Parameter selection method and device for simulating wind, solar and water resources elements in river valley areas

Abstract

The present application discloses a parameter selection method and device for simulating wind, solar and water resources elements in river valley areas, belonging to the field of wind power generation. The method includes: without correcting the thrust coefficient in the WRF model, simulating the wind field and turbulent kinetic energy distribution in the dry hot river valley area with a first resolution grid spacing to obtain a first simulation result; simulating the wind field and turbulent kinetic energy distribution in the dry hot river valley area with a second resolution grid spacing to obtain a second simulation result; comparing the first simulation result with the second simulation result, determining the correction coefficient value at the second resolution; correcting the thrust coefficient according to the wind direction wind turbine angle and the correction coefficient value to obtain a target thrust coefficient, which is used in the simulation process of the WRF model.

Description

Technical Field

[0001] The present application belongs to the field of wind power generation, and specifically relates to a parameter selection method and device for simulating wind, solar and water resource elements in river valley areas. Background Art

[0002] With the rapid development of the wind power industry around the world, the impact of large-scale wind farm construction on local meteorological conditions has gradually attracted attention. The traditional mesoscale numerical weather forecast model alone can no longer fully capture the complex impact of wind farms on meteorological elements such as temperature, humidity, and wind field.

[0003] A wake area of ​​tens of kilometers will be formed downstream of the wind farm. The drop in wind speed and change in turbulence characteristics in this area will affect the local temperature and humidity distribution. The wind turbine wake effect refers to the turbulence and low-speed area generated by the wind turbine when it rotates, which is called the wake effect. This will affect the energy capture of the following wind turbines and reduce the power generation efficiency of the entire wind farm.

[0004] Therefore, it is urgent to develop more comprehensive parameter optimization for wind, solar and water resource element simulation in order to comprehensively and accurately evaluate the feedback effect of wind farms on regional weather systems. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a parameter selection method and device for simulating wind, solar and water resources elements in river valley areas, which can comprehensively and accurately evaluate the feedback effect of wind farms on regional weather systems.

[0006] In a first aspect, an embodiment of the present application provides a parameter selection method for simulating wind, solar and water resources elements in a river valley area, the method comprising:

[0007] Obtain the terrain database, candidate parameterization schemes and wind direction wind turbine angle of the river valley area, where the wind direction wind turbine angle is the angle between the wind direction and the main direction of wind turbine arrangement;

[0008] The pre-processing tool of the Weather Research and Forecasting (WRF) model is used to process the terrain database of the dry hot valley area to obtain the lower boundary data, which includes terrain data, soil data and vegetation data.

[0009] Selecting a target parameterization scheme from candidate parameterization schemes, the target parameterization scheme comprising a target cumulus process parameterization scheme, a target radiation process parameterization scheme and a target microphysical process parameterization scheme;

[0010] Input the lower boundary data into the WRF model and set the target parameterization scheme in the WRF model, which is used to simulate the flow field in the dry hot valley area;

[0011] Without correcting the thrust coefficient in the WRF model, the wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using the first resolution grid spacing to obtain the first simulation result;

[0012] The wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using a second resolution grid to obtain a second simulation result;

[0013] Compare the first simulation result with the second simulation result to determine the correction coefficient value at the second resolution;

[0014] The thrust coefficient is corrected according to the wind direction-wind turbine angle and the correction coefficient value to obtain the target thrust coefficient, which is used in the simulation process of the WRF model.

[0015] In a second aspect, an embodiment of the present application provides a parameter selection device for simulating wind, solar and water resources elements in a river valley area, the device comprising:

[0016] An acquisition module is used to acquire a terrain database of a river valley area, candidate parameterization schemes, and a wind direction wind turbine angle, where the wind direction wind turbine angle is the angle between the wind direction and the main direction of the wind turbine arrangement;

[0017] A processing module is used to process the terrain database of the dry hot valley area using the pre-processing tool of the Weather Research and Forecasting WRF model to obtain the lower boundary data, which includes: terrain data, soil data and vegetation data;

[0018] A selection module is used to select a target parameterization scheme from candidate parameterization schemes, wherein the target parameterization scheme includes a target cumulus process parameterization scheme, a target radiation process parameterization scheme, and a target microphysical process parameterization scheme;

[0019] An input module is used to input the lower boundary data into the WRF model and set the target parameterization scheme in the WRF model. The target parameterization scheme is used to simulate the flow field in the dry hot valley area;

[0020] The first simulation module is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area using a grid spacing of a first resolution without correcting the thrust coefficient in the WRF model to obtain a first simulation result;

[0021] A second simulation module is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area using a grid spacing of a second resolution to obtain a second simulation result;

[0022] A comparison module, used for comparing the first simulation result and the second simulation result to determine the correction coefficient value at the second resolution;

[0023] The correction module is used to correct the thrust coefficient according to the wind direction wind turbine angle and the correction coefficient value to obtain the target thrust coefficient, which is used in the simulation process of the WRF model.

[0024] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0025] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0026] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0027] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the method described in the first aspect.

[0028] In an embodiment of the present application, a terrain database of a river valley area, candidate parameterization schemes and a wind direction wind turbine angle are obtained, wherein the wind direction wind turbine angle is the angle between the wind direction and the main direction of the wind turbine arrangement; the terrain database of the dry hot river valley area is processed by a pre-processing tool of a weather research and forecasting WRF model to obtain fine lower boundary data for subsequent use by the WRF model, wherein the lower boundary data include: terrain data, soil data and vegetation data; a target parameterization scheme is selected from the candidate parameterization schemes, wherein the target parameterization scheme includes a target cumulus process parameterization scheme, a target radiation process parameterization scheme and a target microphysical process parameterization scheme; the lower boundary data is input into the WRF model, and a target parameterization scheme is set in the WRF model, wherein the target parameterization scheme is used to simulate the flow field in the dry hot river valley area; and the WRF model is used to simulate the flow field in the dry hot river valley area without modification. In the case of thrust coefficient in the positive WRF model, the grid spacing of the first resolution is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area to obtain the first simulation result. By exploring the characteristics of the wind field and turbulent kinetic energy distribution in the simulation of the first resolution, a basis is provided for the subsequent determination of the correction coefficient value at the second resolution; the grid spacing of the second resolution is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area to obtain the second simulation result; the first simulation result and the second simulation result are compared to analyze the differences between the two in key parameters such as wind field and turbulent kinetic energy, so as to determine the correction coefficient value at the second resolution according to the law of the difference; the thrust coefficient is corrected according to the wind direction wind turbine angle and the correction coefficient value to obtain the target thrust coefficient. The target thrust coefficient can better simulate the change of the overall thrust coefficient of the wind field with wind direction. The target thrust coefficient is used in the simulation process of the WRF model. The WRF optimized based on the target thrust coefficient can accurately capture the influence of wind turbine wake and can comprehensively and accurately evaluate the feedback effect of wind farms on regional weather systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a parameter selection method for simulating wind, solar and water resources elements in a river valley area provided in an embodiment of the present application;

[0030] Figure 2 is a flow chart of a simulation result determination method provided in an embodiment of the present application;

[0031] Figure 3 It is a structural diagram of a parameter selection device for simulating wind, solar and water resources elements in a river valley area provided in an embodiment of the present application;

[0032] Figure 4 It is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application belong to the scope of protection of this application.

