CN115841265A - High-resolution onshore wind power generation resource evaluation method and system - Google Patents
High-resolution onshore wind power generation resource evaluation method and system Download PDFInfo
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Abstract
The invention discloses a high-resolution onshore wind power generation resource evaluation method and a high-resolution onshore wind power generation resource evaluation system.
Description
Technical Field
The invention belongs to the field of electrical engineering, and particularly relates to a high-resolution onshore wind power generation resource evaluation method and system.
Background
In recent years, due to the increasing severity of global warming climate problems and the imminent exhaustion of fossil energy, there is a need for the development of pollution-free renewable energy to fill the gap in demand for electricity due to the shortage of fossil energy. Wind energy is used as one of renewable energy sources, is wide in distribution, rich in storage amount, clean and environment-friendly, and contributes to relieving greenhouse effect. Research has shown that the total wind energy reserve in the global atmosphere is about 3.8 × 1016kWh, including 4.3 × 1012kWh, which is about 10 times the available water energy worldwide. In order to further exploit wind energy resources, regional wind power generation resources need to be evaluated. The existing wind power generation resource assessment method generally comprises the steps of directly obtaining wind speed data of a target area, obtaining wind power data of the target area by combining a wind speed-wind power conversion relation, and assessing wind power generation resources of the target area.
The existing wind power generation resource assessment is mainly carried out based on methods such as radar detection, satellite inversion, meteorological model numerical simulation, mesoscale numerical simulation, computational Fluid Dynamics (CFD) numerical simulation, geographic Information System (GIS) and the like. The method based on radar detection, satellite inversion, geographic Information System (GIS) and the like often has the problems of low wind power resource evaluation accuracy caused by non-uniform observation heights and non-uniform time resolution; the method based on meteorological model numerical simulation, mesoscale numerical simulation and computational fluid mechanics numerical simulation mainly focuses on that the evaluation of the total wind power resource space amount does not combine with the actual landform to match the geographic space resources, and the wind power resource distribution condition in the target area cannot be determined.
Disclosure of Invention
The invention aims to provide a high-resolution onshore wind power generation resource evaluation method.
Another object of the present invention is to provide a high-resolution onshore wind power generation resource evaluation system.
The invention adopts the following technical scheme:
a high-resolution onshore wind power generation resource assessment method comprises the following steps:
constructing a plane grid model of a target area;
acquiring the earth surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model;
screening out grids capable of building the wind driven generator as grids to be evaluated according to the digital elevation data of each grid in the planar grid model;
calculating the available area of each grid to be evaluated according to the ground surface coverage data of each grid to be evaluated;
determining the mountable capacity of each grid to be evaluated according to the available area of each grid to be evaluated, the rated capacity of a typical wind driven generator and the diameter data of blades;
calculating the wind power resource capacity factor of each grid to be evaluated according to the historical wind speed data of each grid and the hub height data of a typical wind driven generator;
and calculating the wind power resource capacity of each grid to be evaluated according to the machine-installable capacity and the wind power resource capacity factor of the grid to be evaluated, and evaluating the wind power resources of each grid to be evaluated according to the size of the wind power resource capacity of each grid to be evaluated.
Furthermore, the length range of the grid in the planar grid model is 0.01-0.25 degrees of latitude, and the width range of the grid is 0.01-0.25 degrees of longitude.
Further, the surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model are obtained by the following methods:
acquiring earth surface coverage data, digital elevation data and historical wind speed data of a target area;
and unifying the resolution of the earth surface coverage data, the digital elevation data and the historical wind speed data of the target area into the grid resolution of the constructed plane grid model by adopting a KNN algorithm.
