High-temporal-spatial-resolution photovoltaic resource prediction system along highway

Abstract

The invention discloses a high-temporal-spatial-resolution photovoltaic resource prediction system along an expressway, which is based on a WRF-Chem-Solar mode, combines CMIP6 meteorological data and DPEC emission source data to simulate future weather climate, and establishes an expressway photovoltaic resource fine evaluation and prediction method by using a radiation space downscaling and terrain correction technology. The method comprises the following steps: acquiring solar energy related meteorological prediction data such as high-temporal-spatial-resolution total radiation, horizontal plane and inclined direct radiation, scattered radiation, air temperature and the like along an expressway, and carrying out modeling analysis on a resource evaluation result through PVLIB-Python to obtain a photovoltaic development potential evaluation and prediction product along the expressway. And fine evaluation and accurate prediction of photovoltaic resources with high temporal-spatial resolution along the national expressways are realized.

Description

Technical Field

[0001] The invention belongs to the field of meteorological science and technology, and in particular relates to a photovoltaic resource prediction system with high temporal and spatial resolution along a highway. Background Art

[0002] The development and utilization of distributed photovoltaic resources that can be used immediately provides a new way to supply electricity to meet the growing electricity demand of highways. Moreover, with the rapid development of photovoltaic technology, the efficient development and utilization of solar energy resources along highways has broad development space and huge application potential.

[0003] The highway photovoltaic resource assessment and prediction system is aimed at the detailed assessment of highway photovoltaic resources and the prediction of future solar power generation. It takes into account the impact of clouds and aerosols on solar radiation. Based on the dual-carbon target emission source list, historical and future global climate projection data, it adopts meteorological-chemical coupling model and improved cloud microphysical parameterization scheme. It can achieve high temporal and spatial resolution solar radiation and power generation assessment and prediction along highways in the next few decades.

[0004] In addition, the system also incorporates radiation spatial downscaling technology, which can increase the resolution of solar radiation data to a level sufficient to cover a single highway section, which is essential for the refined assessment of photovoltaic resources. Through this high-resolution assessment, the potential and economic benefits of building solar power generation facilities on specific sections can be determined more accurately. At the same time, the system also takes into account the impact of terrain. Through terrain correction technology, photovoltaic resources in complex terrain conditions along the highway can be more accurately assessed. This is especially important for highway sections in mountainous areas or with complex terrain. Through this terrain correction, the accuracy of photovoltaic resource assessment can be ensured, thereby providing more reliable data support for the design and layout of solar power generation facilities.

[0005] The system can provide important scientific and technological support for the detailed evaluation of highway photovoltaic resources and the prediction of future power generation. This is of great significance for promoting the green energy transformation of transportation infrastructure such as highways and accelerating the realization of the zero-carbon goal of the transportation industry. Summary of the invention

[0006] Purpose of the invention: In view of the problems existing in the prior art, the present invention provides a photovoltaic resource prediction system with high temporal and spatial resolution along highways. The system is based on the WRF-Chem-Solar model, combines CMIP6 meteorological data with DPEC emission source data to simulate future weather and climate, and uses radiation spatial downscaling and terrain correction technology to establish a detailed evaluation and prediction method for photovoltaic resources on highways, obtains solar energy-related meteorological prediction data such as total radiation, horizontal and inclined direct radiation, scattered radiation, and temperature along highways with high temporal and spatial resolution, and models and analyzes the resource evaluation results through PVLIB-Python to obtain evaluation and prediction products for photovoltaic development potential along highways, thereby realizing detailed evaluation and accurate prediction of photovoltaic resources with high temporal and spatial resolution along highways across the country.