[0034] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0035] The parameter selection method for simulating wind, solar and water resources elements in river valley areas provided in the embodiment of the present application can be applied to at least the following application scenarios, which are described below.

[0036] The thrust coefficient and wind energy utilization coefficient in the Fitch wake parameterization scheme are key parameters in the parameterization scheme of wind turbine wake effect. The following is a brief description of the concepts of the thrust coefficient (Ct) and wind energy utilization coefficient (Cp) in the wake parameterization scheme and their impact on wake simulation:

[0037] Thrust Coefficient (Ct): Ct is defined as the ratio of the thrust generated by the fan on the incoming flow to the incoming flow momentum. It reflects the degree of disturbance of the upstream flow by the fan. The larger the Ct value, the stronger the disturbance of the fan on the incoming flow and the greater the degree of reduction in the initial velocity of the wake.

[0038] Wind energy utilization coefficient (Power Coefficient, Cp), Cp is defined as the ratio of the mechanical power extracted by the fan to the incoming kinetic energy. It reflects the efficiency of the fan in extracting energy from the incoming flow. The higher the Cp value, the higher the fan energy extraction efficiency, and thus the greater the impact on the downstream wake.

[0039] In the Fitch wake parameterization scheme, Ct determines the initial state of the wake, affects the velocity attenuation and diffusion characteristics of the wake, and affects the diffusion speed and concentration distribution of the wake. Ct is the core parameter of the Fitch wake parameterization model, which determines the initial state and energy level of the wake, thereby affecting the overall evolution characteristics of the wake.

[0040] The role of thrust coefficient Ct, Ct determines the degree of disturbance of the fan to the incoming flow, which in turn affects the initial state of the wake; the larger the Ct, the stronger the disturbance of the fan to the incoming flow, and the greater the degree of reduction in the initial velocity of the wake. The accurate estimation of Ct is directly related to the accurate prediction of the wake velocity attenuation and diffusion.

[0041] Ct determines the initial state and energy level of the wake. The accurate estimation of Ct directly determines the simulation accuracy of key parameters such as wake velocity, concentration, and turbulence. Only by reasonably determining Ct can the wake characteristics of different wind turbine types be better reflected, which is of key significance for optimizing the layout of wind farms and improving power generation efficiency.

[0042] The technical terms involved in the embodiments of the present application are explained below:

[0043] Weather Research and Forecasting (WRF) model, WRF is an open source mesoscale numerical weather forecast and atmospheric modeling system, which is widely used in weather forecasting, climate simulation, air quality and other fields.

[0044] WRF adopts a non-static dynamic framework and can simulate atmospheric processes from mesoscale to regional scale. It has a rich physical process parameterization scheme, can be optimized for different application scenarios, supports a variety of initial and boundary condition data, can flexibly select suitable data sources, has good parallel computing performance, and can efficiently utilize supercomputer resources.

[0045] WRF is widely used in mesoscale numerical weather forecasting in dry and hot valley areas. By refining geographical parameters such as terrain and land use, it can better simulate the impact of complex terrain on airflow and weather. Combined with high-resolution observational data assimilation, it can improve the simulation accuracy of local weather systems.

[0046] The WRF modeling process includes: using WPS to pre-process the geographic and meteorological fields, running the core WRF model for numerical simulation, and using post-processing tools to analyze and visualize the simulation results. WRF is a powerful and flexible mesoscale numerical weather model that is widely used in dry and hot valley areas. Through reasonable configuration and optimization, it can greatly improve the simulation accuracy of local weather systems under complex terrain.

[0047] The WRF model includes: a geographic preprocessing system (WRF Preprocessing System, WPS), which is responsible for obtaining geographic information and meteorological field data from various data sources, including terrain height, land cover, temperature, humidity, wind and other parameters, to provide the required initial and boundary conditions for the WRF model.

[0048] WPS mainly includes the following tools:

[0049] geogrid.exe, this tool is responsible for reading geographic information data from various data sources (such as DEM, land cover, etc.); interpolating and resampling the original data according to the simulation area and grid resolution specified by the user; and outputting the geographic parameter files required by the WRF model, including terrain height, land cover type, etc.

[0050] Such geographic preprocessing tools ensure that the WRF model has the required high-resolution geographic information and meteorological field data, which is critical to improving simulation accuracy.

[0051] In response to the problems arising from related technologies, the embodiments of the present application provide a parameter selection method and device for simulating wind, solar and water resource elements in river valley areas, which can comprehensively and accurately evaluate the feedback effect of wind farms on regional weather systems.

[0052] The parameter selection method for simulating wind, solar and water resources elements in river valley areas provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0053] Figure 1 A flow chart of a parameter selection method for simulating wind, solar and water resource elements in river valley areas provided in an embodiment of the present application.

[0054] like Figure 1 As shown, the parameter selection method for simulating wind, solar and water resources elements in river valley areas may include steps 110 to 180. The method is applied to a parameter selection device for simulating wind, solar and water resources elements in river valley areas, as shown below:

[0055] Step 110, obtaining a terrain database of the river valley area, candidate parameterization schemes and wind direction wind turbine angle, where the wind direction wind turbine angle is the angle between the wind direction and the main direction of the wind turbine arrangement;

[0056] The wind turbine layout of a wind farm refers to the arrangement of the relative positions of the wind turbines in the wind farm. Reasonable wind turbine layout is the key to ensuring the power generation efficiency and economy of the wind farm.

[0057] The angle between wind direction and the main direction of wind turbine arrangement is an important factor affecting the power generation efficiency of wind farms. Generally, the dominant direction of wind turbine arrangement should be consistent with the local dominant wind direction as much as possible. This can maximize the use of incoming wind and thus improve the power generation efficiency of the entire wind farm.

