A high-resolution onshore wind power resource assessment method and system

Abstract

The application discloses a high-resolution land wind power generation resource evaluation method and system, the method establishes a plane grid model of a target region, and matches the grid in the model with geographical space resources in combination with ground surface covering data and digital elevation data, screens out the grid with terrain conditions for constructing a wind power generator, and then evaluates the wind power resource potential of the grid in combination with historical wind speed data, so that the distribution of the wind power resource in the target region can be determined.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of electrical engineering, and particularly relates to a high-resolution land wind power generation resource evaluation method and system. BACKGROUND

[0002] In recent years, due to the increasingly serious climate problem of global warming and the exhaustion of fossil energy, in order to fill the gap in electricity demand caused by the shortage of fossil energy, it is necessary to vigorously develop pollution-free renewable energy. As a kind of renewable energy, wind energy is not only widely distributed and abundant, but also clean and friendly to the environment, which helps to alleviate the greenhouse effect. Studies have shown that the total wind energy reserves in the global atmosphere are about 3.8x1016kWh, including 4.3x1012kWh of exploitable wind energy, which is about 10 times the amount of water energy available worldwide. In order to further develop and utilize wind energy resources, it is necessary to evaluate the regional wind power generation resources. The current wind power generation resource evaluation method is generally to directly obtain the wind speed data of the target region, and then combine the wind speed-wind power conversion relationship to obtain the wind power data of the target region, and evaluate the wind power generation resources of the target region.

[0003] The existing evaluation of wind power generation resources is mainly based on radar detection, satellite inversion, meteorological model numerical simulation, mesoscale numerical simulation, computational fluid dynamics (CFD) numerical simulation, geographic information system (GIS) and other methods. Among them, the methods based on radar detection, satellite inversion, geographic information system (GIS) often have the problem of inconsistent observation height and inconsistent time resolution, resulting in low accuracy of wind power resource evaluation; and the methods based on meteorological model numerical simulation, mesoscale numerical simulation, computational fluid dynamics numerical simulation mainly focus on the evaluation of the total amount of wind power resources without matching the geographical space resources with the actual topography, and cannot clearly determine the distribution of wind power resources within the target region. SUMMARY

[0004] The purpose of the present application is to provide a high-resolution land wind power generation resource evaluation method.

[0005] Another purpose of the present application is to provide a high-resolution land wind power generation resource evaluation system.

[0006] To achieve the above purpose, the present application adopts the following technical solutions:

[0007] A high-resolution land wind power generation resource evaluation method, comprising:

[0008] constructing a planar grid model of a target region;

[0009] obtaining ground cover data, digital elevation data and historical wind speed data of each grid in the planar grid model;

[0010] According to the digital elevation data of each grid in the plane grid model, the grids where wind turbines can be built are screened out as the to-be-evaluated grids;

[0011] According to the land cover data of each to-be-evaluated grid, the available area of each to-be-evaluated grid is calculated;

[0012] According to the available area of each to-be-evaluated grid, the rated capacity and the blade diameter data of a typical wind turbine, the installed capacity of each to-be-evaluated grid is determined;

[0013] According to the historical wind speed data of each grid and the hub height data of a typical wind turbine, the wind power resource capacity factor of each to-be-evaluated grid is calculated;

[0014] According to the installed capacity of each to-be-evaluated grid and the wind power resource capacity factor, the wind power resource capacity of each to-be-evaluated grid is calculated, and the wind power resources of each to-be-evaluated grid are evaluated according to the size of the wind power resource capacity of each to-be-evaluated grid.

[0015] Further, the length of each grid in the plane grid model ranges from 0.01° to 0.25° in latitude, and the width ranges from 0.01° to 0.25° in longitude.

[0016] Further, the land cover data, the digital elevation data and the historical wind speed data of each grid in the plane grid model are obtained by the following method:

[0017] Obtain the land cover data, the digital elevation data and the historical wind speed data of the target area;

[0018] Uniform the resolution of the land cover data, the digital elevation data and the historical wind speed data of the target area to the grid resolution of the constructed plane grid model by using the KNN algorithm.

