A design method and system for a bilateral ring network collection system for an offshore wind farm

Abstract

The present invention discloses a bilateral ring network collection system design method and system for an offshore wind farm, the present invention comprises obtaining wind farm data of an offshore wind farm; constructing a bidirectional power flow conservation model for the bilateral ring network collection system of the offshore wind farm based on the wind farm data to achieve a one-to-one matching of the cable position with its optimal rated value that meets the precise redundancy requirement; constructing an objective function with the minimum cable investment cost and the minimum network loss, and adding constraints; convexifying the objective function and the constraints; solving the convexified model with a solver to obtain the optimal topological structure of the bilateral ring network collection system and the total cost composed of the value of its objective function. The present invention can realize the design of the bilateral ring network collection system of an offshore wind farm by considering the precise redundancy requirement, so that the precise rated value requirement and type of each cable can be matched one-to-one with its maximum sorting position, and realize the precise planning of the bilateral ring network collection system.

Description

Technical Field

[0001] The present invention relates to the field of wind power technology, and in particular to a design method and system for a bilateral ring network power collection system for an offshore wind farm. Background Art

[0002] Wind energy is an efficient, clean, widely distributed and abundant renewable energy source. The development of wind energy is now very mature. Compared with onshore wind farms, offshore wind farms have attracted global attention due to their superior wind conditions and more advantageous locations for capturing wind energy resources. The costs associated with the collection and transmission systems usually account for 20% of the total investment in an open wind farm. The planning of the collection system is particularly variable, involving decisions such as cable routing and cable type selection. The planning of the collection system also affects the topology of the open wind farm, thereby affecting power loss and operating costs. The requirement for uniform capacity cable ratings will result in excessive flow redundancy and higher investment costs. Therefore, how to consider precise redundancy requirements to achieve the design of the bilateral ring network collection system of offshore wind farms has become a key technical issue that needs to be solved urgently. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems of the prior art, a method and system for designing a bilateral ring network collection system for an offshore wind farm are provided. The present invention can take into account precise redundancy requirements to realize the design of the bilateral ring network collection system for an offshore wind farm, so that the precise rating requirements and type of each cable can be matched one-to-one with its maximum sorting position, thereby realizing precise planning of the bilateral ring network collection system.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A design method for a bilateral ring network collection system for an offshore wind farm comprises the following steps:

[0006] S1, obtain wind farm data of offshore wind farms;

[0007] S2, based on wind farm data, builds a bidirectional power flow conservation model for the bilateral ring network collection system of the offshore wind farm to achieve a one-to-one matching of the cable position with its optimal rating that meets the precise redundancy requirements;

[0008] S3, construct an objective function with the minimum cable investment cost and network loss, and add constraints of the capacity-constrained vehicle routing problem, cable sequencing constraints, and DC power flow constraints;

[0009] S4, convexifying the objective function and constraint conditions;

[0010] S5, using a solver to solve the convexified model to obtain the optimal topological structure of the bilateral ring network collection system and the total cost composed of the value of its objective function.

[0011] Optionally, step S2 includes:

[0012] S2.1, construct a bidirectional power flow conservation model consisting of a forward power flow conservation model and a reverse power flow conservation model, wherein the function expression of the forward power flow conservation model is:

[0013] ,

[0014] In the above formula, Represents a collection of cable lines, represents the maximum sequence position set, k is the number of the cable sorting position, k is 0 to the maximum sequence position set number A positive integer, The binary sorting variable representing the cable (i, j) with the largest sequence position k in the positive direction, i, j are the node numbers, and the forward power conservation model is used to ensure that the power flowing out of node i in the positive direction is 1 more than the power flowing in; the function expression of the reverse power conservation model is:

[0015] ,

[0016] In the above formula, Represents the binary order variable of the cable (i, j) with the largest order position k in the reverse direction. The reverse power flow conservation model is used to ensure that the power flowing out of node i in the reverse direction is 1 more than the power flowing into node i, and:

[0017] ,

[0018] In the above formula, is the maximum rated power of the cable, is the capacity of a wind turbine, Express Round down;

[0019] S2.2, determine the maximum sorting position of any cable (i, j) according to the following formula :

[0020] ,

[0021] In the above formula, k and h are the maximum sequential position sets. The maximum order position in, max means taking the maximum value, A binary sorting variable representing the cable (i, j) with the largest sequential position h in the reverse direction is used to determine the relationship between each cable and its exact rating requirement, thereby achieving a one-to-one matching of the cable position with its optimal rating that meets the exact redundancy requirement.

