Remote sensing map-based dispersed wind power site selection and constant volume method and system

Abstract

The invention relates to the technical field of scattered wind power site selection, and discloses a scattered wind power site selection and constant volume method and system based on a remote sensing map, and the method comprises the steps: collecting the remote sensing map data of a target region, and calculating the wind speed data of the target region; analyzing the sensitivity of the target area according to the wind speed data and remote sensing map data of the meteorological station, and evaluating the wind energy resource of the target area; performing multi-factor comprehensive evaluation by using a GIS technology in combination with wind energy resource evaluation and sensitivity analysis results, and screening out an optimal potential dispersed wind power site selection area; and calculating the optimal layout and capacity configuration of the wind turbine generator according to the wind energy resource evaluation result of the site selection area, and optimizing the configuration scheme of the wind turbine generator. By introducing multi-factor comprehensive evaluation, a dynamic adjustment mechanism and a comprehensive scoring system, the scientificity, accuracy and operability of dispersed wind power site selection are improved. The site selection process is optimized, the environmental influence is reduced, and the economical efficiency and social acceptability of the project are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dispersed wind power site selection, and in particular to a dispersed wind power site selection and capacity determination method and system based on remote sensing maps. Background Art

[0002] With the growth of global energy demand and the improvement of environmental protection awareness, the development and utilization of renewable energy has received more and more attention. As a clean and renewable energy source, wind energy has great development potential. However, the site selection and capacity determination of decentralized wind power is a complex process that requires consideration of multiple factors, such as wind energy resources, topography, land use type, environmental and social sensitivity. Traditional site selection methods mainly rely on on-site surveys and empirical judgments, and it is difficult to find the best solution. Therefore, there is an urgent need for a decentralized wind power site selection and capacity determination method based on remote sensing maps. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to scientifically evaluate environmental sensitivity and social sensitivity and exclude areas that are not suitable for construction, thereby optimizing site selection decisions and ensuring the safety, operability and economy of the project.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for site selection and capacity determination of distributed wind power based on remote sensing maps, comprising:

[0006] Collect remote sensing map data of the target area and calculate wind speed data of the target area;

[0007] Analyze the sensitivity of the target area and evaluate the wind energy resources in the target area based on the wind speed data from the meteorological station and the remote sensing map data;

[0008] Combined with the results of wind energy resource assessment and sensitivity analysis, GIS technology is used to conduct a comprehensive evaluation of multiple factors to screen out the best potential decentralized wind power site selection areas;

[0009] Based on the wind energy resource assessment results in the site selection area, the optimal layout and capacity configuration of wind turbines are calculated, and the configuration plan of wind turbines is optimized.

[0010] As a preferred solution of the method for site selection and capacity determination of distributed wind power based on remote sensing maps described in the present invention, wherein: the remote sensing map data of the target area includes topography, vegetation coverage and land use type;

[0011] Calculating wind speed data in the target area includes acquiring and preprocessing data using Landsat satellite images;

[0012] The daily wind speed values ​​at the meteorological stations were collected, and the inverse distance weighted method was used to interpolate the daily average wind speed grid map of the target area.

[0013] As a preferred solution of the method for site selection and capacity determination of distributed wind power based on remote sensing maps described in the present invention, wherein: analyzing the sensitivity of the target area includes analyzing the environmental sensitivity and social sensitivity of the target area, including ecological protection areas, residential areas and transportation facilities, and excluding areas that are not suitable for the construction of distributed wind power;

[0014] Assess geological disaster risks and ensure the safety of site selection;

[0015] Assess traffic conditions and accessibility to the power grid to ensure the feasibility and cost-effectiveness of the project.

[0016] As a preferred solution of the method for site selection and capacity determination of dispersed wind power based on remote sensing maps described in the present invention, wherein: evaluating the wind energy resources in the target area includes calculating the annual average wind speed and the monthly average wind speed in the target area according to the longitude and latitude data of the meteorological station;

[0017] Calculate the wind energy density, the formula is expressed as:

[0018]

[0019] in, represents air density, V represents the volume of air flowing through unit area per unit time; S represents the air circulation area perpendicular to the wind direction; t represents the air circulation time; W represents wind energy; w represents the wind energy flowing through a unit cross-section perpendicular to the wind direction per unit time.

