Wind resource micro-siting method and system based on economy

Abstract

The invention discloses a wind resource micro-siting method and system based on economy, and the method comprises the steps: obtaining the spatial geographic information and long-sequence meteorological data of a target wind power plant, and the geographic constraint information of a wind power prohibited construction region, and constructing a high-resolution three-dimensional geographic information system platform; based on the three-dimensional geographic information system platform, wind resource refined evaluation is carried out, whole-field wind energy distribution parameters and single-station theoretical wind energy parameters are obtained, and the wind energy distribution parameters at least comprise wind speed Weibull distribution parameters and a wind direction rose diagram; an optimization objective function of micro-siting is defined, and compliance risks are avoided from the source by taking a construction forbidding area as a rigid constraint to be embedded in an optimization model; and high-precision terrain and meteorological data are fused in a three-dimensional geographic information platform, and wind field high-resolution numerical simulation based on a computational fluid mechanics principle is performed, so that the precision of wind resource space-time distribution evaluation in a complex terrain is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of wind power technology, and in particular to an economically viable micro-situation method and system for wind resources. Background Technology

[0002] In the context of large-scale wind power development, the scientific nature of micro-site selection directly determines the economic benefits of wind farms. Traditional micro-site selection methods often rely on engineers' experience or only optimize power generation as a single objective, lacking systematic consideration. The main shortcomings are as follows: 1) Poor environmental adaptability: Insufficient accuracy in assessing the spatiotemporal heterogeneity of wind resources in complex terrains (mountains, high altitudes), and often neglecting the rigid constraints of land use planning, ecological red lines, airport airspace restrictions, and other prohibited construction areas, leading to high compliance risks. 2) Single optimization objective: Most methods focus solely on maximizing power generation, failing to systematically incorporate power loss due to wake effects and construction costs such as roads, power collection lines, and foundations directly determined by the turbine location coordinates into a unified optimization framework, making it difficult to achieve optimal life-cycle benefits. 3) Lagging economic evaluation: Technical solutions are disconnected from economic benefit analysis; economic evaluation is often conducted only after the layout plan is determined, failing to dynamically balance power generation benefits and cost inputs during the optimization process. Summary of the Invention

[0003] The purpose of this invention is to overcome the above-mentioned technical deficiencies and propose an economically-based micro-location method and system for wind resources, so as to solve the problems of insufficient consideration of environmental constraints, single optimization objectives and disconnection from economic efficiency in traditional methods.

[0004] To achieve the above-mentioned technical objectives, in a first aspect, the present invention provides an economically-based micro-location method for wind resources, comprising: Acquire spatial geographic information of the target wind farm, long-sequence meteorological data, and geographic constraint information of wind power construction prohibited areas, and construct a high-resolution three-dimensional geographic information system platform; Based on the aforementioned three-dimensional geographic information system platform, a refined assessment of wind resources is conducted to obtain the wind energy distribution parameters of the entire field and the theoretical wind energy parameters of a single unit. The wind energy distribution parameters include at least the Weibull distribution parameters of wind speed and the wind rose diagram. Define an optimization objective function for micro-site selection. The objective function is a comprehensive objective that integrates the total power generation of the entire site, the total wake loss, and the total construction cost over the entire life cycle. Also set spatial geometric constraints on the available wind turbine locations and geographical prohibition constraints based on the wind power construction prohibition areas. The objective function is solved iteratively using an intelligent optimization algorithm. Under the conditions of satisfying the spatial geometric constraints and geographical prohibition constraints, the optimal set of wind turbine location coordinates is searched to generate a micro-site selection scheme. A full life-cycle techno-economic evaluation is conducted on the generated micro-site selection schemes, the net present value of the schemes is calculated, and the final scheme is selected by combining sensitivity analysis.

[0005] Optionally, the refined wind resource assessment specifically includes: Long-series reanalysis meteorological data of the target wind farm are acquired, and the time series of the reanalysis meteorological data is corrected and the representativeness is verified by using on-site wind measurement data. Based on the corrected long-series wind data, the shape parameter k and scale parameter c of the Weibull distribution of wind speed at each grid point are obtained by fitting using the maximum likelihood method or the least squares method, and a wind rose diagram is generated statistically. On the aforementioned 3D geographic information system platform, high-precision terrain data, Weibull distribution parameters, and wind rose diagram data are integrated, and a 3D wind field simulation is performed using computational fluid dynamics methods to generate wind speed and wind power density distribution maps with a spatial resolution of no less than 100m×100m, which serve as inputs for the theoretical wind energy parameters of a single unit.

