A numerical simulation method and device for offshore wind farms based on wave-air field coupling

Abstract

The present invention discloses a method and device for numerical simulation of offshore wind farms based on wave-air field coupling. The invention utilizes a parallel coupler to couple the atmospheric model, ocean model and wave model of an open computing framework in real time, realizes the variable interaction between the three component models of ocean-wave-atmosphere, and completes the numerical simulation and resource assessment of offshore wind farms considering the sea-air effect. Based on this, a wind farm parameterized model considering the sub-grid wake interference effect is added to obtain the wind resource characteristics of the offshore wind farm area, and realize high-fidelity numerical simulation of offshore wind farms under the action of sea and air. The present invention forms a numerical simulation method and device that takes into account the multi-physics and multi-scale coupling between wave and air field, which not only improves the numerical simulation method of offshore wind farms, but also significantly improves the accuracy of offshore wind resource prediction.

Description

Technical Field

[0001] The present invention belongs to the field of numerical simulation, and in particular relates to a method and device for numerical simulation of offshore wind farms based on ocean wave and air field coupling. Background Art

[0002] Offshore wind farms operate at the sea-air boundary, a sensitive zone for energy and material exchange, involving complex multi-physics, multi-scale interactions involving the ocean, waves, atmosphere, and offshore wind farms, including complex atmospheric-ocean dynamic coupling, heat, and material exchange. To date, the mechanisms of sea-wave-air-field coupling in offshore wind farm operating areas remain incompletely understood, and effective, reliable, and highly credible research methods are still lacking. This has limited the innovative development of offshore wind power technology and severely hampered the efficient development and utilization of offshore wind resources.

[0003] Weather research and forecasting of mesoscale numerical weather simulation systems are important tools for studying atmospheric physical processes in the air-sea boundary layer and flow field characteristics of offshore wind farms. In recent years, in order to more accurately forecast offshore wind resources, coupling air-sea models in atmospheric models has become a research focus. For example, application number: CN202010062642.X discloses a weather forecasting method and system based on a mesoscale air-sea coupling model. By constructing a multi-mode weather simulation system by constructing multiple mesoscale atmospheric models, deep learning calculations are performed on historical data to obtain ocean model parameters, and finally weather forecast results are obtained. Application number: CN 202211384677.0 discloses an integrated numerical forecast system for meteorological and hydrological elements of a regional air-sea wave coupling model and its operation method, including a coupler MCT and a post-processing module POST, which realizes the coupling of the three modes of atmosphere, ocean and waves. Application number CN201510001502.0 provides an integrated coupling method for ocean and meteorological models, employing coupler technology to facilitate the development and maintenance of sub-component models of the air-sea coupling model. The "non-flux correction" coupling method ensures the conservation of flux at the air-sea interface. Application number CN 201910533456.7 provides a numerical forecasting method for the wind-wave-current coupled ocean environment based on empirical correction. By constructing an empirical correction field and adding it to the simulation results at the initial forecast time, a more accurate initial forecast field is constructed. Numerical forecasts are then carried out using an atmosphere-ocean coupling numerical model to obtain wind, wave, and current forecast results. Application number CN 202211712024.0 provides a wind power resource prediction method and system based on air-sea-wave coupling. This method incorporates a wind power resource prediction model based on coupled atmospheric, ocean, and wave models to obtain regional wind power resource estimates.

[0004] However, existing patents rarely consider the effects and impacts of offshore wind farms while employing multi-model coupling. The influence of wake turbulence during offshore wind farm operation inevitably leads to changes in downstream wind speeds. Therefore, it is necessary to further incorporate wind farm models, taking into account the effects of waves and air, and develop a numerical simulation method and apparatus for offshore wind farms based on wave-air field coupling. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems existing in the existing technology and provide a method and device for numerical simulation of offshore wind farms based on wave-air field coupling.

