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

Abstract

The application relates to the technical field of offshore wind power transmission system planning, and provides an offshore wind power transmission system planning method, device, equipment and storage medium, wherein the method comprises the following steps: determining a voltage level corresponding to an offshore wind power collection system; according to the voltage level, adopting an improved K-means method to divide wind power generators, so as to obtain a wind power generator division result; according to the wind power generator division result, calculating the capacity and site selection result of each offshore transformer substation by means of an improved gravity center method; according to the wind power generator division result and the capacity and site selection result of each offshore transformer substation, constructing a target function corresponding to an offshore wind power transmission system planning scheme, and determining a constraint condition corresponding to the target function; and according to the constraint condition, performing optimal solution on the target function, so as to obtain an optimal offshore wind power transmission system planning scheme. Through the technical scheme, the construction cost is reduced, the efficient utilization of wind power is ensured, and the occurrence of 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 particularly relates to an offshore wind power transmission system planning method, device, equipment and storage medium. BACKGROUND

[0002] In recent years, with the rapid development of offshore wind power, offshore wind power bases develop in a large-scale and deep-sea trend, and the capacity of a single wind turbine breaks through 10 MW or more, which puts forward higher requirements for offshore wind power transmission system planning and design. As a key link connecting offshore wind turbine generators and power transmission systems, the planning and design of the power collection system is directly related to the stable transmission and efficient distribution of wind power. However, in the related art, there is a lack of partition, site selection and planning scheme for offshore wind power bases. SUMMARY

[0003] Embodiments of the present application provide an offshore wind power transmission system planning method, device, equipment and storage medium, aiming at solving the technical problems in the related art.

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

[0005] determining a voltage level corresponding to an offshore wind power collection system;

[0006] According to the voltage level, the wind turbine generators of the offshore wind power transmission system are partitioned by using an improved K-means method to obtain wind turbine generator partition results;

[0007] According to the wind turbine generator partition results, the capacity and site selection results of each offshore substation are calculated by using an improved gravity center method;

[0008] According to the wind turbine generator partition results and the capacity and site selection results of each offshore substation, a target function corresponding to an offshore wind power transmission system planning scheme is constructed, and a constraint condition corresponding to the target function is determined;

[0009] According to the constraint condition, the target function is optimized and solved to obtain an optimal offshore wind power transmission system planning scheme.

[0010] In an embodiment, optionally, the voltage level includes a first voltage level and a second voltage level, the voltage of the first voltage level is less than the voltage of the second voltage level, the wind turbine generator partition results corresponding to the first voltage level include large partition results and small partition results, the wind turbine generator partition results corresponding to the second voltage level include small partition results, the offshore substations corresponding to the first voltage level include offshore booster stations and offshore collection stations, and the offshore substations corresponding to the second voltage level include offshore collection stations.

[0011] In an embodiment, optionally, according to the voltage level, the wind turbines of the offshore wind power transmission system are partitioned by using the improved K-means method 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 turbines is determined by using the elbow method;

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

[0014] In an embodiment, optionally, according to the number of partitions, the wind turbines are partitioned by using the improved K-means method to obtain a wind turbine partition result, including:

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

[0016] Select a reference coordinate of the wind turbines in the offshore wind power transmission system;

[0017] Calculate the distance value between the position coordinates of each wind turbine and the reference coordinate;

[0018] According to the distance value, calculate the average distance value from the reference coordinate to the position coordinates of each wind turbine;

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

[0020] According to the number of partitions, select a corresponding number of wind turbines in the front as cluster centers;

[0021] According to the cluster centers, partition the wind turbines to obtain a wind turbine partition result.