[0022] Optionally, the function expression of the objective function constructed in step S3 is:

[0023] ,

[0024] In the above formula, represents the investment cost, t Indicates planning time, T Indicates the planning period, r represents the discount rate, represents the total number of hours in a year, represents the electricity price, represents the operating cost, A function representing the value of the constraint relaxation factor.

[0025] Optionally, the calculation function expression of the function of the investment cost, the operating cost and the value of the limiting relaxation factor is:

[0026] ,

[0027] ,

[0028] ,

[0029] In the above formula, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position k in the reverse direction, for k and h The maximum value of constitutes the cost of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), for all node sets; for k and h The maximum value of constitutes the resistance of the cable (i, j) at the maximum sorting position, is the power flowing on cable (i, j), is a parameter of the big M method, which is used to penalize solutions or constraints that are not in the feasible domain. is the relaxation factor of cable (i, j) of the square root of the branch power flow equation, is the relaxation factor of cable (i, j) of the square root of the branch power loss equation, For wind turbines, is the set of all binary auxiliary variables y.

[0030] Optionally, in step S3, the function expression of the constraint condition of the vehicle routing problem with capacity constraints is:

[0031] ,

[0032] ,

[0033] ,

[0034] ,

[0035] ,

[0036] ,

[0037] In the above formula, is the outgoing cable of the substation node (s,i), is the incoming cable (j, s) of the substation node, cable (s, i) and cable (j, s) are both cable line sets The cables in m is the number of circuits, is the wind turbine node set, is the substation node set, is the total number of wind turbines, Indicates the maximum rated power of the cable. is the active power of the wind turbine, For outgoing cables, To flow into the cable, For wind turbines i Eliminate the accumulated power of the sub-path, For wind turbines j Eliminate the accumulated power of the sub-path, for all node sets;

[0038] The functional expression of the cable sorting constraint is:

[0039] ,

[0040] ,

[0041] ,

[0042] In the above formula, k and h are the maximum sequential position sets. The maximum sequential position in A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position h in the reverse direction, is the binary ordered variable of cable (i, j), cable (i, j) is the set of cable lines The cables in , i, j are the node numbers;

[0043] The function expression of the DC power flow constraint condition is:

[0044] ,

[0045] ,

[0046] ,

[0047] ,

[0048] ,

[0049] In the above formula, represents the phase angle of the substation, Indicates i The voltage phase angle of the typhoon generator set, and As its upper and lower limits, is the set of substation nodes, is the power flowing on cable (i, j), Indicates i The active power of the typhoon generator set, For each wind turbine node i The relaxation coefficient of power balance, M is the parameter of the big M method, For wind turbines, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position h in the reverse direction, Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates j The voltage phase angle of the typhoon generator set, Represents a collection of cable runs.

[0050] Optionally, when the objective function and the constraint condition are convexed in step S4, it includes according to the following formula:

[0051] ,

[0052] The investment cost in the objective function The model is convexed to obtain the convexed investment cost shown in the following formula: :

[0053] ,

[0054] And the constraints between active power and voltage phase angle after convexification are:

[0055] ,

[0056] in, represents a binary auxiliary variable to describe the product of the positive and negative sort positions, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, The binary sorting variable representing the cable (i, j) with the maximum sequence position h in the reverse direction, k and h are the maximum sequence position sets The maximum sequential position in is the set of all binary auxiliary variables y, for k and h The maximum value of constitutes the cost of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, Indicates i The voltage phase angle of the typhoon generator set, Indicates j The voltage phase angle of the typhoon generator set, is the power flowing on cable (i, j), is the relaxation factor of cable (i, j) of the square root of the branch power flow equation, is the binary ordering variable of cable (i, j), Parameters for the Big M method.

