Geographic space discretization method and system for wind and light energy development potential

Abstract

The invention discloses a geographic space discretization method and system for wind and light energy development potential, and relates to the technical field of energy space layout, and the method comprises the steps: building an original grid map layer; the land area where the wind and light resource power station cannot be built in the original grid layer is removed, and a processed grid layer is obtained; calculating theoretical installation potential, power generation potential and development cost of the photovoltaic and wind generating sets in each independent geographic unit; establishing an optimization objective function, and utilizing the optimization objective function to carry out successive optimization on the geographic space features in the processed grid layer; and when the bearable wind and light energy installation potential of the available geographic space area is not greater than the renewable energy to-be-built installation capacity, optimization is stopped, and the geographic space discretization of the wind and light energy development potential is completed. According to the method, not only is the scientificity of wind and light resource configuration effectively improved, but also the economical efficiency of wind and light resource development and utilization is ensured, and a data support and an optimization scheme are provided for an energy geographic space layout decision.

Description

Technical Field

[0001] The present invention relates to the technical field of energy space layout, and more specifically to a method and system for geographical space discretization of wind and solar energy development potential. Background Art

[0002] At present, the misaligned layout of wind and solar endowments and electricity demand has limited the development of wind and solar energy to a certain extent. Compared with traditional fossil energy, clean energy production of the same amount of electricity occupies a larger area of ​​land, and land resources have gradually become an influencing factor restricting the development and utilization of clean energy. Geographical environmental conditions are an important criterion for judging the feasibility of renewable energy infrastructure construction. Many studies have established corresponding geographical environmental conditions assessment systems and assessment systems for areas suitable for wind and solar energy development. However, the current method of geographic spatial discretization of wind and solar energy development potential needs to be deepened. How to achieve the geographic spatial discretization of wind and solar energy development potential is a key problem that technicians in this field need to solve urgently. Summary of the invention

[0003] In view of this, the present invention provides a method and system for geographical space discretization of wind and solar energy development potential to solve the problems existing in the background technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] A geospatial discretization method for the development potential of wind and solar energy, including:

[0006] Collect raw data and create original raster layers;

[0007] Eliminate the land area that is not suitable for building wind and solar power stations in the original raster layer, and obtain the raster layer that supports the development and construction of wind and solar resources;

[0008] Calculate the theoretical installed capacity, power generation potential, and development cost of photovoltaic and wind power generation in each independent geographic unit in the raster layer;

[0009] Based on the theoretical installed capacity potential, power generation potential and development cost, an optimization objective function is established, and the geographic spatial features in the raster layer are successively optimized using the optimization objective function;

[0010] When the available geographical space area can carry wind and solar energy installed capacity is not greater than the planned installed capacity of renewable energy, the optimization is stopped and the geographical space discretization of wind and solar energy development potential is completed.

[0011] Optionally, the geographic grid accuracy of the original raster layer is resampled to 1 km*1 km, and the original data includes wind power density, light radiation intensity, capacity factor, terrain slope, land use type, and energy infrastructure; the land types to be eliminated include land use type where wind and solar power stations cannot be built, terrain slope, distance from urban areas, distance from roads, and boundaries of nature reserves.

[0012] Optionally, estimate the onshore wind energy density ρ with reference to the wind turbine wind The calculation formula is as follows:

[0013] ρ wind =P turbine ·(n 2 ·D turbine 2 ) -1 ;

[0014] Where P turbine is the rated power of the onshore wind turbine; D turbine is the rotor diameter of the onshore wind turbine; n is the ratio of the side length of the onshore wind turbine to the rotor diameter.

[0015] According to the wind power density and unit pixel area in different regions, the theoretical installed capacity potential of onshore wind turbines P wind The calculation formula is as follows:

[0016] P wind =S wind ·ρ wind ;

[0017] In the formula, ρ wind represents the onshore wind power density; S wind Represents the installation pixel area of ​​the onshore wind turbine.

[0018] Optionally, the theoretical power generation potential E of the wind turbine wind The calculation formula is as follows:

[0019] E wind =P wind ·T h ·C f ;

[0020] Where P wind is the theoretical installed capacity of wind turbines; T h is the number of hours per year; C f It is the capacity factor of the wind turbine, which represents the ratio of the power generation of the wind turbine in a period of time to the maximum power generation at rated power.

