WO2023103455A1 - 一种大规模海上风电场集电系统拓扑结构优化方法及系统 - Google Patents

一种大规模海上风电场集电系统拓扑结构优化方法及系统 Download PDF

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WO2023103455A1
WO2023103455A1 PCT/CN2022/114078 CN2022114078W WO2023103455A1 WO 2023103455 A1 WO2023103455 A1 WO 2023103455A1 CN 2022114078 W CN2022114078 W CN 2022114078W WO 2023103455 A1 WO2023103455 A1 WO 2023103455A1
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collection system
power collection
topology
wind farm
offshore wind
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French (fr)
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林伟伟
叶荣
林毅
黄海
方朝雄
唐雨晨
陈小月
苑玉宽
文习山
杨建军
李景一
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国网福建省电力有限公司
国网福建省电力有限公司经济技术研究院
武汉大学
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Priority to JP2022566693A priority Critical patent/JP7518199B2/ja
Publication of WO2023103455A1 publication Critical patent/WO2023103455A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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  • the present invention relates to the field of wind power generation, and more specifically, to a method and system for optimizing the topology of a large-scale offshore wind farm power collection system.
  • Wind power has the characteristics of low noise and no pollution, and has gradually become the main form of global renewable energy development.
  • Offshore wind energy resources are abundant, and offshore wind turbines have the advantages of large power generation and do not occupy land, and have become a hot spot in the field of new energy research.
  • the power collection system of the offshore wind farm is to collect the electric energy generated by the wind turbines and send them to the power grid through the offshore substation. It is mainly composed of fans, submarine cables, and booster stations. Wind energy is an important structure for centralized power delivery.
  • the structural design of the power collection system is directly related to the investment cost.
  • the optimization design of the power collection system of the offshore wind farm is to find a more economical electrical wiring topology.
  • the purpose of the present invention is to provide a large-scale offshore wind farm power collection system topology optimization method and system.
  • the optimization design method can effectively design the power collection system topology with the lowest life cycle cost considering submarine cable selection.
  • the technical solution of the present invention is: a method for optimizing the topology of a large-scale offshore wind farm power collection system, comprising the following steps:
  • Step S1 sort the wind turbines, and input the capacity of the wind turbines and the ampacity limits of different types of submarine cables;
  • Step S2. Calculate the number of wind turbines that can be connected to different types of submarine cables according to the current carrying capacity limits of different types of submarine cables;
  • Step S3 run the improved single-parent genetic algorithm based on the matrix of the adjustment variable in the breakpoint chromosome generation formula to generate the topology structure of the collector system with the lowest life cycle cost considering the maximum fan capacity that different submarine cable models can bear.
  • step S2 the formula for calculating the number of fans that can be connected to different types of submarine cables is:
  • floor is a function of rounding down
  • S cmax is the maximum capacity allowed to be connected to the collector submarine cable
  • P is the rated capacity of the wind turbine.
  • step S3 is specifically implemented as follows:
  • Step S31 create an initial population
  • the population coding method adopts integer coding: the initial population is to directly generate the initial population that meets the current carrying capacity limit of the submarine cable, and the specific generation method is as follows:
  • Two chromosomes are used to represent the topological structure of the collector system.
  • One of the chromosomes is the wind turbine sequence chromosome, denoted as XL, and the length is the total number of wind turbines in the wind farm;
  • the other chromosome is the breakpoint chromosome, denoted as DD, and the length is set
  • the number of feeders of the power system is reduced by 1 to ensure that the topology of the power collection system is divided into multiple feeders;
  • Step S32 calculating the objective function according to the topological structure represented by the two chromosomes, the objective function is the life cycle cost of the collector system;
  • Step S33 judging whether the termination condition is satisfied, and adopting the maximum genetic algebra as the termination condition of evolution;
  • Step S34 performing a selection operation, dividing every eight individuals of the population into one group, and selecting a topological structure corresponding to the minimum objective function of each group;
  • Step S35 performing a genetic operator operation, performing flipping, exchanging and sliding operations on a minimum topology structure corresponding to the minimum objective function of each selected group to generate a new population;
  • Step S36 repeating steps S33-S35, performing repeated iterative operations until the termination condition is met, and outputting the optimal topology structure.
  • the breakpoint chromosome generation formula is:
  • n max is the maximum number of wind turbines that can be connected to a single feeder
  • N t is the number of feeders in the collector system
  • cumsum is the cumulative sum function
  • j is the adjusted variable.
  • n max is the maximum number of fans that can be connected to a single feeder
  • N t is the number of feeders in the collector system
  • N is the total number of fans
  • the capacity of the wind turbines connected to each feeder is controlled not to exceed the maximum capacity that the submarine cable can bear.
  • the adjustment variable j is a randomly generated matrix with 1 row and N t ⁇ 1 columns.