[0020] To evaluate the wind energy resource potential in different regions, the formula is expressed as:

[0021]

[0022] in, represents the average wind energy density, V represents the wind speed at any time, and T represents the total number of hours.

[0023] As a preferred solution of the remote sensing map-based decentralized wind power site selection and capacity determination method of the present invention, wherein: screening out the best potential decentralized wind power site selection area includes assigning weights according to the importance of each factor;

[0024] Use GIS software to conduct a comprehensive evaluation of multiple factors; overlay each indicator layer, perform weighted calculation according to the pre-set weights, obtain the comprehensive score of each potential site, and select the area with the highest score as the preliminary site selection area.

[0025] As a preferred solution of the method for site selection and capacity determination of distributed wind power based on remote sensing maps described in the present invention, wherein: screening out the best potential distributed wind power site selection area also includes, according to the principal component analysis method, dividing the factors affecting the distributed wind power site selection area into three indicator layers; the first layer is the target layer, which is the suitability of distributed wind power, the second layer is the influencing factor layer, and the third layer is the sub-indicator layer selected in each influencing factor layer;

[0026] Introducing the dispersed wind power site selection suitability index Z, the formula is expressed as:

[0027]

[0028] Among them, Y i is the weight of the influencing factor, C ij is the sub-index layer, X ij is the sub-indicator layer weight;

[0029] The value range of Z is 0 to 1;

[0030] When the value of Z is close to 1, it means that the current area is suitable for establishing distributed wind power;

[0031] When the value of Z is close to 0, it means that it is not suitable to carry out wind power projects.

[0032] As a preferred solution of the remote sensing map-based decentralized wind power site selection and capacity determination method described in the present invention, wherein: calculating the optimal layout and capacity configuration of wind turbines includes, based on wind energy resource assessment results and on-site survey data, calculating the optimal layout and capacity configuration of wind turbines in each potential site selection area;

[0033] Optimize the configuration of wind turbines based on local climate characteristics and wind energy resource distribution;

[0034] Plan the spacing and arrangement between wind turbines to improve the overall efficiency of distributed wind power.

[0035] A decentralized wind power site selection and capacity determination system based on remote sensing maps, wherein:

[0036] A data collection module collects remote sensing map data of the target area and calculates wind speed data of the target area;

[0037] The analysis module analyzes the sensitivity of the target area and evaluates the wind energy resources in the target area based on the wind speed data of the meteorological station and the remote sensing map data;

[0038] The screening module combines the wind energy resource assessment and sensitivity analysis results, uses GIS technology to conduct a comprehensive multi-factor evaluation, and screens out the best potential decentralized wind power site selection areas;

[0039] The optimization module calculates the optimal layout and capacity configuration of wind turbines based on the wind energy resource assessment results in the site selection area, and optimizes the configuration plan of wind turbines.

[0040] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0041] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0042] Beneficial effects of the invention: The remote sensing map-based decentralized wind power site selection and capacity determination method provided by the invention can accurately screen out areas suitable for decentralized wind power construction and avoid high-risk and high-conflict areas through dual sensitivity analysis and comprehensive consideration of environmental sensitivity and social sensitivity. By introducing multi-factor comprehensive evaluation, dynamic adjustment mechanism and comprehensive scoring system, the scientificity, accuracy and operability of decentralized wind power site selection are improved. The site selection process is optimized, the environmental impact is reduced, the economic and social acceptance of the project is improved, and the sustainable development and practical application of wind power projects are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 An overall flow chart of a method for site selection and capacity determination of distributed wind power based on remote sensing maps provided in the first embodiment of the present invention;

[0045] Figure 2 A distributed wind power site suitability distribution map of a distributed wind power site selection and capacity determination method based on remote sensing maps is provided as a first embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0047] Example 1, reference Figure 1 and Figure 2 , which is an embodiment of the present invention, provides a method for site selection and capacity determination of distributed wind power based on remote sensing maps, including:

[0048] S1: Collect remote sensing map data of the target area and calculate the wind speed data of the target area.