[0021] Optionally, the levelized cost of electricity (LCOE) is selected as the development cost indicator in the renewable energy development area, and the wind power development cost LCOE wind The calculation formula is as follows:

[0022]

[0023] In the formula, LCOE wind P is the electricity cost per point; wind is the installed capacity potential of each point; μ fix is the total investment cost; R y is the ratio of operation and maintenance cost to investment cost; r d is the discount rate; E wind is the power generation potential of wind power; T is the operation period; μ line is the unit transmission line cost; d line is the length of the transmission line.

[0024] Optional, the theoretical installed capacity of the photovoltaic power generation unit P solar The calculation formula is as follows:

[0025] P solar =P w ·Ω f ·S solar ;

[0026] Where P w is the rated power of the photovoltaic module per unit area; Ω f is the area factor, which represents the area ratio of the land that can be covered by the photovoltaic panels. It is inversely proportional to the latitude. The higher the latitude, the smaller the area factor. solar The area suitable for laying photovoltaic modules in each pixel.

[0027] Optionally, the theoretical power generation potential E of the photovoltaic generator set solar The calculation formula is as follows:

[0028] E solar =R A ·S solar ·T h ·η·k;

[0029] In the formula, R A is the light radiation intensity; T h is the number of hours per year; η is the conversion efficiency coefficient; k is the correction factor.

[0030] Optional, photovoltaic power generation development cost LCOE solar The calculation formula is as follows:

[0031]

[0032] In the formula, LCOE solar P is the electricity cost per point; solar The theoretical installed capacity of each point; μ fix is the total investment cost per unit installed capacity; R y is the ratio of operation and maintenance cost to investment cost; r d is the discount rate; E solar is the theoretical power generation potential of photovoltaic power generation; T is the operation period; μ linr is the unit transmission line cost; d line is the length of the transmission line.

[0033] Optionally, the objective function can be expressed as:

[0034]

[0035] In the formula, min is the minimization objective function, max is the maximization objective function, P wind and P solar Represents the theoretical installed capacity potential of each suitable location; E wind and E solar Represents the theoretical power generation potential of each suitable location; LCOE wind and LCOE solar Represents the electricity cost of each suitable point, S wind and S solar Represents the area of ​​each suitable point; ε is the floating coefficient; P T Represents the planned installed capacity. Among them, the suitable point means the suitable location for building wind power generation and photovoltaic power generation. When the most suitable point is identified, the second most suitable point is found in turn. And so on, when the available geographical space area can carry wind and solar energy installed capacity is not greater than the planned installed capacity of non-hydropower renewable energy P T When , stop searching for the optimal solution.

[0036] A geospatial discretization system for wind and solar energy development potential, including:

[0037] The basic layer establishment module collects raw data and establishes the original raster layer;

[0038] The grid layer elimination module eliminates the land area that cannot be used to build wind and solar power stations in the original grid layer, and obtains the grid layer that supports the development and construction of wind and solar resources;

[0039] The installed capacity quantification module calculates the theoretical installed capacity, power generation potential, and development cost of photovoltaic and wind turbines in each independent geographic unit in the raster layer;

[0040] The optimization function building module establishes the optimization objective function based on the theoretical installed capacity potential, power generation potential and development cost;

[0041] The geospatial identification module uses the optimization objective function to perform sequential optimization in the geospatial space. When the wind and solar energy installed capacity potential of the available geospatial area is not greater than the planned installed capacity of renewable energy, the optimization is stopped to complete the geospatial discretization of the wind and solar energy development potential.

[0042] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for geographic spatial discretization of wind and solar energy development potential, including: collecting original data and establishing an original grid layer; eliminating land areas in the original grid layer that are not suitable for the construction of wind and solar power generating units, and obtaining a grid layer that supports the development and construction of wind and solar resources; calculating the theoretical installed capacity potential, power generation potential, and development cost of photovoltaic and wind power generating units in each independent geographic unit; establishing an optimization objective function based on the obtained theoretical installed capacity potential, power generation potential, and development cost, and using the optimization objective function to successively optimize the geographic spatial features in the processed grid layer; when the wind and solar energy installed capacity potential that can be carried by the available geographic space area is not greater than the planned installed capacity of renewable energy, stop optimizing and complete the geographic spatial discretization of the wind and solar energy development potential. The present invention systematically collects and analyzes raw data, eliminates areas that are not suitable for building power stations, calculates theoretical installed capacity potential, power generation potential and development cost, and establishes an optimization objective function for gradual optimization. It not only effectively improves the scientific nature of wind and solar resource allocation, but also ensures the economic feasibility of wind and solar resource development and utilization, providing data support and optimization solutions for energy geographic spatial layout decisions. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0044] Figure 1 The present invention provides a flow chart of the method. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The embodiment of the present invention discloses a method for discretizing the geographic space of wind and solar energy development potential, such as Figure 1 As shown, including:

