CN117391394B - Wind-solar facility joint construction potential evaluation method based on geospatial data - Google Patents
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Abstract
The invention discloses a wind-solar facility joint construction potential evaluation method based on geospatial data, which comprises S1, space grid treatment of multi-source heterogeneous data; s2, determining construction potential grades of wind power and photovoltaic power stations; s3, building a wind-light resource relation model; s4, determining the combined construction potential of the wind and light facilities. The advantages are that: by establishing a relational model of wind-light meteorological element data, the construction potential of regional wind and photovoltaic power stations is weighed and distributed, a complete flow of wind-light facility combined construction potential evaluation is formed, the influence of constructed construction factors and limited construction factors is increased on the basis of a potential evaluation model, geospatial processing is carried out on the wind-light facility combined construction potential evaluation model, the wind-light facility combined construction potential is evaluated more objectively and truly, and the comprehensive and efficient utilization of wind-light renewable energy sources is promoted.
Description
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to a wind-solar facility joint construction potential evaluation method based on geospatial data.
Background
Compared with single wind power generation and single photovoltaic power generation, the wind-solar combined power generation can more efficiently utilize wind energy and light energy resources, realize mutual complementation among energy sources, provide more stable electric energy output, and provide important assistance for promoting comprehensive and efficient utilization of renewable energy sources and promoting realization of 'carbon peak and carbon neutralization'. And analyzing the combined construction potential of the wind-light facilities, and planning wind-light combined power generation construction projects to realize comprehensive utilization of wind-light resources. The regional centralized wind power and photovoltaic construction potential is influenced by factors such as land utilization, constructed construction conditions, terrain conditions, limited construction conditions and the like, and the wind and light combined construction potential is directly related to regional wind and light resource conditions.
The existing wind-light power generation facility construction potential is evaluated, the research on wind-light combined power generation potential is relatively few, the analysis on single wind power generation and single photovoltaic power generation is mostly carried out, and the selected evaluation factors are single. The satellite remote sensing technology is an important means for evaluating the wind-solar power generation potential, and various data required by potential evaluation can be obtained through extraction or inversion of remote sensing image data. In the existing related researches, potential evaluation is mostly carried out by combining wind energy or light energy resource data, land utilization data and topographic data, and analysis on the constructed capacity applying condition and the construction limiting condition is lacked, so that the calculated construction potential is relatively high; and research on wind-solar combined power generation and complementarity is mainly focused on the design of a power generation system, and analysis on the regional wind-solar combined power generation potential is lacking.
Disclosure of Invention
The invention aims to provide a wind-solar facility joint construction potential evaluation method based on geospatial data, so as to solve the problems in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a wind-solar facility joint construction potential evaluation method based on geospatial data, which comprises the following steps,
S1, spatial grid processing of multi-source heterogeneous data:
Extracting wind speed, solar total radiation, land utilization space distribution information, constructed wind and light facility distribution, gradient slope direction and limiting wind power construction and photovoltaic construction areas based on multi-source heterogeneous data including acquired meteorological site observation data, satellite remote sensing data, topographic data and land management data, and performing space grid treatment on the extracted data by constructing a uniform geographic grid;
s2, determining the construction potential grade of the wind power station and the photovoltaic power station:
calculating evaluation factors including land bearing, constructed construction capacity, topography conditions and policy limitation conditions, and correspondingly building a wind power facility construction potential evaluation model and a photovoltaic facility construction potential evaluation model based on the wind power evaluation factors and the photovoltaic evaluation factors respectively so as to respectively determine construction potential grades of wind power and photovoltaic power stations;
S3, building a wind-light resource relation model:
Performing data processing and multiple linear regression on wind speed data and total solar radiation quantity to establish a wind-solar resource relation model;
S4, determining the combined construction potential of wind and light facilities:
And constructing a centralized wind-light facility joint construction potential analysis model according to the wind-light resource relation model, the wind-power facility construction potential evaluation model and the photovoltaic facility construction potential evaluation model so as to determine the wind-light facility joint construction potential.