[0049] Collect remote sensing map data of the target area, including topography, vegetation cover, land use type and other information;

[0050] Landsat satellite images are used for data acquisition and preprocessing to ensure high resolution and accuracy of the images.

[0051] The daily wind speed values ​​at the meteorological stations were collected, and the inverse distance weighted method was used to interpolate the daily average wind speed grid map of the target area.

[0052] Furthermore, by collecting multi-dimensional remote sensing map data (such as terrain, vegetation, land use, etc.) and meteorological data (daily wind speed), reliable basic data support is provided for subsequent wind energy resource assessment and site selection decisions. Using Landsat satellite images for high-resolution preprocessing ensures the accuracy of the data, and interpolating wind speed data through the inverse distance weighted method can accurately predict the wind speed distribution in the region, comprehensively reflect the wind energy potential and environmental characteristics of the target area, and lay the foundation for scientific and reasonable decentralized wind power site selection.

[0053] S2: Analyze the sensitivity of the target area and evaluate the wind energy resources in the target area based on the wind speed data of the meteorological station and the remote sensing map data.

[0054] Calculate the annual average wind speed and monthly average wind speed in the target area based on the latitude and longitude data of the meteorological station;

[0055] Use formulas to calculate wind energy density and assess the wind energy resource potential in different regions;

[0056] Combined with topographic and geomorphic data, the impact of terrain on wind energy resources is analyzed to optimize the assessment results of wind energy resources.

[0057] Among them, the calculation of wind energy and wind energy density is as follows:

[0058] The calculation formula of wind energy is:

[0059]

[0060] Among them, ρ represents the air density, V represents the volume of air flowing through a unit area per unit time; S represents the air circulation area perpendicular to the wind direction; t represents the air circulation time; W represents the wind energy; w represents the wind energy flowing through a unit cross-section perpendicular to the wind direction per unit time.

[0061] The wind energy flowing through a unit cross section perpendicular to the wind direction per unit time is:

[0062]

[0063] w is the wind energy density at a certain moment.

[0064] From the above formula, it can be concluded that wind energy density is proportional to the cube of wind speed, so the prediction of wind speed distribution is particularly important. Wind energy density is an indicator of the wind energy potential of a place, because it not only indicates the frequency of occurrence of various wind speeds, but also the relationship with air density. The wind energy density obtained by the above formula is only the wind energy density at a certain moment, and it cannot directly evaluate the level of wind energy resources. Therefore, it is necessary to take the average wind energy density over a period of time to estimate the level of wind energy resources in a certain area. The details are as follows:

[0065] To evaluate the wind energy resource potential in different regions, the formula is expressed as:

[0066]

[0067] in, represents the average wind energy density, V represents the wind speed at any time, and T represents the total number of hours.

[0068] Analyze the environmental sensitivity and social sensitivity of the target area, including ecological protection areas, residential areas, transportation facilities and other factors, and exclude areas that are not suitable for the construction of decentralized wind power;

[0069] When the coverage ratio of ecological protection areas in the target area exceeds 15%, or the area of ​​core ecological functional areas (such as wetlands and forest reserves) exceeds 10% of the total area, the area is judged as a highly environmentally sensitive area and is not suitable for the construction of distributed wind power. In addition, for geological disaster risk assessment, if the predicted earthquake magnitude in the area exceeds 6, or the frequency of floods has exceeded three times in the past ten years, the area is also excluded from the scope of suitable construction of distributed wind power.

[0070] When the density of residential areas in the target area exceeds 500 people per square kilometer, or the main transportation facilities (such as highways and railways) are less than 2 kilometers away from the proposed decentralized wind power site, the area is determined to be a high social sensitivity area and needs to be excluded. In addition, if the distance to the existing grid access point in the area exceeds 10 kilometers, the area is limited in terms of economy and operability and should also be excluded from the scope of suitable construction.