[0019] Further, the method of uniforming the resolution of the land cover data, the digital elevation data and the historical wind speed data of the target area to the grid resolution of the constructed plane grid model by using the KNN algorithm is as follows:

[0020] 1) Construct a training set according to the plane grid model, and the i-th training sample in the training set is denoted as (X i ,Y i ), X i is the central point longitude of the i-th training sample, i.e., the central point longitude of the i-th grid in the plane grid model; Y i is the central point latitude of the i-th training sample, i.e., the central point latitude of the i-th grid in the plane grid model; i = 1, 2, …, I, I is the total number of training samples;

[0021] According to the land cover data / digital elevation data / historical wind speed data, a test set is constructed, and a jth test sample in the test set is denoted as (X j ,Y j ), X j is a central point longitude of the jth test sample, i.e., a central point longitude of a jth grid in the land cover data / digital elevation data / historical wind speed data; Y j is a central point latitude of the jth test sample, i.e., a central point latitude of the jth grid in the land cover data / digital elevation data / historical wind speed data; j = 1, 2, …, J, and J is a total number of test samples;

[0022] 2) The spatial distance between each test sample in the test set and each training sample in the training set is calculated respectively, wherein the spatial distance between the jth test sample in the test set and the ith training sample in the training set is calculated according to the following formula:

[0023] d ji = R · arcos [cos(Y i ) · cos(Y j ) · cos(X i -X j ) + sin(Y i ) · sin(Y j )]

[0024] wherein R is the radius of the earth, R is 6371 km; X i and Y i are the central point longitude and latitude of the ith training sample respectively; X j and Y j are the central point longitude and latitude of the jth test sample respectively.

[0025] 3) The spatial distance between each test sample and different training samples is sorted respectively, and the training sample having the smallest spatial distance with each test sample is found.

[0026] 4) The land cover data / digital elevation data / historical wind speed data contained in the test sample are assigned to the corresponding training sample, and the land cover data / digital elevation data / historical wind speed data under the grid resolution of the plane grid model are obtained.

[0027] Further, the specific steps of screening the grid on which the wind turbine can be constructed according to the digital elevation data of each grid in the plane grid model include:

[0028] The height data and slope data of each grid in the plane grid model are obtained, and the grid with a height less than or equal to 3000 meters and a slope less than or equal to 10° is screened.

[0029] Further, the specific step of calculating the available area of each to-be-evaluated grid according to the land cover data of each to-be-evaluated grid comprises:

[0030] acquiring the land cover type of each grid in the planar grid model, and calculating the available area of each to-be-evaluated grid according to the available area coefficient corresponding to the land cover type.

[0031] Further, the specific step of determining the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid and the rated capacity and blade diameter data of a typical wind turbine comprises:

[0032] determining the unit land area of the typical wind turbine according to the rated capacity data and blade diameter data of the typical wind turbine, and obtaining the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid.

[0033] Further, the specific step of calculating the wind power resource capacity factor of each to-be-evaluated grid according to the historical wind speed data of each grid and the hub height data of the typical wind turbine comprises:

[0034] acquiring the historical wind speed data of each to-be-evaluated grid

[0035] extrapolating the acquired historical wind speed data to obtain the historical wind speed data of each to-be-evaluated grid at an arbitrary height, and further obtaining the historical wind speed data of each to-be-evaluated grid at the hub height of the typical wind turbine;

[0036] obtaining the historical output power of each to-be-evaluated grid when the typical wind turbine is used according to the wind speed-power curve corresponding to the typical wind turbine and the historical wind speed data of each to-be-evaluated grid at the hub height of the typical wind turbine;

[0037] calculating the wind power resource capacity factor of each to-be-evaluated grid according to the historical output power of each to-be-evaluated grid when the typical wind turbine is used.

