CN115048790A - Method and system for predicting rapid downscaling of wind power - Google Patents

Abstract

The present invention provides a method and system for rapid downscaling of wind power prediction. Among them, the method includes: performing directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stability, obtaining the wind acceleration factor and wind direction declination angle of the whole wind farm, and using them as the key parameters of the basic database for wind power forecasting to generate a grid file; associate the average state of the area represented by the meteorological forecast data with the three-dimensional spatial wind flow field in the microscale model based on its resolution; according to the different wind directions and different atmospheric stability in the meteorological forecast results, the gridded model is applied. The file performs linear interpolation on the wind direction and thermal stability in the basic database of wind power prediction; calculates the wind power prediction result according to the linear interpolation results. The invention can reflect the influence of different atmospheric thermal stability, wind direction and other meteorological parameters on the wind power by establishing a basic database of wind power prediction, and at the same time perform fast wind power prediction through interpolation, so that it can meet the needs of business operation.

Description

A method and system for rapid downscaling of wind power prediction

technical field

The invention belongs to the field of wind farm meteorology, and in particular relates to a method and system for rapid downscaling of wind power prediction.

Background technique

Through the search of domestic and foreign papers, academic conferences, scientific literature, patents and other databases, it is found that:

国家实验室开发了以数值天气预报+WAsP+功率曲线为基础的Prediktor风电功率预测系统，1993年在丹麦东开始应用。1994年

实验室联合丹麦技术大学合作开发了Prediktor与WPPT相结合的Zephyr风电功率预测系统，在丹麦全国范围内得到应用，并推广到西班牙、爱尔兰、美国、日本等国家和地区。西班牙电网公司的SIPREOLICO风电功率预测系统采用西班牙气象局数值预报产品和欧洲中期天气预报中心数值天气预报产品，通过8个风电功率预测模型，进行风电功率集合预报，再结合荷兰AEOLIS预测服务公司、西班牙工程技术研究所(IIC)和西班牙METEOLOGICA专业风能预报和风电功率预测公司的风电功率预测产品，最终得到未来48小时和10天的风电功率预测。我国的风电功率预测工作起步较晚，2008年11月，中国电力科学研究院开发了我国首套具有自主知识产权的风电功率预测系统WPFS。目前中国电力科学研究院与美国大气科学研究中心(NCAR)发展了具有实时快速更新同化和集合预报技术的电力数值天气预报系统，为风电场风电功率预测提供基础数值天气预报。Denmark

The National Laboratory developed the Prediktor wind power prediction system based on numerical weather forecast + WAsP + power curve, which was applied in eastern Denmark in 1993. year 1994

The laboratory cooperated with the Technical University of Denmark to develop the Zephyr wind power prediction system combining Prediktor and WPPT, which has been applied nationwide in Denmark and extended to Spain, Ireland, the United States, Japan and other countries and regions. The SIPREOLICO wind power prediction system of the Spanish Power Grid Corporation adopts the numerical forecast products of the Spanish Meteorological Agency and the numerical weather forecast products of the European Medium-Range Weather Forecast Center, and uses 8 wind power forecast models to make ensemble forecasts of wind power, combined with the Netherlands AEOLIS forecast service company, Spain The wind power forecast products of the Institute of Engineering and Technology (IIC) and the Spanish METEOLOGICA professional wind energy forecast and wind power forecast company finally get the wind power forecast for the next 48 hours and 10 days. my country's wind power forecasting work started late. In November 2008, China Electric Power Research Institute developed my country's first wind power forecasting system WPFS with independent intellectual property rights. At present, China Electric Power Research Institute and the US Center for Atmospheric Research (NCAR) have developed a power numerical weather forecast system with real-time rapid update assimilation and ensemble forecasting technology, which provides basic numerical weather forecast for wind power forecasting of wind farms.

There are two main types of wind power forecasting methods commonly used in China. One is the statistical analysis and forecasting method based on the historical time series of wind power for short-term wind power forecasting; , the neural network technology is simulated as a black box for wind power forecast, because the influence of atmospheric thermal stability, wind direction and other meteorological parameters on wind power cannot be considered at the same time. Especially for the complex terrain and coastal wind farms in my country, due to the strong vertical movement of the near-surface atmosphere, the thermal stability is mostly stable and unstable except for being neutral. It has a great influence, resulting in poor domestic wind power prediction accuracy. Therefore, the mature foreign systems cannot be directly applied to my country's wind farms. Therefore, a fast dynamic downscaling method for wind power prediction that adapts to the characteristics of my country's wind farms is required.

