Method for evaluating macroscopic wake effect of large-base wind power plant

Abstract

The invention provides a macroscopic wake effect evaluation method for a large-base wind power plant. The method comprises the following steps: S1, acquiring meteorological reanalysis data, terrain and land utilization data and fan data; s2, preprocessing the obtained data, correcting meteorological reanalysis data, and obtaining a more accurate initial and boundary condition meteorological data set of a simulation area; s3, defining a model domain, and generating a computational grid; encrypting the space layer; interpolating the data into a computational grid so as to construct a computational domain; s4, carrying out natural wind resource condition simulation according to the computational domain by adopting a mesoscale meteorological model; s5, performing macroscopic wake effect condition simulation according to the computational domain by adopting a mesoscale meteorological model coupled fan model, and evaluating the macroscopic wake effect; and S6, checking an evaluation result, and improving parameterized configuration of the model. According to the method, the problem that the power generation capacity of the wind power plant is wrongly evaluated due to the fact that mesoscale meteorological conditions are not known is solved.

Description

Technical Field

[0001] The invention relates to the technical field of wind power generation, and in particular to a method for evaluating macroscopic wake effects of a large-scale wind farm. Background Art

[0002] Traditional wake models based on CFD can accurately analyze the flow details at the blade element scale, wind turbine scale, and (independent) wind farm scale, that is, they are suitable for simulation of laboratory-scale or large-scale wind turbine blade rotation, wind turbines or wind turbine groups in smaller-scale areas. If the flow and operation characteristics of large-scale wind farms and the atmospheric environmental effects are explored, this method will face many limitations, such as the difficulty of wind farm modeling, the large amount of grid data required, the large consumption of computing resources, and the high computing power requirements.

[0003] However, with the large-scale development of wind farms, there is an urgent need for macro-evaluation of large-scale base wind farms (installed capacity is usually 1 million kilowatts or more) and evaluation of the wake effect of wind turbines between adjacent wind farms and within wind farms.

[0004] In view of this, the inventor of the present application has designed a method for evaluating the macroscopic wake effect of a large-scale wind farm in order to overcome the above technical problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the many limitations of traditional models in the prior art, such as the difficulty in wind farm modeling, the large amount of grid data required, the large consumption of computing resources and the high computing power requirements, and to provide a method for evaluating the macroscopic wake effect of large-scale wind farms.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] The present invention provides a method for evaluating the macroscopic wake effect of a large-scale wind farm, which is characterized in that the evaluation method comprises the following steps: 1 , obtain meteorological reanalysis data, topographic and land use data, and wind turbine data; S 2 , preprocess the acquired data, correct the meteorological reanalysis data, and obtain a more accurate meteorological data set of initial and boundary conditions in the simulation area; S 3 , define the model domain and generate the computational grid; encrypt the spatial layer; interpolate the data into the computational grid to construct the computational domain; S 4 , using a mesoscale meteorological model to simulate the natural wind resource situation according to the calculation domain; S 5 , using a mesoscale meteorological model coupled with a wind turbine model to simulate the macro wake effect according to the computational domain, and to evaluate the macro wake effect; S 6, verify the evaluation results of macroscopic wake effects, and improve the parameterization configuration of mesoscale meteorological models and wind turbine models.

[0008] According to one or more embodiments of the present invention, step S 1 In: The meteorological reanalysis data are the initial and boundary condition data of the simulation time period obtained through the global meteorological reanalysis data set; the terrain and land use data are high-resolution terrain data; the terrain and land use data are used to define the terrain characteristics of the model domain; the wind turbine data include the site coordinates, wind turbine model, wind turbine hub height, wind turbine rated power, wind turbine thrust coefficient, wind turbine impeller diameter and power generation of each wind turbine unit in the target wind farm.

[0009] According to one or more embodiments of the present invention, step S 2 The following steps are included: 21 , correcting the meteorological reanalysis data used as initial and boundary conditions, using the wind tower data to correct the errors of the meteorological reanalysis data, and obtaining a more accurate meteorological data set of initial and boundary conditions for the simulation area; S 22 , perform spatial statistical downscaling on the obtained meteorological data set to improve the spatial resolution.

[0010] According to one or more embodiments of the present invention, step S 21 The wind profile fitting formula Perform system error correction; where, is the observation data from the wind tower, V Z To reanalyze the data; Where Z is the surface roughness, α is the wind shear index, and Z r is the altitude; the error correction of the reanalysis data is Perform: Select a historical year as the year for establishing the revised model, and calculate based on the time period of the revised model establishment year Then use the calculated The reanalysis data of the simulation year are corrected to obtain a more accurate meteorological dataset of initial and boundary conditions in the simulation area.

[0011] According to one or more embodiments of the present invention, step S 22 The following steps are included: 221 , use bilinear interpolation to interpolate low-resolution meteorological data sets to make them correspond to the spatial points of high-resolution data, and convert the grid field into a discrete field; S 222 Based on low-resolution meteorological data, after standardizing the data, the empirical orthogonal function analysis method is used to reduce the dimension and filter the large-scale prediction factors. The empirical orthogonal function used is: P t =PCt ×EOF t +N t ; Among them, P t The numerical matrix representing the factor field of low-resolution meteorological data at time t; EOF t represents the spatial modal matrix of this factor field; PC t Represents EOF t The coefficient matrix that varies with time; N t Represents the noise matrix; select PC t As a factor entering the stepwise regression model; S 223 , based on the measured data sets of multiple observation sites and through the step S 222 The factors of the stepwise regression model are screened out, and the stepwise regression model is established. The relationship can be expressed as: Among them, A and b are the regression coefficients determined in the sliding training; PC t is the regression model factor; is the measured data set of the nth observation station; S 224 , for each grid point of the interpolated meteorological data set, follow the steps S moment by moment 223 The established stepwise regression model is used for regression correction to obtain meteorological data with high spatial resolution.

