CN111680408A - Wind resource map drawing method and device for offshore wind power - Google Patents
Wind resource map drawing method and device for offshore wind power Download PDFInfo
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Abstract
The invention discloses a method and a device for drawing a wind resource map of offshore wind power, wherein the method comprises the following steps: collecting wind resource related data and performing data arrangement; carrying out downscaling analysis on the wind field simulation data, and adjusting the wind field according to the terrain data to generate a downscaling grid field; adjusting the downscaling grid field based on actual measurement data, and correcting wind direction frequency distribution; counting wind resource parameters, wherein the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density; drawing according to the wind resource parameters to generate a wind resource map. The method can fully consider the potential influence of the marine hydrological factors on the distribution of the offshore wind resources, and improve the accuracy and the applicability of the drawing of the wind resource map.
Description
Technical Field
The invention relates to the technical field of offshore wind power, in particular to a method and a device for drawing a wind resource map for offshore wind power.
Background
Compared with onshore wind power, offshore wind power has the unique advantages of more stable wind energy resources, more considerable development scale, more efficient power generation efficiency, more saved land utilization, more convenient power grid access and the like as a new stage and a new direction of wind energy utilization, and therefore the offshore wind power is generally concerned in the world.
The offshore wind resource map is used as a main carrier and an intuitive result of wind resource investigation and evaluation, is a prerequisite condition for formulating offshore wind power development planning and wind power plant macroscopic layout, and is also input data for subsequently performing project model selection, machine position arrangement, power generation amount estimation and economic index measurement and calculation. Therefore, the method has important significance in drawing the offshore wind resource map with high spatial and temporal resolution and high precision.
At present, the drawing method of the domestic offshore wind resource map is basically consistent with the drawing method of the onshore wind resource map. Firstly, obtaining a wind speed, a wind direction, an air pressure, an air temperature and a relative humidity simulation result under a certain space-time resolution by a meteorological numerical simulation means; then after the actual measurement data is corrected, the annual average wind speed and the wind power density at different heights are displayed in a grading way by using a specified color code to form a thematic map layer; and finally, drawing a map by using geographic information element points.
However, in the process of research and practice of the prior art, the inventor of the present invention finds that the existing drawing technology of the offshore wind resource map still has several disadvantages, such as that the potential influence of marine hydrological conditions on the offshore wind resource is difficult to reflect by a simple meteorological numerical simulation result; quantitative evaluation of lack of representativeness and representative range when using measured data for correction; and the wind speed and the wind power density of each height independent are difficult to comprehensively reflect the whole wind conditions of the impeller range (30-200 m away from the sea surface) of the wind generating set. Therefore, a method for drawing a wind resource map of offshore wind power, which can solve the above problems, is urgently needed.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a method and a device for drawing a wind resource map for offshore wind power, which can fully consider the potential influence of marine hydrological factors on the distribution of offshore wind resources and improve the precision and the applicability of drawing the wind resource map.
In order to solve the above problem, an embodiment of the present invention provides a method for drawing a wind resource map for offshore wind power, which at least includes the following steps:
collecting wind resource related data and performing data arrangement; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement;
carrying out downscaling analysis on the wind field simulation data, and adjusting the wind field according to the terrain data to generate a downscaling grid field;
adjusting the downscaling grid field based on actual measurement data, and correcting wind direction frequency distribution;
counting wind resource parameters, wherein the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density;
drawing according to the wind resource parameters to generate a wind resource map.
Preferably, the wind resource related data includes mesoscale meteorological pattern data, marine wind measurement data, wave observation data, terrain data, water depth data, established offshore wind farm coordinates and basic geographic information data.
As a preferred scheme, the marine anemometry data inspection and processing specifically comprises the following steps:
screening off offshore anemometry data collected by an offshore anemometry tower, a buoy and an island meteorological station, wherein the effective data integrity rate of the offshore anemometry data is lower than 90%;
and carrying out unreasonable data processing and default data processing on the offshore wind measuring data.
As a preferred scheme, the sorting wave observation data specifically comprises:
counting the monthly average wave height in the wave observation data according to a preset height range;
counting the total average period of the wave observation data according to a preset time range;
and counting the monthly average wave height range and the average period interval with the highest occurrence frequency.
As a preferred scheme, the down-scaling analysis is performed on the wind field simulation data, the wind field is adjusted according to the terrain data, and a down-scaling grid field is generated, specifically including:
defining a downscaling grid standard, taking a Cartesian coordinate as a reference coordinate system, and setting grid horizontal resolution and vertical layering height;
simulating the mesoscale meteorological model to the wind speed UmAnd wind direction DmU converted to wind vectormComponent sum vmA component;
weighting u in horizontal direction by inverse distancemComponent sum vmInterpolating components into the downscale grid;
the u is interpolated vertically by linear interpolationmComponent sum vmThe components are interpolated to the vertical hierarchy of the downscale grid.