[0006] Optionally, the objective function F is expressed as: F = max(α*AEP_net - β*C_total) Where AEP_net is the net power generation of all wind turbines after considering the wake effect, converted into the annual equivalent full-load hours; C_total is the present value of the total life cycle cost of the wind farm converted to the initial construction period; α is the economic conversion coefficient of power generation, which is related to the grid-connected electricity price; and β is the cost weighting coefficient.

[0007] Optionally, the formula for calculating the net power generation (AEP_net) of all wind turbines in the field is as follows: AEP_net=Σ_{i=1}^{N}[P_i*(1-Loss_wake_i)*T] Where N is the total number of wind turbines, P_i is the theoretical power generation of the i-th wind turbine without wake effect, Loss_wake_i is the comprehensive wake loss rate of the i-th wind turbine calculated based on the engineering wake model, and T is the number of operating hours.

[0008] Optionally, the present value of the total lifecycle cost C_total includes: equipment procurement cost C_equip, construction cost C_construct, and operation and maintenance cost C_o&m; wherein, the construction cost C_construct includes the cost of the wind turbine foundation, road construction cost, and power collection line cost, and this cost is related to the wind turbine coordinates, terrain slope, and spatial distribution of prohibited construction areas, and the cost is estimated based on path and engineering quantity through the three-dimensional geographic information system platform.

[0009] Optionally, the intelligent optimization algorithm is a genetic algorithm; the set of wind turbine location coordinates is used as the chromosome of the genetic algorithm for encoding; the fitness value of each generation of individuals is calculated by the objective function F; the crossover and mutation operations of the algorithm need to be modified in combination with the spatial geometric constraints and geographical prohibition constraints to ensure that the newly generated turbine location coordinates do not fall into the prohibited construction area and meet the minimum spacing requirements.

[0010] Optionally, the full life-cycle techno-economic evaluation introduces a micro-level site selection economic coefficient K, used for normalized horizontal economic comparison of different schemes. The formula for calculating the economic coefficient K is as follows: K=(NPV*(1-R_risk)) / (C_capex*PBP) Wherein: NPV is the net present value of the micro-location scheme; R_risk is the project risk adjustment coefficient obtained based on sensitivity analysis, with a value range of 0~1; C_capex is the static total investment cost of the scheme; PBP is the investment payback period of the scheme; and the higher the value of the economic coefficient K, the higher the risk-adjusted net present value obtained per unit investment cost and per unit payback period, and the better the economic performance.

[0011] Optionally, the project risk adjustment factor R_risk is determined through the following steps: Wind speed uncertainty and grid-connected electricity price fluctuation were selected as key uncertainty factors. A Monte Carlo simulation based on the key uncertainties was performed on the net present value (NPV) of the micro-location scheme to obtain the probability distribution of NPV; The value of R_risk is determined by mapping the probability that the NPV is lower than the benchmark value and combining it with the industry average risk level.

[0012] To achieve the above-mentioned technical objectives, in a second aspect, the present invention provides an economically viable wind resource micro-location system, comprising: The data acquisition and platform construction module is used to acquire spatial geographic information of the target wind farm, long-sequence meteorological data, and geographic constraint information of wind power prohibited construction areas, and to build a high-resolution three-dimensional geographic information system platform. The wind resource assessment module, based on the three-dimensional geographic information system platform, performs a refined assessment of wind resources, and obtains the wind energy distribution parameters of the entire field and the theoretical wind energy parameters of a single unit. The wind energy distribution parameters include at least the Weibull distribution parameters of wind speed and the wind rose diagram. The optimization modeling module is used to define the optimization objective function for micro-site selection. The objective function is a comprehensive objective that integrates the total power generation of the entire site, the total wake loss, and the total construction cost over the entire life cycle. It also sets the spatial geometric constraints of the available wind turbine locations and the geographical prohibition constraints based on the wind power construction prohibition areas. The optimization solution module is used to iteratively solve the objective function using an intelligent optimization algorithm. Under the conditions of satisfying the spatial geometric constraints and geographical prohibition constraints, it searches for the optimal set of wind turbine location coordinates and generates a micro-site selection scheme. The economic evaluation module is used to perform a full life-cycle techno-economic evaluation of the generated micro-site selection schemes, calculate the net present value of the schemes, and combine sensitivity analysis to screen the final schemes.