[0022] In an embodiment, optionally, according to the wind turbine partition result, the capacity and site selection result of each offshore substation are calculated by using the improved barycenter method, including:

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

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

[0025] The economic objective function C1 includes:

[0026]

[0027] wherein c partition represents the total cost of large partition planning, C m represents the total cost of small partition, C cs represents the cost of offshore collection station, C cs,cable represents the total cost of offshore collection submarine cable, C ca,cable,loss represents the loss of offshore collection submarine cable, C connect,cable represents the cost of connecting submarine cable between offshore collection 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] wherein c ts represents the cost of offshore booster station, m represents the partition number, w i represents the wind turbine capacity, d i represents the distance from each wind turbine to offshore booster station, h i represents the unit distance submarine cable cost, P cable,loss represents the partition submarine cable active power loss, U cable represents the voltage level of power collection system, R cable represents the unit distance impedance of line, i energy,price represents the on-grid price of offshore wind power project; i loss represents the discount rate; q represents the average life of wind turbine, d station represents the distance from offshore booster station to offshore collection station, P ts represents the offshore booster station capacity in small partition;

[0034] wherein the reliability objective function C2 includes:

[0035] min C2 = (T fau + T fix )gi energy,price qs

[0036] wherein, T fau represents the time spent from failure to detection; T fix represents the time spent from land to offshore wind farm repair; g represents the amount of electricity generated by the offshore wind power transmission system per unit time; i energy,price represents the on-grid price of the offshore wind power project; q represents the average service life of the wind turbine; s represents the annual failure rate of different wind turbines;

[0037] The constraint conditions include: a submarine cable rated current flow constraint, a wind turbine output constraint, a wind turbine device phase angle safety constraint, and a wind turbine device voltage safety constraint.

[0038] In an embodiment, optionally, the optimal offshore wind power transmission system planning scheme is obtained by optimizing and solving the target function according to the constraint conditions.

[0039] The optimal offshore wind power transmission system planning scheme is obtained by optimizing and solving the target function according to the constraint conditions using a quantum particle swarm optimization algorithm.

[0040] In a second aspect, the embodiments of the present application provide an offshore wind power transmission system planning device, which comprises:

[0041] A grade determination module is configured to determine a voltage grade corresponding to the offshore wind power collection system.

[0042] A partition module is configured to partition wind turbines of the offshore wind power transmission system according to the voltage grade using an improved K-means method to obtain a wind turbine partition result.

[0043] A site selection module is configured to calculate capacities and site selection results of each offshore substation by using an improved gravity center method according to the wind turbine partition result.

[0044] A planning module is configured to construct a target function corresponding to an offshore wind power transmission system planning scheme according to the wind turbine partition result and the capacities and site selection results of each offshore substation, and determine constraint conditions corresponding to the target function.

[0045] A solving module is configured to optimize and solve the target function according to the constraint conditions to obtain an optimal offshore wind power transmission system planning scheme.

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

[0047] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps of the offshore wind power transmission system planning method.

[0048] In the scheme realized by the 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; the wind turbine generators are partitioned by using the improved K-means method according to the voltage level, so as to obtain the wind turbine generator partition result; the capacity and site selection result of each offshore transformer substation are calculated by using the improved gravity center method according to the wind turbine generator partition result; the target function corresponding to the offshore wind power transmission system planning scheme is constructed according to the wind turbine generator partition result and the capacity and site selection result of each offshore transformer substation, and the constraint condition corresponding to the target function is determined; and the optimal offshore wind power transmission system planning scheme is obtained by optimizing and solving the target function according to the constraint condition. In the present application, the construction cost can be reduced by partitioning and site selection, the efficient use of wind power is ensured, and the occurrence of wind curtailment is prevented. The collection system planning ensures that the wind energy generated by the offshore wind turbine generators can be safely and reliably transmitted to the onshore power grid, and meets the increasing demand for electricity in the coastal areas. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[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 partitioning schematic diagram 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 the offshore wind power transmission system planning method according to an embodiment of the present application is shown.

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

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

[0055] Figure 6A 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 sending-out system planning device according to an embodiment of the present application is shown.

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

DETAILED DESCRIPTION

[0058] In order to better understand the technical solutions 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 some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall 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 the specific embodiments, and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0061] Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The embodiments described below and the features in the embodiments can be combined with each other as long as they do not conflict.

[0062] Please refer to Figure 1 , Figure 1 A schematic flow chart of an offshore wind power sending-out system planning method according to an embodiment of the present application is shown. The offshore wind power sending-out system planning method is used to solve the technical problems of slow and low efficiency of integrated setting calculation operation of power grid in the related art.

