Wind field wind resource fine simulation method and system based on micro-scale coupling in WRF-CFD

Abstract

The invention discloses a wind field wind resource fine simulation method, system and device based on WRF-CFD medium and micro-scale coupling and a storage medium, and belongs to the technical field of wind energy resource evaluation.The method comprises the steps that mesoscale meteorological analysis data is collected and calculated through a mesoscale WRF numerical simulation technology; performing fitting processing to obtain a fitting function between the wind speed component and the height; extracting wind speed contour information at a micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining a boundary condition of a micro-scale CFD model; performing directional calculation and correction on the wind speed of each sector by using a virtual anemometer tower; performing grid division on the wind power plant area to generate a refined grid corresponding to the wind power plant area; and driving numerical simulation of the micro-scale CFD model, and carrying out wind power plant regional range stroke resource evaluation. According to the method, the complex characteristics of the wind field can be comprehensively reflected, the wind resource evaluation precision and the calculation efficiency are remarkably improved, and the resource utilization is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of wind energy resource assessment, and in particular to a method, system, device and storage medium for fine simulation of wind resources in a wind farm based on micro-scale coupling in WRF-CFD. Background Art

[0002] The WRF model is a meteorological model used for numerical weather prediction and atmospheric research. It has the most advanced numerical methods and numerical assimilation techniques, uses an improved physical process scheme, and has the ability to be multiple nested and easily located in different geographical locations. It focuses on horizontal grids with a resolution of several kilometers to tens of kilometers, improves the simulation and forecast accuracy of important weather characteristics at different scales, from cloud scale to weather scale, and adapts well to the needs of different applications from idealized research to operational forecasting, and is widely used in wind resource simulation, wind speed forecasting and other businesses.

[0003] Since WRF is a mesoscale model, it focuses on simulating weather phenomena on scales from a few kilometers to hundreds of kilometers. It generally only considers horizontal grids with a resolution of 1-10 kilometers, and is unable to achieve fine-scale simulations of small-scale resolutions of tens of meters. Especially in complex terrain, the forced change of wind speed by terrain is difficult to be reflected in WRF. Therefore, a downscaling method is generally used to convert the low-resolution wind speed data output by WRF into high-resolution fine data. Currently, downscaling methods are mainly divided into statistical downscaling and dynamic downscaling.

[0004] Among them, the statistical downscaling method uses years of observational data to establish the statistical relationship between mesoscale climate conditions and regional climate elements, and uses independent observational data to test this relationship. Finally, this relationship is applied to the mesoscale climate information output by the global climate model to predict the climate change trend of regional elements. Its disadvantage is that it requires sufficient observational data to establish a statistical model, and cannot be used in areas where the correlation between mesoscale climate elements and regional climate elements is poor.

[0005] Dynamical downscaling is when the mesoscale model drives the small-scale model to obtain a more refined wind field. For example, the mesoscale simulation results provide the real meteorological background of the entire region as the entry condition of the CFD model. The CFD model refines the mesoscale simulation results, which has the disadvantages of large computational complexity and time-consuming computer. Affected by the boundary conditions provided by the global climate model, the dynamic model has a relatively large systematic error in climate simulation.

[0006] Traditional wind resource assessment methods mostly use single-scale numerical simulations, such as mesoscale weather models (WRF) and microscale computational fluid dynamics models (CFD). However, due to complex factors such as climate conditions, terrain changes, and wind turbine layout, a single model often cannot accurately capture the detailed characteristics of the wind field. Therefore, there is an urgent need for a comprehensive method that integrates mesoscale and microscale models to improve the accuracy of wind energy resource assessment.

[0007] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0008] The technical problem to be solved by the present invention is: to provide a method, system, device and storage medium for fine simulation of wind resources in a wind farm based on micro-scale coupling in WRF-CFD, convert the data format based on the coupling strategy of the WRF-CFD interface, obtain the turbulence by the precursor flow periodically running on OpenFOAM, and verify the simulated incoming wind by comparing with the WRF output, so as to solve the problems of low efficiency of data coupling and fusion, insufficient spatial resolution, etc.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is: a wind resource fine simulation method of a wind farm based on microscale coupling in WRF-CFD, comprising the following steps:

[0010] Using the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area, and fitting the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height;

[0011] Extracting wind speed profile information at the microscale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining the boundary conditions of the microscale CFD model;

[0012] Use a virtual wind tower to perform directional calculation and correction on the wind speed in each sector to determine the corrected wind speed data;

[0013] Use OpenFOAM to mesh the wind farm area and generate a refined mesh corresponding to the wind farm area;

[0014] Drive micro-scale CFD models to perform numerical simulations and conduct regional-scale wind resource assessments for wind farms.

