A method and system for rapid downscaling of wind power prediction

Abstract

The present invention provides a method and system for rapid downscaling of wind power prediction. The method includes: performing directional calculations on a micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain the wind farm-wide wind acceleration factor and wind direction deviation angle, which are used as key parameters of the wind power prediction basic database to generate a gridded file; establishing an association between the meteorological forecast data and the three-dimensional spatial wind flow field in the micro-scale model based on the average state in the area represented by its resolution; according to different wind directions and different atmospheric stabilities in the meteorological forecast results, linearly interpolating the wind direction and thermal stability in the wind power prediction basic database by using the gridded file; and calculating the wind power prediction result according to the linear interpolation result. The present invention can reflect the influence of meteorological parameters such as different atmospheric thermal stabilities and wind directions on wind power by establishing a wind power prediction basic database, and at the same time, can perform rapid wind power prediction through interpolation, so as to meet the needs of operational operation.

Description

Technical Field

[0001] The present invention belongs to the field of wind farm meteorology, and particularly relates to a method and a system for rapid dynamic downscaling of wind power prediction. Background Art

[0002] Through the retrieval of databases such as domestic and foreign papers, academic conferences, scientific and technological literature, patents, etc., it is found that:

[0003] Denmark The National Laboratory developed the Prediktor wind power prediction system based on numerical weather prediction + WAsP + power curve, which began to be applied in the east of Denmark in 1993. In 1994 The laboratory jointly developed with the Technical University of Denmark the Zephyr wind power prediction system combining Prediktor and WPPT, which was applied nationwide in Denmark and promoted to countries and regions such as Spain, Ireland, the United States, and Japan. The SIPREOLICO wind power prediction system of the Spanish grid company uses numerical forecast products of the Spanish Meteorological Agency and numerical weather prediction products of the European Centre for Medium-Range Weather Forecasts. Through 8 wind power prediction models, ensemble forecasting of wind power is carried out, and then combined with wind power prediction products of the Dutch AEOLIS Forecast Service Company, the Spanish Institute of Engineering and Technology (IIC), and the Spanish METEOLOGICA professional wind energy forecasting and wind power prediction company, and finally the wind power prediction for the next 48 hours and 10 days is obtained. The work of wind power prediction in China started relatively late. In November 2008, the China Electric Power Research Institute developed the first set of wind power prediction system WPFS with independent intellectual property rights in China. At present, the China Electric Power Research Institute and the National Center for Atmospheric Research (NCAR) in the United States have developed a power numerical weather forecasting system with real-time rapid update assimilation and ensemble forecasting technology, providing basic numerical weather forecasting for wind farm wind power prediction.

[0004] There are mainly two types of wind power prediction methods commonly used in China. One is the statistical analysis prediction method based on the historical time series of wind power for short-term wind power prediction; the other is to adopt the combination of mesoscale weather forecasting models and wind farm measured data, and simulate the neural network technology as a black box for wind power forecasting. Since it is impossible to consider the influence of meteorological parameters such as atmospheric thermal stability and wind direction on wind power at the same time. Especially for complex terrain and coastal wind farms in China, due to the strong vertical movement of the near-surface atmosphere, the thermal stability shows stable and unstable states in addition to neutral, which has a great impact on the wind profile, wind shear index, and wind speed distribution, resulting in poor wind power prediction accuracy in China. Therefore, the mature foreign systems cannot be directly applied to wind farms in China, so a rapid dynamic downscaling method for wind power prediction suitable for the characteristics of wind farms in China is needed.

[0005] Disadvantages of the prior art: In the process of wind power prediction, the meteorological forecast results are directly downscaled to the fan positions through model calculation and then the wind-to-wind power conversion is carried out, resulting in low calculation efficiency; it is difficult to simultaneously consider the influence of meteorological parameters such as atmospheric thermal stability and wind direction on wind power when simulating the neural network technology as a black box for wind power forecasting. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes a technical solution for a method of rapid downscaling of wind power prediction to solve the above technical problems.