[0013] The beneficial effects of this invention include: 1. By embedding prohibited construction areas as rigid constraints into the optimization model, compliance risks are avoided from the source; and by integrating high-precision terrain and meteorological data into a 3D geographic information platform, high-resolution numerical simulation of wind fields based on computational fluid dynamics principles is carried out, which significantly improves the accuracy of wind resource spatiotemporal distribution assessment under complex terrain. 2. A multi-objective optimization function integrating power generation, wake, and construction cost was constructed, which makes the optimization process directly oriented towards the economic efficiency of the wind farm throughout its entire life cycle, overcoming the drawbacks of the traditional method of "technology first, economy second"; 3. By introducing the economic coefficient K, the net present value, investment payback period, and risk assessment results of the project are integrated into a comprehensive quantitative indicator, providing a clear and reliable basis for investment decisions and realizing a deep integration of technical solutions and economic evaluation. Attached Figure Description

[0014] Figure 1 This is a flowchart of the economical wind resource micro-location method according to an embodiment of the present invention; Figure 2 This is a structural diagram of an economically-based wind resource micro-location system according to an embodiment of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] In the context of large-scale wind power development, the scientific nature of micro-site selection directly determines the economic benefits of wind farms. Traditional micro-site selection methods often rely on engineers' experience or only optimize power generation as a single objective, lacking systematic consideration. The main shortcomings are as follows: 1) Poor environmental adaptability: Insufficient accuracy in assessing the spatiotemporal heterogeneity of wind resources in complex terrains (mountains, high altitudes), and often neglecting the rigid constraints of land use planning, ecological red lines, airport airspace restrictions, and other prohibited construction areas, leading to high compliance risks. 2) Single optimization objective: Most methods focus solely on maximizing power generation, failing to systematically incorporate power loss due to wake effects and construction costs such as roads, power collection lines, and foundations directly determined by the turbine location coordinates into a unified optimization framework, making it difficult to achieve optimal life-cycle benefits. 3) Lagging economic evaluation: Technical solutions are disconnected from economic benefit analysis; economic evaluation is often conducted only after the layout plan is determined, failing to dynamically balance power generation benefits and cost inputs during the optimization process.

[0017] To address the issues of insufficient consideration of environmental constraints, singular optimization objectives, and disconnect from economic efficiency in the aforementioned methods, a micro-site selection method for wind resources based on economic efficiency is proposed.

[0018] Reference Figure 1 , Figure 1 A flowchart of an economical wind resource micro-situation method is provided for embodiments of the invention. The method includes: S101. Acquire spatial geographic information of the target wind farm, long-sequence meteorological data, and geographic constraint information of wind power construction prohibited areas, and construct a high-resolution three-dimensional geographic information system platform. This step first requires collecting detailed geographic data of the target area, including but not limited to digital elevation models, land use types, vegetation cover, and surface roughness, to accurately characterize the topography. Simultaneously, long-term (usually no less than 20 years) reanalysis meteorological data is acquired as the basic climate background and validated in conjunction with on-site wind tower data. Furthermore, prohibited or restricted areas for wind power construction must be clearly defined, such as ecological protection zones, military zones, residential areas, and infrastructure corridors. Integrating all the above information, a high-resolution 3D geographic information system platform with spatial analysis and simulation capabilities is constructed, serving as the spatial data foundation and computational carrier for all subsequent evaluations and optimizations.

[0019] S102. Based on the three-dimensional geographic information system platform, conduct a refined assessment of wind resources, and obtain the wind energy distribution parameters of the entire field and the theoretical wind energy parameters of a single unit. The wind energy distribution parameters include at least the Weibull distribution parameters of wind speed and the wind rose diagram. The detailed assessment of wind resources specifically includes: Long-series reanalysis meteorological data of the target wind farm are acquired, and the time series of the reanalysis meteorological data is corrected and the representativeness is verified by using on-site wind measurement data. Based on the corrected long-series wind data, the shape parameter k and scale parameter c of the Weibull distribution of wind speed at each grid point are obtained by fitting using the maximum likelihood method or the least squares method, and a wind rose diagram is generated statistically. On the aforementioned 3D geographic information system platform, high-precision terrain data, Weibull distribution parameters, and wind rose diagram data are integrated, and a 3D wind field simulation is performed using computational fluid dynamics methods to generate wind speed and wind power density distribution maps with a spatial resolution of no less than 100m×100m, which serve as inputs for the theoretical wind energy parameters of a single unit.