Disadvantages of the prior art: In the process of wind power prediction, the weather forecast results are directly downscaled to the wind turbine point through model calculation, and then the wind-to-wind power conversion is performed, and the calculation efficiency is low; the neural network technology is simulated as a black box for wind power conversion. It is difficult to simultaneously consider the influence of meteorological parameters such as atmospheric thermal stability and wind direction on wind power in power forecasting.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned technical problem, the present invention proposes a technical scheme of a method for rapid downscaling of wind power prediction, so as to solve the above-mentioned technical problem.

A first aspect of the present invention discloses a method for rapid downscaling of wind power prediction, the method comprising:

Step S1, collecting and mapping the terrain elevation and roughness data of the wind farm and a certain area around the wind farm, and establishing a micro-scale wind flow field model capable of representing the local effect of the wind farm and its surrounding area;

Step S2, performing directional calculation on the micro-scale wind flow field model according to different wind directions and different degrees of thermal stability, to obtain the wind acceleration factor and wind direction declination of the entire wind farm;

Step S3, generating a gridded file with the wind acceleration factor and wind direction declination of the wind farm as the key parameters of the basic database for wind power prediction;

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

Step S5, performing linear interpolation on the wind direction and thermal stability in the wind power prediction basic database by applying the gridded file according to different wind directions and different atmospheric stability in the weather forecast result through the association;

Step S6, obtaining the wind acceleration factor and wind direction declination angle of the 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 according to the wind speed and wind direction information in the weather forecast data, and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time.

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 capable of representing the local effects of the wind farm and its surrounding area includes:

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

According to the method of the first aspect of the present invention, in the step S2, the calculation method of the wind acceleration factor Δk for the entire field of the wind farm includes:

Among them, U(z) represents the wind speed at the height z above the mountain ground, and U ₀ (z) represents the wind speed at the height z above the flat ground.

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

Selecting an altitude position where the weather forecast data is coupled with the microscale wind flow field model.

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 according to the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time includes: :

Calculate the wind parameter downscaling forecast results of the wind farm by using the wind speed and wind direction information in the meteorological forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time;

According to the downscaling forecast results, combined with the coordinates of the wind farm site and the power curve, the wind power forecast results of each wind turbine are obtained.

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:

Among them, P(V) is the wind power, V is the wind speed, V _cut -in is the cut-in wind speed of the wind turbine, V _rated is the rated wind speed of the wind turbine, V _cut-out is the cut-out wind speed of the wind turbine, S(V) is the wind power output, P _max is the maximum power output.

According to the method of the first aspect of the present invention, in the step S4, the specific method of the association includes: 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 condition of the three-dimensional wind flow field in the micro-scale wind flow field model, the extrapolation simulation is carried out.

A second aspect of the present invention discloses a system for rapid downscaling of wind power prediction, the system comprising:

The first processing module is configured to collect and map the terrain elevation and roughness data of the wind farm and a certain area around the wind farm, and establish a micro-scale wind flow field model that can characterize the local effect of the wind farm and its surrounding area;

The second processing module is configured to perform directional calculation on the micro-scale wind flow field model according to different wind directions and different degrees of thermal stability, so as to obtain the wind acceleration factor and the wind direction declination angle of the entire wind farm;

The third processing module is configured to generate a gridded file by using the wind acceleration factor and wind direction declination of the wind farm as key parameters of the basic database for wind power prediction;

The fourth processing module is configured to associate the weather forecast data based on the average state of the area represented by its resolution with the three-dimensional spatial wind flow field in the microscale model, and perform extrapolation simulation;

The fifth processing module is configured to, through the association, perform linear interpolation on the wind direction and thermal stability in the wind power prediction basic database by applying the gridded file according to different wind directions and different atmospheric stability in the weather forecast result ;

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

The seventh processing module is configured to calculate the wind power prediction result according to the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time.