[0006] The present invention is achieved through the following technical solutions: First, a numerical simulation method for offshore wind farms based on wave-air field coupling, the method comprises the following steps:

[0007] (1) Obtaining target sea area data, including meteorological data, topography, water depth and ocean forecast data of the target sea area;

[0008] (2) Based on the data of the target sea area in step (1), grid division is performed for the ocean, wave and atmospheric models and the wind farm parameterized model, and the initial fields and boundary fields of the ocean, wave and atmospheric models are respectively produced;

[0009] (3) Based on the grids, initial fields, and boundary fields of the ocean, wave, and atmosphere models obtained in step (2), set model parameters for different models according to the sensitivity requirements of each model;

[0010] (4) Through the distributed MCT parallel coupler, the data exchange interval of the coupling process is set to complete the coupling of the atmospheric model, ocean model and wave model, and realize the transmission and data exchange of simulation components between each model;

[0011] (5) Based on the coupling model in step (4), the wind farm parameterized model is added to the atmospheric model. By modifying the wind farm parameterized control file in the atmospheric model, turning on the wind farm parameterization switch, adding the wind turbine configuration file and wind turbine location information required by the wind farm parameterized model, a mesoscale ocean-wave-atmosphere-wind farm coupled numerical prediction model of the target sea area is obtained;

[0012] (6) Based on the ocean-wave-atmosphere-wind farm coupled numerical prediction model in step (5), the small-scale wind field, wave field, flow field and wind farm power output data of the target sea area are obtained through numerical simulation.

[0013] Furthermore, in step (1), the ocean forecast data includes ocean temperature, salinity, water level, flow rate and tidal change data.

[0014] Furthermore, in step (2), the grid division, wherein the atmospheric model and the wind farm parameterized model use the same set of structured grids, and the ocean model and the wave model use the same set of structured grids; the initial field and boundary field are the initial settings and boundary conditions for starting the atmospheric model and the ocean model, and the wave model relies on the data transmitted by the atmospheric model and the ocean model as boundary conditions for simulation.

[0015] Furthermore, in step (3), the model parameters are set as input and output settings, time step settings and physical parameterization scheme settings in the atmospheric model simulation process, including microphysical process scheme, cumulus parameterization scheme, longwave radiation scheme, shortwave radiation scheme, planetary boundary layer scheme and near-ground scheme; boundary layer scheme settings, turbulent mixing scheme settings, stretching coefficient settings, time step settings and output format settings in the ocean model simulation process; time step settings, model operation mode settings, low friction scheme, shallow water breaking wave settings, white hat dissipation settings and output format settings in the wave model simulation process.

[0016] Furthermore, in step (4), the transmission of simulation components and data exchange between the modes are as follows: the atmospheric mode transmits the simulated sea surface wind speed to the wave mode through the distributed parallel coupler, and transmits the simulated sea surface wind speed, atmospheric pressure, sea surface temperature, sea surface shear stress, net heat flux, sensible heat flux, latent heat flux, shortwave radiation flux and longwave radiation flux to the ocean mode; the ocean mode transmits the simulated sea surface temperature to the atmospheric mode, and transmits the simulated ocean current velocity and water level to the wave mode; the wave mode transmits the simulated wave height, wave direction, spectral peak, period and wavelength to the ocean mode and the atmospheric mode; and so on and so forth, completing data transmission and interaction within the entire solution time step.

[0017] Furthermore, in step (5), the improved wind farm parameterized model is a wind farm parameterized model that adds subgrid effects, taking into account the wake effects of multiple wind turbines within a unit grid, and characterizing the wind turbine interference effect by successively placing wind turbines within the unit grid to induce momentum loss. That is, a wind farm parameterized model that considers subgrid interference is developed as follows:

[0018] The wind speed loss per unit grid caused by the placement of n wind turbines is:

[0019]

[0020] in is the horizontal velocity component u in the grid ijk ,v ijk The amount of change, is the spatial correction coefficient, is the layout correction coefficient; where n is the number of wind turbines placed, m represents the placement order of wind turbines, and C T is the thrust coefficient, Represents the incoming air velocity of the mth placed wind turbine. Δt is the time step of the WRF model, N ij is the total number of wind turbines in the grid (i, j), Δt / (N ij -1) represents the time interval between the placement of the wind turbine. k Indicates the height of the vertical grid of the kth layer, A ijk Represents the area intercepted by the wind rotor rotating surface on the vertical layer k of the grid (i, j).

[0021] In a second aspect, the present invention also provides a numerical simulation device for offshore wind farms based on wave-air field coupling, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, it implements the numerical simulation method for offshore wind farms based on wave-air field coupling.

[0022] In a third aspect, the present invention further provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the aforementioned method for numerical simulation of offshore wind farms based on wave-air field coupling.

[0023] In a fourth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the aforementioned method for numerical simulation of offshore wind farms based on wave-air field coupling.