Preferably, step S1 comprises in particular,
S11, acquiring long-time sequence wind speed data and solar total radiation data obtained by regional weather station observation;
Acquiring regional satellite remote sensing data, and extracting 6 kinds of land utilization space distribution information including cultivated land, woodland, grassland, water area, construction land and bare land in the region by using a method combining automatic classification of a computer and manual visual interpretation;
obtaining regional DEM topographic data to extract gradient slope information;
Acquiring regional land management data to extract a region limiting wind power construction and photovoltaic construction;
s12, determining the dimension of the geographic grids, and constructing uniform geographic grids;
s13, matching the gradient slope data into a geographic grid by adopting a nearest neighbor resampling method;
For point type vector data such as wind power generation facilities, counting the number of points in each grid according to the space position, and assigning the total number of points to the geographic grid;
For vector data limiting the construction area and land utilization of the surface types, counting the areas of different types of pattern spots in each grid according to the space positions, and assigning the areas to the geographic grids;
for wind speed data and solar radiation data, wind speed and total solar radiation are assigned to the geographic grid using interpolation methods that take into account the terrain conditions.
Preferably, the step S12 is specifically that,
Using modelsDetermining the dimension of the geographic grid so as to construct a uniform geographic grid;
Wherein Area is the Area of the region; grid is the geographic Grid size; and (4) representing rounding of the calculation result.
Preferably, in step S13, the process of the present invention,
The interpolation formula for wind speed is that,
Wherein, wind is the grid Wind speed value; i is the grid number; h is the elevation of the target interpolation grid; h max is the highest elevation in the region; t i is the weight of the ith grid, t i＝1/di,di is the Euclidean distance from the ith grid to the target interpolation grid;
the interpolation formula for the total solar radiation is,
Wherein Solar is the total Solar radiation value of the grid; a is a grid slope parameter, and for the north hemisphere south or the south hemisphere north, a is 1, and other orientations are 0.5.
Preferably, step S2 specifically includes,
S21, calculating land bearing factors according to land utilization data, respectively endowing different suitability grades of 1-10 grades to land utilization types available for wind power and photovoltaic, and endowing unavailable types to be 0;
Calculating constructed capacity factors, counting the distribution area of the built wind power facilities and the distribution area of the photovoltaic facilities in a single grid, and calculating the constructed capacity factors of wind power and the constructed capacity factors of photovoltaic according to the proportion of the constructed distribution area in the grid;
calculating gradient factors, for wind power, gradually reducing suitability grades at intervals of 3 degrees, and setting an area with gradient greater than or equal to 30 degrees as 0; for the photovoltaic, the suitability level is gradually reduced at intervals of 2 degrees, and the area with the gradient of more than or equal to 20 degrees is set to be 0;
Calculating a slope factor, wherein the slope factor is increased clockwise by 0 DEG in the north direction, when the slope is increased from 0 DEG to 180 DEG, the suitability grade is increased from 1 to 10 at intervals of 18 DEG, and when the slope is increased from 180 DEG to 360 DEG, the suitability grade is reduced from 10 to 1; wind power does not calculate a slope factor;
Calculating a limit construction factor, counting the area of limiting wind power facility construction in a single grid, limiting the area of limiting photovoltaic facility construction, and calculating the wind power limit construction factor and the photovoltaic limit construction factor according to the area occupation ratio of a limit construction area in the grid;
S22, superposing all grid meshes of the wind power evaluation factors in space, constructing a wind power construction potential evaluation model, directly giving the potential of the grid mesh with one or more wind power evaluation factor values of 0 to 0, and calculating the construction potential of other grids according to the wind power construction potential evaluation model;
s23, superposing all the grids of the photovoltaic evaluation factors in space, constructing a photovoltaic construction potential evaluation model, directly giving the potential of the grid with one or more photovoltaic evaluation factors with 0 to 0, and calculating the construction potential of other grids according to the photovoltaic construction potential evaluation model.
Preferably, the suitability level assignment in step S21 is specifically,
For wind power, the grid value of cultivated land, water area and construction land is 0, the woodland is 5, the grassland is 8, and the bare land is 10;
For photovoltaics, the grid where cultivated land, water area and construction land are located is assigned 0, the woodland is 3, the grassland is 9, and the bare land is 10.
Preferably, in step S21,
The calculation formula of the established capacity factor of the wind power is as follows,
The calculation formula of the photovoltaic constructed capacity factor is as follows,
The calculation formula of the wind power limit construction factor is as follows,
The photovoltaic limit construction factor is calculated as,
Wherein alpha i is the constructed capacity factor of wind power in the ith grid; SW i is the distribution area of the wind power facilities already built in the ith grid; beta i is the photovoltaic built capacity factor in the ith grid; SP i is the distribution area of the photovoltaic facilities already built in the ith grid; gamma i is the wind power limit construction factor in the ith grid; DW i is the building area of the wind power facility limited in the ith grid; delta i is the photovoltaic limit construction factor in the ith grid; DP i is the construction area of the photovoltaic facility in the ith grid; SG is the mesh area.
