Offshore wind power delivery system planning method, device, equipment and storage medium

Abstract

The invention relates to the technical field of offshore wind power sending-out system planning, and provides an offshore wind power sending-out system planning method, device and equipment and a storage medium, and the method comprises the steps: determining a voltage level corresponding to an offshore wind power collection system; according to the voltage grade, the improved K-means method is adopted to partition the wind turbine generator set, and a wind turbine generator set partition result is obtained; calculating the capacity and site selection result of each offshore substation through an improved gravity center method according to the wind turbine generator partition result; constructing a target function corresponding to an offshore wind power delivery system planning scheme according to a wind turbine generator partition result and the capacity and site selection result of each offshore substation, and determining a constraint condition corresponding to the target function; and according to the constraint condition, carrying out optimization solution on the target function to obtain an optimal offshore wind power delivery system planning scheme. Through the technical scheme, the construction cost is reduced, efficient utilization of wind power is ensured, and the wind curtailment phenomenon is prevented.

Description

[Technical field]

[0001] The present application relates to the technical field of offshore wind power transmission system planning, and in particular to an offshore wind power transmission system planning method, device, equipment and storage medium. [Background technology]

[0002] With the rapid development of offshore wind power in recent years, offshore wind power bases have shown a trend of large-scale and deep sea development, with the capacity of a single wind turbine exceeding 10MW and above, which has put forward higher requirements for the planning and design of offshore wind power transmission systems. The planning and design of the power collection system, as a key link connecting offshore wind turbines and the transmission system, is directly related to the stable transmission and efficient distribution of wind power. However, in the current relevant technologies, there is a lack of zoning, site selection and planning schemes for offshore wind power bases. [Summary of the invention]

[0003] The embodiments of the present application provide a method, device, equipment and storage medium for planning an offshore wind power transmission system, aiming to solve the technical problems existing in the related technologies.

[0004] In a first aspect, an embodiment of the present application provides a method for planning an offshore wind power transmission system, comprising:

[0005] Determine the voltage level corresponding to the offshore wind power collection system;

[0006] According to the voltage level, the wind turbines of the offshore wind power transmission system are partitioned by using an improved K-means method to obtain a wind turbine partitioning result;

[0007] According to the wind turbine group zoning results, the capacity and site selection results of each offshore substation are calculated by using an improved centroid method;

[0008] According to the wind turbine group zoning results and the capacity and site selection results of each offshore substation, construct an objective function corresponding to the offshore wind power transmission system planning scheme, and determine the constraint conditions corresponding to the objective function;

[0009] According to the constraint conditions, the objective function is optimized to obtain an optimal offshore wind power transmission system planning scheme.

[0010] In one embodiment, optionally, the voltage level includes a first voltage level and a second voltage level, the voltage of the first voltage level is lower than the voltage of the second voltage level, the wind turbine group zoning results corresponding to the first voltage level include large zoning results and small zoning results, the wind turbine group zoning results corresponding to the second voltage level include small zoning results, the offshore substations corresponding to the first voltage level include offshore boost stations and offshore collection stations, and the offshore substations corresponding to the second voltage level include offshore collection stations.

[0011] In one embodiment, optionally, based on the voltage level, an improved K-means method is used to partition the wind turbines in the offshore wind power transmission system to obtain a wind turbine partition result, including:

[0012] According to the voltage level and the upper limit of the offshore substation capacity, the number of partitions corresponding to the wind turbine generator set is determined by the elbow method;

[0013] According to the number of partitions, the wind turbine group is partitioned by using an improved K-means method to obtain a wind turbine group partition result.

[0014] In one embodiment, optionally, partitioning the wind turbine group using an improved K-means method according to the number of partitions to obtain a wind turbine group partition result includes:

[0015] Obtaining the position coordinates of each wind turbine in the offshore wind power transmission system;

[0016] Selecting the reference coordinates of the wind turbine generator set in the offshore wind power transmission system;

[0017] Calculating the distance between the position coordinates of each wind turbine generator set and the reference coordinates;

[0018] According to the distance values, calculating the average distance value from the reference coordinate to the position coordinate of each wind turbine generator set;

[0019] Sorting all wind turbines in descending order according to the average distance value;

[0020] According to the number of partitions, selecting a corresponding number of wind turbines ranked first as cluster centers;

[0021] The wind turbine group is partitioned according to the cluster center to obtain a wind turbine group partition result.

[0022] In one embodiment, optionally, according to the wind turbine group zoning result, the capacity and site selection result of each offshore substation is calculated by an improved center of gravity method, including:

[0023] For each wind turbine group partition, the capacity of each wind turbine group is taken as part of the overall mass of the wind turbine group partition, the center of gravity corresponding to the wind turbine group partition is calculated, and the center of gravity is determined as the capacity and site selection result of the offshore substation, wherein each wind turbine group partition corresponds to an offshore substation.

