A renewable energy site selection method considering ecological service value

Abstract

The application relates to a renewable energy site selection method considering ecological service value and belongs to the technical field of renewable energy site selection. The application considers the ecosystem influence of energy deployment, and proposes a renewable energy site selection method which comprehensively considers energy supply potential, economic, social, technical cost and ecosystem influence, and the method comprises the following steps: collecting basic data, processing obtained geographic information data; screening ecosystem service types, and determining various indexes involved in site selection; setting a limiting factor, and removing unfeasible patches in a region to be selected; calculating traditional index values and ecosystem service value related index values; determining the index weight by using an analytic hierarchy process; and quantitatively evaluating site selection suitability to obtain a renewable energy suitability distribution map. Therefore, the economic, social, technical and ecological comprehensive benefits of renewable energy site selection are optimized.

Description

Technical Field

[0001] This application belongs to the field of renewable energy site selection technology, specifically relating to a renewable energy site selection method that takes into account the value of ecosystem services. Background Technology

[0002] With rapid urbanization and industrialization, energy-related carbon emissions have increased significantly, exerting a substantial impact on climate change. Renewable energy technologies (such as wind, solar, and biomass energy) are widely recognized as one of the most effective ways to reduce carbon emissions. However, the deployment of renewable energy facilities often brings about substantial changes to the ecosystem, inevitably affecting the quality of ecosystem services provided by these ecosystems. Therefore, a comprehensive renewable energy site selection strategy should proactively identify the potential impacts of site selection on ecosystem services.

[0003] In existing research, the site selection for renewable energy mainly considers energy potential factors, which are decisive criteria closely related to the production potential of renewable energy, such as wind power and photovoltaic power. A search revealed that Chinese invention patent application CN114819756A, published on July 29, 2022, discloses a method, device, and equipment for intelligent site selection of wind turbines based on a classification model. The method involves: obtaining an initial set of site selection areas; obtaining the historical average wind speed, historical high-frequency wind direction set, historical average turbine capacity coefficient, historical average turbulence intensity, and average altitude corresponding to each initial site selection area to form the regional wind force parameters for each initial site selection area; determining input vectors based on the wind force parameters for each region; inputting each input vector into a classification model to obtain its corresponding classification value; obtaining the target classification value that exceeds the classification threshold from among the classification values; and determining the initial site selection areas corresponding to each target classification value to form a candidate site selection area set. While this invention can quickly obtain a set of candidate sites and reduce the difficulty of obtaining site selection results, it only considers energy potential indicators and cannot accurately judge the technical, economic, and ecosystem suitability of the site selection.

[0004] Some scholars have proposed a multi-criteria decision-making approach to assess the site selection of renewable energy, considering multiple economic, social, and technological factors in addition to energy potential indicators. These factors generally include distance from the power grid, topographic and geological conditions, and land use type (Ge Shilong et al., Energy Infrastructure Site Selection Decision Based on Spatial and Technological Heterogeneity. Journal of Systems Management, 2018, 27(01):23-31+39). Combining geographic information technology with multi-criteria decision-making methods is more suitable for spatial planning of site selection. Existing applications mainly utilize various fuzzy methods such as the Analytic Hierarchy Process (AHP) to handle the inherent uncertainty in human subjective judgment (Shao Meng et al., Study on Tidal Energy Zoning of Shandong Province Based on GIS and Multi-criteria Decision-Making. Journal of Ocean University of China (Natural Science Edition), 2021, 51(12):107-114). These methods greatly improve the accuracy and reliability of renewable energy site selection assessment, but rarely consider the potential impact of site selection on ecosystem services. While a few studies have used ecosystem service-related indicators, such as bird sanctuaries and historical sites, as limiting areas for site selection, this approach is clearly insufficient to reflect the full impact of ecosystem services. Ecosystem service assessment methods are maturing, but their application is primarily in renewable energy assessment, rarely in renewable energy site selection. Currently, some scholars are considering incorporating ecosystem impacts into renewable energy site selection. The report "Ecologically Friendly Spatial Layout of Renewable Energy Development in China (2016-2030)" uses a landscape-scale analysis method of "development systems planning" to assess the balance between existing centralized wind and solar power projects and ecological protection in China, and provides planning recommendations for the spatial layout of ecologically friendly centralized wind and solar power development in the near and medium term, offering solutions for balancing renewable energy site selection and ecosystem service impacts. However, these methods do not easily produce accurately quantified site selection assessment results, and their studies do not further identify the impacts of different ecosystem service types. In reality, the impact of renewable energy deployment on different ecosystem service types is uneven. The site selection of renewable energy sources should further consider the extent of their impact on different ecosystems (Galparsoro I, Korta M et al., 2021. A new framework and tool for ecological risk assessment of wave energy converters projects, Renewable and Sustainable Energy Reviews. 151, 111539). Summary of the Invention