[0063] As shown in Figure 1 , according to the offshore wind power sending-out system planning method of an embodiment of the present application, the flow includes:

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

[0065] In one embodiment, the voltage levels include a first voltage level and a second voltage level, the voltage of the first voltage level is less than the voltage of the second voltage level, the wind turbine partitioning result corresponding to the first voltage level includes a large partitioning result and a small partitioning result, the wind turbine partitioning result corresponding to the second voltage level includes a small partitioning result, the offshore substation corresponding to the first voltage level includes an offshore booster station and an offshore collection station, and the offshore substation corresponding to the second voltage level includes an offshore collection station.

[0066] In one specific embodiment, the first voltage level can be 35 kV, and the second voltage level can be 66 kV. The voltage level of the offshore wind power base power collection system affects the capacity and site selection results of the partitioning, booster station and collection station. When the voltage level of the offshore wind power base power collection system is 35 kV, the offshore wind turbine is connected to the 110 kV or 220 kV offshore booster station through the 35 kV AC submarine cable, and then is sent to the offshore collection station through the submarine cable, is boosted and converted, and then is sent out through the ±320 kV, ±400 kV or ±525 kV DC submarine cable; when the voltage level of the power collection system is 66 kV, the offshore wind turbine is directly connected to the offshore collection station through the 66 kV AC submarine cable, is boosted and converted, and then is sent to the onshore power grid through the DC submarine cable, without the need to set up an offshore booster station, and only the offshore collection station site selection needs to be completed on the basis of a large partitioning.

[0067] By comparing the technical and economic performance of the voltage levels of 35 kV and 66 kV, the investment in switchgear and transformers is increased in the scheme of using the voltage level of 66 kV, but the construction cost of the submarine cable is significantly reduced. Considering that the design method is applicable to a large-scale offshore wind power base of tens of millions of kilowatts, the length of the 66 kV submarine cable with the same sending capacity is much less than that of the 35 kV submarine cable, and the supporting electrical equipment is also less, so the power collection system with the voltage level of 66 kV is preferentially selected. Then, by analyzing the regional characteristics, combining the actual capacity to be planned and the distance between wind farms, setting the upper limit value of the preliminary estimated regional capacity, and selecting the preliminary scheme of the voltage level of the power collection system.

[0068] When the capacity of a single zone of the offshore wind power base is large, if the voltage level of the AC power collection submarine cable is 35 kV, the number of wind turbines connected is limited, and more submarine cables will be needed to collect the power, which will greatly increase the construction cost of the submarine cable. In this case, the 66 kV power collection system will have better economic benefits. Therefore, before the partitioning and site selection planning results are determined, the voltage level of the power collection system needs to be determined first.

[0069] In step S102, according to the voltage level, the wind turbines of the offshore wind power sending-out system are partitioned by using the improved K-means method to obtain a wind turbine partitioning result.

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

[0071] As shown in Figure 3 In an embodiment, step S102 optionally includes:

[0072] Step S301, according to the voltage level and the upper limit of the offshore substation capacity, the elbow method is used to determine the number of partitions corresponding to the wind turbine;

[0073] In this step, in the offshore wind power base partition planning, different partition numbers k will result in different partition capacities. In this paper, the elbow method is used to obtain the partition number k. The core of the elbow method is the sum of the squared errors (SSE), which represents the clustering error of all samples, and the size of the clustering error reflects the pros and cons of the clustering effect.

[0074]

[0075] where V i is the i-th cluster, p is the point in the cluster, r i ri is the centroid, V i reflects the mean value of all samples in the cluster. With the increase of k value, the compactness between each cluster will increase, and the size of SSE value will decrease. When a point appears in the elbow diagram that makes the slope change significantly, and the slope becomes smaller after the point, the number corresponding to the point is the optimal partition number.

[0076] Step S302, according to the number of partitions, the improved K-means method is used to partition the wind turbine, and the wind turbine partition result is obtained.

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

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

[0079] Step S3022, selecting a reference coordinate of the wind turbine in the offshore wind power transmission system;

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

[0081] ​Step S3024, according to the distance value, the average distance value of the reference coordinate to the position coordinate of each wind turbine is calculated;

[0082] Step S3025, all wind turbines are sorted in descending order according to the average distance value;

[0083] Step S3026, according to the number of partitions, the corresponding number of wind turbines in the front of the sorting are selected as the clustering center;

[0084] Step S3027, according to the clustering center, the wind turbines are partitioned to obtain the wind turbine partition result.