[0024] In one embodiment, optionally, the objective function includes an economic objective function and a reliability objective function;

[0025] Among them, the economic objective function C 1 include:

[0026]

[0027] Among them, c partition represents the total cost of large-scale partition planning, C m represents the total cost of the small partition, C cs represents the cost of offshore terminal, C cs,cable represents the total cost of offshore cable aggregation, C ca,cable,loss represents the loss of the offshore cable, C connect,cable Represents the cost of connecting submarine cables between offshore aggregation stations;

[0028]

[0029] c cab =w i d i h i

[0030]

[0031] e dis =1-1 / (1+i loss ) q

[0032] c cs,cable =d station h i P ts

[0033] Among them, c ts represents the cost of the offshore substation, m represents the partition number, w i represents the capacity of wind turbine, d i Indicates the distance from each wind turbine to the offshore booster station, h i P represents the cost of submarine cable per unit distance. cable,loss Indicates the active power loss of the partitioned submarine cable, U cable Indicates the voltage level of the collector system, R cable Represents the unit distance impedance of the line, i energy,price represents the on-grid electricity price of offshore wind power projects; i loss represents the discount rate; q represents the average life of the wind turbine, d station Indicates the distance from the offshore booster station to the offshore collection station, P ts It indicates the capacity of offshore booster station in the sub-area;

[0034] Among them, the reliability objective function C 2 include:

[0035] min C 2 =(T fau +Tfix )gi energy,price qs

[0036] Among them, T fau It represents the time from the occurrence of a fault to the detection of the fault; T fix represents the time taken to repair the offshore wind power base from onshore; g represents the amount of electricity generated by the offshore wind power transmission system per unit time; i energy,price represents the on-grid electricity price of offshore wind power projects; q represents the average life of wind turbines; s represents the annual failure rate of different wind turbines;

[0037] The constraint conditions include: rated current carrying capacity constraint of submarine cable, output constraint of wind turbine generator set, phase angle safety constraint of wind turbine generator set equipment and voltage safety constraint of wind turbine generator set equipment.

[0038] In one embodiment, optionally, optimizing and solving the objective function according to the constraint condition to obtain an optimal offshore wind power transmission system planning scheme includes:

[0039] According to the constraints, the objective function is optimized using a quantum particle swarm optimization algorithm to obtain an optimal offshore wind power transmission system planning scheme.

[0040] In a second aspect, an embodiment of the present application provides an offshore wind power transmission system planning device, including:

[0041] A level determination module, used to determine the voltage level corresponding to the offshore wind power collection system;

[0042] A partitioning module is used to partition the wind turbines of the offshore wind power transmission system according to the voltage level by using an improved K-means method to obtain a partitioning result of the wind turbines;

[0043] A site selection module, used for calculating the capacity and site selection results of each offshore substation by using an improved center of gravity method according to the wind turbine group zoning results;

[0044] A planning module, used to construct an objective function corresponding to the offshore wind power transmission system planning scheme according to the wind turbine group zoning results and the capacity and site selection results of each offshore substation, and determine the constraint conditions corresponding to the objective function;

[0045] The solution module is used to optimize the objective function according to the constraint conditions to obtain the optimal offshore wind power transmission system planning scheme.

[0046] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned offshore wind power transmission system planning method when executing the computer program.

[0047] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned offshore wind power transmission system planning method are implemented.

[0048] In the scheme implemented by the above offshore wind power transmission system planning method, device, system, equipment and storage medium, the voltage level corresponding to the offshore wind power collection system is determined; according to the voltage level, the wind turbine group is partitioned by the improved K-means method to obtain the wind turbine group partitioning result; according to the wind turbine group partitioning result, the capacity and site selection results of each offshore substation are calculated by the improved center of gravity method; according to the wind turbine group partitioning result and the capacity and site selection results of each offshore substation, the objective function corresponding to the offshore wind power transmission system planning scheme is constructed, and the constraints corresponding to the objective function are determined; according to the constraints, the objective function is optimized to obtain the optimal offshore wind power transmission system planning scheme. In the present invention, zoning and site selection can reduce construction costs, ensure efficient use of wind power, and prevent the occurrence of wind abandonment. The collection system planning ensures that the wind energy generated by the offshore wind turbine generator set can be safely and reliably transmitted to the onshore power grid to meet the growing power demand in coastal areas.

Brief Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A schematic flow chart of an offshore wind power transmission system planning method according to an embodiment of the present application is shown.

[0051] Figure 2 A schematic diagram of the partitioning of an offshore wind power transmission system according to an embodiment of the present application is shown.

[0052] Figure 3 A schematic flow chart of step S102 in a method for planning an offshore wind power transmission system according to an embodiment of the present application is shown.

