapping cables is equal to the total number of laid cables minus the path construction state variable.

[0046] In some embodiments, the constraint of describing the power aggregation at the wind turbine node and the cable routing in step S4 is modeled using power layer discretization and inter-layer promotion mechanism; specifically including: assigning a unique power layer identifier variable to the power output cable of each wind turbine; defining the number of outgoing and incoming cables on each power layer at the node; establishing a layered conservation relationship, so that for non-highest power layers, the difference between the outgoing and incoming cable numbers is equal to the difference between the cable number promoted from the current layer (i.e. the power identifier variable of the current layer) and the cable number promoted from the previous layer to the current layer (i.e. the power identifier variable of the previous layer); at the same time, it is constrained that only when a cable is merged into a certain power layer, a cable of that layer is allowed to be promoted to a higher layer.

[0047] In some embodiments, the constraint of ensuring power transmission not exceeding the limit and node power conservation in step S4 includes: the total power transmitted on each candidate path is equal to the sum of the power carried by each cable on the path, where 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 outgoing paths and the total power of all incoming paths is equal to the rated power of the wind turbine.

[0048] In some embodiments, the node degree constraint of limiting the number of connection cables between wind turbines and substation nodes in step S4 includes: constraining each wind turbine node to have and only have one outgoing cable path; constraining the substation node to have no outgoing cable path; constraining the total number of cables accessing each substation node to be within a preset lower limit and upper limit range.

[0049] In some embodiments, the construction process of the geometric constraint of preventing cable space intersection in step S4 includes: traversing all candidate cable pairs, judging whether their corresponding line segments intersect in space through vector cross product operation, and recording all intersecting cable pairs to form an intersection relationship set; to reduce the constraint scale, for any two non-overlapping node pairs and a third node, construct a mutually exclusive triple, which includes the candidate arcs in the positive and negative directions connecting the node pairs and all candidate arcs connected to the third node and intersecting the line segments of the aforementioned node pairs; impose a constraint that at most one candidate arc can be selected to be constructed in each such mutually exclusive triple.

[0050] Step 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 use of sea and optimizes the total cost.

[0051] In some embodiments, the solving of the mixed integer linear programming model in step S5 is solved by using a commercial mathematical programming solver, which can directly process offshore wind farm planning scenarios containing hundreds of wind turbines and obtain high-quality feasible solutions within an acceptable engineering time.

[0052] Step S6, evaluate the degree of intensification of the layout scheme, if the preset requirements are not met, adjust the intensive incentive coefficient and return to step S5, otherwise output the final planning result.

[0053] In some embodiments, the evaluation of the degree of intensification of the layout scheme in step S6 and the adjustment of the coefficient specifically include:

[0054] S61, scheme evaluation: for the power collection system layout scheme obtained by solving step S5, calculate the total length of the cable parallel laying as the core index for quantitatively evaluating the degree of intensification;

[0055] S62, judgment and feedback: according to the total length of the parallel laying calculated, judge whether the degree of intensification meets the requirements, if the length does not meet the requirements, increase the intensive incentive coefficient in the objective function of the mixed integer linear programming model in step S4; if it is judged that the degree of intensification is too high, reduce the intensive incentive coefficient;

[0056] S63, iterative optimization: substitute the adjusted intensive incentive coefficient into the model, return to step S5 to solve again, thereby forming an iterative optimization cycle of "solving-evaluating-adjusting", until the final planning scheme that meets the requirements of intensification and economic targets is obtained.

[0057] The offshore wind farm intensive power collection system planning method based on the quantification of cable parallel laying provided by the present application has the following main technical advantages: by innovatively establishing the length of cable parallel laying as a quantifiable core optimization index, and constructing a unified mixed integer programming model integrating construction cost, operation loss and intensive incentive item, the defects of traditional methods relying on artificial experience, lacking of quantitative standard and difficulty in coordinating economic and intensive are fundamentally overcome, thereby ensuring the engineering feasibility of the planning scheme while realizing the efficient balance of policy requirements and economic targets, not only significantly improving the solving efficiency and scheme quality of large-scale wind farm power collection system layout, but also greatly reducing the cost of artificial intervention, providing a stable, reliable and economic planning means for deep-sea wind power intensive development.