[0005] This application provides a renewable energy site selection method that considers the value of ecosystem services, in order to address the problem that existing renewable energy site selection methods do not adequately quantify the impact of ecosystem services.

[0006] This application provides a renewable energy site selection method that considers the value of ecosystem services, including the following steps:

[0007] (1) Obtain basic geographic information data of the site selection area and perform preprocessing;

[0008] (2) Determine the indicators used to evaluate site suitability, including two categories: traditional indicators and ecosystem service indicators;

[0009] (3) Set limiting factors and eliminate infeasible site selection patches in the site selection area;

[0010] (4) Calculate the indicators to obtain the initial data for each indicator;

[0011] (5) The weights of each indicator are determined using the analytic hierarchy process (AHP);

[0012] (6) Obtain the comprehensive value of the indicator based on the initial data and weights of the indicator;

[0013] Among them, the initial data of each indicator are scaled to the range of 0 to 1 using the min-max standardization method, and then the two types of indicators are respectively obtained by linear weighting method to obtain the comprehensive value of traditional indicators and the comprehensive value of ecosystem service indicators.

[0014] (7) Evaluate the site selection area based on the combined values ​​of the two types of indicators; present the spatial distribution results of the evaluation and obtain a renewable energy suitability distribution map;

[0015] Among them, the comprehensive value of traditional indicators is the economic benefit level value, the comprehensive value of ecosystem service indicators is the ecosystem service loss value, and the ratio of the economic benefit level value to the ecosystem service loss value is the site selection suitability efficiency value; the suitability of each plot in the site selection area is evaluated based on the site selection suitability efficiency value.

[0016] Optionally, in step (1), the preprocessing of the basic geographic information data of the site selection area includes: using the land use layer of the resource and environment data cloud platform, and using geographic information processing software to perform raster data resampling processing according to the classification of cultivated land, forest, grassland, water area, wetland and built-up area land.

[0017] Optionally, in step (2), the determination of ecosystem service indicators includes: selecting and determining ecosystem service subclass indicators from the international general classification of ecosystem services.

[0018] Optionally, ecosystem service sub-category indicators can be selected from the international general classification of ecosystem services using an expert scoring method.

[0019] Optionally, step (7) includes: removing areas smaller than 1 km² 2 In the region, geographic information software is used to present the final calculated site suitability efficiency values ​​of each plot. The area with the highest site suitability efficiency value is selected as the most suitable site location. The natural discontinuity classification method is used to evaluate the spatial distribution results of site suitability and obtain a renewable energy suitability distribution map.

[0020] Optionally, the renewable energy source is wind power. When selecting a site for a wind farm, the traditional indicators include wind speed, slope, distance from the city, distance from the road, and distance from the power grid. The ecosystem service impact indicators include food supply indicators, bird habitat indicators, and cultural ecosystem service indicators.

[0021] Optionally, in step (3), the limiting factors are five items, including: airport and its vicinity within 5km; scenic spots, nature reserves, highways, railways within 500 meters; residential areas, forests, lakes, wetlands; areas with a slope of more than 15°; and power grids within 250 meters.

[0022] Optionally, the food supply index is calculated as follows:

[0023] Using food crop yields to represent food supply services, the total food production is calculated using the following formula:

[0024]

[0025] Where Y represents total output, and i represents the type of grain crop. i，t Output represents the output of grain crop i in the t-th statistical year. i，t The sown area represents the sown area of ​​grain crop i in the t-th statistical year, and n represents the total number of years to be counted.

