Method and system for evaluating wind power plant layout suitability from macroscopic perspective

Abstract

The invention provides a method and a system for evaluating the layout suitability of a wind power plant from a macroscopic perspective, and belongs to the technical field of prediction or optimization specially suitable for administrative or management purposes. Firstly, natural geographic data and social economic data are collected and preprocessed; determining a wind power plant site selection alternative area according to the preprocessed natural geographic data and social economic data; determining a wind power plant suitability evaluation index system and a weight; the wind power plant suitability evaluation index system comprises natural geographic factors and social economic factors of the wind power plant layout; based on natural geographic factors and social economic factors of the wind power plant layout, site selection natural condition optimization and social economic condition optimization are carried out on the wind power plant site selection alternative area; and performing overlay analysis and reclassification on respective optimization results of the wind power plant site selection natural condition optimization and the social economic condition optimization to obtain a final wind power plant site selection suitability evaluation result. According to the scheme, the suitability of the wind power plant layout can be evaluated macroscopically.

Description

Technical Field

[0001] The present application relates to the field of prediction or optimization technology specifically suitable for administrative or management purposes, and in particular to a method and system for evaluating the suitability of wind farm layout from a macroscopic perspective. Background Art

[0002] Wind energy is one of the most promising energy sources among all renewable energy sources in the energy market. The site selection of wind farms is the primary task of wind power generation. Site selection is crucial to the future development of wind farms and is an important link in the success of wind power projects. It may also become a bottleneck restricting the development of wind power. At present, there are many technologies and model methods to support decision-making in the macro-calculation of wind energy installed capacity and the macro-layout of wind energy development, but there are still some problems and challenges. For example, there is no research work on the appropriate spatial site selection decision of wind farms across the country that comprehensively considers social economy, natural geography and other factors, which is not conducive to the macro-calculation of wind energy installed capacity and the macro-layout of wind energy development.

[0003] Therefore, it is necessary to provide an improved technical solution to address the above-mentioned deficiencies in the prior art. Summary of the invention

[0004] The purpose of this application is to provide a method and system for evaluating the suitability of wind farm layout from a macro perspective, so as to solve or alleviate the above-mentioned problems existing in the process of wind farm site selection.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] This application provides a method for evaluating the suitability of wind farm layout from a macro perspective, including:

[0007] Collect and pre-process physical geographic and socio-economic data;

[0008] Determine the candidate sites for wind farms based on pre-processed physical geographic data and socio-economic data;

[0009] Determine a wind farm suitability evaluation index system and construct a hierarchical model, wherein the wind farm suitability evaluation index system includes natural geographical factors and socio-economic factors of wind farm layout;

[0010] Based on the natural geographical factors and socio-economic factors of the wind farm layout, the natural conditions and socio-economic conditions of the wind farm site selection candidate areas are optimized;

[0011] The optimization results of natural conditions optimization and socio-economic conditions optimization for wind farm site selection are superimposed and reclassified to obtain the final wind farm site suitability evaluation results.

[0012] In some optional embodiments, the socio-economic factors include but are not limited to: the distance between each wind farm site selection area and the power plant, the distance between each wind farm site selection area and the city's power consumption center, and the distance between each wind farm site selection area and the road.

[0013] In some optional embodiments, the distance between each wind farm site selection area and the city's power consumption center is calculated according to the following steps:

[0014] The population data and GDP data were superimposed with the national city-level vector data, and the patches with the highest population and GDP in each city were selected as the urban power consumption centers;

[0015] Among them, population data and GDP data are social economic data stored in raster format, and national city-level vector data are natural geographic data stored in vector format;

[0016] By converting raster to surface, the patch where the urban power consumption center is located is converted into vector surface data, and the Euclidean distance between it and each wind farm site selection area is calculated to obtain the distance between each wind farm site selection area and the urban power consumption center.

[0017] In some optional embodiments, the natural geographical factors include but are not limited to: wind speed factor and slope factor.

[0018] In some optional embodiments, the hierarchical structure model includes a target layer, a solution layer, and an indicator layer;

[0019] The target layer is the optimal wind farm site selection area, the solution layer is the candidate area for wind farm site selection, and the indicator layer includes natural geographical factors and socio-economic factors for wind farm layout.

[0020] In some optional embodiments, the weights are determined according to the following steps:

[0021] Construct a hierarchical model based on the target layer, solution layer, and indicator layer;

[0022] The pairwise comparison method was used to evaluate the relative importance of the elements at each level and form a judgment matrix;

[0023] The weight value of each factor relative to the target layer is calculated based on the judgment matrix.