[0012] According to one or more embodiments of the present invention, step S 3 The following steps are included: 31 , determine the spatial range and resolution of wake simulation, select the number and configuration of nested models, and generate the computational grid; S 32 , perform spatial layer encryption, divide the calculation space into several vertical layers through the mass-based mixed pressure coordinate system, and encrypt each layer; S 33 , the step S 1 The terrain and land use data in step S 2 The processed initial and boundary condition meteorological data are interpolated to the step S 31 and the step S 32 In the generated grid, the data format is converted to form multiple computational domains.

[0013] According to one or more embodiments of the present invention, step S 31In: The horizontal grid of the computational domain adopts Arakawa-C grid, and multiple 3-4 layers of one-way nested grid computational domains are superimposed. The horizontal grid resolution of each computational domain ranges from the wind farm area to the surrounding area, and the value is between 200 meters and 15 kilometers; the horizontal grid resolution in the wind farm area is less than 5 times the diameter of the wind rotor; different computational domains are nested to adjust the definition domain of wind farms in different areas; the processing calculation formula for the final simulation results of the computational domain superposition area is as follows: Where V is the final simulated meteorological element result; n is the number of overlapping computational domains in the same grid area; It is the simulation result of meteorological elements at the zth altitude layer in the i-th row, j-th column and the n-th computational domain.

[0014] According to one or more embodiments of the present invention, step S 32 Middle: Using the terrain data in the entire computational domain, the computational space is divided into several vertical layers through a mass-based mixed pressure coordinate system. The calculation formula is: Where p is the air pressure, p t is the top pressure, p s is the surface air pressure, η is the atmospheric vertical coordinate system; the atmospheric boundary layer below 1 km in the vertical layer is encrypted by at least 20 layers.

[0015] According to one or more embodiments of the present invention, step S 4 The following steps are included: 41 Based on the step S 3 The generated computational domain sets the integration time step of each layer of grids and the parameterization scheme of the physical process, and obtains the simulation results of the target time period of the computational domain by continuously updating and solving the physical equations and performing time integration from the start time to the end time of the evaluation period; S 42 , adopt the step S 31 The overlapping grid results are processed by the method to obtain the natural wind resource situation in the target wind farm area when there is no wind turbine; the natural wind resource situation in the target wind farm area when there is no wind turbine includes the simulated wind speed in the target area within the target time period.

[0016] According to one or more embodiments of the present invention, step S 5 The fan model described in the embodiment adopts the Fitch model, and the step S 5 The following steps are included: 51 Based on step S 3 The generated computational domain adds step S in the target area 1 Based on the wind turbine data in the calculation domain, the mesoscale meteorological model is coupled with the Fitch model to simulate the wake effect and power output characteristics of the wind turbine, and the simulation results of the target time period in the calculation domain are obtained; S52 , adopt the step S 31 The overlapping grid results are processed by the method of S to obtain the wind resource situation after the wind farm in the target area is built; the wind resource situation after the wind farm in the target area is built includes the simulated wind speed in the target area within the target time period; S 53 , by determining the wake effect length and quantifying the power generation loss, the macroscopic wake effect is evaluated.

[0017] According to one or more embodiments of the present invention, step S 53 The length of the medium wake effect is determined by the wind speed loss ratio, and the wind speed loss ratio formula is: Among them, v with out fitch For step S 42 The simulated wind speed in the target area and target time period obtained in with fitch For step S 52 The simulated wind speed in the target area and target time period obtained in .

[0018] According to one or more embodiments of the present invention, step S 53 The method for quantifying power generation loss in the present invention comprises the following steps: 531 , the total power generation of the wind farm is calculated by the following formula: Where P is the power generated per unit time; ρ is the air density; A is the swept area of ​​the wind wheel; v is the wind speed; C p is the power coefficient, which indicates the efficiency of converting wind energy into electrical energy; S 532 , multiply the power generation per unit time by time to calculate the total power generation of the wind farm within a certain period of time: E = P·t; where E is the total power generation; P is the power generation per unit time; t is time; S 533 , calculate the power generation loss, the power generation loss calculation formula is: Among them, η loss is the power generation loss, E with out fitch Use step S 42 The power generation calculated by simulating wind speed in the target area and target time period finally obtained; E with fitch Use step S 52 The generated power is calculated from the simulated wind speed in the target area and target time period finally obtained.