[0047] Collect raw data and create original raster layers;

[0048] Eliminate the land area in the original raster layer that cannot be used to build wind and solar power stations, and obtain the raster layer that supports the development and construction of wind and solar resources;

[0049] Calculate the theoretical installed capacity, power generation potential, development cost, etc. of wind and photovoltaic generators in each independent geographic unit in the raster layer;

[0050] Based on the theoretical installed capacity potential, power generation potential, development cost and land area, an optimization objective function is established, and the geographic spatial features in the raster layer are successively optimized using the optimization objective function.

[0051] When the available geographical space area's potential for wind and solar energy installed capacity is no greater than the planned installed capacity of renewable energy, the search for optimization is stopped and the geographical space discretization of the wind and solar energy development potential is completed.

[0052] In a specific embodiment, the geographic grid accuracy of the original raster layer is resampled to 1 km * 1 km. The original data includes wind power density, light radiation intensity, capacity factor, terrain slope, land use type, energy infrastructure, etc. The land types to be eliminated include land use type where wind and solar power stations cannot be built, terrain slope, distance from urban areas, distance from roads, and boundaries of nature reserves, etc.

[0053] In a specific embodiment, the onshore wind energy density ρ is estimated with reference to the wind turbine wind The calculation formula is as follows:

[0054] ρ wind =P turbine· (n 2 ·D turbine 2 ) -1 ;

[0055] Where P turbine is the rated power of the onshore wind turbine; D turbine is the rotor diameter of the onshore wind turbine; n is the ratio of the land length of the onshore wind turbine to the rotor diameter;

[0056] According to the wind power density and unit pixel area in different regions, the theoretical installed capacity potential of onshore wind turbines P wind The calculation formula is as follows:

[0057] P wind =Swind ·ρ wind ;

[0058] In the formula, ρ wind represents the onshore wind power density; S wind Represents the pixel area where onshore wind turbines are installed.

[0059] In a specific embodiment, the theoretical power generation potential E of the wind turbine generator system is wind The calculation formula is as follows:

[0060] E wind =P wind ·T h ·C f ;

[0061] Where P wind is the theoretical installed capacity of wind turbines; T h is the number of hours per year; C f It is the capacity factor of wind power generation, which represents the ratio of the power generation of a wind turbine over a period of time to the maximum power generation at rated power.

[0062] In a specific embodiment, the levelized cost of electricity is selected as the development cost indicator in the renewable energy development area, and the wind power development cost calculation formula is as follows:

[0063]

[0064] In the formula, LCOE wind P is the electricity cost per point; wind The theoretical installed capacity of each point; μ fix is the total investment cost per unit installed capacity; R y is the ratio of operation and maintenance cost to investment cost; r d is the discount rate; E wind is the theoretical power generation potential of the wind turbine; T is the operation period; μ line is the unit transmission line cost; d line is the length of the transmission line.

[0065] In a specific embodiment, the theoretical installed capacity potential P of the photovoltaic power generation system solar The calculation formula is as follows:

[0066] P solar =P w ·Ω f ·S solar ;

[0067] Where P w is the rated power of the photovoltaic module per unit area; Ω fis the area factor, which represents the area ratio of the land that can be covered by the photovoltaic panels. It is inversely proportional to the latitude. The higher the latitude, the smaller the area factor. solar The area suitable for laying photovoltaic modules in each pixel.

[0068] In a specific embodiment, the theoretical power generation potential E of the photovoltaic power generation system solar The calculation formula is as follows:

[0069] E solar =R A ·S solar ·T h ·η·k;

[0070] In the formula, R A is the light radiation intensity; T h is the annual utilization hours; η is the conversion efficiency coefficient, including the conversion efficiency of photovoltaic cells, the array efficiency of photovoltaic modules, the efficiency of inverters, etc.; k is the correction factor, including attenuation coefficient, shading coefficient, temperature reduction, tilt angle correction coefficient, etc.

[0071] In a specific embodiment, the photovoltaic power generation development cost LCOE solar The calculation formula is as follows:

[0072]

[0073] In the formula, LCOE solar P is the electricity cost per point; solar The theoretical installed capacity of each point; μ fix is the total investment cost per unit installed capacity; R y is the ratio of operation and maintenance cost to investment cost; r d is the discount rate; E solar is the theoretical power generation potential of photovoltaic power generation; T is the operation period; μ line is the unit transmission line cost; d line is the length of the transmission line.