[0024] In some optional embodiments, the candidate wind farm site selection area is determined based on the pre-processed natural geographical data and socio-economic data, specifically:

[0025] Comprehensively consider the influencing factors of geographical location, economy and environment to determine the exclusion criteria for wind farm site selection and identify areas that are not suitable for wind farm site selection;

[0026] The unsuitable areas for wind farm site selection are deducted from the possible site selection areas in the study area to obtain the alternative sites for wind farm site selection.

[0027] In some optional embodiments, the unsuitable areas for wind farm site selection include but are not limited to: unsuitable terrain areas, unsuitable ecological areas, and unsuitable structural areas.

[0028] In some optional embodiments, the optimization results of the natural conditions optimization and the socio-economic conditions optimization of the wind farm site selection are superimposed and analyzed and reclassified to obtain the final wind farm site suitability evaluation result, specifically:

[0029] Based on the reclassification results of the optimization of natural conditions and socio-economic conditions for wind farm site selection, the suitability classification map of slope factors, the suitability classification map of wind speed factors, the classification map of distances from power plants, the classification map of distances from urban power consumption centers, and the classification map of distances from roads are drawn;

[0030] The highest-level areas in each grading map are extracted, and the extracted results are superimposed to obtain the spatial distribution map of wind farm site suitability.

[0031] The present application also provides a system for evaluating the suitability of wind farm layout from a macroscopic perspective, including:

[0032] Data collection module, used to collect and pre-process physical geographic data and socio-economic data;

[0033] A candidate area determination module is used to determine the candidate areas for wind farm site selection based on pre-processed natural geographical data and socio-economic data;

[0034] The index embodies a construction module, which is used to determine the wind farm suitability evaluation index system and weights; the wind farm suitability evaluation index system includes natural geographical factors and socio-economic factors of wind farm layout;

[0035] Optimization module, used to optimize the natural conditions and social economic conditions of the wind farm site selection area based on the natural geographical factors and social economic factors of the wind farm layout;

[0036] The superposition and reclassification module is used to superimpose and reclassify the optimization results of the natural conditions optimization and socio-economic conditions optimization of wind farm site selection to obtain the final wind farm site suitability evaluation results.

[0037] The technical solution of the embodiment of the present application has the following beneficial effects:

[0038] In the technical solution of the present application, firstly, natural geographical data and socio-economic data are collected and pre-processed; based on the pre-processed natural geographical data and socio-economic data, the candidate sites for wind farms are determined; the wind farm suitability evaluation index system and weights are determined; the wind farm suitability evaluation index system includes natural geographical factors and socio-economic factors of wind farm layout; based on the natural geographical factors and socio-economic factors of wind farm layout, the site selection natural conditions and socio-economic conditions of the candidate sites for wind farms are optimized; the optimization results of the optimization of natural conditions and socio-economic conditions of wind farm site selection are superimposed and analyzed and reclassified to obtain the final evaluation results of the suitability of wind farm site selection. This solution comprehensively considers natural and social factors, and by optimizing natural conditions and socio-economic conditions separately, it can more comprehensively evaluate the overall suitability of each candidate area, ensure that the wind farm not only has natural resource advantages, but also can be coordinated with local economic development and social needs, and improve the scientific nature of wind farm site selection. At the same time, by setting the weights of different factors, the relative importance of different factors in the wind farm site selection process can be quantitatively evaluated to ensure that different factors are reasonably reflected in the decision-making and improve the objectivity and accuracy of the evaluation results. By superimposing the results of natural condition optimization and socio-economic condition optimization, we can make trade-offs in multiple dimensions to balance the impact of natural and socio-economic factors. The reclassification process can help decision makers adjust the suitability level according to the weight setting, ensure the optimal solution for comprehensive site selection, and be closer to the actual construction and operation needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:

[0040] Figure 1 A schematic flow chart of a method for evaluating the suitability of a wind farm layout from a macroscopic perspective according to some embodiments of the present application.

[0041] Figure 2 A technical logic diagram of a method for evaluating the suitability of a wind farm layout from a macroscopic perspective according to some embodiments of the present application.