As a preferred scheme, the down-scaling analysis of the wind field simulation data and the adjustment of the wind field according to the terrain data to generate a down-scaling grid field further include:
if the islands are detected to exist in the downscaling grid, calculating the adjustment effect of the terrain on the wind field;
and constraining the whole downscaling grid by adopting a three-dimensional wind field radiationless relation to generate an adjusted downscaling grid field.
As a preferred scheme, the adjusting the downscaling grid field based on actual measurement data and correcting the wind direction frequency distribution specifically include:
will wind vector u0Component sum v0Component, synthesized into horizontal wind speed U0And wind direction D0；
Calculating sea surface drag coefficient Cd；
Monthly average H from the wave observation datamAnd TmSimulating seasonal variation characteristics of waves to the sea surface drag coefficient CdAdjusting to form a downscaling grid C with 10m height wind speed and wave changedDistributing;
according to the horizontal wind speed U0Fitting a logarithmic law to the vertical profile of the wind speed and adjusting the CdCorrecting the original wind speed vertical profile by adopting a boundary layer similarity theory according to the length L of the Monin-obuff output by the mesoscale meteorological model;
estimating the representative range R of the offshore anemometer tower, the buoy and the island meteorological station according to the Hsieh modelr；
For offshore anemometry tower, buoy and island meteorological station D during anemometrysAccording to 16 piecesCarrying out frequency statistics on wind direction sectors, calculating the frequency difference between adjacent sectors, and determining the form of wind direction frequency distribution;
for offshore anemometry tower, buoy and island meteorological station D during anemometrysAnd downscaling grid D in synchronism with site anemometry0Fitting according to von Mises distribution;
counting the corresponding representative ranges R estimated by the offshore anemometer tower, buoy and island meteorological station during anemometryrTaking RrDefining an influence area A by taking median as radiusrScreening lattice points in the Ar range, and calculating von Mises distribution parameter mu obtained by fitting the lattice points and the sitesiDifference d mu ofiAnd for mu of lattice point not in the period of anemometryiCorrecting;
adopting inverse distance weighting method to make the non-independent ArThe grid points within the range are corrected.
An embodiment of the present invention further provides a wind resource map drawing apparatus for offshore wind power, including:
the data sorting module is used for collecting wind resource related data and sorting data; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement;
the downscaling analysis module is used for performing downscaling analysis on the wind field simulation data and adjusting the wind field according to the topographic data to generate a downscaling grid field;
the wind field correction module is used for adjusting the downscale grid field based on actual measurement data and correcting wind direction frequency distribution;
the wind resource parameter counting module is used for counting wind resource parameters, and the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density;
and the wind resource map generation module is used for drawing according to the wind resource parameters to generate a wind resource map.
An embodiment of the present invention provides a terminal device for wind resource mapping of offshore wind power, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the wind resource mapping method for offshore wind power as described above is implemented.
An embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus on which the computer-readable storage medium is located is controlled to execute the wind resource mapping method for offshore wind power as described above.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a method and a device for drawing a wind resource map for offshore wind power, wherein the method comprises the following steps: collecting wind resource related data and performing data arrangement; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement; carrying out downscaling analysis on the wind field simulation data, and adjusting the wind field according to the terrain data to generate a downscaling grid field; adjusting the downscaling grid field based on actual measurement data, and correcting wind direction frequency distribution; counting wind resource parameters, wherein the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density; drawing according to the wind resource parameters to generate a wind resource map.
Compared with the prior art, the method can fully consider the potential influence of the marine hydrological factors on the distribution of the offshore wind resources, and improve the accuracy and the applicability of the drawing of the wind resource map; the method comprises the steps of collecting mesoscale meteorological model simulation data, offshore field wind measurement data, wave observation data and underwater topography data, combining a statistic-power mixed downscaling technology, a sea surface drag coefficient parameterization scheme considering a wave effect and an offshore wind measurement data representative range estimation function, and realizing generation of a high-spatial-resolution offshore wind field simulation result and result correction based on actual measurement hydrometeorological data; in addition, the analysis results of the equivalent wind speed and the wind power density of the impeller are supplemented, and the representativeness and the applicability of the wind resource map are further enhanced.