[0058] Step 120, using the pre-processing tool of the Weather Research and Forecasting WRF model to process the terrain database of the dry hot valley area to obtain lower boundary data, the lower boundary data includes: terrain data, soil data and vegetation data;

[0059] A high-precision terrain database for the dry and hot valley region was established, and the WPS pre-processing tool in the WRF mesoscale numerical weather forecast model was used to obtain a detailed lower boundary.

[0060] Establishing a high-precision terrain database in the dry and hot valley area is the basis for conducting research on mesoscale numerical weather forecast models. The global digital elevation model (DEM) data, such as SRTM, ASTER GDEM and other high-precision terrain data, combined with high-resolution satellite image data, can be used to classify land cover.

[0061] Data preprocessing: Use WPS's geographic preprocessing tool geogrid.exe to read and convert the above terrain and land cover data into different formats, and interpolate and resample the terrain and land cover data according to the selected simulation area and resolution.

[0062] Generate lower boundary conditions. The geogrid.exe tool will output the preprocessed terrain elevation, land cover type and other parameters as the initial lower boundary condition file required by the WRF model. These high-resolution lower boundary data can better express the complexity of the dry hot valley terrain. The above lower boundary conditions can be further corrected and verified using measured terrain data or high-resolution satellite images to ensure that the accuracy of the terrain data can meet the needs of mesoscale numerical weather simulation.

[0063] Therefore, using WRF's WPS pre-processing tool, combined with high-precision terrain and land cover data, more refined lower boundary conditions can be established for mesoscale numerical weather forecast simulations in dry and hot valley areas. This is crucial to improving the simulation accuracy of forecast models in complex terrain areas.

[0064] Specifically, first, collect high-precision terrain data of the dry and hot valley area; pre-process the high-precision terrain data, which may include: clipping, splicing, interpolation and other processing to form processed terrain data; secondly, establish a terrain database through the processed terrain data; store the processed terrain data in a suitable format to obtain a terrain database; establish a geographic reference system to determine the spatial range and resolution of the processed terrain data set. Then, use the terrain data in WPS pre-processing:

[0065] In the geogrid.exe program of WPS, specify to use the self-built terrain database; run geogrid.exe to generate geographic input data using the self-built terrain data; then run other WPS tools to generate a complete WRF model input.

[0066] Finally, the geographic input data generated above were used as the lower boundary conditions in the WRF model.

[0067] Therefore, the self-built high-precision terrain database can be used in WRF to better describe the geographical characteristics of the dry and hot valley area, which helps to improve the accuracy of the WRF model's simulation of weather processes in the region.

[0068] Among them, in the WRF numerical weather forecast model, the terrain data of the dry and hot valley area as the lower boundary condition mainly includes the following aspects:

[0069] Terrain height is used to describe the complex terrain of the river valley area in detail, including peaks, valleys, slopes and other undulating features, which directly affect the air flow movement, heat and momentum exchange process.

[0070] Surface roughness refers to the length of surface roughness, which affects the momentum exchange process. The surface cover types in the river valley vary greatly, and the roughness also varies significantly.

[0071] Soil properties, including soil temperature and moisture, determine surface heat and water flux. Soil types and water content vary greatly in river valleys.

[0072] Vegetation parameters, such as leaf area index (LAI), vegetation cover fraction, etc., affect momentum, heat and water vapor exchange processes.

[0073] Water body characteristics (Water body), the temperature and ice conditions of water bodies such as rivers and lakes, have an important impact on the local climate.

[0074] These lower boundary parameters need to be converted into a format that can be used by the WRF model through the WPS pre-processing program. Accurately describing the complex geographical environment characteristics of the dry and hot valley area is the key to improving the accuracy of WRF simulation.

[0075] Step 130, selecting a target parameterization scheme from the candidate parameterization schemes, the target parameterization scheme comprising a target cumulus process parameterization scheme, a target radiation process parameterization scheme, and a target microphysical process parameterization scheme;

[0076] In a possible embodiment, the candidate parameterization schemes include: a plurality of cumulus process parameterization schemes, a plurality of radiation process parameterization schemes, and a plurality of microphysical process parameterization schemes; step 130 includes:

[0077] Obtain the climate characteristics and target weather processes of the river valley area;

[0078] According to the climate characteristics and target weather processes, the target parameterization scheme is selected from the candidate parameterization schemes.

[0079] WRF provides a variety of parameterization schemes for physical processes such as cumulus, radiation, and microphysics. Different parameterization schemes describe atmospheric processes differently, and it is necessary to select a suitable combination of parameterization schemes based on the study area and weather conditions. The following is an explanation of the cumulus parameterization, radiation parameterization, and microphysics parameterization schemes:

[0080] Cumulus parameterization is a very important physical process in numerical weather forecast models. The role of cumulus parameterization includes: simulating convective clouds and precipitation processes, the impact of clouds on radiation, heat and momentum transfer, and the feedback of cloud microphysical processes on the large-scale environment.

[0081] The main parameterization schemes for cumulus parameterization include: Kain-Fritsch scheme, Grell-Freitas scheme, new Tiedtke scheme, BMJ scheme, etc. The selection process of the parameterization scheme includes: considering the climate characteristics of the simulation area and the target weather process, considering that different schemes will have different simulation effects on processes of different scales, and needing to be evaluated and selected according to actual needs. The main considerations for the parameterization scheme include: convection triggering conditions, convection intensity and vertical flux, cloud microphysical processes, sinking motion and stability, cloud-radiation feedback, etc. Therefore, cumulus parameterization is a very critical physical process description in numerical weather forecast models, and it is necessary to select a suitable parameterization scheme based on actual conditions.

[0082] In numerical weather forecast models, radiation parameterization is also a very important physical process.

[0083] The functions of radiation parameterization include: simulating the transmission of short-wave and long-wave radiation fluxes in the atmosphere; calculating the radiation balance of the surface and various layers of the atmosphere; and the impact of radiation processes on the distribution of atmospheric temperature and humidity.

[0084] The main parameterization schemes for radiation parameterization include:

[0085] Involving shortwave radiation, including: Dudhia scheme, RRTMG scheme;

[0086] Involving long-wave radiation, including: RRTM scheme, GFDL scheme;

[0087] The selection process of the parameterized scheme includes: based on the climate characteristics of the simulated area and the target weather process, and considering that different schemes have certain differences in computational efficiency and accuracy, it is necessary to evaluate and select according to actual needs.