[0015] Furthermore, the use of the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area further includes the following steps:

[0016] Determine the scope of the wind farm area, and collect meteorological reanalysis data and high-precision terrain data in the wind farm area;

[0017] The meteorological reanalysis data and the high-precision terrain data are used as background fields, and the mesoscale WRF numerical simulation technology is used to perform calculations including the wind farm area;

[0018] Based on the calculation results, mesoscale meteorological analysis data with a horizontal resolution of 1-3km are obtained, and the output includes wind speed, air pressure, turbulence intensity and spatial distribution of meteorological elements.

[0019] Furthermore, the fitting process of the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height comprises the following steps:

[0020] The mesoscale meteorological analysis data obtained by the mesoscale WRF numerical simulation technology is processed using the interpolation function to obtain the wind field data at a series of grid points on the side boundary.

[0021] Polynomial fitting is performed on each series of vertical points on the side boundary to determine fitting functions u(z), v(z), w(z) and k(z) between wind speed components and height h.

[0022] Furthermore, the extracting of wind speed profile information at the microscale modeling boundary based on the mesoscale meteorological analysis data and the fitting function to determine the boundary conditions of the microscale CFD model further includes the following steps:

[0023] The average wind speed at two different wind measurement heights was selected, and the wind shear value was determined using multi-layer data fitting to obtain the exponential inlet wind profile.

[0024] The mesoscale data is used to solve the complete inlet wind profile parameters, and the inlet wind profile parameters are determined as the boundary conditions of the microscale CFD model.

[0025] Furthermore, the average wind speeds v1 and v2 at two different wind measurement heights z1 and z2 satisfy the following relationship:

[0026]

[0027] Where α is the wind shear coefficient.

[0028] Furthermore, the exponential inlet wind profile is:

[0029]

[0030] Among them, u ref is the reference wind speed, h is the height above the ground, h ref is the reference height and α is the wind shear coefficient.

[0031] Furthermore, the method of solving the complete inlet wind profile parameters using mesoscale data further includes the following steps:

[0032] Take the reference height h ref The wind speed time series is 1m, and the logarithm of both sides of the exponential inlet wind profile is taken to calculate the average wind speed corresponding to different heights in the wind speed time series;

[0033] Using the average wind speed corresponding to different heights, the reference wind speed u is calculated using the least squares method. ref Fitting with the wind shear coefficient α can obtain the complete inlet wind profile parameters.

[0034] Furthermore, the average wind speed corresponding to different heights in the wind speed time series is obtained by the following formula:

[0035]

[0036] The complete inlet wind profile parameters are:

[0037]

[0038] Where n is the number of selected moments; u h is the instantaneous wind speed at height h at time n; is the average wind speed at height h; k is the number of mesoscale meteorological analysis data layers, h k is the height of the kth layer of mesoscale meteorological analysis data; h0 is the reference height.

[0039] Furthermore, the mesoscale meteorological analysis data has five layers, which are wind speed data corresponding to heights of 10m, 50m, 70m, 80m, and 100m respectively.

[0040] Furthermore, the use of a virtual wind tower to perform directional calculation and correction on the wind speed of each divided sector and determine the corrected wind speed data further includes the following steps:

[0041] Use RANS to perform sector-by-sector CFD directional calculations to determine wind acceleration factors, which represent the velocity relationship between different locations under the influence of local complex geographical environments such as topography and roughness;

[0042] Consider the actual wind tower data and correct the wind speed in each sector, and further perform wake calculation and power generation evaluation;

[0043] A mesoscale numerical simulation is performed on the target area, with one year being simulated daily. Each grid point can be considered as a wind tower, and the wind speed sequence of 8760 hours a year at that point is obtained. The corrected wind field data of several equally distributed virtual wind towers in the target area within the simulation period are determined.

[0044] Furthermore, the micro-scale CFD model is driven to perform numerical simulation to assess wind resources in the wind farm area, including the following steps:

[0045] Write a script to monitor the output of the WRF model, use the WRF extraction tool, an open source interface tool of OpenFOAM, to convert the WRF .nc file into an OpenFOAM readable file, fit and update the atmospheric boundary layer fitting function of OpenFOAM every time WRF outputs data, and drive OpenFOAM to perform a calculation;

[0046] Build the required grid scale from a list of points with latitude, longitude and elevation information, write it in bc.dat format, help the WRF extraction tool automatically locate and extract information from the WRF output file, including wind speed vector, temperature and pressure, interpolate the time series in the WRF output file based on the constructed grid, set the start date and offset, import the WRF results of different forecast times, and align the OpenFOAM simulation with the real-time data;

[0047] A RANS-based unsteady solver processes the initial conditions and drives OpenFOAM calculations to run a precursor method based on wind speed and direction extracted from WRF to perform a regional-scale wind resource assessment for the wind farm.

