A planning and design method for a double-sided annular power collection system of an offshore wind farm

Abstract

The application provides a kind of offshore wind farm double-sided annular power collection system planning and design method, comprising the following steps: S1: obtaining all candidate construction cables in the planning area: receiving all node coordinates and the maximum distance, the node includes offshore wind turbine and offshore substation, for each node, list all optional construction cables between it and its surrounding nodes, select the cable less than the maximum distance as the candidate construction cable;S2: obtain all candidate cable combinations that occur intersection, and impose constraints so that candidate cables that can occur intersection cannot be constructed at the same time;S3: based on capacity constraint vehicle routing planning and power system expansion planning to establish power collection system planning model;S4: solve offshore wind farm power collection system planning optimization model using commercial solver.The technical scheme of the application can guide the design of large-scale offshore wind farm power collection system topology structure that meets the "N-1" rule, and has better stability.

Description

Technical Field

[0001] This invention relates to the field of power system planning, and in particular to a planning and design method for a double-sided ring-shaped power collection system for offshore wind farms. This method can guide the topology design of power collection systems for large-scale offshore wind farms that meet the "N-1" criterion, and is applicable to the planning and design of power collection systems for offshore wind farms in my country. Background Technology

[0002] With increasing global focus on carbon emissions and climate change, renewable energy is receiving growing attention. Offshore wind power is one of the most promising forms of energy application. According to a 2021 report by the Global Wind Energy Council, the levelized cost of energy (LCOE) for offshore wind power has fallen dramatically, making it one of the most competitive energy sources and increasingly playing a vital role in promoting cross-sectoral collaboration and decarbonization. As of the end of 2020, the total installed capacity of global offshore wind farms was approximately 35 GW. This figure is projected to reach approximately 270 GW by 2030 and 2000 GW by 2050, contributing to the achievement of net-zero emissions. Most offshore wind farms have more than 3,000 hours of effective utilization per year, significantly more than onshore wind farms, which to some extent offsets the additional costs associated with offshore wind farms.

[0003] In recent years, offshore wind farms have been expanding on a large scale into deep-sea areas. However, due to the various operating conditions and extreme weather in these regions, higher economic and reliability requirements have been placed on offshore wind farms. The consequences of a failure are more severe compared to onshore wind farms. Therefore, the planning and design of the power collection system for offshore wind farms are particularly important, as the reliability of the system can be improved by changing the topology, such as single / double-sided ring topologies. Figure 1a , Figure 1b and Figure 1c As shown. Furthermore, optimizing the topology design of the power collection system can reduce investment costs and power losses, thereby improving the economic benefits of offshore wind farm projects.

[0004] Currently, the planning problem of power collection systems is often modeled as the minimum spanning tree problem and the Capacitated Vehicle Routing Problem (CVRP). The algorithms used to solve this problem are mostly heuristic algorithms, such as minimum spanning tree and particle swarm optimization. However, the convergence performance of heuristic algorithms often varies depending on the example, making it impossible to guarantee convergence. Furthermore, it is impossible to obtain the optimality distance between the result obtained by the heuristic algorithm and the global optimal solution, thus making it impossible to determine whether the result obtained by the heuristic algorithm has fallen into a local optimum. This means that it is impossible to prove that the result has reached the global optimum. Sometimes, the local optimal solution and the global optimal solution differ greatly, which will result in a huge waste of investment costs in practical engineering.

[0005] Existing research has mostly focused on the design of radial collector systems, with few studies paying attention to the application of the "N-1" safe power supply criterion (also known as the single fault safety test rule, which in this working environment means that if any line in the collector system is disconnected due to a fault in normal operation, the fan should still be able to maintain normal power supply) in the collector system. As a result, the issue of power supply reliability has not been well studied.