[0007] Technical solution: To achieve the above invention purpose, the present invention adopts the following technical solution: A photovoltaic resource prediction system with high temporal and spatial resolution along a highway, comprising the following steps: S1, data preparation: obtain DPEC emission source data and CMIP6 meteorological field data; S2, data processing: (1) DPEC emission source data processing: DPEC emission sources are processed into three-dimensional grids on a hourly basis to form three-dimensional grid values ​​that reflect the temporal and spatial characteristics of various emission sources; (2) CMIP6 meteorological data processing: CMIP6 meteorological data adopt 18 CMIP6 models, namely ACCESS-CM2, ACCESS-ESM1–5, CanESM5, BCC-CSM2-MR, FGOALS-f3-L, FGOALS-g3, EC-Earth3, EC-Earth3-Veg, IPSL-CM6A-LR, AWI-CM-1-1-MR, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MIROC6, MRI-ESM2-0, NorESM2-LM, CESM2, CESM2-WACCM, GFDL-ESM4, and the above 18 CMIP6 models are integrated and converted into three-dimensional grids; S3, build WRF-Chem-Solar model: Based on the WRF-Chem-Solar model, perform the following settings and operations on the model: (1) Model parameter setting: a double nested grid model is used, with a first-layer nesting resolution of 50 km or higher and a local area of ​​10 km or higher resolution; (2) Microphysical parameter setting: The Dudhia microphysical model is used to calculate the mutual transformation process of water phases including cloud water, rain water, snow and hail; the CBMZ model is used to calculate the cloud convection parameterization; the MOSAIC model is used to calculate the physical properties and interactions of water phases in different particle size segments; (3) Initial field setting: The CMIP6 meteorological field data uses the current simulation data as the initial condition; the chemical field of the DPEC emission source uses the last hour simulation data of the previous day as the initial field condition; (4) WRF-Chem-Solar model calculation scheme setting: The model uses the 24 hours before the start of the simulation as the spin-up time, and uses the last 24 hours as the simulation result output to obtain gridded meteorological products of future solar energy resources with high temporal and spatial resolution; S4, WRF-Chem-Solar model post-processing: The gridded meteorological products obtained in step S3 are interpolated through the SES2 radiation transfer model to achieve radiation spatial downscaling; S5, terrain correction: Based on the DEM complex terrain solar radiation correction model along the highway, the temporal and spatial distribution of solar radiation under complex terrain conditions along the highway is calculated; S6, Photovoltaic potential assessment and prediction: The PVLIB-Python numerical simulation system is used to model and analyze the performance of photovoltaic systems by using the radiation spatial downscaling and terrain-corrected meteorological data. The photovoltaic potential assessment and prediction products along the highways under different inclination angles, different tracking methods and different photovoltaic materials are obtained.

[0008] Furthermore, the DPEC emission source data in step S1 adopts the Ambitious-pollution-Neutral-goal scenario, which is one of the six emission scenarios in the second scenario data set of the DPEC model; and CMIP6 adopts the SP1-2.6 scenario.

[0009] Furthermore, after the DPEC emission source data in step S2 is three-dimensionally gridded, the data resolution is 10km*10km, the vertical direction is 41 layers, and the layer height is 2km.

[0010] Furthermore, the interpolation process of the SES2 radiation transfer model in step S4 includes: (1) Horizontal downscaling interpolation: Search and determine the points to be interpolated The four nearest neighboring points around, the longitude and latitude coordinates of the four nearest neighboring points are , , , , the interpolation points to be downscaled in the horizontal direction The value at It is calculated by the following formula: , In the formula, , , and Represent the values ​​of the four nearest neighbor points respectively. Indicates coordinate points The value of Indicates coordinate points The value of (2) Vertical downscaling interpolation: The input variables of the WRF-Chem-Solar model are linearly interpolated in the vertical direction. The variable value T of each input variable in the pressure layer to be interpolated is calculated by the following formula: , In the formula, T represents the variable value of the pressure layer to be inserted, p represents the pressure value of the pressure layer to be inserted, and T a represents the variable value of the a-th pressure layer, T b represents the variable value of the b-th pressure layer, p a Indicates the pressure value of the a-th pressure layer, p b represents the air pressure value of the b-th air pressure layer; the input variables include temperature, air pressure, ozone concentration, carbon dioxide concentration, nitric oxide concentration, methane concentration and oxygen concentration; For pressures above 100 mb, the water vapor mixing ratio is replaced by the mid-latitude atmospheric climate profile; for pressures below 100 mb, the water vapor mixing ratio of the interpolated layer is qq _ need m By calculating the following formula, , In the formula, the subscript i represents the number of layers before interpolation, the subscript m represents the number of interpolation layers, and P i represents the air pressure of the i-th layer, P i-1 represents the air pressure of the i-1th layer, P i+1 represents the air pressure of the i+1th layer, Indicates the pressure difference between the layer before interpolation and the previous layer, vapor i-1 represents the water vapor content of the i-1th layer, qq i represents the cumulative amount of water vapor from the ground to the i-th layer, qq i-1 represents the cumulative amount of water vapor from the ground to the i-1th layer, qq1 i-1 represents the water vapor accumulation of the i-1th layer; qq m represents the accumulated water vapor from the ground to the interpolation layer, qq i+1 represents the water vapor accumulation of the i+1th layer, P mrepresents the air pressure of the interpolation layer, P m+1 Indicates the air pressure of the previous layer of the interpolation layer, qq _ need m represents the water vapor mixing ratio of the interpolation layer, qq m+1 Represents the accumulated water vapor from the ground to the previous layer of the interpolation layer.

