DISTRIBUTED WIND RESOURCE ASSESSMENT USING COMPUTATIONAL MODELING … · 2019-06-05 · distributed...
Transcript of DISTRIBUTED WIND RESOURCE ASSESSMENT USING COMPUTATIONAL MODELING … · 2019-06-05 · distributed...
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DEPARTMENT OF MECHANICAL ENGINEERING
ENERGY AND COMPUTATIONAL MODELING LAB
DISTRIBUTED WIND RESOURCE ASSESSMENT USING COMPUTATIONAL MODELING FOR OFF-GRID, KILOWATT-
SIZE WIND TURBINES
MAY 22-23, 2019
4TH INTERNATIONAL HYBRID POWER SYSTEMS WORKSHOP CRETE, GREECE
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THOMAS L. ACKER
ANDREW M. MULLEN, JAYNE A. SANDOVAL, JOSE ALVAREZ GUERRERO
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OUTLINE
Background
Objectives
Met towers and wind turbines
Meteodyn WT modeling software
Modeling domain
Results
Conclusions
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DISTRIBUTED WIND RESOURCE ASSESSMENT
2015 Distributed Wind Resource Assessment (DWRA) Workshop sponsored by the U.S. Department of Energy
DWRA
Wind turbines that are “behind the meter,” kW to multi-MW installations
Predict Annual Energy Output (AEP) of a distributed wind turbine
The following challenges and barriers of DWRA were identified:
accuracy of the current approach is low and inconsistent
little verification of existing standards/rules of thumb
lack of available data to properly conduct the assessment
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COST OF DISTRIBUTED WIND
For small wind turbine installations, it is often not economically feasible to conduct a traditional wind resource assessment where a met mast is installed
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Wind Turbine Capacities Mean Installed Cost ($/𝑘𝑘𝑘𝑘)
Installed Cost Std. Dev. (±$/𝑘𝑘𝑘𝑘)
< 10 𝑘𝑘𝑘𝑘 $7,645 $2,431
10-100 𝑘𝑘𝑘𝑘 $6,118 $2,101
100−1000 𝑘𝑘𝑘𝑘 $3,751 $1,376
1-10 𝑀𝑀𝑘𝑘 $2,346 $770
Met Mast Height Complete Met Mast Cost ($)
10 𝑚𝑚 5,000-7,000
34 𝑚𝑚 9,000-10,000
50 𝑚𝑚 19,000-20,000
Table 2: National Renewable Energy Laboratory Wind Turbine Installed Costs
Table 1: NRG Systems Complete Met Mast Costs
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MAP OF ANNUAL ENERGY PRODUCTION
Alternative to met mast measurements is numerical prediction
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OBJECTIVES
Investigate the accuracy in predicting wind speed and annual energy production (AEP) using numerical modeling software
Meteodyn WT
Multiple wind speed and wind turbine data sources available
Best practices
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2.4 kW Skystream DWECS installed on the Scharf’s property in Doney Park, AZ
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LOCATION IN ARIZONA, USA
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MODEL DOMAIN – TERRAIN, ROUGHNESS, DATA
USGS Data Sources: National Digital Elevation Map (DEM, 10m)
National Land Surface Database (NLCD roughness)
DEM NLCD
Star School
Leupp
Flagstaff
~ 100 km
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PERSPECTIVE VIEW OF DOMAIN
The domain is centered near Doney Park, a small community northeast of Flagstaff, AZ.
Elevation approximately 2,100 𝑚𝑚
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Turbines
Nova Met Mast
Google Earth Pro image of the surrounding terrain in east Flagstaff and Doney Park
Prevailing wind direction
SW
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OROGRAPHY AND MESH GENERATION
Boundary Conditions
Orography
Roughness
Forest density
Thermal stability… defines inlet boundary wind speed profile
Refinement points and mesh generation
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Meteodyn WT
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COMPUTATIONAL DOMAIN
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METEODYN WT SOLUTION METHOD
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Meteodyn WT solves the Reynolds-Averaged Navier Stokes (RANS) equations
steady state, nonlinear, incompressible, isothermal, non-dimensional
Computes speed-up ratios
Δ𝑆𝑆 =Δ𝑈𝑈𝑈𝑈0
=𝑈𝑈𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 − 𝑈𝑈0
𝑈𝑈0
Performs “Directional Calculations”
“Synthesize” output wind speeds or wind power predictions by dimensiolizating the speed-up ratios using wind speed data
Δ𝑆𝑆 + ⇨
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DATA SOURCES
Star School
Leupp
Flagstaff
Location Description of Data SourceAnderson Met tower with measurements at 30-m and 10-m; 10-min ave.
Metz Met tower with measurements at 30-m and 10-m; 10-min ave.Meteor Met tower with measurements at 30-m and 9-m; 10-min ave.