[0057] Optionally, step S4 further includes establishing a linear relationship between the square root of the power loss of each cable and its maximum sorting position according to the big M method:

[0058] ,

[0059] ,

[0060] ,

[0061] in, represents the network loss of cable (i, j), represents a binary auxiliary variable to describe the product of the positive and negative sort positions, for k and h The maximum value of constitutes the resistance of the cable (i, j) at the maximum sorting position, Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates i The voltage phase angle of the typhoon generator set, Indicates j The voltage phase angle of the typhoon generator set, are the parameters of the big M method, The relaxation factor of cable (i, j) is the square root of the branch power flow equation.

[0062] In addition, the present invention also provides a bilateral ring network collection system design system for an offshore wind farm, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the bilateral ring network collection system design method for an offshore wind farm.

[0063] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the bilateral ring network collection system design method for an offshore wind farm through a processor.

[0064] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the bilateral ring network collection system design method for an offshore wind farm through a processor.

[0065] Compared with the prior art, the design method of the bilateral ring network collection system for offshore wind farms of the present invention mainly has the following advantages: the present invention includes obtaining wind farm data of offshore wind farms; constructing a bidirectional power flow conservation model for the bilateral ring network collection system of offshore wind farms based on the wind farm data to achieve a one-to-one matching of the cable position with its optimal rated value that meets the precise redundancy requirements; constructing an objective function with the minimum cable investment cost and the minimum network loss, and adding constraints; convexifying the model of the objective function and the constraints; solving the convexified model with a solver to obtain the optimal topological structure of the bilateral ring network collection system and the total cost composed of the value of its objective function. The present invention can realize the design of the bilateral ring network collection system of offshore wind farms by considering the precise redundancy requirements, so that the precise rated value requirements and types of each cable can be matched one-to-one with its maximum sorting position, and realize the precise planning of the bilateral ring network collection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0067] Figure 2 Schematic diagram of the topological structure of the bilateral ring network power collection system in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] like Figure 1 As shown, the design method of the bilateral ring network collection system for an offshore wind farm in this embodiment includes the following steps:

[0069] S1, obtain wind farm data of offshore wind farms;

[0070] S2, based on wind farm data, builds a bidirectional power flow conservation model for the bilateral ring network collection system of the offshore wind farm to achieve a one-to-one matching of the cable position with its optimal rating that meets the precise redundancy requirements;

[0071] S3, construct an objective function with the minimum cable investment cost and network loss, and add constraints of the capacity-constrained vehicle routing problem, cable sequencing constraints, and DC power flow constraints;

[0072] S4, convexifying the objective function and constraint conditions;

[0073] S5, using a solver to solve the convexified model to obtain the optimal topological structure of the bilateral ring network collection system and the total cost composed of the value of its objective function.

[0074] Figure 2Schematic diagram of the topological structure of the bilateral ring network power collection system in this embodiment, where the "S" in the left box represents an offshore substation, WT1 to WT7 are 7 wind turbines in the bilateral ring network power collection system, and Br1 to Br8 are 8 cables in the bilateral ring network power collection system. A binary sorting variable representing the cable (i, j) with the largest sequential position k in the positive direction, where i and j are the node numbers. = represents the binary sorting variable of the cable (i, j) with the largest order position k in the reverse direction. Based on the topological structure of the bilateral ring network collection system of the offshore wind farm, step S2 in this embodiment includes:

[0075] S2.1, construct a bidirectional power flow conservation model consisting of a forward power flow conservation model and a reverse power flow conservation model, wherein the function expression of the forward power flow conservation model is:

[0076] ,

[0077] In the above formula, Represents a collection of cable lines, represents the maximum sequence position set, k is the number of the cable sorting position, k is 0 to the maximum sequence position set number A positive integer, The binary sorting variable representing the cable (i, j) with the largest sequence position k in the positive direction, i, j are the node numbers, and the forward power conservation model is used to ensure that the power flowing out of node i in the positive direction is 1 more than the power flowing in; the function expression of the reverse power conservation model is:

[0078] ,

[0079] In the above formula, Represents the binary order variable of the cable (i, j) with the largest order position k in the reverse direction. The reverse power flow conservation model is used to ensure that the power flowing out of node i in the reverse direction is 1 more than the power flowing into node i, and:

[0080] ,

[0081] In the above formula, is the maximum rated power of the cable, is the capacity of a wind turbine, Express rounded down; for example, if the capacity of a wind turbine is is 5 MW, while the maximum power rating of the cable is 39.4 MW, then is 7, which means that the cable with the maximum rated power can support up to 7 wind turbines;

[0082] S2.2, determine the maximum sorting position of any cable (i, j) according to the following formula :

[0083] ,

[0084] In the above formula, k and h are the maximum sequential position sets. The maximum order position in, max means taking the maximum value, The binary sorting variable representing the cable (i, j) with the largest order position h in the reverse direction is used to determine the relationship between each cable and its exact rating requirement, so as to achieve a one-to-one matching between the cable position and its optimal rating that meets the exact redundancy requirement. Figure 2 Cables in Br3 For and is equal to 1, and and Equal to 0. Maximum sorting position of cable (2,3) is equal to 5, the exact rating of cable (2,3) is equal to 5 wind turbines, so the relationship between each cable and its exact rating requirement has been determined.

[0085] The functional expression of the objective function constructed in step S3 of this embodiment is:

[0086] ,

[0087] In the above formula, represents the investment cost, t Indicates planning time, T Indicates the planning period, r represents the discount rate, represents the total number of hours in a year, represents the electricity price, represents the operating cost, A function representing the value of the restriction relaxation factor. In this embodiment, T =20, r =0.08, , In this embodiment, the calculation function expression of the function of the investment cost, the operating cost and the value of the limiting relaxation factor is:

[0088] ,

[0089] ,

[0090] ,

[0091] In the above formula, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position k in the reverse direction, for k and h The maximum value of constitutes the cost of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), for all node sets; for k and h The maximum value of constitutes the resistance of the cable (i, j) at the maximum sorting position, is the power flowing on cable (i, j), is a parameter of the big M method, which is used to penalize solutions or constraints that are not in the feasible domain. is the relaxation factor of cable (i, j) of the square root of the branch power flow equation, is the relaxation factor of cable (i, j) of the square root of the branch power loss equation, For wind turbines, is the set of all binary auxiliary variables y.

[0092] The constraint condition of the vehicle routing problem with capacity constraints is a well-known mathematical problem, which means that each customer point must be visited and can only be visited once, and the route of each vehicle must start from the garage and end at the garage. The planning of the offshore wind farm collection system with bilateral ring topology also needs to meet the above constraints, specifically, it needs to meet the requirement that the outflow cable of the substation node is equal to the inflow cable of the substation node and equal to the number of loops. Therefore, in step S3 of this embodiment, the function expression of the constraint condition of the vehicle routing problem with capacity constraints is:

[0093] ,

[0094] ,

[0095] ,

[0096] ,

[0097] ,

[0098] ,

[0099] In the above formula, is the outgoing cable of the substation node (s,i), is the incoming cable (j, s) of the substation node, cable (s, i) and cable (j, s) are both cable line sets The cables in m is the number of circuits, is the wind turbine node set, is the substation node set, is the total number of wind turbines, Indicates the maximum rated power of the cable. is the active power of the wind turbine, For outgoing cables, To flow into the cable, For wind turbines i Eliminate the accumulated power of the sub-path, For wind turbines j Eliminate the accumulated power of the sub-path, is the set of all nodes; in the above function expression, it is first limited that the outgoing cable of the substation node is equal to the incoming cable of the substation node and equal to the number of loops, and then by defining m The minimum value of is used to improve convergence. Finally, it is necessary to satisfy that the outgoing cable and the incoming cable of each wind turbine node are equal to 1, which is used to ensure the ring topology of the collection system, eliminate sub-loops, and ensure that all loops start and end at the offshore substation.