[0011] Furthermore, in step S2, the 18 CMIP6 models are integrated and calculated to generate the integrated corrected GCM data GCM mvt , , In the formula, GCM mvt represents the global climate model after integrated correction; GCM represents the global climate model; GCM' represents the interannual disturbance term of the global climate model GCM; It represents the average value of the long-term trend LT of the reanalysis data in the historical period H; denoted by represents the average value of the multi-model ensemble MME long-term trend LT in the historical period H; It represents the average deviation of the long-term trend of GCM data relative to the reanalysis dataset in the historical period; represents the standard deviation of the detrended reanalysis data over the historical period, represents the standard deviation of the detrended GCM data; r s Represents the ratio of the standard deviation of the detrended reanalysis data to the standard deviation of the detrended GCM data over the historical period.

[0012] Beneficial effects: Compared with the prior art, the present invention has the following advantages: (1) The system can predict photovoltaic resources at a specific time in the future and obtain photovoltaic resource prediction products with high temporal and spatial resolution (hourly or 15 minutes, hundred meters or tens of meters) along the highway; and consider the impact of different inclination angles, different tracking methods and different photovoltaic materials on photovoltaic potential prediction.

[0013] (2) The system includes a cloud-aerosol-radiation mutual feedback mechanism, which fully considers the impact of clouds and aerosols on solar radiation.

[0014] (3) The system uses radiation spatial downscaling technology, which has higher resolution than the traditional WRF model and can easily obtain solar radiation data at a scale of hundreds or tens of meters along the highway.

[0015] (4) The system adopts terrain correction technology. Compared with the traditional planar model, it fully considers the influence of complex terrain along the highway and can more accurately evaluate and predict the photovoltaic potential under complex terrain conditions along the highway. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the photovoltaic resource prediction system with high temporal and spatial resolution along the highway of the present invention; Figure 2 This is a schematic diagram of bilinear interpolation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0018] like Figure 1 As shown, the present invention adopts the following scheme to obtain high temporal and spatial resolution photovoltaic resource detailed assessment and prediction products along the highway for the next few decades or hundreds of years, so as to achieve accurate estimation of photovoltaic resources.

[0019] S1, data preparation: ①DPEC emission source: The present invention adopts the Ambitious-pollution-Neutral-goal scenario, one of the six emission scenarios in the second scenario data set (v1.1) of the DPEC model, that is, the carbon neutral climate scenario.

[0020] ②CMIP6 meteorological field data: CMIP6 (Coupled Model Intercomparison Project Phase6) is the sixth phase of the global climate model comparison program. CMIP6 data covers multiple levels from the ground to the atmosphere, including temperature, precipitation, wind speed, humidity and other meteorological variables. This system uses the SSP1-2.6 scenario.

[0021] S2, DPEC emission source treatment for WRF-Chem-Solar model: The WRF-Chem-Solar model is a numerical weather forecast model specifically used for solar resource assessment and forecasting needs. It is based on the WRF-Solar model and is further combined with the WRF-Chem model to combine the aerosol chemistry module with the radiation module in an online coupling manner.

