Coupling Mesoscale with CFD modeling Identifying high value development opportunities before wind monitoring Karim Fahssis, Aravind Nair, Sushant Kumar Ecoren Energy India Private Limited, th Plot No 481, 4th Floor, 36 Square, Road No 36, Jubilee Hills, Hyderabad 500 033, India Phone: +91 40 23551374 Email:
[email protected]
Abstract Ecoren Energy is developing, constructing, owning and operating wind power projects built for lasting success. In order to identify high value opportunities in the wind energy sector in India and then create and implement a plan to convert those opportunities into operating assets, Ecoren is putting a lot of efforts on investigating wind resource patterns over the whole country. Becoming the leading IPP in India also means getting projects operate as fast as possible and using the one year monitoring campaign to validate the pre-assessment work rather than to prospect for windy sites. A unique method of in-house Mesoscale modeling coupled with high resolution CFD modeling is presented and validated in this report. Keywords: Mesoscale, WRF, CFD, downscaling, coupling, site selection, RMSE Introduction The method validated in this report is based on a coupling approach of Mesoscale outputs with CFD inputs. It is noted that this paper is not a recommendation of a particular approach or a data providers; we are sharing in-house experiences with new modeling approaches. The most important advantage of the method is to earn confidence on the project feasibility at the very early stages of project development in terms of both “quantity” (wind speed, PLF) and “quality” (shear, turbulence, inflow angles) of the wind. Before installing a mast, we need to know if the future plants will produce enough electricity and if the turbines will perform well. Ecoren has developed its own method of site prospection, selection and ranking before having on-site mast data. Five sites were used to validate that methodology and they were chosen because they are representative of the Indian diversity in terms of topographical, land cover and climate conditions. Results of this validation are reported in this document. Methods Method 1: “ Meso1” The first method is based on in-house Mesoscale modeling. Meso1 is a software calculating meteorological parameters based on the Mesoscale model WRF. WRF is a fully compressible and non-hydrostatic model. Its vertical coordinate is a terrain-following hydrostatic pressure coordinate. The grid staggering is the Arakawa C-grid. The model uses the Rungend rd nd th Kutta 2 and 3 order time integration schemes and 2 to 6 order advection schemes in both horizontal and vertical directions. It uses a time-split small step for acoustic and gravity-wave modes. The dynamics conserves scalar variables. Method 2: “Meso1+cfd” The second method is based on in-house Mesoscale modeling (Meso1) coupled with CFD modeling. The CFD model solves the non-linear Navier-Stokes momentum equation. This model assumes an incompressible and steady state flow and uses a k-l turbulence model based on Yamada (1983) and Arritt (1987). Without solving the conservation of energy equation, the model takes into account the temperature gradient effects through an adjusted turbulence length scale based on thermal stability in the turbulent kinetic energy (TKE) equation. The initial conditions at the inlet are set using the surface roughness, a corresponding reference logarithmic wind speed profile in the surface layer and an Ekman wind speed profile for the remainder of the planetary boundary layer (PBL). In post-processing, the program calculates a directional speed ratio between every point and the mast and applies those ratios to the observed wind speeds. Both roughness (length and density) and thermal stability (10 different thermal stability classes available) models can be adjusted to tune the model and fit the measured profile. This calibration procedure has been performed on all the sites. As Mesoscale-CFD coupling is a new trend of the wind resource assessment community worldwide, WRF data can now automatically be inputted into the CFD model. The size of the CFD zone of interest is the resolution of the Mesoscale computation that had been used to generate
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the WRF data. Coupling these two technologies should be based on the well-known principle that Mesoscale models cannot capture well terrain local effects (ridges, valleys, trees,…) and CFD microscale models cannot capture well larger scale thermal effects (low-level jets, solar heating induced convection, etc.). As a result, in order to get a high level of accuracy for both resource and load assessment at the pre-feasibility stage, Mesoscale wind data set are generated at the highest possible height above ground level (terrain impact gets lower for ascending heights) and then CFD modeling is used to “downscale" these data to the turbine height level and with high resolution simulations. Method 3: “Meso2” Mesoscale data are generated by the data provider “Meso2”. The data are accessible via a web platform on a 2.5km resolution grid at anemometers height, no microscale downscaling performed. Method 4: “Meso3+CFD” WRF Mesoscale data are generated by the data provider “Meso3” at 150m height on a 5km resolution grid, high resolution CFD modeling (25m) is used in-house for downscaling data at anemometers heights.