[0042] Figure 3 A structural block diagram of a system for evaluating the suitability of wind farm layout from a macroscopic perspective according to some embodiments of the present application. DETAILED DESCRIPTION

[0043] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present disclosure belongs. The terms used herein are only for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0045] Method Embodiment

[0046] This embodiment provides a method for evaluating the suitability of wind farm layout from a macro perspective, aiming to solve the problems of where wind farms should be built nationwide, how to avoid the limitations of a single evaluation method and model, how to consider social economy, natural geography and other factors, and reveal the area and spatial distribution of suitable wind farm layouts nationwide. The method includes steps S101 to S105, which are as follows:

[0047] Step S101: Collect and pre-process physical geographic data and socio-economic data.

[0048] Among them, the natural geographic data may include: terrain data (such as digital elevation model DEM data), climate data (wind speed, wind direction), land use data, nature reserves, high-standard farmland, forest land, etc.; social and economic data may include power plant data (such as the location of power access points, power plant location, etc.), road network data, population density, GDP, etc. The above data can be obtained from public data sets, or obtained from relevant departments through application, which is not limited in this embodiment.

[0049] Step S102: determining a candidate site for a wind farm based on the pre-processed physical geographic data and socio-economic data.

[0050] Wind farm site selection is a complex task, involving economic technology, natural geography, social politics, environmental protection and many other aspects. In this embodiment, the candidate site selection area for wind farms refers to the candidate area with certain potential for wind farm development that is selected from many possible areas through analysis and evaluation in the early stage of macro-calculation of wind energy installed capacity and macro-layout of wind energy development. After preliminary research, these areas are considered to be likely to meet the natural conditions, socio-economic conditions and technical requirements for wind farm construction, but further detailed analysis and evaluation are needed to determine the best site selection.

[0051] The principles for wind farm site selection include the restrictive principle and the suitability principle.

[0052] The principle of suitability is to comprehensively consider factors such as the demand for electricity, traffic conditions, power grid development, land use, and environmental protection in the area where the wind farm is located, and select areas with certain wind energy resource potential, rich wind resources, stable wind speed and direction, and basic conditions for developing wind energy from a large area (such as the entire territory of China). In this embodiment, based on natural geographical data, the regions across the country can be preliminarily screened through data such as wind energy resources, geographical conditions, and infrastructure to obtain preliminary candidate areas.

[0053] The restrictive principle is used to exclude areas that obviously do not have development potential or are prohibited from development. In some embodiments, the wind farm site selection candidate areas are determined based on the restrictive principle, specifically: the wind farm site selection exclusion criteria are determined by comprehensively considering the influencing factors of geographical location, economy and environment, and the unsuitable wind farm site selection areas are determined; the unsuitable wind farm site selection areas are deducted from the possible site selection areas in the study area to obtain the wind farm site selection candidate areas.

[0054] Considering the construction cost and wind energy utilization efficiency, ecological protection concept, safety issues, relevant policies and regulations, terrain and other factors, the exclusion criteria for wind farm site selection may include but are not limited to: avoiding nature reserves and forests, minimizing the occupation of cultivated land, avoiding basic farmland, and excluding areas with a terrain undulation greater than 500 meters and a slope greater than 10°. The above-mentioned exclusion areas constitute unsuitable areas for wind farm site selection (unfeasible areas, unsuitable site selection areas) and include but are not limited to: unsuitable terrain areas, unsuitable ecological areas, and unsuitable structural areas. Based on the exclusion criteria, the exclusion rules can be expressed as:

[0055] A=S T -B (1)

[0056] B=K+E+C (2)

[0057] In the formula, A represents the suitable construction area, S T represents the total area, B represents the area unsuitable for construction, K represents the area unsuitable for terrain, E represents the area unsuitable for ecology, and C represents the area unsuitable for structure.

[0058] The exclusion ranges of topographically unsuitable areas, ecologically unsuitable areas, and structurally unsuitable areas are explained in detail below.

[0059] First, the terrain is not suitable for the area

[0060] The unsuitable terrain area can be determined according to the following steps:

[0061] (1) Areas with slopes > 10° are set as unsuitable areas;

[0062] (2) Considering that areas with elevations greater than 2000 m are at higher altitudes, as the air density decreases, the effectiveness of wind energy decreases, so 2000 m is used as the altitude cutoff;

[0063] (3) For terrain relief, areas with relief greater than 500 m are designated as unsuitable areas.