[0085] In this embodiment, the K-means algorithm is an unsupervised learning algorithm, which obtains the partition result according to the offshore generator micro-siting and the minimum Euclidean distance between wind turbines. The selection of the initial clustering center affects the final clustering result. The traditional K-means clustering algorithm randomly selects the initial clustering center point, which is easy to cause the problem of poor partition result due to the too close selection of the initial clustering center. Selecting the initial clustering center based on the maximum distance to improve the clustering effect can avoid the problem of poor partition effect caused by the proximity of the clustering center.

[0086] Suppose a region has l wind turbines, and the coordinates of these wind turbines are (a i ,b i ), respectively, and (a o ,b o ) is selected as the reference coordinate, wherein o, i = 1, 2,..., l, and the distance between two wind turbines is e i . For the selection of the reference coordinate, first calculate the average distance between the reference coordinate and each wind turbine:

[0087]

[0088] 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 the clustering center point. After determining the clustering center, the offshore wind power base partition is completed by improving the K-means method.

[0089] Step S103, according to the wind turbine partition result, the capacity and site selection result of each offshore substation are calculated by improving the gravity method;

[0090] In one embodiment, step S103 includes:

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

[0092] Suppose that there are n wind turbines in the offshore wind power base, the coordinates of each wind turbine are (x i ,y i ), (i∈1,2,...,n), the initial selected offshore substation is located at (x0,y0), wherein the distance from each wind turbine to the offshore substation is d i ; the total cost of the partitioned submarine cable is c cab ; the unit distance submarine cable cost is h i ; there are offshore wind turbines with different capacities in the same partition of the offshore wind power base, 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] The active loss P cable,loss of the partitioned submarine cable is

[0096]

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

[0098]

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

[0100] Therefore, the total cost c m of the partition is

[0101]

[0102] wherein c ts is the cost of offshore substation, m represents the number of subarea, m∈1, 2, …, n. Since the distance between offshore wind turbine and offshore substation determines d i , the above formula is substituted into the following formula when calculating the total cost of a subarea:

[0103]

[0104] The offshore substation location with the minimum total cost is equivalent to solving the extreme value problem of the function c m (x 0, y0). According to the function extreme value principle, the partial derivatives of the above formula are solved and set to 0, and the following formula is obtained:

[0105]

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

[0107] The result of the kth iteration is obtained as follows:

[0108]

[0109] wherein, If H k <H k-1 , it indicates that there is still optimization space for the total cost of the subarea; otherwise, (x k-1 *, y k-1 *) is the optimal location of the offshore substation in the subarea.

[0110] If the voltage level of the power collection system is 35 kV, each small subarea should include an offshore substation, and the substation in each small subarea is collected to the offshore collection station of the corresponding large subarea for unified sending; if the voltage level is 66 kV, the offshore wind turbine can be directly connected to the offshore collection station for unified sending. The power collection system topologies of 35 kV and 66 kV voltage levels are shown in Figure 4 and Figure 5 .

[0111] The total cost function of the offshore collection station location is as follows:

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

[0113] wherein c cs,cable is the total cost of the offshore collection process, d station is the distance from the offshore substation to the offshore collection station, and Pts For offshore booster station capacity in small partition.

[0114] The above formula is improved by the center method to obtain the capacity and site selection results of the offshore collection station. The algorithm can consider the difference in the capacity of each booster station, and make the capacity and site selection results of the offshore collection station closer to the large-capacity booster station, reduce the line loss, and improve the economy of the planning results.

[0115] Total cost c of large partition planning partition For economy

[0116]

[0117] Wherein, c cs is the cost of offshore collection station, c cs,cable,loss is the offshore collection cable loss, c connect,cable is the interconnection cable cost between collection stations.

[0118] By increasing the interconnection cable between offshore collection stations, the power can be sent to the nearby another collection station through the interconnection cable when any offshore collection station fails, improving the reliability and reducing the failure loss.