Nova Met tower with measurements at 30-m and 20-m; 10-min ave.Sharf 2.4 kW Skystream 3.7 turbine; 10-m hub height; hourly energy
Rogers 2.4 kW Skystream 3.7 turbine; 10-m hub height; hourly energyLeupp Three 2.4 kW Skystream 3.7 turbine; 21.3-m hub; hourly energy
Anderson (km)
Metz (km)
Meteor (km)
Nova (km)
Sharf (km)
Rogers (km)
Leupp (km)
Anderson - 14.1 14.7 40.3 37.5 37.6 29.7Metz 14.1 - 14.4 52.6 48.8 48.9 40.1
Meteor 14.7 14.4 - 54.1 51.8 51.9 28.0Nova 40.3 52.6 54.1 - 6.0 6.0 48.6Sharf 37.5 48.8 51.8 6.0 - 0.14 49.6
Rogers 37.6 48.9 51.9 6.0 0.14 - 49.7Leupp 29.7 40.1 28.0 48.6 49.6 49.7 -
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PREDICT MEAN W.S. AT ANDERSON WITH METZ
~ 5% to 15% error with 20° interval
14 km separation 14
Star School
Leupp
Flagstaff
20
40
0%
5%
10%
15%
10 m 30 m
4.6%
15.0%
6.7%
17.3%
Dir
ecti
on In
terv
al
Perc
ent
Erro
r
Height of Wind Speed Measurement on Met Mast
Source data: Anderson 30-m Prediction site: Metz
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PREDICT MEAN W.S. AT ANDERSON WITH METZ
~ 1% to 3% error with 20° interval
40 km separation 15
Star School
Leupp
Flagstaff
20
40
0%
5%
10%
15%
20 m 33 m
3.3%
1.1%
0.3%1.7%
Dir
ecti
on In
terv
al
Perc
ent
Erro
r
Height of Wind Speed Measurement on Met Mast
Source data: Anderson 30-m Prediction site: Nova
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PREDICT AEP AT SCHARF & ROGERS WITH NOVA
~ 3% error with 20° interval
6 km separation 16
Star School
Leupp
Flagstaff 2.7% 2.9%
7.2%
5.0%
S C H A R F R O G E R S
% E
RR
OR
NOVA PREDI CTI NG SCHARF AND ROGERS % ERROR
20 Degree Interval 40 Degree Interval
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PREDICT AEP AT LEUPP WITH METEOR
~ 25% error with 20° interval
28 km separation 17
Star School
Leupp
Flagstaff
53% 53%
26%28%
N O V A 2 0 D E G
N O V A 4 0 D E G
M E T E O R C R A T E R 2 0
D E G
M E T E O R C R A T E R 4 0
D E G
% E
RR
OR
LOCATION
L e u p p E n e r gy P r e d ic ti o n
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DIURNAL AND ANNUAL PROFILES
0
1
2
3
4
5
6
7
8
12:00 A
M1:00 A
M2:00 A
M3:00 A
M4:00 A
M5:00 A
M6:00 A
M7:00 A
M8:00 A
M9:00 A
M10:00
AM
11:00 A
M12:00
PM1:00 PM2:00 PM3:00 PM4:00 PM5:00 PM6:00 PM7:00 PM8:00 PM9:00 PM10:00
PM11:00
PM
Aver
age
Win
d Sp
eed
(m/s
)
Meteodyn Prediction Real Met Mast Data
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AEP
(KW
H)
SHARFActual AEP (kWh) 20 Degree 40 Degree
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CONCLUSIONS
Meteodyn accuracy good but varies depending on data and location
Wind Speed errors 1% to 23%
AEP errors 3% to 25%
Time correlation very good
20° direction interval generally better than 40°
Solver settings
10-m Digital Elevation Data
USGS NLCD roughness data
Normal forest density
Neutral stability
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QUESTIONS?
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DIRECTIONAL MESHES
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240° mesh 270° mesh
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COMPUTATIONS AND RESULTS SYNTHESIS
The Directional Computations (DCs)
generate a surface grid from the orography file
bind roughness lengths, 𝑧𝑧0, to the surface grid cells
canopy heights are defined as 𝑑𝑑 = 20𝑧𝑧0
one mesh for each directional sector, adding up to 360°
solve the RANS equations
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Δ𝑆𝑆 + ⇨
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DATA SOURCES AND MEAN VALUES
Location
Mean Wind Speed Description
Anderson at 10-m 4.57 m/s 3 years of wind speed data; 2006-2008Anderson at 30-m 5.34 m/s 3 years of wind speed data; 2006-2008
Metz at 10-m 5.05 m/s 3 years of wind speed data; 2006-2008Metz at 30-m 5.65 m/s 3 years of wind speed data; 2006-2008Nova at 20-m 3.05 m/s 2-mo. wind speed data: Aug-July 2013Nova at 33-m 3.54 m/s 2-mo. wind speed data: Aug-July 2014
Location AEP DescriptionSharf 2.895 MWh 1 year production
Rogers 2.942 MWh 1 year productionLeupp 2.537 MWh 4-year average production
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DATA SOURCES AND ERROR RANGE
Source Data Prediction Distance Error Range DescriptionAnderson at 10-m Metz at 10- and 30-m 14.1 km 10% to 23% 3 years of wind speed data; 2006-2008Anderson at 30-m Metz at 10- and 30-m 14.1 km 4% to 17% 3 years of wind speed data; 2006-2008
Metz at 10-m Anderson at 10- and 30-m 14.1 km 1% to 10% 3 years of wind speed data; 2006-2008Metz at 30-m Anderson at 10- and 30-m 14.1 km 10% to 17% 3 years of wind speed data; 2006-2008
Anderson at 10-m Nova at 20-m and 33-m 40.3 km 7% to 12% 2-mo. wind speed data: Aug-July 2011Anderson at 30-m Nova at 20-m and 33-m 40.3 km 0% to 3% 2-mo. wind speed data: Aug-July 2012
Nova at 20-m Anderson at 10-and 30-m 40.3 km 1% to 5% 2-mo. wind speed data: Aug-July 2013Nova at 33-m Anderson at 10-and 30-m 40.3 km 2% to 4% 2-mo. wind speed data: Aug-July 2014Nova at 20-m Sharf & Rogers 6 km 3% to 7% 1-year coincident wind and power dataNova at 20-m Leupp 48.6 km 53% No coincident wind and power dataMeteor at 9-m Leupp 28 km 26% to 28% 2-year coincident wind and power data