Preferably, step S3 comprises in particular,
S31, respectively performing matrix dimension conversion according to the wind speed of the selected area and grid data of total solar radiation, and converting two-dimensional data into one-dimensional data according to the sequence of transverse priority to obtain wind speed sample data and total solar radiation sample data;
s32, respectively carrying out normalization processing on the wind speed sample data and the solar total radiation sample data;
S33, performing multiple linear regression on the normalized wind speed sample data and the total solar radiation sample data by using a least square method to obtain a functional relation between solar radiation and wind speed, namely a relation model of wind and light resources.
Preferably, step S4 specifically includes,
S41, calculating the weight of each wind power and photovoltaic according to a functional relation between solar radiation and wind speed;
S42, calculating a derivative function of a functional relation of solar radiation and wind speed, analyzing the correlation of solar total radiation and wind speed, constructing different wind-solar facility joint construction potential evaluation models according to the value of the derivative function,
Wherein Z i is the combined construction potential of wind and light facilities in the ith grid; w i is the wind power weight in the ith grid; f i is the photovoltaic weight in the ith grid; p i is an i-th grid wind power construction potential evaluation model; a i is the wind power evaluation factor in the ith grid; k i is the weight corresponding to the wind power evaluation factor in the ith grid; y i is an ith grid photovoltaic construction potential evaluation model; s i is the photovoltaic evaluation factor in the ith grid; h i is the weight corresponding to the photovoltaic evaluation factor in the ith grid; n is the number of grids within the region with a potential other than 0; g' θ (x) is the derivative of the solar radiation as a function of wind speed G θ (x);
G' θ (x) >0 indicates that the total solar radiation in the region is proportional to wind speed; g' θ (x) <0 indicates that the total solar radiation in the region is inversely proportional to wind speed; the simultaneous presence of G 'θ (x) >0 and G' θ (x) <0 indicates that the total solar radiation is proportional to the wind speed at some stages and inversely proportional at some stages.
10. The geospatial data based method for evaluating joint construction potential of wind and light facilities according to claim 9, wherein: the step S41 is specifically performed by,
Order theI.e./>Solving to obtain/>The wind power weight W i is obtained; /(I)Namely the photovoltaic weight F i;
wherein G i is solar radiation calculated by using a functional expression of solar radiation and wind speed; θ (θ 0,θ1,θ2,…,θm) as a parameter by Acquiring the value of each parameter; g θ (x) is a polynomial fit of degree m,/> The wind speed sample data after normalization; /(I)Is normalized solar radiation sample data.
The beneficial effects of the invention are as follows: the invention is based on satellite remote sensing technology, and the construction potential of regional wind and optical power stations is weighed and distributed by establishing a relation model of wind-light meteorological element data, so that a complete flow of wind-light facility joint construction potential evaluation is formed. The method has the advantages that the influence of constructed construction factors and limited construction factors is increased on the basis of the traditional potential evaluation model, factors such as land bearing, constructed construction capacity, terrain conditions and limited construction are comprehensively considered, the factors are subjected to geospatial treatment, the combined construction potential of wind and light facilities is evaluated more truly and objectively, the comprehensive and efficient utilization of wind and light renewable energy sources is facilitated, and important assistance is provided for promoting the realization of 'carbon peak and carbon neutralization'.
Drawings
FIG. 1 is a flow chart of a method for evaluating the joint construction potential of a wind turbine facility in an embodiment of the invention;
FIG. 2 is a spatial distribution diagram of regional land utilization information in an embodiment of the present invention;
FIG. 3 is a graph of regional wind power limit construction factor results in an embodiment of the invention;
FIG. 4 is a graph of regional wind power construction potential results in an embodiment of the invention;
FIG. 5 is a graph of the results of a regional photovoltaic construction potential in an embodiment of the present invention;
FIG. 6 is a graph of the combined construction potential results of a regional wind and solar facility in an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the invention.
