Fan laying method, device and equipment for wind power plant, medium and program product

Abstract

The invention relates to the technical field of electronics, and discloses a fan laying method, device and equipment for a wind power plant, a medium and a program product. Basic fund cost data irrelevant to the number of fans in the wind power plant, wind speed and wind direction statistical data and a pre-constructed fan laying optimization model are obtained; the fan layout optimization model comprises a target function and a constraint condition, the target function is constructed by taking the maximum comprehensive investment return rate of the wind power plant as a target, and the comprehensive investment return rate of the wind power plant is calculated through predicted electricity selling income data and cost data of the wind power plant; and solving the target function by using the basic fund cost data, the wind speed and wind direction statistical data and the constraint conditions to obtain the target fan number and the target position information of each fan, and determining a wind power plant layout scheme according to the target fan number and the target position information of each fan, so that the comprehensive investment yield of the wind power plant is highest, and the wind power plant layout efficiency is improved. And the scientificity, the economical efficiency and the feasibility of wind power plant fan layout are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of electronic technology, and in particular to a method, device, equipment, medium and program product for deploying wind turbines in a wind farm. Background Art

[0002] Microsite selection is a key step in wind farm design. It involves optimizing the specific installation locations for wind turbines after the macrosite is determined. The goal is to determine the optimal location for each wind turbine within a limited area through scientific analysis and evaluation to maximize power generation, minimize costs, and minimize environmental impact.

[0003] Currently, wind turbine layout plans within wind farms are typically determined based on empirical rules and simplified models. These decisions often rely on engineers' experience or historical data, employing simplified spacing rules (such as fixed row and column spacing, minimum wake distances), or terrain avoidance principles (avoiding obstacles and steep slopes). This approach lacks scientific and quantitative analysis, easily overlooks the combined impact of complex environmental factors, and leads to a series of systemic flaws. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, equipment, medium and program product for the layout of wind turbines in a wind farm to solve the problem in the related art that the layout plan of wind turbines in a wind farm is determined based on empirical rules and simplified models, lacks scientific quantitative analysis, and easily ignores the comprehensive influence of complex environmental factors, resulting in a series of systemic defects.

[0005] In a first aspect, the present invention provides a method for deploying wind turbines in a wind farm, the method comprising:

[0006] Obtain basic capital cost data, wind speed and direction statistical data, and a pre-built wind turbine layout optimization model that are independent of the number of wind turbines in the wind farm. The wind turbine layout optimization model includes an objective function and constraints. The objective function is constructed with the goal of maximizing the comprehensive investment return rate of the wind farm. The comprehensive investment return rate of the wind farm is calculated through the predicted electricity sales revenue data and cost data of the wind farm. The predicted electricity sales revenue data is calculated through the power generation corresponding to different wind turbines in the wind farm. The power generation of each wind turbine is determined based on the location information and wind speed and direction statistical data of the corresponding wind turbine. The cost data is calculated based on basic capital cost data and construction and operation and maintenance cost data. The construction and operation and maintenance cost data is determined based on the number of wind turbines and the location information of each wind turbine. The objective function is solved using the basic capital cost data, wind speed and direction statistical data, and the constraints to obtain the target number of wind turbines and the target location information of each wind turbine. The layout plan of the wind farm is determined using the target number of wind turbines and the target location information of each wind turbine.

[0007] The wind turbine layout method of the wind farm provided by the present invention obtains basic capital cost data, wind speed and direction statistical data, and a pre-built wind turbine layout optimization model that are independent of the number of wind turbines in the wind farm. The wind turbine layout optimization model includes an objective function and constraint conditions. The objective function is constructed with the goal of maximizing the comprehensive investment return rate of the wind farm. The comprehensive investment return rate of the wind farm is calculated by the predicted electricity sales revenue data and cost data of the wind farm. The predicted electricity sales revenue data is calculated by the power generation corresponding to different wind turbines in the wind farm. The power generation of each wind turbine is determined according to the location information of the corresponding wind turbine and the wind speed and direction statistical data. The cost data is obtained by calculating the cost of the basic data. The method is based on the capital cost data and construction and operation and maintenance cost data. The construction and operation and maintenance cost data are determined according to the number of wind turbines and the location information of each wind turbine. The objective function is solved using the basic capital cost data, wind speed and direction statistical data and constraint conditions to obtain the target number of wind turbines and the target location information of each wind turbine. The wind farm layout plan is determined according to the target number of wind turbines and the target location information of each wind turbine, so that the comprehensive investment return rate of the wind farm is maximized, which significantly improves the scientificity, economy and feasibility of the wind farm wind turbine layout, provides strong technical support for wind farm investment decisions, and at the same time reduces investment risks, improves resource utilization efficiency and supports sustainable development.

[0008] In an optional embodiment, the constraints include a constraint on the number of wind turbines and multiple preset constraints, and the objective function is solved using basic capital cost data, wind speed and direction statistical data, and the constraints to obtain the target number of wind turbines and the location information of each wind turbine. The steps include: partitioning the wind turbine number constraint according to preset partition intervals to obtain multiple group constraints, and the wind turbine number constraint ranges of different group constraints are different; solving the objective function using each group constraint information, multiple preset constraints, basic capital cost data, and wind speed and direction statistical data to obtain the number of wind turbines under the corresponding group constraints and the location information of each wind turbine; determining the target number of wind turbines and the target location information of each wind turbine based on the number of wind turbines and the location information of each wind turbine corresponding to different group constraints.

[0009] The method provided by this optional implementation manner partitions the wind turbine number constraint according to preset partition intervals to obtain multiple grouping constraints, and uses the multiple grouping constraints to solve subsequent objective functions, thereby effectively improving the iteration speed of the optimization calculation.

