resource assessment overview and mit full breeze case...
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Resource Assessment Overview and MIT Full Breeze Case Study
Cy Chan, EECS PhD CandidateWind Energy Projects in Action
MIT Wind Week – January 18, 2011
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Resource Assessment
• Characterize the wind resource at a particular location
• Multi-faceted problem:– Speed, direction, shear, veer– Temperature– Uncertainty: data, model
• Talk focus: wind speed and direction
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Assessment Techniques
• Statistical– Measure-Correlate-Predict (MCP)
• Estimates statistical parameters of wind speed• Regression: Linear or more advanced
– Neural networks• Function approximation via learning
– Kernel density estimation• Can treat uncertainty explicitly by generating
distributions versus point estimates
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Assessment Techniques
• Physical– Computational Fluid Dynamics– Model topography and local terrain– Combine historical weather patterns with
computational model
• Physical and Statistical hybrids• Ensemble models
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MCP Overview
• Measure: Data at target site• Retrieve: Long-term data from a reference site
(e.g. nearby weather station) with high correlation
• Correlate: Target data with reference data during overlapping time frame
• Predict: Long-term behavior of target site based on long-term behavior of reference
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A Simple Correlation Approach
• Binned linear regression:– Estimate parameters a and b such that model
equation ŷ(x) = ax + b minimizes squared error |ŷ – y|2 over training data
– Binned linear regression creates a separate model for each of a number of wind direction bins
– Determine approximate linear relationship for each wind direction bin independently
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Measure Correlate Predict
• Alternate methods (Rogers, Manwell, et al, 2005):– Monomial regression:
– Two dimensional regression:• Given wind vector x = (x1, x2) at reference site,
determine wind vector y = (y1, y2) at target site using matrix-vector equation: y = Ax + b
xbayaxy b
logloglog +=⇒=
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Measure Correlate Predict
• Existing methods (cont’d)– Variance Ratio method:
– Neural Networks– Kernel Density Estimation:
• Learn joint density estimate from data• Explicit treatment of model uncertainty• Can be used for forecasting
( )xy xyx
y µµ σσ −+=
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Full Breeze Project Overview
• Student-run project to assess the installation of a small wind turbine on campus
• Two potential sites near Briggs athletic field chosen for assessment
• Comparative analysis of wind resource, community and environmental impact, and finance
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Full Breeze Test Sites
• Collected temperature, wind speed, and wind direction at various heights (15 to 34m)
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Comparison of Test Sites
3
3.5
4
4.5
5
5.5
Dec Partial Jan Feb Mar Partial
Mea
n W
ind
Spee
d [m
/s]
Mean Wind Speed
MT 1 15m MT 1 20m MT 1 26m
MT 2 20m MT 2 34m
0
20
40
60
80
100
120
140
160
180
Dec Partial Jan Feb Mar Partial
Win
d Po
wer
Den
sity
[W/m
^2]
Mean Wind Power Density
MT 1 15m MT 1 20m MT 1 26m
MT 2 20m MT 2 34m
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How to Compare Site 1 to Site 2?
• Wind Shear relates wind speed to altitude:v/v0 = (h/h0)a
– a is the wind shear exponent (depends on surface roughness)
• Speed increases with height– Wind power density at Site 1 at 26m appears
roughly the same as Site 2 at 20m– Site 2 appears to be much stronger than Site 1– Is it in the long term?
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Test/Reference Site Correlations
• Logan Airport good candidate for correlation:– Over 10 years of data available– Pearson’s correlation coefficient = Cov(x, y) / (Std(x)Std(y))
r = 0.84 to 0.85 for wind speedr = 0.96 to 0.97 for wind direction (wrapped)
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Blue – Met Tower 1 Green – Met Tower 2 Logan Wind Rose
Binned Linear Regression Results
• Regression slopes a (from y = ax + b) on left• Met Tower 1 out performs Met Tower 2 under most prevailing
wind directions• Winds often come from 270 to 330 range, shifting overall
advantage to Met Tower 2
5%
10%
15%
WEST EAST
SOUTH
NORTH
0 - 22 - 44 - 66 - 88 - 1010 - 1212 - 1414 - 16>=16
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MCP Results:Observations to Estimations
0
20
40
60
80
100
120
140
160
180
Dec Partial Jan Feb Mar Partial
Win
d Po
wer
Den
sity
[W/m
^2]
Observed Mean Wind Power Density
MT 1 15m MT 1 20m MT 1 26m
MT 2 20m MT 2 34m
0
20
40
60
80
100
120
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Win
d Po
wer
Den
sity
[W/m
^2]
Estimated Wind Power Density
MT 1 15m MT 1 20m MT 1 26m
MT 2 20m MT 2 34m
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Why the Divergence?
Blue – Met Tower 1 Green – Met Tower 2Blue – Observed Green – Long-Term Trend
• Long-term wind direction distribution more spread out• Site 2 still better than Site 1, but not by as much as
observed during data gathering period
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More Information
• Visit http://windenergy.mit.edu for much more information on Project Full Breeze, the MIT Wind Energy Group, and Wind Energy Projects in Action (WEPA)
• Visit http://people.csail.mit.edu/cychan/ for more on my research in wind energy and computer science
• Thanks!