[0007] The first aspect of the present invention discloses a method for rapid downscaling of wind power prediction, the method comprising:

[0008] Step S1: Collect and map the terrain elevation and roughness data of a wind farm and its surrounding area within a certain range, and establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding area;

[0009] Step S2: Perform directional calculations on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain the wind farm full-field wind acceleration factor and the wind direction deviation angle;

[0010] Step S3: Use the wind farm full-field wind acceleration factor and the wind direction deviation angle as key parameters of the wind power prediction basic database to generate a grid file;

[0011] Step S4: Establish an association between the meteorological forecast data and the three-dimensional space wind flow field in the micro-scale wind flow field model based on the average state of the area represented by its resolution, and perform extrapolation simulation;

[0012] Step S5: Through the association, perform linear interpolation on the wind direction and thermal stability in the wind power prediction basic database according to different wind directions and different atmospheric stabilities in the meteorological forecast results by using the grid file;

[0013] Step S6: Obtain the wind farm full-field wind acceleration factor and the wind direction deviation angle corresponding to the wind direction and atmospheric stability state in the meteorological forecast data at each forecast time point according to the ratio of the linear interpolation;

[0014] Step S7: Calculate the wind power prediction result through the wind speed and wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and the wind direction deviation angle at the corresponding time.

[0015] According to the method of the first aspect of the present invention, in the step S1, the method for establishing a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding area includes:

[0016] Based on the steady, adiabatic, and incompressible Reynolds-averaged Navier-Stokes equations, a micro-scale wind flow field model is established that can characterize the local effects of a wind farm and its surrounding areas.

[0017] According to the method of the first aspect of the present invention, in the step S2, the calculation method of the full-field wind acceleration factor Δk of the wind farm includes:

[0018]

[0019] where U(z) represents the wind speed at a height z above the mountain ground, and U 0 (z) represents the wind speed at a height z above the flat ground.

[0020] According to the method of the first aspect of the present invention, in the step S4, the method further includes:

[0021] Select the height position where the meteorological forecast data is coupled with the micro-scale wind flow field model.

[0022] According to the method of the first aspect of the present invention, in the step S7, the method for calculating the wind power prediction result by using the wind speed, wind direction information in the meteorological forecast data, the full-field wind acceleration factor of the wind farm at the corresponding time, and the wind direction deviation angle includes:

[0023] Calculate the downscaling prediction result of the wind parameters of the wind farm by using the wind speed, wind direction information in the meteorological forecast data, the full-field wind acceleration factor of the wind farm at the corresponding time, and the wind direction deviation angle;

[0024] According to the downscaling prediction result, combined with the coordinates and power curve of the wind turbine positions in the wind farm, obtain the wind power prediction result of each wind turbine.

[0025] According to the method of the first aspect of the present invention, in the step S7, the calculation relationship between the wind power and the wind speed at the corresponding moment is:

[0026]

[0027] where P(V) is the wind power, V is the wind speed, V 切入 is the cut-in wind speed of the wind turbine, V 额定 is the rated wind speed of the wind turbine, V 切出 is the cut-out wind speed of the wind turbine, S(V) is the wind power output, P max is the maximum power output.

[0028] According to the method of the first aspect of the present invention, in the step S4, the specific method of association includes: after downscaling the meteorological forecast data, based on the wind speed, wind direction, and other relevant meteorological parameters in the area represented by its resolution, as the input conditions of the three-dimensional spatial wind flow field in the micro-scale wind flow field model, extrapolation simulation is performed.