[0100] Cable sequencing constraints are original constraints for offshore wind farm collection system planning to meet the cable accurate rating requirements. and represents the binary sorting variable of cable (i, j). The functional expression of the cable sorting constraint is:

[0101] ,

[0102] ,

[0103] ,

[0104] In the above formula, k and h are the maximum sequential position sets. The maximum sequential position in A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position h in the reverse direction, is the binary ordered variable of cable (i, j), cable (i, j) is the set of cable lines The cables in the diagram are: i, j are node numbers; Indicates positive / negative power flow from the node iFlow to Node j , the positive / negative sort positions are k , if there is a cable between two wind turbines, there is only one positive sequence position and one negative sequence position.

[0105] For DC power flow constraints, the DC power flow model linearizes the voltage phase angle and active power. Considering the uniform cable rating requirements, the optimal power flow with the minimum power loss can be solved according to the mixed integer quadratic programming method. However, the above method cannot directly calculate the offshore wind farm collection system planning considering the precise redundancy requirements, so the function expression of the DC power flow constraint in this embodiment is:

[0106] ,

[0107] ,

[0108] ,

[0109] ,

[0110] ,

[0111] In the above formula, represents the phase angle of the substation, Indicates i The voltage phase angle of the typhoon generator set, and As its upper and lower limits, is the set of substation nodes, is the power flowing on cable (i, j), Indicates i The active power of the typhoon generator set, For each wind turbine node i The relaxation coefficient of power balance, M is the parameter of the big M method, For wind turbines, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position h in the reverse direction, Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates j The voltage phase angle of the typhoon generator set, The above function expression first establishes a three-dimensional DC power flow model for offshore wind farms, and then imposes constraints on the power collected on the cables, which need to satisfy the power balance of each wind turbine node and keep the voltage angle within a feasible range. For each wind turbine node i The relaxation coefficient of power balance, the parameter M of the big M method is an infinite positive number, and the big M method is used to define the first i Typhoon turbines and j The voltage phase angle of the typhoon generator set and the cable ( i , j ) collected in the active power ( ) is related to each other, if , the active power is independent of the voltage phase angle, where indicates the maximum sort position ( k and h Maximum value of the cable ( i , j ) conductivity.

[0112] In step S4, the objective function and the constraint condition are convexed by a desired convexing method as required. In this embodiment, the big M method is specifically adopted. Specifically, when the objective function and the constraint condition are convexed by a model in step S4, the following formula is included:

[0113] ,

[0114] The investment cost in the objective function The model is convexed to obtain the convexed investment cost shown in the following formula: :

[0115] ,

[0116] And the constraints between active power and voltage phase angle after convexification are:

[0117] ,

[0118] in, represents a binary auxiliary variable to describe the product of the positive and negative sort positions, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, The binary sorting variable representing the cable (i, j) with the maximum sequence position h in the reverse direction, k and h are the maximum sequence position sets The maximum sequential position in is the set of all binary auxiliary variables y, for k and hThe maximum value of constitutes the cost of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, Indicates i The voltage phase angle of the typhoon generator set, Indicates j The voltage phase angle of the typhoon generator set, is the power flowing on cable (i, j), is the relaxation factor of cable (i, j) of the square root of the branch power flow equation, is the binary ordering variable of cable (i, j), Parameters for the Big M method.

[0119] In this embodiment, step S4 also includes establishing a linear relationship between the square root of the power loss of each cable and its maximum sorting position according to the Big M method:

[0120] ,

[0121] ,

[0122] ,

[0123] in, represents the network loss of cable (i, j), represents a binary auxiliary variable to describe the product of the positive and negative sort positions, for k and h The maximum value of constitutes the resistance of the cable (i, j) at the maximum sorting position, Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates i The voltage phase angle of the typhoon generator set, Indicates j The voltage phase angle of the typhoon generator set, are the parameters of the big M method, The relaxation factor of cable (i, j) is the square root of the branch power flow equation.