[0088] The main considerations for parameterization schemes include:

[0089] The factors affecting radiation transmission, such as clouds, water vapor, greenhouse gases, the characteristics of the ground surface (albedo, emissivity, etc.), and the influence of atmospheric stratification on radiation transmission. Therefore, radiation parameterization is an indispensable physical process description in numerical weather forecast models, and it is necessary to select a suitable parameterization scheme according to actual conditions.

[0090] In numerical weather forecast models, microphysical parameterization is a key part of simulating cloud and precipitation processes. The role of microphysical parameterization includes: describing cloud droplets, ice crystals, raindrops and other cloud microphysical processes, calculating condensation, collision, sublimation and other microphysical processes in cloud and precipitation processes, and cloud microphysics feedback on radiation, convection and momentum processes.

[0091] Main parameterization scheme: simple scheme (Kessler);

[0092] Complex schemes (WSM3, WSM6, Thompson, Morrison, etc.).

[0093] The selection process of parameterization scheme includes: considering the climate characteristics of the simulated area and the target weather process, taking into account the characteristics of simple schemes with high efficiency but low accuracy, and taking into account the characteristics of complex schemes with more detailed simulation but large amount of calculation. The main considerations of parameterization schemes include: the initial spectral distribution of cloud droplets and ice crystals, the parameterization formula of cloud microphysical processes, the conversion mechanism between different phases, the sedimentation velocity of cloud droplets and ice crystals, etc. Therefore, microphysical parameterization is a crucial description of physical processes in numerical weather forecast models, and it is necessary to select a suitable parameterization scheme based on actual conditions.

[0094] In the WRF model, it is very important to carry out the optimization of the physical process parameterization scheme, which is crucial to improving the simulation accuracy. The optimization process of the parameterization scheme combination includes:

[0095] First, based on historical observation data, a set of candidate parameterization scheme combinations are selected, and the WRF model is used to simulate historical cases respectively. The deviations between the simulation results and the observations are compared, and the parameterization scheme combinations are adjusted according to the size of the deviation to find the optimal configuration.

[0096] The key physical processes include: cumulus parameterization, which determines the description of convective processes and affects precipitation simulation. Radiation parameterization, which determines the radiation transfer process between the surface and the atmosphere. Microphysical parameterization, which determines the description of precipitation processes and affects energy flux.

[0097] In the steps involving optimization criteria and verification, we can minimize the simulation deviation based on factors such as precipitation, temperature, and humidity. We can also conduct targeted optimization based on research objectives, such as forecasting local weather in dry and hot valleys, and ensure the universality and robustness of the optimization scheme through methods such as cross-validation.

[0098] In summary, conducting a combination optimization of physical process parameterization schemes is a systematic task, which requires in-depth analysis based on the specific research area and objectives.

[0099] Step 140, inputting the lower boundary data into the WRF model, and setting a target parameterization scheme in the WRF model, the target parameterization scheme being used to simulate the flow field in the dry hot valley area;

[0100] Step 150, simulating the wind field and turbulent kinetic energy distribution in the dry hot valley region using a grid spacing of a first resolution without correcting the thrust coefficient in the WRF model to obtain a first simulation result;

[0101] Since the correction coefficient in the optimization scheme affects the simulation results, it is necessary to determine the value of the correction coefficient. Here, a typical river valley can be selected to carry out a 1 km resolution simulation without correcting the thrust coefficient. The influence of wind turbine arrangement on the wind field and turbulent kinetic energy is explicitly analyzed through the exchange of wind field and turbulent kinetic energy between grid points. The first simulation results at the first resolution are compared to determine the value of the correction coefficient at the second resolution grid.

[0102] like Figure 2 As shown, in a possible embodiment, step 150 includes:

[0103] Step 210, dividing the river valley area using a grid spacing of a first resolution to obtain a plurality of grids;

[0104] Step 220, running the WRF model to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area;

[0105] Step 230, explicitly analyzing the exchange of wind field and turbulent kinetic energy distribution between adjacent grids to obtain the exchange of wind field and turbulent kinetic energy between adjacent grid points;

[0106] Step 240, determining the data on the influence of the wind turbine arrangement on the flow field according to the wind field and turbulent kinetic energy exchange between adjacent grid points, wherein the first simulation result includes the data on the influence of the wind turbine arrangement on the flow field.

[0107] Run the WRF simulation to obtain the wind field and turbulent kinetic energy distribution in the area, that is, the three-dimensional wind field data (u, v, w components) of the simulation output, including: u component, that is, the east-west wind speed data; v component, that is, the north-south wind speed data; w component, that is, the vertical wind speed data; through in-depth analysis of the three components of u, v, and w, we can fully understand the three-dimensional characteristics of the wind field, providing valuable reference for the design and operation optimization of wind farms.

[0108] In a 1 km resolution WRF simulation, the following methods can be used to analyze the wind field and turbulent kinetic energy exchange between adjacent grid points:

[0109] Wind field analysis: Extract the three-dimensional wind field data (u, v, w components) output by the simulation; calculate the wind speed gradient between adjacent horizontal grid points; analyze the wind speed gradient distribution at different height layers to obtain the spatial variation characteristics of the wind field; focus on the wind field changes at the height of the wind turbines and analyze the mutual influence between the units.

[0110] Turbulent kinetic energy analysis: Extract turbulent kinetic energy (TKE) data from simulation output; calculate TKE gradients between adjacent grid points; analyze the distribution characteristics of TKE gradients and identify areas where turbulence intensity changes dramatically;

[0111] Combined with wind field analysis, explore how wind turbine layout affects the transmission of turbulent kinetic energy; output key parameters such as wind field vector, wind speed gradient, and turbulent kinetic energy distribution; intuitively reflect the wind field and turbulent kinetic energy exchange between adjacent grid points. Finally, conduct quantitative analysis, calculate the statistical characteristics of wind speed difference and TKE difference between adjacent grid points, analyze the distribution law of wind speed difference and TKE difference at different altitudes, and quantify the influence of wind turbine layout on flow field and turbulent kinetic energy.

[0112] Among them, the first resolution is 1 km and the second resolution is 3 km.

[0113] Therefore, we can deeply explore the exchange characteristics of wind field and turbulent kinetic energy between grids in 1 km resolution simulation, and provide a basis for the subsequent determination of correction coefficients at 3 km grid spacing.