As shown in fig. 1, in this embodiment, a method for evaluating the joint construction potential of wind and light facilities based on geospatial data is provided, which specifically includes the following parts,
1. Spatial meshing processing of multi-source heterogeneous data:
based on the acquired multi-source heterogeneous data including meteorological site observation data, satellite remote sensing data, topographic data and land management data, extracting wind speed, solar total radiation, land utilization space distribution information, constructed wind and light facility distribution, gradient slope direction and limited wind power construction and photovoltaic construction areas, and performing space grid treatment on the extracted data by constructing a uniform geographic grid.
Specifically, the method comprises the following steps of,
1.1 Extraction of related data in Multi-Source heterogeneous data
(1) And acquiring long-time sequence wind speed data and solar total radiation data obtained by regional weather station observation.
(2) The method comprises the steps of obtaining regional satellite remote sensing data, and extracting 6 kinds of land utilization space distribution information such as cultivated land, forest land, grassland, water area, construction land, bare land and the like in a region by utilizing a method of combining automatic classification of a computer with manual visual interpretation.
(3) And obtaining regional DEM topographic data to extract gradient and slope information.
(4) And acquiring regional land management data to extract regions limiting wind power construction and photovoltaic construction.
1.2, Determining the dimension of the geographic grid, and constructing a uniform geographic grid;
specifically: using models Determining the dimension of the geographic grid so as to construct a uniform geographic grid; wherein Area is the Area of the region; grifd is the geographic grid size; and (4) representing rounding of the calculation result. In this embodiment, the area of the area is 29.4 ten thousand square kilometers, and the size of the geographic grid is 1000 meters×1000 meters.
1.3 Data gridding treatment
Performing space gridding treatment on all extracted output by adopting the following space method: for gradient and slope data, matching the gradient and slope data into a geographic grid by adopting a nearest neighbor resampling method; for point type vector data such as wind power generation facilities, counting the number of points in each grid according to the space position, and assigning the total number of points to the geographic grid; for vector data limiting the construction area and land utilization of the surface types, counting the areas of different types of pattern spots in each grid according to the space positions, and assigning the areas to the geographic grids; referring to fig. 2, the result of the land utilization data grid of the region to be evaluated in this embodiment is shown.
For wind speed data and solar radiation data, wind speed and total solar radiation are assigned to the geographic grid using interpolation methods that take into account the terrain conditions.
The interpolation formula for wind speed is that,
Wherein, wind is the grid Wind speed value; i is the grid number; h is the elevation of the target interpolation grid; h max is the highest elevation in the region; t i is the weight of the ith grid, t i＝1/di,di is the Euclidean distance from the ith grid to the target interpolation grid;
the interpolation formula for the total solar radiation is,
Wherein Solar is the total Solar radiation value of the grid; a is a grid slope parameter, and for the north hemisphere south direction (slope direction 180 DEG-360 DEG) or the south hemisphere north direction (slope direction 0 DEG-180 DEG), a is 1, and other directions are 0.5.
2. And determining the construction potential grade of the wind power station and the photovoltaic power station:
And calculating evaluation factors including land bearing capacity, constructed construction capacity, topography conditions and policy limitation conditions, and correspondingly building a wind power facility construction potential evaluation model and a photovoltaic facility construction potential evaluation model based on the wind power evaluation factors and the photovoltaic evaluation factors respectively so as to determine construction potential grades of wind power and photovoltaic power stations respectively.
Specifically, the method comprises the following steps of,
2.1 Evaluation factor calculation
(1) According to land utilization data, land bearing factors are calculated, different suitability grades of 1-10 grades are respectively assigned to land utilization types available for wind power and photovoltaic, and unavailable types are assigned to 0.
Specifically: for wind power, the grid value of cultivated land, water area and construction land is 0, the woodland is 5, the grassland is 8, and the bare land is 10; for photovoltaics, the grid where cultivated land, water area and construction land are located is assigned 0, the woodland is 3, the grassland is 9, and the bare land is 10.
(2) Calculating constructed capacity factors, counting the distribution area (SW i) of the constructed wind power facilities and the distribution area (SP i) of the breadth facilities in a single grid, and calculating the constructed capacity factors of wind power and the constructed capacity factors of photovoltaic according to the occupied ratio of the constructed distribution areas in the grid.
The calculation formula of the established capacity factor of the wind power is as follows,
The calculation formula of the photovoltaic constructed capacity factor is as follows,
Alpha i is the constructed capacity factor of wind power in the ith grid; SW i is the distribution area of the wind power facilities already built in the ith grid; beta i is the photovoltaic built capacity factor in the ith grid; SP i is the distribution area of the photovoltaic facilities already built in the ith grid; SG is the mesh area.