Further, unifying the resolutions of the earth surface coverage data, the digital elevation data and the historical wind speed data of the target area into the grid resolution of the constructed plane grid model by adopting a KNN algorithm by adopting the following method:
1) Constructing a training set according to the plane grid model, and recording the ith training sample in the training set as (X) i ,Y i )，X i The center point longitude of the ith training sample is the center point longitude of the ith grid in the planar grid model; y is i The latitude of the center point of the ith training sample is the latitude of the center point of the ith grid in the planar grid model; i =1, 2.., I is the total number of training samples;
constructing a test set according to the earth surface coverage data/digital elevation data/historical wind speed data, and recording the jth test sample in the test set as (X) j ,Y j )，X j The longitude of the center point of the jth test sample is the longitude of the center point of the jth grid in the earth surface coverage data/digital elevation data/historical wind speed data; y is j The latitude of the center point of the jth test sample is the latitude of the center point of the jth grid in the earth surface coverage data/digital elevation data/historical wind speed data; j =1,2.. J, J is the total number of test samples;
2) Respectively 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 radius of the earth, and R is 6371km; x i And Y i Respectively the longitude and the latitude of the center point of the ith training sample; x j And Y j Respectively the longitude and the latitude of the central point of the jth test sample;
3) Respectively sequencing the spatial distance between each test sample and different training samples, and finding out the training sample with the minimum spatial distance with each test sample;
4) And giving the earth surface coverage data/digital elevation data/historical wind speed data contained in the test sample to the corresponding training sample to obtain the earth surface coverage data/digital elevation data/historical wind speed data under the grid resolution of the plane grid model.
Further, the specific step of screening out the grids capable of constructing the wind driven generator according to the digital elevation data of each grid in the planar grid model comprises:
and acquiring height data and gradient data of each grid in the planar grid model, and screening out the grids with the height less than or equal to 3000 m and the gradient less than or equal to 10 degrees.
Further, the specific step of calculating the available area of each grid to be evaluated according to the ground surface coverage data of each grid to be evaluated includes:
and acquiring the ground surface coverage type of each grid in the planar grid model, and calculating the available area of each grid to be evaluated according to the available area coefficient corresponding to the ground surface coverage type.
Further, the specific step of determining the available capacity of each grid to be evaluated according to the available area of each grid to be evaluated, the rated capacity of a typical wind turbine and the diameter data of the blades comprises the following steps:
and determining the unit occupied area of the typical wind driven generator according to the rated capacity data and the blade diameter data of the typical wind driven generator, and obtaining the available capacity of each grid to be evaluated according to the available area of each grid to be evaluated.
Further, the specific step of calculating the wind power resource capacity factor of each grid to be evaluated according to the historical wind speed data of each grid and the hub height data of the typical wind driven generator comprises the following steps:
obtaining historical wind speed data of each grid to be evaluated
Extrapolating according to the acquired historical wind speed data to obtain historical wind speed data of each grid to be evaluated at any height, and further obtaining historical wind speed data of each grid to be evaluated at the height of a hub of a typical wind driven generator;
obtaining historical output power of each grid to be evaluated when the typical wind driven generator is adopted according to a wind speed-wind power curve corresponding to the typical wind driven generator and historical wind speed data of each grid to be evaluated under the height of a hub of the typical wind driven generator;
and calculating the wind power resource capacity factor of each grid to be evaluated according to the historical output power of each grid to be evaluated when a typical wind driven generator is adopted.
Further, the surface coverage data adopts a type I surface coverage data set in the MCD12Q 1; the digital elevation data adopts an STRM digital elevation set
And the historical wind speed data adopts a historical wind speed data set of a European mesoscale forecasting center.