[0006] In summary, existing technologies for solving power collection system planning problems suffer from unstable methods for obtaining optimal solutions and the inability to guarantee power supply reliability. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention proposes a planning and design method for a double-sided ring-shaped power collection system in offshore wind farms, which solves the technical problems that current power collection system planning cannot stably obtain the optimal solution and cannot guarantee power supply reliability.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A planning and design method for a bifacial ring-shaped power collection system in an offshore wind farm includes the following steps:

[0010] S1: Obtain all candidate construction cables within the planning area: Receive the coordinates of all nodes and the preset maximum distance. The nodes include offshore wind turbines and offshore substations. For each node, list all possible construction cables between it and its surrounding nodes, and select the cables with a distance less than the maximum distance as candidate construction cables.

[0011] S2: Obtain all candidate cable combinations that may cross, and impose constraints so that candidate cables that may cross cannot be constructed simultaneously;

[0012] S3: Establish a power collection system planning model based on capacity-constrained vehicle routing planning and power system extended planning;

[0013] S4: Solve the planning and optimization model of offshore wind farm collection system using a commercial solver.

[0014] Furthermore, in step S1, the candidate construction cable connected to the offshore substation can be further designed, and is not limited to the maximum distance limitation.

[0015] Further, step S2 includes the following steps:

[0016] S2-1: Settings , where V represents the set of all nodes;

[0017] S2-2: Determine if line segments AB and CD intersect. If they intersect, add the current line segment combination to L. CAC LCAC This represents the set of all candidate cable combinations where a crossing occurs;

[0018] S2-3: For L CAC All cross-candidate cable combinations make x AB +x CD ≤1, where x ij This indicates whether a candidate cable (i,j) between nodes i and j should be constructed, when x ij When the value is 1, it indicates that the candidate cable (i,j) is constructed.

[0019] Furthermore, step S2-2 includes the following steps:

[0020] If and only if At that time, determine that line segments AB and CD intersect;

[0021] Iterate through all candidate cable combinations and make judgments, adding the candidate cable combinations that intersect to set L. CAC Specifically:

[0022]

[0023] Further, step S3 includes the following steps:

[0024] S3-1: A preliminary planning model for a bi-faceted ring topology power collection system is established based on a capacity-constrained vehicle routing planning model. The capacity-constrained vehicle routing planning problem is improved to adapt to power collection system planning.

[0025] S3-2: An extended programming model for power systems based on mixed-integer linear programming is used to approximate and optimize the total power loss in the power collection system;

[0026] Furthermore, step S3-1 specifically involves,

[0027] Establish the objective function for the current collector system planning model:

[0028]

[0029] Here, we assume a complete directed graph G = (V, L), where V = {V...} Sub V W}, including V Sub Substation nodes and V W In the context of wind turbine nodes, L represents all candidate cables between nodes, where L = {(i,j)∈V×V:i≠j}. Each candidate cable (i,j) has an investment cost c. ij And binary investment decision variable x ij , Σ (i,j)∈L c ijx ij Represents the investment and construction cost of the power collection system; ηΣ (i,j)∈L r ij P ij 2 Representing the grid loss cost over the operating life of an offshore wind farm, η is a coefficient that converts the power loss at a given moment to the power loss over the operating life of the offshore wind farm. It is calculated as η = planned operating life × annual full-load hours, r ij Let P be the resistance of cable ij. ij The active power flowing through cable ij; This represents the total value of the reduced wind power generation capacity. This represents the wind power loss at point i of the wind turbine.

[0030] Furthermore, in step S3-1, the constraints of the power system extended planning model are:

[0031]

[0032] In formula (2), and The decision variable representing the direction of the power flow is a binary variable, when x ij =1, meaning that when cable ij is constructed, when the virtual power flow in (i,j) flows from node i to node j, When the virtual current in (i,j) flows from node j to node i L represents the set of all cables; in formulas (3) and (4), V W The formula represents the set of wind turbine nodes. It indicates that the number of inflow and outflow power cables for each wind turbine node is strictly 1, which is a constraint required by the double-sided ring topology. Formulas (5) and (6) are for MTZ-sub-ring elimination constraints.

[0033]

[0034] Represents the accumulated power u upon reaching node i. i Formula (7) represents the accumulated power u at node i. i Not less than the electrical energy P generated by the wind turbine W The power transmission capacity is not greater than the upper limit of cable ij. In formula (8), L CAC A collection of cross-cable combinations.