(3) The gradient factor is calculated, and for wind power, the suitability level gradually decreases at intervals of 3 degrees, for example, the gradient is 0-3 degrees, the grid value is 10, 3-6 degrees is 9, and the like, and the region with the gradient of 30 degrees or more is 0. For photovoltaic, the suitability level gradually decreases at intervals of 2 °, for example, the gradient is 0 to 2 °, the grid value is 10,2 to 4 ° is 9, and the like, and the region having a gradient of 20 ° or more is 0.
(4) Calculating a slope factor, wherein the slope factor is increased clockwise by 0 DEG in the north direction, when the slope is increased from 0 DEG to 180 DEG, the suitability grade is increased from 1 to 10 at intervals of 18 DEG, and when the slope is increased from 180 DEG to 360 DEG, the suitability grade is reduced from 10 to 1; wind power does not calculate the slope factor.
(5) Calculating a limit construction factor, counting the area (DW i) for limiting the construction of wind power facilities in a single grid, and the area (DP i) for limiting the construction of photovoltaic facilities, and respectively calculating the wind power limit construction factor and the photovoltaic limit construction factor according to the area occupation ratio of the limit construction area in the grid.
The calculation formula of the wind power limit construction factor is as follows,
The photovoltaic limit construction factor is calculated as,
Wherein, gamma i is the wind power limit construction factor in the ith grid; DW i is the building area of the wind power facility limited in the ith grid; delta i is the photovoltaic limit construction factor in the ith grid; DO i limits the photovoltaic utility building area within the ith grid. Referring to fig. 3, a calculation result of wind power limit construction factors of an area to be evaluated in the embodiment is shown.
2.2, Superposing all wind power evaluation factor grids in space to construct a wind power construction potential evaluation modelThe potential of a grid with a certain or a plurality of wind power evaluation factor values of 0 is directly assigned to 0, and then the construction potential of other grids is calculated according to a wind power construction potential evaluation model;
Wherein P represents a wind power construction potential level (rounded according to rounding); a represents wind power evaluation factors including gradient factors, land resource bearing factors, constructed construction capacity factors and construction limiting factors; k represents the weight corresponding to the evaluation factor, wherein the gradient factor is 0.2, the land resource bearing factor is 0.2, the constructed capacity factor is 0.2, and the construction factor is limited to be 0.4; n represents the number of grids within the region with a potential other than 0, and i represents the grid number. Referring to fig. 4, the wind power construction potential evaluation result of the region to be evaluated in the embodiment is shown, and the grade numbers from 1 to 10 indicate that the wind power construction potential is gradually increased.
2.3, Superposing all the photovoltaic evaluation factor grids in space to construct a photovoltaic construction potential evaluation modelAnd directly assigning the potential of the grid with a certain or a plurality of photovoltaic evaluation factor values of 0 to 0, and calculating the construction potential of other grids according to the photovoltaic construction potential evaluation model.
Wherein Y represents a photovoltaic construction potential grade; s represents evaluation factors including gradient factors, slope factors, land resource bearing factors, constructed construction capacity factors and construction limiting factors; s represents the weight corresponding to the evaluation factor, wherein the gradient factor is 0.1, the slope factor is 0.3, the land resource bearing factor is 0.1, the constructed capacity factor is 0.1, and the construction factor is limited to be 0.4; n represents the number of grids within the region with a potential other than 0, and i represents the grid number. Fig. 5 shows the evaluation result of the photovoltaic construction potential of the region to be evaluated in the present embodiment, and the grade numbers from 1 to 10 indicate that the construction potential of the photovoltaic facility is gradually increased.
3. Building a wind-light resource relation model:
And carrying out data processing and multiple linear regression on the wind speed data and the total solar radiation quantity to establish a wind-solar resource relation model.
Specifically, the method comprises the following steps of,
3.1, Respectively performing matrix dimension conversion according to the wind speed of the selected area and grid data of total solar radiation, and converting two-dimensional data into one-dimensional data according to the sequence of transverse priority to obtain wind speed sample data and total solar radiation sample data;
(w i,fi)(i＝1,2,3,…k),wi is wind speed sample data, f i is solar total radiation sample data, k is total number of grids, and i is grid number.