A high resolution onshore wind power generation resource assessment system comprising:
the model building module is used for building a plane grid model of the target area;
the data acquisition module is used for acquiring the earth surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model;
the data processing module is used for screening out grids capable of building the wind driven generator as grids to be evaluated according to the digital elevation data of each grid in the planar grid model; calculating the available area of each grid to be evaluated according to the earth surface coverage data of each grid to be evaluated; determining the mountable capacity of each grid to be evaluated according to the available area of each grid to be evaluated, the rated capacity of a typical wind driven generator and the diameter data of blades; calculating the wind power resource capacity factor of each grid to be evaluated according to the historical wind speed data of each grid and the hub height data of a typical wind driven generator;
and the evaluation module is used for calculating the wind power resource capacity of each grid to be evaluated according to the machine-installable capacity and the wind power resource capacity factor of the grid to be evaluated and evaluating the wind power resource of each grid to be evaluated according to the wind power resource capacity of each grid to be evaluated.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a high-resolution onshore wind power generation resource evaluation method and system.
The evaluation method provided by the invention designs the length and the width of the grid, establishes a high-resolution planar grid model, unifies the spatial scale of the ground surface coverage data, the digital elevation data and the historical wind speed data of the target area based on the resolution of the planar grid model to obtain the high-resolution ground surface coverage data, the digital elevation data and the historical wind speed data, and evaluates the wind power generation resources of the target area by combining the high-resolution data, so that the obtained evaluation result has the advantages of high refinement degree and high accuracy.
Drawings
FIG. 1 is a flow chart of an evaluation method of the present invention;
Detailed Description
The first embodiment is as follows:
a high-resolution onshore wind power generation resource assessment method, as shown in fig. 1, comprising:
1) Constructing a plane grid model of a target area; the length range of the grid in the constructed planar grid model is 0.01-0.25 degrees of latitude, and the width range is 0.01-0.25 degrees of longitude. The scale of the grid in the planar grid model in this example selects the resolution of the digital elevation dataset and determines that the grid is 0.05 latitude long and 0.05 longitude wide (approximately 5.57 km).
2) And acquiring the earth surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model. The specific method of the step comprises the following steps:
acquiring surface coverage data and digital elevation data of a target area; the surface coverage data in this example used a type I surface coverage data set in MCD12Q1 with a spatial resolution of 500m, about 0.00463 ° for near the equator; the digital elevation data adopts an STRM digital elevation set, and the spatial resolution of the data set is 0.05 degrees of longitude and 0.05 degrees of latitude; the historical wind speed data employs a historical wind speed data set from the european mesoscale forecasting center with a spatial resolution of 0.25 ° longitude x 0.25 ° latitude.
And unifying the resolution of the ground surface coverage data, the digital elevation data and the historical wind speed data of the target area into the grid resolution of the constructed planar grid model by adopting a KNN algorithm. The specific steps of unifying the resolution include
Firstly, a training set is constructed according to the plane grid model, and the ith training sample in the training set is marked as (X) i ,Y i )，X i The longitude of the central point of the ith training sample is the longitude of the central point of the ith grid in the plane grid model; y is i The latitude of the center point of the ith training sample is the latitude of the center point of the ith grid in the planar grid model; i =1, 2.., I is the total number of training samples;
constructing a test set according to the earth surface coverage data/historical wind speed data, and recording the jth test sample in the test set as (X) j ,Y j )，X j The longitude of the center point of the jth test sample is the longitude of the center point of the jth grid in the earth surface coverage data/historical wind speed data; y is j The latitude of the center point of the jth test sample is the latitude of the center point of the jth grid in the earth surface coverage data/historical wind speed data; j =1,2.. J, J is the total number of test samples;
secondly, setting a parameter K in the KNN algorithm to be 1, and respectively calculating the spatial distance between each test sample in the test set and each training sample in the training set, wherein the spatial distance calculation formula between the jth test sample in the test set and the ith training sample in the training set is as follows:
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 radius of the earth, and R is 6371km; x i And Y i Respectively the longitude and the latitude of the center point of the ith training sample; x j And Y j Respectively the longitude and the latitude of the central point of the jth test sample;
thirdly, respectively sequencing the spatial distance between each test sample and different training samples, and finding out the training sample with the minimum spatial distance with each test sample;
and fourthly, giving the ground surface coverage data/the historical wind speed data contained in the test sample to the corresponding training sample to obtain the ground surface coverage data/the historical wind speed data under the grid resolution of the plane grid model.