[0071] Furthermore, by accurately assessing the wind energy resource potential and sensitivity factors in the target area, and comprehensively considering environmental and social sensitivity, the scientific and sustainable site selection of decentralized wind power is ensured. By calculating wind energy density and predicting wind speed distribution, the assessment of wind energy resources is optimized, and factors such as terrain, ecological protection areas, residential areas, and transportation facilities are combined to exclude areas that are not suitable for construction, thereby reducing environmental risks, social conflicts, and improving the safety, economy, and social acceptance of wind power projects.

[0072] S3: Combined with the results of wind energy resource assessment and sensitivity analysis, GIS technology is used to conduct a comprehensive evaluation of multiple factors to screen out the best potential decentralized wind power site selection areas.

[0073] Assign weights to each factor based on its importance; for example, wind energy resources, terrain conditions, and land use types can be assigned weights of 0.6, 0.2, and 0.2, respectively; these weights can be adjusted to reflect the importance of different factors based on specific circumstances;

[0074] Use GIS software (ArcGIS) to conduct a comprehensive evaluation of multiple factors; superimpose each indicator layer together, perform weighted calculation according to the pre-set weights, and obtain a comprehensive score for each potential site;

[0075] Preliminary site selection and optimization Based on the comprehensive evaluation results, the areas with the highest scores are selected as preliminary site selection areas; detailed surveys are conducted on these areas to verify the accuracy of remote sensing map data and collect more detailed information, such as soil conditions, groundwater levels, vegetation coverage, etc.

[0076] The four main factors affecting the site selection of decentralized wind power are studied in detail: wind energy resource factor W, land use type L and terrain condition P. According to the hierarchical analysis method in operations research, the many factors affecting the site selection of decentralized wind power are classified and a set of appropriate evaluation index hierarchical structures are constructed. There will be different index structures for different needs and decision variables. The decision variable of the present invention is the suitability of decentralized wind power macro-site selection. The value coefficient is the product of the score and weight of each index. Considering that there are many factors affecting site selection, such as the potential danger of geological disasters, whether the frequency of wind direction is stable, and sudden meteorological disasters, etc., they cannot be expressed by a unified quantitative standard, but can only be expressed by some qualitative degree words. Therefore, according to the principal component analysis method, in this case, only a few factors with more obvious influence can be studied, while ignoring some minor factors. The three influencing factors are mainly obtained and divided into three index layers: the first layer is the target layer, that is, the suitability of decentralized wind power, the second layer is the influencing factor layer divided into three categories, and the third layer is the sub-index layer selected in each influencing factor layer.

[0077] Comprehensive evaluation weight table of each influencing factor:

[0078]

[0079] Combining the various indicators mentioned above, the dispersed wind power site selection suitability index Z can be introduced:

[0080]

[0081] The value range of the distributed wind power suitability index Z is [0, 1]. The closer the value is to 1, the more suitable it is for establishing distributed wind power. Otherwise, it is not suitable for carrying out wind power projects.

[0082] Furthermore, through a systematic multi-factor comprehensive evaluation method, combined with key factors such as wind energy resources, land use type and terrain conditions, the suitability of decentralized wind power site selection is scientifically evaluated. By using the analytic hierarchy process (AHP) and principal component analysis (PCA), the complex factors affecting site selection are quantified into specific evaluation indicators, and weighted calculations are performed according to preset weights to ensure the scientificity and accuracy of site selection decisions. The suitability index Z provides decision makers with a clear quantitative standard to help identify the most optimal site selection area and effectively improve the success rate and sustainability of decentralized wind power construction.

[0083] S4: Based on the wind energy resource assessment results of the site selection area, calculate the optimal layout and capacity configuration of wind turbines, and optimize the configuration plan of wind turbines.

[0084] Conduct detailed on-site surveys in the preliminary site selection areas to verify the accuracy of remote sensing map data.

[0085] Gather detailed information including soil conditions, groundwater levels, vegetation cover, etc.

[0086] Assess the possibility of grid access and construction difficulty to ensure the feasibility of the project.