[0038] Further, the land cover data adopts the type I land cover data set in MCD12Q1; the digital elevation data adopts the STRM digital elevation set

[0039] , and the historical wind speed data adopts the historical wind speed data set of the European Centre for Medium-Range Weather Forecasts.

[0040] A high-resolution onshore wind power resource evaluation system comprises:

[0041] a model construction module, configured to construct a planar grid model of a target region;

[0042] A data acquisition module is configured to acquire land cover data, digital elevation data, and historical wind speed data of each grid in the planar grid model;

[0043] A data processing module is configured to filter out grids where wind power generators can be constructed from the planar grid model according to the digital elevation data of each grid, as to-be-evaluated grids; calculate the available area of each to-be-evaluated grid according to the land cover data of each to-be-evaluated grid; determine the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid, the rated capacity of a typical wind power generator, and the blade diameter data; and calculate the wind power resource capacity factor of each to-be-evaluated grid according to the historical wind speed data of each grid and the hub height data of the typical wind power generator.

[0044] An evaluation module is configured to calculate the wind power resource capacity of each to-be-evaluated grid according to the installable capacity of each to-be-evaluated grid and the wind power resource capacity factor, and evaluate the wind power resource of each to-be-evaluated grid according to the size of the wind power resource capacity of each to-be-evaluated grid.

[0045] Compared with the prior art, the present application has the following beneficial effects:

[0046] The present application provides a high-resolution onshore wind power resource evaluation method and system, which establishes a planar grid model of a target region, matches the grids in the model with geographical space resources in combination with land cover data and digital elevation data, filters out grids with terrain conditions for constructing wind power generators, and evaluates the wind power resource potential of the filtered grids in combination with historical wind speed data, so that the distribution of wind power resources in the target region can be determined.

[0047] The evaluation method of the present application designs the length and width of the grid, establishes a high-resolution planar grid model, and unifies the spatial scales of the land cover data, digital elevation data, and historical wind speed data of the target region based on the resolution of the planar grid model, to obtain high-resolution land cover data, digital elevation data, and historical wind speed data, which are combined to evaluate the wind power resources of the target region, so that the evaluation results have the advantages of high refinement and high accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The flowchart of the evaluation method of the present application is shown in the figure; DETAILED DESCRIPTION

[0049] Example 1

[0050] A high-resolution onshore wind power resource evaluation method, as shown in the figure, comprises: Figure 1

[0051] ​1) Construct a planar grid model of the target area; the length of the grid in the constructed planar grid model ranges from 0.01° to 0.25° latitude, and the width ranges from 0.01° to 0.25° longitude. In this example, the scale of the grid in the planar grid model is selected according to the resolution of the digital elevation data set, and the length of the grid is determined to be 0.05° latitude and the width to be 0.05° longitude (about 5.57 km).

[0052] 2) Obtain the land cover data, digital elevation data, and historical wind speed data of each grid in the planar grid model. The specific method of this step includes:

[0053] Obtain the land cover data, digital elevation data of the target area; in this example, the land cover data uses the I-type land cover data set in MCD12Q1, and the spatial resolution of this data set is 500 m, and the spatial resolution near the equator is about 0.00463°; the digital elevation data uses the STRM digital elevation set, and the spatial resolution of this data set is 0.05° longitude * 0.05° latitude; the historical wind speed data uses the historical wind speed data set of the European Centre for Medium-Range Weather Forecasts, and the spatial resolution of this data set is 0.25° longitude * 0.25° latitude.