[0058] The specific embodiments of the present application, algorithm examples and experimental verification are further described below.

[0059] A method for planning an intensive power collection system of an offshore wind farm based on cable parallel laying quantity breaks through the limitations of traditional intensive sea use without unified quantitative indicators and relying on manual adjustment, and realizes the balance between intensive sea use requirements and economic goals through quantitative definition and mathematical model integrated optimization. Specifically, it can include taking the cable parallel laying length as the core quantitative indicator of intensification, building a multi-objective mixed integer linear programming model integrating cable construction cost, life cycle power loss cost and intensive reward items, designing parallel cable number, cable anti-crossing, wind turbine node degree and other engineering constraints, thereby generating a power collection system layout that meets the requirements of intensive sea use and is more economical, breaking through the traditional inefficient mode of "first conventional planning + then manual binding".

[0060] The mathematical model of the power collection system planning considering intensive sea use is as follows:

[0061] Objective function

[0062] Build a complete weighted directed graph in the wind farm , wherein is a node set containing transformer nodes and wind turbine nodes , and represents all candidate cable sets connecting the nodes. The objective function of the model can be represented as:

[0063] (1)

[0064] wherein represents the sum of the construction cost and the life cycle network loss cost of the offshore wind farm; represents the cable from node to node , is the number of wind turbines carried by the cable, is the maximum number of wind turbines that can be carried by the cable currently used; represents the sum of the construction cost and the network loss cost per unit length of the cable carrying wind turbines; represents the engineering length of the cable ; represents the number of cables carrying k wind turbines arranged in parallel at . is the parallel reward coefficient, which represents the reward obtained by the overlapping cable per unit length in the objective function. The size of this coefficient can represent the "aggressiveness" of the given scheme in intensification; is the number of overlapping cables, is the length of the cable at , represents the number of cables carrying k wind turbines arranged in parallel at . Lengths and of the second and more cables laid in parallel.

[0065] Constraints

[0066] Constraints for parallel laying of submarine cables

[0067] (2)

[0068] (3)

[0069] (4)

[0070] (5)

[0071] In constraint (2) is the number of cables laid at , denoted as the sum of the number of cables carrying various wind turbines ; constraints (3) and (4) denote the range of the number of cables at , where is the construction variable of the line , and denotes that the cable is constructed at , otherwise not constructed, is the maximum number of cables allowed to be laid in parallel; in constraint (5) denotes the number of cables overlapped (counting from the second one) at .

[0072] Constraints for submarine cable capacity and power conservation

[0073] (6)

[0074] (7)

[0075] In constraint (6) denotes the total power at , which is the sum of the power of all cables at this location; constraint (7) is the power conservation constraint at nodes, which denotes that for each wind turbine node, the difference between the outgoing power and the incoming power at this node is the rated power of the node .

[0076] Constraints for power layer balancing and promotion selection

[0077] For ease of description, the carrying status of each cable is discretized into several power layers: a cable carrying wind turbines is said to be in the th layer. Let denote the outgoing power at node in the Number of cables in layer, For node At layer Number of cables in layer, variable Indicates whether the cable actually sent out from the fan is at layer , and for each fan node, the cable actually sent out from the node can only be at one power layer, so there is:

[0078] (8)

[0079] And for each fan node , the "connection selection" that occurs is only two: either a new outgoing cable with power layer 1 is started ( ), or the power layer of an incoming cable is raised by one and continued to be sent out ( ), indicating that the cable actually accesses the fan and bears its power. The corresponding layered conservation relationship is:

[0080] (9)

[0081] (10)

[0082] Constraint (9) (10) can be intuitively understood as: on each layer, the number of roots leaving the node is equal to the number of arriving roots, plus the number of "promoted from the previous layer", and minus the number of "promoted to a higher layer and left this layer". In order to avoid the cable "promoted from the empty layer", the following feasibility constraint is added:

[0083] (12)

[0084] That is, only when there are incoming cables in layer , can one of them be promoted to layer . The above constraint uses the concept of power layer to completely describe the power distribution of the parallel laid submarine cables at the fan.