[0024] The beneficial effects of the present invention are as follows: through distributed parallel couplers, the atmospheric model, ocean model and wave model are coupled. On this basis, the role and influence of offshore wind farms are taken into consideration, and an improved wind farm parameterized model is added to form a numerical simulation method and device for offshore wind farms based on wave and air field coupling, which reduces the uncertainty of offshore wind resource forecasts and offshore wind farm power predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A schematic flow chart of the numerical simulation method for offshore wind farms based on wave-air field coupling provided by the present invention;

[0027] Figure 2Comparison of the spatial distribution of ERA5 data between the WRF model and the coupled model for an offshore wind farm in Hangzhou Bay;

[0028] Figure 3 This is a structural schematic diagram of a numerical prediction device for offshore wind farms based on wave-air field coupling provided by the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments.

[0030] This paper mainly constructs a numerical simulation method and device for offshore wind farms based on wave-atmosphere coupling, which considers the role and impact of offshore wind farms while coupling the atmospheric, ocean, and wave models. The specific steps are as follows:

[0031] (1) Obtain meteorological data of the target sea area based on meteorological observation stations or lidar data, and obtain ocean forecast data such as topography, water depth, sea temperature, salinity, water level, current velocity, and tidal changes of the target sea area from the high-precision global ocean bathymetric database and HYCOM global forecast data;

[0032] (2) Based on the data of the target sea area, the grids are divided for different modes, and the initial fields and boundary fields of different modes are produced. The atmospheric model and the wind farm parameterization model use the same set of structured grids, and the ocean model and the wave model use the same set of structured grids. The initial fields and boundary fields are the initial settings and boundary conditions for the start-up of the atmospheric model and the ocean model. The wave model relies on the data transmitted by the atmospheric model and the ocean model as boundary conditions for simulation.

[0033] (3) According to the sensitivity requirements, the model parameters are set for different modes. The input and output settings, time step settings and physical parameterization scheme settings in the atmospheric model simulation process need to be set, including microphysical process scheme, cumulus parameterization scheme, longwave radiation scheme, shortwave radiation scheme, planetary boundary layer scheme, near-surface scheme, etc.; boundary layer scheme settings, turbulent mixing scheme settings, stretch coefficient settings, time step settings, output format settings, etc. in the ocean model simulation process; time step settings, model operation mode settings, low friction scheme, shallow water breaking wave settings, white hat dissipation settings, output format settings, etc. in the wave model simulation process;

[0034] (4) Through the distributed MCT parallel coupler, that is, the MCT (Model Coupling Toolkit) parallel coupler based on Fortran-based MPI distributed storage, this technology realizes the modular parallel synchronous coupling of each component mode in an advanced "plug-and-play" manner, completing the coupling of the atmospheric model, ocean model and wave model. The atmospheric model transmits the simulated sea surface wind speed to the wave model, and transmits the simulated sea surface wind speed, atmospheric pressure, sea surface temperature, sea surface shear stress, net heat flux, sensible heat flux, latent heat flux, shortwave radiation flux and longwave radiation flux to the ocean model; the ocean model transmits the simulated sea surface temperature to the atmospheric model, and transmits the simulated seawater flow velocity and water level to the wave model; the wave model transmits the simulated wave height, wave direction, spectrum peak, period and wavelength to the ocean model and the atmospheric model;

[0035] (5) Based on the coupling mode in step (4), the improved wind farm parameterization model is added to the atmospheric model. By modifying the wind farm parameterization control file in the atmospheric model, turning on the wind farm parameterization switch, and adding the wind turbine configuration file and wind turbine location information required by the wind farm parameterization model; the improved wind farm parameterization model is a wind farm parameterization model with sub-grid effect added. For this model, please refer to the model disclosed in CN202210802451.1. The original wind farm parameterization model ignores the wake effect of multiple wind turbines in the unit grid and "accumulates and sums" the effects of all wind turbines in the unit grid, which will overestimate the wake effect and power output to a certain extent. Therefore, based on the ideas of "time difference" and "spatial Gaussian correction", the wind turbine interference effect is characterized by successively "putting" the induced momentum loss of wind turbines in the unit grid (the loss amount is related to the spacing between wind turbines), that is, a wind farm parameterization model considering the sub-grid interference effect is developed.

[0036] The wind speed loss per unit grid caused by the placement of n wind turbines is:

[0037]

[0038] in, is the horizontal velocity component u in the grid ijk ,v ijk The amount of change, is the spatial correction coefficient, is the layout correction coefficient:

[0039] α=dxdy / δx 2

[0040]

[0041] Where n is the number of wind turbines placed, m represents the order in which wind turbines are placed, and C T is the thrust coefficient, Represents the incoming air velocity of the mth placed wind turbine. Δt is the time step of the WRF model, N ij is the total number of wind turbines in the grid (i, j), Δt / (N ij -1) represents the time interval between the placement of the wind turbine. k Indicates the height of the vertical grid of the kth layer, A ijk represents the area intercepted by the rotor rotating surface on the vertical layer k of the grid (i, j), d is the distance between wind turbines, D cr is the critical distance (the wind turbine wake interference is negligible outside this distance), dx and dy are the horizontal grid sizes of the WRF model, and δx 2 To limit the area.