[0053] Figure 4 A topological diagram of a power collection system with a voltage level of 35 kV according to an embodiment of the present application is shown.

[0054] Figure 5 A topological diagram of a power collection system with a voltage level of 66 kV according to an embodiment of the present application is shown.

[0055] Figure 6 A flow chart of a quantum particle swarm algorithm according to an embodiment of the present application is shown.

[0056] Figure 7 A block diagram of an offshore wind power transmission system planning device according to an embodiment of the present application is shown.

[0057] Figure 8 A schematic structural diagram of a computer device according to an embodiment of the present application is shown. [Specific implementation method]

[0058] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0059] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0060] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0061] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0062] See also Figure 1 , Figure 1 A schematic flow chart of an offshore wind power transmission system planning method according to an embodiment of the present application is shown. The offshore wind power transmission system planning method is used to solve technical problems such as slow speed and low efficiency of integrated setting calculation of power grid in related technologies.

[0063] like Figure 1 As shown, according to an embodiment of the present application, a method for planning an offshore wind power transmission system includes:

[0064] Step S101, determining the voltage level corresponding to the offshore wind power collection system;

[0065] In one embodiment, optionally, the voltage level includes a first voltage level and a second voltage level, the voltage of the first voltage level is lower than the voltage of the second voltage level, the wind turbine group zoning results corresponding to the first voltage level include large zoning results and small zoning results, the wind turbine group zoning results corresponding to the second voltage level include small zoning results, the offshore substations corresponding to the first voltage level include offshore boost stations and offshore collection stations, and the offshore substations corresponding to the second voltage level include offshore collection stations.

[0066] In a specific embodiment, the first voltage level may be 35kV, and the second voltage level may be 66kV. The voltage level of the offshore wind power base collection system affects the capacity and site selection results of the sub-areas, booster stations and collection stations. When the voltage level of the offshore wind power base collection system is 35kV, the offshore wind turbines are connected to the 110kV or 220kV offshore booster station through a 35kV AC submarine cable, and then sent to the offshore collection station through the submarine cable. After boosting and conversion, they are sent out through a DC submarine cable with a voltage level of ±320kV, ±400kV or ±525kV; when the voltage level of the collection system is 66kV, the offshore wind turbines are directly connected to the offshore collection station through a 66kV AC submarine cable, and after boosting and conversion, they are sent to the onshore power grid through a DC submarine cable. There is no need to set up an offshore booster station, and only the site selection of the offshore collection station needs to be completed based on the large sub-areas.

[0067] By comparing the technical and economic benefits of voltage levels of 35kV and 66kV, the investment in switchgear and transformers in the scheme with a voltage level of 66kV has increased, but the construction cost of the submarine cable has been significantly reduced. Considering that this design method is suitable for large-scale offshore wind power bases of tens of millions of kilowatts, the length of the submarine cable with the same transmission capacity of 66kV is much shorter than that of 35kV, and the supporting electrical equipment is also less, so the collection system with a voltage level of 66kV is preferred. Then conduct a regional characteristic analysis, combine the capacity to be planned for the actual project and the distance between wind farms, set the upper limit of the regional capacity of the preliminary estimate, and select a preliminary plan for the voltage level of the collection system.

[0068] When the capacity of a single area of ​​an offshore wind power base is large, if a 35kV AC collection cable is used, the number of wind turbines connected is limited, and more cables will be needed to collect electricity, which will greatly increase the construction cost of the cable. In this case, choosing a 66kV collection system will have better economic benefits. Therefore, before clarifying the zoning and site selection planning results, the voltage level of the collection system must be clarified first.

[0069] Step S102, partitioning the wind turbines in the offshore wind power transmission system using an improved K-means method according to the voltage level to obtain a wind turbine partitioning result;

[0070] In this step, the offshore wind power base is first divided into zones, and this zone is defined as a large zone, such as Figure 2 As shown in the figure, the offshore wind power base is divided into five large zones: I, II, III, IV, and V; the large zones are further divided and defined as small zones. For example, large zone II is further divided into two small zones: II-1 and II-2.

[0071] like Figure 3 As shown, in one embodiment, optionally, step S102 includes:

[0072] Step S301, determining the number of partitions corresponding to the wind turbine generator set by using the elbow method according to the voltage level and the upper limit of the offshore substation capacity;

[0073] In this step, in the zoning planning of offshore wind power bases, different number of partitions k will lead to different partition capacities. This paper uses the elbow method to obtain the number of partitions k. The core of the elbow method is the sum of squared errors (SSE), which represents the clustering error of all samples. The size of the clustering error reflects the quality of the clustering effect.