Drawings
Fig. 1 is a schematic flow chart of a wind resource mapping method for offshore wind power according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart of a downscaling analysis of wind farm simulation data according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a downscale grid provided by a first embodiment of the present invention;
fig. 4 is a schematic flow chart of wind field correction based on measured data according to a first embodiment of the present invention;
FIG. 5 is a schematic flow chart of generating a wind resource map according to a first embodiment of the present invention;
FIG. 6 is a schematic diagram of a wind resource map provided in accordance with a first embodiment of the present invention;
fig. 7 is a color scale table corresponding to the grading of the average wind speed and the average wind power of the wind resource map according to the first embodiment of the present invention;
fig. 8 is a schematic structural diagram of a wind resource map drawing apparatus for offshore wind power according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
Firstly, the invention provides an application scenario, such as providing a wind resource map drawing method for offshore wind power, and drawing a wind resource map of offshore wind power.
The first embodiment of the present invention:
please refer to fig. 1-7.
As shown in fig. 1, the embodiment provides a wind resource map drawing method for offshore wind power, which at least includes the following steps:
s101, collecting wind resource related data and performing data arrangement; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement;
in a preferred embodiment, the wind resource-related data comprises mesoscale meteorological pattern data, marine wind measurements data, wave observation data, terrain data, water depth data, established offshore wind farm coordinates, and basic geographic information data.
Specifically, for step S101, the mesoscale meteorological pattern data, the marine anemometry data, the wave observation data, the terrain and water depth data, the coordinates of the established marine wind farm, and other basic geographic information data are collected.
Wherein the mesoscale meteorological pattern data comprise wind speed U of each altitude hourly every 20 yearsmWind direction DmAir density ρ, Monning-obufhoff length L; hourly wind speed U of 10m heightm,10Wind direction Dm,10Simulating a result; longitude and latitude of grid points and potential height G of the grid points; and the grid resolution is 3-9 km.
The offshore wind measurement data comprises an offshore wind measurement tower, a buoy and an island meteorological station hourly average wind speed W for continuously observing the offshore wind measurement tower, the buoy and the island meteorological station for one yearsWind direction DsAnd observing starting and stopping time according to the longitude and latitude of the observation point, the model of the instrument and maintenance records.
The wave observation includes an hourly average wave height HmAverage period TmAnd observation ofAnd (4) observing start and stop time according to the longitude and latitude, the instrument model and maintenance records.
In a preferred embodiment, the marine anemometry data inspection and processing specifically comprises:
screening off offshore anemometry data collected by an offshore anemometry tower, a buoy and an island meteorological station, wherein the effective data integrity rate of the offshore anemometry data is lower than 90%;
and carrying out unreasonable data processing and default data processing on the offshore wind measuring data.
Specifically, the offshore wind measuring tower, the buoy and the island meteorological station with effective data integrity rate lower than 90% are not adopted by the wind measuring data inspection method and the reference value according to the relevant regulations of wind power plant wind energy resource assessment method (GB/T18710) and offshore wind power plant wind energy resource measurement and ocean hydrology observation specification (NB/T31029).
Wherein, the anemometry data processing comprises unreasonable and default data processing. For the offshore anemometer tower, if the data of only one height layer is missing or unreasonable, the data is compensated or corrected according to the correlation analysis between the height layer and other height layers of the same tower; if all the height layer data are not detected or are not reasonable in a certain period of time, the data are not processed. For buoys and island meteorological stations, if data are continuously absent or unreasonable time is less than 8 hours, the data are compensated or corrected according to the correlation analysis between the stations and other stations around; if the continuous absence or unreasonable length is more than 8 hours, no treatment is performed.
The correlation analysis is to calculate the correlation coefficient between the same data of each height layer or each station, determine the interpolation priority according to the magnitude of the correlation coefficient, preferentially select other height layers or other stations with the highest correlation coefficient, and establish linear regression relation interpolation missing or unreasonable data, wherein the calculation formula is as follows:
y＝ax+b；
in the formula, x is other height layer or other station data (wind speed or wind direction) for interpolation, y is height layer or station data (wind speed or wind direction) with missing or unreasonable detection, and constants a and b are determined according to a least square method.
In a preferred embodiment, the sorting wave observation data specifically includes:
counting the monthly average wave height in the wave observation data according to a preset height range;
counting the total average period of the wave observation data according to a preset time range;
and counting the monthly average wave height range and the average period interval with the highest occurrence frequency.