  • the matrix of the adjustment variable j has the following two constraints:
  • step S32 the calculation formula of the objective function is as follows:
  • C is the life cycle cost of the collector system
  • C 1 is the one-time investment cost
  • C 2 is the operation and maintenance cost
  • C 3 is the line power loss
  • C 4 is the fault power loss
  • C 5 is the fault maintenance cost.
  • the one-time investment cost is the total construction cost of the power collection system, including the purchase and construction costs of major equipment such as cables and switches. Its calculation formula is:
  • N t is the number of feeders of the collector system
  • C S is the cost of purchasing and installing circuit breakers (10,000 yuan/piece)
  • L i is the length of the i-th power-collecting submarine cable (unit: km).
  • the operation and maintenance cost of the power collection system mainly comes from the overhaul and maintenance of the power collection submarine cable. Its calculation formula is:
  • N t is the number of feeders of the collector system, is the number of collector submarine cables connected to the kth feeder line, is the maintenance cost of the i-th power-collecting submarine cable per unit length (unit: 10,000 yuan/(km ⁇ a)); L i is the length of the i-th power-collecting submarine cable (unit: km); T is the cost of the offshore wind farm Life cycle (unit: year).
  • the power loss of the line mainly comes from the economic loss caused by the power loss caused by the operation of the power collection system. Its calculation formula is as follows:
  • N t is the number of feeders of the collector system, is the number of collector submarine cables connected to the kth feeder, m is the additional loss coefficient of the submarine cable, 1.4 is taken for the three-core submarine cable, I i is the ampacity of the i collector submarine cable (unit: kA), R i is the resistance per unit length of the i-th power-collecting submarine cable ( ⁇ /km), L i is the length of the i-th power-collecting submarine cable (unit: km), ⁇ is the on-grid electricity price (unit: yuan/kW h), t is the annual utilization hours of the offshore wind farm (unit: h/year), and T is the life cycle of the offshore wind farm (unit: year).
  • P i is the total power (MW) of the fans connected to the i-th collector submarine cable
  • U (unit: kV) is the voltage level of the collector line
  • Fault power loss refers to the economic loss caused by failure of circuit breakers, cables and other components to output power output, which is equivalent to the loss of due income of wind farms under normal working conditions. Its calculation formula is as follows:
  • N t is the number of feeders of the collector system, is the number of collector submarine cables connected to the kth feeder
  • Q i is the probability that the follow-up fan power cannot be sent out due to the failure of the ith collector submarine cable (unit: times/a)
  • L i is the length of the i-th power-collecting submarine cable (unit: km)
  • MW fan power
  • ⁇ is the on-grid electricity price (unit: yuan/kW*h)
  • T is the life cycle of the offshore wind farm (unit: year)
  • q s is the circuit breaker Device failure rate (unit: times/a), is the average output of the total fan power (MW) connected to the k-th collector feeder
  • t s is the fault maintenance time of the circuit breaker (unit:
  • Failure repair cost refers to the cost of repairing components such as circuit breakers and cables after failure.
  • the formula for calculating the breakdown maintenance cost is:
  • N t is the number of feeders of the collector system, is the number of collector submarine cables connected to the kth feeder line, is the failure rate of the i-th power-collecting submarine cable (unit: times/(km*a))
  • L i is the length of the i-th power-collecting submarine cable (unit: km)
  • q s is the failure rate of the circuit breaker (unit: times/a)
  • T is the life cycle of the offshore wind farm (unit: year).
  • step S34 the objective function is the smallest, that is, the life cycle cost of the power collection system is the lowest.
  • the optimization model is established as follows:
  • n is the number of wind turbines that can be connected to each feeder
  • S cmax is the maximum capacity allowed to be connected to the collector submarine cable
  • P is the rated capacity of the wind turbine
  • A, B, C, and D are the maximum capacity of any four wind turbines. represent the points, Indicates the cross product calculation, ⁇ indicates the dot product calculation.
  • the present invention also provides a large-scale offshore wind farm power collection system topology optimization system, including a memory, a processor, and computer program instructions stored on the memory and capable of being run by the processor.
  • the processor runs the computer program instructions , the method steps as described above can be realized.
  • the present invention has the following beneficial effects: the method of the present invention is simple and practical, considering the constraints of the current carrying capacity of submarine cables in large-scale offshore wind farms, and taking the whole life cycle cost as the optimization target, the optimal life cycle cost can be obtained
  • the topological structure contributes to the economical operation of the offshore wind farm power collection system.
  • Fig. 1 is a schematic flow chart of a method for topology design of a collection system of an offshore wind farm according to the present invention
  • Fig. 2 is a kind of schematic flow chart of improved single-parent genetic algorithm of the present invention.
  • Fig. 3 is the coordinates of the fan of the offshore wind farm and the booster station in the embodiment
  • Fig. 4 is the schematic diagram of the topological structure obtained with the least one-time investment cost in the embodiment
  • Fig. 5 is a schematic diagram of a topology structure obtained by taking the least life cycle cost in the embodiment.