[0022] The present invention is based on DPEC emission sources, and the emission source processing mode of the WRF-Chem-Solar model is used for localized processing to support a higher resolution and refined three-dimensional grid tracing mode. The processing process aims to provide hourly three-dimensional grid emission sources. The horizontal resolution of the grid can reach 10km*10km or higher, and there are 41 layers in the vertical direction, mainly concentrated below 2km.

[0023] S3, CMIP6 large-scale meteorological field processing: The meteorological field data of the present invention uses 18 CMIP6 model prediction data integration, namely ACCESS-CM2, ACCESS-ESM1-5, CanESM5, BCC-CSM2-MR, FGOALS-f3-L, FGOALS-g3, EC-Earth3, EC-Earth3-Veg, IPSL-CM6A-LR, AWI-CM-1-1-MR, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MIROC6, MRI-ESM2-0, NorESM2-LM, CESM2, CESM2-WACCM, GFDL-ESM4, a total of 18 models, which surpass the limitations of a single model, and the meteorological prediction effect is better than that of a single model, and the reliability is stronger. The WPS (WRF preprocessing system) module of the WRF-Chem-Solar mode converts the meteorological field data of CMIP6 into the three-dimensional grid data required by the mode, and these data are then used as the initial field and boundary conditions of the WRF-Chem-Solar mode.

[0024] The 18 CMIP6 models of the present invention are based on the WPS module, and the integrated calculation is performed through the following formula to generate the integrated corrected GCM data GCM mvt , , In the formula, GCM mvt represents the global climate model after integrated correction; GCM represents the global climate model; GCM' represents the interannual disturbance term of the global climate model GCM; It represents the average value of the long-term trend LT of the reanalysis data in the historical period H; denoted by represents the average value of the multi-model ensemble MME long-term trend LT in the historical period H; It represents the average deviation of the long-term trend of GCM data relative to the reanalysis dataset in the historical period; represents the standard deviation of the detrended reanalysis data over the historical period, represents the standard deviation of the detrended GCM data; r s Represents the ratio of the standard deviation of the detrended reanalysis data to the standard deviation of the detrended GCM data over the historical period.

[0025] The present invention adopts the integrated prediction data of 18 CMIP6 models to replace the conventional single CMIP6 model, and the prediction effect is better than that of a single model.

[0026] S4, using the WRF-Chem-Solar model to predict the weather field in the next few decades or hundreds of years: (1) Model parameter setting: Based on the WRF-Chem-Solar model, the model adopts double nested grid technology. The first layer of nesting is 50 km or higher resolution, and the second layer of nesting is further improved to 10 km or higher resolution, which significantly improves the temporal and spatial resolution; (2) Model parameter setting: In terms of microphysical parameterization scheme, the model of the present invention has also made important improvements. The parameterization scheme adopts scheme D+CBMZ+MOSAIC 4bin. Scheme D is the Dudhia microphysical scheme, which mainly considers the mutual transformation process of water components such as cloud water, rain water, snow and hail. The CBMZ scheme refers to an improved version of the Kain-Fritsch cumulus convection parameterization scheme (KF), that is, a revised version of the Kain-Fritsch cumulus convection parameterization scheme (CBMZ). It is mainly used to describe the cumulus convection process, and simulates the development of convective clouds and precipitation formation by parameterizing the physical mechanism of cumulus convection. The MOSAIC scheme is a binned microphysical parameterization scheme, and 4bin means that the water components are divided into 4 particle size segments for simulation. It simulates the microphysical processes of clouds and precipitation more accurately by describing the physical properties of water components in different particle size segments and their interactions in detail. Morrison can replace Kessler in the microphysics scheme, and the Morrison scheme is further optimized based on WRF-Chem4.2. The cloud droplet activation process is embedded in the Morrison scheme and the cloud droplet number concentration is updated in real time. The cloud droplet mass concentration, cloud droplet radius, cloud water path and other cloud physics parameters are improved to improve the simulation of cloud-radiation feedback process. (3) Initial field setting: The CMIP6 meteorological field data uses the current simulation data as the initial condition; the chemical field of the DPEC emission source uses the last hour simulation data of the previous day as the initial field condition; (4) In terms of calculation scheme, the WRF-Chem-Solar model performs simulation one day in advance every day. The first 24 hours of the simulation are used as the spin-up time, and the last 24 hours are used as the simulation results.

