Mountain wind farm wind resource simulation method considering thermal stability

Abstract

The application discloses a mountainous wind farm wind resource simulation method considering thermal stability, and the method comprises the following steps: calculating the flux Richardson number based on the mountainous wind farm environment data; determining the atmospheric stability parameter distribution characteristics according to the flux Richardson number; establishing the near-surface turbulence characteristic similarity function by combining the atmospheric stability distribution characteristics with the Monin-Obukhov similarity theory; calculating the turbulence characteristics at different positions of the mountainous wind farm according to the near-surface turbulence characteristic similarity function; establishing the mountainous wind farm turbulence control equation based on the mountainous wind farm turbulence characteristics; adopting the wall function to correct the turbulence viscosity, and perfecting the mountainous wind farm turbulence control equation; and simulating the mountainous wind farm wind resource based on the perfected mountainous wind farm turbulence control equation. According to the simulation method, the mountainous wind farm wind resource evaluation result is more consistent with the measured data, and the mountainous wind farm turbulence control equation can capture the flow characteristics in the field more carefully.

Description

Technical Field

[0001] This invention relates to a method for simulating wind resources in mountain wind farms that takes into account thermal stability, and belongs to the field of mountain wind resource technology. Background Technology

[0002] Thermal stability refers to the impact of atmospheric temperature distribution on wind fields. In mountainous areas, due to the complexity of the terrain, atmospheric temperature is affected by factors such as topographic relief and ground radiation, leading to spatial and temporal variations in thermal stability. These variations in thermal stability significantly affect the morphology and wind speed distribution of wind fields, posing technical obstacles to wind resource assessment in mountainous wind farms.

[0003] As wind power development deepens, more and more areas with easily exploitable wind energy resources have been fully utilized. This has prompted the wind power industry to turn its attention to areas with more challenging wind resource development, such as mountainous regions. Mountainous regions typically feature significant topographic relief and complex landforms. Compared to plains, mountain wind farms experience higher and more stable wind speeds, providing favorable conditions for their development.

[0004] In mountainous regions, undulating terrain can obstruct or accelerate airflow between mountains and valleys, creating localized airflow variations. Surface radiation in mountainous areas is also affected by topography, which can block and reflect radiation, leading to differences in temperature distribution across different regions. Furthermore, the interaction between atmospheric turbulence and topography can influence thermal stability, causing variations in thermal stability both spatially and temporally.

[0005] Variations in thermal stability significantly impact wind field morphology and speed distribution, posing a technical obstacle to wind resource assessment in mountainous wind farms. Different thermal stability conditions lead to varying wind speed and direction distributions. In a stable boundary layer, wind speeds are lower and more influenced by topography, while in an unstable boundary layer, wind speeds are higher and less affected by topography. This variation necessitates considering the wind energy utilization potential under different thermal stability conditions during the assessment process. Furthermore, changes in thermal stability also affect the layout and design of wind turbines. In a stable boundary layer, the location and layout of turbines need to pay closer attention to the influence of topography to minimize mutual interference between turbines. In an unstable boundary layer, turbine location selection can be more flexible to better capture high-speed winds. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention aims to provide a method for simulating wind resources in mountainous wind farms that considers thermal stability. Specifically addressing the wind resource assessment problem in mountainous wind farms, this invention proposes atmospheric stability parameters and combines thermal stability with the turbulent characteristics of mountainous wind farms to establish a set of turbulent control equations that consider thermal stability. Based on the established turbulence model, a wall function is introduced to correct the turbulent viscosity, thus refining the turbulent control equations for mountainous wind farms and enabling accurate assessment of their wind energy resources.

[0007] To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0008] In a first aspect, the present invention provides a method for simulating wind resources in mountainous wind farms that considers thermal stability, comprising:

[0009] Step 1) Calculate the flux Richardson number based on the acquired mountain wind farm environmental data; wherein the mountain wind farm environmental data includes at least the spatial location of the measuring equipment and real-time potential temperature, wind direction, and wind speed data;

[0010] Step 2) Determine the atmospheric stability distribution characteristics based on the flux Richardson number;

[0011] Step 3) Establish a similarity function for near-surface turbulence characteristics by combining atmospheric stability distribution characteristics with Monin-Obukhov similarity theory;

[0012] Step 4) Calculate the turbulence characteristics at different locations in the mountain wind farm based on the near-surface turbulence characteristic similarity function;

[0013] Step 5) Establish the turbulence control equations for the mountain wind farm based on the turbulence characteristics at different locations;

[0014] Step 6) Use wall functions to correct turbulent viscosity and improve the turbulence control equations for mountain wind farms;

[0015] Step 7) Based on the well-established turbulence control equations for mountain wind farms, the wind resources of mountain wind farms are simulated.