3) Screening out grids capable of building the wind driven generator as grids to be evaluated according to the digital elevation data of each grid in the planar grid model, wherein the specific steps comprise:
and acquiring height data and gradient data of each grid in the planar grid model, and screening out the grids with the height of less than or equal to 3000 meters and the gradient of less than 10 degrees.
4) And acquiring the ground surface coverage type of each grid in the planar grid model, wherein the ground surface coverage type comprises 17 types of cities, buildings, farmlands, wastelands and the like, the available area coefficients corresponding to different ground surface coverage types are different, and the available area of each grid to be evaluated is calculated according to the available area coefficients corresponding to the ground surface coverage types shown in the table 1.
TABLE 1 17 land usable area coefficient corresponding to land cover type
| Ground surface covering | Coefficient of usable area | Ground surface covering | Coefficient of usable area |
| Water body | 0％ | Thin tree grassland | 75％ |
| Evergreen coniferous forest | 0％ | Grass land | 90％ |
| Evergreen broad-leaved forest | 0％ | Permanent wetland | 0％ |
| Deciduous coniferous forest | 0％ | Farmland | 2％ |
| Deciduous broad-leaved forest | 0％ | Town and built-up area | 0％ |
| Mixed forest | 0％ | Farmland and natural vegetation mosaic | 2％ |
| Canopy shrub forest | 15％ | Ice and snow | 0％ |
| Sparse shrubbery | 50％ | Wasteland | 80％ |
| Grass land for forest | 45％ |
5) When natural wind passes through a wind power plant group, the input wind speeds of upstream and downstream wind power generation sets distributed along the wind direction are different, specifically, when the natural wind blows to the downstream wind power generation sets through the upstream wind power generation sets, the shielding of the upstream wind power generation sets can generate strong turbulence to the downstream wind power generation sets, so that the input wind speed of the downstream wind power generation sets is smaller than the input wind speed of the upstream wind power generation sets, and the phenomenon is called wake effect. Under the regular arrangement, the larger the distance between the wind turbine generator sets is, the smaller the influence of the wake effect is, and the smaller the difference between the wind speeds of the upstream unit and the downstream unit is. The results of the Swedish FFA wind power plant actually measured show that: the two wind generating sets are arranged in a row, the distance between the two wind generating sets is five times the diameter of an impeller, and when 12m/s of incoming wind is blown along the direction parallel to the straight line where the two wind generating sets are located, the output power of the wind generating sets in the wake flow area is only about 60% under the condition of no interference; when the distance is 10 times of the diameter of the impeller, the wind generating set in the wake area is not influenced by the upstream wind generating set, and the output power of the wind generating set is 100% under the condition of no interference. Therefore, according to the rated capacity data and the impeller diameter of a typical fan and the wake effect of the fan, the land area occupied by each fan can be determined, the mountable capacity of the grid to be evaluated can be obtained according to the available area of each grid to be evaluated, and the wind power mountable capacity evaluation result with high resolution is formed.
In the embodiment, a typical fan is assumed to be installed in all wind power plants, the installed capacity of the fan is 1.1MW, the diameter of an impeller is 82m, the cut-in wind speed is 3m/s, the cut-out wind speed is 21m/s, the height of a hub of the fan is 60m, and the installation distance of the fan is 10 times the diameter of the impeller to avoid a wake effect. Therefore, the fan occupies about 2.69 square kilometers of area, and the machine-installable capacity of the grid to be evaluated is further calculated according to the parameters.
6) Calculating the wind power resource capacity factor of each grid to be evaluated according to historical wind speed data and hub height data of a typical wind driven generator, and the specific steps comprise:
acquiring historical wind speed data of each grid to be evaluated; in this example, historical wind speed data published by the European mesoscale forecasting center at heights of 10m and 50m from the ground in nearly 10 years and with a time resolution of 1 hour is adopted.