[0087] Capacity planning specifically includes:

[0088] Based on the wind energy resource assessment results and on-site survey data, the optimal layout and capacity configuration of wind turbines in each potential site selection area are calculated.

[0089] Optimize the configuration of wind turbines based on local climate characteristics and distribution of wind energy resources.

[0090] Plan the spacing and arrangement between wind turbines to improve the overall efficiency of distributed wind power.

[0091] The economic analysis specifically includes:

[0092] Conduct economic analysis on wind power projects in each potential site selection area, including investment cost, operation and maintenance costs, expected benefits, etc.

[0093] Evaluate the economic benefits of the project based on factors such as market electricity prices.

[0094] Use financial models to make long-term economic benefit forecasts to ensure the sustainability of the project.

[0095] In S7, the maximum output of the fan is calculated using the following formula:

[0096]

[0097] Where P represents the power generation of the wind turbine blade area along the direction of wind; represents air density; A represents wind direction area; V represents wind speed; K represents wind force coefficient.

[0098] The final site selection and capacity determination include:

[0099] Taking into account the technical feasibility and economic analysis results, the best decentralized wind power site selection and capacity configuration plan is selected.

[0100] Furthermore, the best optimization of decentralized wind power site selection and wind turbine configuration is ensured through comprehensive wind energy resource assessment, site investigation, capacity planning and economic analysis. The accuracy of remote sensing data is verified through detailed site investigation, and the feasibility and construction difficulty of the project are evaluated in combination with detailed information such as soil, groundwater, vegetation, etc. At the same time, the overall efficiency of decentralized wind power is improved by optimizing the layout, spacing and arrangement of wind turbines. The economic analysis combines factors such as investment cost, maintenance cost and market electricity price to ensure the long-term sustainability of the project. The final site selection and capacity determination process ensures the maximization of the project in terms of technical feasibility and economic benefits.

[0101] Embodiment 2 is an embodiment of the present invention, which provides a decentralized wind power site selection and capacity determination system based on remote sensing maps, including:

[0102] The data collection module collects remote sensing map data of the target area and calculates the wind speed data of the target area.

[0103] The analysis module analyzes the sensitivity of the target area and evaluates the wind energy resources in the target area based on the wind speed data of the meteorological station and the remote sensing map data.

[0104] The screening module combines the wind energy resource assessment and sensitivity analysis results, uses GIS technology to conduct a comprehensive multi-factor evaluation, and screens out the best potential decentralized wind power site selection areas.

[0105] The optimization module calculates the optimal layout and capacity configuration of wind turbines based on the wind energy resource assessment results in the site selection area, and optimizes the configuration plan of wind turbines.

[0106] Embodiment 3, an embodiment of the present invention, is different from the first two embodiments in that:

[0107] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0109] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0110] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0111] Example 4 is an embodiment of the present invention, which provides a method and system for site selection and capacity determination of distributed wind power based on remote sensing maps. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0112] The target area is a mountainous area with great potential for wind energy resources, with a total area of ​​1,000 square kilometers. The climate conditions in this area are relatively changeable, and it is suitable as a potential area for decentralized wind power construction. Through the method of the present invention, decentralized wind power site selection and capacity determination experiments are carried out in this area.

[0113] First, collect remote sensing map data of the target area, covering information such as topography, vegetation cover, and land use type. The data comes from Landsat satellite images with a resolution of 30 meters. After standardization and geometric correction, it provides basic data for wind energy resource analysis.

[0114] In terms of wind speed data collection, there are 8 meteorological stations in the target area, and daily wind speed data were obtained from 2018 to 2020. Based on these data, the inverse distance weighted method (IDW) was used to interpolate the target area to generate a daily wind speed grid map with a resolution of 1 km. The results show that the average annual wind speed in the area is about 6.2m / s, which is relatively stable and belongs to a medium wind energy area.

[0115] Next, a sensitivity analysis was conducted, including environmental sensitivity (ecological protection areas, residential areas, transportation facilities, etc.), social sensitivity (regional economy, population density, etc.), and natural disaster risks (such as earthquakes, floods, etc.) were evaluated. The results showed that there were no ecological protection areas in the area, but there were a small number of residential areas and more complex traffic conditions, so special evaluation was required. The geological disaster risk assessment showed that the area was relatively stable and had low flood and earthquake risks.