[0114] In a possible embodiment, step 220 includes:

[0115] Set the horizontal resolution grid of the first resolution in the WRF model;

[0116] Add wind turbine location and parameter information to the WRF model;

[0117] Run the WRF model to obtain the wind field and turbulent kinetic energy distribution in the valley area.

[0118] Firstly, a typical river valley terrain is selected as the study area, a 1 km horizontal resolution grid is set in the WRF model, and the detailed terrain data and surface characteristic parameters of the study area are input; a suitable physical parameterization scheme is set to ensure that the complex flow field in the area can be accurately simulated; the location and parameter information of the wind turbine are added to the WRF model, but the thrust coefficient is not corrected; the WRF simulation is run to obtain the wind field and turbulent kinetic energy distribution in the area; the wind field and turbulent kinetic energy exchange between adjacent grid points is analyzed to obtain the impact data of wind turbine arrangement on the flow field.

[0119] Step 160, simulating the wind field and turbulent kinetic energy distribution in the dry hot valley region using a second resolution grid spacing to obtain a second simulation result;

[0120] Step 170, comparing the first simulation result and the second simulation result to determine a correction coefficient value at a second resolution;

[0121] In a possible embodiment, step 170 includes:

[0122] Analyze the first simulation results and the second simulation results to determine the difference in wind field and turbulent kinetic energy;

[0123] Determine the test correction coefficient value at the second resolution according to the wind field difference and the turbulence kinetic energy difference;

[0124] The test correction coefficient value is input into the WRF model, and the wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using the second resolution grid spacing to obtain an updated second simulation result;

[0125] When the first simulation result and the updated second simulation result are consistent, the test correction coefficient value is determined as the correction coefficient value.

[0126] The 1 km resolution simulation results obtained in 1 are used as a reference, and the horizontal resolution is adjusted to 3 km in the WRF model; other physical parameters are kept unchanged and the simulation with 3 km resolution is run; the simulation results of 1 km and 3 km are compared, and the differences between the two in key parameters such as wind field and turbulent kinetic energy are analyzed; according to the law of the differences, the thrust coefficient value that needs to be corrected at the 3 km grid spacing is determined; that is, according to the differences in wind field and turbulent kinetic energy, the test correction coefficient value at the second resolution is determined.

[0127] Input the test correction factor value into the WRF model and rerun the 3 km resolution simulation to verify whether the correction factor can effectively eliminate the difference between the 1 km and 3 km simulation results. If the first simulation result and the updated second simulation result are consistent, the test correction factor value is determined as the correction factor value.

[0128] The above-mentioned steps of analyzing the first simulation result and the second simulation result to determine the wind field difference and the turbulent kinetic energy difference include:

[0129] determining a percentage difference between an average wind speed distribution at a first resolution and an average wind speed distribution at a second resolution;

[0130] The percentage difference in turbulence intensity between the turbulence intensity distribution at the first resolution and the turbulence intensity distribution at the second resolution is determined.

[0131] Specifically, the wind speed distribution diagram and turbulence intensity distribution diagram under the simulation of 1 km and 3 km grid spacing are drawn, and the wind speed distribution diagram and turbulence intensity distribution diagram under the simulation of 1 km and 3 km grid spacing are compared.

[0132] Extract the values ​​of key parameters (such as average wind speed, turbulence intensity, etc.) and calculate the difference percentages under the two grid spacings. According to the difference law, establish the correction function relationship between thrust coefficient and grid spacing.

[0133] Analyze the difference between the simulation results at 1 km and 3 km grid spacing. The specific calculation methods may include:

[0134] Average wind speed difference, calculate the percentage difference between the average wind speed at 1 km and 3 km grid spacing; analyze the relationship between the difference and grid spacing, and fit a simple linear or exponential function.

[0135] Turbulence intensity difference: calculate the percentage difference between turbulence intensity at 1 km and 3 km grid spacing; study the functional relationship between turbulence intensity difference and grid spacing;

[0136] Power coefficient difference, calculate the percentage difference of power coefficient under 1 km and 3 km grid spacing; establish the relationship function between power coefficient and grid spacing.

[0137] Therefore, we can first use high-resolution simulation to analyze the impact of wind turbine layout on the wind farm, and then determine the correction coefficient required for low-resolution simulation, thereby improving the simulation accuracy of the WRF model in wind farms with complex terrain.

[0138] The above-mentioned step of determining the test correction coefficient value at the second resolution according to the wind field difference and the turbulent kinetic energy difference includes:

[0139] Analyze the reasons for the differences based on the percentage differences in average wind speed and turbulence intensity;

[0140] According to the cause of the difference, the test correction coefficient value at the second resolution is determined.

[0141] Comparative analysis of wind farm parameters, including: comparison of average wind speed distribution under 1 km and 3 km grid spacing simulations; analysis of the difference in wind speed vertical profiles under the two grid spacings; study of the changes in wind speed direction distribution under the two grid spacings; comparison of turbulent kinetic energy distribution: comparison of turbulence intensity distribution under 1 km and 3 km grid spacings; analysis of the difference in turbulent kinetic energy density under the two grid spacings; evaluation of the impact of turbulence on wind turbine performance;

[0142] The systematic differences in key parameters at 1 km and 3 km grid spacings are summarized, and the causes of the differences, such as numerical calculation accuracy and terrain modeling, are analyzed to determine the thrust coefficient correction required at 3 km grid spacing.

[0143] Step 180, the thrust coefficient is corrected according to the wind direction wind turbine angle and the correction coefficient value to obtain a target thrust coefficient, and the target thrust coefficient is used in the simulation process of the WRF model.

[0144] In a possible embodiment, step 180 includes:

[0145] Determine the sine value of the wind direction fan angle;

[0146] According to the sine value of the wind direction-fan angle and the correction coefficient value, the thrust coefficient is corrected to obtain the target thrust coefficient.

[0147] The target thrust coefficient is calculated as shown in formula (1):

[0148] C T '= C T [ (1-α)+α sin(θ)](1)

[0149] Among them, θ is the angle between wind direction and the main direction of wind turbine arrangement, that is, the wind direction-wind turbine angle; the value range of θ is [0,π / 4];

[0150] α is the correction coefficient; the value range of α is [0,1);

[0151] C T is the thrust coefficient;

[0152] C T ' is the corrected thrust coefficient, that is, the target thrust coefficient.