[0016] In some embodiments, step 1) specifically includes:

[0017] Environmental data of mountain wind farms are acquired, wherein the environmental data of mountain wind farms are obtained by measuring equipment such as three-dimensional ultrasonic anemometer and eddy covariance measurement system;

[0018] Based on the environmental data of the mountain wind farm, the flux Richardson number R is calculated. f ;

[0019]

[0020]

[0021] In the formula, g is the acceleration due to gravity; θ' represents the average potential temperature; u represents the potential temperature fluctuation. * Friction velocity is used to characterize Reynolds stress. The influence of; U is the incoming wind speed; z is the height above the ground; u′, v′, w′ represent the longitudinal, lateral and vertical wind speed component fluctuations, respectively.

[0022] In some embodiments, step 2) determining the atmospheric stability distribution characteristics based on the flux Richardson number specifically includes:

[0023] By establishing a one-to-one functional relationship between the atmospheric stability parameter ζ and the flux Richardson number, the atmospheric stability parameter distribution function f(ζ) of the mountain wind farm is calculated to characterize the atmospheric stability distribution characteristics of the mountain wind farm at different terrain locations.

[0024]

[0025]

[0026] In the formula, z is the height above the ground; L is the Monin-Obukhov length, which reflects the relative dominance between mechanical turbulence and buoyant turbulence generated by thermal effects; R f is the Richardson number for flux; k is the topographic factor; A > 0 is the scaling factor.

[0027] In some embodiments, step 3) establishes a near-surface turbulence characteristic similarity function by combining atmospheric stability distribution characteristics with Monin-Obukhov similarity theory, specifically including:

[0028] By combining atmospheric stability distribution characteristics with the Monin-Obukhov similarity theory, any dimensionless turbulent characteristic in the near-surface layer depends on atmospheric stability, and a near-surface turbulent characteristic function is established accordingly; the near-surface turbulent characteristic includes the dimensionless vertical gradient φ of wind speed and potential temperature. m and φ h Dimensionless turbulent kinetic energy φ k and its dissipation rate φ ε The definition is as follows:

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, ζ is the atmospheric stability parameter; k is the turbulent kinetic energy; z is the altitude above the ground; u * U is the friction velocity, and U is the incoming airflow velocity; θ is the average potential temperature. * Characteristic potential temperature, For Reynolds stress; C μ ε is a constant; ε is the turbulent dissipation rate.

[0034] In some embodiments, step 4) calculates the turbulence characteristics at different locations in the mountain wind farm based on the near-surface turbulence characteristic similarity function, specifically including:

[0035] The different locations of the mountain wind farm include at least: the entrance of the mountain wind farm and the top boundary;

[0036] Turbulence characteristics at the entrance of a mountain wind farm:

[0037]

[0038]

[0039]

[0040] Turbulence characteristics at the top boundary of a mountain wind farm:

[0041]

[0042]

[0043]

[0044] In the formula, U(z), k0, and ε0 represent the turbulence characteristics at the entrance of the mountain wind farm, which are the vertical wind speed gradient, turbulence intensity, and dissipation rate at the entrance, respectively; z0 is the ground clearance at the entrance; L is the Monin-Obukhov length; and φ is the dimensionless vertical gradient of wind speed and potential temperature. m and φ h Dimensionless turbulent kinetic energy φ k and its dissipation rate φ ε k is the turbulent kinetic energy, z is the height above the ground; u * Where C is the friction velocity, U is the incoming airflow velocity; μ z is a constant; ε is the turbulent dissipation rate; top This refers to the height of the top boundary above the ground. The three terms represent the turbulence characteristics at the top boundary of the mountain wind farm: the vertical wind speed gradient at the top boundary, the turbulence intensity at the top boundary, and the dissipation rate at the top boundary.