[0116] Based on the above wind energy resource assessment and sensitivity analysis, GIS technology was used to conduct a comprehensive multi-factor evaluation, taking into account factors such as wind energy resources, land use type, transportation convenience and ecological sensitivity. By setting weights (wind energy resources 0.4, land use type 0.2, transportation convenience 0.2, ecological sensitivity 0.2), the comprehensive score of each potential site was calculated, and the three best decentralized wind power site selection areas were selected.

[0117] In the process of capacity planning and wind turbine layout, the optimal layout of each site selection area is calculated in combination with the field survey data. According to the actual situation of the annual average wind speed and the field survey, the optimal wind turbine layout is determined to be an arrangement with a spacing of 6 times the diameter of the wind turbine, ensuring that there is enough wind speed space between each wind turbine to improve the overall power generation efficiency. The experimental results are shown in Table 1.

[0118] Table 1 Experimental data table

[0119]

[0120] It can be seen from the table that the decentralized wind power site selection and capacity determination method of the present invention can more accurately and scientifically select decentralized wind power sites for target areas compared with existing traditional methods. First, from the perspective of the average annual wind speed of wind energy resources, the wind speed in area C is relatively high (8.0m / s), and the land use type is grassland and forest, which provides more space for decentralized wind power construction, while the wind speed in areas A and B is relatively low. Areas with higher wind energy resources (such as area C) obviously have better wind power generation potential.

[0121] Secondly, combining the ecological sensitivity and traffic convenience scores, although region C has a low ecological sensitivity (score of 5), it has a high comprehensive score due to its convenient transportation and more suitable land use type for decentralized wind power construction. Regions A and B differ in ecological sensitivity and traffic convenience. Region A has a comprehensive score of 78, and a relatively low traffic convenience score. The comprehensive score shows that region C has better feasibility for wind power projects.

[0122] In terms of capacity planning and wind turbine layout, the present invention ensures that the wind turbine configuration in each area can achieve the best power generation efficiency by setting the wind turbine spacing to 6 times the wind turbine diameter (to maximize wind turbine utilization). By evaluating different wind speeds, terrain conditions and land use types, the capacity of area C is determined to be 180MW, which is much higher than the other two areas. This capacity configuration can maximize the use of the wind energy resources in the area.

[0123] Finally, the economic analysis results show that region C is not only rich in wind energy resources, but also has a short payback period (6.8 years) and an IRR of 14%, which is more optimized than the payback period and IRR of regions A and B, showing strong economic feasibility. These analysis results show that the method of the present invention can effectively improve the economic benefits of the project in the site selection and capacity determination of decentralized wind power.

[0124] Compared with traditional methods, the present invention not only improves the accuracy of site selection, but also reduces risks and improves economic benefits through comprehensive application of wind energy resource assessment, sensitivity analysis, GIS technology and precise layout optimization. This innovative method effectively makes up for the deficiency of existing technologies that fail to fully combine various multi-factor analyses, and has strong innovation and application prospects.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for site selection and capacity determination of distributed wind power based on remote sensing maps, characterized in that: include: Collect remote sensing map data of the target area and calculate wind speed data of the target area; Analyze the sensitivity of the target area and evaluate the wind energy resources in the target area based on the wind speed data of the meteorological station and the remote sensing map data; Combined with the results of wind energy resource assessment and sensitivity analysis, GIS technology is used to conduct a comprehensive evaluation of multiple factors to screen out the best potential decentralized wind power site selection areas; Based on the wind energy resource assessment results in the site selection area, the optimal layout and capacity configuration of wind turbines are calculated, and the configuration plan of wind turbines is optimized.

2. The method for site selection and capacity determination of distributed wind power based on remote sensing maps according to claim 1, characterized in that: The remote sensing map data of the target area includes topography, vegetation coverage and land use type; Calculating wind speed data in the target area includes acquiring and preprocessing data using Landsat satellite images; The daily wind speed values ​​at the meteorological stations were collected, and the inverse distance weighted method was used to interpolate the daily average wind speed grid map of the target area.