[0048] The present invention also provides a wind resource fine simulation system for a wind farm based on micro-scale coupling in WRF-CFD, comprising:

[0049] A fitting function acquisition module is used to collect and calculate the mesoscale meteorological analysis data of the wind farm area using the mesoscale WRF numerical simulation technology, and to fit the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height;

[0050] A boundary condition determination module, used to extract wind speed profile information at the microscale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determine the boundary conditions of the microscale CFD model;

[0051] A wind speed data correction module is used to use a virtual wind tower to perform directional calculation and correction on the wind speed of each sector and determine the corrected wind speed data;

[0052] A refined grid generation module is used to mesh the wind farm area using OpenFOAM and generate a refined grid corresponding to the wind farm area;

[0053] The wind resource assessment module is used to drive the micro-scale CFD model to perform numerical simulation and conduct wind resource assessment in the wind farm area.

[0054] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for fine simulation of wind resources in a wind farm based on microscale coupling in WRF-CFD as described above are implemented.

[0055] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for fine simulation of wind resources in a wind farm based on microscale coupling in WRF-CFD as described above are implemented.

[0056] The beneficial effects of the present invention are as follows: the present invention can more comprehensively reflect the complex characteristics of the wind field and significantly improve the accuracy of wind resource assessment through the mesoscale and microscale coupling technology; it can effectively reduce data processing time, improve computing efficiency, and optimize resource utilization through the multi-scale data fusion algorithm; it is not only suitable for wind energy resource assessment, but can also be extended to other meteorological and environmental fields, providing support for various renewable energy projects, and has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a schematic diagram of a flow chart of a wind farm wind resource fine simulation method based on micro-scale coupling in WRF-CFD in an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of the working process of a wind farm wind resource fine simulation method based on micro-scale coupling in WRF-CFD in an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the process of obtaining mesoscale meteorological analysis data in an embodiment of the present invention;

[0061] Figure 4 Schematic diagram of the process of confirming the fitting function in an embodiment of the present invention;

[0062] Figure 5 Schematic diagram of the process of confirming boundary conditions in an embodiment of the present invention;

[0063] Figure 6 Schematic diagram of the process of obtaining wind profile parameters in an embodiment of the present invention;

[0064] Figure 7 Schematic diagram of the process of obtaining wind field data for correction in an embodiment of the present invention;

[0065] Figure 8 The rose diagram of the average wind speed of the No. 2 wind tower and the adjacent WRF grid data in the embodiment of the present invention;

[0066] Fig. 9 It is a schematic diagram of the structure of a wind farm wind resource fine simulation system based on micro-scale coupling in WRF-CFD in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0068] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation method.

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0070] like Figure 1 to Figure 2 The wind resource fine simulation method of the wind farm based on micro-scale coupling in WRF-CFD includes the following steps:

[0071] Use the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area, and fit the mesoscale meteorological analysis data to obtain the fitting function between the wind speed component and the height;

[0072] Based on the mesoscale meteorological analysis data and fitting functions, the wind speed profile information at the microscale modeling boundary is extracted to determine the boundary conditions of the microscale CFD model;

[0073] Use a virtual wind tower to perform directional calculation and correction on the wind speed in each sector to determine the corrected wind speed data;

[0074] Use OpenFOAM to mesh the wind farm area and generate a refined mesh corresponding to the wind farm area;

[0075] Drive micro-scale CFD models to perform numerical simulations and conduct regional-scale wind resource assessments for wind farms.

[0076] The present invention uses mesoscale and microscale coupling technology to more comprehensively reflect the complex characteristics of wind fields and significantly improve the accuracy of wind resource assessment. It uses a multi-scale data fusion algorithm to effectively reduce data processing time, improve computing efficiency, and optimize resource utilization. It is not only suitable for wind energy resource assessment, but can also be extended to other meteorological and environmental fields, providing support for various renewable energy projects, and has a wide range of applications.

[0077] The data format is converted through the coupling strategy of the WRF-CFD interface, and the turbulence can be obtained by periodically running the precursor flow on OpenFOAM. The simulated incoming wind is verified by comparing with the WRF output, thus solving the problems of low efficiency of data coupling and fusion, insufficient spatial resolution, etc.

[0078] It should be noted that when performing grid division, the terrain elevation data can be imported into the grid generation tool independently programmed in Python for grid division, and the resolution of the refined grid can be set to 30m.

[0079] like Figure 3 As shown, based on the above embodiment, using the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area further includes the following steps:

[0080] Determine the scope of the wind farm area and collect meteorological reanalysis data and high-precision terrain data in the wind farm area; be able to determine the geographical boundaries of the simulation to ensure that our calculations can cover the entire area where the wind farm is located. Meteorological reanalysis data provides past meteorological information, and high-precision terrain data provides ground undulations; these two types of data provide the basis for accurate meteorological simulation;

[0081] Meteorological reanalysis data and high-precision terrain data are used as background fields, and the mesoscale WRF numerical simulation technology is used to perform calculations including the wind farm area. The WRF model is a powerful meteorological simulation tool that can simulate the meteorological conditions within the wind farm area, thereby predicting the meteorological conditions in the wind farm area through numerical simulation technology.