[0026] Based on total output and grain crop production potential data, initial data for food supply indicators are obtained using the following formula:

[0027]

[0028] Among them, y j Let be the average crop yield per unit area of ​​grid j, and let be the initial data for the food supply index of grid j;

[0029] Optionally, the calculation of the bird habitat index includes:

[0030] Using a maximum entropy model for species distribution, five consecutive years of bird records from a sub-database of the Global Biodiversity Information Database were selected, environmental variables were collected, and niche modeling software was used to simulate bird habitat services.

[0031] Optionally, the calculation of the cultural ecosystem service index includes:

[0032] The recreation opportunity spectrum method is used for evaluation, including the recreation potential index (RPI) and accessibility. The RPI is obtained by combining multiple potential recreation opportunities. The RPI is then integrated with the accessibility map to obtain the cultural ecosystem service index.

[0033] The potential recreational opportunities include: nature reserves and scenic areas that can provide important historical and educational services; areas within 500 meters of the coastline; and different land use types, which are listed in ascending order of equidistant values ​​as farmland, grassland, forest, water, and wetland.

[0034] Compared with existing technologies, the technical solution of this application achieves the following beneficial effects:

[0035] The renewable energy site selection method considering the value of ecosystem services proposed in this application integrates ecosystem service assessment into the renewable energy site selection process. By establishing ecosystem service indicators and quantifying them, these indicators are incorporated into the site selection analysis and evaluation method. This approach can fully quantify the impact of ecosystem services during the renewable energy site selection process and generate accurate and quantitative site selection assessment results.

[0036] Specifically:

[0037] (1) The renewable energy site selection method of this application combines the existing research on the accounting methods of traditional indicators and the ecosystem measurement model to account for the impact of ecosystem services. It adopts the method of analytic hierarchy process and the combination of the maximum-minimum standardization method and the ratio fusion method to quantify the site selection suitability efficiency value of each region in the study area. It can present a renewable energy suitability distribution map and accurately recommend the site selection location of renewable energy.

[0038] (2) The renewable energy site selection method of this application designs a site selection framework that includes ecosystem service impact indicators, which fills the gap in the field of renewable energy site selection that considers ecological impact.

[0039] (3) The renewable energy site selection method of this application is applicable to all types of renewable energy, including wind energy, solar energy, biomass energy, tidal energy, etc. It is also applicable to renewable energy site selection research at various research levels, including regional, national and global levels, and the application scope of this method is relatively wide. Attached Figure Description

[0040] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a renewable energy location method according to an embodiment of this application;

[0042] Figure 2 This is another schematic diagram of the renewable energy site selection method according to an embodiment of this application. Detailed Implementation

[0043] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be described in detail below with reference to the accompanying drawings and embodiments.

[0044] In this embodiment, renewable energy site selection takes wind farm site selection as the object and Shandong Province, China as the site selection area.

[0045] The renewable energy site selection method considering ecosystem service value described in this embodiment, such as Figure 1 and Figure 2 As shown, the details are as follows:

[0046] S1: Obtain basic geographic information data of the site selection area and perform preprocessing.

[0047] Specifically, in this embodiment, five consecutive years of daily average wind speed distribution data were obtained from the China Meteorological Data Network; remote sensing monitoring data, elevation data, crop growth potential data, economic intensity, and population density data for land use in China were obtained from the Resource, Environment and Science Data Center of the Chinese Academy of Sciences; airport location distribution data were obtained from the Civil Aviation Administration of China; tourist attraction distribution data were obtained from the website of the Ministry of Culture and Tourism of China; biological distribution data were obtained using the Global Biodiversity Information Database; and ArcGIS software was used to perform related processing such as unifying the coordinate system on the data, and the corresponding distribution data for Shandong Province was obtained after extraction. Other data were cited from yearbooks and existing research by others.