[0027] In the setting of initial field and boundary conditions, the present invention adopts CMIP6 cold start for meteorological field and uses the last simulation structure of the previous day as the initial field for chemical field. Such setting enables the model to combine meteorological field and chemical field more accurately and further consider the interaction between cloud, aerosol and radiation. Finally, the simulation obtains grid meteorological products related to solar energy resources with high temporal and spatial resolution (hourly or 15 minutes, 3 kilometers or finer) from 2015 to 2060 or longer.

[0028] Through the above parameter settings, the present invention can predict the spatiotemporal changes of solar energy resources in a certain month, season, year, multiple years or longer in the future; the microphysical parameterization scheme has been locally improved, and the cloud-aerosol-radiation interaction has been fully considered, and the spatiotemporal resolution has been significantly improved.

[0029] S5, Radiative spatial downscaling based on the SES2 radiative transfer model: The present invention uses the SES2 radiation transfer model to perform interpolation processing to achieve radiation spatial downscaling. The data output by the WRF-Chem-Solar model cannot be directly applied to the SES2 model. The SES2 model requires 91 layers, while the WRF-Chem-Solar model outputs only 34 layers of data. The model output data needs to be interpolated before it can be used. The present invention performs interpolation processing according to the following method: (1) Horizontal downscaling interpolation In the process of horizontal downscaling, this scheme adopts the bilinear interpolation method, which uses the four station values ​​near the grid point to perform linear interpolation in the horizontal and vertical directions to obtain the value of the grid point. This method shows good results in spatial downscaling, especially in the refined forecast of precipitation data. The process of bilinear interpolation is as follows: ① Determine the nearest neighbor point: First determine the four nearest neighbor points around the interpolation point , , , , forming a small rectangular area, and the interpolation point P is located inside this rectangle; ② Linear interpolation: Linear interpolation is performed in the x direction and the y direction. For the x direction, the two endpoint values ​​R of the row where the interpolation point is located are used. 1 , R 2 Perform linear interpolation; for the y direction, use the two endpoint values ​​of the column where the interpolation point is located for linear interpolation, such as Figure 2 As shown. The interpolation point P is calculated by the following formula, that is, the point to be interpolated The value at , , In the formula, , , and Represent the values ​​of the four nearest neighbor points respectively. Indicates coordinate points The value of Indicates coordinate points The value of (2) Vertical downscaling interpolation In the vertical downscaling process, input variables such as carbon dioxide, nitrous oxide, methane, oxygen, ozone, temperature, specific humidity, cloud liquid water content, and cloud ice crystal content are used, among which carbon dioxide, nitrous oxide, methane, oxygen, and ozone are set as constants. In addition, since the extrapolation method for water vapor at 100 mb will produce a large error, the temperature of water vapor above 100 mb in the present invention is replaced by the mid-latitude atmospheric climate profile, while the WRF-Chem model output data is used below 100 mb, as follows: The variable value T of each input variable in the pressure layer to be inserted, , In the formula, T represents the variable value of the pressure layer to be inserted, p represents the pressure value of the pressure layer to be inserted, and T a represents the variable value of the a-th pressure layer, T b represents the variable value of the b-th pressure layer, p a Indicates the pressure value of the a-th pressure layer, p b represents the air pressure value of the b-th air pressure layer; the input variables include temperature, air pressure, ozone concentration, carbon dioxide concentration, nitric oxide concentration, methane concentration and oxygen concentration; For pressures above 100 mb, the water vapor mixing ratio is replaced by the mid-latitude atmospheric climate profile; for pressures below 100 mb, the water vapor mixing ratio of the interpolated layer is qq _ need m By calculating the following formula, , In the formula, the subscript i represents the number of layers before interpolation, the subscript m represents the number of interpolation layers, and P i represents the air pressure of the i-th layer, P i-1 represents the air pressure of the i-1th layer, P i+1 represents the air pressure of the i+1th layer, Indicates the pressure difference between the layer before interpolation and the previous layer, vapor i-1 represents the water vapor content of the i-1th layer, qq i represents the cumulative amount of water vapor from the ground to the i-th layer, qq i-1 represents the cumulative amount of water vapor from the ground to the i-1th layer, qq1 i-1 represents the water vapor accumulation of the i-1th layer; qq mrepresents the accumulated water vapor from the ground to the interpolation layer, qq i+1 represents the water vapor accumulation of the i+1th layer, P m represents the air pressure of the interpolation layer, P m+1 Indicates the air pressure of the previous layer of the interpolation layer, qq _ need m represents the water vapor mixing ratio of the interpolation layer, qq m+1 Represents the accumulated water vapor from the ground to the previous layer of the interpolation layer.