According to the system of the second aspect of the present invention, the first processing module is specifically configured to establish a micro-scale wind flow field model capable of representing the local effects of the wind farm and its surrounding areas, including:

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

According to the system of the second aspect of the present invention, the second processing module is specifically configured such that the calculation of the wind acceleration factor Δk for the entire field of the wind farm includes:

Among them, U(z) represents the wind speed at the height z above the mountain ground, and U ₀ (z) represents the wind speed at the height z above the flat ground.

According to the system of the second aspect of the present invention, the fourth processing module is specifically configured as:

Selecting an altitude position where the weather forecast data is coupled with the microscale wind flow field model.

According to the system of the second aspect of the present invention, the seventh processing module is specifically configured to calculate the wind power according to the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time. Predicted results include:

Calculate the wind parameter downscaling forecast results of the wind farm by using the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time;

According to the downscaling forecast results, combined with the coordinates of the wind farm site and the power curve, the wind power forecast results of each wind turbine are obtained.

According to the system of 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:

Among them, P(V) is the wind power, V is the wind speed, V _cut -in is the cut-in wind speed of the wind turbine, V _rated is the rated wind speed of the wind turbine, V _cut-out is the cut-out wind speed of the wind turbine, S(V) is the wind power output, P _max is the maximum power output.

According to the system of the second aspect of the present invention, the fourth processing module is specifically configured to, the association includes: down-scaling the weather forecast data based on the wind speed, wind direction and other relevant meteorological parameters in the area represented by the resolution, As the input condition of the three-dimensional wind flow field in the micro-scale wind flow field model, the extrapolation simulation is carried out.

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, and when the processor executes the computer program, the processor implements the steps in the method for rapid downscaling of wind power prediction according to any one of the first aspects of the present disclosure.

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

The scheme proposed by the present invention reflects the influence of different atmospheric thermal stability, wind direction and other meteorological parameters on wind power by establishing a basic database of wind power prediction, and at the same time performs fast wind power prediction through interpolation, so that it can meet the needs of business operation.

Description of drawings

In order to illustrate the specific embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description The drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

1 is a flowchart of a method for rapid downscaling of wind power prediction according to an embodiment of the present invention;

2 is a flowchart of a method for rapid downscaling of wind power prediction according to an embodiment of the present invention;

3 is a structural diagram of a system for rapid downscaling of wind power prediction according to an embodiment of the present invention;

FIG. 4 is a structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

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

Step S1, collecting and mapping the terrain elevation and roughness data of the wind farm and a certain area around the wind farm, and establishing a micro-scale wind flow field model capable of representing the local effect of the wind farm and its surrounding area;

Step S2, performing directional calculation on the micro-scale wind flow field model according to different wind directions and different degrees of thermal stability, to obtain the wind acceleration factor and wind direction declination of the entire wind farm;

Step S3, generating a gridded file with the wind acceleration factor and wind direction declination of the wind farm as the key parameters of the basic database for wind power prediction;

Step S4, establishing an association between the meteorological forecast data based on the average state of the area represented by its resolution and the three-dimensional space wind flow field in the microscale model, and performing extrapolation simulation;

Step S5, performing linear interpolation on the wind direction and thermal stability in the wind power prediction basic database by applying the gridded file according to different wind directions and different atmospheric stability in the weather forecast result through the association;

Step S6, obtaining the wind acceleration factor and wind direction declination angle of the wind farm 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;

Step S7: Calculate the wind power prediction result according to the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time.

In step S1, collect and map 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.

In some embodiments, in the step S1, the method for establishing a micro-scale wind flow field model capable of representing the local effects of the wind farm and its surrounding areas includes:

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

where the Reynolds-averaged Navier-Stokes equation:

在等式中被作为湍流通量，也叫做雷诺应力项。在动量方程中出现的雷诺应力项需要被模拟：where _u is the fluid velocity, p is the fluid pressure, ρ is the fluid density, μ is the fluid dynamic viscosity, and Fi is other forces.

is used in the equation as the turbulent flux, also known as the Reynolds stress term. The Reynolds stress term that appears in the momentum equation needs to be modeled:

v _T =k ^1/2 L _T

where 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 Charlson number Rif, which 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.

Specifically, the weather forecast results of a wind farm in Shanxi are selected for wind power prediction, and the input weather forecast data is first analyzed to obtain a resolution of 9 kilometers.