[0048] The preprocessing of basic geographic information data for the site selection area includes: using the land use layer of the resource and environment data cloud platform, and using geographic information processing software to perform raster data resampling processing according to the classification of cultivated land, forest, grassland, water area, wetland and built-up area land.

[0049] S2: Determine the indicators used to evaluate site suitability, including both traditional indicators and ecosystem service indicators.

[0050] In other words, based on traditional renewable energy site selection indicators and considering the ecosystem services they affect, the various indicators involved in renewable energy site selection are clarified. These traditional renewable energy site selection indicators include energy supply potential, economic, social, and technological indicators, with specific indicators determined based on the type of renewable energy, the background of the study area, and existing research findings. Ecosystem service value-related indicators are determined according to the internationally accepted classification of ecosystem services, including allocation services, regulation and maintenance services, and cultural services. These subcategories of ecosystem services affected by renewable energy site selection are screened using an expert scoring method.

[0051] The method for determining the ecosystem service indicators is as follows: sub-category indicators of ecosystem services are selected from the internationally recognized classification of ecosystem services by using an expert scoring method.

[0052] Specifically, in this embodiment, based on previous research, five traditional indicators were selected for wind farm site selection in the study area: wind speed, slope, distance from the city, distance from the road, and distance from the power grid, with wind speed representing the energy potential. Through literature review and expert scoring, three ecosystem service impact indicators were determined: food supply, bird habitat, and cultural ecosystem services.

[0053] S3: Set limiting factors to eliminate infeasible site selection patches in the site selection area.

[0054] In this step, limiting factors are set to eliminate infeasible patches within the proposed selection area. The limiting factors are mainly considered from the perspectives of ecological protection concepts, safety, and relevant policies and regulations. Geographic information processing technology is used to eliminate areas unsuitable for renewable energy site selection in the geographic system. Areas that need to be excluded include nature reserves, scenic areas and historical sites, and specific land use types such as forests, lakes, residential areas, and roads.

[0055] In this embodiment, considering policy and regulatory requirements, engineering safety, and ecological protection, five restrictive conditions were selected: airports and their vicinity within 5 km; scenic spots, nature reserves, highways, and railways within 500 meters; residential areas, forests, lakes, and wetlands; areas with a slope greater than 15°; and power grids within 250 meters. These restricted areas were created using a buffer tool in ArcGIS software, and after trimming, feasible deployment areas excluding the restricted areas were obtained.

[0056] S4 calculates the indicators to obtain the initial data for each indicator.

[0057] This step calculates relevant indicators related to energy potential, socio-economic factors, technological advancements, and ecosystem service value involved in renewable energy site selection. The calculation of traditional indicators such as energy potential, economic, social, and technological aspects of renewable energy site selection is based on existing research methods, with appropriate improvements to geographic information data processing methods. The selected ecosystem service indicators are calculated using existing research and corresponding ecosystem service measurement models for each indicator.

[0058] In this embodiment, the calculation methods for five traditional indicators (wind speed, slope, distance from power grid, distance from road, and distance from city) and three ecosystem service indicators (food supply, bird habitat, and cultural ecosystem services) are as follows:

[0059] The wind speed index uses five consecutive years of daily average wind speed distribution data obtained from the China Meteorological Data Network. Kriging interpolation is used to form wind profiles, and then the root mean square error and mean absolute error methods are used to determine the optimal interpolation map.

[0060] Slope data is generated using the slope tool in ArcGIS, which uses elevation data.

[0061] The three indicators—distance from the city, distance from the road, and distance from the power grid—are obtained by loading the data for each indicator into ArcGIS and using a distance analysis tool.

[0062] The calculation method for the food supply index is as follows:

[0063] Using grain crop yields to represent food supply services, the total grain output of Shandong Province is calculated using the following formula:

[0064]

[0065] Where Y represents total output (tons / square kilometer), using multi-year averages to mitigate market and climate fluctuations over many years. Where i represents the type of food crop, in this embodiment i represents the five major food crops (rice, wheat, corn, beans, and potatoes); food crops i，t Output represents the output of grain crop i in the t-th statistical year. i，t The sown area represents the sown area of ​​grain crop i in the t-th statistical year; n represents the total number of years to be counted. In this embodiment, n is 5, that is, the relevant average value of 5 years is counted.