[0064] If any of the above conditions (1), (2), and (3) is met, the area is defined as an unsuitable terrain area and is excluded from the preliminary candidate areas. The expression is as follows:

[0065] K=S T -S Pi -RDLS (3)

[0066] S P =(AD / HD)×100% (4)

[0067]

[0068] Where K is the unsuitable terrain area; S P Represents the terrain slope, S Pi indicates the area with a slope of >10°, AD indicates the elevation difference, HD indicates the horizontal distance; RDLS indicates the terrain relief; ALT indicates the average altitude in the area; H max and H min is the relative elevation within the set area; is the flat area within the set area; S T is the total area of ​​the set area; 500 is the benchmark mountain height value in China.

[0069] Second, ecologically unsuitable areas

[0070] Natural heritage sites, national parks, nature reserves, forest parks, wetland parks, geological parks, scenic spots, major bird migration routes and migration areas are prohibited areas for wind farm projects. Among them, nature reserves are further divided into three categories: ecosystem type protection areas, biological species protection areas and natural heritage protection areas. The construction of wind farms will have a negative impact on the ecological environment. Therefore, the above areas can be organized into layers, and the layer overlay technology of the geographic information system (GIS) can be used to exclude the above areas from the preliminary candidate areas as ecologically unsuitable areas.

[0071] Third, structurally unsuitable areas

[0072] Structurally unsuitable areas refer to areas that are not suitable for wind farm development due to geological structure, urban construction and other reasons. Specifically, they include: geological disaster risk areas, residential and urban areas, military restricted areas, cultural heritage protection areas, etc. The above areas can be organized into layers and excluded from the preliminary candidate areas using the layer overlay technology of the geographic information system.

[0073] Step S103: determining a wind farm suitability evaluation index system and constructing a hierarchical structure model; the wind farm suitability evaluation index system includes natural geographical factors and socio-economic factors of wind farm layout.

[0074] The socio-economic factors include, but are not limited to: the distance between each wind farm site selection area and the power plant, the distance between each wind farm site selection area and the city's power consumption center, and the distance between each wind farm site selection area and the road.

[0075] Natural geographical factors include but are not limited to: wind speed factor and slope factor.

[0076] In this embodiment, evaluation indicators are selected based on natural geographical data and socio-economic data, and evaluation indicators are established starting from wind energy resources, natural conditions, traffic environment, location conditions, etc., and finally five single factors are selected for grading, including the distance of each wind farm site selection area from the power plant, the distance of each wind farm site selection area from the urban power consumption center, the distance of each wind farm site selection area from the road, wind speed factor, and slope factor.

[0077] Among them, the wind speed factor is used to reflect the quality of wind resources. This factor directly affects the amount of wind power generated, thereby affecting its power generation cost.

[0078] The slope factor is used to reflect the size of the terrain slope, which affects the installation, operating efficiency, infrastructure and maintenance costs of the wind turbine.

[0079] The distance from the power plant affects the transmission cost, transmission loss, and grid access, which in turn has a significant impact on the economic feasibility, technical feasibility, and overall benefits of the wind farm. The farther the distance, the higher the transmission cost, the greater the power loss, and the more construction and maintenance challenges. On the contrary, a wind farm site that is closer can help improve the project's economic benefits and grid-connected flexibility, and reduce environmental impacts and technical risks.

[0080] The distance from the city's power consumption center is used to measure the distance between the wind farm and the main power demand area. Wind farms close to the city's power consumption center can reduce transmission costs, reduce power loss, and improve the stability of power transmission. At the same time, they can better meet the city's power demand and enhance the economic benefits of the wind farm. In the macro selection study of wind farms, the distance from the city's power consumption center should take into account the overall planning of the city. It is necessary to leave enough development space for the city's future planning, but it should not be too far away to cause excessive power transportation costs. When selecting a site for a wind farm, attention should be paid to maintaining an appropriate distance from nearby residents, factories, enterprises and institutions (points) to minimize noise pollution.

[0081] The distance from the road is used to reflect the convenience of wind farm transportation and construction efficiency. Areas rich in wind energy resources are generally in relatively remote areas. Most sites need to widen existing roads and build new roads to meet the transportation of equipment. When selecting a site for a wind farm, you should understand the transportation conditions around the candidate wind farm. For sites with similar wind conditions, try to choose those that are close to existing roads and have convenient external transportation to help reduce road investment.