[0035] Furthermore, in step S3-2, in the power system extended planning model based on mixed-integer linear programming, the optimal power flow is based on DC power flow, specifically as follows:

[0036]

[0037] Among them, V Sub L is the set of substation nodes. j This represents the set of all cables connected to node j. Let P represent the power of the wind turbine at node j. Formulas (9) and (10) represent the power balance constraints at the wind turbine node and substation node j, respectively. Formula (11) represents the cable power P when the cable between nodes i and j is constructed in the DC power flow model. ij and node voltage phase angle θ i and θ j The relationship between the two. Formula (12) sets the substation node as the reference node and its voltage phase angle as 0; Formulas (13)-(14) respectively limit the transmission power on the cable and the power loss at the wind turbine node.

[0038] Furthermore, step S3 also includes the following steps:

[0039] The mathematical model for constructing a k-degree center tree is as follows:

[0040]

[0041]

[0042] in, Indicates rounding, |V W | represents the number of elements in the wind turbine node set, i.e., the number of wind turbines, y ij The decision variable representing whether cable (i,j) is in the k-degree center tree is 1 when cable (i,j) is in the k-degree center tree, and 0 otherwise. and Let represent the decision variable for whether cable (i,j) is in the k-degree center tree and connected to a substation node. If cable (i,j) is not in the k-degree center tree and connected to a substation node, then otherwise Y 0 This refers to the number of cables connected to substation nodes but not in the k-degree center tree.

[0043] Further, step S4 specifically involves establishing a mathematical model in MATLAB software using the modeling tool YALMIP based on this method, inputting the coordinate information of the offshore wind turbine and offshore substation, and calling a commercial solver to perform planning optimization.

[0044] The beneficial effects of this invention are:

[0045] This invention provides a planning and design method for a double-sided ring collector system in offshore wind farms. Based on a mathematical optimization method using mixed-integer linear programming, it studies the planning of double-sided ring collector systems. This invention improves the capacity-constrained vehicle routing problem and incorporates an extended power system planning model to adapt to collector system planning, satisfying the "N-1" safe power supply criterion. The model established in this invention is a mixed-integer linear programming model, which can be solved by calling a solver. It solves the convergence instability problem of heuristic algorithms used for planning problems and can obtain the optimality distance between the current solution and the global optimal solution. In some embodiments, considering that the multiple traveling salesman problem provides a lower bound for the capacity-constrained vehicle routing problem, this invention incorporates a k-degree center tree model to accelerate the solution of the lower bound of the proposed model. Attached Figure Description

[0046] Figure 1a This is a schematic diagram of a radial structure;

[0047] Figure 1b This is a schematic diagram of a single-sided ring structure;

[0048] Figure 1c This is a schematic diagram of a double-sided ring structure;

[0049] Figure 2 This is a schematic diagram illustrating the determination of candidate construction cables in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of a candidate cable for a power collection system containing 30 wind turbines in an embodiment of the present invention;

[0051] Figure 4 This is an example diagram of the cross cable combination {AB,CD} in an embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of a capacity-constrained vehicle routing model in an embodiment of the present invention;

[0053] Figure 6 This is a schematic diagram of a k-degree center tree in an embodiment of the present invention;

[0054] Figure 7 This is a flowchart of the method in an embodiment of the present invention;

[0055] Figure 8a This is a schematic diagram of the radial topology result in Example 1 of the present invention;

[0056] Figure 8b Example 2 in this embodiment of the invention: A schematic diagram of Google OR-Tools results;

[0057] Figure 8c Example 3 in this embodiment of the invention: A schematic diagram of the result of the method of the present invention. Detailed Implementation

[0058] To make the technical solutions and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be fully described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0059] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0060] In some embodiments, the present invention provides a planning and design method for an offshore wind farm power collection system, comprising the following steps.