[0066] Furthermore, using crop production potential data, a spatial dataset of food crop yields was obtained. Specifically, based on total yield and food crop production potential data, initial data for food supply indicators were obtained using the following formula:

[0067]

[0068] Among them, y j Let be the average crop yield per unit area of ​​grid j, and let be the initial data for the food supply index of grid j;

[0069] The calculation of the bird habitat index includes: using a maximum entropy model species distribution model, selecting bird records from a sub-database of the Global Biodiversity Information Database for five consecutive years, collecting environmental variables, and using niche model software to simulate bird habitat services.

[0070] Specifically, the spatial distribution of bird habitats was predicted using a maximum entropy model for species distribution. Five consecutive years of bird records from a sub-database of the Global Biodiversity Information Database were selected. In this embodiment, records from urban areas with a large number of data collectors were excluded to reduce data bias. A set of environmental variables was collected, including annual average temperature / precipitation, average temperature / precipitation during the wettest, driest, hottest, and coldest seasons, annual temperature range, isotherm, and precipitation seasonality (bioclimatic group), land use type, elevation, slope, and aspect (geographic group), and population density, economic density, and road distance (socioeconomic group). Bird habitat services were simulated using Maxent 3.4.1 software. The final output was represented as a logistic probability distribution. Output values ​​ranged from 0 to 1, with values ​​closer to 1 indicating suitable habitat for birds, and vice versa. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the prediction results.

[0071] The calculation of the cultural ecosystem service index includes: evaluation using the recreation opportunity spectrum method, including the Recreation Potential Index (RPI) and accessibility; obtaining the RPI through a comprehensive index of multiple potential recreation opportunities; and fusing the RPI with an accessibility map to obtain the cultural ecosystem service index; the potential recreation opportunities include: nature reserves and scenic spots that can provide important historical and educational services, areas within 500 meters of the coastline, and different land use types; the land use types are arranged in ascending order of equidistant values ​​as farmland, grassland, forest, water area, and wetland.

[0072] Specifically, the cultural ecosystem service index is evaluated using the recreation opportunity spectrum method, including the Recreation Potential Index (RPI) and accessibility. The RPI is obtained through a comprehensive index of multiple potential recreational opportunities. In this embodiment, three typical recreational areas were selected as potential recreation providers: (1) Nature reserves and scenic spots that can provide important historical and educational services. These places are assigned a value of 1, while others are assigned a value of 0. (2) Areas within 500 meters of the coastline that can provide beautiful and unique scenery were selected. These places are also assigned a value of 1, while others are assigned a value of 0. (3) Different land use types also provide their own unique landscapes. Isostatic values ​​from 0.2 to 1 were assigned to farmland (= 0.2), grassland (= 0.4), forest (= 0.6), water area (= 0.8), and wetland (= 1). Then, the raster maps of these three areas were aggregated and linearly scaled to the range of 0-1 according to their minimum and maximum values ​​to finally obtain the comprehensive index RPI. The distances to roads and urban areas were determined using Euclidean distance. The distances to roads and urban areas were categorized into 4 and 5 classes respectively using the natural discontinuity method, with each combination assigned a value from 1 to 5. Here, 1 represents the easiest accessibility, and 5 represents the least accessible. Finally, the Reachability Point Index (RPI) was fused with the accessibility map (RPI / accessibility value) to obtain the cultural ecosystem service index. In this study, grids with high RPI and high accessibility imply higher potential for cultural ecosystem service provision.

[0073] S5 uses the analytic hierarchy process (AHP) to determine the weights of each indicator.

[0074] The analytic hierarchy process (AHP) was used to determine the weights among traditional indicators and among ecosystem service indicators. The aggregation matrix was calculated by taking the geometric mean of the elements in each expert comparison matrix, checking the level of consistency, and then converting it into weight values.

[0075] Specifically, in this embodiment, based on the determined indicators, a questionnaire is distributed to experts, requesting them to evaluate the importance of each traditional indicator in wind farm site selection and the importance of wind farm site selection on ecosystem services, according to the judgment criteria shown in Table 1 below.