The micro-scale wind flow field model of the wind farm area is established by fluid mechanics simulation software, and the 100-meter-precision topographic file and the 300-meter-precision roughness file of the wind farm are selected to reflect the topographic and landform characteristics of the wind farm area.

In step S2, directional calculation is performed on the micro-scale wind flow field model according to different wind directions and different degrees of thermal stability, and the wind acceleration factor and wind direction declination angle of the entire wind farm are obtained.

Specifically, the 10° sector step size and 10 thermal stability degrees of the wind farm area are calculated, and a total of 360 models with 100-meter resolution directional calculation results are used to describe the input and output characteristics of the micro-scale model under different conditions.

In step S3, a gridded file is generated using the wind acceleration factor and wind direction declination of the entire wind farm as key parameters of the basic database for wind power prediction.

In some embodiments, in the step S3, the acceleration effect of the mountain on the wind flow is usually quantitatively described by a wind acceleration factor, and the calculation method of the wind acceleration factor Δk for the entire field of the wind farm includes:

Among them, U(z) represents the wind speed at the height z above the mountain ground, and U ₀ (z) represents the wind speed at the height z above the flat ground.

Specifically, the overall wind acceleration factor and wind direction declination angle of the wind farm obtained by 360 directional calculations are used as the key parameters of the basic database for wind power prediction, and a gridded txt file is generated, as shown in Table 1, as the subsequent wind power prediction interpolation. Base.

Table 1

In step S4, the average state of the weather forecast data based on the area represented by its resolution is associated with the three-dimensional spatial wind flow field in the microscale model, and extrapolation simulation is performed, so as to improve accuracy and reduce uncertainty sex.

In some embodiments, in the step S4, the height position where the weather forecast data is coupled with the micro-scale wind flow field model is selected to ensure that the position is in the high-altitude range, can provide effective weather forecast data, and at the same time can Reflects the contribution of near-Earth parameters to the microscale model.

The specific method of association includes: 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 condition of the three-dimensional spatial wind flow field in the micro-scale wind flow field model, and performing external analysis. Push simulation.

Specifically, based on the average state in the 9-kilometer area in the meteorological forecast results, a meteorological forecast input unit is established, which is associated with the three-dimensional spatial wind flow field in the microscale model, and the extrapolation simulation settings are performed; the coupling between the meteorological forecast data and the microscale model is selected. The altitude position is 400 meters.

In step S5, through the association, linear interpolation is performed on the wind direction and thermal stability in the basic database of wind power prediction by applying the gridded file according to different wind directions and different atmospheric stability in the weather forecast result.

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

表示由采样点(x_i，y_i)至插值点(x,y)的距离。where _zi is the attribute value of the discrete point,

Represents the distance from the sampling point (x _i , y _i ) to the interpolation point (x, y).

In step S6, according to the linear interpolation ratio, the wind direction and the wind acceleration factor and the wind direction declination angle corresponding to the wind direction and the atmospheric stability state in the meteorological forecast data at each forecast time point are obtained.

In step S7, the wind power prediction result is calculated according to the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time.

In some embodiments, in the step S7, the method for calculating the wind power prediction result according to the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time includes:

Calculate the wind parameter downscaling forecast results of the wind farm by using the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time;

According to the downscaling forecast results, combined with the coordinates of the wind farm site and the power curve, the wind power forecast results of each wind turbine are obtained.

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

Among them, P(V) is the wind power, V is the wind speed, V _cut -in is the cut-in wind speed of the wind turbine, V _rated is the rated wind speed of the wind turbine, V _cut-out is the cut-out wind speed of the wind turbine, S(V) is the wind power output, P _max is the maximum power output.

To sum up, the solution proposed by the present invention can reflect the influence of different atmospheric thermal stability, wind direction and other meteorological parameters on wind power by establishing a basic database for wind power prediction, and at the same time perform fast wind power prediction through interpolation, so that it can meet the needs of business operation. .