[0010] In an optional embodiment, the step of determining the target number of wind turbines and the target position information of each wind turbine based on the number of wind turbines corresponding to different grouping constraints and the position information of each wind turbine includes: calculating the optimal comprehensive investment rate of return within the group under the corresponding grouping constraints based on the number of wind turbines under each grouping constraint and the position information of each wind turbine; based on the optimal comprehensive investment rate of return within the group obtained under different grouping constraints, taking the number of wind turbines corresponding to the group with the largest optimal comprehensive investment rate of return within the group as the target number of wind turbines, and taking the position information of each wind turbine corresponding to the group with the largest optimal comprehensive investment rate of return within the group as the target position information of the corresponding wind turbine.

[0011] In an optional embodiment, the method further includes: determining power generation data, cost data, wake loss ratio and rate of return of the corresponding wind turbine according to the target position information of each wind turbine.

[0012] In an optional embodiment, the method further includes: generating wind turbine layout information based on the target position information of each wind turbine; generating yield convergence curve information based on the yield of each wind turbine; determining cost distribution information based on the cost data of each wind turbine; and sending the wind turbine layout information, yield convergence curve information and cost distribution information to a display terminal for display.

[0013] In an optional embodiment, the basic capital cost data in the wind farm that is independent of the number of wind turbines is determined by the following steps: obtaining the land lease cost data, survey and design cost data, change point station construction cost data and road construction cost data of the wind farm; and determining the basic capital cost data based on the land lease cost data, survey and design cost data, change point station construction cost data and road construction cost data.

[0014] In the second aspect, the present invention provides a wind turbine layout device for a wind farm, which includes: an acquisition module for acquiring basic capital cost data, wind speed and direction statistical data, and a pre-built wind turbine layout optimization model that is independent of the number of wind turbines in the wind farm. The wind turbine layout optimization model includes an objective function and constraints. The objective function is constructed with the goal of maximizing the comprehensive investment return rate of the wind farm. The comprehensive investment return rate of the wind farm is calculated through the predicted electricity sales revenue data and cost data of the wind farm. The predicted electricity sales revenue data is calculated through the power generation corresponding to different wind turbines in the wind farm. It is found that the power generation of each wind turbine is determined based on the location information of the corresponding wind turbine and the statistical data of wind speed and direction. The cost data is calculated through basic capital cost data and construction and operation and maintenance cost data. The construction and operation and maintenance cost data is determined based on the number of wind turbines and the location information of each wind turbine. The solution module is used to solve the objective function using the basic capital cost data, wind speed and direction statistical data and constraint conditions to obtain the target number of wind turbines and the target location information of each wind turbine. The layout module is used to determine the layout plan of the wind farm using the target number of wind turbines and the target location information of each wind turbine.

[0015] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the wind turbine deployment method for a wind farm according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the wind turbine deployment method for a wind farm according to the first aspect or any corresponding embodiment thereof.

[0017] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the wind turbine deployment method for a wind farm according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 is a schematic flow chart of a method for deploying wind turbines in a wind farm according to an embodiment of the present invention;

[0020] Figure 2 is a schematic flow chart of a wind turbine deployment method for another wind farm according to an embodiment of the present invention;

[0021] Figure 3 is a schematic flow chart of another method for deploying wind turbines in a wind farm according to an embodiment of the present invention;

[0022] Figure 4 is a structural block diagram of a wind turbine deployment device for a wind farm according to an embodiment of the present invention;

[0023] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0025] Conventional technologies typically determine wind turbine layouts within wind farms based on empirical rules and simplified models. These strategies rely on engineers' experience or historical data, employing simplified spacing rules (such as fixed row and column spacing, minimum wake distances), or terrain avoidance principles (avoiding obstacles and steep slopes). This approach lacks scientific and quantitative analysis, easily overlooks the combined impact of complex environmental factors, and leads to a series of systemic flaws.

[0026] In view of this, a method for deploying wind turbines in a wind farm provided in an embodiment of the present application can be applied to a server to implement the deployment of wind turbines in a wind farm. The method provided by the present invention obtains basic capital cost data, wind speed and direction statistical data, and a pre-built wind turbine deployment optimization model that are independent of the number of wind turbines in the wind farm. The wind turbine deployment optimization model includes an objective function and constraints. The objective function is constructed with the goal of maximizing the comprehensive investment return rate of the wind farm. The comprehensive investment return rate of the wind farm is calculated by the predicted electricity sales revenue data and cost data of the wind farm. The predicted electricity sales revenue data is calculated by the power generation corresponding to different wind turbines in the wind farm. The power generation of each wind turbine is determined based on the location information of the corresponding wind turbine and the wind speed and direction statistical data. The cost data is obtained by calculating the basic capital cost data. The data is calculated based on the construction and operation cost data. The construction and operation cost data is determined according to the number of wind turbines and the location information of each wind turbine. The objective function is solved using the basic capital cost data, wind speed and direction statistical data and constraint conditions to obtain the target number of wind turbines and the target location information of each wind turbine. The wind farm layout plan is determined according to the target number of wind turbines and the target location information of each wind turbine, so that the comprehensive investment return rate of the wind farm is the highest, which significantly improves the scientificity, economy and feasibility of the wind farm wind turbine layout, provides strong technical support for wind farm investment decisions, and at the same time reduces investment risks, improves resource utilization efficiency and supports sustainable development.