[0054] The resolution of the land cover data, digital elevation data, and historical wind speed data of the target area is unified to the grid resolution of the constructed planar grid model using the KNN algorithm. Since the grid resolution of the planar grid model constructed in this example is consistent with the digital elevation data, only the resolution of the land cover data and the historical wind speed data needs to be unified. The specific steps of unifying the resolution include

[0055] First, construct a training set according to the planar grid model, and the i-th training sample in the training set is denoted as (X i ,Y i ), X i is the central point longitude of the i-th training sample, i.e., the central point longitude of the i-th grid in the planar grid model; Y i is the central point latitude of the i-th training sample, i.e., the central point latitude of the i-th grid in the planar grid model; i = 1, 2,..., I, and I is the total number of training samples;

[0056] Construct a test set according to the land cover data / historical wind speed data, and the j-th test sample in the test set is denoted as (X j ,Y j ), X j is the central point longitude of the j-th test sample, i.e., the central point longitude of the j-th grid in the land cover data / historical wind speed data; Y jYj is the latitude of the center point of the jth test sample, i.e., the latitude of the center point of the jth grid in the land cover data / historical wind speed data; j = 1, 2,..., J, J is the total number of test samples;

[0057] Second, set the parameter K in the KNN algorithm to 1, and calculate the spatial distance between each test sample in the test set and each training sample in the training set, wherein the spatial distance between the jth test sample in the test set and the ith training sample in the training set is calculated according to the following formula:

[0058] d ji = R arcos [cos (Y i ) cos (Y j ) cos (X i -X j ) + sin (Y i ) sin (Y j )]

[0059] Wherein, R is the radius of the earth, R takes 6371km; X i and Y i are the longitude and latitude of the center point of the ith training sample; X j and Y j are the longitude and latitude of the center point of the jth test sample;

[0060] Third, sort the spatial distance between each test sample and different training samples, and find the training sample with the smallest spatial distance for each test sample;

[0061] Fourth, assign the land cover data / historical wind speed data contained in the test sample to the corresponding training sample, to obtain the land cover data / historical wind speed data under the grid resolution of the plane grid model.

[0062] 3) According to the digital elevation data of each grid in the plane grid model, the grid that can be constructed for wind power generation is selected as the to-be-evaluated grid, and the specific steps include:

[0063] Obtain the height data and slope data of each grid in the plane grid model, and select the grid with a height less than or equal to 3000 meters and a slope less than 10°.

[0064] 4) Obtain the land cover type of each grid in the plane grid model, and the land cover type composition includes 17 types such as city and building, farmland, and wasteland. Different land cover types correspond to different available area coefficients. According to the available area coefficients corresponding to the land cover types shown in Table 1, the available area of each to-be-evaluated grid is calculated.

[0065] Table 1 Available area coefficients of 17 types of land cover

[0066] Land cover Fraction of area covered Land cover Fraction of area covered Water body 0％ Savanna 75％ Evergreen needleleaf forest 0％ Grassland 90％ Evergreen broadleaf forest 0％ Permanent wetland 0％ Deciduous needleleaf forest 0％ Cropland 2％ Deciduous broadleaf forest 0％ Urban and built-up area 0％ Mixed forest 0％ Cropland and natural vegetation mosaic 2％ Dense shrubland 15％ Ice and snow 0％ Open shrubland 50％ Barren or sparsely vegetated 80％ Wooded grassland 45％

[0067] 5) When natural wind passes through the wind farm group, the input wind speed of the upstream and downstream wind turbines distributed along the wind direction is not the same. Specifically, when the natural wind blows through the upstream wind turbine to the downstream wind turbine, the upstream wind turbine will generate strong turbulence to the downstream wind turbine due to the shielding of the upstream wind turbine, which will make the input wind speed of the downstream wind turbine smaller than that of the upstream wind turbine. This phenomenon is called wake effect. In a regular arrangement, the larger the spacing between the wind turbine groups, the smaller the influence of the wake effect, and the smaller the wind speed difference between the upstream and downstream wind turbines. The actual measurement results of the Swedish FFA wind farm show that when two wind turbines are arranged in a row with a spacing of five times the impeller diameter, and a 12m / s incoming wind blows along the direction parallel to the straight line where the two wind turbines are located, the output power of the wind turbine in the wake area is only about 60% of the case without interference; when the spacing is changed to 10 times the impeller diameter, the wind turbine in the wake area is not affected by the upstream wind turbine, and its output power is 100% of the case without interference. Therefore, according to the rated capacity data and impeller diameter of a typical wind turbine, and considering the wake effect of the wind turbine reasonably, the land area required by each wind turbine can be determined, and then the installable capacity of each evaluation grid can be obtained according to the available area of each evaluation grid, forming a high-resolution wind power installable capacity evaluation result.