  • the present invention provides a method for optimizing the topology of a large-scale offshore wind farm power collection system, comprising the following steps:
  • Step S1 sort and label the wind turbines, and input the position coordinates of each wind turbine and the offshore step-up station, the capacity of the wind turbines, the model of the submarine cable, and the ampacity limit of different types of submarine cables;
  • Step S2. Calculate the number of wind turbines that can be connected to different types of submarine cables according to the current carrying capacity limits of different types of submarine cables;
  • Step S3 running the improved parthenogenetic algorithm to generate the topological structure of the power collecting system with the lowest cost in consideration of different types of submarine cables and the whole life cycle.
  • the present invention also provides a large-scale offshore wind farm power collection system topology optimization system, including a memory, a processor, and computer program instructions stored on the memory and capable of being run by the processor.
  • the processor runs the computer program instructions , the method steps as described above can be realized.
  • described method comprises the following steps:
  • Step 1 Input the location coordinates of the wind turbine and the offshore step-up station, the capacity of the wind turbine, the type of submarine cable to be used, and the current carrying capacity limit of different types of submarine cables.
  • Step 2 Calculate the number of wind turbines that can be connected to different types of submarine cables according to the current carrying capacity limitations of different types of submarine cables.
  • the calculation formula is:
  • floor is a function of rounding down
  • S cmax is the maximum capacity allowed to be connected to the collector submarine cable
  • P is the rated capacity of the wind turbine.
  • Step 3 Run the improved single-parent genetic algorithm to generate the topology structure of the collector system with the lowest cost in consideration of different submarine cable models and the whole life cycle.
  • Step 4 Input the optimal topology.
  • an improved single-parent genetic algorithm includes the following steps:
  • Step 1 Create an initial population, the population number is set to 400, the population coding method adopts integer coding, and two chromosomes are used to jointly represent the topological structure of the power collection system, one of which is the wind turbine sequence chromosome, denoted as XL, and the length is wind farm total number of fans.
  • the other chromosome is the feeder break point chromosome, denoted as DD, whose length is the number of feeders in the collector system minus one, to ensure that the topology of the collector system is divided into multiple feeders.
  • DD feeder break point chromosome
  • the feeder breakpoint chromosome DD is:
  • the corresponding collector system topology has 4 feeders, and the specific connection methods are 1-2-5-7, 1-8-11-13-14, 1-4-9-3-6, 1-10-12 -15-16.
  • No. 1 is the offshore step-up station, and the serial number in the chromosome XL of the wind turbine path means adding 1 to the serial number of the wind turbine.
  • n max is the maximum number of fans that can be connected to a single feeder
  • N t is the number of feeders in the collector system
  • N is the total number of fans.
  • the adjustment variable j is a randomly generated matrix of 1 row N t -1 columns, and two restrictions are added to it: (1) The value range of each element in the matrix of the adjustment variable j is [0, n max ), ( 2) The accumulative sum of all elements in the matrix of the adjustment variable j is [dn max +1, n max ].
  • the breakpoint chromosome generation formula is:
  • cumsum is the cumulative sum function
  • j is the adjustment variable
  • chromosome matrix of a certain group of paths is [2-4-6-8-10-12-14-16-15-13-3-5-7-9-11].
  • j is a matrix with 1 row of N t -1 columns, and the value of each element needs to be in the interval [0, 4), and the generated j matrix is set to [1 0 1 2];
  • No. 1 is an offshore step-up station.
  • Step 2 Calculate the objective function; the objective function is the total cost of the whole life cycle.
  • Step 3 Judging whether the termination condition is met, and adopting the maximum genetic algebra as the termination condition of evolution.
  • Step 4 Perform a selection operation, divide the population into 50 groups, and select a topology corresponding to the minimum objective function of each group.
  • Step 5 Carry out the genetic operator operation, perform flipping, exchanging and sliding operations on the topological structure corresponding to the minimum objective function of each selected group to generate a new population.
  • Step 6 Repeat steps 3, 4, and 5 to perform iterative operations until the termination condition is reached, and output the optimal topology.
  • Figure 3 shows the coordinates of wind turbines and booster stations in the offshore wind farm in the embodiment.
  • the total capacity of the wind farm in the embodiment is 240MW, and the capacity of a single wind turbine is 6.45MW.
  • the parameters of the circuit breaker and various types of submarine cables are shown in Table 1 and Table 2.
  • On-grid electricity price ⁇ 0.85 yuan/kWh, the annual utilization hours are 4000h, and the design cycle is 25 years.