[0027] According to an embodiment of the present invention, an embodiment of a method for deploying wind turbines in a wind farm is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] In this embodiment, a method for deploying wind turbines in a wind farm is provided, which can be used for the above-mentioned server. Figure 1 FIG. 1 is a flow chart of a method for deploying wind turbines in a wind farm according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0029] Step S101 : obtaining basic capital cost data, wind speed and direction statistics, and a pre-built wind turbine layout optimization model that is independent of the number of wind turbines in the wind farm. The wind turbine layout optimization model includes an objective function and constraint conditions.

[0030] Exemplarily, the objective function is constructed with the goal of maximizing the wind farm's comprehensive investment return rate. The wind farm's comprehensive investment return rate is calculated using the wind farm's predicted electricity sales revenue data and cost data. The predicted electricity sales revenue data is calculated based on the power generation corresponding to each wind turbine in the wind farm. The power generation of each wind turbine is determined based on the corresponding wind turbine's location information and wind speed and direction statistics, which are used to characterize wind speed and direction information at different locations. Cost data is calculated using basic capital cost data and construction and operation and maintenance cost data. Construction and operation and maintenance cost data is determined based on the number of wind turbines and the location information of each wind turbine. Basic capital cost data unrelated to the number of wind turbines in the wind farm may include, but is not limited to, land lease cost data, road construction cost data, survey and design cost data, and substation construction cost data. Constraints primarily include constraints on the number of wind turbines, layout location constraints, investment limit constraints, investment return rate constraints, and wind turbine spacing constraints. The objective function maximizes the wind farm's comprehensive investment return rate. The objective function is: maxIRR, where IRR represents the comprehensive investment return rate.

[0031] Step S102 : solving the objective function using basic capital cost data, wind speed and direction statistical data, and constraint conditions to obtain the target number of wind turbines and target position information of each wind turbine.

[0032] For example, in an embodiment of the present application, a genetic algorithm can be used to iteratively solve the objective function. First, a population of several wind turbine layout schemes is randomly generated. The genetic information of each individual in the population includes the number of wind turbines and location parameters, that is, the decision variables of the optimization problem. The comprehensive investment rate of return of each scheme is calculated as the fitness value. Selection, crossover, and mutation: select the scheme with higher fitness as the parent generation; generate the offspring scheme through crossover operation; perform mutation operation on the offspring scheme, introduce randomness, and gradually approach the optimal solution. The calculation is terminated after reaching the preset number of iterations or the rate of return converges.

[0033] Step S103 : determining a layout plan of the wind farm using the target number of wind turbines and target location information of each wind turbine.

[0034] Exemplarily, a layout plan of wind turbines in a wind farm is determined according to a target number of wind turbines and target position information of each wind turbine.

[0035] The wind turbine layout method of the wind farm provided in this embodiment obtains basic capital cost data, wind speed and direction statistical data, and a pre-built wind turbine layout optimization model that are independent of the number of wind turbines in the wind farm. The wind turbine layout optimization model includes an objective function and constraint conditions. The objective function is constructed with the goal of maximizing the comprehensive investment return rate of the wind farm. The comprehensive investment return rate of the wind farm is calculated by the predicted electricity sales revenue data and cost data of the wind farm. The predicted electricity sales revenue data is calculated by the power generation corresponding to different wind turbines in the wind farm. The power generation of each wind turbine is determined based on the location information of the corresponding wind turbine and the wind speed and direction statistical data. The cost data is obtained by calculating the cost of the basic data. The method is based on the capital cost data and construction and operation and maintenance cost data. The construction and operation and maintenance cost data are determined according to the number of wind turbines and the location information of each wind turbine. The objective function is solved using the basic capital cost data, wind speed and direction statistical data and constraint conditions to obtain the target number of wind turbines and the target location information of each wind turbine. The wind farm layout plan is determined according to the target number of wind turbines and the target location information of each wind turbine, so that the comprehensive investment return rate of the wind farm is maximized, which significantly improves the scientificity, economy and feasibility of the wind farm wind turbine layout, provides strong technical support for wind farm investment decisions, and at the same time reduces investment risks, improves resource utilization efficiency and supports sustainable development.

[0036] In this embodiment, a method for deploying wind turbines in a wind farm is provided, which can be used for the above-mentioned server. Figure 2 FIG. 1 is a flow chart of a method for deploying wind turbines in a wind farm according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0037] Step S201: Obtain basic capital cost data, wind speed and direction statistics, and a pre-built wind turbine placement optimization model that are independent of the number of wind turbines in the wind farm. The wind turbine placement optimization model includes an objective function and constraints. The objective function is constructed with the goal of maximizing the wind farm's comprehensive investment return rate. The wind farm's comprehensive investment return rate is calculated using the wind farm's predicted electricity sales revenue data and cost data. The predicted electricity sales revenue data is calculated based on the power generation corresponding to different wind turbines in the wind farm. The power generation of each wind turbine is determined based on the location information of the corresponding wind turbine and wind speed and direction statistics. The cost data is calculated based on basic capital cost data and construction and operation and maintenance cost data. The construction and operation and maintenance cost data is determined based on the number of wind turbines and the location information of each wind turbine. For details, please see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0038] In some optional implementations, basic capital cost data in a wind farm that is independent of the number of wind turbines is determined by the following steps:

[0039] Step a1: Obtain land lease fee data, survey and design fee data, variable point station construction fee data, and road construction fee data of the wind farm.

[0040] For example, in the embodiment of the present application, the land lease fee (C land ) is calculated based on the wind farm area and land rental unit price. The calculation process is shown in the following formula:

[0041] C land =A×P land

[0042] Among them, A is the area of ​​wind farm, P land The rent per unit area.