Method 5: “Meso3+DS”
WRF Mesoscale data are generated and downscaled via the data provider’s own downscaling method (“DS”), data are provided by the data provider “Meso3” at 90m resolution at anemometers heights. Case Study 1 MH1 (Madhya Pradesh) Wind Monitoring & site description
25km
25km Site Location : Madhya Pradesh (India) Terrain type : extremely complex (slopes>30%, forests) Measurement heights : 30m and 50m Wind monitoring period : 11/01/2007 to 01/01/2008 Data recovery rate : 94%
Annual Mean wind speed Meso1+cfd
Meso1
Meso2
Meso3+cfd
Meso3+DS
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
(90m res.)
50
3.05%
0.48%
-25.7%
25.7%
-0.64%
30
4.06%
NA
-35.7%
26.9%
NA
Height (m)
Annual mean wind speed modeling errors for different techniques
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Weibull parameters In the tables below we compare the outputs of different modeling techniques for Weibull parameters. The Weibull parameters were derived from the real frequency distribution using the maximum likelihood method. Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
50
-7.49%
-17.48%
-34.82%
16.03%
30
-7.26%
NA
-41.23%
15.89%
Height (m)
A scale paremeter modeling errors for different techniques Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
50
-21.5%
-25.7%
-21%
-42.6%
30
-22.3%
NA
-26.3%
-43%
Height (m)
k shape parameters modeling errors for different techniques Wind shear (after CFD calibration of roughness and stability)
Error
Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
0%
60%
270%
0%
Wind shear modeling errors for different techniques Frequency distributions
Measured wind rose (blue) and MESO1 modeled wind roses (red) MESO1 gets the right energy producing sector (315 degrees).
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Case study 2: AP1 (Andhra Pradesh) Site description & Wind monitoring
18km
18km • • • • •
Site Location : Andhra Pradesh (India) Wind monitoring period : 18/12/2010 to 05/04/2012 Measurement heights : 50m, 65m and 80m Terrain type : moderately complex (hills and forests) Data recovery rate : 97%
Annual Mean wind speed Meso1+cfd
Meso1
Meso2
Meso3+cfd
Meso3+DS
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
(90m res.)
80
0.18%
4.96%
17.83%
26.47%
25.92%
65
0.57%
5.75%
18.01%
27.11%
NA
50
-0.99%
4.34%
15.58%
25.44%
NA
Height (m)
Annual mean wind speed modeling errors for different techniques Weibull parameters In the tables below we compare the outputs of different modeling techniques for Weibull parameters. The Weibull parameters were derived from the real frequency distribution using the maximum likelihood method. Height (m)
Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
80
-0.65%
4.03%
17.58%
25.16%
65
0.68%
5.76%
17.97%
26.86%
50
-0.87%
4.36%
15.88%
25.13%
A scale parameter modeling errors for different techniques
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Height (m)
Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
80
-6.52%
-9.13%
-10.48%
-22.57%
65
-6.52%
-9.13%
-11.83%
-22.52%
50
-4.44%
-7.11%
-12.44%
-20.76%
k shape modeling errors for different techniques Wind shear (after CFD calibration of roughness and stability) Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
16%
35%
35%
14%
Wind shear modeling errors for different techniques Frequency distributions In the tables below we compare the outputs of different modeling techniques for Annual Mean Wind Speed calculation. MESO1 gets the correct energy producing sector (247.5 degree).