[0029] The second aspect of the present invention discloses a system for rapid downscaling of wind power prediction, and the system includes:

[0030] A first processing module, configured to collect and map the terrain elevation and roughness data of a wind farm and a certain area around it, and establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas;

[0031] A second processing module, configured to perform directional calculations on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain the wind acceleration factor and wind direction deviation angle of the entire wind farm;

[0032] A third processing module, configured to use the wind acceleration factor and wind direction deviation angle of the entire wind farm as key parameters of the wind power prediction basic database to generate a grid file;

[0033] A fourth processing module, configured to establish an association between the average state in the area represented by the resolution of the meteorological forecast data and the three-dimensional spatial wind flow field in the micro-scale model, and perform extrapolation simulation;

[0034] A fifth processing module, configured to perform linear interpolation on the wind direction and thermal stability in the wind power prediction basic database by using the grid file according to different wind directions and different atmospheric stabilities in the meteorological forecast results through the association;

[0035] A sixth processing module, configured to obtain the wind acceleration factor and wind direction deviation angle of the entire wind farm corresponding to the wind direction and atmospheric stability state in the meteorological forecast data at each forecast time point according to the ratio of the linear interpolation;

[0036] A seventh processing module, configured to calculate the wind power prediction result through the wind speed and wind direction information in the meteorological forecast data and the wind acceleration factor and wind direction deviation angle of the entire wind farm at the corresponding time.

[0037] According to the system of the second aspect of the present invention, the first processing module is specifically configured to, the establishment of the micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas includes:

[0038] Based on the steady, adiabatic, and incompressible Reynolds-averaged Navier-Stokes equations, establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas.

[0039] For the system according to the second aspect of the present invention, the second processing module is specifically configured such that the calculation of the wind farm full-field wind acceleration factor Δk includes:

[0040]

[0041] where U(z) represents the wind speed at a height z above the mountain ground, and U 0 (z) represents the wind speed at a height z above the flat ground.

[0042] For the system according to the second aspect of the present invention, the fourth processing module is specifically configured to:

[0043] Select the height position where the meteorological forecast data is coupled with the micro-scale wind flow field model.

[0044] For the system according to the second aspect of the present invention, the seventh processing module is specifically configured such that the calculation of the wind power prediction result by using the wind speed, wind direction information in the meteorological forecast data, and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time includes:

[0045] Calculate the downscaling prediction result of the wind farm wind parameters by using the wind speed, wind direction information in the meteorological forecast data, and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time;

[0046] According to the downscaling prediction result, and in combination with the coordinates and power curve of the wind farm turbine positions, obtain the wind power prediction result of each wind turbine.

[0047] For the system according to the second aspect of the present invention, the seventh processing module is specifically configured such that the calculation relationship between the wind power and the wind speed at the corresponding moment is:

[0048]

[0049] where P(V) is the wind power, V is the wind speed, V 切入 is the cut-in wind speed of the wind turbine, V 额定 is the rated wind speed of the wind turbine, V 切出 is the cut-out wind speed of the wind turbine, S(V) is the wind power output, and P max is the maximum power output.

[0050] For the system according to the second aspect of the present invention, the fourth processing module is specifically configured such that the association includes: After downscaling the meteorological forecast data, use the wind speed, wind direction, and other relevant meteorological parameters on the area represented by its resolution as the input conditions of the three-dimensional space wind flow field in the micro-scale wind flow field model for extrapolation simulation.

[0051] A third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in a method for rapid downscaling of wind power prediction according to any one of the first aspects of the present disclosure are implemented.

[0052] A fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a method for rapid downscaling of wind power prediction according to any one of the first aspects of the present disclosure are implemented.

[0053] The solution proposed by the present invention reflects the influence of meteorological parameters such as different atmospheric thermal stabilities and wind directions on wind power by establishing a basic database for wind power prediction. At the same time, rapid wind power prediction is carried out through interpolation, so as to meet the needs of operational operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 FIG. is a flowchart of a method for rapid downscaling of wind power prediction according to an embodiment of the present invention;

[0056] Figure 2 FIG. is a flowchart of a method for rapid downscaling of wind power prediction according to an embodiment of the present invention;

[0057] Figure 3 FIG. is a structural diagram of a system for rapid downscaling of wind power prediction according to an embodiment of the present invention;

[0058] Figure 4 FIG. is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0060] A first aspect of the present invention discloses a method for rapid downscaling of wind power prediction.Figure 1 As shown in the flowchart of a method for rapid downscaling of wind power prediction according to an embodiment of the present invention, as Figure 1 and Figure 2 shown, the method includes:

[0061] Step S1: Collect and survey the terrain elevation and roughness data of the wind farm and a certain area around it, and establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding area;

[0062] Step S2: Perform directional calculations on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain the wind farm full-field wind acceleration factor and wind direction deviation angle;

[0063] Step S3: Use the wind farm full-field wind acceleration factor and wind direction deviation angle as key parameters of the wind power prediction basic database to generate a grid file;

[0064] Step S4: Establish an association between the meteorological forecast data based on the average state of the area represented by its resolution and the three-dimensional spatial wind flow field in the micro-scale model, and perform extrapolation simulation;

[0065] Step S5: Through the association, according to different wind directions and different atmospheric stabilities in the meteorological forecast results, linearly interpolate the wind direction and thermal stability in the wind power prediction basic database using the grid file;

[0066] Step S6: Obtain the wind farm full-field wind acceleration factor and wind direction deviation angle corresponding to the wind direction and atmospheric stability state in the meteorological forecast data at each forecast time point according to the linear interpolation ratio;

[0067] Step S7: Calculate the wind power prediction result through the wind speed and wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time.

[0068] In step S1, collect and survey the terrain elevation and roughness data of the wind farm and a certain area around it, and establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding area.

[0069] In some embodiments, in the step S1, the method for establishing a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding area includes:

[0070] Based on the steady, adiabatic, and incompressible Reynolds-averaged Navier-Stokes equations, establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding area.

[0071] Among them, the Reynolds-averaged Navier-Stokes equations:

[0072]

[0073]

[0074] Wherein, u is the fluid velocity, p is the fluid pressure, ρ is the fluid density, μ is the dynamic viscosity of the fluid, and F i is other acting forces. is taken as the turbulent flux in the equation, also called the Reynolds stress term. The Reynolds stress term appearing in the momentum equation needs to be simulated:

[0075]

[0076] v T = k 1 / 2 L T

[0077]

[0078]

[0079] Wherein, v T is the turbulent viscosity. L T is the length scale, k is the Karman constant, z is the surface height, and S m is solved based on the flux Richardson number Rif, and the solution of Rif is directly determined by the thermal stability. Rif takes a positive value when the atmospheric stability is in a stable state, a negative value when it is unstable, and 0 when it is in a neutral state.

[0080] Specifically, the meteorological forecast results of a wind farm in Shanxi are selected for wind power prediction. First, the input meteorological forecast data is parsed to obtain a resolution of 9 kilometers.

[0081] A micro-scale wind flow field model of the wind farm area is established using fluid mechanics simulation software. The 100-meter precision terrain file and 300-meter precision roughness file of the wind farm surveyed by itself are selected to reflect the terrain and geomorphic characteristics of the wind farm area.

[0082] In step S2, the micro-scale wind flow field model is directionally calculated according to different wind directions and different thermal stabilities to obtain the wind farm full-field wind acceleration factor and wind direction deviation angle.

[0083] Specifically, the directional calculation results of 10° sector steps and 10 thermal stabilities in the wind farm area, a total of 360 models with a 100-meter resolution, are calculated to describe the input-output characteristics of the micro-scale model under different conditions.

[0084] In step S3, the wind farm full-field wind acceleration factor and wind direction deviation angle are used as the key parameters of the wind power prediction basic database to generate a grid file.

[0085] In some embodiments, in the step S3, the acceleration effect of the mountain on the wind flow is usually quantitatively described by the wind acceleration factor. The calculation method of the wind farm-wide wind acceleration factor Δk includes:

[0086]

[0087] where U(z) represents the wind speed at a height z above the mountain ground, and U 0 (z) represents the wind speed at a height z above the flat ground.

[0088] Specifically, the 360 wind farm-wide wind acceleration factors and wind direction deflection angles calculated by orientation are used as key parameters of the wind power prediction basic database, and a gridded txt file is generated, as shown in Table 1, as the interpolation basis for subsequent wind power prediction.