[0045] In some embodiments, step 5) establishes the turbulence control equations for the mountain wind farm based on the turbulence characteristics at different locations within the mountain wind farm, specifically including:

[0046] Based on the turbulence characteristics at different locations in a mountain wind farm, for the flow in the plane perpendicular to the mainstream direction, without considering the effects of wind turbine force, pressure gradient force, and Coriolis force, and neglecting molecular viscosity, a set of turbulence control equations for a mountain wind farm under equilibrium conditions is established:

[0047]

[0048]

[0049]

[0050] In the formula,

[0051]

[0052]

[0053]

[0054] Where ρ is air density; U is incoming wind speed; t is time; z is height above ground; μ t denoted as turbulent viscosity; u′w′ as Reynolds stress; k as turbulent kinetic energy; and g as gravitational acceleration. ε is the mean potential temperature; ε is the turbulent dissipation rate; ζ is the atmospheric stability parameter; C μ u is a constant; * σ is the frictional velocity; k and σ ε Prantl numbers for k and ε, respectively; C ε1 C ε2 and C ε3 Here are the parameters for the turbulence model, where C 1ε and C 2ε The constant is the dimensionless vertical gradient φ of wind speed and potential temperature. m and φ h Dimensionless turbulent kinetic energy φ k and its dissipation rate φ ε ,φ′ ε , φ′ m , φ′ h , φ′ k These are the first derivatives of the corresponding functions; φ″ ε and φ″ k It is the second derivative of the corresponding function.

[0055] In some embodiments, step 6) uses wall functions to correct turbulent viscosity and improve the turbulent control equations of the mountain wind farm, specifically including:

[0056] The turbulent viscosity is corrected using wall functions; based on the corrected turbulent viscosity, the turbulence control equations for the mountain wind farm are improved.

[0057]

[0058]

[0059]

[0060] Where, μ t,p The corrected turbulent viscosity is given by ρ, where ρ is the air density, U is the incoming wind speed, t is time, and z is the height above the ground. denoted as Reynolds stress; k is turbulent kinetic energy; g is gravitational acceleration. ε is the mean potential temperature; ε is the turbulent dissipation rate; ζ is the atmospheric stability parameter; C μ u is a constant; * σ is the frictional velocity; k and σ ε Prantl numbers for k and ε, respectively; C ε1 C ε2 and C ε3 Here are the parameters for the turbulence model, where C 1ε and C 2ε The constant is the dimensionless vertical gradient φ of wind speed and potential temperature. m and φ h Dimensionless turbulent kinetic energy φ k and its dissipation rate φ ε ,φ′ ε , φ′ m , φ′ h , φ′ k These are the first derivatives of the corresponding functions; φ″ ε and φ″ k This is the second derivative of the corresponding function;

[0061] The improved turbulence control equations can simulate reasonable turbulent viscosity, and the wind speed automatically maintains horizontal uniformity, i.e., DU / Dt = 0.

[0062] Furthermore, in some embodiments, wall functions are used to correct turbulent viscosity, including:

[0063]

[0064] In the formula, μ t,p This is the corrected turbulent viscosity; u *ρ is the friction velocity; k is the turbulent kinetic energy; p is the center of the first grid layer closest to the ground; z p Let p be the height above the ground; τ k The wall shear stress is μ; z is the height above the ground; L is the Monin-Obukhov length; μ t This refers to turbulent viscosity.

[0065] In some embodiments, step 7) specifically includes: based on the improved turbulence control equations, configuring simulated boundary conditions according to the site environment of the mountain wind farm, and simulating the spatiotemporal distribution pattern of wind resources within the mountain wind farm.

[0066] In a second aspect, the present invention provides a wind resource simulation device for mountain wind farms that takes into account thermal stability, including a processor and a storage medium;

[0067] The storage medium is used to store instructions;

[0068] The processor is configured to operate according to the instructions to execute the method according to the first aspect.

[0069] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0070] Fourthly, the present invention provides an apparatus comprising,

[0071] Memory;

[0072] processor;

[0073] as well as

[0074] Computer programs;

[0075] The computer program is stored in the memory and configured to be executed by the processor to implement the method described in the first aspect above.