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Abstract

本发明涉及一种大规模海上风电场集电系统拓扑结构优化方法及系统。所述方法，包括：构建考虑可靠性与经济性的全寿命周期成本模型，包括一次性投资成本、运营维护成本、线路电能损耗、故障电能损失、故障维修成本；采用生成满足载流量约束条件的拓扑结构，根据生成后的拓扑结构完成海缆选型，计算各拓扑结构的全寿命周期成本，以全寿命周期成本作为改进遗传算法的目标函数，获得最优的集电系统拓扑结构。本发明简单实用，考虑大规模海上风电场海缆载流量约束条件，以全寿命周期成本作为优化目标，有助于海上风电场集电系统的经济、可靠运行。

Description

一种大规模海上风电场集电系统拓扑结构优化方法及系统 技术领域
本发明涉及风力发电领域，并且更具体的，尤其是涉及一种大规模海上风电场集电系统拓扑结构优化方法及系统。
背景技术
风力发电具有噪声小，无污染等特点，逐渐成为全球可再生能源发展的主要形式。海上风能资源丰富、海上风机具有发电量大且不占陆地等优点，已经成为新能源研究领域的热点方向。海上风电场集电系统是将风电机组发出的电能汇集起来并通过海上变电站送入电网，主要由风机、海底电缆、升压站等构成，海上风电场的集电系统，是风场汇集风电机组风能进而进行功率集中外送的重要结构。集电系统的结构设计，直接关系着投资成本，海上风电场集电系统优化设计是为了寻找更经济的电气接线拓扑方式。
目前国内外专家针对海上风电场集电系统拓扑结构优化已经进行了一些研究，但随着海上风电场规模不断增加，风机容量不断增大，单条馈线上所能连接的风机台数越来越少，因此在集电系统拓扑结构优化时应充分考虑集电线路的载流量约束条件，同时，为兼顾集电系统经济性与可靠性，建立全寿命周期成本优化的模型优化海上风电场集电系统拓扑结构更加合理。
发明内容
本发明的目的在于提供一种大规模海上风电场集电系统拓扑结构优化方法及系统，该优化设计方法能够有效地设计出考虑海缆选型的全寿命周期成本最小的集电系统拓扑结构。
为实现上述目的，本发明的技术方案是：一种大规模海上风电场集电系统拓扑结构优化方法，包括如下步骤：
步骤S1、对风机进行排序，并输入风机容量、不同型号海底电缆的载流量限制；
步骤S2、根据不同型号海底电缆的载流量限制计算不同型号海底电缆所能连接的风机数；
步骤S3、运行基于断点染色体生成公式中调节变量的矩阵的改进的单亲遗传算法，生成考虑不同海底电缆型号所能承受的最大风机容量的全寿命周期成本最低的集电系统拓扑结构。
在本发明一实施例中，步骤S2中，不同型号海底电缆所能连接的风机数的计算公式为：
Figure PCTCN2022114078-appb-000001
式中：floor为向下取整函数，S cmax为集电海底电缆所允许连接的最大容量，P为风电机组的额定容量。
在本发明一实施例中，所述步骤S3具体实现如下：
步骤S31、创建初始种群，种群编码方式采用整数编码：初始种群为直接生成满足海底电缆的载流量限制的初始种群，具体生成方式为：
采用两段染色体来共同表示集电系统的拓扑结构，其中一个染色体为风机序列染色体，记为XL，长度为风电场的总风机数；另一个染色体为断点染色体，记为DD，长度为集电系统的馈线数减1，确保集电系统的拓扑结构划分为多条馈线；
步骤S32、根据两段染色体所代表的拓扑结构计算目标函数，目标函数为集电系统的全寿命周期成本；
步骤S33、判断是否满足终止条件，采用最大遗传代数作为进化的终止条件；
步骤S34、进行选择操作，将种群个体每八个分为1组，选择每一组目标函数最小对应的一种拓扑结构；
步骤S35、进行遗传算子操作，对选中的每一组目标函数最小对应的一种最小拓扑结构进行翻转、交换和滑移操作生成新的种群；
步骤S36、重复步骤S33-S35，进行反复迭代运算，直至达到终止条件，输出最优拓扑结构。
在本发明一实施例中，所述断点染色体生成公式为：
DD＝n max×[1:N t-1]-cumsum(j)
式中：n max为单条馈线最多可连接风机台数，N t为集电系统馈线数，cumsum为累加和函数，j为调节变量。
在本发明一实施例中，在生成断点染色体时，设立两个变量，分别为自由点变量d和调节变量j，其中：
d＝n max×N t-N
式中：n max为单条馈线最多可连接风机台数，N t为集电系统馈线数，N为总风机台数；
通过调节变量j确保断点间隔小于单条馈线最多可连接风机台数，控制每条馈线所连接的风机容量不超过海底电缆所能承受的最大容量。
在本发明一实施例中，调节变量j为一个随机生成的1行N t-1列的矩阵。
在本发明一实施例中，调节变量j的矩阵，有以下两个限制条件：
(1)调节变量j的矩阵中各个元素取值区间为[0，n max)；
(2)调节变量j的矩阵中所有元素累加和区间为[d-n max+1，d]。
在本发明一实施例中，步骤S32中，目标函数计算公式如下：
C＝C 1+C 2+C 3+C 4+C 5
其中，C为集电系统全寿命周期成本，C 1为一次性投资成本，C 2为运营维护成本，C 3为线路电能损耗，C 4为故障电能损失，C 5为故障维修成本。
一次性投资成本是集电系统建设施工总费用，包括电缆、开关等主要设备的购置与施工费用。其计算公式为：
Figure PCTCN2022114078-appb-000002
式中：N t为集电系统馈线数，C S为断路器购置与安装成本(万元/个)，