[0089] Table 1

[0090]

[0091] In step S4, the meteorological forecast data is associated with the three-dimensional spatial wind flow field in the microscale model based on the average state in the area represented by its resolution, and extrapolation simulation is carried out to improve the accuracy and reduce the uncertainty.

[0092] In some embodiments, in the step S4, the height position where the meteorological forecast data is coupled with the microscale wind flow field model is selected to ensure that this position is in the high-altitude range, can provide effective meteorological forecast data, and can reflect the contribution of near-surface parameters to the microscale model.

[0093] The specific method of association includes: After downscaling the meteorological forecast data, the wind speed, wind direction, and other relevant meteorological parameters in the area represented by its resolution are used as the input conditions for the three-dimensional spatial wind flow field in the microscale wind flow field model, and extrapolation simulation is carried out.

[0094] Specifically, based on the average state in the 9-kilometer area in the meteorological forecast result, a meteorological forecast input unit is established, associated with the three-dimensional spatial wind flow field in the microscale model, and extrapolation simulation settings are carried out; the height position where the meteorological forecast data is coupled with the microscale model is selected to be 400 meters.

[0095] In step S5, through the association, according to different wind directions and different atmospheric stabilities in the meteorological forecast result, linear interpolation is performed on the wind direction and thermal stability in the wind power prediction basic database using the gridded file.

[0096] Specifically, the specific formula can refer to the inverse distance weighted interpolation formula:

[0097]

[0098] where z i is the attribute value of the discrete point, representing the distance from the sampling point (x i , y i ) to the interpolation point (x, y).

[0099] In step S6, according to the linear interpolation ratio, the wind farm full-field wind acceleration factor and wind direction deviation angle corresponding to the wind direction and atmospheric stability state at each forecast time point are obtained from the meteorological forecast data.

[0100] In step S7, the wind power prediction result is calculated based on the wind speed and wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time.

[0101] In some embodiments, in step S7, the method for calculating the wind power prediction result by using the wind speed and wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time includes:

[0102] Calculating the downscaling prediction result of the wind farm wind parameters by using the wind speed and wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time;

[0103] According to the downscaling prediction result, combined with the coordinates and power curves of the wind turbine positions in the wind farm, the wind power prediction result of each wind turbine is obtained.

[0104] The calculation relationship between wind power and the wind speed at the corresponding moment is,

[0105]

[0106] where P(V) is the wind power, V is the wind speed, V 切入 is the cut-in wind speed of the wind turbine, V 额定 is the rated wind speed of the wind turbine, V 切出 is the cut-out wind speed of the wind turbine, S(V) is the wind power output, and P max is the maximum power output.

[0107] In summary, the solution proposed by the present invention can reflect the influence of meteorological parameters such as different atmospheric thermal stabilities and wind directions on wind power by establishing a wind power prediction basic database, and at the same time, perform rapid wind power prediction through interpolation, so as to meet the needs of operational operation.

[0108] The second aspect of the present invention discloses a system for rapid downscaling of wind power prediction. Figure 3 For the structural diagram of a system for rapid downscaling of wind power prediction according to an embodiment of the present invention; as Figure 3 shown, the system 100 includes:

[0109] The first processing module 101 is configured to collect and survey the terrain elevation and roughness data of a certain area range around the wind farm and its vicinity, and establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas;

[0110] The second processing module 102 is configured to perform directional calculations on the micro-scale wind flow field model according to different wind directions and different thermal stabilities, and obtain the wind farm full-field wind acceleration factor and wind direction deflection angle;

[0111] The third processing module 103 is configured to generate a grid file using the wind farm full-field wind acceleration factor and wind direction deflection angle as the key parameters of the wind power prediction basic database;

[0112] The fourth processing module 104 is configured to establish an association between the meteorological forecast data and the three-dimensional spatial wind flow field in the micro-scale model based on the average state of the area represented by its resolution, and perform extrapolation simulation;

[0113] The fifth processing module 105 is configured to perform linear interpolation on the wind direction and thermal stability in the wind power prediction basic database by using the grid file according to different wind directions and different atmospheric stabilities in the meteorological forecast results through the association;

[0114] The sixth processing module 106 is configured to obtain the wind farm full-field wind acceleration factor and wind direction deflection angle corresponding to the wind direction and atmospheric stability state in the meteorological forecast data at each forecast time point according to the linear interpolation ratio;

[0115] The seventh processing module 107 is configured to calculate the wind power prediction result through the wind speed and wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deflection angle at the corresponding time.