Further carrying out extrapolation according to the acquired historical wind speed data with the height of 10m and 50m from the ground to obtain the historical wind speed data of each grid to be evaluated at any height, and further obtaining the historical wind speed data of each grid to be evaluated at the height of a hub of a typical wind driven generator according to the least square law of indexes, wherein the calculation method comprises the following steps:
in the formula, V z Indicating the wind speed, V, at the hub height of the fan a Representing the wind speed at 10m from the ground, the least-squares coefficient of friction alpha LS Can be calculated by the following formula:
in the formula, V i Is h i The wind speed at the height of the wind,and N is the total of all wind speed data except the wind speed data at the height to be obtained, N =2 is taken in the example, and the least square friction coefficient is calculated according to the formula by utilizing the wind speed data at the position 10m away from the ground and the wind speed data at the position 50m away from the ground.
Obtaining historical output power of each grid to be evaluated when the typical wind driven generator is adopted according to a wind speed-wind power curve corresponding to the typical wind driven generator and historical wind speed data of each grid to be evaluated under the height of a hub of the typical wind driven generator;
calculating the wind power resource capacity factor of each grid to be evaluated according to the historical output power of each grid to be evaluated when a typical wind driven generator is adopted, wherein the capacity factor is calculated according to the following formula:
wherein CF represents a wind power capacity factor, p, corresponding to a certain grid w(t) Representing the output power, C, of a typical fan in the grid at time t w The rated installed capacity corresponding to a typical fan.
7) And calculating the wind power resource capacity of each grid to be evaluated according to the machine-installable capacity and the wind power resource capacity factor of the grid to be evaluated, and evaluating the wind power resources of each grid to be evaluated according to the size of the wind power resource capacity of each grid to be evaluated.
Example two:
a high resolution onshore wind power generation resource assessment system comprising:
the model building module is used for building a plane grid model of the target area;
the data acquisition module is used for acquiring the earth surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model;
the data processing module is used for screening out grids capable of building the wind driven generator as grids to be evaluated according to the digital elevation data of each grid in the planar grid model; calculating the available area of each grid to be evaluated according to the ground surface coverage data of each grid to be evaluated; determining the mountable capacity of each grid to be evaluated according to the available area of each grid to be evaluated, the rated capacity of a typical wind driven generator and the diameter data of blades; calculating the wind power resource capacity factor of each grid to be evaluated according to the historical wind speed data of each grid and the hub height data of a typical wind driven generator;
and the evaluation module is used for calculating the wind power resource capacity of each grid to be evaluated according to the machine-installable capacity and the wind power resource capacity factor of the grid to be evaluated and evaluating the wind power resource of each grid to be evaluated according to the wind power resource capacity of each grid to be evaluated.
Example three:
in the embodiment, the method in the first embodiment is adopted to evaluate wind power resources in onshore regions of China, wherein the length of the grid in the constructed planar grid model is 0.05-degree latitude, and the width of the grid is 0.05-degree longitude; the surface coverage data used are the type I surface coverage data set in MCD12Q1, and the 17 surface coverage types and land usable area coefficients used are shown in table 1 in example one. The adopted digital elevation data is an STRM digital elevation set; typical wind turbine parameters used are shown in table 2, the rated power of the wind turbine is 1.1MW, the cut-in wind speed is 3m/s, the cut-out wind speed is 21m/s, the diameter of the impeller is 82m, and the height of the hub suitable for the virtual wind turbine is selected to be 60m.
TABLE 2 parameter table of a typical blower
According to the method, a distribution diagram of the installed wind power capacity (MW/km 2) of land areas in China is obtained, and the installed wind power capacity of land areas in China is mainly concentrated in areas such as the northern part of Xinjiang, moneast, monwest and the like, and meanwhile, the installed wind power capacity of areas such as the Qinghai, gansu, guangxi, hebei, tibet and the like is rich, so that the method has a considerable development prospect.