Figure PCTCN2022114078-appb-000003
为第k条馈线所连接集电海缆数量，
Figure PCTCN2022114078-appb-000004
为单位长度的第i条集电海缆购置成本(单位：万元/km)；
Figure PCTCN2022114078-appb-000005
为单位长度的第i条集电海缆施工费用(单位：万元/km)，L i为第i条集电海缆的长度(单位：km)。
集电系统运营维护成本主要来自集电海缆的检修、保养维护等。其计算公式为为：
Figure PCTCN2022114078-appb-000006
式中：N t为集电系统馈线数，
Figure PCTCN2022114078-appb-000007
为第k条馈线所连接集电海缆数量，
Figure PCTCN2022114078-appb-000008
为单位长度的第i条集电海缆维护成本(单位：万元/(km·a))；L i为第i条集电海缆的长度(单位：km)；T为海上风电场的生命周期(单位：年)。
线路电能损耗主要来自集电系统运行过程中造成的电能损耗所造成的经济损失。其计算公式如下：
Figure PCTCN2022114078-appb-000009
Figure PCTCN2022114078-appb-000010
式中：N t为集电系统馈线数，
Figure PCTCN2022114078-appb-000011
为第k条馈线所连接集电海缆数量，m为海缆的附加损耗系数，三芯海缆取1.4，I i为第i条集电海缆的载流量(单位：kA)，R i为第i条集电海缆单位长度的电阻(Ω/km)，L i为第i条集电海缆的长度(单位：km)，α为上网电价(单位：元/kW·h)，t为海上风电场的年利用小时数(单位：h/年)，T为海上风电场的生命周期(单位：年)。P i为第i条集电海缆所连接的风机总功率(MW)，U(单位：kV)为集电线路电压等级,
Figure PCTCN2022114078-appb-000012
为风电机组功率因数，
Figure PCTCN2022114078-appb-000013
为风电机组功率因数角。
故障电能损失是指由于断路器、电缆等部件发生故障后不能输出电能输出所造成的经济损失，相当于风电场在正常工况下损失了应得的收入。其计算公式如下：
Figure PCTCN2022114078-appb-000014
Figure PCTCN2022114078-appb-000015
式中：N t为集电系统馈线数，
Figure PCTCN2022114078-appb-000016
为第k条馈线所连接集电海缆数量，Q i为第i条集电海缆故障导致后续风机功率无法送出的概率(单位：次/a)，
Figure PCTCN2022114078-appb-000017
为第i条集电海缆故障率(单位：次/(km*a))，L i为第i条集电海缆的长度(单位：km)，
Figure PCTCN2022114078-appb-000018
为第i条集电海缆所连接的风机功率平均出力(MW)，
Figure PCTCN2022114078-appb-000019
为第i条集电海缆的故障维护时间(单位：h)，α为上网电价(单位：元/kW*h)，T为海上风电场的生命周期(单位：年)，q s为断路器故障率(单位：次/a)，
Figure PCTCN2022114078-appb-000020
为第k条集电馈线所连接风机总功率平均出力(MW)，t s为断路器的故障维护时间(单位：h)，I i为第i条集电海缆的载流量。
故障维修成本是指是指由于断路器、电缆等部件发生故障后进行修复的费用。故障维修成本计算公式为：
Figure PCTCN2022114078-appb-000021
式中：N t为集电系统馈线数，
Figure PCTCN2022114078-appb-000022
为第k条馈线所连接集电海缆数量，
Figure PCTCN2022114078-appb-000023
为第i条集电海缆故障率(单位：次/(km*a))，L i为第i条集电海缆的长度(单位：km)，
Figure PCTCN2022114078-appb-000024
为单位长度的第i条集电海缆维修成本(单位：万元/km)，q s为断路器故障率(单位：次/a)，
Figure PCTCN2022114078-appb-000025
为断路器维修成本(万元/个)，T为海上风电场的生命周期(单位：年)。
风电场报废后，集电海缆回收会有一定的残值，但回收需要人工和响应的设备费用，可认为两部分互相抵消为0。
在本发明一实施例中，步骤S34中，目标函数最小即集电系统全寿命周期成本最低，考虑实际工程中海底电缆载流量与铺设交叉条件为约束建立优化模型如下：
Figure PCTCN2022114078-appb-000026
式中：n为每条馈线所能连接的风机台数，S cmax为集电海底电缆所允许连接的最大容量，P为风电机组的额定容量，A、B、C、D为任意四台风机所代表的点，
Figure PCTCN2022114078-appb-000027
表示叉积计算，·表示点积计算。
本发明还提供了一种大规模海上风电场集电系统拓扑结构优化系统，包括存储器、处理器以及存储于存储器上并能够被处理器运行的计算机程序指令，当处理器运行该计算机程序指令时，能够实现如上述所述的方法步骤。