[0061] (1) Obtain all candidate construction cables within the planning area

[0062] In the industry, offshore wind farm planning generally includes the following steps: 1. Macro-site selection; 2. Micro-site selection; 3. Power collection system planning; 4. Transmission system planning. The design and planning of the power collection system in this invention is carried out after the micro-site selection stage of the offshore wind farm, at which point the coordinates of the offshore wind turbines and offshore substations are known information. Within a set maximum distance range, all candidate construction cables are listed between adjacent wind turbines (a circle is drawn with a wind turbine as the center and the maximum distance as the radius; all wind turbines within the circle are considered adjacent wind turbines) and between them and the offshore substations. A cable whose length is less than the set maximum distance can be used as a candidate construction cable; otherwise, it cannot be used. In particular, the candidate construction cables connected to the offshore substation can be specifically designed, not just relying on distance. For example, the maximum distance can be further increased to expand the range and increase the number of candidate cables within the range, thereby enriching the selection of candidate construction cables. This helps the model to better optimize the process.

[0063] like Figure 2 As shown, for node 1, draw a circle with the maximum distance range. 2, 3, 5, 6, and 7 are within this range, while 4 and 8 are outside this range. Therefore, the cables between 1-4 and 1-8 are not considered as candidate construction cables, while 1-2, 1-3, 1-5, 1-6, and 1-7 are considered as candidate construction cables.

[0064] And so on, drawing circles with all nodes 2, 3, ... as the center, and all candidate cables within the maximum distance range are considered as candidate construction cables.

[0065] Figure 3 Examples of all candidate cables listed for a power collection system in an offshore wind farm containing 30 wind turbines. Figure 2 and Figure 3 In the diagram, square nodes represent offshore substation nodes, circular nodes represent offshore wind turbine nodes, and dashed lines represent candidate cables.

[0066] (2) Obtain all candidate cable combinations where crossings occur.

[0067] In this step, it is necessary to obtain all candidate cable combinations for crossing and place them into constraints to avoid cable crossing construction. The implementation method will be described in detail below.

[0068] Example of a crossover cable combination {AB, CD} Figure 4 As shown, consider node A as a substation node, B / C / D as wind turbine nodes, and {AD, AB, CD, BD} as cables connecting the wind turbines and the substation. To find all crossing cable combinations and input them into the cross-avoidance constraint, this invention develops a method to determine whether each pair of cables crosses each other. Figure 4 As can be seen, nodes C and D are located on opposite sides of line AB, while nodes A and B are located on opposite sides of line CD. Therefore, line segments AB and CD intersect if and only if... This formula is the condition for finding the cross-cable combination {AB,CD}, using L. CAC This represents the set containing all candidate crossover cable combinations. This condition is used to iterate through all candidate cable combinations to form L. CAC .Right now:

[0069]

[0070] Where V represents the set of all nodes, and A, B, C, and D are nodes.

[0071] Next, let's assume x ij This is a binary variable representing whether the line (i,j) between nodes i and j has been constructed, and includes:

[0072] x AB +x CD ≤1

[0073] This formula indicates that cables AB and CD cannot be constructed simultaneously in the final planning results; this constraint is applied to L. CAC All cable combinations, x ij This is a binary variable, where a value of 1 indicates that line ij has been constructed.

[0074] This step avoids cable crossovers in the collector system by proposing conditions for finding all crossover cable combinations and adding crossover avoidance constraints.

[0075] (3) Establish a power collection system planning model based on capacity-constrained vehicle routing planning and power system extension planning.

[0076] First, a preliminary planning model for a biplane ring topology power collection system is established based on a capacity-constrained vehicle routing planning model. The capacity-constrained vehicle routing planning problem is then improved to adapt to power collection system planning.

[0077] Suppose there is a complete directed graph G = (V, L), where V = {V Sub V W}, including V Sub Substation nodes and V W In the context of wind turbine nodes, L represents all candidate cables between nodes, where L = {(i,j)∈V×V:i≠j}. Each candidate cable (i,j) has an investment cost c. ij (Set according to market prices) and binary investment decision variable x ij Establish the objective function for the current collector system planning model:

[0078]