[0068] In this embodiment, it is assumed that all wind farms are equipped with a certain typical wind turbine, which has a capacity of 1.1 MW, an impeller diameter of 82m, a cut-in wind speed of 3m / s, a cut-out wind speed of 21m / s, and a hub height of 60m. To avoid the wake effect, the spacing between the wind turbines is set to 10 times the impeller diameter. Therefore, the land area occupied by the wind turbine is about 2.69 square kilometers, and the installable capacity of each evaluation grid is further calculated based on this parameter.

[0069] 6) Calculate the wind power resource capacity factor of each evaluation grid according to the historical wind speed data and the hub height data of the typical wind turbine, the specific steps including:

[0070] Obtain the historical wind speed data of each evaluation grid; in this example, the historical wind speed data at a height of 10m and 50m from the ground with a time resolution of 1 hour in the past 10 years published by the European Centre for Medium-Range Weather Forecasts is used.

[0071] Further extrapolate the historical wind speed data obtained at a height of 10m and 50m from the ground to obtain the historical wind speed data of each evaluation grid at any height, and then obtain the historical wind speed data of each evaluation grid at the hub height of the typical wind turbine through the least square exponential law, the calculation method is as follows:

[0072]

[0073] V = V0+ (V10- V0) e- (h / h0) a z V = V0+ (V10- V0) e- (h / h0) a a V = V0+ (V10- V0) e- (h / h0) a LS The least square friction coefficient a can be calculated by the following formula:

[0074]

[0075] V = V0+ (V10- V0) e- (h / h0) a i V = V0+ (V10- V0) e- (h / h0) a i V = V0+ (V10- V0) e- (h / h0) a

[0076] According to the wind speed-power curve corresponding to the typical wind turbine and the historical wind speed data at the hub height of the typical wind turbine of each to-be-evaluated grid, the historical output power of each to-be-evaluated grid when using the typical wind turbine is obtained.

[0077] According to the historical output power of each to-be-evaluated grid when using the typical wind turbine, the wind power resource capacity factor of each to-be-evaluated grid is calculated, wherein the capacity factor is calculated according to the following formula:

[0078]

[0079] CF = 1 / N Σ (P (t) / C), t = 1, 2, …, T w(t) P (t) represents the output power of the typical wind turbine in the grid at time t, and C w C represents the rated installed capacity corresponding to the typical wind turbine.

[0080] 7) According to the installed capacity of the to-be-evaluated grid and the wind power resource capacity factor, the wind power resource capacity of each to-be-evaluated grid is calculated, and the wind power resources of each to-be-evaluated grid are evaluated according to the size of the wind power resource capacity of each to-be-evaluated grid.

[0081] Embodiment two:

[0082] A high-resolution onshore wind power resource evaluation system, comprising:

[0083] A model construction module for constructing a planar grid model of a target region;

[0084] A data acquisition module for acquiring ground cover data, digital elevation data and historical wind speed data of each grid in the planar grid model;

[0085] The data processing module is configured to filter out the grid where the wind power generator can be constructed as an evaluation grid according to the digital elevation data of each grid in the plane grid model; calculate the available area of each evaluation grid according to the land cover data of each evaluation grid; determine the installed capacity of each evaluation grid according to the available area of each evaluation grid, the rated capacity of the typical wind power generator and the blade diameter data; and calculate the wind power resource capacity factor of each evaluation grid according to the historical wind speed data of each grid and the hub height data of the typical wind power generator.

[0086] The evaluation module is configured to calculate the wind power resource capacity of each evaluation grid according to the installed capacity of each evaluation grid and the wind power resource capacity factor, and evaluate the wind power resource of each evaluation grid according to the size of the wind power resource capacity of each evaluation grid.