[0020] S103. Define the optimization objective function for micro-site selection. The objective function is a comprehensive objective that integrates the total power generation, total wake loss and total construction cost throughout the entire life cycle. Set the spatial geometric constraints of the wind turbine placement sites and the geographical prohibition constraints based on the wind power construction prohibition area. The objective function F is expressed as: F = max(α*AEP_net - β*C_total) Where AEP_net is the net power generation of all wind turbines after considering the wake effect, converted into the annual equivalent full-load hours; C_total is the present value of the total life cycle cost of the wind farm converted to the initial construction period; α is the economic conversion coefficient of power generation, which is related to the grid-connected electricity price; and β is the cost weighting coefficient.

[0021] The formula for calculating the net power generation (AEP_net) of all wind turbines in the field is as follows: AEP_net=Σ_{i=1}^{N}[P_i*(1-Loss_wake_i)*T] Where N is the total number of wind turbines, P_i is the theoretical power generation of the i-th wind turbine without wake effect, Loss_wake_i is the comprehensive wake loss rate of the i-th wind turbine calculated based on the engineering wake model, and T is the number of operating hours.

[0022] The present value of the total lifecycle cost C_total includes: equipment procurement cost C_equip, construction cost C_construct, and operation and maintenance cost C_o&m; wherein, the construction cost C_construct includes the cost of wind turbine foundation, road construction cost, and power collection line cost, and this cost is related to the wind turbine coordinates, terrain slope, and spatial distribution of prohibited construction areas, and the cost is estimated based on path and engineering quantity through the three-dimensional geographic information system platform.

[0023] S104. The objective function is solved iteratively using an intelligent optimization algorithm. Under the conditions of satisfying the spatial geometric constraints and geographical prohibition constraints, the optimal set of wind turbine location coordinates is searched to generate a micro-site selection scheme. The intelligent optimization algorithm is a genetic algorithm; the set of wind turbine location coordinates is used as the chromosome of the genetic algorithm for encoding; the fitness value of each generation of individuals is calculated by the objective function F; the crossover and mutation operations of the algorithm need to be modified in combination with the spatial geometric constraints and geographical prohibition constraints to ensure that the newly generated turbine location coordinates do not fall into the prohibited construction area and meet the minimum spacing requirements.

[0024] S105. Conduct a full life-cycle techno-economic evaluation of the generated micro-location schemes, calculate the net present value of the schemes, and screen the final schemes in conjunction with sensitivity analysis. The life-cycle techno-economic evaluation introduces a micro-level site selection economic coefficient K, which is used to compare the horizontal economic efficiency of different schemes after normalization. The formula for calculating the economic coefficient K is as follows: K=(NPV*(1-R_risk)) / (C_capex*PBP) Wherein: NPV is the net present value of the micro-location scheme; R_risk is the project risk adjustment coefficient obtained based on sensitivity analysis, with a value range of 0~1; C_capex is the static total investment cost of the scheme; PBP is the investment payback period of the scheme; and the higher the value of the economic coefficient K, the higher the risk-adjusted net present value obtained per unit investment cost and per unit payback period, and the better the economic performance.

[0025] The project risk adjustment factor R_risk is determined through the following steps: Wind speed uncertainty and grid-connected electricity price fluctuation were selected as key uncertainty factors. A Monte Carlo simulation based on the key uncertainties was performed on the net present value (NPV) of the micro-location scheme to obtain the probability distribution of NPV; The value of R_risk is determined by mapping the probability that the NPV is lower than the benchmark value and combining it with the industry average risk level.

[0026] Based on the above embodiments, the present invention avoids compliance risks from the source by embedding the prohibited construction area as a rigid constraint into the optimization model; and by integrating high-precision terrain and meteorological data in a three-dimensional geographic information platform to conduct high-resolution numerical simulation of wind fields based on the principles of computational fluid dynamics, the accuracy of wind resource spatiotemporal distribution assessment under complex terrain is significantly improved.