[0082] According to the calculation results, mesoscale meteorological analysis data with a horizontal resolution of 1-3km is obtained, and the output includes wind speed, air pressure, turbulence intensity and spatial distribution of meteorological elements; through precise meteorological and terrain data input, combined with the powerful computing power of the WRF model, detailed meteorological analysis data of the wind farm area is obtained. It can better understand and predict the wind energy resources of the wind farm, thereby improving the efficiency and economic benefits of the wind farm.

[0083] like Figure 4 As shown, based on the above embodiment, the mesoscale meteorological analysis data is fitted and processed to obtain a fitting function between the wind speed component and the height, further comprising the following steps:

[0084] The mesoscale meteorological analysis data obtained by the mesoscale WRF numerical simulation technology is processed using the interpolation function to obtain the wind field data at a series of grid points on the side boundary.

[0085] Polynomial fitting is performed on each series of vertical points on the side boundary to determine the fitting functions u(z), v(z), w(z) and k(z) between the wind speed components and the height h. Through polynomial fitting, we can obtain the functions u(z), v(z), w(z) that describe the changes of wind speed components with height, and the function k(z) that describes the changes of turbulence intensity with height. The above functions are the key inputs for micro-scale wind field simulation, ensuring that the simulation results are more in line with the vertical structure of the actual wind field, thereby improving the accuracy and reliability of the simulation.

[0086] The above processing is to facilitate the import of meteorological data output by WRF into CFD and solve the problem that the two modes cannot directly exchange data due to different resolutions. The EC reanalysis data source is mainly used. Through fitting processing, the wind speed at different altitudes can be predicted more accurately. The obtained fitting function can help build a more refined wind field model, especially in complex terrain areas. The evaluation process combines mesoscale and microscale data to improve the availability and consistency of data and reduce the uncertainty in wind resource assessment.

[0087] like Figure 5 As shown, based on the above embodiment, the wind speed profile information at the microscale modeling boundary is extracted based on the mesoscale meteorological analysis data and the fitting function, and the boundary conditions of the microscale CFD model are determined, further comprising the following steps:

[0088] The average wind speed at two different wind measurement heights was selected, and the wind shear value was determined using multi-layer data fitting to obtain the exponential inlet wind profile.

[0089] The mesoscale data are used to solve the complete inlet wind profile parameters, and the inlet wind profile parameters are determined as the boundary conditions of the microscale CFD model.

[0090] Since the RANS method ignores the time term, the boundary and initial conditions of the RANS equations are solved iteratively, which affects the iterative calculation results, especially the boundary conditions. Generally speaking, there are two ways to set the inlet boundary conditions. One is to use an empirical wind profile model, such as thermal stability level, exponential wind profile, and logarithmic wind profile. The other is to calculate the simulation results of a larger area by nesting interpolation. The quality of the boundary condition setting determines the simulation results of the entire calculation. By combining mesoscale meteorological data and microscale CFD models, the wind speed profile of the wind farm area can be simulated more accurately, thereby improving the accuracy of wind resource assessment and helping to optimize the design of wind farms, including the layout and height of wind turbines, to maximize wind energy capture efficiency.

[0091] Based on the above embodiment, the average wind speeds v1 and v2 at two different wind measurement heights z1 and z2 satisfy the following relationship:

[0092]

[0093] Where α is the wind shear coefficient.

[0094] Wind shear is an atmospheric phenomenon that indicates the change of wind vector in horizontal and vertical distance in the air, including factors such as wind direction and wind speed. It is used to characterize the relationship between the average wind speeds at two heights and reflects how fast the wind speed changes with increasing height. The wind shear α can be calculated by measuring the average wind speeds at two different heights, and then the average wind speeds at other heights can be derived. Wind shear is related to the roughness of the ground. The rougher the surface, the smaller the wind shear coefficient.

[0095] Therefore, combined with mesoscale data, the average wind speed at different heights is used to calculate the true wind shear value, thereby obtaining the reference wind speed at a height of 10m. Compared with the reference wind speed of 10m / s at a height of 10m and the wind shear of 0.1, the reference wind speed and wind shear calculated using mesoscale data are more real and reliable. At the same time, the wind shear value can also represent the regional thermal stability to a certain extent, making the wind shear wind profile more representative.