[0079] The objective function aims to minimize the total cost of constructing the power collection system. The construction cost of each cable is multiplied by its investment decision variable, i.e., Σ. (i,j)∈L c ij x ij The investment and construction cost of the power collection system is represented by ηΣ; the grid loss cost over the operating life of an offshore wind farm is ηΣ. (i,j)∈L r ij P ij 2 η is a coefficient that converts the power loss at a given moment to the power loss over the entire operating life of an offshore wind farm. It is calculated as η = planned operating life × annual full-load hours. ij Let P be the resistance of cable ij. ij Let represent the active power flowing through cable ij. It is worth noting that the node voltage amplitude |U| in the offshore wind farm collector system... i |≈1p.u., therefore the current I flowing through cable ij ij =P ij / U i ≈P ij Therefore, we can reduce power loss. By approximation, we can obtain The objective function also takes into account the total value of the reduced wind power generation. This represents the wind power loss at turbine i, but this term is usually not allowed to have a non-zero value because M is set to a very large constant, such as 10. 7 ¥ / MWh.

[0080] The constraints for the capacity-constrained vehicle routing problem are as follows:

[0081]

[0082] In formula (2), and The decision variable representing the direction of the power flow is a binary variable, when x ij When = 1, that is, when cable ij is constructed, when the virtual power flow in (i,j) flows from node i to node j, When the virtual current in (i,j) flows from node j to node i L represents the set of all cables. In formulas (3) and (4), V W The formula represents the set of wind turbine nodes. It indicates that the number of inflow and outflow power cables for each wind turbine node is strictly 1, which is a constraint required for a two-sided ring topology. Formulas (5) and (6) are for MTZ-sub-ring elimination constraints, using...

[0083]

[0084] Represents the accumulated power u upon reaching node i. i Formula (7) represents the accumulated power u at node i. i Not less than the electrical energy P generated by the wind turbine W The power transmission capacity is not greater than the upper limit of cable ij.

[0085] Figure 5 This is one example.

[0086] In this embodiment, the route "Sub→1→2→3→Sub" is a double-sided loop connecting substation node sub with wind turbine nodes 1, 2, and 3. It can be seen that the accumulated power u... i As the distance increases along the route, u1 = P W u3 = 3P W If and only if and This situation holds true. Cable (2,4) was not invested in because formulas (3) and (4) restrict the number of outgoing / incoming cables at node 2 to 1, therefore x 24 =0, node 4 is reserved for another route connection.

[0087] As mentioned above, avoiding cross constraints eliminates all possible cross cable combinations (i1j1, i2j2) ∈ L. CAC ,have:

[0088]

[0089] In (8), L CACFor the set of cross-cable combinations, in fact, the planning results obtained by solving the vehicle routing planning model based on capacity constraints naturally satisfy the "N-1" power supply criterion. Because if any single cable is interrupted in any route, the power generated by the wind turbine can still be transmitted to the substation in another way, since constraint (7) guarantees that in each route, the cumulative power is lower than the upper limit of the transmission power, i.e.

[0090] In addition to the capacity-constrained vehicle routing model, the power in each submarine cable is also needed to approximate and optimize the total power loss in the power collection system. Therefore, this patent adopts an extended power system planning model based on Mixed-Integer Linear Programming (MILP), where the optimal power flow is based on DC power flow, as shown below.

[0091]

[0092] Among them, V Sub L is the set of substation nodes. j This represents the set of all cables connected to node j. Let P represent the wind turbine power at node j. Formulas (9) and (10) represent the power balance constraints at the wind turbine node and substation node j, respectively. Formula (11) represents the cable power P when the cable between nodes i and j is constructed in the DC power flow model. ij and node voltage phase angle θ i and θ j The relationship between the two. Formula (12) sets the substation node as the reference node, and its voltage phase angle is set to 0. Formulas (13)-(14) respectively limit the transmission power on the cable and the power loss at the wind turbine node.