[0076] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0077] 1. This invention can make the assessment results of mountain wind resources more consistent with the measured data, and the planning results have better economic benefits, and have good application prospects in actual engineering.

[0078] 2. To provide theoretical support for the planning of mountain wind farms that take thermal stability into account. Attached Figure Description

[0079] Figure caption

[0080] Figure 1This is a schematic flowchart of a method for simulating wind resources in a mountain wind farm according to an embodiment of the present invention;

[0081] Figure 2 This is a schematic diagram of terrain modeling of Fangtaishan Mountain with a slope of 30° and a slope of H50, according to an embodiment of the present invention;

[0082] Figure 3 This is a schematic diagram of wind resource distribution under the following embodiment of the present invention: Fangtaishan terrain with an H50 slope of 30° and an incoming wind speed of 4.5 m / s.

[0083] Figure 4 This is a schematic diagram of simulated wind speed changes under different atmospheric stability conditions for Fangtaishan terrain with a slope of 30° and an H50 slope, according to an embodiment of the present invention. Detailed Implementation

[0084] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0085] Example 1

[0086] Firstly, such as Figure 1 As shown in the figure, this embodiment proposes a method for simulating wind resources in mountain wind farms that considers thermal stability. The method includes the following steps:

[0087] Step 1) Calculate the flux Richardson number based on the acquired mountain wind farm environmental data; wherein the mountain wind farm environmental data includes at least the spatial location of the measuring equipment and real-time potential temperature, wind direction, and wind speed data;

[0088] Step 2) Determine the atmospheric stability distribution characteristics based on the flux Richardson number;

[0089] Step 3) Establish a similarity function for near-surface turbulence characteristics by combining atmospheric stability distribution characteristics with Monin-Obukhov similarity theory;

[0090] Step 4) Calculate the turbulence characteristics at different locations in the mountain wind farm based on the near-surface turbulence characteristic similarity function;

[0091] Step 5) Establish the turbulence control equations for the mountain wind farm based on the turbulence characteristics at different locations;

[0092] Step 6) Use wall functions to correct turbulent viscosity and improve the turbulence control equations for mountain wind farms;

[0093] Step 7) Based on the well-established turbulence control equations for mountain wind farms, the wind resources of mountain wind farms are simulated.

[0094] Furthermore, step 1) specifically includes:

[0095] Step 1) Obtain environmental data of the mountain wind farm, wherein the environmental data of the mountain wind farm is obtained by measuring equipment such as a three-dimensional ultrasonic anemometer and an eddy covariance measurement system;

[0096] Based on the environmental data of the mountain wind farm, the flux Richardson number R is calculated. f ;

[0097]

[0098]

[0099] In the formula, g is the acceleration due to gravity; θ' represents the average potential temperature; u represents the potential temperature fluctuation. * Friction velocity is used to characterize Reynolds stress. The influence of; U is the incoming wind speed; z is the height above the ground; u′, v′, w′ represent the longitudinal, lateral and vertical wind speed component fluctuations, respectively.

[0100] Furthermore, step 2) specifically includes:

[0101] By establishing a one-to-one functional relationship between the atmospheric stability parameter ζ and the flux Richardson number, the atmospheric stability parameter distribution function f(ζ) of the mountain wind farm is calculated to characterize the atmospheric stability distribution characteristics of the mountain wind farm at different terrain locations.

[0102]

[0103]

[0104] In the formula, L is the Monin-Obukhov length, which reflects the relative dominance between mechanical turbulence and buoyant turbulence generated by thermal effects; R f is the Richardson number for flux; k is the topographic factor; A > 0 is the scaling factor.

[0105] Furthermore, step 3) specifically includes:

[0106] By combining atmospheric stability distribution characteristics with the Monin-Obukhov similarity theory, any dimensionless turbulent characteristic in the near-surface layer depends on atmospheric stability, and a near-surface turbulent characteristic function is established accordingly; the near-surface turbulent characteristic includes the dimensionless vertical gradient φ of wind speed and potential temperature. m and φ h Dimensionless turbulent kinetic energy φ k and its dissipation rate φ ε The definition is as follows:

[0107]

[0108]

[0109]

[0110]

[0111] In the formula, k is the turbulent kinetic energy; θ * Characteristic potential temperature, For Reynolds stress; C μ ε is a constant; ε is the turbulent dissipation rate.