[0030] After interpolation of the model output data, the temperature, air pressure, water vapor, ozone, aerosol, cloud water, cloud ice, cloud vertical distribution profile, surface reflectivity, and the real-time calculation data of the solar zenith angle of the SES2 model and the background data of greenhouse gases such as carbon dioxide, nitric oxide, methane, oxygen, CFC, etc. are input into the SES2 model. The SES2 model undergoes preprocessing, vertical interpolation, diagnostic quantity calculation, solar radiation calculation, photovoltaic resource prediction, and ground temperature and reflectivity calculation. Photovoltaic resource prediction products with a resolution of hundreds or tens of meters along the highway are obtained, including horizontal or inclined surface total radiation, direct radiation, scattered radiation, ground temperature, etc. in the narrow band (0.25~1.1μm) of photovoltaic power generation.

[0031] Compared with the traditional WRF model, the present invention further combines the radiation spatial downscaling technology to significantly improve the spatiotemporal resolution, and can obtain photovoltaic resource data at a scale of hundreds or tens of meters along the highway at a specific time in the future.

[0032] S6, terrain correction of photovoltaic resource prediction products along highways Compared with horizontal plane radiation, solar radiation along the highway is significantly affected by the terrain, which is mainly due to the differences in the reception of direct sunlight, atmospheric scattering and surrounding surface reflected radiation caused by the undulating terrain. In order to perform effective terrain correction, the present invention adopts the solar radiation correction model under complex terrain using DEM.

[0033] The present invention takes into account the self-shading caused by slope, aspect and terrain, and also considers the anisotropy of radiation and the influence of slope reflected radiation on the total solar radiation under complex terrain in the model, and calculates the temporal and spatial distribution of solar radiation under complex terrain conditions along the highway. By calculating the following formula, , In the formula, represents direct solar radiation, represents the solar diffuse radiation, Indicates that the terrain reflects radiation, while the sun directly radiates By calculating the following formula, , In the formula, is the total direct radiation on the horizontal surface, is the astronomical radiation on the horizontal plane, The astronomical radiation under complex terrain conditions, the unit is MJ / m 2 ; T represents the duration of each day, is the solar-terrestrial correction coefficient, k represents the number of available illumination times, represents the solar constant, and They represent the starting and ending angles of the available illumination time under complex terrain conditions. represents the solar declination, u represents the north-south tilt coefficient of the terrain, v represents the east-west tilt coefficient of the terrain, and w represents the vertical tilt coefficient of the terrain.

[0034] The scattered solar radiation from complex terrain By calculating the following formula, , In the formula, K t represents the clear sky index, K b represents direct transmittance, R b It represents the ratio of astronomical radiation under complex terrain conditions to astronomical radiation on the horizontal plane. V represents the terrain openness. The terrain openness at any point is calculated by the following formula: , Where a i is the maximum elevation angle of the point in one direction, that is, the maximum shielding degree of the point in this direction; n is the number of azimuth angles, and the azimuth angle step length in the present invention is 5°, with a total of 72 azimuth angles in the whole circle. The radius of the shielding range is 20 km.