[0153] For a given WRF grid, the Fitch scheme assumes that the energy absorbed and converted by the wind turbine from the wind farm does not change with wind direction (determined by the number of wind turbines and thrust coefficient). In reality, when the wind direction is perpendicular to the main direction of the wind turbine arrangement, the wind farm loses the most energy; when the wind direction is parallel to the main direction of the wind turbine arrangement, the wind farm loses the least energy. This is the main reason for the simulation error of the Fitch scheme.

[0154] By optimizing the thrust coefficient through the above formula, the Fitch scheme can better simulate the change of the overall thrust coefficient of the wind field with wind direction.

[0155] In an embodiment of the present application, a terrain database of a river valley area, candidate parameterization schemes and a wind direction wind turbine angle are obtained, wherein the wind direction wind turbine angle is the angle between the wind direction and the main direction of the wind turbine arrangement; the terrain database of the dry hot river valley area is processed by a pre-processing tool of a weather research and forecasting WRF model to obtain fine lower boundary data for subsequent use by the WRF model, wherein the lower boundary data include: terrain data, soil data and vegetation data; a target parameterization scheme is selected from the candidate parameterization schemes, wherein the target parameterization scheme includes a target cumulus process parameterization scheme, a target radiation process parameterization scheme and a target microphysical process parameterization scheme; the lower boundary data is input into the WRF model, and a target parameterization scheme is set in the WRF model, wherein the target parameterization scheme is used to simulate the flow field in the dry hot river valley area; and the WRF model is used to simulate the flow field in the dry hot river valley area without modification. In the case of thrust coefficient in the positive WRF model, the grid spacing of the first resolution is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area to obtain the first simulation result. By exploring the characteristics of the wind field and turbulent kinetic energy distribution in the simulation of the first resolution, a basis is provided for the subsequent determination of the correction coefficient value at the second resolution; the grid spacing of the second resolution is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area to obtain the second simulation result; the first simulation result and the second simulation result are compared to analyze the differences between the two in key parameters such as wind field and turbulent kinetic energy, so as to determine the correction coefficient value at the second resolution according to the law of the difference; the thrust coefficient is corrected according to the wind direction wind turbine angle and the correction coefficient value to obtain the target thrust coefficient. The target thrust coefficient can better simulate the change of the overall thrust coefficient of the wind field with wind direction. The target thrust coefficient is used in the simulation process of the WRF model. The WRF optimized based on the target thrust coefficient can accurately capture the influence of wind turbine wake and can comprehensively and accurately evaluate the feedback effect of wind farms on regional weather systems.

[0156] The parameter selection method for wind and solar water resource element simulation in river valley areas provided in the embodiment of the present application can be executed by a parameter selection device for wind and solar water resource element simulation in river valley areas. In the embodiment of the present application, the parameter selection device for wind and solar water resource element simulation in river valley areas performs the parameter selection method for wind and solar water resource element simulation in river valley areas as an example to illustrate the parameter selection device for wind and solar water resource element simulation in river valley areas provided in the embodiment of the present application.

[0157] Figure 3 300 is a block diagram of a parameter selection device for simulating wind, solar and water resources elements in a river valley area provided in an embodiment of the present application. The device 300 includes:

[0158] An acquisition module 310 is used to acquire a terrain database of a river valley area, candidate parameterization schemes, and a wind direction wind turbine angle, where the wind direction wind turbine angle is an angle between the wind direction and the main direction of wind turbine arrangement;

[0159] The processing module 320 is used to process the terrain database of the dry hot valley area using the pre-processing tool of the Weather Research and Forecasting WRF model to obtain the lower boundary data, which includes: terrain data, soil data and vegetation data;

[0160] A selection module 330 is used to select a target parameterization scheme from candidate parameterization schemes, wherein the target parameterization scheme includes a target cumulus process parameterization scheme, a target radiation process parameterization scheme, and a target microphysical process parameterization scheme;

[0161] An input module 340 is used to input the lower boundary data into the WRF model and set a target parameterization scheme in the WRF model, the target parameterization scheme is used to simulate the flow field in the dry hot valley area;

[0162] The first simulation module 350 is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley region using a grid spacing of a first resolution without correcting the thrust coefficient in the WRF model to obtain a first simulation result;

[0163] A second simulation module 360 ​​is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley region using a second resolution grid spacing to obtain a second simulation result;

[0164] A comparison module 370, used to compare the first simulation result and the second simulation result to determine the correction coefficient value at the second resolution;

[0165] The correction module 380 is used to correct the thrust coefficient according to the wind direction wind turbine angle and the correction coefficient value to obtain a target thrust coefficient, and the target thrust coefficient is used in the simulation process of the WRF model.

[0166] In a possible embodiment, the candidate parameterization schemes include: a plurality of cumulus process parameterization schemes, a plurality of radiation process parameterization schemes, and a plurality of microphysical process parameterization schemes; the selection module 330 is specifically used to:

[0167] Obtain the climate characteristics and target weather processes of the river valley area;

[0168] According to the climate characteristics and target weather processes, the target parameterization scheme is selected from the candidate parameterization schemes.

[0169] In a possible embodiment, the first simulation module 350 is specifically configured to:

[0170] Divide the river valley area by using a first resolution grid distance to obtain multiple grids;

[0171] Run the WRF model to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area;

[0172] The exchange of wind field and turbulent kinetic energy distribution between adjacent grids is explicitly analyzed to obtain the exchange of wind field and turbulent kinetic energy between adjacent grid points;

[0173] According to the wind field and turbulent kinetic energy exchange between adjacent grid points, the influence data of the wind turbine arrangement on the flow field are determined, and the first simulation result includes the influence data of the wind turbine arrangement on the flow field.

[0174] In a possible embodiment, the first simulation module 350 is specifically configured to:

[0175] Set the horizontal resolution grid of the first resolution in the WRF model;

[0176] Add wind turbine location and parameter information to the WRF model;

[0177] Run the WRF model to obtain the wind field and turbulent kinetic energy distribution in the valley area.

[0178] In a possible embodiment, the comparison module 370 is specifically used to:

[0179] Analyze the first simulation results and the second simulation results to determine the difference in wind field and turbulent kinetic energy;

[0180] Determine the test correction coefficient value at the second resolution according to the wind field difference and the turbulence kinetic energy difference;

[0181] The test correction coefficient value is input into the WRF model, and the wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using the second resolution grid spacing to obtain an updated second simulation result;

[0182] When the first simulation result and the updated second simulation result are consistent, the test correction coefficient value is determined as the correction coefficient value.