Measured wind rose (blue) and MESO1 modeled wind rose (green) Application: Pre-monitoring conceptual layouts In order to validate the real value of one project development opportunity, we need to understand more than the wind speed at one particular point before and we usually generate conceptual turbine layouts in order to understand the potential capacity and the energy yield of the future wind farms. We use Wind Resource Grids as input to the optimizer software Openwind (Enterprise version) to generate these layouts. In this section, we will be comparing the conceptual layouts generated based on the real mast data and CFD modeling with the conceptual layouts based on Mesoscale MESO1 data coupled with CFD modeling, with Meso3 data coupled with CFD modeling and with the layout based on the WRG directly generated by Meso2 (downscaled at 200m resolution with a mass conservation linear model). We defined the same conditions for the three conceptual layouts: Growing layouts
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Minimum PLF: 30% Maximum array losses: 5% Turbine: GE82.5 (1.62MW, 80 meter hub height) No feasibility restrictions
Final layouts based (from left to right) : on mast data & CFD modeling (1), on MESO1+cfd modeling (2), on Meso2 downscaled (to 200m resolution) (3), on Meso3+cfd modeling (4) Results of the comparison are reported in the table below: WRG type
Number of turbines
Mast+cfd
26
Meso1+cfd
Capacity (MW)
Mean Free wind speed (m/s))
PLF (%)
42.25
6.55
30.20
22
35.75
6.35
30.12
Meso2+DS
52
84.5
6.89
30.76
Meso3+cfd
91
147.875
7.50
35.82
Comparison between post-monitoring layouts with conceptual layouts As we can see on the table above, the projected capacity of a wind farm is highly overestimated based on the Meso2+DS WRG (+100%), and the Meso3+cfd WRG (+250%) and we get estimates closer to post-monitoring estimates when coupling MESO1 Mesoscale modeled data with cfd modeling to generate the WRG (-15%). Additionally, if we consider the conceptual layout generated based on the Meso2 Wind Resource Grid and we run an energy capture of that layout on the postmonitoring WRG, we get a PLF of 25.18%, which is 22% lower than the projected PLF for the same layout. Case study 3: AP2 (Andhra Pradesh) Site description & Wind monitoring
• • • • •
Site Location : Andhra Pradesh (India) Terrain type: extremely complex (ridges slopes>40%) Wind monitoring period : 01/08/2010 to 05/05/2012 Measurement heights : 50m, 65m and 80m Data recovery rate : 99% (Mast J) and 96% (Mast D)
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20 km
8km Annual Mean wind speed In the tables below we compare the outputs of different modeling techniques for Annual Mean Wind Speed calculation. Meso1+cfd
Meso1
Meso2
Meso3+cfd
Meso3+DS
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
(90m res.)
80
1.92%
-2.27%
18.15%
29.14%
17.98%
65
1.43%
-3.21%
15.89%
27.14%
NA
50
2.43%
-3.17%
14.37%
26.49%
NA
Height (m)
Annual mean wind speed modeling errors for different techniques at Mast J
Height (m)
Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
80
-6.66%
-10.48%
5.32%
46.92%
65
-4.05%
-8.45%
7.04%
49.12%
50
-2.95%
-8.10%
6.26%
48.07%
Annual mean wind speed modeling errors for different techniques at Mast D Weibull parameters In the tables below we compare the outputs of different modeling techniques for Weibull parameters. The Weibull parameters were derived from the real frequency distribution using the maximum likelihood method.
Height (m)
Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
80
2.75%
-1.60%
13.32%
30.02%
65
1.52%
-3.15%
10.06%
27.06%
50
2.64%
-3.09%
9.75%
26.46%
80
-5.61%
-9.45%
6.30%
48.36%
65
-3.60%
-7.99%
6.96%
49.38%
50
-2.60%
-7.79%
5.75%
48.45%
A scale parameter modeling errors for different techniques at Mast J (top) and Mast D (bottom)
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Height (m)
Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
80
-10.43%
-10.00%
-7.70%
-4.78%
65
-13.26%
-12.84%
-12.21%
-7.98%
50
-14.95%
-14.53%
-16.39%
-9.92%
80
-27.13%
-27.50%
-22.26%
-22.34%
65
-23.69%
-24.46%
-20.45%
-18.7%
50
-25.54%
-26.28%
-24.61%
-20.82%
k shape parameter modeling errors for different techniques at Mast J (top) and Mast D (bottom) Wind shear (after CFD calibration of roughness and stability) In the tables below we compare the outputs of different modeling techniques for Wind Shear calculation.