[0019] According to one or more embodiments of the present invention, step S 6 The evaluation results of the macro wake effect are tested by using the average deviation of the simulated wind speed loss results and the observed data, and the root mean square error as evaluation indicators. 6 The following steps are included: 61, calculate the average deviation and root mean square error between the simulated wind speed loss results and the observed data; the calculation formula for the average deviation between the simulated wind speed loss results and the observed data is: Where MBE is the average deviation between the simulated wind speed loss result and the observed data; S i , O i are the statistical samples of the i-th simulation and observation respectively; the root mean square error represents the accuracy of the simulation results. The calculation formula is: Among them, RMSE is the root mean square error; S i is the statistical sample of the ith simulation; O i is the statistical sample of the i-th observation; S 62 , according to the average deviation and root mean square error of the simulated wind speed loss result obtained by calculation and the observed data, the step S 4 and the step S 5 The parameters of the mesoscale meteorological model and wind turbine model are tuned to improve the parameterized configuration of the mesoscale meteorological model and wind turbine model.

[0020] The positive and progressive effects of the present invention are:

[0021] The macroscopic wake effect assessment method of a large-scale wind farm of the present invention has at least the following advantages:

[0022] The macroscopic wake effect evaluation method for large-scale wind farms of the present invention is suitable for mesoscale range simulations such as large-scale wind farms or multiple wind farms, and can perform mesoscale calculations on wind resource conditions. Compared with traditional wake effect evaluation methods and wake models, it solves the problem of incorrect evaluation of wind farm power generation capacity due to lack of understanding of mesoscale meteorological conditions, increases calculation efficiency, and reduces calculation resource investment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other features, properties and advantages of the present invention will become more apparent through the following description in conjunction with the accompanying drawings and embodiments, in which the same reference numerals always represent the same features, wherein:

[0024] Figure 1 It is a flow chart of a preferred embodiment of a method for evaluating macroscopic wake effects of a large-scale wind farm according to the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0026] Embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Reference will now be made in detail to preferred embodiments of the present invention, examples of which are shown in the accompanying drawings. Wherever possible, the same reference numerals will be used in all drawings to represent the same or similar parts. In addition, although the terms used in the present invention are selected from well-known and commonly used terms, some of the terms mentioned in the specification of the present invention may be selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant parts of the description herein. In addition, it is required to understand the present invention not only by the actual terms used, but also by the meaning implied by each term.

[0027] The present invention provides a method for evaluating the macroscopic wake effect of a large-scale wind farm, the method comprising the following steps:

[0028] Step S 1 , obtain meteorological reanalysis data, topographic and land use data, and wind turbine data;

[0029] Step S 2 , preprocess the acquired data, correct the meteorological reanalysis data, and obtain a more accurate meteorological data set of initial and boundary conditions in the simulation area;

[0030] Step S 3 , define the model domain and generate the computational grid; encrypt the spatial layer; interpolate the data into the computational grid to construct the computational domain;

[0031] Step S 4 , using a mesoscale meteorological model to simulate the natural wind resource situation according to the calculation domain;

[0032] Step S 5 , using a mesoscale meteorological model coupled with a wind turbine model to simulate the macro wake effect according to the computational domain, and evaluating the macro wake effect;

[0033] Step S 6 , verify the evaluation results of macroscopic wake effects, and improve the parameterization configuration of mesoscale meteorological models and wind turbine models.

[0034] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of a method for evaluating macroscopic wake effects of a large-scale wind farm according to the present invention.

[0035] The macroscopic wake effect assessment method for large-scale base wind farms of the present invention is based on a mesoscale meteorological model and can simulate the wake effect of large-scale wind farm areas with high precision in combination with the atmospheric environmental effects of the mesoscale wind farms, thereby solving the problem of incorrect assessment of the power generation capacity of wind farms due to lack of understanding of mesoscale meteorological conditions, increasing computational efficiency and reducing computational resource investment.

[0036] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 1 middle:

[0037] The meteorological reanalysis data are initial and boundary condition data of a simulation time period obtained through a global meteorological reanalysis data set;

[0038] The terrain and land use data are high-resolution terrain data; the terrain and land use data are used to define the terrain characteristics of the model domain;

[0039] The wind turbine data includes the position coordinates, wind turbine model, wind turbine hub height, wind turbine rated power (MW), wind turbine thrust coefficient, wind turbine impeller diameter and power generation (kW) of each wind turbine unit in the target wind farm.

[0040] It should be noted that the step S 1 Steps to prepare the data.

[0041] Preferably, the meteorological reanalysis data are the initial and boundary condition data of the simulation time period, which are usually from the global meteorological reanalysis data set (such as the reanalysis data set FNL jointly produced by the U.S. Meteorological and Environmental Forecast Center and the U.S. National Center for Atmospheric Research, the European Center for Medium-Range Weather Forecasts reanalysis data set ERA5, China's first generation global atmosphere and land surface reanalysis data set CRA40, etc.). The spatial resolution of the data set is usually 0.25° to 1°, the temporal resolution is generally not less than 6 hours, and the time length is usually not less than 1 year. It includes information such as temperature, humidity, wind speed, and air pressure.

[0042] Preferably, the terrain and land use data are high-resolution terrain data, and the terrain features of the model domain are defined by using high-resolution terrain data. It is advisable to use digital elevation model (DEM, Digital Elevation Model) terrain data such as ASTER GDEM, SRTM 90m, GTOPO30, etc., with a horizontal resolution of less than 100 meters, as mesoscale simulated surface elevation information. The surface roughness value can refer to HJ 169, and it is advisable to use the global land cover data of the European Space Agency Global Coverage Program (ESA Globcover), the global land cover data of the European Space Agency, or the data collected by the European Commission Joint Research Center (EC JRC), the United States Geological Survey (USGS, US Geographical Survey), etc.