[0074]

[0075] Among them, V i is the i-th cluster, p is the point in the cluster, r i ri is the center of mass, V i Reflects the mean of all samples in the cluster. As the k value increases, the compactness between each cluster will increase, and the SSE value will decrease. When a point appears in the elbow graph where the slope changes significantly, and the slope becomes significantly smaller after this point, the number corresponding to this point is the optimal number of partitions.

[0076] Step S302: partition the wind turbine group using an improved K-means method according to the number of partitions to obtain a wind turbine group partition result.

[0077] In one embodiment, optionally, step S302 includes:

[0078] Step S3021, obtaining the position coordinates of each wind turbine in the offshore wind power transmission system;

[0079] Step S3022, selecting the reference coordinates of the wind turbines in the offshore wind power transmission system;

[0080] Step S3023, calculating the distance value between the position coordinates of each wind turbine generator set and the reference coordinates;

[0081] Step S3024, calculating the average distance value from the reference coordinate to the position coordinate of each wind turbine generator set according to the distance value;

[0082] Step S3025, sorting all wind turbines in descending order according to the average distance values;

[0083] Step S3026, selecting a corresponding number of wind turbines ranked first as cluster centers according to the number of partitions;

[0084] Step S3027: partition the wind turbine group according to the cluster center to obtain a wind turbine group partition result.

[0085] In this embodiment, the K-means algorithm is an unsupervised learning algorithm, which obtains the partitioning result based on the micro-site selection of offshore generators and the minimum Euclidean distance between wind turbines. The selection of the initial cluster center affects the final clustering result. The traditional K-means clustering algorithm randomly selects the initial cluster center point, which is prone to the problem of poor partitioning results due to the initial cluster center being selected too close. Selecting the initial cluster center based on the maximum distance to improve the clustering effect can avoid the problem of poor partitioning effect caused by the proximity of cluster centers.

[0086] Assume that there are l wind turbines in an area, and the coordinates of these wind turbines are (a i ,b i ), select (a o ,b o ) is the reference coordinate, where o,i=1,2,...,l, and the distance between two wind turbines is e i For the selection of the reference coordinates, first calculate the average distance from it to each wind turbine:

[0087]

[0088] Will Sort in descending order, take the first 2k coordinate points, calculate the average distance between the selected points again, and select the first k coordinate points as cluster centers. After determining the cluster center, the offshore wind power base is partitioned by improving the K-means method.

[0089] Step S103, according to the wind turbine group zoning result, the capacity and site selection result of each offshore substation are calculated by using an improved centroid method;

[0090] In one embodiment, optionally, step S103 includes:

[0091] For each wind turbine group partition, the capacity of each wind turbine group is taken as part of the overall mass of the wind turbine group partition, the center of gravity corresponding to the wind turbine group partition is calculated, and the center of gravity is determined as the capacity and site selection result of the offshore substation, wherein each wind turbine group partition corresponds to an offshore substation.

[0092] Assume that there are n wind turbines in the offshore wind power base, and the coordinates of each wind turbine are (x i ,y i ), (i∈1,2,...,n), the initial offshore booster station is located at (x 0 ,y 0 ), where the distance between each wind turbine and the offshore booster station is d i ; The total cost of the partitioned submarine cable is c cab ; The cost of the submarine cable per unit distance is h i ; There are offshore wind turbines with different capacities in the same offshore wind power base area, and the capacity of each offshore wind turbine is w i .

[0093] c cab =w i d i h i

[0094] c cab =w i d i h i

[0095] Active power loss of zoned submarine cable P cable,loss for

[0096]

[0097] Among them, U cable is the voltage level of the collector system, R cable is the unit distance impedance of the line. The active power loss cost of the submarine cable of the power collection system is c cable,loss .

[0098]

[0099] Among them, e dis =1-1 / (1+i loss ) q ;i energy,price represents the on-grid electricity price of offshore wind power projects; i loss is the discount rate; q is the average life of the offshore wind turbine.

[0100] Therefore, the total cost c of the partition can be obtained m for

[0101]

[0102] Among them, c ts is the cost of the offshore substation, m represents the partition number, m∈1,2,...,n. Since the distance between the offshore wind turbine and the offshore substation determines the i , when calculating the total cost of a partition, substitute the above formula into the following formula, and we get:

[0103]

[0104] Finding the location of the offshore substation with the minimum total cost is equivalent to calculating the function c m (x 0, y 0 ), find the extreme value problem. According to the function extreme value principle, find the partial derivatives of the above formula respectively, and make the partial derivatives equal to 0, and we get:

[0105]

[0106] Where z = i energy,price e loss / i loss R cable .

[0107] The result of the kth iteration is

[0108]

[0109] in, If H k <H k-1 , indicating that the total cost of the partition can still be optimized; otherwise (x k-1 *,y k-1 *) is the best location for the offshore booster station in this sub-area.