[0087] Embodiment three:

[0088] In this embodiment, the method in embodiment one is used to evaluate the wind power resource of the land area in China, wherein the length of the grid in the constructed plane grid model is 0.05° latitude and the width is 0.05° longitude; the land cover data used is the I-type land cover data set in MCD12Q1; the 17 types of land cover and the land available area coefficient are shown in Table 1 in embodiment one; the digital elevation data used is the STRM digital elevation set; and the parameters of the typical wind power generator are shown in Table 2, wherein the rated power of the wind generator is 1.1 MW, the cut-in wind speed is 3 m / s, the cut-out wind speed is 21 m / s, the impeller diameter is 82 m, and the hub height suitable for the virtual wind generator is 60 m.

[0089] Table 2: Parameters of a certain typical wind generator

[0090]

[0091] In this example, the wind power installed capacity (MW / km2) distribution map of the land area in China is obtained, and it can be known from the distribution map that the wind power installed capacity in China mainly concentrates in the northern Xinjiang, eastern and western Inner Mongolia, etc., and the wind power installed capacity in Qinghai, Gansu, Guangxi, Hebei and Tibet is also relatively rich, which has a considerable development prospect.

[0092] In this example, the wind power capacity factor distribution map of the land area in China is also obtained, and it can be known from the distribution map that the distribution of the wind energy resources in China has the following two characteristics:

[0093] Firstly, the wind energy resources in eastern and western Inner Mongolia, Heilongjiang, northern Liaoning and northwest Xinjiang are particularly rich due to the influence of winter wind.

[0094] Second, the southeast coastal areas of coastal provinces affected by the southeast monsoon, the coastline near the wind energy resources are particularly rich.