[0085] Cable root number conservation constraint

[0086] (13)

[0087] (14)

[0088] Constraints (13) (14) are the cable root number conservation constraints expressed by the big M method, where is the total number of cables of the incoming node . The constraint indicates that the total number of cables of the incoming node If the construction is in the case of the node a new cable is started, then the total number of cables at the node is equal to the total number of incoming cables + 1, otherwise it is equal to the total number of incoming cables.

[0089] Fan and substation node degree constraints

[0090] (15)

[0091] (16)

[0092] (17)

[0093] (18)

[0094] When expressed in graph theory, the fan and booster station are regarded as nodes, and the number of branches connected to them is defined as the degree, which is equivalent to the number of outgoing lines in the directed graph. Equation (15) limits the outgoing cable of the fan to only one edge, and equation (16) specifies that the substation cannot have an outgoing cable edge; equations (17) and (18) use the actual number of cables laid on the edge to specify the range of incoming cable degrees for the substation node, where and represent the lower and upper bounds of the substation incoming degree, respectively, which are usually given by engineering requirements.

[0095] Line non-intersection constraints

[0096] First, according to the candidate cable set, a set of cable pairs with intersection relationships is established , the specific method is: when and only when and are satisfied, it is determined that the line segments and intersect. By traversing all cable combinations and determining whether they intersect, if they intersect, the current line segment combination is added to , where represents the set of all candidate cable combinations that intersect. Considering that the candidate cable set is very large in a large-scale scenario, the number of non-intersection constraints directly applied to each pair of arcs in will reach . Here, a ternary constraint is used: for any unnecessary point pair and any , it is agreed that represents the outgoing line from and intersects with ​All strictly intersecting candidate directed arcs, i.e. arcs satisfying and are defined as a set of mutually exclusive triples:

[0097] (19)

[0098] and thus impose a cable non-crossing constraint of at most one:

[0099] (20)

[0100] The complete planning model for the intensive ECS based on the intensive use of the sea is established as follows:

[0101] (1)

[0102] (2) - (20)

[0103] Therefore, the overall planning scheme flowchart is shown in Figure 2 .

[0104] Experimental verification

[0105] The site of Experiment One contains 125 wind turbines with a single-machine capacity of 16 MW and a voltage level of 66 KV; the site of Experiment Two contains 300 wind turbines with a single-machine capacity of 16 MW and a voltage level of 110 KV; the comparative example schemes used are obtained from the planning software of a design institute and the results of professional engineers; MATLAB (2024a) and Gurobi solver (12.0.3) are used for solving.

[0106] Experiment One: 125 wind turbines in a wind farm, from a real sea wind farm in China

[0107] The coordinates of the wind turbines and substations of the wind farm and the schematic diagram of the candidate sea cables are shown in Figure 3 .

[0108] Comparative Example 1 is the non-intensive ECS planning result obtained from the planning software of a design institute; Example 1 is the ECS planning result without using the intensive mathematical optimization algorithm, i.e. by setting the maximum parallel number of the model proposed in the present application to 1; Comparative Example 2 is the artificial bundling and wiring result based on Comparative Example 1, which is also the conventional path for obtaining an intensive planning scheme in current engineering; Example 2 is the intensive ECS planning result based on the method of the present application. The planning results of Comparative Examples 1 to 3 and Example 2 of Experiment One are shown from top to bottom. Figure 4

[0109] The cost comparison results (cost unit: billion RMB, length unit: km) are shown in Table 1: ​

[0110] Table 1

[0111]

[0112] 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).

[0113] Experiment 2: 300-ton offshore wind farm

[0114] 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.

[0115] 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.

[0116] The cost comparison results (cost unit: RMB 100 million, length unit: kilometers) are shown in Table 2:

[0117] Table 2

[0118]

[0119] 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).

[0120] 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.

[0121] 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.

[0122] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0123] 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.

[0124] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0132] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0133] 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.

[0134] 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, In step S2, 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 of 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 parallel cable laying quantification as described in claim 1, characterized in that, Step S3 involves identifying the geometric relationships of the candidate cable set and constructing a set of cross-relationship constraints, specifically including: 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 quantified parallel cable laying 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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