[0112] Furthermore, step 4) specifically includes:

[0113] The different locations of the mountain wind farm include at least: the entrance of the mountain wind farm and the top boundary;

[0114] Turbulence characteristics at the entrance of a mountain wind farm:

[0115]

[0116]

[0117]

[0118] Turbulence characteristics at the top boundary of a mountain wind farm:

[0119]

[0120]

[0121]

[0122] In the formula, U(z), k0, and ε0 represent the turbulence characteristics at the entrance of the mountain wind farm, which are the vertical wind speed gradient, turbulence intensity, and dissipation rate at the entrance, respectively; z0 is the ground clearance at the entrance; z top The top boundary height above the ground The three terms represent the turbulence characteristics at the top boundary of the mountain wind farm: the vertical wind speed gradient at the top boundary, the turbulence intensity at the top boundary, and the dissipation rate at the top boundary.

[0123] Furthermore, step 5) specifically includes:

[0124] Based on the turbulence characteristics at different locations in a mountain wind farm, for the flow in the plane perpendicular to the mainstream direction, without considering the effects of wind turbine force, pressure gradient force, and Coriolis force, and neglecting molecular viscosity, a set of turbulence control equations for a mountain wind farm under equilibrium conditions is established:

[0125]

[0126]

[0127]

[0128] In the formula,

[0129]

[0130]

[0131]

[0132] Where ρ is the air density; μ t σ is the turbulent viscosity; k and σ ε Prantl numbers for k and ε, respectively; C ε1 C ε2 and C ε3 Here are the parameters for the turbulence model, where C 1ε and C 2ε φ′ is a constant; ε , φ′ m , φ′ h , φ′ k These are the first derivatives of the corresponding functions; φ″ ε and φ″ k It is the second derivative of the corresponding function.

[0133] Furthermore, step 6) specifically includes:

[0134] The turbulent viscosity is corrected using wall functions;

[0135]

[0136] In the formula, μ t,p The corrected turbulent viscosity is represented by p; p represents the center of the first grid layer closest to the ground; z represents the turbulent viscosity. p Let p be the height above the ground; τ k This refers to the wall shear stress.

[0137] Based on the corrected turbulent viscosity μ t,p Improve the turbulence control equations for mountain wind farms:

[0138]

[0139]

[0140]

[0141] The improved turbulence control equations can simulate reasonable turbulent viscosity, and the wind speed automatically maintains horizontal uniformity, i.e., DU / Dt = 0.

[0142] Furthermore, step 7) specifically includes:

[0143] Based on the well-established turbulence control equations, and according to the site environment of the mountain wind farm, simulated boundary conditions are configured to simulate the spatiotemporal distribution of wind resources within the mountain wind farm.

[0144] Application Example: A schematic diagram of terrain modeling for a certain mountainous area with slope H200 and a gradient of 30° is shown below. Figure 2 As shown. The Fangtaishan terrain is divided by its upstream and downstream sections. The downstream section uses a 10D grid, and the upstream section uses a 6D grid. The vertical distance is 1500m. The horizontal and vertical grid resolutions are 40, 40, and 20, respectively. The required densification area and background grid vary depending on the peak cluster terrain; generally, the background grid for Fangtaishan is around 500,000. The densification area is the peak cluster surface, with a densification level of 3, resulting in a total grid count of approximately 2 million. Figure 3 This study describes the wind resource distribution of Fangtai Mountain, a terrain 120m above ground with a 30° slope, under conditions of an inflow wind speed of 4.5m / s. When the airflow passes over the hilly terrain, the boundary layer rapidly increases and a small deceleration zone appears at the foot of the windward side of the hill due to the adverse pressure gradient. The airflow accelerates as it rises along the hill surface due to terrain compression, but because the top of the platform is a smooth plane, the maximum wind speed occurs at the front and rear ends of the platform.