3.2, Respectively carrying out normalization processing on the wind speed sample data and the solar total radiation sample data;
Normalized wind speed sample data Solar total radiation sample data/> W max is the maximum wind speed in the region, f max is the maximum solar radiation in the region, w min is the minimum wind speed in the region, and f min is the minimum solar radiation in the region.
And 3.3, performing multiple linear regression on the normalized wind speed sample data and the total solar radiation sample data by using a least square method to obtain a functional relation between solar radiation and wind speed, namely a relation model of wind and light resources.
Polynomial fitting with G θ (x) m times is used, For wind speed, G i is solar radiation obtained by model calculation, and θ (θ 0,θ1,θ2,…,θm) is a parameter; by solving forObtain/>
4. And (3) determining the combined construction potential of wind and light facilities:
And constructing a centralized wind-light facility joint construction potential analysis model according to the wind-light resource relation model, the wind-power facility construction potential evaluation model and the photovoltaic facility construction potential evaluation model so as to determine the wind-light facility joint construction potential.
Specifically, the method comprises the following steps of,
4.1, Calculating the weight of each wind power and photovoltaic according to a functional relation between solar radiation and wind speed;
Order the I.e./>Solving for the result/>Namely wind power weight, is additionally represented by W i,/>The weight is photovoltaic weight, and is further represented by F i;
4.2, calculating a derivative function of a functional relation formula of solar radiation and wind speed, analyzing the correlation relation of solar total radiation and wind speed, constructing different wind-solar facility joint construction potential evaluation models according to the value of the derivative function,
Z i is the combined construction potential of wind and light facilities in the ith grid, and the calculation result is rounded off by rounding; w i is the wind power weight in the ith grid; f i is the photovoltaic weight in the ith grid; p i is an i-th grid wind power construction potential evaluation model; a i is the wind power evaluation factor in the ith grid; k i is the weight corresponding to the wind power evaluation factor in the ith grid; y i is an ith grid photovoltaic construction potential evaluation model; s i is the photovoltaic evaluation factor in the ith grid; h i is the weight corresponding to the photovoltaic evaluation factor in the ith grid; n is the number of grids within the region with a potential other than 0; g' θ (x) is the derivative of the solar radiation as a function of wind speed G θ (x);
G' θ (x) >0 indicates that the total solar radiation in the region is proportional to wind speed; g' θ (x) <0 indicates that the total solar radiation in the region is inversely proportional to wind speed; the simultaneous presence of G 'θ (x) >0 and G' θ (x) <0 indicates that the total solar radiation is proportional to the wind speed at some stages and inversely proportional at some stages. Referring to fig. 6, the evaluation result of the centralized wind-solar power station combined construction potential of the region to be evaluated in the embodiment is shown, and the grade numbers from 1 to 10 indicate that the construction potential of the photovoltaic facilities is gradually increased.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a wind and light facility joint construction evaluation method based on geospatial data, which is characterized in that a relation model of wind and light meteorological element data is established based on satellite remote sensing technology, construction potential of regional wind and light power stations is balanced and distributed, and a complete flow of wind and light facility joint construction potential evaluation is formed. The method has the advantages that the influence of constructed construction factors and limited construction factors is increased on the basis of the traditional potential evaluation model, factors such as land bearing, constructed construction capacity, terrain conditions and limited construction are comprehensively considered, the factors are subjected to geospatial treatment, the combined construction potential of wind and light facilities is evaluated more truly and objectively, the comprehensive and efficient utilization of wind and light renewable energy sources is facilitated, and important assistance is provided for promoting the realization of 'carbon peak and carbon neutralization'.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which is also intended to be covered by the present invention.