The method also obtains a wind power capacity factor distribution map of the land area in China, and the distribution of the land wind energy resources in China is known to have the following two characteristics:
the first, mongolian, heilongjiang, northern Liaoning and northern Xinjiang areas are affected by winter season wind, and the wind energy resource is particularly rich.
Secondly, one province in the coastal region of the southeast coastal region is influenced by the southeast monsoon, and the wind energy resources near the coastline are particularly rich.
Claims (10)
1. A high-resolution onshore wind power generation resource evaluation method is characterized by comprising the following steps: the method comprises the following steps:
constructing a plane grid model of a target area;
acquiring the earth surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model;
screening out grids capable of building the wind driven generator as grids to be evaluated according to the digital elevation data of each grid in the planar grid model;
calculating the available area of each grid to be evaluated according to the earth surface coverage data of each grid to be evaluated;
determining the mountable capacity of each grid to be evaluated according to the available area of each grid to be evaluated, the rated capacity of a typical wind driven generator and the diameter data of blades;
calculating the wind power resource capacity factor of each grid to be evaluated according to the historical wind speed data of each grid and the hub height data of a typical wind driven generator;
and calculating the wind power resource capacity of each grid to be evaluated according to the machine-installable capacity and the wind power resource capacity factor of the grid to be evaluated, and evaluating the wind power resources of each grid to be evaluated according to the size of the wind power resource capacity of each grid to be evaluated.
2. The high resolution onshore wind power generation resource assessment method according to claim 1, characterized in that: the length range of the grid in the planar grid model is 0.01-0.25 degrees of latitude, and the width range of the grid is 0.01-0.25 degrees of longitude.
3. The high resolution onshore wind power generation resource assessment method according to claim 2, characterized in that: the earth surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model are obtained by the following methods:
acquiring earth surface coverage data, digital elevation data and historical wind speed data of a target area;
and unifying the resolution of the earth surface coverage data, the digital elevation data and the historical wind speed data of the target area into the grid resolution of the constructed plane grid model by adopting a KNN algorithm.
4. The high resolution onshore wind power generation resource assessment method according to claim 3, wherein: the method for unifying the resolutions of the earth surface coverage data, the digital elevation data and the historical wind speed data of the target area into the grid resolution of the constructed planar grid model by adopting the KNN algorithm comprises the following steps:
1) Constructing a training set according to the plane grid model, and recording the ith training sample in the training set as (X) i ,Y i )，X i The longitude of the central point of the ith training sample is the longitude of the central point of the ith grid in the plane grid model; y is i The central point latitude of the ith training sample is the central point latitude of the ith grid in the planar grid model; i =1, 2.., I is the total number of training samples;
constructing a test set according to the earth surface coverage data/digital elevation data/historical wind speed data, and recording the jth test sample in the test set as (X) j ,Y j )，X j The longitude of the center point of the jth test sample is the longitude of the center point of the jth grid in the earth surface coverage data/digital elevation data/historical wind speed data; y is j The latitude of the center point of the jth test sample is the latitude of the center point of the jth grid in the earth surface coverage data/digital elevation data/historical wind speed data; j =1,2.. J, J is the total number of test samples;
2) Respectively 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·ar cos[cos(Y i )·cos(Y j )·cos(X i -X j )+sin(Y i )·sin(Y j )]
wherein R is the radius of the earth, and R is 6371km; x i And Y i Respectively the longitude and the latitude of the center point of the ith training sample; x j And Y j Respectively the longitude and the latitude of the central point of the jth test sample;
3) Respectively sequencing the spatial distance between each test sample and different training samples, and finding out the training sample with the minimum spatial distance with each test sample;
4) And giving the earth surface coverage data/digital elevation data/historical wind speed data contained in the test sample to the corresponding training sample to obtain the earth surface coverage data/digital elevation data/historical wind speed data under the grid resolution of the plane grid model.