[0043] Survey and design fees (C survey ) Based on the complexity of the wind farm and the survey and design unit price, the calculation process is as follows:

[0044] C survey =Q survey ×P survey

[0045] Among them, Q survey is the survey and design workload, P survey Cost per unit of work.

[0046] Road construction costs (C road ) is calculated based on the road length and unit length construction cost. The calculation process is shown in the following formula:

[0047] C road =L road ×P road

[0048] Among them, L road is the total length of the road, P road is the construction cost per unit length.

[0049] Substation construction cost (C substation ) Based on the substation capacity and unit capacity construction cost, the calculation process is as follows:

[0050] C substation =S substation ×P substation

[0051] Among them, S substation is the substation capacity, P substation is the construction cost per unit capacity.

[0052] Step a2: determining basic capital cost data based on the land lease cost data, the survey and design cost data, the change point station construction cost data, and the road construction cost data.

[0053] For example, in the embodiment of the present application, the basic funding cost data is calculated by the following formula:

[0054] C base =C land +C survey +C road +C substation

[0055] Among them, C base Represents basic funding cost data. The meanings of other variables are not repeated here.

[0056] Step S202 : solving the objective function using basic capital cost data, wind speed and direction statistical data, and constraint conditions to obtain the target number of wind turbines and target position information of each wind turbine.

[0057] Specifically, the constraint conditions include the number of wind turbines and a plurality of preset constraints. The above step S202 includes:

[0058] In step S2021 , the wind turbine number constraint is partitioned according to a preset partition interval to obtain a plurality of group constraints, where the wind turbine number constraint ranges of different group constraints are different.

[0059] For example, the preset partition interval can be any interval. The specific content of the preset partition interval is not limited in the embodiment of the present application, and those skilled in the art can determine it according to needs. In the embodiment of the present application, the wind turbine number constraint is determined according to the installed capacity range of the wind farm, and the installed capacity range can be expressed by the following formula:

[0060] P min ≤P farm ≤P max

[0061] Among them, P farm represents the installed capacity of the wind farm, P min Indicates the lower limit of installed capacity, P max Indicates the upper limit of installed capacity.

[0062] Determine the fan model according to the fan selection requirements, and its rated capacity is P turbine Based on this, the upper and lower limits of the number of fans can be obtained as shown in the following formula:

[0063]

[0064] Among them, N min Indicates the lower limit of the number of fans, N max Indicates the upper limit of the number of fans.

[0065] Furthermore, the constraint on the number of wind turbines is as follows:

[0066] N min ≤N≤N max

[0067] Wherein, N is the number of wind turbines, and N is an integer.

[0068] Since the range of the number of units may be large, which will affect the iteration speed of subsequent optimization calculations, it is partitioned and the number of partition intervals is selected as N based on historical experience. gap (preset partition interval), if the subsequent calculation is too fast or too slow, you can adjust it. min to N max All integers between press N gap The number of is grouped from small to large, a total of n groups, and n grouping constraints are obtained, as shown in the following formula:

[0069] N min,1 ≤N1≤N max,1

[0070] …

[0071] N min,j ≤N j ≤N max,j

[0072] …

[0073] N min,n ≤N n ≤N max,n

[0074] Among them, N j represents the number of wind turbines in the jth group constraint, N min,j N represents the lower limit of the number of wind turbines in the jth group constraint. max,j represents the upper limit of the number of wind turbines in the j-th group constraint. The meanings of other variables are not repeated here.

[0075] Step S2022 , using each group constraint information, multiple preset constraints, basic capital cost data, and wind speed and direction statistical data to solve the objective function, and obtain the number of wind turbines under the corresponding group constraints and the location information of each wind turbine.

[0076] For example, in the embodiment of the present application, the multiple preset constraints mainly include an investment limit constraint, an investment rate of return constraint, a wind turbine location constraint, and a wind turbine spacing constraint. The investment limit constraint is shown in the following formula:

[0077] C min ≤C total ≤C max

[0078]

[0079] Among them, C min represents the lower bound of the wind farm cost, C total Represents the cost data of the wind farm, C max represents the upper limit of the wind farm cost; T represents the operating life of the wind farm after completion, which is a known quantity; C t represents the cost data of the wind farm in year t, C base Indicates basic funding cost data, C turbine Indicates construction and operation and maintenance cost data.

[0080] The investment rate of return constraint is as follows:

[0081] IRR min ≤IRR

[0082] Among them, IRR min It represents the lower limit of investment return, and IRR represents the investment rate of return. IRR can be calculated by the following formula:

[0083]

[0084] Among them, R t represents the forecast electricity sales revenue data for year t, which is related to electricity prices and related policies; C t represents the cost data of the tth year, IRR represents the comprehensive investment return rate of the wind farm, P total Represents the annual power generation of the wind farm; P i represents the annual power generation of the i-th wind turbine, T year is the annual effective power generation hours, f frequ,i (v) is the frequency of wind speed v at the position of the i-th fan after flow field calculation, f Power (v) is the value of the wind turbine power curve function at wind speed v. In the embodiment of the present application, based on the historical wind speed and wind direction data of the wind farm, wind resource assessment software (such as WAsP) or an engineering wake model (such as the Jensen model) is used to simulate the wind farm flow field, and the wind speed, wind direction and turbulence intensity at each unit position are calculated considering the wake effect, so as to determine f frequ,i (v) and f power The value of (v).

[0085] The fan position constraint is as follows:

[0086] x min ≤x i ≤x max

[0087] y min ≤y i ≤y max

[0088] Among them, (x i ,y i ) is the location information of the i-th wind turbine, x min and x max Indicates the upper and lower limits of the horizontal coordinate of the fan position, y min and y max Indicates the upper and lower limits of the vertical coordinate of the fan position.