[0110] If the voltage level of the power collection system is 35kV, each sub-division should include an offshore substation, and the substations in each sub-division will be collected and sent out at the offshore collection station of the corresponding large division; if the voltage level is 66kV, the offshore wind turbines can be directly connected to the offshore collection station for unified transmission. The topology of the power collection system of 35kV and 66kV voltage levels is as follows: Figure 4 and Figure 5 shown.

[0111] The total cost function for the location of the offshore terminal is:

[0112] c cs,cable =d station h i P ts

[0113] Among them, c cs,cable is the total cost of the submarine cable during the offshore aggregation process, dstation P is the distance from the offshore booster station to the offshore collection station, ts It is the capacity of offshore substation in small subarea.

[0114] The above formula is used to perform an improved centroid method to calculate the capacity and site selection results of the offshore aggregation station. This algorithm can take into account the differences in the capacity of each booster station and make the capacity and site selection results of the offshore aggregation station closer to the large-capacity booster station, reducing line losses and improving the economy of the planning results.

[0115] Total cost of large-scale zoning planning partition For economical

[0116]

[0117] Among them, c cs is the cost of the offshore terminal, c cs,cable,loss is the loss of the submarine cable at sea, c connect,cable To collect the cost of submarine cables connecting stations.

[0118] By increasing the number of connecting submarine cables between offshore gathering stations, when a failure occurs in any offshore gathering station, the electric energy can be sent to another nearby gathering station through the connecting submarine cables, thereby improving reliability and reducing failure losses.

[0119] Step S104, constructing an objective function corresponding to the offshore wind power transmission system planning scheme according to the wind turbine group zoning results and the capacity and site selection results of each offshore substation, and determining constraint conditions corresponding to the objective function;

[0120] The main indicator considered in planning is economic efficiency, that is, the total cost of large-scale zoning planning c partition , its objective function C 1 The calculation formula is as follows:

[0121]

[0122] Among them, C m is the total cost of the partition, C cs is the cost of the offshore terminal, C cs,cable is the total cost of the submarine cable during the offshore aggregation process, C ca,cable,loss is the loss of the submarine cable at sea, C connect,cable The cost of connecting submarine cables between aggregation stations.

[0123] The reliability index of the power collection system can be converted into an economic form through the relationship between electricity price and failure and repair time. Its objective function C 2 The calculation formula is as follows:

[0124] minC 2 =(T fau +T fix)gi energy,price qs

[0125] Among them, T fau T is the time from the occurrence of a fault to the detection of the fault; fix is the time taken to repair the offshore wind power base from onshore; g is the amount of electricity generated by the offshore wind power base per unit time; i energy,price is the on-grid electricity price of offshore wind power projects; q is the average life of wind turbines; s is the annual failure rate of different wind turbines. The offshore wind power base is far from the shore and the journey takes a long time. The fault repair time of the offshore wind power base should include the total journey time from the shore to the offshore wind power base.

[0126] The constraints for planning the collection system of offshore wind power bases are the rated current carrying capacity of the collection system's submarine cable, the output of wind turbines, the phase angle of wind turbine equipment, and voltage safety:

[0127] (1) Constraints on rated current carrying capacity of submarine cables

[0128] The current carrying capacity of a submarine cable is determined by its cross-sectional area, where the size of the submarine cable is determined by the standardized set G s,cable The rated power of the submarine cable is obtained from the set G P,cable Obtained

[0129] S∈G P,cable

[0130] P cable ∈G P,cable

[0131] (2) Output constraints of offshore wind turbines

[0132] The active and reactive output of offshore wind turbines meet the following constraints

[0133]

[0134] Among them, P wind , Q wind Respectively represent the reactive power and active power output by the wind turbine; P wind,min , P wind,max Respectively represent the minimum and maximum output of offshore wind turbines; tanα wind,min , tanα wind,max Respectively represent the minimum and maximum power factor angles of offshore wind turbines.

[0135] (3) Phase angle safety constraints of offshore wind turbine equipment

[0136]

[0137] Among them, U v,min , U v,maxRespectively represent the lower and upper limits of the voltage amplitude on the AC or DC side; θ ij,min ,θ ij,max Represent the lower limit and upper limit of the voltage phase angle difference between nodes i and j respectively.

[0138] (4) Voltage safety constraints for offshore wind turbine equipment

[0139]

[0140] Among them, P TS,min , P TS,max Respectively represent the minimum and maximum capacity of the offshore converter station; P CS,min , P CS,max They represent the minimum and maximum capacities of offshore collection stations respectively.

[0141] Step S105, optimizing and solving the objective function according to the constraint conditions to obtain an optimal offshore wind power transmission system planning scheme.

[0142] In one embodiment, optionally, step S105 includes:

[0143] According to the constraints, the objective function is optimized using a quantum particle swarm optimization algorithm to obtain an optimal offshore wind power transmission system planning scheme.