3. The method for site selection and capacity determination of distributed wind power based on remote sensing maps according to claim 2, characterized in that: Analyzing the sensitivity of the target area includes analyzing the environmental sensitivity and social sensitivity of the target area, including ecological protection areas, residential areas and transportation facilities, and excluding areas that are not suitable for the construction of decentralized wind power; Assess geological disaster risks and ensure the safety of site selection; Assess traffic conditions and accessibility to the power grid to ensure the feasibility and cost-effectiveness of the project.

4. The method for site selection and capacity determination of distributed wind power based on remote sensing maps according to claim 3, characterized in that: The evaluation of wind energy resources in the target area includes calculating the annual average wind speed and monthly average wind speed in the target area based on the latitude and longitude data of the meteorological station; Calculate the wind energy density, the formula is expressed as: Among them, ρ represents air density, V represents the volume of air flowing through a unit area per unit time; S represents the air circulation area perpendicular to the wind direction; t represents the air circulation time; W represents wind energy; w represents the wind energy flowing through a unit cross section perpendicular to the wind direction per unit time; To evaluate the wind energy resource potential in different regions, the formula is expressed as: in, represents the average wind energy density, V represents the wind speed at any time, and T represents the total number of hours.

5. The method for site selection and capacity determination of distributed wind power based on remote sensing maps according to claim 4, characterized in that: Screening out the best potential decentralized wind power site selection areas includes assigning weights according to the importance of each factor; Use GIS software to conduct a comprehensive evaluation of multiple factors; overlay each indicator layer, perform weighted calculation according to the pre-set weights, obtain the comprehensive score of each potential site, and select the area with the highest score as the preliminary site selection area.

6. The method for site selection and capacity determination of distributed wind power based on remote sensing maps according to claim 5, characterized in that: Screening out the best potential decentralized wind power site selection area also includes, based on the principal component analysis method, dividing the factors affecting the decentralized wind power site selection area into three indicator layers: the first layer is the target layer which is the suitability of decentralized wind power, the second layer is the influencing factor layer, and the third layer is the sub-indicator layer selected in each influencing factor layer; Introducing the dispersed wind power site selection suitability index Z, the formula is expressed as: Among them, Y i is the weight of the influencing factor, C ij is the sub-index layer, X ij is the sub-indicator layer weight; The value range of Z is 0 to 1; When the value of Z is close to 1, it means that the current area is suitable for establishing distributed wind power; When the value of Z is close to 0, it means that it is not suitable to carry out wind power projects.

7. The method for site selection and capacity determination of distributed wind power based on remote sensing maps according to claim 6, characterized in that: Calculating the optimal layout and capacity configuration of wind turbines includes calculating the optimal layout and capacity configuration of wind turbines in each potential site selection area based on wind energy resource assessment results and on-site survey data; Optimize the configuration of wind turbines based on local climate characteristics and wind energy resource distribution; Plan the spacing and arrangement between wind turbines to improve the overall efficiency of distributed wind power.

8. A remote sensing map-based decentralized wind power site selection and capacity determination system using the method according to any one of claims 1 to 7, characterized in that: A data collection module collects remote sensing map data of the target area and calculates wind speed data of the target area; The analysis module analyzes the sensitivity of the target area and evaluates the wind energy resources in the target area based on the wind speed data of the meteorological station and the remote sensing map data; The screening module combines the wind energy resource assessment and sensitivity analysis results, uses GIS technology to conduct a comprehensive multi-factor evaluation, and screens out the best potential decentralized wind power site selection areas; The optimization module calculates the optimal layout and capacity configuration of wind turbines based on the wind energy resource assessment results in the site selection area, and optimizes the configuration plan of wind turbines.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for site selection and capacity determination of distributed wind power based on remote sensing maps described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for site selection and capacity determination of distributed wind power based on remote sensing maps described in any one of claims 1 to 7 are implemented.

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