[0027] Reference Figure 2 , Figure 2 A structural diagram of an economically-based wind resource micro-situation system is provided for embodiments of the invention. The system includes: The data acquisition and platform construction module 100 is used to acquire spatial geographic information of the target wind farm, long-sequence meteorological data, and geographic constraint information of wind power prohibited construction areas, and to build a high-resolution three-dimensional geographic information system platform. The wind resource assessment module 200, based on the three-dimensional geographic information system platform, performs a refined assessment of wind resources and obtains the wind energy distribution parameters of the entire field and the theoretical wind energy parameters of a single unit. The wind energy distribution parameters include at least the Weibull distribution parameters of wind speed and the wind rose diagram. The optimization modeling module 300 is used to define the optimization objective function for micro-site selection. The objective function is a comprehensive objective that integrates the total power generation of the entire site, the total wake loss, and the total construction cost over the entire life cycle. It also sets the spatial geometric constraints of the wind turbine placement sites and the geographical prohibition constraints based on the wind power construction prohibition area. The optimization and solution module 400 is used to iteratively solve the objective function using an intelligent optimization algorithm. Under the conditions of satisfying the spatial geometric constraints and geographical prohibition constraints, it searches for the optimal set of wind turbine location coordinates and generates a micro-site selection scheme. The Economic Evaluation Module 500 is used to perform a full life-cycle techno-economic evaluation on the generated micro-site selection schemes, calculate the net present value of the schemes, and combine sensitivity analysis to screen the final schemes.

[0028] The implementation process of the functions and roles of each module in the above system is detailed in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0029] Based on the above embodiments, this invention also provides an electronic device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the device may also include various necessary network interfaces, a power supply, and other components.

[0030] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by an execution terminal or processor, can implement the methods provided in this invention; the storage medium may include: a USB flash drive, a portable hard drive, or a read-only memory (ROM). Various media that can store program code, such as ROM, RAM, magnetic disks, or optical disks.

[0031] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. An economically-based micro-situation method for wind resources, characterized in that, include: Acquire spatial geographic information of the target wind farm, long-sequence meteorological data, and geographic constraint information of wind power construction prohibited areas, and construct a high-resolution three-dimensional geographic information system platform; Based on the aforementioned three-dimensional geographic information system platform, a refined assessment of wind resources is conducted to obtain the wind energy distribution parameters of the entire field and the theoretical wind energy parameters of a single unit. The wind energy distribution parameters include at least the Weibull distribution parameters of wind speed and the wind rose diagram. Define an optimization objective function for micro-site selection. The objective function is a comprehensive objective that integrates the total power generation of the entire site, the total wake loss, and the total construction cost over the entire life cycle. Also set spatial geometric constraints on the available wind turbine locations and geographical prohibition constraints based on the wind power construction prohibition areas. The objective function is solved iteratively using an intelligent optimization algorithm. Under the conditions of satisfying the spatial geometric constraints and geographical prohibition constraints, the optimal set of wind turbine location coordinates is searched to generate a micro-site selection scheme. A full life-cycle techno-economic evaluation is conducted on the generated micro-site selection schemes, the net present value of the schemes is calculated, and the final scheme is selected by combining sensitivity analysis.

2. The economically-based wind resource micro-situation method according to claim 1, characterized in that, The detailed assessment of wind resources specifically includes: Long-series reanalysis meteorological data of the target wind farm are acquired, and the time series of the reanalysis meteorological data is corrected and the representativeness is verified by using on-site wind measurement data. Based on the corrected long-series wind data, the shape parameter k and scale parameter c of the Weibull distribution of wind speed at each grid point are obtained by fitting using the maximum likelihood method or the least squares method, and a wind rose diagram is generated statistically. On the aforementioned 3D geographic information system platform, high-precision terrain data, Weibull distribution parameters, and wind rose diagram data are integrated, and a 3D wind field simulation is performed using computational fluid dynamics methods to generate wind speed and wind power density distribution maps with a spatial resolution of no less than 100m×100m, which serve as inputs for the theoretical wind energy parameters of a single unit.

3. The economically-based wind resource micro-situation method according to claim 1, characterized in that, The objective function F is expressed as: F = max(α*AEP_net - β*C_total) Where AEP_net is the net power generation of all wind turbines after considering the wake effect, converted into the annual equivalent full-load hours; C_total is the present value of the total life cycle cost of the wind farm converted to the initial construction period; α is the economic conversion coefficient of power generation, which is related to the grid-connected electricity price; and β is the cost weighting coefficient.