[0144] like Figure 6 As shown in Figure 2, the solution process of the quantum particle swarm optimization algorithm includes:

[0145] 1) First, set the parameters;

[0146] 2) Randomly generate population;

[0147] 3) Initialization of particle position;

[0148] 4) Solution space transformation;

[0149] 5) Calculate fitness;

[0150] 6) Determine whether the constraint condition is met. If the result is yes, proceed to the next step; otherwise, return to 2);

[0151] 7) Fitness statistics;

[0152] 8) Realize particle state update;

[0153] 9) Probabilistic variation;

[0154] 10) Construct the Pareto optimal set using the freewheeling interval method;

[0155] 11) Determine whether the convergence condition is met. If yes, proceed to the next step; otherwise, return to 8);

[0156] 12) Obtain the global optimal solution.

[0157] Through the above technical solutions, zoning and site selection can reduce construction costs, ensure efficient use of wind power, and prevent the occurrence of wind abandonment. The planning of the collection system ensures that the wind energy generated by offshore wind turbines can be safely and reliably transmitted to the onshore power grid to meet the growing power demand in coastal areas. At the same time, the topology of the offshore wind power collection system can be reasonably optimized and the cable length of the collection system can be reduced, which can better play the economic and reliability of the collection system planning scheme to achieve efficient collection and transmission of wind power.

[0158] In addition, the present application uses a quantum particle swarm optimization algorithm to improve planning results. The quantum-behaved PSO algorithm (QPSO) is an improvement on the classical particle swarm algorithm (PSO algorithm). It mainly combines the idea of ​​quantum physics to update the method of particle position. When updating the particle position, it focuses on the current local optimal position information and global optimal position information of each particle. The algorithm has a faster convergence speed, can overcome the troubles of local optimal solutions, and can find the global optimal solution faster. When it is used to deal with planning problems, it can take into account multiple goals, help to obtain more reasonable results, and provide a new planning method for practical engineering.

[0159] The above technical solution can be applied to various large-scale offshore wind power transmission, improving the utilization of offshore wind power resources and wind power absorption capacity.

[0160] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0161] Figure 7 A block diagram of an offshore wind power transmission system planning device according to an embodiment of the present application is shown.

[0162] like Figure 7 As shown, in a second aspect, an embodiment of the present application provides an offshore wind power transmission system planning device 70, comprising:

[0163] The level determination module 71 is used to determine the voltage level corresponding to the offshore wind power transmission system;

[0164] A partitioning module 72 is used to partition the wind turbines of the offshore wind power transmission system according to the voltage level by using an improved K-means method to obtain a partitioning result of the wind turbines;

[0165] A site selection module 73, configured to calculate the capacity and site selection results of each offshore substation by using an improved centroid method according to the wind turbine group zoning results;

[0166] A planning module 74 is used to construct an objective function corresponding to the offshore wind power transmission system planning scheme according to the wind turbine group zoning results and the capacity and site selection results of each offshore substation, and determine the constraint conditions corresponding to the objective function;

[0167] The solution module 75 is used to optimize the objective function according to the constraint conditions to obtain an optimal offshore wind power transmission system planning scheme.

[0168] In one embodiment, optionally, the voltage level includes a first voltage level and a second voltage level, the voltage of the first voltage level is lower than the voltage of the second voltage level, the wind turbine group zoning results corresponding to the first voltage level include large zoning results and small zoning results, the wind turbine group zoning results corresponding to the second voltage level include small zoning results, the offshore substations corresponding to the first voltage level include offshore boost stations and offshore collection stations, and the offshore substations corresponding to the second voltage level include offshore collection stations.

[0169] In one embodiment, optionally, the partition module includes:

[0170] A quantity determination unit, used to determine the number of partitions corresponding to the wind turbine generator set by an elbow method according to the voltage level and the upper limit of the offshore substation capacity;

[0171] The partitioning unit is used to partition the wind turbine group according to the number of partitions by using an improved K-means method to obtain a wind turbine group partitioning result.

[0172] In one embodiment, optionally, the partition unit is used to:

[0173] Obtaining the position coordinates of each wind turbine in the offshore wind power transmission system;

[0174] Selecting the reference coordinates of the wind turbine generator set in the offshore wind power transmission system;

[0175] Calculating the distance between the position coordinates of each wind turbine generator set and the reference coordinates;

[0176] According to the distance values, calculating the average distance value from the reference coordinate to the position coordinate of each wind turbine generator set;

[0177] Sorting all wind turbines in descending order according to the average distance value;

[0178] According to the number of partitions, selecting a corresponding number of wind turbines ranked first as cluster centers;

[0179] The wind turbine group is partitioned according to the cluster center to obtain a wind turbine group partition result.