[0089] The fan spacing constraint is as follows:

[0090]

[0091] Among them, D min Indicates the minimum fan spacing. The meanings of other variables are not repeated here.

[0092] In the embodiment of the present application, the construction and operation and maintenance costs (C turbine ) is calculated by the following formula:

[0093] C turbine =C construction +C equipment +C O&M +C decommission

[0094] Among them, C construction Indicates infrastructure cost data, C equipment Represents equipment purchase cost data, C O&M Indicates operation and maintenance cost data, C decommission Represents decommissioning cost data.

[0095] Infrastructure construction costs (C construction ) is calculated based on the terrain conditions and foundation type of the wind turbine location. The calculation process is shown in the following formula:

[0096]

[0097] Among them, f terrain (x i ,y i ) is the terrain adjustment coefficient, P construction For the unit price of infrastructure, (x i ,y i ) is the location information of the i-th wind turbine, N j is the total number of wind turbines, and j indicates the calculation for the jth group.

[0098] Equipment procurement costs (C equipment ) is calculated based on the fan model and market price. The calculation process is as follows:

[0099] C equipment =Nj ×P equipment

[0100] Among them, P equipment The purchase cost of a single fan equipment.

[0101] Operation and maintenance costs (C O&M ) is calculated based on the accessibility of the wind turbine location and the difficulty of operation and maintenance.

[0102]

[0103] Among them, is f O&M (x i ,y i ) Accessibility adjustment coefficient, P O&M The unit price for operation and maintenance.

[0104] Decommissioning costs (C decommission ): Calculated based on the difficulty of wind turbine decommissioning and residual value recovery.

[0105]

[0106] Among them, f decom (x i ,y i ) is the retirement difficulty adjustment coefficient, P decom is the decommissioning unit price, V decom The residual value is the recovery value.

[0107] Step S2023 : determining the target number of wind turbines and the target position information of each wind turbine based on the numbers of wind turbines corresponding to different grouping constraints and the position information of each wind turbine.

[0108] In some optional implementations, the above step S2023 includes:

[0109] Step b1: Calculate the optimal comprehensive investment return rate within the group under the corresponding group constraints based on the number of wind turbines under each group constraint and the location information of each wind turbine.

[0110] For example, in the embodiment of the present application, the comprehensive investment rate of return under each grouping constraint is calculated by the IRR calculation formula, which will not be described in detail here.

[0111] Step b2: Based on the optimal comprehensive investment rate of return within the group obtained under different grouping constraints, the number of wind turbines corresponding to the group with the largest optimal comprehensive investment rate of return within the group is used as the target number of wind turbines, and the position information of each wind turbine corresponding to the group with the largest optimal comprehensive investment rate of return within the group is used as the target position information of the corresponding wind turbine.

[0112] Step S203: Determine the layout plan of the wind farm using the target number of wind turbines and the target location information of each wind turbine. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0113] In this embodiment, a method for deploying wind turbines in a wind farm is provided, which can be used for the above-mentioned server. Figure 3 FIG. 1 is a flow chart of a method for deploying wind turbines in a wind farm according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0114] Step S301: Obtain basic capital cost data, wind speed and direction statistics, and a pre-built wind turbine placement optimization model that are independent of the number of wind turbines in the wind farm. The wind turbine placement optimization model includes an objective function and constraints. The objective function is constructed with the goal of maximizing the wind farm's comprehensive investment return rate. The wind farm's comprehensive investment return rate is calculated using the wind farm's predicted electricity sales revenue data and cost data. The predicted electricity sales revenue data is calculated based on the power generation corresponding to different wind turbines in the wind farm. The power generation of each wind turbine is determined based on the location information of the corresponding wind turbine and wind speed and direction statistics. The cost data is calculated based on basic capital cost data and construction and operation and maintenance cost data. The construction and operation and maintenance cost data is determined based on the number of wind turbines and the location information of each wind turbine. For details, please see Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0115] Step S302: Solve the objective function using the basic capital cost data, wind speed and direction statistics, and constraint conditions to obtain the target number of wind turbines and the target location information of each wind turbine. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0116] Step S303: Determine the layout plan of the wind farm using the target number of wind turbines and the target location information of each wind turbine. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0117] Step S304 : determining the power generation data, cost data, wake loss ratio, and rate of return of the corresponding wind turbine according to the target position information of each wind turbine.

[0118] For example, in this embodiment of the present application, key indicators such as power generation data, cost data, wake loss ratio, and rate of return are output based on the target location information of each wind turbine. The cost data for each wind turbine refers to the construction and operation and maintenance costs of the corresponding wind turbine, which is related to the installation location of the wind turbine.

[0119] Step S305 : generating wind turbine layout information based on the target position information of each wind turbine.

[0120] Illustratively, in an embodiment of the present application, the wind turbine layout information may include but is not limited to a wind turbine layout diagram.

[0121] Step S306 : generating yield convergence curve information based on the yield of each wind turbine.

[0122] Exemplarily, the yield convergence curve information is used to represent the yield information corresponding to different wind turbines.

[0123] Step S307: determining cost distribution information based on the cost data of each wind turbine.

[0124] Illustratively, in an embodiment of the present application, the cost distribution information may include but is not limited to a cost distribution graph.

[0125] Step S308: Send the wind turbine layout information, yield rate convergence curve information, and cost distribution information to a display terminal for display.

[0126] Illustratively, the display terminal may include but is not limited to a display screen. The embodiments of the present application do not limit the specific content of the display terminal, and those skilled in the art may determine it according to needs.