[0043] It should be noted that the ASTER GDEM is a global digital elevation model data of the Spaceborne Thermal Emission and Reflection Radiometer, which is produced based on the detailed observation results of NASA's new generation of earth observation satellite Terra. The SRTM90m is a Shuttle Radar Topography Mission (SRTM) elevation model data. The SRTM data is mainly measured jointly by the National Aeronautics and Space Administration (NASA) and the National Mapping and Surveying Administration (NIMA) of the Department of Defense. The GTOPO30 is a global terrain database with a resolution of 30 arc seconds, about 1 km. It is a data set developed by the United States Geological Survey and is composed of 8 different sources of data elevation splicing.

[0044] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 2 The steps include:

[0045] Step S 21 , correcting the meteorological reanalysis data used as initial and boundary conditions, using the wind tower data to perform error correction on the meteorological reanalysis data, and obtaining a more accurate meteorological data set of initial and boundary conditions for the simulation area;

[0046] Step S 22 , perform spatial statistical downscaling on the obtained meteorological data set to improve the spatial resolution.

[0047] Preferably, the step S 21 The meteorological data sets used as the initial and boundary conditions of the mesoscale meteorological model are corrected, mainly involving the wind speed that has a more direct impact on the wake. The meteorological reanalysis data are corrected for errors using wind tower data.

[0048] Preferably, the step S 22 In the simulation, because the spatial resolution of meteorological data of initial and boundary conditions is usually between 0.25° and 1°, in order to improve the accuracy of the simulation, the meteorological data set can be spatially statistically downscaled to increase the spatial resolution to 0.01. By establishing a mathematical statistical model and analyzing the statistical function relationship between low-resolution meteorological data sets and high-resolution data (such as observation site data), refined background field data can be obtained.

[0049] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 21 The wind profile fitting formula Perform system error correction;

[0050] Among them, V Zr is the observation data from the wind tower, VZ To reanalyze the data; Where Z is the surface roughness, α is the wind shear index, and Z r is the altitude;

[0051] The error correction of reanalysis data is aimed at Perform: Select a historical year as the year for establishing the revised model, and calculate based on the time period of the revised model establishment year Then use the calculated The reanalysis data of the simulation year are corrected to obtain a more accurate meteorological dataset of initial and boundary conditions in the simulation area.

[0052] It should be noted that the wind profile fitting formula middle, Contains surface roughness Z, wind shear index α, altitude Z r Among them, the surface roughness Z is related to seasonal changes, and the wind shear index α is related to daily changes.

[0053] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 22 The steps include:

[0054] Step S 221 , use bilinear interpolation to interpolate low-resolution meteorological data sets to make them correspond to the spatial points of high-resolution data and convert the grid field into a discrete field;

[0055] Step S 222 Based on low-resolution meteorological data, after standardizing the data, the empirical orthogonal function (EOF) analysis method is used to reduce the dimension and filter the large-scale prediction factors. The empirical orthogonal function used is:

[0056] P t =PC t ×EOF t +N t

[0057] Among them, P t The numerical matrix representing the factor field of low-resolution meteorological data at time t; EOF t represents the spatial modal matrix of this factor field; PC t Represents EOF t The coefficient matrix that varies with time; N t represents the noise matrix;

[0058] Select PC t as factors entering the stepwise regression model;

[0059] Step S 223, based on the measured data sets of multiple observation sites and through the step S 222 The factors of the stepwise regression model are screened out, and a stepwise linear regression model is established. The relationship can be expressed as:

[0060]

[0061] Among them, A and b are the regression coefficients determined in the sliding training; PC t is the regression model factor; is the measured data set of the nth observation station;

[0062] Step S 224 , for each grid point of the interpolated meteorological data set, follow the steps S moment by moment 223 The established stepwise linear regression model was used for regression correction to obtain meteorological data with high spatial resolution.

[0063] It should be noted that the step S 222 Based on low-resolution meteorological data, after standardizing the data, the empirical orthogonal function (EOF) analysis method is used to reduce the dimension and filter the large-scale prediction factors. The purpose is to reduce the input amount of data in the stepwise linear regression model, filter out noise and reduce the degree of freedom of the data.

[0064] As described above, the macroscopic wake effect assessment method for large-scale wind farms of the present invention corrects the input meteorological reanalysis data and improves its spatial resolution using a statistical downscaling model. The correction model uses wind tower data to correct the errors of the reanalysis data, taking into account the seasonal and diurnal variations of the system error, and develops a reanalysis data correction method with diurnal and seasonal variation adaptive characteristics. The correction method can reduce the root mean square error of the reanalysis data by more than 75%, ensuring that the root mean square error of wind speed in all years is less than 0.6m / s.

[0065] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 3 The steps include:

[0066] Step S 31 , determine the spatial range and resolution of wake simulation, select the number and configuration of nested models, and generate the computational grid;

[0067] Step S 32 , perform spatial layer encryption, divide the calculation space into several vertical layers through the mass-based mixed pressure coordinate system, and encrypt each layer;

[0068] Step S 33 , the step S 1 The terrain and land use data in step S2 The processed initial and boundary condition meteorological data are interpolated to the step S 31 and the step S 32 In the generated grid, the data format is converted to form multiple computational domains.