[0093] To improve the convergence of large-scale offshore wind farm power collection system planning models, this invention develops and incorporates a k-degree center tree (k-DCT) model. Considering the planning results of offshore wind farm power collection systems with multiple cable routes, it can also be used as a solution to the multi-traveling salesman problem (MTS). The solution to the MTS is a lower bound on the solution to the capacity-constrained vehicle routing problem, because the capacity-constrained vehicle routing problem is a MTS with additional constraints on cable capacity. min The minimum number of cable loops required to transmit all wind power is expressed as follows:

[0094]

[0095] in, Indicates rounding, |V W | represents the number of elements in the set of wind turbine nodes, i.e., the number of wind turbines. Assume we have a set L of cables connected to the substation.Sub The word "remove" (not actually removed, but only hypothetically) Y 0 ≤R min One cable, except L Sub Other cable collections LL Sub Remove R from the middle min -Y 0 A cable, then there is R min One cable is "removed" in the planning results, meaning one cable is "removed" from each cable route (loop). This generates a tree structure. The degree of a substation node is defined as k, then k = 2R. min -Y 0 Thus, a k-degree center tree was obtained.

[0096] Figure 6 An example of a k-degree center tree is shown, where R min =3,Y 0 =2, degree(Sub)=4. There are 3 cable loops. Suppose we "remove" cables (sub,4), (sub,1) and (7,8), then we can get a k-degree center tree with degree(Sub)=4.

[0097] The mathematical model for establishing a k-degree center tree is as follows:

[0098]

[0099] Among them, y ij The decision variable represents whether cable (i,j) is in the k-degree center tree. It is 1 when cable (i,j) is in the k-degree center tree, and 0 otherwise. and Let represent the decision variable for whether cable (i,j) is in the k-degree center tree and connected to a substation node. If cable (i,j) is not in the k-degree center tree and connected to a substation node, then otherwise Y 0 This refers to the number of cables connected to substation nodes but not in the k-degree center tree.

[0100] Formula (16) represents the relationship between cable investment decisions and the k-degree center tree. Formula (17) imposes degree constraints on substation nodes. Formulas (18), (19) and (20) declare the required cable quantity relationship. Formula (21) specifies that the degree of each wind turbine node in the solution of the multiple traveling salesman problem is 2.

[0101] It should be noted that the introduction of the k-degree center tree model in this patent does not affect the feasibility of any solution for the planning of offshore wind farm collection systems, and it can provide a strict lower bound for the planning problem, thereby helping the model converge.

[0102] (4) Use a commercial solver to solve the planning and optimization model of the offshore wind farm collection system.

[0103] The compact form of the planning model for the double-sided ring-shaped power collection system of offshore wind farms proposed in this patent is as follows:

[0104]

[0105] In MATLAB software, this mathematical model can be established using the modeling tool YALMIP. By inputting the coordinates of all offshore wind turbines and offshore substations determined in the micro-site selection process, and then calling commercial solvers such as Gurobi or CPLEX, planning optimization can be performed. The results obtained from solving this model include the planning and construction scheme, investment and construction costs, operating costs, and total costs of the offshore wind farm's power collection system.

[0106] In summary, the process of this invention is as follows: Figure 7 As shown.

[0107] The technical effects of the present invention will be illustrated below with reference to a specific embodiment:

[0108] (1) Basic Overview of the Implementation Examples

[0109] This invention uses the Saint-Brieu offshore wind farm, comprising 62 wind turbines, as an example application scenario. The failure rate fr = 0.0045 times / km / year, and the mean time to repair (MTTR) is set to 1440 hours, or two months. The wind energy price Pr... ele =850 RMB / MWh, the operating life of the offshore wind farm is set at 20 years, and the full load hours of the offshore wind farm are 4000 hours. This patent designs three examples: Example 1 is a radial topology collector system planning; Example 2 uses Google's OR-tools to solve the capacity-constrained vehicle routing problem as a double-sided ring collector system planning scheme; Example 3 is a double-sided ring collector system planning solved using the method proposed in this patent. The YALMIP toolbox in MATLAB software is used for modeling, and the solution is performed on a desktop computer equipped with an Intel Core i7-1165G7 using the commercial solver Gurobi.

[0110] Table 1 shows the key parameters used in the example.

[0111]

[0112] (2) Analysis of Planning Results

[0113] Based on the proposed model, different examples were calculated, and the results are as follows: Figure 8a , Figure 8b , Figure 8c As shown in Table 2.