[0127] The wind turbine deployment method of the wind farm provided by the present application is described below through a specific embodiment.

[0128] Example:

[0129] The method provided in the embodiment of the present application takes into account the diminishing marginal effect of the benefits brought by an increase in the number of wind turbines, and aims to optimize the number and layout of wind turbines in the micro-site selection process of wind farms, and calculates the wake loss ratio of each unit under corresponding conditions and their respective differentiated investment returns to conduct unit-level economic evaluation.

[0130] The implementation steps are as follows:

[0131] 1. Conduct full life cycle cost modeling by classifying the basic capital components that are not related to the number of wind turbines:

[0132] Land lease fee (C land ): Calculated based on the wind farm area and land rental unit price.

[0133] C land =A×P land

[0134] Among them, A is the area of ​​wind farm, P land The rent per unit area.

[0135] Survey and design fees (C survey ): Calculated based on the complexity of the wind farm and the unit price of survey and design.

[0136] Csurvey =Q survey ×P survey

[0137] Among them, Q survey is the survey and design workload, P survey Cost per unit of work.

[0138] Road construction costs (C road ): Calculated based on road length and construction cost per unit length.

[0139] C road =L road ×P road

[0140] Among them, L road is the total length of the road, P road is the construction cost per unit length.

[0141] Substation construction cost (C substation ): Calculated based on substation capacity and unit capacity construction cost.

[0142] C substation =S substation ×P substation

[0143] Among them, S substation is the substation capacity, P substation is the construction cost per unit capacity.

[0144] Basic funding fee (C base )for:

[0145] C base =C land +C survey +C road +C substation

[0146] 2. Grouping by wind farm size and number of wind turbines.

[0147] For the planned wind farm, based on relevant policies and actual needs, determine the installed capacity P of the wind farm. farm Scope:

[0148] P min ≤P farm ≤P max

[0149] Determine the fan model according to the fan selection requirements, and its rated capacity is P turbine .

[0150] Based on this, the upper and lower limits of the number of fans can be obtained:

[0151]

[0152] The number of fans must be within the range of the following formula and must be an integer.

[0153] N min ≤N≤N max

[0154] Since the range of the number of units may be large, which will affect the iteration speed of subsequent optimization calculations, it is partitioned and the number of partition intervals is selected as N based on historical experience. gap If the subsequent calculation is too fast or too slow, you can adjust it. min to N max All integers between press N gap The number of is grouped from small to large, with a total of n groups.

[0155] N min,1 ≤N1≤N max,1

[0156] …

[0157] N min,j ≤N j ≤N max,j

[0158] …

[0159] N min,n ≤N n ≤N max,n

[0160] For each range of wind turbine numbers, calculations and optimizations are performed in parallel according to steps 3-8 below.

[0161] 3. Conduct full life cycle cost modeling for the construction, operation and maintenance parts related to the number of wind turbines:

[0162] Infrastructure construction costs (C construction ): Calculated based on the terrain conditions and foundation type of the wind turbine location.

[0163]

[0164] Among them, f terrain (x i ,y i ) is the terrain adjustment coefficient, P construction For the unit price of infrastructure, (x i ,y i ) is the location parameter of the i-th wind turbine, N j is the total number of wind turbines, and j indicates the calculation for the jth group.

[0165] Equipment procurement costs (Cequipment ): Calculated based on fan model and market price.

[0166] C equipment =N j ×P equipment

[0167] Among them, P equipment The purchase cost of a single fan equipment.

[0168] Operation and maintenance costs (C O&M ): Calculated based on the accessibility of the wind turbine location and the difficulty of operation and maintenance.

[0169]

[0170] Among them, is f O&M (x i ,y i ) Accessibility adjustment coefficient, P O&M The unit price for operation and maintenance.

[0171] Decommissioning costs (C decommission ): Calculated based on the difficulty of wind turbine decommissioning and residual value recovery.

[0172]

[0173] Among them, f decom (x i ,y i ) is the retirement difficulty adjustment coefficient, P decom is the decommissioning unit price, V decom The residual value is the recovery value.

[0174] Construction and operation and maintenance costs (C turbine )for:

[0175] C turbine =C construction +C equipment +C O&M +C decommission

[0176] 4. Establishment of a micro-site selection optimization model with variable number of wind turbines.

[0177] The objective function is to maximize the comprehensive investment return rate of the wind farm.

[0178] maxIRR

[0179] The calculation method is shown in step 7.

[0180] Constraints include the following:

[0181] Investment Limits:

[0182] Cmin ≤C total ≤C max

[0183] Upper and lower limits of the number of fans

[0184] N min,j ≤N j ≤N max,j

[0185] Lower limit of investment return rate:

[0186] IRR min ≤IRR

[0187] Wind farm range:

[0188] x min ≤x i ≤x max

[0189] y min ≤y i ≤y max

[0190] Fan spacing:

[0191]

[0192] 5. Optimize solution initialization.

[0193] Since the above optimization problem is a mixed integer optimization problem with high complexity, it is better to use a heuristic method to solve it. The present invention takes a genetic algorithm as an example.

[0194] First, a population of several wind turbine layout plans is randomly generated. The genetic information of each individual in the population includes the number of wind turbines and location parameters, which are the decision variables of the optimization problem.

[0195] 6. Calculation of annual power generation of wind farm:

[0196] Based on the historical wind speed and direction data of the wind farm, wind resource assessment software (such as WAsP) or engineering wake models (such as the Jensen model) are used to simulate the wind farm flow field, and the wind speed, wind direction and turbulence intensity at the location of each unit are calculated considering the wake effect.