Specifically, the wave observation data arrangement comprises month-by-month wave height-period joint distribution statistics. Average wave height H for each monthmAccording to the ratio of 0 to 0.1m, 0.1 to 0.2m, 0.2 to 0.3m, 0.3 to 0.4m, 0.4 to 0.5m, 0.5 to 1.0m, 1.0 to 1.5m, 1.5 to 2.0m, 2.0 to 2.5m, 2.5 to 3.0m, 3.0 to 3.5m, 3.5 to 4.0m, 4.0 to 4.5m, 4.5 to 5.0m,>5.0m, average period TmAccording to the formula, the amount of the compound is 0-1 s, 1-2 s, 2-3 s, 3-4 s, 4-5 s, 5-6 s, 6-7 s, 7-8 s, 8-9 s, 9-10 s,>Counting the occurrence frequency for 10s, and finding out the H with the highest frequencymAnd TmAn interval.
S102, carrying out scale reduction analysis on wind field simulation data, and adjusting the wind field according to the terrain data to generate a scale reduction grid field;
in a preferred embodiment, the down-scaling analysis of the wind field simulation data and the adjustment of the wind field according to the terrain data to generate a down-scaling grid field specifically include:
defining a downscaling grid standard, taking a Cartesian coordinate as a reference coordinate system, and setting grid horizontal resolution and vertical layering height;
simulating the mesoscale meteorological model to the wind speed UmAnd wind direction DmU converted to wind vectormComponent sum vmA component;
weighting u in horizontal direction by inverse distancemComponent sum vmInterpolating components into the downscale grid;
the u is interpolated vertically by linear interpolationmComponent sum vmThe components are interpolated to the vertical hierarchy of the downscale grid.
In a preferred embodiment, the down-scaling analysis of the wind field simulation data and the adjustment of the wind field according to the terrain data to generate a down-scaling grid field further include:
if the islands are detected to exist in the downscaling grid, calculating the adjustment effect of the terrain on the wind field;
and constraining the whole downscaling grid by adopting a three-dimensional wind field radiationless relation to generate an adjusted downscaling grid field.
Specifically, as shown in fig. 2-3, the step S102 includes the following steps:
a downscaling grid is defined, the coordinate system is Cartesian coordinates, the horizontal resolution is 1km × 1km, and the vertical layering is 10m, 20m, 30m, 40m, 50m, 60m, 70m, 80m, 90m, 100m, 110m, 120m, 130m, 140m, 150m, 160m, 170m, 180m, 190m, 200m, 250m, 300m, 350m, 400m, 450m, 500m, 600m, 700m, 800m, 900m, 1000m, 1200 m.
Simulating the mesoscale meteorological model to the wind speed UmWind direction DmU converted to wind vectormComponent sum vmComponent, the calculation is as follows:
um＝Umcos(270-Dm)
vm＝Umsin(270-Dm)；
weighting u by inverse distance in horizontal directionmAnd vmInterpolating to a downscale grid, the calculation is as follows:
in the formula u0And v0For the downscaled grid point interpolation result, um,iAnd vm,iFor each one isThe simulation result of the mesoscale meteorological pattern grid points, n is the total number of the mesoscale grid points required by interpolation (the search radius is the resolution of the mesoscale meteorological pattern), and lambdaiAs weights of the respective mesoscale meteorological pattern grid values,/iThe distance from each mesoscale meteorological pattern lattice point to the downscale grid.
U is interpolated vertically by linear interpolationmAnd vmInterpolating to vertical layering of the downscaled grid, the calculation is as follows:
Zu＝Gu/g
Zl＝Gl/g；
in the formula u0And v0For the downscaled grid point interpolation result, um uAnd vm lIs the simulation result of the upper and lower two height layers in the mesoscale meteorological model, GuAnd GlThe potential heights of an upper height layer and a lower height layer in the mesoscale meteorological model are shown, g is the gravity acceleration, and z is the height of the downscale grid.
If there are islands in the downscale grid, the Liu and Yocke (1980) schemes are used to calculate1The calculation formula is as follows:
in the formula, w0Is the vertical velocity of the downscaled grid point, htThe terrain elevation (the water surface is 0) of the down-scale grid point, theta is the temperature of the medium-scale meteorological pattern grid point, and z is the height of the down-scale grid.
If the last step is executed, the whole downscaling grid u is subjected to0、v0And w0And (3) constraining by adopting a three-dimensional wind field non-dispersion relation, wherein the calculation formula is as follows:
in the formula, i and j are the lattice point number in the direction of the horizontal lattice X, Y, k is the level number of the vertical lattice, and ztopIs the grid top height, and Δ x and Δ y are the resolutions of the horizontal grid X, Y directions, respectively.