相较于现有技术，本发明具有以下有益效果：本发明方法简单实用，考虑大规模海上风电场海缆载流量约束条件，以全寿命周期成本作为优化目标，可以得到全寿命周期成本最优的拓扑结构，有助于海上风电场集电系统的经济运行。
附图说明
图1为本发明一种海上风电场集电系统拓扑设计方法的流程示意图；
图2为本发明一种改进的单亲遗传算法的流程示意图；
图3为实施例中海上风电场风机及升压站坐标；
图4为实施例中以一次性投资成本最少得出的拓扑结构示意图；
图5为实施例中以全寿命周期成本最少得出的拓扑结构示意图。
具体实施方式
下面结合附图，对本发明的技术方案进行具体说明。
本发明一种大规模海上风电场集电系统拓扑结构优化方法，包括如下步骤：
步骤S1、对风机进行排序并标号，输入每一台风机与海上升压站的位置坐标、风机容量、海底电缆型号、不同型号海底电缆的载流量限制；
步骤S2、根据不同型号海底电缆的载流量限制计算不同型号海底电缆所能连接的风机数；
步骤S3、运行改进的单亲遗传算法，生成考虑不同海底电缆型号和全寿命周期成本最低的集电系统拓扑结构。
本发明还提供了一种大规模海上风电场集电系统拓扑结构优化系统，包括存储器、处理器以及存储于存储器上并能够被处理器运行的计算机程序指令，当处理器运行该计算机程序指令时，能够实现如上述所述的方法步骤。
以下为本发明具体实施实例。
参见图1所示，所述方法，包括以下步骤：
步骤一：输入风机与海上升压站的位置坐标，风机容量，规划使用的海缆型号、不同型号海缆的载流量限制。
步骤二：根据不同型号海缆的载流量限制计算不同型号海缆所能连接的风机数。计算公式为：
Figure PCTCN2022114078-appb-000028
式中：floor为向下取整函数，S cmax为集电海底电缆所允许连接的最大容量，P为风电机组的额定容量。
步骤三：运行改进的单亲遗传算法，生成考虑不同海底电缆型号和全寿命周期成本最低的集电系统拓扑结构。
步骤四：输入最优拓扑结构。
下面描述所述的改进单亲遗传算法的具体过程：
如图2所示，一种改进的单亲遗传算法，包括以下步骤：
步骤一：创建初始种群，种群数量设置为400，种群编码方式采用整数编码，采用两段染色体来共同表示集电系统的拓扑结构，其中一个染色体为风机序列染色体，记为XL，长度为风电场的总风机数。另一个染色体为馈线断点染色体，记为DD，长度为集电系统的馈线数减一，确保集电系统的拓扑结构划分为多条馈线。例如对于一个有15台风机的集电系统，一段路径染色体XL为：
[2-5-7-8-11-13-14-4-9-3-6-10-12-15-16]
馈线断点染色体DD为：
[3-7-11]
其所对应的集电系统拓扑有4条馈线，具体连接方式为1-2-5-7，1-8-11-13-14，1-4-9-3-6，1-10-12-15-16。其中1号为海上升压站，风机路径染色体XL内序列号含义为风机序列号加1。
设立两个变量，分别为自由点变量d和调节变量j。
d＝n max×N t-N
式中：n max为单条馈线最多可连接风机台数，N t为集电系统馈线数，N为总风机台数。
通过调节变量j确保断点间隔小于单挑馈线最多可连接风机台数，控制每条馈线所连接的风机容量不超过海缆所能承受的最大容量。调节变量j为一个随机生成的1行N t-1列的矩阵，并对其增加两条限制条件：(1)调节变量j的矩阵中各个元素取值区间为[0，n max)，(2) 调节变量j的矩阵中所有元素累加和区间为[d-n max+1，n max]。
断点染色体生成公式为：
Figure PCTCN2022114078-appb-000029
式中：cumsum为累加和函数，j为调节变量。
例如：
设单条馈线最多可连接风机台数n max＝4，馈线数N t＝5，总风机台数N＝15。某组路径染色体矩阵为[2-4-6-8-10-12-14-16-15-13-3-5-7-9-11]。
则自由点变量d＝n max×N t-N＝4×5-15＝5；
j为1行N t-1列的矩阵，各个元素取值还需在区间为[0，4)，设某次生成的j矩阵为[1 0 1 2]；
j矩阵元素和的取值区间为[d-n max+1，n max]＝[2,4],设某次随机取值为4。
则：
DD＝n max×[1:N t-1]-cumsum(j)＝4×[1,2,3,4]-[1,1,2,4]
＝[4,8,12,16]-[1,1,2,4]＝[3,7,10,12]
其所对应的拓扑结构连接方式为为1-2-4-6，1-8-10-12-14，1-16-15-13，1-3-5,1-7-9-11。其中1号为海上升压站。
步骤二：计算目标函数；目标函数为全寿命周期总成本。
步骤三：判断是否满足终止条件，采用最大遗传代数作为进化的终止条件。
步骤四：进行选择操作，将种群分为50组，选择每一组目标函数最小对应的一种拓扑结构。
步骤五：进行遗传算子操作，对选中的每一组目标函数最小对应的拓扑结构进行翻转、交换和滑移操作生成新的种群。
步骤六：重复步骤三、四、五，进行反复迭代运算，直至达到终止条件，输出最优拓扑结构。
图3为实施例中海上风电场风机及升压站坐标，实施例所述风电场总容量为240MW，风机单机容量为6.45MW，共有38台风机，集电系统电压等级为35kV。集电海缆共有4种，分别为3*70mm 2、3*150mm 2、3*240mm 2、3*500mm 2。断路器与各种型号海缆参数如表1、表2所示。
表1断路器参数
Figure PCTCN2022114078-appb-000030
表2海底电缆参数