[0145] Wind speed simulations were performed on the Fangtaishan terrain using the established turbulence control equations that consider thermal stability. The results are as follows: Figure 4 As shown: Sequence 0 represents the wind speed calculation results under unstable atmospheric conditions, with the smallest simulated wind speed value and a large error compared to the measured wind speed value; Sequence 2 represents the wind speed calculation results without considering thermal stability, with a peak wind speed of approximately 5.30 m / s, slightly higher than under unstable atmospheric conditions, but still with some calculation error; Sequences 5 and 7 represent the wind speed calculation results under stable atmospheric conditions. It can be seen that Sequence 7 corresponds to the highest peak wind speed calculation under atmospheric stability, approximately 5.85 m / s, followed by Sequence 5, slightly lower than 5.50 m / s. The simulated wind speed values ​​calculated by the turbulence control equation considering thermal stability are closer to the measured values ​​and have good representativeness.

[0146] Example 2

[0147] Secondly, based on Embodiment 1, this embodiment provides a wind resource simulation device for mountain wind farms that considers thermal stability, including a processor and a storage medium;

[0148] The storage medium is used to store instructions;

[0149] The processor is configured to operate according to the instructions to execute the method according to Embodiment 1.

[0150] Example 3

[0151] Thirdly, based on Embodiment 1, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Embodiment 1.

[0152] Example 4

[0153] Fourthly, based on Embodiment 1, this embodiment provides a device, including,

[0154] Memory;

[0155] processor;

[0156] as well as

[0157] Computer programs;

[0158] The computer program is stored in the memory and configured to be executed by the processor to implement the method described in Embodiment 1.

[0159] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0161] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0162] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0163] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for simulating wind resources in mountain wind farms considering thermal stability, characterized in that, include: Step 1) Calculate the flux Richardson number based on the acquired mountain wind farm environmental data; wherein the mountain wind farm environmental data includes at least the spatial location of the measuring equipment and real-time potential temperature, wind direction, and wind speed data; Step 2) Determine the atmospheric stability distribution characteristics based on the flux Richardson number, specifically including: using atmospheric stability parameters The atmospheric stability parameter distribution function of a mountain wind farm is calculated based on the one-to-one functional relationship between flux and Richardson number. This is used to characterize the distribution of atmospheric stability at different terrain locations in mountain wind farms: ； ； In the formula, Height above the ground; The Monin–Obukhov length is used to reflect the relative dominance between mechanical turbulence and buoyant turbulence generated by thermal effects. Richardson's flux number; For terrain factors; , is the scaling factor; Step 3) Establish a similarity function for near-surface turbulence characteristics by combining atmospheric stability distribution characteristics with Monin–Obukhov similarity theory; Step 4) Calculate the turbulence characteristics at different locations in the mountain wind farm based on the near-surface turbulence characteristic similarity function; Step 5) Establish the turbulence control equations for the mountain wind farm based on the turbulence characteristics at different locations; Step 6) Correct the turbulent viscosity using wall functions and improve the turbulent control equations for the mountain wind farm. Specifically, this includes: correcting the turbulent viscosity using wall functions; and improving the turbulent control equations for the mountain wind farm based on the corrected turbulent viscosity. ； ； ； in, For the corrected turbulent viscosity, air density; For incoming air velocity; For time; Height above the ground; Reynolds stress; For turbulent kinetic energy, It is the acceleration due to gravity; The average potential temperature; The turbulent dissipation rate; For atmospheric stability parameters; It is a constant; The friction speed; and They are respectively and The number of Prantl; , and Here are the parameters for the turbulence model, where and Constants; dimensionless vertical gradients of wind speed and potential temperature and Dimensionless turbulent kinetic energy and its dissipation rate , , , , These are the first derivatives of the corresponding functions; and This is the second derivative of the corresponding function; The improved turbulence control equations can simulate reasonable turbulent viscosity, and the wind speed automatically maintains horizontal uniformity. ; Step 7) Based on the well-established turbulence control equations for mountain wind farms, the wind resources of mountain wind farms are simulated.

2. The method for simulating wind resources in mountain wind farms considering thermal stability according to claim 1, characterized in that, Step 1) specifically includes: Environmental data of mountain wind farms are acquired, wherein the environmental data of mountain wind farms are obtained by measuring equipment such as three-dimensional ultrasonic anemometer and eddy covariance measurement system; Based on the environmental data of the mountain wind farm, the flux Richardson number was calculated. ; ； ； In the formula, It is the acceleration due to gravity; The average potential temperature; This is due to potential temperature fluctuations; Friction velocity is used to characterize Reynolds stress. The impact; For incoming air velocity; Height above the ground; , , These represent the fluctuations in longitudinal, lateral, and vertical wind speed components, respectively.