[0183] In a possible embodiment, the comparison module 370 is specifically used to:

[0184] determining a percentage difference between an average wind speed distribution at a first resolution and an average wind speed distribution at a second resolution;

[0185] The percentage difference in turbulence intensity between the turbulence intensity distribution at the first resolution and the turbulence intensity distribution at the second resolution is determined.

[0186] In a possible embodiment, the comparison module 370 is specifically used to:

[0187] Analyze the reasons for the differences based on the percentage differences in average wind speed and turbulence intensity;

[0188] According to the cause of the difference, the test correction coefficient value at the second resolution is determined.

[0189] In a possible embodiment, the correction module 380 is specifically configured to:

[0190] Determine the sine value of the wind direction fan angle;

[0191] According to the sine value of the wind direction-fan angle and the correction coefficient value, the thrust coefficient is corrected to obtain the target thrust coefficient.

[0192] Among them, the first resolution is 1 km and the second resolution is 3 km.

[0193] In an embodiment of the present application, a terrain database of a river valley area, candidate parameterization schemes and a wind direction wind turbine angle are obtained, wherein the wind direction wind turbine angle is the angle between the wind direction and the main direction of the wind turbine arrangement; the terrain database of the dry hot river valley area is processed by a pre-processing tool of a weather research and forecasting WRF model to obtain fine lower boundary data for subsequent use by the WRF model, wherein the lower boundary data include: terrain data, soil data and vegetation data; a target parameterization scheme is selected from the candidate parameterization schemes, wherein the target parameterization scheme includes a target cumulus process parameterization scheme, a target radiation process parameterization scheme and a target microphysical process parameterization scheme; the lower boundary data is input into the WRF model, and a target parameterization scheme is set in the WRF model, wherein the target parameterization scheme is used to simulate the flow field in the dry hot river valley area; and the WRF model is used to simulate the flow field in the dry hot river valley area without modification. In the case of thrust coefficient in the positive WRF model, the grid spacing of the first resolution is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area to obtain the first simulation result. By exploring the characteristics of the wind field and turbulent kinetic energy distribution in the simulation of the first resolution, a basis is provided for the subsequent determination of the correction coefficient value at the second resolution; the grid spacing of the second resolution is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area to obtain the second simulation result; the first simulation result and the second simulation result are compared to analyze the differences between the two in key parameters such as wind field and turbulent kinetic energy, so as to determine the correction coefficient value at the second resolution according to the law of the difference; the thrust coefficient is corrected according to the wind direction wind turbine angle and the correction coefficient value to obtain the target thrust coefficient. The target thrust coefficient can better simulate the change of the overall thrust coefficient of the wind field with wind direction. The target thrust coefficient is used in the simulation process of the WRF model. The WRF optimized based on the target thrust coefficient can accurately capture the influence of wind turbine wake and can comprehensively and accurately evaluate the feedback effect of wind farms on regional weather systems.

[0194] The device provided in the embodiment of the present application can implement each process implemented in the above method embodiment. To avoid repetition, it will not be described again here.

[0195] Optionally, Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0196] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0197] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0198] The memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 402 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 402 is a non-volatile solid-state memory. In a specific embodiment, the memory 402 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0199] The processor 401 implements any one of the methods in the embodiments shown in the figure by reading and executing the computer program instructions stored in the memory 402.

[0200] In one example, the electronic device may further include a communication interface 404 and a bus 410. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 404 are connected via a bus 410 and communicate with each other.

[0201] The communication interface 404 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0202] Bus 410 includes hardware, software or both, and couples the components of the electronic device to each other. For example, but not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable bus or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnect.

[0203] The electronic device can execute the method in the embodiment of the present application, thereby realizing the combination Figure 1 Describe the method.

[0204] In addition, in combination with the method in the above embodiment, the embodiment of the present application can provide a computer-readable storage medium to implement. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the Figure 1 method.

[0205] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0206] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0207] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0208] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A parameter selection method for simulating wind, solar and water resources in river valley areas, characterized in that: The method comprises: Obtaining a terrain database, candidate parameterization schemes, and wind direction wind turbine angles for the river valley area, where the wind direction wind turbine angle is the angle between the wind direction and the wind turbine arrangement; The pre-processing tool of the Weather Research and Forecasting WRF model is used to process the terrain database of the dry hot valley area to obtain the lower boundary data, which includes: terrain data, soil data and vegetation data; the pre-processing tool is the Geographic Pre-Processing System WPS; Select a target parameterization scheme from the candidate parameterization schemes, the target parameterization schemes include a target cumulus process parameterization scheme, a target radiation process parameterization scheme and a target microphysical process parameterization scheme; the candidate parameterization schemes include: multiple cumulus process parameterization schemes, multiple radiation process parameterization schemes and multiple microphysical process parameterization schemes; the cumulus process parameterization schemes include: Kain-Fritsch scheme, Grell-Freitas scheme, new Tiedtke scheme, BMJ scheme; the radiation process parameterization schemes include: Dudhia scheme, RRTMG scheme, RRTM scheme, GFDL scheme; the microphysical process parameterization schemes include: WSM3, WSM6, Thompson, Morrison scheme; Input the lower boundary data into the WRF model and set the target parameterization scheme in the WRF model, which is used to simulate the flow field in the dry hot valley area; Without correcting the thrust coefficient in the WRF model, the wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using the first resolution grid spacing to obtain the first simulation result; The method of simulating the wind field and turbulent kinetic energy distribution in the dry hot valley region using a grid spacing of the first resolution without correcting the thrust coefficient in the WRF model to obtain a first simulation result includes: Divide the river valley area by using a first resolution grid distance to obtain multiple grids; Run the WRF model to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area; The exchange of wind field and turbulent kinetic energy distribution between adjacent grids is explicitly analyzed to obtain the exchange of wind field and turbulent kinetic energy between adjacent grid points; According to the wind field and turbulent kinetic energy exchange between adjacent grid points, the influence data of the wind turbine arrangement on the flow field is determined, and the first simulation result includes the influence data of the wind turbine arrangement on the flow field; The wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using a second resolution grid to obtain a second simulation result; Compare the first simulation result with the second simulation result to determine the correction coefficient value at the second resolution; The thrust coefficient is corrected according to the wind direction-fan angle and the correction coefficient value to obtain the target thrust coefficient. The target thrust coefficient is used in the simulation process of the WRF model. The thrust coefficient is the ratio of the thrust generated by the fan on the incoming flow to the incoming flow momentum.