[0119] Step S104, according to the wind turbine partition result and the capacity and site selection result of each offshore transformer station, a target function corresponding to the offshore wind power transmission system planning scheme is constructed, and the constraint condition corresponding to the target function is determined;

[0120] The main index considered in planning is economy, that is, the total cost c of large partition planning partition The target function C1 calculation formula is as follows:

[0121]

[0122] Wherein, C m is the total cost of the partition, C cs is the cost of offshore collection station, C cs,cable is the total cost of offshore collection process cable, C ca,cable,loss is the offshore collection cable loss, C connect,cable is the interconnection cable cost between collection stations.

[0123] The reliability index of the power collection system can be converted into economic form through the relationship between the electricity price and the failure and repair time. The target function C2 calculation formula is as follows:

[0124] minC2=(T fau +T fix )gi energy,price qs

[0125] Wherein, T fauT is the time spent from failure to detection; T fix T is the time spent from land to offshore wind farm repair; g is the amount of electricity generated by offshore wind farm per unit of time; i energy,price T is the on-grid price of offshore wind power project; q is the average life of wind turbine; s is the annual failure rate of different wind turbines. Offshore wind farms are far from the shore, and the long distance consumes a lot of time. The repair time of offshore wind farm failure should include the total time spent from the shore to the offshore wind farm.

[0126] The rated current carrying capacity of the power collection system cable, the output of the wind turbine, the phase angle and voltage safety of the wind turbine equipment are the constraint conditions for the planning of the power collection system of the offshore wind farm:

[0127] (1) Cable rated current carrying capacity constraint

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

[0129] S∈G P,cable

[0130] P cable ∈G P,cable

[0131] (2) Offshore wind turbine output constraint

[0132] The active and reactive power output of the offshore wind turbine satisfies the following constraints

[0133]

[0134] Where P wind , Q wind represent the reactive and active power output of the wind turbine, respectively; P wind,min , P wind,max represent the minimum and maximum output of the offshore wind turbine, respectively; tanα wind,min , tanα wind,max represent the minimum and maximum power factor angle of the offshore wind turbine, respectively.

[0135] (3) Offshore wind turbine equipment phase angle safety constraint

[0136]

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

[0138] (4) Offshore wind turbine equipment voltage safety constraint

[0139]

[0140] wherein, P TS,min , P TS,max respectively represent the minimum value and the maximum value of the offshore converter station capacity; P CS,min , P CS,max respectively represent the minimum value and the maximum value of the offshore collection station capacity.

[0141] Step S105, according to the constraint condition, the optimal solution to the objective function is obtained to obtain the optimal offshore wind power transmission system planning scheme.

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

[0143] According to the constraint condition, the quantum particle swarm optimization algorithm is used to optimize the solution of the objective function to obtain the optimal offshore wind power transmission system planning scheme.

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

[0145] 1) First, parameter setting is performed;

[0146] 2) Randomly generate a population;

[0147] 3) Particle position initialization;

[0148] 4) Solution space transformation;

[0149] 5) Calculate the 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) Probability mutation;

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

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

[0156] 12) Obtain the global optimal solution. ​

[0157] By the technical solution, the partition and site selection can reduce the construction cost, ensure efficient use of wind power, and prevent the occurrence of wind curtailment. The power 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, meeting the increasing demand for electricity in coastal areas. At the same time, reasonably optimizing the topology of the offshore wind power collection system and reducing the cable length of the collection system can better play the economy and reliability of the collection system planning scheme to achieve efficient collection and transmission of wind power.

[0158] In addition, the application adopts a quantum particle swarm optimization algorithm to improve the planning result. The PSO algorithm with quantum behavior (QPSO) is improved on the basis of the classical particle swarm optimization algorithm (PSO algorithm), which mainly updates the particle position method by combining the idea of quantum physics. When updating the particle position, the current local optimal position information and global optimal position information of each particle are mainly considered. The algorithm has faster convergence speed, can overcome the problem of local optimal solution, and can find the global optimal solution faster. When used to process the planning problem, it can consider multiple objectives and help to get a more reasonable result, providing a new planning method for actual 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 consumption capacity.

[0160] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the application.