[0076] Table 1

[0077]

[0078] The comparison results are shown in Table 2 below. C1 to C5 represent five traditional indicators: wind speed, slope, distance from power grid, distance from road, and distance from city, respectively. E1 to E3 represent three ecosystem service indicators: food supply, bird habitat, and cultural ecosystem services, respectively.

[0079] Table 2

[0080]

[0081]

[0082] The aggregation matrix is ​​calculated by taking the geometric mean of the elements in each expert comparison matrix, and then converting it into weight values, as shown in Table 3 below:

[0083] Table 3

[0084]

[0085] Consistency checks showed that both the CR (traditional indicator judgment matrix) and CR (ecosystem service indicator judgment matrix) were less than 0.1, indicating reliability.

[0086] S6: Obtain the comprehensive value of the indicator based on the initial data and weights of the indicator;

[0087] The initial data of each indicator were rescaled to the range of 0 to 1 using the min-max standardization method, and then the two types of indicators were respectively obtained by using the linear weighting method to obtain the comprehensive value of the traditional indicator and the comprehensive value of the ecosystem service indicator.

[0088] S7: Evaluate the site selection area based on the combined values ​​of the two types of indicators; present the spatial distribution results of the evaluation to obtain a renewable energy suitability distribution map.

[0089] Among them, the comprehensive value of traditional indicators obtained by linear weighting of each traditional indicator is the economic benefit level value, and the comprehensive value of ecosystem service indicators obtained by linear weighting of each ecosystem service indicator is the ecosystem service loss value. The ratio of the economic benefit level value to the ecosystem service loss value is the site selection suitability efficiency value. The suitability of each plot in the site selection area is evaluated based on the site selection suitability efficiency value.

[0090] That is, the feasible site selection is evaluated by using the maximum-minimum standardization method combined with the ratio fusion method;

[0091] The specific calculation method of the max-min standardization method is as follows:

[0092] Positive benefit indicators:

[0093]

[0094] Negative benefit indicators:

[0095]

[0096] Specifically, the maximum-minimum standardization method is used to rescale the values ​​of five indicators—wind speed, slope, distance to the power grid, distance to the road, and distance to the city—to the range of 0 to 1. Positive benefit indicators include wind speed, distance to the road, and distance to the city; negative benefit indicators include slope and distance to the power grid. Then, the linear weighting method is used to merge the normalized standard maps to obtain the economic benefit level value of each map grid.

[0097] The economic benefit level value layer was divided into four categories according to the natural discontinuity method: highly feasible, feasible, less feasible, and infeasible. The area of ​​highly feasible sites in Shandong Province was found to be approximately 32,000 square kilometers.

[0098] The values ​​of three indicators—food supply, bird habitat, and cultural ecosystem services—were rescaled to a range of 0 to 1. A linear weighting method was used to merge and normalize the standard maps, resulting in the distribution of ecosystem service loss values ​​for each map grid. The ratio of economic benefit level to ecosystem service loss value was used as the site suitability efficiency value for quantitative assessment. The calculated site suitability efficiency value for each region represents the maximum potential economic benefit under a given ecological cost. Points with higher efficiency values ​​represent more suitable site locations.

[0099] Specifically, in this embodiment, areas smaller than 1 km² are excluded. 2 In a given region, geographic information software is used to present the final calculated site suitability efficiency values ​​for each plot, and the region with the highest efficiency value is selected as the most suitable site location.

[0100] In this embodiment, the site selection locations with the highest area suitability efficiency values ​​in Shandong Province are mainly distributed in Dongying City and Binzhou City.

[0101] The present application has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present application. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and implementation methods of the present application without departing from the spirit and scope of the present application, and all such modifications and improvements fall within the scope of the present application. The scope of protection of the present application is determined by the appended claims.