[0069] Preferably, the step S 33 The step S 1 The terrain and land use data in step S 2 The processed initial and boundary condition meteorological data are interpolated to the step S 31 and the step S 32 The generated grid is mainly mapped to the model grid through interpolation methods (such as bilinear interpolation), and then the data format is converted to form multiple calculation domains for subsequent calculations.

[0070] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 31 middle:

[0071] The horizontal grid of the computational domain adopts Arakawa-C grid, and multiple 3-4 layers of one-way nested grid computational domains are superimposed. The horizontal grid resolution of each computational domain ranges from 200 meters to 15 kilometers from the wind farm area to the surrounding area covered; wherein the horizontal grid resolution in the wind farm area is less than 5 times the diameter of the wind rotor;

[0072] Different computational domains are used to adjust the nested definition domains of wind farms in different regions; the processing calculation formula for the final simulation results of the computational domain superposition area is as follows:

[0073]

[0074] Where V is the final simulated meteorological element result (e.g. wind speed); n is the number of overlapping computational domains in the same grid area; It is the simulation result of the meteorological element (for example, wind speed) at the zth altitude layer in the i-th row, j-th column and the n-th computational domain.

[0075] It should be noted that the horizontal grid of the computational domain adopts the Arakawa-C grid. In order to consider more mesoscale meteorological conditions, multiple 3-4 layers of one-way nested grids (i.e., D01 to D03 or D04) are used to superimpose the computational domains. The horizontal grid resolution of each computational domain ranges from 200 meters to 15 kilometers from the wind farm area to the large area covering the surrounding area; among them, the horizontal grid resolution in the wind farm area should be less than 5 times the diameter of the wind rotor. Different computational domains are nested and defined according to different areas of concern for the wind farm. The superposition of multiple computational domains can encrypt the key positions of the simulation target area and improve the simulation accuracy.

[0076] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 32 middle:

[0077] Using the terrain data in the entire computational domain, the computational space is divided into several vertical layers through a mass-based mixed pressure coordinate system. The calculation formula is:

[0078]

[0079] Where p is the air pressure, p t is the top pressure, p s is the surface pressure, η is the atmospheric vertical coordinate system;

[0080] The atmospheric boundary layer of the vertical layer is encrypted below 1 km by at least 20 layers.

[0081] It should be noted that the calculation space is divided into several vertical layers through a mass-based mixed pressure coordinate system using the terrain data in the entire calculation domain. After vertical stratification, the intervals between the layers are not uniform, and the overall distribution features are dense in the lower layer and sparse in the upper layer, that is, the layer distribution close to the ground is relatively dense.

[0082] Usually in practical applications, the number of layers is set to 25 to 40. Considering that wind turbines are mainly located in the near-ground area of ​​the model, the vertical layer of the model focuses on the encryption of the lower layer of the model. Therefore, at least 20 layers are encrypted in the atmospheric boundary layer of the model (below 1 km), especially in the range of the wind turbine blades.

[0083] Compared with the traditional mesoscale meteorological model height layer setting, the number of layers set in the present invention is much larger than the number of traditional boundary layer model layers below 1 km in the boundary layer. Its purpose is to better simulate the impact of wind turbines on atmospheric circulation and improve the simulation accuracy by increasing the number of vertical layers of the model within the blade range.

[0084] As described above, the macro wake effect assessment method for large-scale wind farms of the present invention superimposes multiple computational domains on the simulation target area, encrypts the simulation grid (including horizontal layers and vertical space layers) in the key focus area, and improves the accuracy of the simulation results.

[0085] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 4 The steps include:

[0086] Step S 41 Based on the step S 3 The generated computational domain sets the integration time step of each layer of grids and the parameterization scheme of the physical process. By continuously updating and solving the physical equations, the time integration is performed from the start time to the end time of the evaluation period to obtain the simulation results of the target time period of the computational domain.

[0087] Step S 42 , adopt the step S 31 The overlapping grid results are processed by the method to obtain the natural wind resource situation in the target wind farm area when there is no wind turbine; the natural wind resource situation in the target wind farm area when there is no wind turbine includes the simulated wind speed in the target area within the target time period.

[0088] Preferably, the step S 41 Based on the step S 3 The generated multiple computational domains set the integration time step and physical process parameterization scheme of each layer of grid in the mesoscale meteorological model. For example, a computational domain M with three layers of nested grids n (i.e. D01 to D03), n is the number of computational domains. Generally, the integration time step of each layer of grid can be set to 90 seconds, 30 seconds and 10 seconds from the outside to the inside; by continuously updating and solving the physical equations, time integration is performed from the start time to the end time of the evaluation period.

[0089] Preferably, the physical processes in the mesoscale meteorological model used in the present invention adopt a CONUS parameterization scheme combination suitable for simulation in mid-latitude regions.

[0090] In order to increase the accuracy of the simulation results, the present invention uses the observed data to optimize the physical process parameterization scheme. The specific execution steps are shown in step S. 61 and S 62 .

[0091] Preferably, after obtaining the simulation results of the target time periods of the multiple computing domains, the step S 42 Using the step S 31 The overlapping grid results are processed by the method to obtain the final natural wind resource situation in the target wind farm area without wind turbines.