[0042] (6) Form an ocean-wave-atmosphere-wind farm coupled numerical prediction model, and obtain small-scale wind field, wave field, flow field and wind farm power data in the target sea area through numerical simulation.

[0043] The specific implementation effect of the above method is demonstrated below with reference to examples.

[0044] Example

[0045] In this example, an offshore wind farm was used as the research object to explore the simulation effect and advantages of this offshore wind farm numerical prediction model based on wave-air field coupling. The relevant parameters used are set as follows:

[0046] The simulation duration for the target sea area is nine days from February 14, 2022 to February 23, 2022. Since the model needs a certain amount of time to reach a steady state during operation, the analysis period is 7 days from February 15, 2022 to February 22, 2022. The Weather Research and Forecasting Model WRF and the wind farm parameterization model use three-layer nested grids, the innermost layer of which contains the complete bay area, with resolutions of 9km, 3km and 1km respectively. Each nested domain outputs data once every 60 minutes to ensure that the time points of each data are the same. The ocean model ROMS and the wave model SWAN use the same set of two-layer nested grids, with a specific range of 29.6°N~32.1°N, 120.3°E~124.9°E. The resolution of the outermost grid is 2km, and the resolution ratio of the inner and outer grids is 1:3. WRF has a 45-second time step, ROMS has a 60-second time step for large grids and a 30-second time step for small grids, and SWAN has a 600-second time step for large grids and a 300-second time step for small grids. WRF is driven by FNL data from the National Center for Atmospheric Research (NCAR), a global reanalysis dataset. ROMS is driven by forecasts from the HYCOM global ocean model. The SWAN component in the COAWST model is driven by data provided by ROMS and WRF, requiring no additional data input.

[0047] Table 1 shows the comparison of the simulation effects of the WRF mode and the coupled mode on the wind tower and the wind farm measured power in this case; Figure 2 The spatial distribution comparison of the simulated variables of the WRF model and the coupled model with the ERA5 data in this case is shown; Table 2 shows the comparative verification of the simulated variables of the WRF model and the coupled model with the ERA5 data in this case; overall, the coupled model performs better in terms of test parameters such as correlation coefficient, root mean square error, relative root mean square error and consistency.

[0048] Table 1

[0049]

[0050] Table 2

[0051]

[0052] Corresponding to the aforementioned embodiment of a numerical simulation method for an offshore wind farm based on ocean wave and air field coupling, the present invention also provides an embodiment of a numerical prediction device for an offshore wind farm based on ocean wave and air field coupling.

[0053] See also Figure 3An embodiment of the present invention provides a numerical simulation device for an offshore wind farm based on wave-air field coupling, comprising a memory, one or more processors and an I / O device, wherein the memory stores executable code; when the processor executes the executable code, it is used to implement a numerical simulation method for an offshore wind farm based on wave-air field coupling in the above embodiment; the I / O device is used to present the simulation results.

[0054] The embodiment of the numerical simulation device for offshore wind farms based on wave-air field coupling provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, if Figure 3 As shown in the figure, it is a hardware structure diagram of any device with data processing capability where the numerical simulation device of offshore wind farm based on wave-air field coupling provided by the present invention is located. Figure 3 In addition to the processor, memory, I / O device, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual function of the device with data processing capabilities, which will not be described in detail.

[0055] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0056] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present invention. A person of ordinary skill in the art can understand and implement the present invention without inventive work.

[0057] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the numerical simulation method for offshore wind farms based on wave-air field coupling.

[0058] The embodiment described above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Persons skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent substitution or equivalent transformation falls within the scope of protection of the present invention.