Meso1+cfd
Meso1
Meso2
Meso3+cfd
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
Mast J
8.57%
0.28%
0.38%
3.49%
Mast D
-14.27%
-24.81%
-21.25%
-15.58%
Shear modeling errors for different techniques at mast J and mast D Frequency distributions In the tables below we compare the outputs of different modeling techniques for directional frequency distribution. Meso1 gets the correct prevailing direction sector (270 degree) but is not able to get the right secondary direction (90°).
Measured wind rose (blue) and Meso1 modeled wind roses (green) at mast J
Measured wind rose (blue) and Meso1 modeled wind roses (green) at mast D
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Metrics of error The metrics of error for each Mesoscale technique are the mean bias and the root mean square error (RMSE) between the predicted and observed annual mean wind speeds for all the anemometers (all the heights) at the 4 mast locations, as defined below: Where:
is the number of available measured & modeled annual mean wind speed
is the predicted wind speed at mast i based on the reference mast j
is the observed speed at mast i
Meso1+cfd
Meso1
Meso2
Meso3+cfd
Meso3+DS
Height (m)
(25m res.)
(5km res.)
(2.5km res.)
(25m res.)
(90m res.)
RMSE (m/s)
0.06
0.57
0.33
0.61
0.71
RMSE for the different techniques
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Conclusions Five different techniques of evaluating the wind resource before having on site measurement data have been investigated in this report. It is noted that the number of studied sites is limited (three) and duration of wind monitoring is short. Thus, further investigation remains to be done on a larger number of sites or after longer monitoring periods. Based on this preliminary analysis, we can draw the following conclusions: Coupling the WRF data from the Meso1 with a CFD model has proven to be the most reliable method as it is giving the smallest bias and root mean square error (RMSE) on the annual mean wind speed for the case studies described in this report. Using only Mesoscale modeling is not recommended, as it gives significantly higher RMSE: 0.57m/s for Meso1 and 0.33m/s for Meso2 compared to 0.06m/s for the MESO1 coupled with CFD. We can also observe that the Meso3 90m resolution map gives the highest RMSE (0.71 m/s) and we can appreciate the value of using CFD to downscale Meso3 generated Mesoscale data instead of using a linear downscaling model as it slightly brings down the RMSE (0.61 m/s). Weibull distribution parameters seem to be more challenging than annual mean wind speeds from a modeling point of view and we should keep on investigating in order to get better modeling for these parameters as they are important for the generation of pre-monitoring Wind Resource Grids. Directional frequency distributions are fairly well reproduced by the different techniques. However, we observed that Meso2 and Meso3 wind roses sometimes have a bias of 22 degrees for the prevailing directions and this can have impacts on the conceptual layout generations (spacing). Finally, using CFD to downscale the WRF Mesoscale data has proven to be very efficient for wind shear modeling, because we have the ability to tune the model (roughness and stability) to make sure the modeled wind shear will fit the real wind shear. Bibliography Description of the Mesomap system, AWS Truepower, April 2012. Wind Flow Model Performance, AWS Truepower, February 2012. Meso/Microscale atmospheric model integration: benefits to the wind power community and associated challenges, Vestas, EWEA 2011 Meso1, Technical note
Disclaimer The information contained in this paper is intended for guidance only and whilst the information is provided in utmost good faith and has been based on the best information currently available, is to be relied upon at the user’s own risk. No representations or warranties are made with regards to its completeness or accuracy and no liability will be accepted by Ecoren Energy India Private Limited for damages of any nature whatsoever resulting from the use of or reliance on the information. Acceptance of this document by EWEA is on the basis that Ecoren Energy India Private Limited is not in any way to be held responsible for the application or use made of the findings.
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