[0114] Table 2 Results of Power Collection System Planning

[0115]

[0116] Although both Example 2 and Example 3 used a two-sided ring topology for collector system planning, their results still differed. Observation Figure 7 The planning results in Example 2 show that there are intersecting cable sections, which pose reliability issues in actual operation. Since Google OR-tools is based on a capacity-constrained vehicle routing model, this model allows routes of different vehicles to intersect. In contrast, the method proposed in this patent ensures that there are no intersecting cables in the planning results. After considering the cost of EENGs over the lifespan of the offshore wind farm, comparing Example 1 with Examples 2 and 3, the double-sided ring topology as the construction mode for the collection system has significant advantages over the radial topology. The radial design of the collection system (as in Example 1) minimizes the investment cost of submarine cables; however, considering its potentially extremely high EENG cost (224.1 million yuan), the total cost of this construction mode reaches 556 million yuan, far exceeding the 429.4 million yuan and 414.8 million yuan of Examples 2 and 3, respectively. The total cost in Example 3 is 25.9% lower than that in Example 1. The solution of Example 2 did not employ mathematical optimization methods, while Example 3 was solved based on the mathematical model of the proposed method. Therefore, Example 3 can be given an optimality interval of 5.00% (the optimality interval is the relative distance between the optimal feasible solution at the end of the solution process, i.e., the upper bound of the model, and the objective function value of the model relaxation problem, i.e., the lower bound of the model).

[0117] In this embodiment, the Expected Energy Not Generated (EENG) cost is calculated to assess the reliability of the radial current collection system. Taking into account the probability of submarine cable interruption and MTTR, its calculation expression is defined as follows:

[0118]

[0119] Among them, C EENG Pr represents the costs associated with EENG. ele This represents the price of wind energy (unit: ¥ / MWh), MTTR is the mean time to repair (unit: hours), py is the lifespan of an offshore wind farm (unit: years), fr represents the failure rate of submarine cables in an offshore wind farm (unit: times / km / hour), and l ijLet P represent the length of cable (i,j), and the corresponding power transmitted in each cable be P. ij Therefore, MTTR·frΣ (i,j)∈L l ij x ij P ij Let EENG be the value of C. Multiplying it by the corresponding operating years of the wind farm and the price of wind power will give us C. EENG The reliability of radial current collection systems is quantified by the cost of power loss. EENG The smaller the value, the less power loss the current collection system experiences, indicating higher reliability. For a double-sided ring current collection system, since each fan is connected to two cables, under the "N-1" test condition, a failure in a single cable does not affect the power output of its connected fans. Therefore, the cost C of this part of the double-sided ring current collection system is relatively low. EENG =0.

[0120] By setting different calculation examples 1, 2 and 3 and using different methods to solve the model, the planning results obtained are observed to be the best based on the method proposed in this patent for the power collection system planning results, proving that the method proposed in this patent can bring significant economic benefits.

[0121] In the description of this specification, references to terms such as "an embodiment" and "example" refer to specific features, structures, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms are not necessarily intended to refer to corresponding embodiments or examples in a suitable manner.

[0122] It must be pointed out that the above description of the embodiments is not intended to limit the scope of the invention but only to help understand the core ideas of the invention. For those skilled in the art, any improvements to the invention and equivalent alternatives made to the invention without departing from the principles of the invention are also within the scope of protection of the claims of the invention.