[0096] In addition, the boundary conditions of general software are set uniformly, without considering the differences in boundary conditions of different sectors. In this embodiment, mesoscale data is used to generate more realistic exponential wind profiles on the entrance boundaries of each sector, which not only meets the regional representativeness, but also takes into account the sectoral changes of the wind profiles. Generally speaking, for the flow simulation of the wind field, the boundary conditions should be set by selecting the real wind profiles closer to the boundaries, which can be obtained by calculating the interpolation at the boundaries. The resolution of mesoscale data is often equivalent to the wind field simulation area, representing the boundary layer state in a certain area. In order to simplify the calculation, the mesoscale data selected in the field area also has a certain degree of representativeness.

[0097] On the basis of the above embodiment, when there are two average wind speeds at different heights, the wind shear can be calculated, and the mesoscale data is generally multi-layer wind speed, so multi-layer data is used to fit a more accurate wind shear value; the exponential inlet wind profile is:

[0098]

[0099] Among them, u ref is the reference wind speed, h is the height above the ground, h ref is the reference height and α is the wind shear coefficient.

[0100] like Figure 6 As shown, based on the above embodiment, the complete inlet wind profile parameters are solved using mesoscale data, further comprising the following steps:

[0101] Because u ref 、h ref One-to-one correspondence, take the reference height h ref =1m, take the logarithm of both sides of the exponential inlet wind profile, and the exponential inlet wind profile after taking the logarithm is: ln(u) = ln(u ref ·h α )=αln(h)+ln(u ref )

[0102] Calculate and obtain the average wind speed corresponding to different heights in the wind speed time series;

[0103] Using the average wind speed corresponding to different heights, the reference wind speed u is calculated using the least squares method. ref Fitting with the wind shear coefficient α can obtain the complete inlet wind profile parameters.

[0104] The nonlinear relationship of the exponential wind profile is converted into a linear relationship to facilitate subsequent data processing and analysis; by taking the logarithm, the exponential model can be converted into a linear model, thereby simplifying the calculation process; by performing time series analysis on wind speed data at different heights, the average wind speed at different heights can be obtained, providing a basis for subsequent fitting of wind profile parameters; through the above steps, we can obtain complete parameters that describe the change of wind speed with height. These parameters will be used as boundary conditions of the micro-scale CFD model to ensure that the model can accurately simulate the wind speed distribution in the wind farm area.

[0105] Based on the above embodiment, the average wind speed corresponding to different heights in the wind speed time series is obtained by the following formula:

[0106]

[0107] The complete inlet wind profile parameters are:

[0108]

[0109] Where n is the number of selected moments; u h is the instantaneous wind speed at height h at time n; is the average wind speed at height h; k is the number of mesoscale meteorological analysis data layers, h k is the height of the kth layer of mesoscale meteorological analysis data; h0 is the reference height.

[0110] Based on the above embodiment, the number of mesoscale meteorological analysis data layers is 5, which are wind speed data corresponding to heights of 10m, 50m, 70m, 80m and 100m respectively; by setting these heights, the distribution of wind speed change information near the ground surface, different middle-level heights and different high-level heights in the wind farm can be reflected. By using the wind speed data of these different height layers, a detailed wind speed vertical distribution map can be constructed, which is helpful to more accurately evaluate wind resources, optimize the layout of wind farms, and improve the utilization efficiency of wind energy.

[0111] like Figure 7 As shown, based on the above embodiment, a virtual wind tower is used to perform directional calculation and correction on the wind speed of each divided sector to determine the corrected wind speed data, further comprising the following steps:

[0112] Use RANS to perform sector-by-sector CFD directional calculations to determine wind acceleration factors, which represent the velocity relationship between different locations under the influence of local complex geographical environments such as terrain and roughness;

[0113] Considering the actual wind tower data and correcting the wind speed in each sector, its core goal is to calculate the speed relationship between different positions under the influence of local complex geographical environments such as terrain and roughness, that is, the wind acceleration factor. Directional calculation only solves the flow field characteristics under specific wind speed boundary conditions. It is also necessary to consider the actual wind tower data and correct the wind speed in each sector, so as to further perform wake calculation and power generation evaluation;

[0114] However, in actual wind resource assessment, there may be problems such as no wind measurement data or low wind measurement quality. For example, in the development of offshore wind farms, the cost of wind measurement is higher than that on land, and it is difficult to obtain wind measurement data. Or, in complex mountainous areas, the wind measurement time is short, the representative range of wind measurement is small, and the wind measurement quality is not high. Numerical weather forecasting technology has become increasingly mature and can simulate and evaluate the wind resource distribution map with the highest resolution of kilometers or even hundreds of meters in the region. Mesoscale numerical simulation can be performed on the target area, simulating day by day for one year. Each grid point can be considered as a wind tower, and the wind speed sequence of 8760 hours a year at that point can be obtained, and the corrected wind field data within the simulation period of several equally distributed virtual wind towers in the target area can be determined.