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

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

[0163] a grade determination module 71 configured to determine the voltage grade corresponding to the offshore wind power transmission system;

[0164] a partition module 72 configured to partition the wind turbine generators of the offshore wind power transmission system according to the voltage grade by using an improved K-means method to obtain a wind turbine generator partition result;

[0165] a site selection module 73 configured to calculate the capacity and site selection result of each offshore substation according to the wind turbine generator partition result by using an improved gravity center method;

[0166] The planning module 74 is configured to construct a target function corresponding to the offshore wind power transmission system planning scheme according to the wind turbine partition result and the capacity and site selection result of each offshore substation, and determine a constraint condition corresponding to the target function;

[0167] The solving module 75 is configured to perform optimal solution on the target function according to the constraint condition, so as to obtain an optimal offshore wind power transmission system planning scheme.

[0168] In an embodiment, the voltage levels include a first voltage level and a second voltage level, the voltage of the first voltage level is less than the voltage of the second voltage level, the wind turbine partition result corresponding to the first voltage level includes a large partition result and a small partition result, the wind turbine partition result corresponding to the second voltage level includes a small partition result, the offshore substation corresponding to the first voltage level includes an offshore booster station and an offshore collection station, and the offshore substation corresponding to the second voltage level includes an offshore collection station.

[0169] In an embodiment, the partition module includes:

[0170] The number determination unit is configured to determine the partition number of the wind turbines by elbow method according to the voltage level and the upper limit of the offshore substation capacity;

[0171] The partition unit is configured to partition the wind turbines by improved K-means method according to the partition number, so as to obtain the wind turbine partition result.

[0172] In an embodiment, the partition unit is configured to:

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

[0174] Select a reference coordinate of the wind turbines in the offshore wind power transmission system;

[0175] Calculate the distance value between the position coordinates of each wind turbine and the reference coordinate;

[0176] According to the distance value, calculate the average distance value from the reference coordinate to the position coordinates of each wind turbine;

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

[0178] According to the partition number, select the corresponding number of wind turbines in front of the sorting as the clustering centers;

[0179] According to the clustering centers, partition the wind turbines, so as to obtain the wind turbine partition result.

[0180] In one embodiment, the optional site selection module is configured to:

[0181] For each wind farm cluster, the capacity of each wind turbine is taken as a part of the overall quality of the wind farm cluster, the corresponding barycenter of the wind farm cluster is calculated, and the barycenter is determined as the capacity and site selection result of the offshore substation, wherein each wind farm cluster corresponds to an offshore substation.

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

[0183] The economic objective function C1 includes:

[0184]

[0185] wherein c partition represents the total cost of large cluster planning, C m represents the total cost of small cluster, C cs represents the cost of offshore collection station, C cs,cable represents the total cost of offshore collection submarine cable, C ca,cable,loss represents the loss of offshore collection submarine cable, C connect,cable represents the cost of connecting submarine cable between offshore collection stations;

[0186]

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

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

[0189] wherein c ts represents the cost of offshore booster station, m represents the cluster number, w i represents the capacity of wind turbine, d i represents the distance from each wind turbine to offshore booster station, h i represents the cost of submarine cable per unit distance, P cable,loss represents the active loss of cluster submarine cable, U cable represents the voltage level of power collection system, R cable represents the impedance per unit distance of line, i energy,price represents the on-grid price of offshore wind power project; i loss represents the discount rate; q represents the average life of wind turbine, d station represents the distance from offshore booster station to offshore collection station, Pts represents the capacity of the offshore booster station in the small partition;

[0190] The reliability objective function C2 comprises:

[0191] minC2=(T fau +T fix )gi energy,price qs

[0192] Wherein, T fau represents the time spent from failure to detection of failure; T fix represents the time spent from land to offshore wind power base repair; g represents the amount of electricity generated by the offshore wind power transmission system per unit time; i energy,price represents the on-grid price of the offshore wind power project; q represents the average life of the wind turbine; s represents the annual failure rate of different wind turbines;

[0193] The constraint conditions comprise: a submarine cable rated flow constraint, a wind turbine output constraint, a wind turbine device phase angle safety constraint, and a wind turbine device voltage safety constraint.

[0194] In an embodiment, optionally, the solving module is configured to:

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

[0196] Specific limitations of the offshore wind power transmission system planning device can be referred to the limitations of the offshore wind power transmission system planning method described above, which will not be repeated here. Each module in the above offshore wind power transmission system planning device can be realized by software, hardware and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each of the above modules.