Claims

1. A planning and design method for a double-sided ring-shaped power collection system in an offshore wind farm, characterized in that, Includes the following steps: S1: Obtain all candidate construction cables within the planning area: Receive the coordinates of all nodes and the preset maximum distance. The nodes include offshore wind turbines and offshore substations. For each node, list all possible construction cables between it and its surrounding nodes, and select the cables with a distance less than the maximum distance as candidate construction cables. S2: Obtain all candidate cable combinations that may cross, and impose constraints so that candidate cables that may cross cannot be constructed simultaneously; S3: Establish a power collection system planning model based on capacity-constrained vehicle routing planning and power system extended planning; S4: Solve the planning and optimization model of offshore wind farm collection system using a commercial solver; Step S3 includes the following steps: S3-1: A preliminary planning model for a bi-faceted ring topology power collection system is established based on a capacity-constrained vehicle routing planning model. The capacity-constrained vehicle routing planning problem is improved to adapt to power collection system planning. S3-2: An extended programming model for power systems based on mixed-integer linear programming is used to approximate and optimize the total power loss in the power collection system; Step S3-1 specifically involves: Establish the objective function for the current collector system planning model: Among them, assume a fully directed graph. ,in ,include Substation nodes and Wind turbine nodes in the middle, This represents all candidate cables between nodes. For each candidate cable All have investment costs and binary investment decision variables , Represents the investment and construction cost of the power collection system; This represents the network loss cost over the operating life of an offshore wind farm. It is a coefficient that converts the power loss at a certain moment to the power loss over the operating life of an offshore wind farm. Its calculation is as follows: , For cables The resistance, For cables The active power flowing upstream; This represents the total value of the reduced wind power generation capacity. Indicates wind turbine The lost wind power; In step S3-1, the constraints of the power system extended planning model are: In formula (2), and Let be the decision variable representing the direction of the power flow, and be a binary variable. cable When it was built, Virtual trends in the nodes Flow to Node hour, ,when Virtual trends in the nodes Flow to Node hour, , For all cable sets; in formulas (3) and (4), The formula represents the set of wind turbine nodes. It indicates that the number of inflow and outflow power cables for each wind turbine node is strictly 1, which is a constraint required by the double-sided ring topology. Formulas (5) and (6) are for MTZ-sub-ring elimination constraints. Indicates arrival at node Power accumulated over time Formula (7) represents the node Accumulated power Not less than the electrical energy generated by the wind turbine No larger than the cable upper limit of transmission power ;In formula (8), A collection of cross-cable combinations.

2. The method according to claim 1, characterized in that, In step S1, the candidate construction cable connected to the offshore substation can be further designed, and is not limited to the maximum distance limitation.

3. The method according to claim 1, characterized in that, Step S2 includes the following steps: S2-1: Settings , where V represents the set of all nodes; S2-2: Determine if line segments AB and CD intersect. If they intersect, add the current line segment combination to the list. ,in This represents the set of all candidate cable combinations where a crossing occurs; S2-3: For All cross-candidate cable combinations make ,in, It represents a node and Candidate cables between Whether to build or not, when A value of 1 indicates that the candidate cable It was built.

4. The method according to claim 3, characterized in that, Step S2-2 includes the following steps: If and only if and At that time, determine that line segments AB and CD intersect; Iterate through all candidate cable combinations and make judgments, adding the candidate cable combinations that cross each other to the set. Specifically: 。 5. The method according to claim 1, characterized in that, In step S3-2, in the power system extended planning model based on mixed-integer linear programming, the optimal power flow is based on DC power flow, specifically: in, For the set of substation nodes, Represents nodes The collection of all connected cables. Represents a node The wind turbine power is given by formulas (9) and (10), which represent the wind turbine node and substation node, respectively. Power balance constraints at the nodes; Equation (11) represents the node power balance constraints in the DC power flow model. and During the construction of the cable, the cable power Phase angle of node voltage and The relationship between the two; Formula (12) sets the substation node as the reference node and sets its voltage phase angle to 0; Formulas (13)-(14) respectively limit the transmission power on the cable and the power loss at the wind turbine node.

6. The method according to claim 1, characterized in that, Step S3 also includes the following steps: Establish The mathematical model of a degree-centered tree is: in, Indicates rounding down. This represents the number of elements in the wind turbine node set, i.e., the number of wind turbines. To indicate cable Is it in Decision variables in a degree-centered tree, when the cable exist The degree is 1 if it is in the center tree, and 0 otherwise. and To indicate cable Is it in In the degree-centered tree, the decision variables connected to the substation nodes, if the cable Connected to substation nodes Not here In the degree-centered tree, then , If the cable Not connected to substation nodes and not In the degree-centered tree, then , ; To be connected to the substation node but not in The number of cables in the degree center tree.

7. The method according to claim 1, characterized in that, Step S4 specifically involves establishing a mathematical model in MATLAB software using the modeling tool YALMIP based on this method, inputting the coordinate information of the offshore wind turbine and offshore substation, and calling a commercial solver to perform planning optimization.

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