[0115] Therefore, it can be assumed that there are several equally distributed virtual wind towers in the target area to simulate the wind field observation data within the period; the wind speed series data of the virtual wind towers then participate in the wind resource assessment, which has the advantages of low cost, short cycle, and more refined data compared to the traditional establishment of wind towers.

[0116] Based on the above embodiment, the micro-scale CFD model is driven to perform numerical simulation to evaluate the wind resources in the wind farm area, further comprising the following steps:

[0117] Write a script to monitor the output of the WRF model, use the WRF extraction tool, an open source interface tool of OpenFOAM, to convert the .nc file of WRF into an OpenFOAM readable file. Each time WRF outputs data, fit and update the atmospheric boundary layer fitting function of OpenFOAM, and drive OpenFOAM to perform a calculation. The output time resolution can generally be 15 minutes. Write a script to monitor the output of the WRF model to ensure timely response each time the WRF model outputs data. Use the WRF extraction tool, an open source interface tool of OpenFOAM, to convert the .nc file of WRF into an OpenFOAM readable file. This step ensures that the output of the WRF model can be effectively used by OpenFOAM, providing the necessary data support for subsequent simulations. Each time WRF outputs data, fit and update the atmospheric boundary layer fitting function of OpenFOAM, and drive OpenFOAM to perform a calculation. This step enables the simulation of OpenFOAM to reflect the latest output of the WRF model in real time, improving the timeliness and accuracy of the simulation.

[0118] The required grid scale is constructed through a point list of latitude, longitude and elevation information, and written in bc.dat format to help the WRF extraction tool automatically locate and extract information from the WRF output file, including wind speed vector, temperature and pressure, providing OpenFOAM with precise geographic location and terrain information to ensure the geographic accuracy of the simulation; time series interpolation is performed in the WRF output file based on the constructed grid, and the start date and offset are set to import WRF results of different forecast times, and the OpenFOAM simulation is aligned with the real-time data, ensuring the temporal continuity of the simulation data and the consistency with the actual observation data;

[0119] The RANS-based unsteady solver processes the initial conditions and drives OpenFOAM to perform calculations to run a precursor method based on wind speed and direction extracted from WRF to perform regional-scale wind resource assessments for wind farms; this enables the simulation to take into account the effects of turbulence, improving the physical accuracy of the simulation.

[0120] like Figure 8As shown, in order to verify the prediction accuracy of the wind resource fine simulation method for a wind farm based on WRF-CFD mesoscale coupling provided in this embodiment, a CFD modeling analysis was carried out on a wind farm in Shaanxi Province, relying on mesoscale data to provide real boundary conditions for the CFD wind farm, and using wind measurement data to verify the CFD calculation accuracy, and realizing downscaled and refined wind farm simulation; at the same time, the mesoscale data was used as a virtual wind measurement tower for comprehensive calculation, so as to realize the calculation of the power generation of the wind turbine at the machine site in the absence of wind measurement data.

[0121] The wind farm has an annual average wind speed of about 6m / s, an annual effective wind time of more than 4,000 hours, an altitude of 1,430 to 1,730m, a drop of about 300m, and a small overall terrain drop, but a relatively complex terrain. The northern part is relatively flat, the southern side has many gullies, and the terrain is undulating. There is a deep ditch from north to south on the east side. There are two wind towers in the wind farm, about 4km apart: Wind Tower 1 and Wind Tower 2.

[0122] The 80-meter hourly wind observation data of wind tower No. 1 and No. 2 from February 1, 2016 to November 22, 2016 were analyzed and the wind field was simulated using CFD; the coordinates of wind tower No. 1 are 107.494083°E, 37.46755°N, the altitude is 1455m, and the average measured wind speed is 5.96m / s; the coordinates of wind tower No. 2 are 107.522433°E, 37.439533°N, the altitude is 1628m, and the average measured wind speed is 6.75m / s.

[0123] Compare the tower data and mesoscale data to verify the mesoscale data results. The WRF grid point closest to Tower 2 is about 900m south of it. Select this WRF grid point, extract the wind vector at an altitude of 80m, and compare its difference with the tower data. The average wind speed observed by Tower 2 is 6.76m / s, and the average wind speed simulated by WRF is 6.6m / s, which are very close. Figure 8 The comparison results of the sectors of the No. 2 wind tower and the WRF simulation results are shown in Figure 2, where the blue line is the average wind speed of the WRF simulated sector and the red line is the average wind speed of the wind tower sector. Figure 8As shown in the figure, the WRF simulation results can well reflect the average wind speed of each sector in the region, and the change characteristics of the average wind speed of the sector are basically consistent with the observation data. Based on the measured data of wind tower No. 2, the corresponding relationship between wind towers established by directional calculation is used to obtain the forecast results at wind tower No. 1. The average wind speed of wind tower No. 1 is 5.96m / s. After the mesoscale data generation boundary is introduced, the average wind speed deviation is -0.48m / s, which is significantly lower than -0.559m / s, and the error percentage of the average wind speed is reduced from -9.38% to -8.05%. This shows that the inlet wind profile defined by mesoscale data can more accurately depict the flow field characteristics under complex terrain.