Claims

1. A renewable energy site selection method considering ecosystem service value, characterized in that, Includes the following steps: (1) Obtain basic geographic information data of the site selection area and perform preprocessing; (2) Determine the indicators used to evaluate site suitability, including two categories: traditional indicators and ecosystem service indicators; (3) Set limiting factors and eliminate infeasible site selection patches in the site selection area; (4) Calculate the indicators to obtain the initial data for each indicator; (5) The weights of each indicator are determined using the analytic hierarchy process (AHP); (6) Obtain the comprehensive value of the indicator based on the initial data and weights of the indicator; Among them, the initial data of each indicator are rescaled to the range of 0 to 1 using the min-max standardization method, and then the two types of indicators are respectively obtained by linear weighting method to obtain the comprehensive value of traditional indicators and the comprehensive value of ecosystem service indicators. (7) Evaluate the site selection area based on the combined values ​​of the two types of indicators; present the spatial distribution results of the evaluation and obtain a renewable energy suitability distribution map; Among them, the comprehensive value of traditional indicators is the economic benefit level value, and the comprehensive value of ecosystem service indicators is the ecosystem service loss value. The ratio of the economic benefit level value to the ecosystem service loss value is the site selection suitability efficiency value. The suitability of each plot in the site selection area is evaluated based on the site selection suitability efficiency value. The renewable energy source is wind power. When selecting a site for a wind farm, the traditional indicators include wind speed, slope, distance from the city, distance from the road, and distance from the power grid; the ecosystem service indicators include food supply indicators, bird habitat indicators, and cultural ecosystem service indicators. The calculation method for the food supply index is as follows: Using food crop yields to represent food supply services, the total food production is calculated using the following formula: ； Where Y represents total output, and i represents the type of grain crop. i，t Output represents the output of grain crop i in the t-th statistical year. i，t The sown area represents the sown area of ​​grain crop i in the t-th statistical year, and n represents the total number of years to be counted. Based on total output and grain crop production potential data, initial data for food supply indicators are obtained using the following formula: ； Among them, y j Let be the average crop yield per unit area of ​​grid j, and let be the initial data for the food supply index of grid j; The calculation of the bird habitat index includes: using a maximum entropy model species distribution model, selecting bird records from a sub-database of the Global Biodiversity Information Database for five consecutive years, collecting environmental variables, and simulating bird habitat services using niche modeling software; The calculation of the cultural ecosystem service indicators includes: The recreation opportunity spectrum method is used for evaluation, including the recreation potential index (RPI) and accessibility. The RPI is obtained by combining multiple potential recreation opportunities. The RPI is then integrated with the accessibility map to obtain the cultural ecosystem service index. The potential recreational opportunities include: nature reserves and scenic areas that can provide important historical and educational services; areas within 500 meters of the coastline; and different land use types, which are listed in ascending order of equidistant values ​​as farmland, grassland, forest, water, and wetland.

2. The renewable energy site selection method according to claim 1, characterized in that, In step (1), the preprocessing of basic geographic information data of the site selection area includes: using the land use layer of the resource and environment data cloud platform, and using geographic information processing software to perform raster data resampling processing according to the classification of cultivated land, forest, grassland, water area, wetland and built-up area land.

3. The renewable energy site selection method according to claim 1, characterized in that, In step (2), the determination of ecosystem service indicators includes: selecting and determining ecosystem service subclass indicators from the international general classification of ecosystem services.

4. The renewable energy site selection method according to claim 3, characterized in that, Ecosystem service sub-category indicators were selected from the international general classification of ecosystem services using an expert scoring method.

5. The renewable energy site selection method according to claim 1, characterized in that, Step (7) includes: removing areas smaller than 1 km² 2 In the region, geographic information software is used to present the final calculated site suitability efficiency values ​​of each plot. The area with the highest site suitability efficiency value is selected as the most suitable site location. The natural discontinuity classification method is used to evaluate the spatial distribution results of site suitability and obtain a renewable energy suitability distribution map.

6. The renewable energy site selection method according to claim 1, characterized in that, In step (3), the limiting factors are five items, including: airport and its vicinity within 5km; scenic spots, nature reserves, highways, and railways within 500 meters; residential areas, forests, lakes, and wetlands; areas with a slope of more than 15°; and power grids within 250 meters.

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