Figure PCTCN2022114078-appb-000031
上网电价α＝0.85元/度，年利用小时数为4000h，设计周期25年。
如图4所示，以一次性投资成本为目标函数得出的各个成本如表3所示
表3
Figure PCTCN2022114078-appb-000032
如图5所示，以全寿命周期总成本为目标函数得出的各个成本如表4所示
表4
Figure PCTCN2022114078-appb-000033
通过以上实施例可以看出，本发明的一种海上风电场集电系统拓扑结构优化方法与以海缆长度最短为优化目标得出的拓扑结构相比，全寿命周期成本总投资节省2179万元，具有良好的 实用价值。
以上所述实施例仅表达了本发明的一种实施方式，其描述较为具体和详细，但并不能因此而理解为对本发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。
以上是本发明的较佳实施例，凡依本发明技术方案所作的改变，所产生的功能作用未超出本发明技术方案的范围时，均属于本发明的保护范围。

Claims (10)

  1. 一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，包括如下步骤：
    步骤S1、对风机进行排序，并输入风机容量、不同型号海底电缆的载流量限制；
    步骤S2、根据不同型号海底电缆的载流量限制计算不同型号海底电缆所能连接的风机数；
    步骤S3、运行基于断点染色体生成公式中调节变量的矩阵的改进的单亲遗传算法，生成考虑不同海底电缆型号所能承受的最大风机容量的全寿命周期成本最低的集电系统拓扑结构。
  2. 根据权利要求1所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，步骤S2中，不同型号海底电缆所能连接的风机数的计算公式为：
    Figure PCTCN2022114078-appb-100001
    式中：floor为向下取整函数，S cmax为集电海底电缆所允许连接的最大容量，P为风电机组的额定容量。
  3. 根据权利要求1或2所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，所述步骤S3具体实现如下：
    步骤S31、创建初始种群，种群编码方式采用整数编码：初始种群为直接生成满足海底电缆的载流量限制的初始种群，具体生成方式为：
    采用两段染色体来共同表示集电系统的拓扑结构，其中一个染色体为风机序列染色体，记为XL，长度为风电场的总风机数；另一个染色体为断点染色体，记为DD，长度为集电系统的馈线数减1，确保集电系统的拓扑结构划分为多条馈线；
    步骤S32、根据两段染色体所代表的拓扑结构计算目标函数，目标函数为集电系统的全寿命周期成本；
    步骤S33、判断是否满足终止条件，采用最大遗传代数作为进化的终止条件；
    步骤S34、进行选择操作，将种群个体每八个分为1组，选择每一组目标函数最小对应的一种拓扑结构；
    步骤S35、进行遗传算子操作，对选中的每一组目标函数最小对应的一种拓扑结构进行翻转、交换和滑移操作生成新的种群；
    步骤S36、重复步骤S33-S35，进行反复迭代运算，直至达到终止条件，输出最优拓扑结构。
  4. 根据权利要求3所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，所述断点染色体生成公式为：
    DD＝n max×[1:N t-1]-cumsum(j)
    式中：n max为单条馈线最多可连接风机台数，N t为集电系统馈线数，cumsum为累加和函数，j为调节变量。
  5. 根据权利要求4所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，在生成断点染色体时，设立两个变量，分别为自由点变量d和调节变量j，其中：
    d＝n max×N t-N
    式中：n max为单条馈线最多可连接风机台数，N t为集电系统馈线数，N为总风机台数；
    通过调节变量j确保断点间隔小于单条馈线最多可连接风机台数，控制每条馈线所连接的风机容量不超过海底电缆所能承受的最大容量。
  6. 根据权利要求5所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，调节变量j为一个随机生成的1行N t-1列的矩阵。
  7. 根据权利要求6所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，调节变量j的矩阵，有以下两个限制条件：
    (1)调节变量j的矩阵中各个元素取值区间为[0，n max)；
    (2)调节变量j的矩阵中所有元素累加和区间为[d-n max+1，d]。
  8. 根据权利要求3所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，步骤S32中，目标函数计算公式如下：
    C＝C 1+C 2+C 3+C 4+C 5
    其中，C为集电系统全寿命周期成本，C 1为一次性投资成本，C 2为运营维护成本，C 3为线路电能损耗，C 4为故障电能损失，C 5为故障维修成本。