S103, adjusting the downscaling grid field based on actual measurement data, and correcting wind direction frequency distribution;
in a preferred embodiment, the adjusting the downscaling grid field based on actual measurement data and the correcting the wind direction frequency distribution specifically include:
will wind vector u0Component sum v0Component, synthesized into horizontal wind speed U0And wind direction D0；
Calculating sea surface drag coefficient Cd；
Monthly average H from the wave observation datamAnd TmSimulating seasonal variation characteristics of waves to the sea surface drag coefficient CdAdjusting to form a downscaling grid C with 10m height wind speed and wave changedDistributing;
according to the horizontal wind speed U0Fitting a logarithmic law to the vertical profile of the wind speed and adjusting the CdCorrecting the original wind speed vertical profile by adopting a boundary layer similarity theory according to the length L of the Monin-obuff output by the mesoscale meteorological model;
estimating the representative range R of the offshore anemometer tower, the buoy and the island meteorological station according to the Hsieh modelr；
For offshore anemometry tower, buoy and island meteorological station D during anemometrysCarrying out frequency statistics according to 16 wind direction sectors, calculating the frequency difference between adjacent sectors, and determining the form of wind direction frequency distribution;
for offshore anemometry tower, buoy and island meteorological station D during anemometrysAnd downscaling grid D in synchronism with site anemometry0Fitting according to von Mises distribution;
counting the corresponding representative ranges R estimated by the offshore anemometer tower, buoy and island meteorological station during anemometryrTaking RrDefining an influence area A by taking median as radiusrScreening lattice points in the Ar range, and calculating von Mises distribution parameter mu obtained by fitting the lattice points and the sitesiDifference d mu ofiAnd for mu of lattice point not in the period of anemometryiCorrecting;
adopting inverse distance weighting method to make the non-independent ArThe grid points within the range are corrected.
Specifically, for step S103, as shown in FIG. 4, u of the wind vector is determined0Component sum v0Component, composite horizontal wind speed U0And wind direction D0The calculation formula is as follows:
calculating sea surface drag coefficient C by adopting a ZHao (2015) schemedThe calculation formula is as follows:
wherein d is the depth of water, U10For a 10m height wind speed of the downscaling grid, uc,∞For infinite depth of water CdTaking 34m/s as the height wind speed of 10m corresponding to the maximum value; g is the gravity acceleration, b is the fitting parameter, 0.25, L is takenmIs the actual wavelength, L∞At a deep water wave wavelength, H, at infinite depth∞The water depth wave height.
Using the monthly average H according to the statistical result of the wave observation datamAnd TmSimulating sea surface drag coefficient C of seasonal variation characteristic of waves to previous stepdAdjusting to form a downscaling grid C which changes with the wind speed at the height of 10m and the wavesdAnd (4) distribution.
For U of 10-200 m0Fitting the wind speed vertical profile by using a logarithmic law, and calculating as follows:
z′＝ln z
wherein k is the hierarchical number of the vertical grid, R2To determine the coefficients. If R is2If not less than 0.98, C is useddAnd U10Correcting the original wind speed vertical profile by utilizing a boundary layer similarity theory according to the length L of the Morin-obuff output by the mesoscale meteorological model, wherein a calculation formula is as follows;
if R is2<0.98 is not corrected; if islands are arranged in the grids, the highest terrain elevation h above the water surface is usedt,maxThe distance of 20 times of the distance is provided with a buffer area, and the vertical profile of the wind speed in the buffer area is not corrected.
The representative ranges of the offshore anemometer tower, the buoy and the island meteorological station are estimated by using a Hsieh (2000) model, and the calculation formula is as follows:
F(X,z)＝∫f(X,z)dX
in the formula, X is the distance in the windward direction of the survey station, z is the height from the ground, and L is the length of the Morin-obuff. Taking X with F (X, z) reaching 0.9 as the representative range R of the siter。
For offshore anemometer tower, buoy and island meteorological station during anemometry period DsFrequency statistics is carried out according to 16 wind direction sectors, the frequency difference between adjacent sectors is calculated, the statistical frequency difference changes from positive to negative, and the occurrence frequency is changed>The number n of sectors of 5% determines the shape of the wind direction frequency distribution.
For offshore anemometer tower, buoy and island meteorological station during anemometry period DsAnd downscaling grid D in synchronism with site anemometry0Both fit according to von Mises distribution, calculated as follows:
wherein n is the number of sectors determined in the estimated representative range by using the Hsieh (2000) model，I0Modifying Bessel function, omega, for order 0i、μi、κiFor the distribution fitting parameters, matlab or sps software can be used for determination.