[0116] For the system according to the second aspect of the present invention, the first processing module 101 is specifically configured that the establishment of the micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas includes:

[0117] Based on the steady, adiabatic and incompressible Reynolds-averaged Navier-Stokes equations, establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas.

[0118] For the system according to the second aspect of the present invention, the second processing module 102 is specifically configured that the calculation of the wind farm full-field wind acceleration factor Δk includes:

[0119]

[0120] where U(z) represents the wind speed at a height z above the mountain ground, U0 (z) represents the wind speed at a height z above the flat ground surface.

[0121] For the system according to the second aspect of the present invention, the fourth processing module 104 is specifically configured to:

[0122] Select the height position where the meteorological forecast data is coupled with the microscale wind flow field model.

[0123] For the system according to the second aspect of the present invention, the seventh processing module 107 is specifically configured to, the calculation of the wind power prediction result by using the wind speed, wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time includes:

[0124] Calculate the downscaling prediction result of the wind farm wind parameters by using the wind speed, wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time;

[0125] According to the downscaling prediction result, combined with the coordinates and power curve of the wind turbine site in the wind farm, obtain the wind power prediction result of each wind turbine.

[0126] For the system according to the second aspect of the present invention, the seventh processing module 107 is specifically configured to, the calculation relationship between the wind power and the wind speed at the corresponding moment is:

[0127]

[0128] Among them, P(V) is the wind power, V is the wind speed, V 切入 is the cut-in wind speed of the wind turbine, V 额定 is the rated wind speed of the wind turbine, V 切出 is the cut-out wind speed of the wind turbine, S(V) is the wind power output, P max is the maximum power output.

[0129] For the system according to the second aspect of the present invention, the fourth processing module 104 is specifically configured to, the association includes: after downscaling the meteorological forecast data, use the wind speed, wind direction and other relevant meteorological parameters on the area represented by its resolution as the input conditions of the three-dimensional space wind flow field in the microscale wind flow field model, and perform extrapolation simulation.

[0130] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. When the processor executes the computer program stored in the memory, the steps in a method for fast downscaling of wind power prediction according to any one of the first aspects disclosed in the present invention are implemented.

[0131] Figure 4 It is a structural diagram of an electronic device according to an embodiment of the present invention, as Figure 4As shown in the figure, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, near field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0132] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only the structure diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] The fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in the method for fast downscaling of wind power prediction in any one of the first aspects disclosed by the present invention are implemented.

[0134] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as these combinations of technical features do not conflict, they should all be considered to be within the scope described in this specification. The above embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for rapid downscaling of wind power prediction, characterized in that, the method includes: Step S1: Collect and survey the terrain elevation and roughness data of the wind farm and a certain area around it, and establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas; Step S2: Perform directional calculations on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain the wind acceleration factor and wind direction deviation angle of the entire wind farm; Step S3: Use the wind acceleration factor and wind direction deviation angle of the entire wind farm as key parameters of the wind power prediction basic database to generate a grid file; Step S4: Establish an association between the meteorological forecast data based on the average state of the area represented by its resolution and the three-dimensional spatial wind flow field in the micro-scale wind flow field model, and perform extrapolation simulation; Step S5: Through the above association, according to different wind directions and different atmospheric stabilities in the meteorological forecast results, linearly interpolate the wind direction and thermal stability in the wind power prediction basic database using the grid file; Step S6: Obtain the wind acceleration factor and wind direction deviation angle of the entire wind farm corresponding to the wind direction and atmospheric stability state in the meteorological forecast data at each forecast time point according to the ratio of the linear interpolation; Step S7: Calculate the wind power prediction result through the wind speed, wind direction information in the meteorological forecast data and the wind acceleration factor and wind direction deviation angle of the entire wind farm at the corresponding time.