Claims (7)
1. A wind-solar facility joint construction potential evaluation method based on geospatial data is characterized by comprising the following steps: comprises the following steps of the method,
S1, spatial grid processing of multi-source heterogeneous data:
Extracting wind speed, solar total radiation, land utilization space distribution information, constructed wind and light facility distribution, gradient slope direction and limiting wind power construction and photovoltaic construction areas based on multi-source heterogeneous data including acquired meteorological site observation data, satellite remote sensing data, topographic data and land management data, and performing space grid treatment on the extracted data by constructing a uniform geographic grid;
s2, determining the construction potential grade of the wind power station and the photovoltaic power station:
calculating evaluation factors including land bearing, constructed construction capacity, topography conditions and policy limitation conditions, and correspondingly building a wind power facility construction potential evaluation model and a photovoltaic facility construction potential evaluation model based on the wind power evaluation factors and the photovoltaic evaluation factors respectively so as to respectively determine construction potential grades of wind power and photovoltaic power stations;
S3, building a wind-light resource relation model:
Performing data processing and multiple linear regression on wind speed data and total solar radiation quantity to establish a wind-solar resource relation model;
step S3 specifically includes the following,
S31, respectively performing matrix dimension conversion according to the wind speed of the selected area and grid data of total solar radiation, and converting two-dimensional data into one-dimensional data according to the sequence of transverse priority to obtain wind speed sample data and total solar radiation sample data;
s32, respectively carrying out normalization processing on the wind speed sample data and the solar total radiation sample data;
S33, performing multiple linear regression on the normalized wind speed sample data and the total solar radiation sample data by using a least square method to obtain a functional relation between solar radiation and wind speed, namely a relation model of wind and light resources;
S4, determining the combined construction potential of wind and light facilities:
according to the wind-light resource relation model, the wind power facility construction potential evaluation model and the photovoltaic facility construction potential evaluation model, a centralized wind-light facility joint construction potential analysis model is constructed to determine wind-light facility joint construction potential;
step S4 specifically includes the following,
S41, calculating the weight of each wind power and photovoltaic according to a functional relation between solar radiation and wind speed; the step S41 is specifically performed by,
Order theI.e./>Solving to obtain/>The wind power weight W i is obtained; /(I)Namely the photovoltaic weight F i;
wherein G i is solar radiation calculated by using a functional expression of solar radiation and wind speed; θ (θ 0,θ1,θ2,…,θm) as a parameter by Acquiring the value of each parameter; g θ (x) is a polynomial fit of m times, The wind speed sample data after normalization; /(I)Normalized solar radiation sample data;
S42, calculating a derivative function of a functional relation of solar radiation and wind speed, analyzing the correlation of solar total radiation and wind speed, constructing different wind-solar facility joint construction potential evaluation models according to the value of the derivative function,
Wherein Z i is the combined construction potential of wind and light facilities in the ith grid; w i is the wind power weight in the ith grid; f i is the photovoltaic weight in the ith grid; p i is an i-th grid wind power construction potential evaluation model; a i is the wind power evaluation factor in the ith grid; k i is the weight corresponding to the wind power evaluation factor in the ith grid; y i is an ith grid photovoltaic construction potential evaluation model; s i is the photovoltaic evaluation factor in the ith grid; h i is the weight corresponding to the photovoltaic evaluation factor in the ith grid; n is the number of grids within the region with a potential other than 0; g' θ (x) is the derivative of the solar radiation as a function of wind speed G θ (x);
G' θ (x) >0 indicates that the total solar radiation in the region is proportional to wind speed; g' θ (x) <0 indicates that the total solar radiation in the region is inversely proportional to wind speed; the simultaneous presence of G 'θ (x) >0 and G' θ (x) <0 indicates that the total solar radiation is proportional to the wind speed at some stages and inversely proportional at some stages.
2. The geospatial data based method for evaluating the joint construction potential of wind and light facilities according to claim 1, wherein: step S1 specifically includes the following,
S11, acquiring long-time sequence wind speed data and solar total radiation data obtained by regional weather station observation;
Acquiring regional satellite remote sensing data, and extracting 6 kinds of land utilization space distribution information including cultivated land, woodland, grassland, water area, construction land and bare land in the region by using a method combining automatic classification of a computer and manual visual interpretation;
obtaining regional DEM topographic data to extract gradient slope information;
Acquiring regional land management data to extract a region limiting wind power construction and photovoltaic construction;
s12, determining the dimension of the geographic grids, and constructing uniform geographic grids;
s13, matching the gradient slope data into a geographic grid by adopting a nearest neighbor resampling method;
For point type vector data such as wind power generation facilities, counting the number of points in each grid according to the space position, and assigning the total number of points to the geographic grid;
For vector data limiting the construction area and land utilization of the surface types, counting the areas of different types of pattern spots in each grid according to the space positions, and assigning the areas to the geographic grids;
for wind speed data and solar radiation data, wind speed and total solar radiation are assigned to the geographic grid using interpolation methods that take into account the terrain conditions.