[0124] On this basis, the obtained convex model makes the offshore wind farm collection system planning problem completely equivalent to a mixed integer quadratic function. The convex model is solved by a commercial solver to obtain the optimal topological structure of the bilateral ring network collection system and the total cost composed of the value of its objective function. The required commercial solver can be used as needed.

[0125] In order to verify the bilateral ring network collection system design method for offshore wind farms in this embodiment, simulation verification is carried out in this embodiment. Table 1 lists the specifications of 33kV aluminum cross-linked polyethylene cables, including cross-sectional area and corresponding rated capacity, resistance per unit length, inductance and cost.

[0126] Table 1: Specifications of three (single) core aluminum XLPE cables

[0127]

[0128] In Table 1, MVA (Mega Volt Amperes) refers to the capacity of the cable, measured in megavolt-amperes (million volt-amperes). The methods used as comparisons for the method of this embodiment are: scanning method + genetic algorithm; not considering the precise rating requirements of the cable; not considering network losses. Among them, the scanning method + genetic algorithm uses a scanning algorithm to divide all wind turbines into several clusters counterclockwise from the direction of the high-voltage cable to increase the solution speed and prevent cable crossing. The use of genetic algorithm is a heuristic algorithm that is often used in wind farm collection system planning. The collection system planning method that does not consider the precise rating requirements of the cable and does not consider network losses is also compared with the proposed method, and the final results are shown in Table 2.

[0129] Table 2: Comparison of economics of offshore wind farms using 20 / 30 / 42 wind turbines

[0130]

[0131] As shown in Table 2, compared with the scanning method + genetic algorithm, for the systems of 20, 30 and 42 wind turbines, the investment cost of the method of this embodiment saves 9.92%, 9.40% and 2.37% respectively, and the total cost of the method of this embodiment saves 9.45%, 8.89% and 1.76% respectively. Compared with the method without considering the precise rating requirements of the cable, in the 20, 30 and 42 wind turbine systems, the investment cost of the method of this embodiment saves 18.57%, 13.55% and 9.27% ​​respectively, and the total cost of the method of this embodiment saves 16.75%, 11.88% and 7.06% respectively. In terms of investment cost and total cost, the global optimal algorithm proposed by the method of this embodiment is superior to the heuristic algorithm and the method without considering the precise rating requirements of the cable. Although the calculation time of the method of this embodiment is long, it is acceptable for the planning of the wind farm collection system. The solution gap of the wind farm with 20 wind turbines is 0, indicating that the global optimal solution has been found. The solution gaps for wind farms with 30 wind turbines and 42 wind turbines are 4.72% and 7.21% respectively. It is worth mentioning that even if the solution gap is not 0, it may still be the global optimal solution. Although the investment cost of the method in this embodiment is 0.12% higher than that of the method without considering network loss in the 20-wind turbine system, the total cost of the method in this embodiment saves 0.59%. The investment cost and total cost of 30 wind turbines and 42 wind turbines without considering the network loss method are the same as those of the method in this embodiment. In offshore wind farms with 20, 30 and 42 wind turbines, the calculation time of the proposed method is 29987s, 36000s and 540000s respectively. However, in offshore wind farms with 20, 30 and 42 wind turbines, the calculation time of the lossless method is 81.6s, 292.9s and 2553.9s respectively. Therefore, in the planning stage of the large-scale offshore wind farm collection system, the quadratic term of network loss can be retained or discarded according to actual needs.