A second aspect of the present invention discloses a system for rapid downscaling of wind power prediction. FIG. 3 is a structural diagram of a system for rapid downscaling of wind power prediction according to an embodiment of the present invention; as shown in FIG. 3 , the system 100 includes:

The first processing module 101 is configured to collect and map the terrain elevation and roughness data of the wind farm and a certain area around the wind farm, and establish a micro-scale wind flow field model that can characterize the local effect of the wind farm and its surrounding area ;

The second processing module 102 is configured to perform directional calculation on the micro-scale wind flow field model according to different wind directions and different degrees of thermal stability, so as to obtain the wind acceleration factor and wind direction declination angle of the entire wind farm;

The third processing module 103 is configured to generate a gridded file by using the wind acceleration factor and wind direction declination of the entire wind farm as key parameters of the basic database for wind power prediction;

The fourth processing module 104 is configured to associate the weather forecast data with the three-dimensional wind flow field in the microscale model based on the average state of the area represented by the resolution of the weather forecast data, and perform extrapolation simulation;

The fifth processing module 105 is configured to, through the association, apply the gridded file to linearly perform a linear analysis on the wind direction and thermal stability in the wind power prediction basic database according to different wind directions and different atmospheric stability in the weather forecast result. interpolation;

The sixth processing module 106 is configured to obtain, according to the linear interpolation ratio, the wind direction and the wind acceleration factor and the wind direction declination angle corresponding to the wind direction and the atmospheric stability state in the weather forecast data at each forecast time point;

The seventh processing module 107 is configured to calculate the wind power prediction result according to the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time.

According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured to, the establishing a micro-scale wind flow field model capable of representing the local effects of the wind farm and its surrounding area includes:

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

According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to: the calculation of the wind acceleration factor Δk for the entire field of the wind farm includes:

Among them, U(z) represents the wind speed at the height z above the mountain ground, and U ₀ (z) represents the wind speed at the height z above the flat ground.

According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured as:

Selecting an altitude position where the weather forecast data is coupled with the microscale wind flow field model.

According to the system of the second aspect of the present invention, the seventh processing module 107 is specifically configured to calculate the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time. The power prediction results include:

Calculate the wind parameter downscaling forecast results of the wind farm by using the wind speed and wind direction information in the weather forecast data and the wind acceleration factor and wind direction declination of the wind farm at the corresponding time;

According to the downscaling forecast results, combined with the coordinates of the wind farm site and the power curve, the wind power forecast results of each wind turbine are obtained.

According to the system of the second aspect of the present invention, the seventh processing module 107 is specifically configured such that 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 _cut -in is the cut-in wind speed of the wind turbine, V _rated is the rated wind speed of the wind turbine, V _cut-out is the cut-out wind speed of the wind turbine, S(V) is the wind power output, P _max is the maximum power output.

According to the system according to the second aspect of the present invention, the fourth processing module 104 is specifically configured to, the association includes: down-scaling the weather forecast data based on the wind speed, wind direction and other relevant meteorological parameters in the area represented by the resolution. , as the input condition of the three-dimensional wind flow field in the micro-scale wind flow field model, and extrapolate the simulation.

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, 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 the first aspects disclosed in the present disclosure are implemented.

FIG. 4 is a structural diagram of an electronic device according to an embodiment of the present invention. As shown in FIG. 4 , the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through 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 nonvolatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used for wired or wireless communication with an external terminal, and the wireless communication can be realized by WIFI, operator network, near field communication (NFC) or other technologies. The display screen of the electronic device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device may be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the electronic device , or an external keyboard, trackpad, or mouse.

Those skilled in the art can understand that the structure shown in FIG. 4 is only a structural diagram of a part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic equipment to which the solution of the present application is applied. A device may include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.

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

Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features , should be considered to be within the scope of this specification. The above examples only represent several embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the scope of protection of the patent of the present application shall be subject to the appended claims.

Claims (10)

1. A method for fast downscaling of wind power predictions, the method comprising:

s1, collecting and mapping terrain elevation and roughness data of the wind power plant and a certain peripheral area range of the wind power plant, and establishing a micro-scale wind flow field model capable of representing local effect of the wind power plant and the peripheral area range of the wind power plant;

step S2, performing directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;

step S3, generating a gridded file by taking the wind farm full-field wind acceleration factor and the wind direction deflection angle as key parameters of a wind power prediction basic database;

s4, establishing association between the average state of the meteorological forecast data in the area represented by the resolution ratio of the meteorological forecast data and a three-dimensional space wind flow field in the microscale wind flow field model, and carrying out extrapolation simulation;