3. The method for simulating wind resources in mountain wind farms considering thermal stability according to claim 1, characterized in that, Step 3) Establish a similarity function for near-surface turbulence characteristics by combining atmospheric stability distribution characteristics with Monin–Obukhov similarity theory, specifically including: By combining atmospheric stability distribution characteristics with the Monin–Obukhov similarity theory, any dimensionless turbulent property in the near-surface layer depends on atmospheric stability, and a near-surface turbulent property function is established accordingly; the near-surface turbulent property includes the dimensionless vertical gradients of wind speed and potential temperature. and Dimensionless turbulent kinetic energy and its dissipation rate The definition is as follows: ； ； ； ； In the formula, For atmospheric stability parameters; It is turbulent kinetic energy; Height above the ground; For friction speed, For incoming air velocity; The average potential temperature; Characteristic potential temperature, , Reynolds stress; It is a constant; This represents the turbulent dissipation rate.

4. The method for simulating wind resources in mountain wind farms considering thermal stability according to claim 1, characterized in that, Step 4) Calculate the turbulence characteristics at different locations in the mountain wind farm based on the near-surface turbulence characteristic similarity function, specifically including: The different locations of the mountain wind farm include at least: the entrance of the mountain wind farm and the top boundary; Turbulence characteristics at the entrance of a mountain wind farm: ； ； ； Turbulence characteristics at the top boundary of a mountain wind farm: ； ； ； In the formula, , , The turbulence characteristics at the entrance of the mountain wind farm are represented by the vertical wind speed gradient at the entrance, the turbulence intensity at the entrance, and the dissipation rate at the entrance. This refers to the height of the entrance from the ground. The dimensionless vertical gradient of Monin–Obukhov length, wind speed, and potential temperature. and Dimensionless turbulent kinetic energy and its dissipation rate , For turbulent kinetic energy, Height above the ground; For friction speed, For incoming air velocity; It is a constant; The turbulent dissipation rate; This refers to the height of the top boundary above the ground. , , The three terms represent the turbulence characteristics at the top boundary of the mountain wind farm: the vertical wind speed gradient at the top boundary, the turbulence intensity at the top boundary, and the dissipation rate at the top boundary.

5. The method for simulating wind resources in mountain wind farms considering thermal stability according to claim 1, characterized in that, Step 5) Establish the turbulence control equations for the mountain wind farm based on the turbulence characteristics at different locations, specifically including: Based on the turbulence characteristics at different locations in a mountain wind farm, for the flow in the plane perpendicular to the mainstream direction, without considering the effects of wind turbine force, pressure gradient force, and Coriolis force, and neglecting molecular viscosity, a set of turbulence control equations for a mountain wind farm under equilibrium conditions is established: ； ； ； In the formula, ； 、 、 、 ； 、 ； in, air density; For incoming air velocity; For time; Height above the ground; Turbulent viscosity; Reynolds stress; For turbulent kinetic energy, It is the acceleration due to gravity; The average potential temperature; The turbulent dissipation rate; For atmospheric stability parameters; It is a constant; The friction speed; and They are respectively and The number of Prantl; , and Here are the parameters for the turbulence model, where and Constants; dimensionless vertical gradients of wind speed and potential temperature and Dimensionless turbulent kinetic energy and its dissipation rate , , , , These are the first derivatives of the corresponding functions; and It is the second derivative of the corresponding function.

6. The method for simulating wind resources in mountain wind farms considering thermal stability according to claim 1, characterized in that, Wall function correction for turbulent viscosity includes: ； In the formula, This is the corrected turbulent viscosity; The friction speed; For turbulent kinetic energy, To be close to the center of the first layer of grid on the ground; for The height above the ground; This refers to the wall shear stress. Height above the ground; The length of Monin–Obukhov. This refers to turbulent viscosity.

7. The method for simulating wind resources in mountain wind farms considering thermal stability according to claim 1, characterized in that, Step 7) specifically includes: Based on the well-established turbulence control equations, and according to the site environment of the mountain wind farm, simulated boundary conditions are configured to simulate the spatiotemporal distribution of wind resources within the mountain wind farm.

8. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method described in any one of claims 1 to 7.

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