[0092] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 5 The fan model described in the embodiment adopts the Fitch model, and the step S 5 The steps include:

[0093] Step S 51 Based on step S 3 The generated computational domain adds step S in the target area 1 Based on the wind turbine data in the calculation, a mesoscale meteorological model coupled with a Fitch model is used to simulate the wake effect and power output characteristics of the wind turbine, and the simulation results of the target time period in the calculation domain are obtained;

[0094] Step S 52 , adopt the step S 31 The method is used to process the overlapping grid results, and obtain the wind resource situation of the target area after the wind farm is built; the wind resource situation of the target area after the wind farm is built includes the simulated wind speed in the target area within the target time period;

[0095] Step S 53 , by determining the wake effect length and quantifying the power generation loss, the macroscopic wake effect is evaluated.

[0096] It should be noted that step S 51 The Fitch model was proposed by the Swedish Meteorological and Hydrological Institute. Based on the law of conservation of energy, it simplifies each wind turbine in a wind farm into a kinetic energy point sink and a turbulence point source. The kinetic energy point sink represents the conversion of part of the wind kinetic energy obtained by the wind turbine from the atmosphere into electrical energy, while the turbulence point source represents the conversion of the remaining kinetic energy into turbulent kinetic energy.

[0097] Preferably, step S 51 Finally, the simulation results of target time periods in multiple computational domains are obtained.

[0098] Preferably, the physical process of the mesoscale meteorological model coupled with the wind turbine model adopted in the present invention can adopt a combination of the CONUS parameterization scheme suitable for simulation in mid-latitude regions and the MYNN-2.5 planetary boundary layer parameterization scheme that can accurately describe the impact of wind farms on the atmospheric boundary layer.

[0099] In order to increase the accuracy of the simulation results, the present invention uses the observed data to optimize the physical process parameterization scheme. The specific execution steps are shown in step S. 61 and S 62 .

[0100] It should be specifically noted that after obtaining the simulation results of the target time period in the computational domain, step S 52 Using the step S31 The overlapping grid results are processed by the method to obtain the final wind resource situation after the wind farm in the target area is built.

[0101] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 53 The length of the medium wake effect is determined by the wind speed loss ratio, and the wind speed loss ratio formula is:

[0102]

[0103] Among them, v with out fitch For step S 42 The simulated wind speed in the target area and target time period obtained in with fitch For step S 52 The simulated wind speed in the target area and target time period obtained in .

[0104] It should be noted that the wake effect length represents the distance from the farthest end downwind of the wind farm to the wind speed recovery point. Therefore, the position of the wind speed recovery point is determined by the wind speed loss ratio. When the wind speed loss ratio is less than 5%, it should be judged that the wind speed has recovered.

[0105] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 53 The method for quantifying power generation loss in China includes the following steps:

[0106] Step S 531 , the total power generation of the wind farm is calculated by the following formula:

[0107]

[0108] Where P is the power generated per unit time (watt, W); ρ is the air density; A is the swept area of ​​the wind wheel (square meters, m 2 ); v is the wind speed (meters per second, m / s); C p is the power factor, which indicates the efficiency of converting wind energy into electrical energy, and usually ranges from 0 to 0.5;

[0109] Step S 532 , multiply the power generation per unit time by time to calculate the total power generation of the wind farm within a certain period of time. The calculation formula is:

[0110] E=P·t

[0111] Where E is the total power generation; P is the power generation per unit time; t is the time;

[0112] Step S 533, calculate the power generation loss, the power generation loss calculation formula is:

[0113]

[0114] Among them, η loss is the power generation loss; E with out fitch Use step S 42 The power generation calculated by simulating wind speed in the target area and target time period finally obtained; E with fitch Use step S 52 The generated power is calculated from the simulated wind speed in the target area and target time period finally obtained.

[0115] When η loss When <0.9, it is advisable to consider increasing the distance between wind farms to reduce wake losses.

[0116] As a preferred embodiment of the method for evaluating the macroscopic wake effect of a large-scale wind farm according to the present invention, the step S 6 The evaluation results of the macro wake effect are tested by using the average deviation of the simulated wind speed loss results and the observed data, and the root mean square error as evaluation indicators. 6 The steps include:

[0117] Step S 61 , calculate the average deviation and root mean square error between the simulated wind speed loss results and the observed data;

[0118] The calculation formula for the average deviation between the simulated wind speed loss results and the observed data is:

[0119]

[0120] Where MBE is the average deviation between the simulated wind speed loss result and the observed data; S i is the statistical sample of the ith simulation; O i is the statistical sample of the i-th observation;

[0121] The root mean square error characterizes the accuracy of the simulation results and the calculation formula is:

[0122]

[0123] Among them, RMSE is the root mean square error; S i is the statistical sample of the ith simulation; O i is the statistical sample of the i-th observation;

[0124] Step S 62 , according to the evaluation index, the step S 4 and the step S5 The parameters of the mesoscale meteorological model and wind turbine model are tuned to improve the parameterized configuration of the mesoscale meteorological model and wind turbine model.

[0125] It should be noted that in order to quantitatively evaluate the accuracy of the model's assessment of the macroscopic wake effect of the field, evaluation indicators such as the mean bias error (MBE) and root mean square error (RMSE) of the simulated wind speed loss results and the observed data are used. Among them, the mean bias error of the simulated wind speed loss results and the observed data can reflect the overall deviation between the simulated results and the observed results, and the root mean square error is used to characterize the accuracy of the simulation results, which is usually a positive value.