2. A method for rapid downscaling of wind power prediction according to claim 1, characterized in that, in the step S1, the method for establishing a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas includes: Based on the steady, adiabatic and incompressible Reynolds-averaged Navier-Stokes equations, establish a micro-scale wind flow field model that can characterize the local effects of the wind farm and its surrounding areas.

3. A method for rapid downscaling of wind power prediction according to claim 1, characterized in that, in the step S2, the calculation method of the wind acceleration factor Δk of the entire wind farm includes: Among them, U(z) represents the wind speed at a height of z above the mountain ground, and U 0 (z) represents the wind speed at a height of z above the flat ground.

4. A method for rapid downscaling of wind power prediction according to claim 1, characterized in that, in the step S4, the method further includes: Select the height position where the meteorological forecast data is coupled with the micro-scale wind flow field model.

5. A method for rapid downscaling of wind power prediction according to claim 1, characterized in that, in the step S7, the method for calculating the wind power prediction result through the wind speed, wind direction information in the meteorological forecast data and the wind acceleration factor and wind direction deviation angle of the entire wind farm at the corresponding time includes: Calculate the downscaling prediction result of the wind parameters of the wind farm through the wind speed, wind direction information in the meteorological forecast data and the wind acceleration factor and wind direction deviation angle of the entire wind farm at the corresponding time; According to the downscaling prediction result, combined with the coordinates and power curve of the wind turbine location in the wind farm, obtain the wind power prediction result of each wind turbine.

6. A method for rapid downscaling of wind power prediction according to claim 5, characterized in that, in the step S7, the calculation relationship between the wind power and the wind speed at the corresponding moment is: Among them, P(V) is the wind power, V is the wind speed, V 切入 is the cut-in wind speed of the wind turbine, V 额定 is the rated wind speed of the wind turbine, V 切出 is the cut-out wind speed of the wind turbine, S(V) is the wind power output, P max is the maximum power output.

7. A method for rapid downscaling of wind power prediction according to claim 1, characterized in that, in the step S4, the specific method of association includes: after downscaling the meteorological forecast data, based on the wind speed, wind direction and other relevant meteorological parameters in the area represented by its resolution, as the input conditions of the three-dimensional spatial wind flow field in the micro-scale wind flow field model, and performing extrapolation simulation.

8. A system for rapid downscaling of wind power prediction, characterized in that, the system includes: A first processing module, configured to collect and survey the terrain elevation and roughness data of a wind farm and a certain area range around it, and establish a micro-scale wind flow field model capable of characterizing the local effects of the wind farm and its surrounding areas; A second processing module, configured to perform directional calculations on the micro-scale wind flow field model according to different wind directions and different thermal stabilities, and obtain the wind farm full-field wind acceleration factor and wind direction deviation angle; A third processing module, configured to use the wind farm full-field wind acceleration factor and wind direction deviation angle as key parameters of the wind power prediction basic database to generate a grid file; A fourth processing module, configured to establish an association between the average state of the meteorological forecast data in the area represented by its resolution and the three-dimensional spatial wind flow field in the micro-scale model, and perform extrapolation simulation; A fifth processing module, configured to perform linear interpolation on the wind direction and thermal stability in the wind power prediction basic database by using the grid file according to different wind directions and different atmospheric stabilities in the meteorological forecast results through the association; A sixth processing module, configured to obtain the wind farm full-field wind acceleration factor and wind direction deviation angle corresponding to the wind direction and atmospheric stability state in the meteorological forecast data at each forecast time point according to the ratio of the linear interpolation; A seventh processing module, configured to calculate the wind power prediction result through the wind speed and wind direction information in the meteorological forecast data and the wind farm full-field wind acceleration factor and wind direction deviation angle at the corresponding time.

9. An electronic device, characterized in that, the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps in the method for rapid downscaling of wind power prediction according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method for rapid downscaling of wind power prediction according to any one of claims 1 to 7 are implemented.

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