[0197] The annual power generation of the wind farm is calculated based on the wind turbine power curve and the full-field flow field data under different wind conditions obtained from the above calculations.

[0198] For any wind turbine, its annual power generation is:

[0199]

[0200] Among them, P iis the annual power generation of the i-th wind turbine, T year is the annual effective power generation hours, f frequ,i (v) is the frequency of wind speed v at the position of the i-th fan after flow field calculation, f power (v) is the value of the wind turbine power curve function at wind speed v.

[0201] The annual power generation of the entire wind farm is:

[0202]

[0203] 7. Calculation of comprehensive investment return rate.

[0204] Comprehensive investment return rate (IRR) of wind farms:

[0205]

[0206] R t is the electricity sales revenue in year t, which is related to electricity prices and related policies.

[0207] R t =f(P total )

[0208] P total is the annual power generation of the entire wind farm.

[0209] C t is the cost in year t, which is related to the capital planning of construction investment.

[0210]

[0211] Directly solving IRR requires iterative calculations. If the algorithm does not converge or the accuracy is insufficient, the result will be unstable. Use mature numerical methods (such as Newton iteration method, bisection method) and set reasonable convergence conditions (such as error range ∈ ≤ 10 -6 ) to solve.

[0212] The iterative solution for optimizing the number and layout of wind turbines includes:

[0213] Fitness calculation: that is, calculate the comprehensive investment return rate (IRR) of each plan in step 7 above as the fitness value.

[0214] Selection, crossover, and mutation: Select the solution with higher fitness as the parent; generate the offspring solution through crossover operation; perform mutation operation on the offspring solution to introduce randomness.

[0215] Repeat steps 6 and 7 above to perform the calculation.

[0216] Iterative optimization: Repeat the selection, crossover, and mutation steps to gradually approach the optimal solution.

[0217] Termination condition: reaching the preset number of iterations or the yield convergence.

[0218] Result output: For each range of wind turbine numbers, the corresponding results can be obtained by performing calculations and optimizations in parallel according to steps 3-8 above. The optimal result within the total range can be obtained by comparing the comprehensive profit rates.

[0219] Optimal number of wind turbines: Output the optimal number of wind turbines that meets the constraints.

[0220] Optimal layout plan: output wind turbine location and its corresponding key indicators such as power generation, cost, wake loss ratio, and rate of return.

[0221] Visual display: Generate wind turbine layout diagrams, yield convergence curves, and cost distribution diagrams to assist in decision-making.

[0222] The method provided in the embodiments of this application can significantly improve the scientificity, economy, and feasibility of wind farm micro-site selection through refined modeling of full life cycle costs, dynamic marginal benefit analysis, and multi-dimensional constraint collaborative optimization, providing strong technical support for wind farm investment decisions, while reducing investment risks, improving resource utilization efficiency, and supporting sustainable development. At the same time, this method can also be used to evaluate the necessity and economy of upgrading existing wind farms, such as adding wind turbines. This method provides a full-chain optimization tool for wind farm planning, from technology to economy, significantly improving the market competitiveness and investment value of wind power projects.

[0223] This embodiment also provides a wind turbine deployment device for a wind farm, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0224] This embodiment provides a wind turbine deployment device for a wind farm, such as Figure 4 Shown, including:

[0225] Acquisition module 401 is used to obtain basic capital cost data, wind speed and direction statistics, and a pre-built wind turbine layout optimization model that are unrelated to the number of wind turbines in the wind farm. The wind turbine layout optimization model includes an objective function and constraints. The objective function is constructed with the goal of maximizing the comprehensive investment return rate of the wind farm. The comprehensive investment return rate of the wind farm is calculated using the predicted electricity sales revenue data and cost data of the wind farm. The predicted electricity sales revenue data is calculated based on the power generation corresponding to different wind turbines in the wind farm. The power generation of each wind turbine is determined based on the location information of the corresponding wind turbine and the wind speed and direction statistics. The cost data is calculated based on the basic capital cost data and construction and operation and maintenance cost data. The construction and operation and maintenance cost data is determined based on the number of wind turbines and the location information of each wind turbine.

[0226] A solution module 402 is configured to solve the objective function using basic capital cost data, wind speed and direction statistics, and constraints to obtain target number of wind turbines and target location information for each wind turbine;

[0227] The layout module 403 is configured to determine a layout plan of the wind farm using the target number of wind turbines and target location information of each wind turbine.

[0228] In some optional implementations, the constraints include a wind turbine number constraint and a plurality of preset constraints, and the solution module includes:

[0229] A processing submodule is used to partition the wind turbine number constraint according to a preset partition interval to obtain multiple group constraints, and the wind turbine number constraint ranges of different group constraints are different;

[0230] The solution submodule is used to solve the objective function using the group constraint information, multiple preset constraints, basic capital cost data, and wind speed and direction statistical data to obtain the number of wind turbines under the corresponding group constraints and the location information of each wind turbine;

[0231] The determination submodule is used to determine the target number of fans and the target position information of each fan based on the number of fans corresponding to different grouping constraints and the position information of each fan.

[0232] In some optional implementations, determining the submodule includes:

[0233] A calculation unit, configured to calculate the optimal comprehensive investment rate of return within a group under the corresponding group constraints based on the number of wind turbines under each group constraint and the location information of each wind turbine;

[0234] The determination unit is used to determine the number of wind turbines corresponding to the group with the largest optimal comprehensive investment return rate within the group based on the optimal comprehensive investment return rate within the group obtained under different grouping constraints, and to determine the position information of each wind turbine corresponding to the group with the largest optimal comprehensive investment return rate within the group as the target position information of the corresponding wind turbine.