[0035] The amount of solar radiation that a mountain receives from the surrounding terrain It depends on the albedo of the mountain and the openness of the terrain, and is calculated by the following formula: , Where Q represents the total solar radiation on the horizontal surface, in MJ / m 2 , a s is the surface albedo.

[0036] Compared with the traditional plane model, the present invention fully considers the influence of the complex terrain along the highway, can more accurately evaluate and predict the photovoltaic potential under the complex terrain conditions along the highway, and the product is more reliable.

[0037] S7, accurate assessment and prediction of photovoltaic potential along highways: Using the PVLIB-Python numerical simulation system, the meteorological data after radiation spatial downscaling and terrain correction (i.e., the photovoltaic resource prediction products with a resolution of hundreds or tens of meters along the highway obtained in the above steps) are input into the photovoltaic system performance modeling and analysis, thereby obtaining photovoltaic potential assessment and prediction products along the highway under different inclination angles, different tracking methods and different photovoltaic materials. The above photovoltaic system performance modeling and analysis includes solar irradiance calculation, photovoltaic module and system performance simulation, photovoltaic system modeling, shadow analysis, etc.

[0038] Invention point: Consider the impact of different inclination angles, different tracking methods and different photovoltaic materials on photovoltaic potential evaluation and prediction.

[0039] The above descriptions are only some embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A photovoltaic resource prediction system with high temporal and spatial resolution along highways, characterized in that The following steps are involved: S1, data preparation: obtain DPEC emission source data and CMIP6 meteorological field data; S2, data processing: (1) DPEC emission source data processing: DPEC emission sources are processed into three-dimensional grids on a hourly basis to form three-dimensional grid values ​​that reflect the temporal and spatial characteristics of various emission sources; (2) CMIP6 meteorological data processing: CMIP6 meteorological data adopt 18 CMIP6 models, namely ACCESS-CM2, ACCESS-ESM1–5, CanESM5, BCC-CSM2-MR, FGOALS-f3-L, FGOALS-g3, EC-Earth3, EC-Earth3-Veg, IPSL-CM6A-LR, AWI-CM-1-1-MR, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MIROC6, MRI-ESM2-0, NorESM2-LM, CESM2, CESM2-WACCM, GFDL-ESM4, and the above 18 CMIP6 models are integrated and converted into three-dimensional grids; S3, build WRF-Chem-Solar model: Based on the WRF-Chem-Solar model, perform the following settings and operations on the model: (1) Model parameter setting: a double nested grid model is used, with a first-layer nesting resolution of 50 km or higher and a local area of ​​10 km or higher resolution; (2) Microphysical parameter setting: The Dudhia microphysical model is used to calculate the mutual transformation process of water phases including cloud water, rain water, snow and hail; the CBMZ model is used to calculate the cloud convection parameterization; the MOSAIC model is used to calculate the physical properties and interactions of water phases in different particle size segments; (3) Initial field setting: The CMIP6 meteorological field data uses the current simulation data as the initial condition; the chemical field of the DPEC emission source uses the last hour simulation data of the previous day as the initial field condition; (4) WRF-Chem-Solar model calculation scheme setting: The model uses the 24 hours before the start of the simulation as the spin-up time, and uses the last 24 hours as the simulation result output to obtain gridded meteorological products of future solar energy resources with high temporal and spatial resolution; S4, WRF-Chem-Solar model post-processing: The gridded meteorological products obtained in step S3 are interpolated through the SES2 radiation transfer model to achieve radiation spatial downscaling; S5, terrain correction: Based on the DEM complex terrain solar radiation correction model along the highway, the temporal and spatial distribution of solar radiation under complex terrain conditions along the highway is calculated; S6, Photovoltaic potential assessment and prediction: The PVLIB-Python numerical simulation system is used to model and analyze the performance of photovoltaic systems by using the radiation spatial downscaling and terrain-corrected meteorological data. The photovoltaic potential assessment and prediction products along the highways under different inclination angles, different tracking methods and different photovoltaic materials are obtained.