[0132] In summary, due to the high reliability of the bilateral ring network topology, it is widely used in the collection system of offshore wind farms. However, the unified cable current rating requirement will lead to excessive planning investment in the collection system. Therefore, the method of this embodiment discloses a scheme for designing a bilateral ring network collection system for offshore wind farms considering precise redundancy requirements. The scheme constructs a mixed integer quadratic programming model to minimize the cable cost and total power loss, and obtains the global minimum of the total cable investment cost and total power loss during the entire life cycle of the offshore wind farm. The method of this embodiment adopts a bidirectional flow conservation method to construct a bidirectional tidal current conservation model to match the cable position with its optimal rating that meets the precise redundancy requirements. Then, by considering the coupling characteristics between the cable impedance parameters at each position and the topology of the offshore wind farm collection system, the large M method is used to convexify the high-dimensional global routing optimization problem using an equivalent substitution method, so that the offshore wind farm collection system planning problem can be completely equivalent to a mixed integer quadratic function by convexifying the complex power loss optimization problem, so as to ensure that the global optimal solution is obtained within a feasible computing time, and the precise planning of the bilateral ring network collection system of the offshore wind farm is realized.

[0133] In addition, this embodiment also provides a bilateral ring network collection system design system for an offshore wind farm, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the bilateral ring network collection system design method for an offshore wind farm.

[0134] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the bilateral ring network collection system design method for an offshore wind farm through a processor.

[0135] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the bilateral ring network collection system design method for an offshore wind farm through a processor.

[0136] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0137] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A design method for a bilateral ring network collection system for an offshore wind farm, characterized in that: The steps include: S1, obtain wind farm data of offshore wind farms; S2, based on wind farm data, builds a bidirectional power flow conservation model for the bilateral ring network collection system of the offshore wind farm to achieve a one-to-one matching of the cable position with its optimal rating that meets the precise redundancy requirements; S3, construct an objective function with the minimum cable investment cost and network loss, and add constraints of the capacity-constrained vehicle routing problem, cable sequencing constraints, and DC power flow constraints; S4, convexifying the objective function and constraint conditions; S5, using a solver to solve the convexified model to obtain the optimal topological structure of the bilateral ring network collection system and the total cost composed of the value of its objective function; Step S2 includes: S2.1, construct a bidirectional power flow conservation model consisting of a forward power flow conservation model and a reverse power flow conservation model, wherein the function expression of the forward power flow conservation model is: ， In the above formula, Represents a collection of cable lines, represents the maximum sequence position set, k is the number of the cable sorting position, k is 0 to the maximum sequence position set number A positive integer, The binary sorting variable representing the cable (i, j) with the largest sequence position k in the positive direction, i, j are the node numbers, and the forward power conservation model is used to ensure that the power flowing out of node i in the positive direction is 1 more than the power flowing in; the function expression of the reverse power conservation model is: ， In the above formula, Represents the binary order variable of the cable (i, j) with the largest order position k in the reverse direction. The reverse power flow conservation model is used to ensure that the power flowing out of node i in the reverse direction is 1 more than the power flowing into node i, and: ， In the above formula, is the maximum rated power of the cable, is the capacity of a wind turbine, Express Round down; S2.2, determine the maximum sorting position of any cable (i, j) according to the following formula : ， In the above formula, k and h are the maximum sequential position sets. The maximum order position in, max means taking the maximum value, A binary sorting variable representing the cable (i, j) with the largest sequential position h in the reverse direction is used to determine the relationship between each cable and its exact rating requirement, thereby achieving a one-to-one matching of the cable position with its optimal rating that meets the exact redundancy requirement.

2. The method for designing a bilateral ring network collection system for an offshore wind farm according to claim 1, characterized in that: The functional expression of the objective function constructed in step S3 is: ， In the above formula, represents the investment cost, t Indicates planning time, T Indicates the planning period, r represents the discount rate, represents the total number of hours in a year, represents the electricity price, represents the operating cost, A function representing the value of the constraint relaxation factor.

3. The design method of a bilateral ring network collection system for an offshore wind farm according to claim 2 is characterized in that: The calculation function expression of the function of the investment cost, the operating cost and the value of the limiting relaxation factor is: ， ， ， In the above formula, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position k in the reverse direction, for k and h The maximum value of constitutes the cost of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), for all node sets; for k and h The maximum value of constitutes the resistance of the cable (i, j) at the maximum sorting position, is the power flowing on cable (i, j), is a parameter of the big M method, which is used to penalize solutions or constraints that are not in the feasible domain. is the relaxation factor of cable (i, j) of the square root of the branch power flow equation, is the relaxation factor of cable (i, j) of the square root of the branch power loss equation, For wind turbine set, is the set of all binary auxiliary variables y.