[0197] In an embodiment, a computer device is provided, which can be a client or a server, and its internal structure diagram can be as shown in Figure 8As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through 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 operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is used to communicate with the external server through the network connection. The computer program is executed by the processor to realize the functions or steps of the client side of the offshore wind power transmission system planning method.

[0198] It should be understood that the processor can be a central processing unit (CPU), and the processor can 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 can be a microprocessor or the processor can 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 on the memory and executable on the processor, the processor executing the computer program to implement the method of the first aspect embodiment.

[0200] It should be noted that the functions or steps that the computer readable storage medium or the electronic device can achieve described above can be referred to the related description in the foregoing method embodiments, and to avoid repetition, they will not be described one by one here.

[0201] It should be understood that the term "and / or" used herein only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0202] It should be understood that, although the terms first, second, etc. can be used herein to describe various sets of elements, these elements should not be limited by these terms. These terms are only used to distinguish one set of elements from another set of elements. For example, a first setting unit can also be termed a second setting unit, and similarly, a second setting unit can also be termed a first setting unit without departing from the scope of embodiments of the present application.

[0203] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "upon determining" or "in response to determining" or "upon detecting [the stated condition or event]" or "in response to detecting [the stated condition or event]."

[0204] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is only a logical function division, and there can be another division manner for the actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units.

[0205] In addition, each function unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist alone physically, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be realized in the form of hardware, or in the form of hardware plus software function 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. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present 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 but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), 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), etc.

[0207] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of offshore wind power transmission system planning, characterized by, The method comprises the following steps: determining the voltage level corresponding to the offshore wind power collection system; According to the voltage level, the wind turbine units of the offshore wind power transmission system are partitioned by using the improved K-means method to obtain the wind turbine unit partition result; According to the wind turbine unit partition result, the capacity and site selection result of each offshore substation are calculated by using the improved gravity center method; According to the wind turbine unit partition result and the capacity and site selection result of each offshore substation, a target function corresponding to the offshore wind power transmission system planning scheme is constructed, and the constraint condition corresponding to the target function is determined; According to the constraint condition, the optimal offshore wind power transmission system planning scheme is obtained by optimizing the target function; According to the voltage level, the wind turbine units of the offshore wind power transmission system are partitioned by using the improved K-means method to obtain the wind turbine unit partition result, comprising: According to the voltage level and the upper limit of the capacity of the offshore substation, the elbow method is used to determine the number of partitions corresponding to the wind turbine units; According to the number of partitions, the wind turbine units are partitioned by using the improved K-means method to obtain the wind turbine unit partition result; The method according to the number of partitions, the wind turbine units are partitioned by using the improved K-means method to obtain the wind turbine unit partition result, comprising: Obtain the position coordinates of each wind turbine unit in the offshore wind power transmission system; Select the reference coordinates of the wind turbine units in the offshore wind power transmission system; Calculate the distance value between the position coordinates of each wind turbine unit and the reference coordinates; According to the distance value, the average distance value from the reference coordinates to the position coordinates of each wind turbine unit is calculated; All wind turbine units are sorted in descending order according to the average distance value; According to the number of partitions, the corresponding number of wind turbine units in the front of the sorting are selected as the cluster centers; According to the cluster centers, the wind turbine units are partitioned to obtain the wind turbine unit partition result; According to the wind turbine unit partition result, the capacity and site selection result of each offshore substation are calculated by using the improved gravity center method, comprising: For each wind turbine unit partition, the capacity of each wind turbine unit is taken as a part of the overall quality of the wind turbine unit partition, the gravity center corresponding to the wind turbine unit partition is calculated, and the gravity center is determined as the capacity and site selection result of the offshore substation, wherein each wind turbine unit partition corresponds to an offshore substation.

2. The method of claim 1, wherein, The voltage level includes a first voltage level and a second voltage level, the voltage of the first voltage level is less than the voltage of the second voltage level, the wind turbine unit partition result corresponding to the first voltage level includes a large partition result and a small partition result, the wind turbine unit partition result corresponding to the second voltage level includes a small partition result, and the offshore substation corresponding to the first voltage level includes an offshore booster station and an offshore collection station. The offshore substation corresponding to the second voltage level includes an offshore collection station.