Claims

1. A method of high resolution onshore wind power resource assessment, characterized in that: The method comprises the following steps: constructing a plane grid model of a target area; obtaining land cover data, digital elevation data and historical wind speed data of each grid in the plane grid model; screening out grids where wind turbines can be built from the plane grid model according to the digital elevation data of each grid in the plane grid model, as to-be-evaluated grids; calculating the available area of each to-be-evaluated grid according to the land cover data of each to-be-evaluated grid; determining the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid, the rated capacity and the blade diameter data of a typical wind turbine; calculating the wind power resource capacity factor of each to-be-evaluated grid according to the historical wind speed data of each grid and the hub height data of the typical wind turbine; calculating the wind power resource capacity of each to-be-evaluated grid according to the installable capacity of each to-be-evaluated grid and the wind power resource capacity factor, and evaluating the wind power resource of each to-be-evaluated grid according to the size of the wind power resource capacity of each to-be-evaluated grid; the length of each grid in the plane grid model ranges from 0.01° to 0.25° in latitude, and the width ranges from 0.01° to 0.25° in longitude; the land cover data, the digital elevation data and the historical wind speed data of each grid in the plane grid model are obtained by the following method: obtaining land cover data, digital elevation data and historical wind speed data of a target area; unifying the resolution of the land cover data, the digital elevation data and the historical wind speed data of the target area to the grid resolution of the constructed plane grid model by using a KNN algorithm; the method of unifying the resolution of the land cover data, the digital elevation data and the historical wind speed data of the target area to the grid resolution of the constructed plane grid model by using a KNN algorithm comprises the following steps: 1) constructing a training set according to the plane grid model, wherein the i-th training sample in the training set is denoted as (X i ,Y i ), X i is the central point longitude of the i-th training sample, i.e. the central point longitude of the i-th grid in the plane grid model; Y i is the central point latitude of the i-th training sample, i.e. the central point latitude of the i-th grid in the plane grid model; i = 1, 2, …, I, I is the total number of training samples; According to the land cover data / digital elevation data / historical wind speed data, a test set is constructed, and a jth test sample in the test set is denoted as (X j ,Y j ), X j is a central point longitude of the jth test sample, that is, a central point longitude of a jth grid in the land cover data / digital elevation data / historical wind speed data; Y j is a central point latitude of the jth test sample, that is, a central point latitude of the jth grid in the land cover data / digital elevation data / historical wind speed data; j = 1, 2, …, J, and J is a total number of test samples; 2) calculating the spatial distance between each test sample in the test set and each training sample in the training set, wherein the spatial distance between the jth test sample in the test set and the ith training sample in the training set is calculated according to the following formula: d ji = R * arcos [cos(Y i ) * cos(Y j ) * cos(X i -X j ) + sin(Y i ) * sin(Y j )] ) Wherein, R is the earth radius, R takes 6371km; X i and Y i are the central point longitude and latitude of the i-th training sample respectively; X j and Y j are the central point longitude and latitude of the j-th test sample respectively. 3) sorting the spatial distance between each test sample and different training samples, and finding the training sample with the smallest spatial distance from each test sample; 4) assigning the land cover data / digital elevation data / historical wind speed data contained in the test sample to the corresponding training sample to obtain the land cover data / digital elevation data / historical wind speed data under the grid resolution of the plane grid model; the specific steps of determining the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid, the rated capacity and the blade diameter data of a typical wind turbine comprise: determining the unit land area of the typical wind turbine according to the rated capacity data and the blade diameter data of the typical wind turbine, and then obtaining the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid; the specific steps of calculating the wind power resource capacity factor of each to-be-evaluated grid according to the historical wind speed data of each grid and the hub height data of the typical wind turbine comprise: obtaining the historical wind speed data of each to-be-evaluated grid According to the obtained historical wind speed data, extrapolation is performed to obtain historical wind speed data of each to-be-evaluated grid at any height, and further to obtain historical wind speed data of each to-be-evaluated grid at a hub height of a typical wind turbine; According to a wind speed-power curve corresponding to the typical wind turbine and the historical wind speed data of each to-be-evaluated grid at the hub height of the typical wind turbine, historical output power of each to-be-evaluated grid when the typical wind turbine is used is obtained; According to the historical output power of each to-be-evaluated grid when the typical wind turbine is used, wind power resource capacity factors of the to-be-evaluated grids are calculated. The ground surface coverage data adopts the I-type ground surface coverage data set in the MCD12Q1, the digital elevation data adopts the STRM digital elevation set, and the historical wind speed data adopts the historical wind speed data set of the European Centre for Medium-Range Weather Forecasts.

2. The high-resolution onshore wind power resource assessment method of claim 1, wherein: The specific steps of screening the grids on which wind turbines can be constructed according to the digital elevation data of each grid in the plane grid model include: Height data and slope data of each grid in the plane grid model are obtained, and grids with a height less than or equal to 3000 meters and a slope less than or equal to 10° are screened.

3. The high-resolution onshore wind power resource assessment method of claim 2, wherein: The specific steps of calculating the available area of each to-be-evaluated grid according to the ground surface coverage data of the to-be-evaluated grid include: The ground surface coverage type of each grid in the plane grid model is obtained, and the available area of each to-be-evaluated grid is calculated according to the available area coefficient corresponding to the ground surface coverage type.