Statistical determination of R in a representative range estimated by using Hsieh (2000) model during the wind measurement period of offshore wind measuring tower, buoy and island meteorological stationrTaking RrDefining an influence area A by taking median as radiusrScreening ArGrid points within the range, calculating von Mises distribution parameter mu determined by distribution fitting at the grid points and the sitesiDifference d mu ofiAnd for mu of lattice point in other time intervaliThe calculation formula is corrected as follows:
in the formula, mui sIs the distribution parameter of von Mises of offshore anemometry towers, buoys and island meteorological stations, namely mui 0Is ArVon Mises distribution parameter, μ, of grid points within rangei,all 0Is ArVon Mises distribution parameters for the entire simulation period of the grid points within the range.
For multiple A at the same timerWithin the range or not at any ArGrid points of the range are corrected by adopting an inverse distance weighting method, and the two previous stations with the nearest distance are takeniAnd (6) carrying out interpolation.
S104, wind resource parameters are counted, wherein the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density;
specifically, for step S104, the statistical wind resource parameter includes: average wind speed of each altitude, average wind power density of each altitude, wind direction frequency of each altitude, Weibull distribution parameter of each altitude, equivalent wind speed of the impeller and equivalent wind power density of the impeller.
Statistics integratorAverage wind speed U of each time period0,mThe calculation formula is as follows:
wherein T is the whole time period, U0,tThe wind speed value of the downscaling grid at the t hour is obtained.
Counting the average wind power density D of the whole time intervalWPThe calculation formula is as follows:
in the formula, ρtAir density at the t hour.
And taking 16 wind direction sector statistics according to the corrected von Mises distribution of the wind direction frequency.
Fitting Weibull distribution shape parameter K, calculated as:
wherein P is U in the whole T period0Higher than average wind speed U in sequence0,mГ is a Gamma function.
Fitting Weibull distribution scale parameter A, and calculating as follows:
taking 80m, 90m, 100m, 110m and 120m as the typical hub height H of the wind generating sethubTaking 100m, 120m, 140m, 160m, 180m and 200m as the typical impeller diameter D of the wind generating setrotEstablishing a corresponding relation between the height of each hub and the diameter of each impeller, namely that the height of 80m corresponds to the diameters of 100m and 120m impellers, the height of 90m corresponds to the diameters of 100m, 120m and 140m impellers, the height of 100m corresponds to the diameters of 100m, 120m, 140m and 160m impellers, the height of 110m corresponds to the diameters of 100m, 120m, 140m, 160m and 180m impellers, and the height of 120m corresponds to the diameters of 100m, 120m, 140m, 160m, 180m and 20 impellers0m impeller diameter.
Establishing each vertical height layer z of the downscaling grid according to the hub height and the impeller diameter determined in the previous stepkSwept area as kThe calculation formula is as follows:
where dz is the height difference between the vertical height levels of the defined downscale grid and nz is the number of vertical height levels within the impeller.
Counting the equivalent wind speed U of the impeller0,REWSThe calculation formula is:
counting impeller equivalent wind power density DWP,REWPThe calculation formula is:
and S105, drawing according to the wind resource parameters to generate a wind resource map.
Specifically, for step S105, the average wind speed U at each altitude is plotted0,mSpatial distribution map, plotting average wind power density D of each heightWPA spatial distribution diagram, a Weibull distribution shape parameter spatial distribution diagram of each height, a Weibull distribution parameter shape parameter spatial distribution diagram, a wind direction frequency distribution diagram of each height,drawing typical hub height equivalent impeller wind speed U0,REWSA space distribution diagram, drawing the equivalent wind power density D of the impeller with the typical hub heightWP,REWPAnd (5) carrying out spatial distribution.
The height of the wind resource map includes: 10m, 30m, 50m, 70m, 90m, 100m, 120m, 150m and 200 m. The typical hub height of the wind resource map is consistent with the hub height determined above.
Specifically, as shown in fig. 5, the generating of the wind resource map includes, after wind resource parameters are counted, representing an average wind speed and an average wind power density in the map by means of classification and color scale, setting water depth data representation, setting geographic information data representation, and generating a final wind resource map result (as shown in fig. 6). The average wind speed and the average wind power density in the map are represented by color blocks, the grading and corresponding color marks are shown in fig. 7, and other information marks in the map comprise administrative divisions, ports, established and planned offshore wind power sites. The water depth data in the map is indicated by contour lines. The wind direction frequency distribution in the map is represented in a wind direction rose diagram form, and the format can refer to appendix D of wind power plant wind energy resource assessment method (GB/T18710).