[0126] It should be noted that the observation data used to verify the results adopts the measured data of multiple wind towers in the wind farm, and the average wind speed loss ratio is calculated by using the difference in average wind speed in the target time period before and after the wind farm in the target area is built. At the same time, the method of the present invention takes into account the influence of the annual change component of the climatic conditions of the wind power base on the wind speed change. When using the observation data to calculate the average wind speed loss ratio, the reanalysis data is used to exclude the influence of the long-term natural change of regional wind resources, thereby improving the accuracy of the observation data.

[0127] Since the macro wake effect assessment method for large-scale wind farms of the present invention takes into account the influence of annual changes in regional wind resources, the deviation existing in the test data of the traditional wind tower observation data used as the test data for macro wake simulation results is adjusted, and the accuracy of the observation data used for testing is improved.

[0128] The macroscopic wake effect evaluation method of the large-scale wind farm of the present invention is applied to a large-scale wind farm in Shanxi (the installed capacity is usually 1 million kilowatts or more), and the wind speed change before and after the wind farm is connected to the grid is obtained. The average wake wind speed loss ratio before and after the wind farm is connected to the grid is 5.64%, and the average wake wind speed loss ratio before and after the actual grid connection is 4.82%, which is basically consistent with the simulation results.

[0129] In summary, the macroscopic wake effect evaluation method for large-scale wind farms of the present invention is suitable for mesoscale range simulations such as large-scale wind farms or multiple wind farms, and can perform mesoscale calculations on wind resource conditions. Compared with traditional wake effect evaluation methods and wake models, it solves the problem of incorrect evaluation of wind farm power generation capacity due to lack of understanding of mesoscale meteorological conditions, increases calculation efficiency, and reduces calculation resource investment.

[0130] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these are only examples, and the protection scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A method for evaluating the macroscopic wake effect of a large-scale wind farm, characterized in that: The evaluation method comprises the following steps: S1. Obtain meteorological reanalysis data, topographic and land use data, and wind turbine data; S2, preprocess the acquired data, correct the meteorological reanalysis data, and obtain a more accurate meteorological data set of initial and boundary conditions in the simulation area; S3, define the model domain and generate the computational grid; encrypt the spatial layer; interpolate the data into the computational grid to construct the computational domain; S4, using a mesoscale meteorological model to simulate the natural wind resource situation according to the calculation domain; S5. Using a mesoscale meteorological model coupled with a wind turbine model to simulate the macro wake effect according to the calculation domain, and evaluate the macro wake effect; S6. Verify the evaluation results of the macroscopic wake effect and improve the parameterization configuration of the mesoscale meteorological model and the wind turbine model.

2. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 1, characterized in that: In step S1: The meteorological reanalysis data are initial and boundary condition data of a simulation time period obtained through a global meteorological reanalysis data set; The terrain and land use data are high-resolution terrain data; the terrain and land use data are used to define the terrain characteristics of the model domain; The wind turbine data includes the position coordinates of each wind turbine unit in the target wind farm, the wind turbine model, the wind turbine hub height, the wind turbine rated power, the wind turbine thrust coefficient, the wind turbine impeller diameter and the generated power.

3. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 1, characterized in that: The step S2 comprises the following steps: S 21 , correcting the meteorological reanalysis data used as initial and boundary conditions, using the wind tower data to perform error correction on the meteorological reanalysis data, and obtaining a more accurate meteorological data set of initial and boundary conditions for the simulation area; S 22 , perform spatial statistical downscaling on the obtained meteorological data set to improve the spatial resolution.

4. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 3, characterized in that: The step S 21 The wind profile fitting formula Perform system error correction; Among them, V Zr is the observation data from the wind tower, V Z To reanalyze the data; Where Z is the surface roughness, α is the wind shear index, and Z r is the altitude; The error correction of reanalysis data is aimed at Perform: Select a historical year as the year for establishing the revised model, and calculate based on the time period of the revised model establishment year Then use the calculated The reanalysis data of the simulation year are corrected to obtain a more accurate meteorological dataset of initial and boundary conditions in the simulation area.

5. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 3, characterized in that: The step S 22 The steps include: S 221 , use bilinear interpolation to interpolate low-resolution meteorological data sets to make them correspond to the spatial points of high-resolution data and convert the grid field into a discrete field; S 222 Based on low-resolution meteorological data, after standardizing the data, the empirical orthogonal function analysis method is used to reduce the dimension and filter the large-scale prediction factors. The empirical orthogonal function used is: P t =PC t ×EOF t +N t Among them, P t The numerical matrix representing the factor field of low-resolution meteorological data at time t; EOF t represents the spatial modal matrix of this factor field; PC t Represents EOF t The coefficient matrix that varies with time; N t represents the noise matrix; Select PC t as factors entering the stepwise regression model; S 223 , based on the measured data sets of multiple observation sites and through the step S 222 The factors of the stepwise regression model are screened out, and the stepwise regression model is established. The relationship can be expressed as: Among them, A and b are the regression coefficients determined in the sliding training; PC t is the regression model factor; is the measured data set of the nth observation station; S 224 , for each grid point of the interpolated meteorological data set, follow the steps S moment by moment 223 The established stepwise regression model is used for regression correction to obtain meteorological data with high spatial resolution.

6. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 3, characterized in that: The step S3 comprises the following steps: S 31 , determine the spatial range and resolution of wake simulation, select the number and configuration of nested models, and generate the computational grid; S 32 , perform spatial layer encryption, divide the calculation space into several vertical layers through the mass-based mixed pressure coordinate system, and encrypt each layer; S 33 , interpolating the terrain and land use data in step S1 and the initial and boundary condition meteorological data processed in step S2 to the data in step S 31 and the step S 32 In the generated grid, the data format is converted to form multiple computational domains.

7. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 6, characterized in that: The step S 31 middle: The horizontal grid of the computational domain adopts Arakawa-C grid, and multiple 3-4 layers of one-way nested grid computational domains are superimposed. The horizontal grid resolution of each computational domain ranges from 200 meters to 15 kilometers from the wind farm area to the surrounding area covered; wherein the horizontal grid resolution in the wind farm area is less than 5 times the diameter of the wind rotor; Different computational domains are used to adjust the nested definition domains of wind farms in different regions; the processing calculation formula for the final simulation results of the computational domain superposition area is as follows: Where V is the final simulated meteorological element result; n is the number of overlapping computational domains in the same grid area; It is the simulation result of meteorological elements at the zth altitude layer in the i-th row, j-th column and the n-th computational domain.

8. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 7, characterized in that: The step S 32 middle: Using the terrain data in the entire computational domain, the computational space is divided into several vertical layers through a mass-based mixed pressure coordinate system. The calculation formula is: Where p is the air pressure, p t is the top pressure, p s is the surface pressure, η is the atmospheric vertical coordinate system; The atmospheric boundary layer of the vertical layer is encrypted below 1 km by at least 20 layers.

9. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 7, characterized in that: The step S4 comprises the following steps: S 41 , based on the computational domain generated in step S3, setting the integration time step of each layer of grids and the parameterization scheme of the physical process, performing time integration from the start time to the end time of the evaluation period by continuously updating and solving the physical equations, and obtaining the simulation result of the target time period of the computational domain; S 42 , adopt the step S 31 The overlapping grid results are processed by the method to obtain the natural wind resource situation in the target wind farm area when there is no wind turbine; the natural wind resource situation in the target wind farm area when there is no wind turbine includes the simulated wind speed in the target area within the target time period.

10. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 9, characterized in that: The wind turbine model in step S5 adopts the Fitch model, and step S5 includes the following steps: S 51 Based on the calculation domain generated in step S3, the wind turbine data in step S1 is added in the target area, and the wake effect and power output characteristics of the wind turbine are simulated by coupling the mesoscale meteorological model with the Fitch model to obtain the simulation results of the target time period of the calculation domain; S 52 , adopt the step S 31 The method is used to process the overlapping grid results, and obtain the wind resource situation of the target area after the wind farm is built; the wind resource situation of the target area after the wind farm is built includes the simulated wind speed in the target area within the target time period; S 53 , by determining the wake effect length and quantifying the power generation loss, the macroscopic wake effect is evaluated.

11. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 10, characterized in that: The step S 53 The length of the medium wake effect is determined by the wind speed loss ratio, and the wind speed loss ratio formula is: Among them, v withoutfitch For step S 42 The simulated wind speed in the target area and target time period obtained in withfitch For step S 52 The simulated wind speed in the target area and target time period obtained in .

12. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 10, characterized in that: The step S 53 The method for quantifying power generation loss in China includes the following steps: S 531 , the total power generation of the wind farm is calculated by the following formula: Where P is the power generated per unit time; ρ is the air density; A is the swept area of ​​the wind wheel; v is the wind speed; C p is the power factor, which indicates the efficiency of converting wind energy into electrical energy; S 532 , multiply the power generation per unit time by time to calculate the total power generation of the wind farm within a certain period of time. The calculation formula is: E=P·t Where E is the total power generation; P is the power generation per unit time; t is the time; S 533 , calculate the power generation loss, the power generation loss calculation formula is: Among them, η loss is the power generation loss, E withoutfitch Use step S 42 The power generation calculated by simulating wind speed in the target area and target time period finally obtained; E withfitch Use step S 52 The generated power is calculated from the simulated wind speed in the target area and target time period finally obtained.

13. The method for evaluating the macroscopic wake effect of a large-scale wind farm according to claim 1, characterized in that: The evaluation result of checking the macro wake effect in step S6 uses the average deviation between the simulated wind speed loss result and the observed data, and the root mean square error as evaluation indicators. Step S6 includes the following steps: S 61 , calculate the average deviation and root mean square error between the simulated wind speed loss results and the observed data; The calculation formula for the average deviation between the simulated wind speed loss results and the observed data is: Where MBE is the average deviation between the simulated wind speed loss result and the observed data; S i , O i are the statistical samples of the i-th simulation and observation respectively; The root mean square error characterizes the accuracy of the simulation results and the calculation formula is: Among them, RMSE is the root mean square error; S i is the statistical sample of the ith simulation; O i is the statistical sample of the i-th observation; S 62 , according to the average deviation and root mean square error between the calculated simulated wind speed loss results and the observed data, the parameters of the mesoscale meteorological model and the wind turbine model in the step S4 and the step S5 are tuned to improve the parameterized configuration of the mesoscale meteorological model and the wind turbine model.

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