3. The geospatial data based method for evaluating the joint construction potential of wind and light facilities according to claim 2, wherein: the step S12 is specifically performed by,
Using modelsDetermining the dimension of the geographic grid so as to construct a uniform geographic grid;
Wherein Area is the Area of the region; grid is the geographic Grid size; and (4) representing rounding of the calculation result.
4. The geospatial data based method for evaluating the joint construction potential of wind and light facilities according to claim 2, wherein: in step S13 of the process,
The interpolation formula for wind speed is that,
Wherein, wind is the grid Wind speed value; i is the grid number; h is the elevation of the target interpolation grid; h max is the highest elevation in the region; t i is the weight of the ith grid, t i＝1/di,di is the Euclidean distance from the ith grid to the target interpolation grid;
the interpolation formula for the total solar radiation is,
Wherein Solar is the total Solar radiation value of the grid; a is a grid slope parameter, and for the north hemisphere south or the south hemisphere north, a is 1, and other orientations are 0.5.
5. The geospatial data based method for evaluating the joint construction potential of wind and light facilities according to claim 1, wherein: step S2 specifically includes the following,
S21, calculating land bearing factors according to land utilization data, respectively endowing different suitability grades of 1-10 grades to land utilization types available for wind power and photovoltaic, and endowing unavailable types to be 0;
Calculating constructed capacity factors, counting the distribution area of the built wind power facilities and the distribution area of the photovoltaic facilities in a single grid, and calculating the constructed capacity factors of wind power and the constructed capacity factors of photovoltaic according to the proportion of the constructed distribution area in the grid;
calculating gradient factors, for wind power, gradually reducing suitability grades at intervals of 3 degrees, and setting an area with gradient greater than or equal to 30 degrees as 0; for the photovoltaic, the suitability level is gradually reduced at intervals of 2 degrees, and the area with the gradient of more than or equal to 20 degrees is set to be 0;
Calculating a slope factor, wherein the slope factor is increased clockwise by 0 DEG in the north direction, when the slope is increased from 0 DEG to 180 DEG, the suitability grade is increased from 1 to 10 at intervals of 18 DEG, and when the slope is increased from 180 DEG to 360 DEG, the suitability grade is reduced from 10 to 1; wind power does not calculate a slope factor;
Calculating a limit construction factor, counting the area of limiting wind power facility construction in a single grid, limiting the area of limiting photovoltaic facility construction, and calculating the wind power limit construction factor and the photovoltaic limit construction factor according to the area occupation ratio of a limit construction area in the grid;
S22, superposing all grid meshes of the wind power evaluation factors in space, constructing a wind power construction potential evaluation model, directly giving the potential of the grid mesh with one or more wind power evaluation factor values of 0 to 0, and calculating the construction potential of other grids according to the wind power construction potential evaluation model;
s23, superposing all the grids of the photovoltaic evaluation factors in space, constructing a photovoltaic construction potential evaluation model, directly giving the potential of the grid with one or more photovoltaic evaluation factors with 0 to 0, and calculating the construction potential of other grids according to the photovoltaic construction potential evaluation model.
6. The geospatial data based method for evaluating joint construction potential of wind and light facilities according to claim 5, wherein: the suitability level assignment in step S21 is specifically,
For wind power, the grid value of cultivated land, water area and construction land is 0, the woodland is 5, the grassland is 8, and the bare land is 10;
For photovoltaics, the grid where cultivated land, water area and construction land are located is assigned 0, the woodland is 3, the grassland is 9, and the bare land is 10.
7. The geospatial data based method for evaluating joint construction potential of wind and light facilities according to claim 5, wherein: in step S21, the step of determining the position of the object,
The calculation formula of the established capacity factor of the wind power is as follows,
The calculation formula of the photovoltaic constructed capacity factor is as follows,
The calculation formula of the wind power limit construction factor is as follows,
The photovoltaic limit construction factor is calculated as,
Wherein alpha i is the constructed capacity factor of wind power in the ith grid; SW i is the distribution area of the wind power facilities already built in the ith grid; beta i is the photovoltaic built capacity factor in the ith grid; SP i is the distribution area of the photovoltaic facilities already built in the ith grid; gamma i is the wind power limit construction factor in the ith grid; DW i is the building area of the wind power facility limited in the ith grid; delta i is the photovoltaic limit construction factor in the ith grid; DP i is the construction area of the photovoltaic facility in the ith grid; SG is the mesh area.
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