4. The economically-based wind resource micro-situation method according to claim 3, characterized in that, The formula for calculating the net power generation (AEP_net) of all wind turbines in the field is as follows: AEP_net=Σ_{i=1}^{N}[P_i*(1-Loss_wake_i)*T] Where N is the total number of wind turbines, P_i is the theoretical power generation of the i-th wind turbine without wake effect, Loss_wake_i is the comprehensive wake loss rate of the i-th wind turbine calculated based on the engineering wake model, and T is the number of operating hours.

5. The economically-based wind resource micro-situation method according to claim 3, characterized in that, The present value of the total lifecycle cost C_total includes: equipment procurement cost C_equip, construction cost C_construct, and operation and maintenance cost C_o&m; wherein, the construction cost C_construct includes the cost of wind turbine foundation, road construction cost, and power collection line cost, and this cost is related to the wind turbine coordinates, terrain slope, and spatial distribution of prohibited construction areas, and the cost is estimated based on path and engineering quantity through the three-dimensional geographic information system platform.

6. The economically-based wind resource micro-situation method according to claim 1, characterized in that, The intelligent optimization algorithm is a genetic algorithm; the set of wind turbine location coordinates is used as the chromosome of the genetic algorithm for encoding; the fitness value of each generation of individuals is calculated by the objective function F; the crossover and mutation operations of the algorithm need to be modified in combination with the spatial geometric constraints and geographical prohibition constraints to ensure that the newly generated turbine location coordinates do not fall into the prohibited construction area and meet the minimum spacing requirements.

7. The economically-based wind resource micro-situation method according to claim 1, characterized in that, The life-cycle techno-economic evaluation introduces a micro-level site selection economic coefficient K, which is used to compare the horizontal economic efficiency of different schemes after normalization. The formula for calculating the economic coefficient K is as follows: K=(NPV*(1-R_risk)) / (C_capex*PBP) Wherein: NPV is the net present value of the micro-location scheme; R_risk is the project risk adjustment coefficient obtained based on sensitivity analysis, with a value range of 0~1; C_capex is the static total investment cost of the scheme; PBP is the investment payback period of the scheme; and the higher the value of the economic coefficient K, the higher the risk-adjusted net present value obtained per unit investment cost and per unit payback period, and the better the economic performance.

8. The economically-based wind resource micro-situation method according to claim 7, characterized in that, The project risk adjustment factor R_risk is determined through the following steps: Wind speed uncertainty and grid-connected electricity price fluctuation were selected as key uncertainty factors. A Monte Carlo simulation based on the key uncertainties was performed on the net present value (NPV) of the micro-location scheme to obtain the probability distribution of NPV; The value of R_risk is determined by mapping the probability that the NPV is lower than the benchmark value and combining it with the industry average risk level.

9. An economically-based wind resource micro-situation system, characterized in that, include: The data acquisition and platform construction module is used to acquire spatial geographic information of the target wind farm, long-sequence meteorological data, and geographic constraint information of wind power prohibited construction areas, and to build a high-resolution three-dimensional geographic information system platform. The wind resource assessment module, based on the three-dimensional geographic information system platform, performs a refined assessment of wind resources, and obtains the wind energy distribution parameters of the entire field and the theoretical wind energy parameters of a single unit. The wind energy distribution parameters include at least the Weibull distribution parameters of wind speed and the wind rose diagram. The optimization modeling module is used to define the optimization objective function for micro-site selection. The objective function is a comprehensive objective that integrates the total power generation of the entire site, the total wake loss, and the total construction cost over the entire life cycle. It also sets the spatial geometric constraints of the available wind turbine locations and the geographical prohibition constraints based on the wind power construction prohibition areas. The optimization solution module is used to iteratively solve the objective function using an intelligent optimization algorithm. Under the conditions of satisfying the spatial geometric constraints and geographical prohibition constraints, it searches for the optimal set of wind turbine location coordinates and generates a micro-site selection scheme. The economic evaluation module is used to perform a full life-cycle techno-economic evaluation of the generated micro-site selection schemes, calculate the net present value of the schemes, and combine sensitivity analysis to screen the final schemes.

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