[0180] In one embodiment, optionally, the site selection module is used to:

[0181] For each wind turbine group partition, the capacity of each wind turbine group is taken as part of the overall mass of the wind turbine group partition, the center of gravity corresponding to the wind turbine group partition is calculated, and the center of gravity is determined as the capacity and site selection result of the offshore substation, wherein each wind turbine group partition corresponds to an offshore substation.

[0182] In one embodiment, optionally, the objective function includes an economic objective function and a reliability objective function;

[0183] Among them, the economic objective function C 1 include:

[0184]

[0185] Among them, c partition represents the total cost of large-scale partition planning, C m represents the total cost of the small partition, C cs represents the cost of offshore terminal, C cs,cable represents the total cost of offshore cable aggregation, C ca,cable,loss represents the loss of the offshore cable, C connect,cable Represents the cost of connecting submarine cables between offshore aggregation stations;

[0186]

[0187] e dis =1-1 / (1+i loss ) q

[0188] c cs,cable =d station h i P ts

[0189] Among them, c ts represents the cost of the offshore substation, m represents the partition number, w i represents the capacity of wind turbine, d i Indicates the distance from each wind turbine to the offshore booster station, h i P represents the cost of submarine cable per unit distance. cable,loss Indicates the active power loss of the partitioned submarine cable, U cable Indicates the voltage level of the collector system, R cable Represents the unit distance impedance of the line, i energy,pricerepresents the on-grid electricity price of offshore wind power projects; i loss represents the discount rate; q represents the average life of the wind turbine, d station Indicates the distance from the offshore booster station to the offshore collection station, P ts It indicates the capacity of offshore booster station in the sub-area;

[0190] Among them, the reliability objective function C 2 include:

[0191] minC 2 =(T fau +T fix )gi energy,price qs

[0192] Among them, T fau It represents the time from the occurrence of a fault to the detection of the fault; T fix represents the time taken to repair the offshore wind power base from onshore; g represents the amount of electricity generated by the offshore wind power transmission system per unit time; i energy,price represents the on-grid electricity price of offshore wind power projects; q represents the average life of wind turbines; s represents the annual failure rate of different wind turbines;

[0193] The constraint conditions include: rated current carrying capacity constraint of submarine cable, output constraint of wind turbine generator set, phase angle safety constraint of wind turbine generator set equipment and voltage safety constraint of wind turbine generator set equipment.

[0194] In one embodiment, optionally, the solution module is used to:

[0195] According to the constraint conditions, the objective function is optimized using a quantum particle swarm optimization algorithm to obtain an optimal wind power planning solution.

[0196] For the specific definition of the offshore wind power transmission system planning device, please refer to the definition of the offshore wind power transmission system planning method above, which will not be repeated here. Each module in the above-mentioned offshore wind power transmission system planning device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0197] In one embodiment, a computer device is provided. The computer device may be a client or a server. The internal structure diagram thereof may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the client side of a method for planning an offshore wind power transmission system.

[0198] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0199] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method described in the first aspect of the embodiment is implemented when the processor executes the computer program.

[0200] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or electronic device can refer to the relevant description in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0201] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0202] It should be understood that, although the terms first, second, etc. may be used to describe the setting unit in the embodiments of the present application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of the present application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0203] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0204] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0205] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0206] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0207] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for planning an offshore wind power transmission system, characterized in that: include: Determine the voltage level corresponding to the offshore wind power collection system; According to the voltage level, the wind turbines of the offshore wind power transmission system are partitioned by using an improved K-means method to obtain a wind turbine partitioning result; According to the wind turbine group zoning results, the capacity and site selection results of each offshore substation are calculated by using the improved center of gravity method; According to the wind turbine group zoning results and the capacity and site selection results of each offshore substation, construct an objective function corresponding to the offshore wind power transmission system planning scheme, and determine the constraint conditions corresponding to the objective function; According to the constraint conditions, the objective function is optimized to obtain an optimal offshore wind power transmission system planning scheme.

2. The method according to claim 1, characterized in that The voltage level includes a first voltage level and a second voltage level, the voltage of the first voltage level is lower than the voltage of the second voltage level, the wind turbine group zoning results corresponding to the first voltage level include large zoning results and small zoning results, the wind turbine group zoning results corresponding to the second voltage level include small zoning results, the offshore substations corresponding to the first voltage level include offshore boost stations and offshore collection stations, and the offshore substations corresponding to the second voltage level include offshore collection stations.

3. The method according to claim 1, characterized in that According to the voltage level, the wind turbines in the offshore wind power transmission system are partitioned using the improved K-means method to obtain the wind turbine partition results, including: According to the voltage level and the upper limit of the offshore substation capacity, the number of partitions corresponding to the wind turbine generator set is determined by the elbow method; According to the number of partitions, the wind turbine group is partitioned by using an improved K-means method to obtain a wind turbine group partition result.