[0235] In some optional embodiments, the above device includes:

[0236] The first determination module is used to determine the power generation data, cost data, wake loss ratio and rate of return of the corresponding wind turbine according to the target position information of each wind turbine.

[0237] In an optional embodiment, the above device includes:

[0238] A second determination module is configured to generate wind turbine layout information based on target position information of each wind turbine;

[0239] A third determination module generates yield convergence curve information based on the yield of each wind turbine;

[0240] A fourth determination module determines cost distribution information based on the cost data of each wind turbine;

[0241] The generation module is used to send wind turbine layout information, yield convergence curve information and cost distribution information to the display terminal for display.

[0242] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0243] The wind turbine deployment device of the wind farm in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0244] The embodiment of the present invention also provides a computer device having the above Figure 4 The wind turbine layout device of the wind farm shown.

[0245] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of a GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0246] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0247] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0248] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0249] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0250] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0251] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0252] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0253] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for laying out wind turbines in a wind farm, characterized in that: The method comprises: Obtain basic capital cost data, wind speed and direction statistical data, and a pre-built wind turbine layout optimization model that are unrelated to the number of wind turbines in the wind farm, the wind turbine layout optimization model including an objective function and constraints, the objective function being constructed with the goal of maximizing the comprehensive investment return rate of the wind farm, the comprehensive investment return rate of the wind farm being calculated using the predicted electricity sales revenue data and cost data of the wind farm, the predicted electricity sales revenue data being calculated using the power generation corresponding to different wind turbines in the wind farm, the power generation of each wind turbine being determined based on the location information and wind speed and direction statistical data of the corresponding wind turbine, the cost data being calculated using basic capital cost data and construction and operation and maintenance cost data, the construction and operation and maintenance cost data being determined based on the number of wind turbines and the location information of each wind turbine; Solving the objective function using the basic capital cost data, wind speed and direction statistical data, and the constraint conditions to obtain a target number of wind turbines and target position information of each wind turbine; The target number of wind turbines and target location information of each wind turbine are used to determine a layout plan for the wind farm.

2. The method according to claim 1, characterized in that The constraints include a constraint on the number of wind turbines and a plurality of preset constraints. The steps of solving the objective function using the basic capital cost data, wind speed and direction statistical data, and the constraints to obtain the target number of wind turbines and the location information of each wind turbine include: Partitioning the wind turbine number constraint according to a preset partition interval to obtain multiple group constraints, where the wind turbine number constraint ranges of different group constraints are different; Solving the objective function using the group constraint information, the multiple preset constraints, the basic capital cost data, and wind speed and direction statistical data to obtain the number of wind turbines under the corresponding group constraints and the location information of each wind turbine; The target number of fans and the target position information of each fan are determined based on the number of fans corresponding to different grouping constraints and the position information of each fan.

3. The method according to claim 2, characterized in that The step of determining the target number of wind turbines and the target position information of each wind turbine based on the number of wind turbines corresponding to different grouping constraints and the position information of each wind turbine includes: Based on the number of wind turbines under each group constraint and the location information of each wind turbine, the optimal comprehensive investment rate of return within the group under the corresponding group constraint is calculated; Based on the optimal comprehensive investment return rate within the group obtained under different grouping constraints, the number of wind turbines corresponding to the group with the largest optimal comprehensive investment return rate within the group is used as the target number of wind turbines, and the position information of each wind turbine corresponding to the group with the largest optimal comprehensive investment return rate within the group is used as the target position information of the corresponding wind turbine.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: The power generation data, cost data, wake loss ratio and rate of return of the corresponding wind turbine are determined according to the target position information of each wind turbine.

5. The method according to claim 4, characterized in that The method further comprises: Generate wind turbine layout information based on target position information of each wind turbine; Generate yield convergence curve information based on the yield of each wind turbine; Determining cost distribution information based on cost data of each wind turbine; The wind turbine layout information, yield rate convergence curve information and cost distribution information are sent to a display terminal for display.

6. The method according to claim 1, characterized in that The basic capital cost data of the wind farm that is independent of the number of wind turbines is determined by the following steps: Obtain data on land lease costs, survey and design costs, variable station construction costs, and road construction costs for wind farms; The basic capital cost data is determined based on the land lease cost data, the survey and design cost data, the change point station construction cost data, and the road construction cost data.

7. A wind turbine deployment device for a wind farm, characterized in that: The device comprises: an acquisition module for acquiring basic capital cost data, wind speed and direction statistical data, and a pre-built wind turbine layout optimization model that are independent of the number of wind turbines in a wind farm, the wind turbine layout optimization model including an objective function and constraints, the objective function being constructed with the goal of maximizing the comprehensive investment return rate of the wind farm, the comprehensive investment return rate of the wind farm being calculated by using the predicted electricity sales revenue data and cost data of the wind farm, the predicted electricity sales revenue data being calculated by using the power generation corresponding to different wind turbines in the wind farm, the power generation of each wind turbine being determined based on the location information of the corresponding wind turbine and the wind speed and direction statistical data, the cost data being calculated based on basic capital cost data and construction and operation and maintenance cost data, the construction and operation and maintenance cost data being determined based on the number of wind turbines and the location information of each wind turbine; a solving module, configured to solve the objective function using the basic capital cost data, the wind speed and direction data, and the constraint conditions to obtain target number of wind turbines and target position information of each wind turbine; The layout module is used to determine the layout plan of the wind farm by using the target number of wind turbines and the target position information of each wind turbine.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the wind turbine deployment method for a wind farm according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the wind turbine deployment method for a wind farm according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the wind turbine deployment method for a wind farm according to any one of claims 1 to 6.

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