[0082] Traditional urban power consumption centers are usually estimated using power grid data through methods such as power load density analysis, power demand forecasting, and power grid layout analysis. These methods are difficult to accurately estimate potential power demand, and the difficulty in obtaining national power grid data leads to their insufficient applicability. Therefore, in this embodiment, the distance between each wind farm site selection area and the urban power consumption center is calculated according to the following steps: the population data and GDP data are superimposed with the national city-level vector data, and the patches with the highest population and GDP in each city are selected as the urban power consumption centers; wherein the population data and GDP data are social and economic data stored in raster format, and the national city-level vector data are natural geographic data stored in vector format; by converting raster to surface, the patches where the urban power consumption centers are located are converted into vector surface data, and the Euclidean distance between them and each wind farm site selection area is calculated to obtain the distance between each wind farm site selection area and the urban power consumption center.

[0083] Compared with traditional methods such as power load density analysis, electricity demand forecasting, and grid layout analysis, the method of superimposing population density on GDP to determine the location of urban power consumption centers has the advantages of convenient data acquisition, adaptability to urban development, reflection of potential power consumption demand, and greater flexibility and efficiency. Especially when the study area is large (such as the national scope) and data is limited, this method can better adapt to changes and provide effective preliminary guidance.

[0084] There are many factors that affect the site selection of wind farms. Simple qualitative analysis cannot complete systematic quantitative evaluation. This embodiment adopts the Analytical Hierarchy Process (AHP) to establish an evaluation model (i.e., a hierarchical structure model) for the site selection of wind farms to optimize the site selection decision. AHP is a weighted decision analysis method that combines qualitative and quantitative factors and has special advantages in multi-dimensional decision-making.

[0085] Specifically, the hierarchical model includes the target layer, the scheme layer, and the indicator layer; the target layer is the optimal wind farm site selection area, the scheme layer is the alternative area for wind farm site selection, and the indicator layer includes the natural geographical factors and socio-economic factors of the wind farm layout, including the distance from each wind farm site selection area to the power plant, the distance from each wind farm site selection area to the urban power consumption center, the distance from each wind farm site selection area to the road, the wind speed factor, and the slope factor. On this basis, the weights are determined according to the following steps: construct a hierarchical model based on the target layer, the scheme layer, and the indicator layer; use the paired comparison method to evaluate the relative importance of the elements at each level to form a judgment matrix; and calculate the weight value of each factor relative to the target layer based on the judgment matrix.

[0086] For example, the elements in the target layer, solution layer, and indicator layer can be paired up, and a 9-level scale can be used to compare the relative importance of each pair of elements at the same level, and score and quantify them to obtain a judgment matrix; then, based on the judgment matrix, the eigenvector method or arithmetic mean method is used to calculate the weight of each factor corresponding to the target layer, and finally the consistency ratio CR is used to evaluate the consistency between the decision maker and his response.

[0087] The AHP method is applied to the site selection of wind farms. The target layer of the hierarchical model is the optimal wind farm site selection area, the scheme layer is the alternative area for wind farm site selection, and the indicator layer is five single factor indicators. The five single factors can be compared pairwise to determine their relative importance, and then the weight of each factor can be calculated.

[0088] Step S104: Based on the natural geographical factors and socio-economic factors of the wind farm layout, the natural conditions and socio-economic conditions of the wind farm site selection candidate areas are optimized.

[0089] Step S105: superimpose and analyze the optimization results of the natural conditions optimization and the socio-economic conditions optimization of the wind farm site selection and reclassify them to obtain the final wind farm site suitability evaluation result.

[0090] In this embodiment, after determining each factor and its related weight, the Euclidean distance, data statistics, spatial analysis, weight overlay, reclassification and other functions provided in ArcGIS software are used to evaluate the suitability of the wind farm site selection within the study area, and the evaluation results of the suitability of the wind farm site selection are obtained, and the three slope suitability levels of high, medium and low are calculated.

[0091] Euclidean distance analysis is used to evaluate the distance between the candidate areas for wind farm site selection and other geographical elements (such as power plants, urban power consumption centers, and roads). For example, the candidate areas for wind farm site selection, power plants, urban power consumption centers, and roads can be grouped into different layers according to different elements and input into ArcGIS software, and the Spatial Analyst Tools->Distance->Euclidean Distance tool can be selected. After setting the parameters, the above function can be run to obtain the distance between the candidate areas for wind farm site selection and these elements.

[0092] Data statistics are used to organize, analyze and calculate different factor data during the site selection evaluation process, and to evaluate the distribution of various factors and their suitability. Specifically, statistical information within the wind farm site selection candidate area can be extracted based on the attribute data of each factor. For example, the Zonal Statistics tool in ArcGIS software can be used to calculate the average or total value of a factor (such as wind speed, terrain slope, etc.) in each area; the maximum, minimum, average, etc. within the area can also be calculated. Different statistical results can serve as the basis for the next step of suitability analysis.