2. The method according to claim 1, characterized in that The step of selecting a target parameterization scheme from candidate parameterization schemes includes: Obtain the climate characteristics and target weather processes of the river valley area; According to the climate characteristics and target weather processes, the target parameterization scheme is selected from the candidate parameterization schemes.

3. The method according to claim 1, characterized in that The WRF model is run to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area, including: Set the horizontal resolution grid of the first resolution in the WRF model; Add wind turbine location and parameter information to the WRF model; Run the WRF model to obtain the wind field and turbulent kinetic energy distribution in the valley area.

4. The method according to claim 1, characterized in that: The comparing the first simulation result and the second simulation result to determine the correction coefficient value at the second resolution includes: Analyze the first simulation results and the second simulation results to determine the difference in wind field and turbulent kinetic energy; Determine the test correction coefficient value at the second resolution according to the wind field difference and the turbulence kinetic energy difference; The test correction coefficient value is input into the WRF model, and the wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using the second resolution grid spacing to obtain an updated second simulation result; In the case where the first simulation result and the updated second simulation result are consistent, determining the test correction coefficient value as the correction coefficient value; The analyzing the first simulation result and the second simulation result to determine the wind field difference and the turbulent kinetic energy difference comprises: determining a percentage difference between an average wind speed distribution at a first resolution and an average wind speed distribution at a second resolution; determining a percentage difference in turbulence intensity between a turbulence intensity distribution at a first resolution and a turbulence intensity distribution at a second resolution; Determining the test correction coefficient value at the second resolution according to the wind field difference and the turbulence kinetic energy difference includes: Analyze the reasons for the differences based on the percentage differences in average wind speed and turbulence intensity; According to the cause of the difference, the test correction coefficient value at the second resolution is determined.

5. The method according to claim 1, characterized in that The method of correcting the thrust coefficient according to the wind direction wind turbine angle and the correction coefficient value to obtain the target thrust coefficient includes: Determine the sine value of the wind direction fan angle; According to the sine value of the wind direction-fan angle and the correction coefficient value, the thrust coefficient is corrected to obtain the target thrust coefficient.

6. The method according to any one of claims 1 to 5, characterized in that: The first resolution is 1 km and the second resolution is 3 km.

7. A parameter selection device for simulating wind, solar and water resources elements in river valley areas, characterized in that: The device comprises: An acquisition module is used to acquire a terrain database of a river valley area, candidate parameterization schemes, and a wind direction wind turbine angle, where the wind direction wind turbine angle is the angle between the wind direction and the wind turbine arrangement; The processing module is used to process the terrain database of the dry and hot valley area using the pre-processing tool of the Weather Research and Forecasting WRF model to obtain the lower boundary data, which includes: terrain data, soil data and vegetation data; the pre-processing tool is the Geographic Pre-Processing System WPS; A selection module is used to select a target parameterization scheme from candidate parameterization schemes, wherein the target parameterization schemes include a target cumulus process parameterization scheme, a target radiation process parameterization scheme and a target microphysical process parameterization scheme; the candidate parameterization schemes include: multiple cumulus process parameterization schemes, multiple radiation process parameterization schemes and multiple microphysical process parameterization schemes; the cumulus process parameterization schemes include: Kain-Fritsch scheme, Grell-Freitas scheme, new Tiedtke scheme, BMJ scheme; the radiation process parameterization schemes include: Dudhia scheme, RRTMG scheme, RRTM scheme, GFDL scheme; the microphysical process parameterization schemes include: WSM3, WSM6, Thompson, Morrison scheme; An input module is used to input the lower boundary data into the WRF model and set the target parameterization scheme in the WRF model. The target parameterization scheme is used to simulate the flow field in the dry hot valley area; The first simulation module is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area using a grid spacing of a first resolution without correcting the thrust coefficient in the WRF model to obtain a first simulation result; The first simulation module is specifically used for: Divide the river valley area by using a first resolution grid distance to obtain multiple grids; Run the WRF model to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area; The exchange of wind field and turbulent kinetic energy distribution between adjacent grids is explicitly analyzed to obtain the exchange of wind field and turbulent kinetic energy between adjacent grid points; According to the wind field and turbulent kinetic energy exchange between adjacent grid points, the influence data of the wind turbine arrangement on the flow field is determined, and the first simulation result includes the influence data of the wind turbine arrangement on the flow field; A second simulation module is used to simulate the wind field and turbulent kinetic energy distribution in the dry hot valley area using a grid spacing of a second resolution to obtain a second simulation result; A comparison module, used for comparing the first simulation result and the second simulation result to determine the correction coefficient value at the second resolution; The correction module is used to correct the thrust coefficient according to the wind direction-fan angle and the correction coefficient value to obtain the target thrust coefficient. The target thrust coefficient is used in the simulation process of the WRF model. The thrust coefficient is the ratio of the thrust generated by the fan on the incoming flow to the incoming flow momentum.

8. The device according to claim 7, characterized in that The candidate parameterization schemes include: multiple cumulus process parameterization schemes, multiple radiation process parameterization schemes and multiple microphysical process parameterization schemes; the selection module is specifically used for: Obtain the climate characteristics and target weather processes of the river valley area; According to the climate characteristics and target weather processes, the target parameterization scheme is selected from the candidate parameterization schemes.

9. The device according to claim 7, wherein the first simulation module is specifically configured to: Set the horizontal resolution grid of the first resolution in the WRF model; Add wind turbine location and parameter information to the WRF model; Run the WRF model to obtain the wind field and turbulent kinetic energy distribution in the valley area.

10. The device according to claim 7, wherein the comparison module is specifically used for: Analyze the first simulation results and the second simulation results to determine the difference in wind field and turbulent kinetic energy; Determine the test correction coefficient value at the second resolution according to the wind field difference and the turbulence kinetic energy difference; The test correction coefficient value is input into the WRF model, and the wind field and turbulent kinetic energy distribution in the dry hot valley area are simulated using the second resolution grid spacing to obtain an updated second simulation result; In the case where the first simulation result and the updated second simulation result are consistent, determining the test correction coefficient value as the correction coefficient value; Comparison module, specifically used for: determining a percentage difference between an average wind speed distribution at a first resolution and an average wind speed distribution at a second resolution; determining a percentage difference in turbulence intensity between a turbulence intensity distribution at a first resolution and a turbulence intensity distribution at a second resolution; Comparison module, specifically used for: Analyze the reasons for the differences based on the percentage differences in average wind speed and turbulence intensity; According to the cause of the difference, the test correction coefficient value at the second resolution is determined.

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