step S5, according to different wind directions and different atmospheric stabilities in the weather forecast result, the gridded file is applied to carry out linear interpolation on the wind direction and the thermal stability in the wind power prediction basic database through the correlation;

step S6, obtaining wind direction, wind direction deflection angle of the wind power plant full field corresponding to the atmospheric stability state in the weather forecast data of each forecast time point according to the proportion of the linear interpolation;

and step S7, calculating a wind power prediction result according to the wind speed and wind direction information in the meteorological forecast data and the wind power plant full-field wind acceleration factor and the wind direction deflection angle at the corresponding time.

2. The method for wind power prediction to rapidly downscale according to claim 1, wherein in the step S1, the method for establishing a micro-scale wind flow field model capable of characterizing local effects of the wind farm and the surrounding area comprises:

and establishing a micro-scale wind flow field model capable of representing the local effect of the wind power plant and the surrounding area thereof based on a constant, adiabatic and incompressible Reynolds average Navier-Stokes equation.

3. The method for wind power prediction to rapidly downscale according to claim 1, wherein in the step S2, the method for calculating the wind farm full wind acceleration factor Δ k comprises:

wherein U (z) represents the wind speed at the height z above the ground of the mountain land, U ₀ (z) represents the wind speed at a height z above the flat ground.

4. The method for wind power prediction fast downscaling according to claim 1, wherein in the step S4, the method further includes:

selecting a height position at which the weather forecast data is coupled to the microscale wind-flow-field model.

5. The method for fast downscaling of wind power prediction according to claim 1, wherein in the step S7, the method for calculating the wind power prediction result through the wind speed, wind direction information and wind farm full-field wind acceleration factor and wind direction declination at the corresponding time in the meteorological forecast data comprises:

calculating a wind power plant wind parameter downscaling forecasting result according to wind speed and wind direction information in the meteorological forecasting data, a wind power plant full-field wind acceleration factor and a wind direction deflection angle at corresponding time;

and according to the downscaling prediction result, combining the coordinates and the power curve of the wind power plant machine position point to obtain a wind power prediction result of each fan.

6. The method for wind power prediction to rapidly downscale according to claim 5, wherein in the step S7, the wind power and the wind speed at the corresponding time are calculated as follows:

wherein P (V) is wind power, V is wind speed, and V is _{Cutting into} For wind turbines to cut into wind speed, V _{Rated value} Rated wind speed V of the wind turbine _{Cutting out} Cutting out wind speed for wind turbine, S (V) wind power output, P _max Is the maximum power output.

7. The method for wind power prediction fast downscaling according to claim 1, wherein in the step S4, the specific method for associating includes: and (3) carrying out extrapolation simulation by taking the wind speed, the wind direction and other related meteorological parameters on the area represented by the resolution ratio of the downscaled meteorological forecast data as the input conditions of the three-dimensional space wind flow field in the microscale wind flow field model.

8. A system for fast downscaling of wind power predictions, characterized in that the system comprises:

the first processing module is configured to collect and map terrain elevation and roughness data of the wind power plant and a certain area range around the wind power plant, and establish a micro-scale wind flow field model capable of representing a local effect of the wind power plant and a surrounding area;

the second processing module is configured to perform directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;

the third processing module is configured to generate a gridded file by taking the wind farm full-field wind acceleration factor and the wind direction deflection angle as key parameters of a wind power prediction basic database;

the fourth processing module is configured to correlate the weather forecast data with a three-dimensional space wind flow field in the micro-scale model based on the average state of the area represented by the resolution of the weather forecast data, and perform extrapolation simulation;

a fifth processing module, configured to apply the gridded file to perform linear interpolation on wind direction and thermal stability in a wind power prediction base database according to different wind directions and different atmospheric stabilities in a weather forecast result through the association;

the sixth processing module is configured to obtain the wind direction in the meteorological forecast data at each forecast time point and the wind direction deflection angle of the corresponding wind power plant in the atmospheric stability state according to the proportion of the linear interpolation;

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

9. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor, when executing the computer program, implements the steps of a method for wind power prediction fast downscaling according to any one of claims 1-7.

10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of a method of wind power prediction of a fast droop scale of any one of claims 1 to 7.

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