[0093] Spatial analysis is used to integrate data from different factors for overall analysis, such as terrain slope analysis, land use type analysis, road network density analysis, etc. For example, a digital elevation model (DEM) can be used to calculate the slope distribution in the study area, that is, the slope grid can be calculated using the Slope tool of ArcGIS to ensure that the slope of the site selection area is suitable for wind farm construction. The land use type of a specific area can also be extracted through the Clip or Extract by Mask tool to identify which areas are suitable for wind farm construction and which areas are not suitable for wind farm construction.

[0094] Weighted overlay refers to the process of calculating the weight of each factor based on a multi-criteria decision-making method (such as the analytic hierarchy process, AHP), and overlaying natural geographical factors, economic and social factors to generate a comprehensive suitability evaluation result for a wind farm. Weighted overlay can combine the influence of multiple natural and socio-economic factors, such as wind speed, slope, land use, etc. Among them, each influencing factor can be organized in the form of a raster layer, and then the Weighted Overlay tool of ArcGIS is used to perform the weighted overlay operation to generate a comprehensive wind farm site suitability layer, showing the suitability scores of different regions. Regions with higher scores are more suitable for wind farm construction.

[0095] Reclassification is to reclassify the results after weight superposition for further analysis and visualization. By dividing the suitability results into multiple levels (such as high suitability, medium suitability, low suitability, etc.), the suitability of wind farm construction in different regions can be evaluated more intuitively.

[0096] Then, the candidate areas for wind farm site selection are optimized according to the degree of suitability to obtain the most suitable area for building a wind farm. The formula for suitability optimization is:

[0097]

[0098] In the formula, i represents the serial number of the candidate area, and T i Indicates transportation, WEP i Indicates wind power potential, S i represents the distance to the power consumption center of the city (obtained based on population and GDP data), S Pi Indicates areas with a terrain slope >10°, EPT i represents electric transportation; C1, C2, C3, C4, and C5 represent the weights of each index (i.e., the index value of each factor).

[0099] When processing the data of various indicators, since the standards used by each index are different, the standards of the results obtained are also different. In order to unify the dimensions and resolution of each factor, it is also necessary to standardize the values ​​of each factor and resample the results to make the data resolution consistent.

[0100] In some embodiments, step S105 specifically includes: based on the reclassification results of the natural conditions optimization and socio-economic conditions optimization of the wind farm site selection, drawing a slope factor suitability grading map, a wind speed factor suitability grading map, a distance grading map from the power plant, a distance grading map from the urban power consumption center, and a distance grading map from the road; extracting the highest-level areas in each grading map, and superimposing the extracted results to obtain a spatial distribution map of the wind farm site suitability.

[0101] Taking China as an example, we can draw a distribution map of areas that are not suitable for wind farm site selection in China based on wind farm site exclusion criteria such as forest distribution, distribution of areas with slopes greater than 10°, distribution of areas with terrain undulations greater than 500 meters, nature reserves, and high-standard farmland. Then, we can exclude these unsuitable areas from the preliminary candidate areas to obtain the final wind farm site selection candidate areas and draw a map of China's wind farm site selection candidate areas.

[0102] After the natural conditions for wind farm site selection are optimized, suitability classification based on slope factor (i.e., slope factor suitability classification map) and suitability classification map based on wind speed factor (i.e., wind speed factor suitability classification map) are drawn respectively. In the slope factor suitability classification map, the slope suitability of each wind farm candidate area is divided into three levels: high, medium, and low, and visualized on the map with different colors. In the wind speed factor suitability classification map, the wind speed suitability of each wind farm candidate area is divided into three levels: high, medium, and low, and visualized on the map with different colors.

[0103] Based on the results of the optimization of socio-economic conditions, the suitability of each factor is divided into three levels: high, medium, and low. A nationwide distance classification map is drawn for distances from power plants, distance classification maps for distances from urban power consumption centers, and distance classification maps for distances from roads. The suitability classification results are visualized on the map with different colors.

[0104] Finally, the highest-level areas in each classification map are extracted, and the extracted results are superimposed to obtain the spatial distribution map of wind farm site suitability, that is, to obtain the nationwide wind farm layout suitability assessment results.