3. The method of claim 1, wherein, The target function includes an economic target function and a reliability target function; Wherein, the economic target function C1 includes: wherein c partition represents the total cost of large partition planning, C m represents the total cost of small partition, C cs represents the cost of offshore gathering station, C cs,cable represents the total cost of offshore gathering submarine cable, C ca,cable,loss represents the loss of offshore gathering submarine cable, C connect,cable represents the cost of connecting submarine cable between offshore gathering stations; c cab = w i d i h i e dis = 1 - 1 / (1 + i loss ) q c cs,cable = d station h i P ts wherein, c ts represents the cost of offshore substation, m represents the subarea number, w i represents the wind turbine capacity, d i represents the distance from each wind turbine to offshore substation, h i represents the unit distance cost of submarine cable, P cable,loss represents the active power loss of subarea submarine cable, U cable represents the voltage level of power collection system, R cable represents the unit distance impedance of line, i energy,price represents the on-grid price of offshore wind power project; i loss represents the discount rate; q represents the average life of wind turbine, d station represents the distance from offshore substation to offshore collection station, P ts represents the offshore substation capacity in small subarea; Wherein, the reliability target function C2 includes: minC2 = (T fau + T fix )gi energy,price qs where T fau represents the time spent from the occurrence of the fault to the detection of the fault; T fix represents the time spent from the land to the offshore wind power base for repair; 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 the offshore wind power project; q represents the average service life of the wind turbine generator; s represents the annual failure rate of different wind turbine generators; The constraint conditions include: a submarine cable rated current flow constraint, a wind turbine output constraint, a wind turbine device phase angle safety constraint, and a wind turbine device voltage safety constraint.

4. The method of claim 1, wherein, According to the constraint conditions, the target function is optimized and solved to obtain an optimal offshore wind power transmission system planning scheme, including: According to the constraint conditions, the target function is optimized and solved to obtain an optimal offshore wind power transmission system planning scheme.

5. An offshore wind power transmission system planning apparatus characterized by comprising: Comprise: A level determination module configured to determine a voltage level corresponding to the offshore wind power collection system; A partitioning module configured to partition wind turbines of the offshore wind power transmission system according to the voltage level using an improved K-means method to obtain a wind turbine partitioning result; A site selection module configured to calculate capacities and site selection results of each offshore substation by using an improved gravity center method according to the wind turbine partitioning result; A planning module configured to construct a target function corresponding to an offshore wind power transmission system planning scheme according to the wind turbine partitioning result and the capacities and site selection results of each offshore substation, and determine constraint conditions corresponding to the target function; A solving module configured to optimize and solve the target function according to the constraint conditions to obtain an optimal offshore wind power transmission system planning scheme; The partitioning module comprises: A quantity determination unit configured to determine a partitioning quantity corresponding to the wind turbines by using an elbow method according to the voltage level and an upper limit of the offshore substation capacity; A partitioning unit configured to partition the wind turbines according to the partitioning quantity using the improved K-means method to obtain a wind turbine partitioning result; The partitioning unit is configured to: Obtain position coordinates of each wind turbine in the offshore wind power transmission system; Select a reference coordinate of the wind turbines in the offshore wind power transmission system; Calculate distance values between the position coordinates of each wind turbine and the reference coordinate; Calculate average distance values from the reference coordinate to the position coordinates of each wind turbine according to the distance values; Sort all wind turbines in descending order according to the average distance values; Select a corresponding number of wind turbines in the front of the sorting as clustering centers according to the partitioning quantity; Partition the wind turbines according to the clustering centers to obtain a wind turbine partitioning result; The site selection module is configured to: For each wind turbine partitioning, calculate a gravity center corresponding to the wind turbine partitioning by taking the capacity of each wind turbine as a part of the overall quality of the wind turbine partitioning, and determine the gravity center as the capacity and site selection result of the offshore substation, wherein each wind turbine partitioning corresponds to one offshore substation.

6. A computer device, comprising: Comprise: At least one processor; And a memory connected in communication with the at least one processor; Wherein the memory stores instructions executable by the at least one processor, the instructions are configured to execute the method of any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Computer executable instructions are stored, and the computer executable instructions are used to execute the method of any one of claims 1 to 4.

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