[0074] In a specific embodiment, the objective function can be expressed as:

[0075]

[0076] In the formula, min is the minimization objective function, max is the maximization objective function, P wind and P solar Represents the theoretical installed capacity potential of each suitable location; E wind and E solar Represents the theoretical power generation potential of each suitable location; LCOE wind and LCOE solarRepresents the electricity cost of each suitable point, S wind and S solar Represents the area of ​​each suitable point; ε is the floating coefficient; P T Represents the planned installed capacity. Among them, the suitable point means the suitable location for the construction of wind power generation and photovoltaic power generation. When the most suitable point is identified, the second most suitable point is found in turn, and so on. When the available geographical space area can carry the wind and solar energy installed capacity is not greater than the planned installed capacity of non-hydropower renewable energy P T When , stop searching for the optimal solution.

[0077] Based on ArcGIS, we conduct superposition, elimination and fusion between multidimensional layers, comprehensively consider the wind and solar resource endowments in different regions, superimpose wind power density, solar radiation intensity, capacity factor, terrain slope, land use type, energy infrastructure, etc., eliminate unusable land use type, terrain slope, distance from urban areas, distance from roads, and boundaries of nature reserves, and identify the geographic spatial layout suitable for the development of wind and solar energy. Combined with the calculation method of wind and solar power generation per unit pixel, the theoretical installed capacity, power generation potential, development cost, etc. of wind and solar generators are quantified. The installed capacity potential is used to reflect the maximum installed capacity that can be achieved under existing technical conditions, and the power generation potential is used to reflect the maximum power generation that can be achieved under existing technical conditions. The development cost involves the full life cycle cost from equipment procurement, installation to operation and maintenance. It not only effectively improves the scientific nature of wind and solar resource allocation, but also ensures the economic nature of wind and solar resource development and utilization, and provides data support and optimization solutions for energy geographic spatial layout decisions.

[0078] A geospatial discretization system for wind and solar energy development potential, including:

[0079] The basic layer establishment module collects raw data and establishes the original raster layer;

[0080] The grid layer elimination module eliminates the land area that cannot be used to build wind and solar power stations in the original grid layer, and obtains the grid layer that supports the development and construction of wind and solar resources;

[0081] The installed capacity quantification module calculates the theoretical installed capacity, power generation potential, development cost, etc. of photovoltaic generators and wind turbines in each independent geographic unit in the raster layer;

[0082] The optimization function building module establishes the optimization objective function based on the theoretical installed capacity potential, power generation potential, development cost and land area;

[0083] The geospatial identification module uses the optimization objective function to perform sequential optimization in the geospatial space. When the wind and solar energy installed capacity potential of the available geospatial area is not greater than the planned installed capacity of renewable energy, the optimization is stopped to complete the geospatial discretization of the wind and solar energy development potential.

[0084] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0085] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for discretizing the geospatial potential of wind and solar energy development, characterized in that: include: Collect raw data and create original raster layers; Eliminate the land area that is not suitable for building wind and solar power stations in the original raster layer, and obtain the raster layer that supports the development and construction of wind and solar resources; Calculate the theoretical installed capacity, power generation potential, and development cost of photovoltaic and wind power generation in each independent geographic unit in the raster layer; Based on the theoretical installed capacity potential, power generation potential and development cost, an optimization objective function is established, and the geographic spatial features in the raster layer are successively optimized using the optimization objective function; When the available geographical space area's potential for wind and solar energy installed capacity is no greater than the planned installed capacity of renewable energy, the search for optimization is stopped and the geographical space discretization of the wind and solar energy development potential is completed.

2. A method for geographical space discretization of wind and solar energy development potential according to claim 1, characterized in that: The geographic grid accuracy of the original raster layer is resampled to 1 km*1 km. The original data include wind power density, light radiation intensity, capacity factor, terrain slope, land use type, and energy infrastructure. The land types to be eliminated include land use type where wind and solar power stations cannot be built, terrain slope, distance from urban areas, distance from roads, and boundaries of nature reserves.

3. The method for geospatial discretization of wind and solar energy development potential according to claim 1, characterized in that: Estimation of onshore wind power density ρ with reference to wind turbines wind The calculation formula is as follows: r wind =P turbine ·(n 2 .D turbine 2 ) -1 ; Where P turbine is the rated power of the onshore wind turbine; D turbine is the rotor diameter of the onshore wind turbine; n is the ratio of the land length of the onshore wind turbine to the rotor diameter. According to the wind power density and unit pixel area in different regions, the theoretical installed capacity potential of onshore wind turbines P wind The calculation formula is as follows: P wind =S wind ·r wind ; In the formula, ρ wind represents the onshore wind power density; S wind Represents the installation pixel area of ​​the onshore wind turbine.