[0105] For example, the method for evaluating the suitability of wind farm layout from a macroscopic perspective provided in this embodiment can be performed according to Figure 2 Implementation of the technical process shown, including:

[0106] Step 1: Data collection and preprocessing

[0107] Collect physical geographic data and socio-economic data. Physical geographic data include land use data, nature reserves, high-standard farmland, forest land, DEM data, wind speed data, etc. Socio-economic data include power plant data, road network data, population density, GDP, etc.

[0108] Step 2: Determine the unsuitable area for site selection

[0109] Based on factors such as nature reserves, forests, high-standard farmland, slopes, and terrain undulations, we determine ecologically unsuitable, structurally unsuitable, and topographically unsuitable areas, and combine the three to determine unsuitable areas for site selection. These unsuitable areas are then deducted from the preliminary candidate areas to obtain the candidate areas for wind farm site selection.

[0110] Step 3: Determine the site selection criteria for suitable areas

[0111] The distance from the power plant, the distance from the city (power consumption) center, and the distance from the road are selected as socioeconomic factors, and the wind speed and terrain are selected as natural geographical factors to construct a wind farm suitability evaluation index system. Among them, the distance from the power plant is used to reflect the power transportation capacity, the wind speed is used to reflect the wind potential, the terrain slope is used to reflect the wind turbine installation, transportation efficiency and maintenance cost, the distance from the road is used to reflect the transportation conditions, and the distance from the city power consumption center is used to measure the transportation cost and loss of wind energy on the way. This indicator is mainly determined based on the population and GDP distribution. Then the AHP hierarchical analysis method is used to determine the weight, and the weight of power transportation is 0.2412, the weight of wind potential is 0.5068, the weight of population and GDP is 0.0459, the weight of terrain slope is 0.07323, and the weight of transportation is 0.1337.

[0112] Step 4: Optimize based on natural conditions and socio-economic conditions

[0113] ArcGIS software is used to overlay and spatialize the optimization results, that is, the optimization results are drawn into a spatial distribution map of China's wind farm site suitability.

[0114] In summary, the method provided in the present embodiment first collects and pre-processes natural geographical data and socio-economic data. Secondly, absolute restrictions are determined from the three aspects of ecology, structure and terrain to exclude unsuitable areas. Then, in suitable areas, a suitability evaluation index system (distance from power plants, distance from urban power consumption centers, distance from roads, wind speed, terrain slope) and the weights of each index are determined through a bottom-up hierarchical structure evaluation system. Finally, the results are weighed and optimized based on natural conditions such as wind speed and slope and socio-economic conditions such as population comprehensive index and GDP, and ArcGIS software tools are used to realize site suitability sorting. Through the technical steps of the above-mentioned wind farm site selection, the suitability of the wind farm layout can be evaluated from a macro perspective, thereby revealing the area and spatial distribution of the suitable layout of China's wind farms.

[0115] The method provided in this embodiment can help design an optimization strategy for site selection of wind farms in China, provide a reference for reasonable planning of future site selection, and provide a theoretical basis and basis for the layout, site selection, and planning of large wind farms.

[0116] The methodology provided in this example can contribute to the standard wind farm site selection assessment currently in use. In particular, the developed paradigm can help planners identify the most suitable sites for wind energy development because it provides a comprehensive grading tool that can assess the suitability of potential sites at an early stage of planning.

[0117] System Example

[0118] This embodiment provides a system for evaluating the suitability of wind farm layout from a macro perspective, such as Figure 3 As shown, the system includes: a data collection module 301, a candidate area determination module 302, an indicator embodiment construction module 303, an optimization module 304 and a superposition and reclassification module 305. Among them:

[0119] Data collection module 301, used to collect physical geographic data and socio-economic data and perform pre-processing;

[0120] The candidate area determination module 302 is used to determine the candidate area for the wind farm site selection based on the pre-processed natural geographical data and socio-economic data;

[0121] The indicator embodiment construction module 303 is used to determine the wind farm suitability evaluation indicator system and weights; the wind farm suitability evaluation indicator system includes natural geographical factors and socio-economic factors of wind farm layout;

[0122] An optimization module 304 is used to optimize the natural conditions and social economic conditions of the wind farm site selection candidate area based on the natural geographical factors and social economic factors of the wind farm layout;

[0123] The superposition and reclassification module 305 is used to perform superposition analysis and reclassification on the optimization results of the natural conditions optimization and the socio-economic conditions optimization of the wind farm site selection, so as to obtain the final evaluation result of the suitability of the wind farm site selection.

[0124] The system for evaluating the suitability of wind farm layout from a macro perspective provided in this embodiment can implement the steps and processes of the method for evaluating the suitability of wind farm layout from a macro perspective provided in any of the above embodiments, and achieve the same technical effects, which will not be described in detail here.