4. A high resolution onshore wind power resource assessment system characterized by: It comprises: a model construction module, configured to construct a plane grid model of a target region; a data acquisition module, configured to acquire ground surface coverage data, digital elevation data and historical wind speed data of each grid in the plane grid model; a data processing module, configured to screen grids on which wind turbines can be constructed as to-be-evaluated grids according to the digital elevation data of each grid in the plane grid model, and calculate the available area of each to-be-evaluated grid according to the ground surface coverage data of the to-be-evaluated grid; determine the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid, the rated capacity and the blade diameter data of the typical wind turbine, and calculate the wind power resource capacity factor of each to-be-evaluated grid according to the historical wind speed data of each grid and the hub height data of the typical wind turbine; an evaluation module, configured to calculate the wind power resource capacity of each to-be-evaluated grid according to the installable capacity of the to-be-evaluated grid and the wind power resource capacity factor, and evaluate the wind power resource of each to-be-evaluated grid according to the size of the wind power resource capacity of the to-be-evaluated grid. The length of each grid in the plane grid model ranges from 0.01° to 0.25° in latitude, and the width ranges from 0.01° to 0.25° in longitude. The ground surface coverage data, the digital elevation data and the historical wind speed data of each grid in the plane grid model are obtained by the following method: acquire ground surface coverage data, digital elevation data and historical wind speed data of a target region; unify the resolution of the ground surface coverage data, the digital elevation data and the historical wind speed data of the target region to the grid resolution of the constructed plane grid model by using a KNN algorithm. The method for unifying the resolutions of the target area's land cover data, digital elevation data and historical wind speed data to the grid resolution of the constructed plane grid model is as follows: 1) constructing a training set according to the plane grid model, wherein the i-th training sample in the training set is denoted as (X i ,Y i ), X i is the central point longitude of the i-th training sample, i.e. the central point longitude of the i-th grid in the plane grid model; Y i is the central point latitude of the i-th training sample, i.e. the central point latitude of the i-th grid in the plane grid model; i = 1, 2, …, I, I is the total number of training samples; According to the land cover data / digital elevation data / historical wind speed data, a test set is constructed, and a jth test sample in the test set is denoted as (X j ,Y j ), X j is a central point longitude of the jth test sample, i.e., a central point longitude of a jth grid in the land cover data / digital elevation data / historical wind speed data; Y j is a central point latitude of the jth test sample, i.e., a central point latitude of the jth grid in the land cover data / digital elevation data / historical wind speed data; j = 1, 2, …, J, and J is a total number of test samples. 2) The spatial distance between each test sample in the test set and each training sample in the training set is calculated respectively, wherein the spatial distance between the jth test sample in the test set and the ith training sample in the training set is calculated according to the following formula: d ji = R * arcos [cos(Y i ) @ cos(Y j ) @ cos(X i - X j ) + sin(Y i ) @ sin(Y j )] ) Wherein, R is the earth radius, R takes 6371km; X i and Y i are the central point longitude and latitude of the i-th training sample respectively; X j and Y j are the central point longitude and latitude of the j-th test sample respectively. 3) The spatial distance between each test sample and different training samples is sorted respectively, and the training sample with the minimum spatial distance to each test sample is found; 4) The land cover data / digital elevation data / historical wind speed data contained in the test sample is assigned to the corresponding training sample, and the land cover data / digital elevation data / historical wind speed data of the plane grid model at the grid resolution are obtained; The specific steps for determining the installable capacity of each to-be-evaluated grid according to the available area of each to-be-evaluated grid and the rated capacity and blade diameter data of a typical wind turbine include: The unit land area of the typical wind turbine is determined according to the rated capacity data and blade diameter data of the typical wind turbine, and the installable capacity of each to-be-evaluated grid is obtained according to the available area of each to-be-evaluated grid; The specific steps for calculating the wind power resource capacity factor of each to-be-evaluated grid according to the historical wind speed data of each grid and the hub height data of the typical wind turbine include: The historical wind speed data of each to-be-evaluated grid is obtained The historical wind speed data of each to-be-evaluated grid at any height is obtained by extrapolation according to the obtained historical wind speed data, and the historical wind speed data of each to-be-evaluated grid at the hub height of the typical wind turbine is obtained; The historical output power of each to-be-evaluated grid when using the typical wind turbine is obtained according to the wind speed-power curve corresponding to the typical wind turbine and the historical wind speed data of each to-be-evaluated grid at the hub height of the typical wind turbine; The wind power resource capacity factor of each to-be-evaluated grid is calculated according to the historical output power of each to-be-evaluated grid when using the typical wind turbine; The land cover data uses the I-type land cover data set in MCD12Q1, the digital elevation data uses the STRM digital elevation set, and the historical wind speed data uses the historical wind speed data set of the European Centre for Medium-Range Weather Forecasts.

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