4. The method according to claim 3, characterized in that The wind turbine group is partitioned by using an improved K-means method according to the number of partitions to obtain a wind turbine group partition result, including: Obtaining the position coordinates of each wind turbine in the offshore wind power transmission system; Selecting the reference coordinates of the wind turbine generator set in the offshore wind power transmission system; Calculating the distance between the position coordinates of each wind turbine generator set and the reference coordinates; According to the distance values, calculating the average distance value from the reference coordinate to the position coordinate of each wind turbine generator set; Sorting all wind turbines in descending order according to the average distance value; According to the number of partitions, selecting a corresponding number of wind turbines ranked first as cluster centers; The wind turbine group is partitioned according to the cluster center to obtain a wind turbine group partition result.

5. The method according to claim 1, characterized in that According to the wind turbine group zoning results, the capacity and site selection results of each offshore substation are calculated by using the improved center of gravity method, including: For each wind turbine group partition, the capacity of each wind turbine group is taken as part of the overall mass of the wind turbine group partition, the center of gravity corresponding to the wind turbine group partition is calculated, and the center of gravity is determined as the capacity and site selection result of the offshore substation, wherein each wind turbine group partition corresponds to an offshore substation.

6. The method according to claim 1, characterized in that The objective function includes an economic objective function and a reliability objective function; Wherein, the economic objective function C1 includes: Among them, c partition represents the total cost of large-scale partition planning, C m represents the total cost of the small partition, C cs represents the cost of offshore terminal, C cs,cable represents the total cost of offshore cable aggregation, C ca,cable,loss represents the loss of the offshore cable, C connect,cable Represents the cost of connecting submarine cables between offshore aggregation stations; c cab =w i d i h i yes dis =1-1 / (1+i loss ) q c cs,cable =d station h i P ts Among them, c ts represents the cost of the offshore substation, m represents the partition number, w i represents the capacity of wind turbine, d i Indicates the distance from each wind turbine to the offshore booster station, h i P represents the cost of submarine cable per unit distance. cable,loss Indicates the active power loss of the partitioned submarine cable, U cable Indicates the voltage level of the collector system, R cable Represents the unit distance impedance of the line, i energy,price represents the on-grid electricity price of offshore wind power projects; i loss represents the discount rate; q represents the average life of the wind turbine, d station Indicates the distance from the offshore booster station to the offshore collection station, P ts It indicates the capacity of offshore booster station in the sub-area; Wherein, the reliability objective function C2 includes: <h2 style=";text-align:left;direction:ltr">minC2=(T<h2 style=";text-align:left;direction:ltr"> fau <h2 style=";text-align:left;direction:ltr"> +T<h2 style=";text-align:left;direction:ltr"> fix <h2 style=";text-align:left;direction:ltr"> )gi<h2 style=";text-align:left;direction:ltr"> energy,price <h2 style=";text-align:left;direction:ltr"> qs Among them, T fau It represents the time from the occurrence of a fault to the detection of the fault; T fix represents the time taken to repair the offshore wind power base from onshore; g represents the amount of electricity generated by the offshore wind power transmission system per unit time; i energy,price represents the on-grid electricity price of offshore wind power projects; q represents the average life of wind turbines; s represents the annual failure rate of different wind turbines; The constraint conditions include: rated current carrying capacity constraint of submarine cable, output constraint of wind turbine generator set, phase angle safety constraint of wind turbine generator set equipment and voltage safety constraint of wind turbine generator set equipment.

7. The method according to claim 1, characterized in that According to the constraint conditions, the objective function is optimized to obtain an optimal offshore wind power transmission system planning scheme, including: According to the constraints, the objective function is optimized using a quantum particle swarm optimization algorithm to obtain an optimal offshore wind power transmission system planning scheme.

8. An offshore wind power transmission system planning device, characterized in that: include: A level determination module, used to determine the voltage level corresponding to the offshore wind power collection system; A partitioning module is used to partition the wind turbines of the offshore wind power transmission system according to the voltage level by using an improved K-means method to obtain a partitioning result of the wind turbines; A site selection module, used for calculating the capacity and site selection results of each offshore substation by using an improved center of gravity method according to the wind turbine group zoning results; A planning module, for constructing an objective function corresponding to an offshore wind power transmission system planning scheme according to the wind turbine group zoning results and the capacity and site selection results of each offshore substation, and determining constraint conditions corresponding to the objective function; A solution module is used to optimize and solve the objective function according to the constraint conditions to obtain an optimal offshore wind power transmission system planning scheme.

9. A computer device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, wherein the instructions are configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer executable instructions are stored, and the computer executable instructions are used to execute the method according to any one of claims 1 to 7.

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