[0125] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the suitability of wind farm layout from a macroscopic perspective, characterized in that: include: Collect and pre-process physical geographic and socio-economic data; Determine the candidate sites for wind farms based on pre-processed physical geographic data and socio-economic data; Determine a wind farm suitability evaluation index system and construct a hierarchical model, wherein the wind farm suitability evaluation index system includes natural geographical factors and socio-economic factors of wind farm layout; Based on the natural geographical factors and socio-economic factors of the wind farm layout, optimize the natural conditions and socio-economic conditions of the wind farm site selection candidate areas; The optimization results of natural conditions optimization and socio-economic conditions optimization for wind farm site selection are superimposed and reclassified to obtain the final wind farm site suitability evaluation results.

2. The method according to claim 1, characterized in that The social and economic factors include, but are not limited to: the distance between each wind farm site selection area and the power plant, the distance between each wind farm site selection area and the urban power consumption center, and the distance between each wind farm site selection area and the road.

3. The method according to claim 2, characterized in that Follow the steps below to calculate the distance between each wind farm site selection area and the city's power consumption center: The population data and GDP data were superimposed with the national city-level vector data, and the patches with the highest population and GDP in each city were selected as the urban power consumption centers; Among them, population data and GDP data are social economic data stored in raster format, and national city-level vector data are natural geographic data stored in vector format; By converting raster to surface, the patch where the urban power consumption center is located is converted into vector surface data, and the Euclidean distance between it and each wind farm site selection area is calculated to obtain the distance between each wind farm site selection area and the urban power consumption center.

4. The method according to claim 1, characterized in that: The natural geographical factors include but are not limited to: wind speed factor and slope factor.

5. The method according to claim 1, characterized in that The hierarchical structure model includes a target layer, a scheme layer, and an indicator layer; The target layer is the optimal wind farm site selection area, the solution layer is the candidate area for wind farm site selection, and the indicator layer includes natural geographical factors and socio-economic factors for wind farm layout.

6. The method according to claim 5, characterized in that Follow these steps to determine the weights: Construct a hierarchical model based on the target layer, solution layer, and indicator layer; The pairwise comparison method was used to evaluate the relative importance of the elements at each level and form a judgment matrix; The weight value of each factor relative to the target layer is calculated based on the judgment matrix.

7. The method according to claim 6, characterized in that Based on the pre-processed natural geographical data and socio-economic data, the candidate sites for wind farms are determined, specifically: Comprehensively consider the influencing factors of geographical location, economy and environment to determine the exclusion criteria for wind farm site selection and identify areas that are not suitable for wind farm site selection; The unsuitable areas for wind farm site selection are deducted from the possible site selection areas in the study area to obtain the alternative sites for wind farm site selection.

8. The method according to claim 7, characterized in that The unsuitable areas for wind farm site selection include but are not limited to: unsuitable terrain areas, unsuitable ecological areas, and unsuitable structural areas.

9. The method according to claim 1, characterized in that: The optimization results of the natural conditions optimization and the socio-economic conditions optimization of the wind farm site selection are superimposed and analyzed and reclassified to obtain the final wind farm site suitability evaluation results, specifically: Based on the reclassification results of the optimization of natural conditions and socio-economic conditions for wind farm site selection, the slope factor suitability classification map, wind speed factor suitability classification map, distance classification map from power plants, distance classification map from urban power consumption centers, and distance classification map from roads are drawn; The highest-level areas in each grading map are extracted, and the extracted results are superimposed to obtain the spatial distribution map of wind farm site suitability.

10. A system for evaluating the suitability of wind farm layout from a macroscopic perspective, characterized in that: include: Data collection module, used to collect and pre-process physical geographic data and socio-economic data; A candidate area determination module is used to determine the candidate areas for wind farm site selection based on pre-processed natural geographical data and socio-economic data; The indicators embody the building blocks, which are used to determine the wind farm suitability evaluation indicator system and weights; The wind farm suitability evaluation index system includes natural geographical factors and socio-economic factors of wind farm layout; Optimization module, used to optimize the natural conditions and social economic conditions of the wind farm site selection area based on the natural geographical factors and social economic factors of the wind farm layout; The superposition and reclassification module is used to superimpose and reclassify the optimization results of the natural conditions optimization and socio-economic conditions optimization of wind farm site selection to obtain the final wind farm site suitability evaluation results.

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