Claims

1. A numerical simulation method for offshore wind farms based on wave-air field coupling, characterized in that: The steps of this method are as follows: (1) Obtain target sea area data, including meteorological data, topography, water depth and ocean forecast data of the target sea area; (2) Based on the data of the target sea area in step (1), grid division is performed for the ocean, wave and atmospheric models and the wind farm parameterized model, and the initial fields and boundary fields of the ocean, wave and atmospheric models are generated respectively; (3) Based on the grids, initial fields, and boundary fields of the ocean, wave, and atmosphere models obtained in step (2), set model parameters for different models according to the sensitivity requirements of each model; (4) Through the distributed MCT parallel coupler, the data exchange interval of the coupling process is set to complete the coupling of the atmospheric model, ocean model and wave model, and realize the transmission and data exchange of simulation components between the models; (5) Based on the coupling model in step (4), the wind farm parameterization model is added to the atmospheric model. By modifying the wind farm parameterization control file in the atmospheric model, the wind turbine configuration file and wind turbine location information required by the wind farm parameterization model are added to obtain the mesoscale ocean-wave-atmosphere-wind farm coupled numerical prediction model of the target sea area. The improved wind farm parameterization model is a wind farm parameterization model with subgrid effect added. The wake effect of multiple wind turbines in a unit grid is considered. The wind turbine interference effect is characterized by successively placing the wind turbine-induced momentum loss in the unit grid. That is, the wind farm parameterization model considering the subgrid interference effect is developed as follows: The wind speed loss per unit grid caused by the placement of n wind turbines is: in , is the horizontal velocity component in the grid , The amount of change, is the spatial correction coefficient, is the layout correction coefficient; n is the number of wind turbines placed, m represents the placement order of wind turbines, is the thrust coefficient, represents the incoming air velocity received by the mth placed wind turbine; ∆t is the time step of the WRF model, is the total number of wind turbines in grid (i, j), ∆t / ( -1) represents the time interval for placing wind turbines; Indicates the height of the vertical grid at the kth layer, represents the area intercepted by the rotor rotating surface on the vertical layer k of the grid (i, j); (6) Based on the ocean-wave-atmosphere-wind farm coupled numerical prediction model in step (5), the small-scale wind field, wave field, flow field and wind farm power output data of the target sea area are obtained through numerical simulation.

2. The numerical simulation method for offshore wind farms based on wave-air field coupling according to claim 1 is characterized in that: In step (1), the ocean forecast data includes ocean temperature, salinity, water level, flow rate and tidal change data.

3. The numerical simulation method for offshore wind farms based on wave-air field coupling according to claim 1 is characterized in that: In step (2), the grid division is as follows: the atmospheric model and the wind farm parameterized model use the same set of structured grids, and the ocean model and the wave model use the same set of structured grids; the initial field and boundary field are the initial settings and boundary conditions for starting the atmospheric model and the ocean model, and the wave model relies on the data transmitted by the atmospheric model and the ocean model as boundary conditions for simulation.

4. The numerical simulation method for offshore wind farms based on wave-air field coupling according to claim 1, characterized in that: In step (3), the model parameters are set as input and output settings, time step settings and physical parameterization scheme settings during the atmospheric model simulation process, including microphysical process scheme, cumulus parameterization scheme, longwave radiation scheme, shortwave radiation scheme, planetary boundary layer scheme and near-surface scheme; boundary layer scheme settings, turbulent mixing scheme settings, stretching coefficient settings, time step settings and output format settings during the ocean model simulation process; time step settings, model operation mode settings, low friction scheme, shallow water breaking wave settings, white hat dissipation settings and output format settings during the wave model simulation process.

5. The numerical simulation method for offshore wind farms based on wave-air field coupling according to claim 1, characterized in that: In step (4), the transmission of simulation components and data exchange between the modes are as follows: the atmospheric mode transmits the simulated sea surface wind speed to the wave mode through the distributed parallel coupler, and transmits the simulated sea surface wind speed, atmospheric pressure, sea surface temperature, sea surface shear stress, net heat flux, sensible heat flux, latent heat flux, short-wave radiation flux and long-wave radiation flux to the ocean mode; the ocean mode transmits the simulated sea surface temperature to the atmospheric mode, and transmits the simulated ocean current velocity and water level to the wave mode; the wave mode transmits the simulated wave height, wave direction, spectral peak, period and wavelength to the ocean mode and the atmospheric mode; and so on and so forth, completing the data transmission and interaction within the entire solution time step.

6. A numerical simulation device for offshore wind farms based on wave-air field coupling, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, a numerical simulation method for an offshore wind farm based on wave-air field coupling according to any one of claims 1 to 5 is implemented.

7. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, a numerical simulation method for an offshore wind farm based on wave-air field coupling according to any one of claims 1 to 5 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for numerical simulation of an offshore wind farm based on wave-air field coupling as described in any one of claims 1 to 5 is implemented.

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