2. The photovoltaic resource prediction system with high temporal and spatial resolution along the highway according to claim 1 is characterized by: The DPEC emission source data in step S1 adopts the Ambitious-pollution-Neutral-goal scenario, one of the six emission scenarios in the second scenario data set of the DPEC model; CMIP6 adopts the SP1-2.6 scenario.

3. The photovoltaic resource prediction system with high temporal and spatial resolution along the highway according to claim 1 is characterized in that: After the DPEC emission source data in step S2 is three-dimensionally gridded, the data resolution is 10km*10km, with 41 layers in the vertical direction and a layer height of 2km.

4. The photovoltaic resource prediction system with high temporal and spatial resolution along the highway according to claim 1 is characterized in that: The interpolation process of the SES2 radiation transfer model in step S4 includes: (1) Horizontal downscaling interpolation: Search and determine the points to be interpolated The four nearest neighboring points around, the longitude and latitude coordinates of the four nearest neighboring points are , , , , the interpolation points to be downscaled in the horizontal direction The value at It is calculated by the following formula: ， In the formula, , , and Represent the values ​​of the four nearest neighbor points, Indicates coordinate points The value of Indicates coordinate points The value of (2) Vertical downscaling interpolation: The input variables of the WRF-Chem-Solar model are linearly interpolated in the vertical direction. The variable value T of each input variable in the pressure layer to be interpolated is calculated by the following formula: ， In the formula, T represents the variable value of the pressure layer to be inserted, p represents the pressure value of the pressure layer to be inserted, and T a represents the variable value of the a-th pressure layer, T b represents the variable value of the b-th pressure layer, p a Indicates the pressure value of the a-th pressure layer, p b represents the air pressure value of the b-th air pressure layer; the input variables include temperature, air pressure, ozone concentration, carbon dioxide concentration, nitric oxide concentration, methane concentration and oxygen concentration; For pressures above 100 mb, the water vapor mixing ratio is replaced by the mid-latitude atmospheric climate profile; for pressures below 100 mb, the water vapor mixing ratio of the interpolated layer is qq _ need m By calculating the following formula, ， In the formula, the subscript i represents the number of layers before interpolation, the subscript m represents the number of interpolation layers, and P i represents the air pressure of the i-th layer, P i-1 represents the air pressure of the i-1th layer, P i+1 represents the air pressure of the i+1th layer, Indicates the pressure difference between the layer before interpolation and the previous layer, vapor i-1 represents the water vapor content of the i-1th layer, qq i represents the cumulative amount of water vapor from the ground to the i-th layer, qq i-1 represents the cumulative amount of water vapor from the ground to the i-1th layer, qq1 i-1 represents the water vapor accumulation in the i-1th layer; qq m represents the accumulated water vapor from the ground to the interpolation layer, qq i+1 represents the water vapor accumulation of the i+1th layer, P m represents the air pressure of the interpolation layer, P m+1 Indicates the air pressure of the previous layer of the interpolation layer, qq _ need m represents the water vapor mixing ratio of the interpolation layer, qq m+1 Represents the accumulated water vapor from the ground to the previous layer of the interpolation layer.

5. The photovoltaic resource prediction system with high temporal and spatial resolution along the highway according to claim 1 is characterized by: In step S2, the 18 CMIP6 models are integrated and calculated to generate the integrated corrected GCM data GCM mvt , ， In the formula, GCM mvt represents the global climate model after integrated correction; GCM represents the global climate model; GCM' represents the interannual disturbance term of the global climate model GCM; It represents the average value of the long-term trend LT of the reanalysis data in the historical period H; denoted by represents the average value of the multi-model ensemble MME long-term trend LT in the historical period H; It represents the average deviation of the long-term trend of GCM data relative to the reanalysis dataset in the historical period; represents the standard deviation of the detrended reanalysis data over the historical period, represents the standard deviation of the detrended GCM data; r s Represents the ratio of the standard deviation of the detrended reanalysis data to the standard deviation of the detrended GCM data over the historical period.

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