In a specific embodiment, as shown in fig. 7, the embodiment further provides another wind resource map drawing method for offshore wind power, which includes the following specific steps: firstly, collecting and arranging wind resource related data, wherein the wind resource related data comprises terrain data, water depth data, wave data, mesoscale meteorological pattern data, wind measurement data and geographic information data; secondly, performing downscaling analysis on the mesoscale meteorological model data, correcting a wind speed vertical profile according to terrain data, water depth data and wave data, and correcting wind direction frequency distribution according to anemometry data; after the correction is completed, wind resource parameters are counted, and the comprehensive information is drawn; and generating a wind resource map according to the terrain data, the water depth data, the geographic information data and the comprehensive information in the steps.
The method for drawing the wind resource map for offshore wind power provided by the embodiment at least comprises the following steps: collecting wind resource related data and performing data arrangement; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement; carrying out downscaling analysis on the wind field simulation data, and adjusting the wind field according to the terrain data to generate a downscaling grid field; adjusting the downscaling grid field based on actual measurement data, and correcting wind direction frequency distribution; counting wind resource parameters, wherein the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density; drawing according to the wind resource parameters to generate a wind resource map.
Compared with the prior art, the method provided by the embodiment fully considers the potential influence of the marine hydrological factors on the distribution of the offshore wind resources, and can obtain better applicability and higher precision. The main performance is as follows:
1. adding a sea surface drag coefficient parameterization scheme considering the wave effect, and adapting to wind field correction under different water depths and different wind speeds;
2. adding a representative range estimation function of offshore wind measurement data to reduce the error of a spatial interpolation algorithm;
3. the equivalent impeller wind speed and wind power density drawing results are added, and the method is suitable for resource assessment and unit model selection facing large-capacity wind generating sets and large-scale offshore wind farm groups in the future.
Second embodiment of the invention:
please refer to fig. 8.
As shown in fig. 8, the present embodiment provides a wind resource map drawing apparatus for offshore wind power, including:
the data sorting module 100 is used for collecting wind resource related data and sorting data; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement;
the downscaling analysis module 200 is configured to perform downscaling analysis on the wind field simulation data, and adjust the wind field according to the topographic data to generate a downscaling grid field;
a wind field correction module 300, configured to adjust the downscale grid field based on actual measurement data, and correct wind direction frequency distribution;
a wind resource parameter statistics module 400, configured to count wind resource parameters, including an average wind speed, an average wind power density, a wind direction frequency, a Weibull distribution parameter, an impeller equivalent wind speed, and an impeller equivalent wind power density;
and a wind resource map generation module 500, configured to perform drawing according to the wind resource parameter to generate a wind resource map.
The wind resource map drawing device for offshore wind power provided by the embodiment can fully consider the potential influence of marine hydrological factors on the distribution of offshore wind resources, and improve the drawing precision and applicability of the wind resource map; the method comprises the steps of collecting mesoscale meteorological model simulation data, offshore field wind measurement data, wave observation data and underwater topography data, combining a statistic-power mixed downscaling technology, a sea surface drag coefficient parameterization scheme considering a wave effect and an offshore wind measurement data representative range estimation function, and realizing generation of a high-spatial-resolution offshore wind field simulation result and result correction based on actual measurement hydrometeorological data; in addition, the analysis results of the equivalent wind speed and the wind power density of the impeller are supplemented, and the representativeness and the applicability of the wind resource map are further enhanced.
An embodiment of the present invention provides a terminal device for wind resource mapping of offshore wind power, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the wind resource mapping method for offshore wind power as described above is implemented.
An embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus on which the computer-readable storage medium is located is controlled to execute the wind resource mapping method for offshore wind power as described above.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the modules may be a logical division, and in actual implementation, there may be another division, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The foregoing is directed to the preferred embodiment of the present invention, and it is understood that various changes and modifications may be made by one skilled in the art without departing from the spirit of the invention, and it is intended that such changes and modifications be considered as within the scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (10)
1. A wind resource map drawing method for offshore wind power is characterized by at least comprising the following steps:
collecting wind resource related data and performing data arrangement; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement;
carrying out downscaling analysis on the wind field simulation data, and adjusting the wind field according to the terrain data to generate a downscaling grid field;
adjusting the downscaling grid field based on actual measurement data, and correcting wind direction frequency distribution;
counting wind resource parameters, wherein the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density;
drawing according to the wind resource parameters to generate a wind resource map.
2. The method of claim 1, wherein the wind resource mapping data comprises mesoscale meteorological pattern data, offshore wind measurements data, wave observation data, terrain data, water depth data, established offshore wind farm coordinates, and basic geographic information data.