4. The method for geospatial discretization of wind and solar energy development potential according to claim 1, characterized in that: Theoretical power generation potential E of wind turbines wind The calculation formula is as follows: E wind =P wind ·T h ·C f ; Where P wind is the theoretical installed capacity of wind turbines; T h is the number of hours per year; C f It is the capacity factor of the wind turbine, which represents the ratio of the power generation of the wind turbine in a period of time to the maximum power generation at rated power.

5. The method for geospatial discretization of wind and solar energy development potential according to claim 1, characterized in that: In the renewable energy development area, the levelized cost of electricity is selected as the development cost indicator. The calculation process of wind power development cost is as follows: In the formula, LCOE wind P is the electricity cost per point; wind is the installed capacity potential of each point; μ fix is the total investment cost; R y is the ratio of operation and maintenance cost to investment cost; rd is the discount rate; E wind is the power generation potential of wind power; T is the operation period; μ line is the unit transmission line cost; d line is the length of the transmission line.

6. A method for geographical space discretization of wind and solar energy development potential according to claim 1, characterized in that: Theoretical installed capacity potential of photovoltaic power generation units P solar The calculation formula is as follows: P solar =P w ·Ω f ·S solar ; Where P w is the rated power of the photovoltaic module per unit area; Ω f is the area factor, which represents the area ratio of the land that can be covered by the photovoltaic panels. It is inversely proportional to the latitude. The higher the latitude, the smaller the area factor. solar The area suitable for laying photovoltaic modules in each pixel.

7. The method for geospatial discretization of wind and solar energy development potential according to claim 1, characterized in that: Theoretical power generation potential E of photovoltaic generator set solar The calculation formula is as follows: E solar =R A ·S solar ·T h ·η·k; In the formula, R A is the light radiation intensity; T h is the number of hours per year; η is the conversion efficiency coefficient; k is the correction factor.

8. The method for geospatial discretization of wind and solar energy development potential according to claim 1, characterized in that: Photovoltaic power generation development cost LCOE solar The calculation formula is as follows: In the formula, LCOE solar P is the electricity cost per point; solar The theoretical installed capacity of each point; μ fix is the total investment cost per unit installed capacity; R y is the ratio of operation and maintenance cost to investment cost; r d is the discount rate; E solar is the theoretical power generation potential of the photovoltaic generator set; T is the operation period; μ line is the unit transmission line cost; d line is the length of the transmission line.

9. The method for geospatial discretization of wind and solar energy development potential according to claim 1, characterized in that: The objective function is expressed as: In the formula, min is the minimization objective function, max is the maximization objective function, P wind and P solar Represents the theoretical installed capacity potential of each suitable location; E wind and E solar Represents the theoretical power generation potential of each suitable location; LCOE wind and LCOE solar Represents the electricity cost of each suitable point, S wind and S solar Represents the area of ​​each suitable point; ε is the floating coefficient; P T represents the planned installed capacity; the suitable location means the suitable location for building wind power generation and photovoltaic power generation. After the most suitable location is identified, the next most suitable location is found in turn, and so on. When the available geographical space area can carry the wind and solar energy installed capacity is not greater than the planned installed capacity of non-hydropower renewable energy P T When , stop searching for the optimal solution.

10. A geographical space discretization system for wind and solar energy development potential, characterized in that: A method for geographically discretizing the potential of wind and solar energy development according to any one of claims 1 to 9, comprising: The basic layer establishment module collects raw data and establishes the original raster layer; The grid layer elimination module eliminates the land area that cannot be used to build wind and solar power stations in the original grid layer, and obtains the grid layer that supports the development and construction of wind and solar resources; The installed capacity quantification module calculates the theoretical installed capacity, power generation potential, and development cost of photovoltaic and wind turbines in each independent geographic unit in the raster layer; The optimization function building module establishes the optimization objective function based on the theoretical installed capacity potential, power generation potential and development cost; The geospatial identification module uses the optimization objective function to perform sequential optimization in the geospatial space. When the wind and solar energy installed capacity potential of the available geospatial area is not greater than the planned installed capacity of renewable energy, the optimization is stopped to complete the geospatial discretization of the wind and solar energy development potential.

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