5. The high resolution onshore wind power generation resource assessment method according to claim 2, characterized in that: the specific steps of screening out the grids capable of constructing the wind driven generator according to the digital elevation data of each grid in the planar grid model comprise:
and acquiring height data and gradient data of each grid in the planar grid model, and screening out the grids with the height of less than or equal to 3000 meters and the gradient of less than or equal to 10 degrees.
6. The high resolution onshore wind power generation resource assessment method according to claim 2, characterized in that: the specific steps of calculating the available area of each grid to be evaluated according to the surface coverage data of each grid to be evaluated comprise:
and acquiring the ground surface coverage type of each grid in the planar grid model, and calculating the available area of each grid to be evaluated according to the available area coefficient corresponding to the ground surface coverage type.
7. The high resolution onshore wind power generation resource assessment method according to claim 2, characterized in that: the specific step of determining the available capacity of each grid to be evaluated according to the available area of each grid to be evaluated, the rated capacity of a typical wind turbine and the diameter data of the blades comprises the following steps:
and determining the unit occupied area of the typical wind driven generator according to the rated capacity data and the blade diameter data of the typical wind driven generator, and obtaining the available capacity of each grid to be evaluated according to the available area of each grid to be evaluated.
8. The high resolution onshore wind power generation resource assessment method according to claim 2, characterized in that: the specific steps of calculating the wind power resource capacity factor of each grid to be evaluated according to the historical wind speed data of each grid and the hub height data of a typical wind driven generator comprise:
obtaining historical wind speed data of each grid to be evaluated
Extrapolating according to the acquired historical wind speed data to obtain historical wind speed data of each grid to be evaluated at any height, and further obtaining historical wind speed data of each grid to be evaluated at the height of a hub of a typical wind driven generator;
obtaining historical output power of each grid to be evaluated when the typical wind driven generator is adopted according to a wind speed-wind power curve corresponding to the typical wind driven generator and historical wind speed data of each grid to be evaluated under the height of a hub of the typical wind driven generator;
and calculating the wind power resource capacity factor of each grid to be evaluated according to the historical output power of each grid to be evaluated when a typical wind driven generator is adopted.
9. The high resolution onshore wind power generation resource assessment method according to claim 1, characterized in that: the earth surface coverage data adopts an I-type earth surface coverage data set in MCD12Q 1; the digital elevation data adopts an STRM digital elevation set; the historical wind speed data adopts a historical wind speed data set of a European mesoscale forecasting center.
10. A high-resolution onshore wind power generation resource evaluation system is characterized in that: the method comprises the following steps:
the model building module is used for building a plane grid model of the target area;
the data acquisition module is used for acquiring the earth surface coverage data, the digital elevation data and the historical wind speed data of each grid in the planar grid model;
the data processing module is used for screening out grids capable of building the wind driven generator as grids to be evaluated according to the digital elevation data of each grid in the planar grid model; calculating the available area of each grid to be evaluated according to the ground surface coverage data of each grid to be evaluated; determining the available capacity of each grid to be evaluated according to the available area of each grid to be evaluated, the rated capacity of a typical wind driven generator and the diameter data of blades; calculating the wind power resource capacity factor of each grid to be evaluated according to the historical wind speed data of each grid and the hub height data of a typical wind driven generator;
and the evaluation module is used for calculating the wind power resource capacity of each grid to be evaluated according to the machine-installable capacity and the wind power resource capacity factor of the grid to be evaluated and evaluating the wind power resource of each grid to be evaluated according to the wind power resource capacity of each grid to be evaluated.
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| CN118279751A (en) * | 2024-05-31 | 2024-07-02 | 华东师范大学 | Offshore wind turbine installed capacity calculation method and system based on Sentinel-2 image and Unet model |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118279751A (en) * | 2024-05-31 | 2024-07-02 | 华东师范大学 | Offshore wind turbine installed capacity calculation method and system based on Sentinel-2 image and Unet model |
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