  9. 根据权利要求8所述的一种大规模海上风电场集电系统拓扑结构优化方法，其特征在于，步骤S34中，目标函数最小即集电系统全寿命周期成本最低，考虑实际工程中海底电缆载流量与铺设交叉条件为约束建立优化模型如下：
    Figure PCTCN2022114078-appb-100002
    式中：n为每条馈线所能连接的风机台数，S cmax为集电海底电缆所允许连接的最大容量，P 为风电机组的额定容量，A、B、C、D为任意四台风机所代表的点，
    Figure PCTCN2022114078-appb-100003
    表示叉积计算，·表示点积计算。
  10. 一种大规模海上风电场集电系统拓扑结构优化系统，其特征在于，包括存储器、处理器以及存储于存储器上并能够被处理器运行的计算机程序指令，当处理器运行该计算机程序指令时，能够实现如权利要求1-9任一所述的方法步骤。
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117876030A (zh) * 2023-12-07 2024-04-12 华南理工大学 海上风电场机组和集电网络的协调规划方法
CN118035608A (zh) * 2024-04-12 2024-05-14 广东电网有限责任公司珠海供电局 代价数据的获取方法、装置、存储介质和处理器

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114186488A (zh) * 2021-12-06 2022-03-15 国网福建省电力有限公司经济技术研究院 一种大规模海上风电场集电系统拓扑结构优化方法及系统
CN114723299A (zh) * 2022-04-12 2022-07-08 江苏方天电力技术有限公司 一种考虑海缆线损计算的海上风电场可调容量评估方法
CN118484903A (zh) * 2022-06-30 2024-08-13 三峡大学 一种用于海缆集电系统拓扑优化的混合整数规划模型

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106410842A (zh) * 2016-09-29 2017-02-15 上海电力学院 一种基于全寿命周期的海上风电场集电系统成本计算方法
CN106503839A (zh) * 2016-10-14 2017-03-15 上海电力学院 一种海上风电场环形集电网络分层规划方法
CN106712076A (zh) * 2016-11-18 2017-05-24 上海电力学院 一种海上风电场集群规模下的输电系统优化方法
WO2019141041A1 (zh) * 2018-01-22 2019-07-25 佛山科学技术学院 一种风电场机组布局多目标优化方法
CN114186488A (zh) * 2021-12-06 2022-03-15 国网福建省电力有限公司经济技术研究院 一种大规模海上风电场集电系统拓扑结构优化方法及系统

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112487659B (zh) 2020-12-15 2022-08-02 国网江苏省电力有限公司经济技术研究院 一种海上风电场集电系统优化设计方法和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106410842A (zh) * 2016-09-29 2017-02-15 上海电力学院 一种基于全寿命周期的海上风电场集电系统成本计算方法
CN106503839A (zh) * 2016-10-14 2017-03-15 上海电力学院 一种海上风电场环形集电网络分层规划方法
CN106712076A (zh) * 2016-11-18 2017-05-24 上海电力学院 一种海上风电场集群规模下的输电系统优化方法
WO2019141041A1 (zh) * 2018-01-22 2019-07-25 佛山科学技术学院 一种风电场机组布局多目标优化方法
CN114186488A (zh) * 2021-12-06 2022-03-15 国网福建省电力有限公司经济技术研究院 一种大规模海上风电场集电系统拓扑结构优化方法及系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LU, SIYAO: "Research on Power Cable Optimization and Life Cycle Cost Management of Offshore Wind Farm", MASTER'S THESIS, no. 04, 1 June 2017 (2017-06-01), CN, pages 1 - 95, XP009546073 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117876030A (zh) * 2023-12-07 2024-04-12 华南理工大学 海上风电场机组和集电网络的协调规划方法
CN118035608A (zh) * 2024-04-12 2024-05-14 广东电网有限责任公司珠海供电局 代价数据的获取方法、装置、存储介质和处理器

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