4. The method for designing a bilateral ring network power collection system for an offshore wind farm according to claim 3, characterized in that: In step S3, the function expression of the constraint condition of the vehicle routing problem with capacity constraints is: ， ， ， ， ， ， In the above formula, is the outgoing cable of the substation node (s,i), is the incoming cable (j, s) of the substation node, cable (s, i) and cable (j, s) are both cable line sets The cables in m is the number of circuits, is the wind turbine node set, is the substation node set, is the total number of wind turbines, Indicates the maximum rated power of the cable. is the active power of the wind turbine, For outgoing cables, To flow into the cable, For wind turbines i Eliminate the accumulated power of the sub-path, For wind turbines j Eliminate the accumulated power of the sub-path, for all node sets; The functional expression of the cable sorting constraint is: ， ， ， In the above formula, k and h are the maximum sequential position sets. The maximum sequential position in A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position h in the reverse direction, is the binary ordered variable of cable (i, j), cable (i, j) is the set of cable lines The cables in , i, j are the node numbers; The function expression of the DC power flow constraint condition is: ， ， ， ， ， In the above formula, represents the phase angle of the substation, Indicates i The voltage phase angle of the typhoon generator set, and As its upper and lower limits, is the set of substation nodes, is the power flowing on cable (i, j), Indicates i The active power of the typhoon generator set, For each wind turbine node i The relaxation coefficient of power balance, M is the parameter of the big M method, For wind turbine set, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, A binary ordering variable representing the cable (i, j) with the largest order position h in the reverse direction, Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates j The voltage phase angle of the typhoon generator set, Represents a collection of cable runs.

5. The method for designing a bilateral ring network collection system for an offshore wind farm according to claim 4, characterized in that: When the objective function and the constraint conditions are convexified in step S4, the following formula is included: ， The investment cost in the objective function The model is convexed to obtain the convexed investment cost shown in the following formula: : ， And the constraints between active power and voltage phase angle after convexification are: ， in, represents a binary auxiliary variable to describe the product of the positive and negative sort positions, A binary ordering variable representing the cable (i, j) with the largest order position k in the positive direction, The binary sorting variable representing the cable (i, j) with the maximum sequence position h in the reverse direction, k and h are the maximum sequence position sets The maximum sequential position in is the set of all binary auxiliary variables y, for k and h The maximum value of constitutes the cost of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, Indicates i The voltage phase angle of the typhoon generator set, Indicates j The voltage phase angle of the typhoon generator set, is the power flowing on cable (i, j), is the relaxation factor of cable (i, j) of the square root of the branch power flow equation, is the binary sorting variable of cable (i,j), Parameters for the Big M method.

6. The method for designing a bilateral ring network power collection system for an offshore wind farm according to claim 5, characterized in that: Step S4 also includes establishing a linear relationship between the square root of the power loss of each cable and its maximum sorting position according to the Big M method: ， ， ， in, represents the network loss of cable (i, j), represents a binary auxiliary variable to describe the product of the positive and negative sort positions, for k and h The maximum value of constitutes the resistance of the cable (i, j) at the maximum sorting position, Indicates that k and h The maximum value of constitutes the conductivity of the cable (i, j) at the maximum sorting position, is the length of cable (i,j), Indicates i The voltage phase angle of the typhoon generator set, Indicates j The voltage phase angle of the typhoon generator set, are the parameters of the big M method, The relaxation factor of cable (i, j) is the square root of the branch power flow equation.

7. A bilateral ring network collection system design system for an offshore wind farm, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the bilateral ring network collection system design method for an offshore wind farm as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute, through a processor, the method for designing a bilateral ring network collection system for an offshore wind farm as claimed in any one of claims 1 to 6.

9. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute, through a processor, the method for designing a bilateral ring network collection system for an offshore wind farm as claimed in any one of claims 1 to 6.

Images