[0124] like Fig. 9 As shown, the present invention also provides a wind resource fine simulation system for a wind farm based on micro-scale coupling in WRF-CFD, comprising:

[0125] The fitting function acquisition module is used to collect and calculate the mesoscale meteorological analysis data of the wind farm area using the mesoscale WRF numerical simulation technology, and to fit the mesoscale meteorological analysis data to obtain the fitting function between the wind speed component and the height;

[0126] The boundary condition determination module is used to extract the wind speed profile information at the boundary of microscale modeling based on the mesoscale meteorological analysis data and the fitting function, and determine the boundary conditions of the microscale CFD model;

[0127] A wind speed data correction module is used to use a virtual wind tower to perform directional calculation and correction on the wind speed of each sector and determine the corrected wind speed data;

[0128] A refined grid generation module is used to mesh the wind farm area using OpenFOAM and generate a refined grid corresponding to the wind farm area;

[0129] The wind resource assessment module is used to drive the micro-scale CFD model to perform numerical simulation and conduct wind resource assessment in the wind farm area.

[0130] The present invention uses mesoscale and microscale coupling technology to more comprehensively reflect the complex characteristics of wind fields and significantly improve the accuracy of wind resource assessment. It uses a multi-scale data fusion algorithm to effectively reduce data processing time, improve computing efficiency, and optimize resource utilization. It is not only suitable for wind energy resource assessment, but can also be extended to other meteorological and environmental fields, providing support for various renewable energy projects, and has a wide range of applications.

[0131] The specific working process and technical effects of the above system are the same as those in the above embodiment and will not be described in detail here.

[0132] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the wind farm wind resource fine simulation method based on WRF-CFD mesoscale coupling are implemented, including using the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area, and fitting the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height; extracting the wind speed profile information at the microscale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining the boundary conditions of the microscale CFD model; using a virtual wind tower to perform directional calculation and correction on the wind speed of each divided sector, and determining the corrected wind speed data; using OpenFOAM to grid the wind farm area, and generate a refined grid corresponding to the wind farm area; driving the microscale CFD model to perform numerical simulation, and performing wind resource assessment in the wind farm area.

[0133] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for fine simulation of wind resources in a wind farm based on micro-scale coupling in WRF-CFD are implemented, including using the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area, and fitting the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height; extracting the wind speed profile information at the micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining the boundary conditions of the micro-scale CFD model; using a virtual wind tower to perform directional calculation and correction on the wind speed of each divided sector, and determine the corrected wind speed data; using OpenFOAM to grid the wind farm area, and generate a refined grid corresponding to the wind farm area; driving the micro-scale CFD model to perform numerical simulation, and perform wind resource assessment in the wind farm area.

[0134] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A wind resource simulation method based on microscale coupling in WRF-CFD, characterized in that: The following steps are involved: Using the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area, and fitting the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height; Extracting wind speed profile information at the microscale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining the boundary conditions of the microscale CFD model; Use a virtual wind tower to perform directional calculation and correction on the wind speed in each sector to determine the corrected wind speed data; Use OpenFOAM to mesh the wind farm area and generate a refined mesh corresponding to the wind farm area; Drive micro-scale CFD models to perform numerical simulations and conduct regional-scale wind resource assessments for wind farms.

2. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 1 is characterized in that: The method of using the mesoscale WRF numerical simulation technology to collect and calculate the mesoscale meteorological analysis data of the wind farm area further includes the following steps: Determine the scope of the wind farm area, and collect meteorological reanalysis data and high-precision terrain data in the wind farm area; The meteorological reanalysis data and the high-precision terrain data are used as background fields, and the mesoscale WRF numerical simulation technology is used to perform calculations including the wind farm area; Based on the calculation results, mesoscale meteorological analysis data with a horizontal resolution of 1-3km are obtained, and the output includes wind speed, air pressure, turbulence intensity and spatial distribution of meteorological elements.

3. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 1 is characterized in that: The fitting process of the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height comprises the following steps: The mesoscale meteorological analysis data obtained by the mesoscale WRF numerical simulation technology is processed using the interpolation function to obtain the wind field data at a series of grid points on the side boundary. Polynomial fitting is performed on each series of vertical points on the side boundary to determine fitting functions u(z), v(z), w(z) and k(z) between wind speed components and height h.

4. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 1 is characterized in that: The step of extracting wind speed profile information at the microscale modeling boundary based on the mesoscale meteorological analysis data and the fitting function and determining the boundary conditions of the microscale CFD model further comprises the following steps: The average wind speed at two different wind measurement heights was selected, and the wind shear value was determined using multi-layer data fitting to obtain the exponential inlet wind profile. The mesoscale data is used to solve the complete inlet wind profile parameters, and the inlet wind profile parameters are determined as the boundary conditions of the microscale CFD model.

5. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 4 is characterized in that: The average wind speeds v1 and v2 at two different wind measurement heights z1 and z2 satisfy the following relationship: Where α is the wind shear coefficient.

6. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 5 is characterized in that: The exponential inlet wind profile is: Among them, u ref is the reference wind speed, h is the height above the ground, h ref is the reference altitude and α is the wind shear coefficient.

7. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 6 is characterized in that: The method of solving the complete inlet wind profile parameters using mesoscale data further includes the following steps: Take the reference height h ref The wind speed time series is 1m, and the logarithm of both sides of the exponential inlet wind profile is taken to calculate the average wind speed corresponding to different heights in the wind speed time series; Using the average wind speed corresponding to different heights, the reference wind speed u is calculated using the least squares method. ref Fitting with the wind shear coefficient α can obtain the complete inlet wind profile parameters.

8. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 7 is characterized in that: The average wind speed corresponding to different heights in the wind speed time series is obtained by the following formula: The complete inlet wind profile parameters are: Where n is the number of selected moments; u h is the instantaneous wind speed at height h at time n; is the average wind speed at height h; k is the number of mesoscale meteorological analysis data layers, h k is the height of the kth layer of mesoscale meteorological analysis data; h0 is the reference height.

9. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 8 is characterized in that: The mesoscale meteorological analysis data has five layers, which are wind speed data corresponding to heights of 10m, 50m, 70m, 80m and 100m respectively.

10. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 1, characterized in that: The method of using a virtual wind tower to perform directional calculation and correction on the wind speed of each sector to determine the corrected wind speed data further includes the following steps: Use RANS to perform sector-by-sector CFD directional calculations to determine wind acceleration factors, which represent the velocity relationship between different locations under the influence of local complex geographical environments such as topography and roughness; Consider the actual wind tower data and correct the wind speed in each sector, and further perform wake calculation and power generation evaluation; A mesoscale numerical simulation is performed on the target area, with one year being simulated daily. Each grid point can be considered as a wind tower, and the wind speed sequence of 8760 hours a year at that point is obtained. The corrected wind field data of several equally distributed virtual wind towers in the target area within the simulation period are determined.

11. The wind resource fine simulation method of wind farm based on micro-scale coupling in WRF-CFD according to claim 1, characterized in that: The driving micro-scale CFD model performs numerical simulation to conduct wind resource assessment in the wind farm area, including the following steps: Write a script to monitor the output of the WRF model, use the WRF extraction tool, an open source interface tool of OpenFOAM, to convert the WRF .nc file into an OpenFOAM readable file, fit and update the atmospheric boundary layer fitting function of OpenFOAM every time WRF outputs data, and drive OpenFOAM to perform a calculation; Build the required grid scale from a list of points with latitude, longitude and elevation information, write it in bc.dat format, help the WRF extraction tool automatically locate and extract information from the WRF output file, including wind speed vector, temperature and pressure, interpolate the time series in the WRF output file based on the constructed grid, set the start date and offset, import the WRF results of different forecast times, and align the OpenFOAM simulation with the real-time data; A RANS-based unsteady solver processes the initial conditions and drives OpenFOAM calculations to run a precursor method based on wind speed and direction extracted from WRF to perform a regional-scale wind resource assessment for the wind farm.

12. A wind resource fine simulation system based on micro-scale coupling in WRF-CFD, characterized in that: include: A fitting function acquisition module is used to collect and calculate the mesoscale meteorological analysis data of the wind farm area using the mesoscale WRF numerical simulation technology, and to fit the mesoscale meteorological analysis data to obtain a fitting function between the wind speed component and the height; A boundary condition determination module, used to extract wind speed profile information at the microscale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determine the boundary conditions of the microscale CFD model; A wind speed data correction module is used to use a virtual wind tower to perform directional calculation and correction on the wind speed of each sector and determine the corrected wind speed data; A refined grid generation module is used to mesh the wind farm area using OpenFOAM and generate a refined grid corresponding to the wind farm area; The wind resource assessment module is used to drive the micro-scale CFD model to perform numerical simulation and conduct wind resource assessment in the wind farm area.

13. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for fine simulation of wind resources in a wind farm based on microscale coupling in WRF-CFD are implemented as claimed in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for fine simulation of wind resources in a wind farm based on microscale coupling in WRF-CFD as described in any one of claims 1 to 11.

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