3. The method for drawing the wind resource map for offshore wind power according to claim 1, wherein the offshore wind measurement data inspection and processing specifically comprises:
screening off offshore anemometry data collected by an offshore anemometry tower, a buoy and an island meteorological station, wherein the effective data integrity rate of the offshore anemometry data is lower than 90%;
and carrying out unreasonable data processing and default data processing on the offshore wind measuring data.
4. The method for drawing a wind resource map for offshore wind power according to claim 1, wherein the sorting of the wave observation data specifically comprises:
counting the monthly average wave height in the wave observation data according to a preset height range;
counting the total average period of the wave observation data according to a preset time range;
and counting the monthly average wave height range and the average period interval with the highest occurrence frequency.
5. The method for drawing the wind resource map for offshore wind power as recited in claim 1, wherein the down-scaling analysis is performed on the wind field simulation data, the wind field is adjusted according to the terrain data, and a down-scaling grid field is generated, specifically comprising:
defining a downscaling grid standard, taking a Cartesian coordinate as a reference coordinate system, and setting grid horizontal resolution and vertical layering height;
simulating the mesoscale meteorological model to the wind speed UmAnd wind direction DmU converted to wind vectormComponent sum vmA component;
weighting u in horizontal direction by inverse distancemComponent sum vmInterpolating components into the downscale grid;
the u is interpolated vertically by linear interpolationmComponent sum vmThe components are interpolated to the vertical hierarchy of the downscale grid.
6. The method for drawing the wind resource map for offshore wind power as recited in claim 5, wherein the downscaling analysis is performed on the wind farm simulation data, and the wind farm is adjusted according to the terrain data to generate a downscaled grid farm, further comprising:
if the islands are detected to exist in the downscaling grid, calculating the adjustment effect of the terrain on the wind field;
and constraining the whole downscaling grid by adopting a three-dimensional wind field radiationless relation to generate an adjusted downscaling grid field.
7. The method for wind resource mapping at offshore wind power as recited in claim 1, wherein the adjusting the downscaling grid field and the correcting the wind direction frequency distribution based on actual measurement data specifically comprises:
will wind vector u0Component sum v0Component, synthesized into horizontal wind speed U0And wind direction D0；
Calculating sea surface drag coefficient Cd；
Monthly average H from the wave observation datamAnd TmSimulating seasonal variation characteristics of waves to the sea surface drag coefficient CdAdjusting to form a downscaling grid C with 10m height wind speed and wave changedDistributing;
according to the horizontal wind speed U0Fitting a logarithmic law to the vertical profile of the wind speed and adjusting the CdCorrecting the original wind speed vertical profile by adopting a boundary layer similarity theory according to the length L of the Monin-obuff output by the mesoscale meteorological model;
estimating the representative range R of the offshore anemometer tower, the buoy and the island meteorological station according to the Hsieh modelr；
For offshore anemometry tower, buoy and island meteorological station D during anemometrysCarrying out frequency statistics according to 16 wind direction sectors, calculating the frequency difference between adjacent sectors, and determining the form of wind direction frequency distribution;
for offshore anemometry tower, buoy and island meteorological station D during anemometrysAnd downscaling grid D in synchronism with site anemometry0Fitting according to von Mises distribution;
counting the corresponding representative ranges R estimated by the offshore anemometer tower, buoy and island meteorological station during anemometryrTaking RrDefining an influence area A by taking median as radiusrScreening grid points in Ar range, calculating the points and stationsVon Mises distribution parameter mu obtained by point fittingiDifference d mu ofiAnd for mu of lattice point not in the period of anemometryiCorrecting;
adopting inverse distance weighting method to make the non-independent ArThe grid points within the range are corrected.
8. A wind resource map drawing device for offshore wind power, comprising:
the data sorting module is used for collecting wind resource related data and sorting data; the data arrangement comprises offshore anemometry data inspection and processing and wave observation data arrangement;
the downscaling analysis module is used for performing downscaling analysis on the wind field simulation data and adjusting the wind field according to the topographic data to generate a downscaling grid field;
the wind field correction module is used for adjusting the downscale grid field based on actual measurement data and correcting wind direction frequency distribution;
the wind resource parameter counting module is used for counting wind resource parameters, and the wind resource parameters comprise average wind speed, average wind power density, wind direction frequency, Weibull distribution parameters, impeller equivalent wind speed and impeller equivalent wind power density;
and the wind resource map generation module is used for drawing according to the wind resource parameters to generate a wind resource map.
9. Terminal device for wind resource mapping for offshore wind power, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the wind resource mapping method for offshore wind power according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus on which the computer-readable storage medium is located to perform a wind resource mapping method for offshore wind power as claimed in any one of claims 1 to 7.
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