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Wind Power: Optimization at All Levels. Jaime Carbonell www.cs.cmu.edu/~jgc 11-September-2009. Wind Turbines (that work). HAWT: Horizontal Axis. VAWT: Vertical Axis. Wind Turbines (flights of fancy). Wind Power Factoids. Potential: 10X to 40X total US electrical power .01X in 2009 - PowerPoint PPT Presentation
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Wind Power: Wind Power: Optimization at All LevelsOptimization at All Levels
Jaime CarbonellJaime Carbonell
www.cs.cmu.edu/~jgcwww.cs.cmu.edu/~jgc
11-September-200911-September-2009
Wind Turbines (that work)Wind Turbines (that work)
HAWT: Horizontal Axis VAWT: Vertical Axis
Wind Turbines (flights of Wind Turbines (flights of fancy)fancy)
Wind Power FactoidsWind Power Factoids Potential: Potential: 10X to 40X total US electrical power10X to 40X total US electrical power
.01X in 2009.01X in 2009 Cost of wind: Cost of wind: $.02 – $.06/kWh$.02 – $.06/kWh
Cost of coal $.02 – $.03 (other fossils are more)Cost of coal $.02 – $.03 (other fossils are more) Cost of solar $.25/kWh – Photon Consulting Cost of solar $.25/kWh – Photon Consulting
““may reach $.10 by 2010” Photon Consultingmay reach $.10 by 2010” Photon Consulting
State with largest existing wind generationState with largest existing wind generation Texas (7.9 MW) – Greatest capacity: DakotasTexas (7.9 MW) – Greatest capacity: Dakotas
Wind farm construction is semi recession proofWind farm construction is semi recession proof Duke Energy to build wind farm in Wyoming – Reuters Sept 1, 2009Duke Energy to build wind farm in Wyoming – Reuters Sept 1, 2009 Government accelerating R&D, keeping tax creditsGovernment accelerating R&D, keeping tax credits
Grid requires upgrade to support scalable Grid requires upgrade to support scalable wind wind
Top Wind Power ProducersTop Wind Power Producersin TWh for Q2 2008in TWh for Q2 2008
CountryCountry Wind TWhWind TWh Total TWhTotal TWh % Wind% Wind
GermanyGermany 4040 585585 7%7%
USAUSA 3535 4,1804,180 < 1%< 1%
SpainSpain 2929 304304 10%10%
IndiaIndia 1515 727727 2%2%
DenmarkDenmark 99 4545 20%20%
Sustained Wind-Energy Sustained Wind-Energy DensityDensity
From: National Renewable Energy Laboratory, public domain, 2009
Yet Another Wind MapYet Another Wind Map
US Wind Farms in 2006US Wind Farms in 2006
Inside a Wind TurbineInside a Wind Turbine
GE Wind Energy's 3.6 megawatt wind turbine
From Wikipedia
Power CalculationPower Calculation
Wind kinetic energy:Wind kinetic energy: Wind power: Wind power:
Electrical power:Electrical power: CCb b .35 (<.593 “Betz limit”) .35 (<.593 “Betz limit”)
Max value of Max value of
NNg g .75 generator efficiency .75 generator efficiency
NNt t .95 transmission efficiency .95 transmission efficiency
221 vmE airk
3221 vrP airwind
windtgbgenerated PNNCP
3231
241
1
2
1
2
1
21 vv
vv
vv
airdtdE vrP
Wind v & E match Weibull Wind v & E match Weibull Dist.Dist.
Weibull Distribution:Weibull Distribution:
Red Red = = WeibullWeibull distribution of wind speed over distribution of wind speed over timetime
BlueBlue = = Wind energyWind energy (P = dE/dt) (P = dE/dt)
kxkxkkW exp),( )1(
Data from Lee Ranch, Colorado wind farm
Optimization OpportunitiesOptimization Opportunities Site selection Site selection
Altitude, wind strength, constancy, grid access, …Altitude, wind strength, constancy, grid access, … Turbine selectionTurbine selection
Design (HAWTs vs VAWTs), vendor, size, quantity,Design (HAWTs vs VAWTs), vendor, size, quantity, Turbine Height: “7Turbine Height: “7thth root law” root law”
Greater precision for local conditionsGreater precision for local conditions Local topography (hills, ridges, …) Local topography (hills, ridges, …)
Turbulence caused by other turbinesTurbulence caused by other turbines Prevailing wind strengths, direction, variancePrevailing wind strengths, direction, variance Ground stability (support massive turbines)Ground stability (support massive turbines)
Grid upgrades: extensions, surge capacity, …Grid upgrades: extensions, surge capacity, … Non-power constraints/preferencesNon-power constraints/preferences
Environmental (birds, aesthetics, power lines, …)Environmental (birds, aesthetics, power lines, …) Cause radar clutter (e.g. near airports, air bases)Cause radar clutter (e.g. near airports, air bases)
ggh
ggh
hgh
vv PPPg
h 43.07 3
7
World’s Largest Wind Turbine (7+Megawatts, 400+ feet tall)
Oops...Oops... What’s wrong with this picture?What’s wrong with this picture?
• Proximity of turbines
• Orientation w.r.t. prevaling winds
• Ignoring local topography
• …
Near Palm Springs, CA
Economic OptimizationEconomic Optimization
$1M-3M/MW capacity$1M-3M/MW capacity $3M-20M/turbine$3M-20M/turbine QuestionsQuestions
Economy of scale?Economy of scale? NPV & longevity?NPV & longevity? Interest rate?Interest rate? Operational costs?Operational costs?
Price of ElectricityPrice of Electricity 8% improvement in 25B invested = $2B8% improvement in 25B invested = $2B Price of storage vs upgrade of grid transmission Price of storage vs upgrade of grid transmission
vs bothvs both
Penultimate Optimization Penultimate Optimization ChallengeChallenge
Objective FunctionObjective Function Construction: cost, time, risk, capacity, …Construction: cost, time, risk, capacity, … Grid: access & upgrade cost,Grid: access & upgrade cost, Operation: cost/year, longevity, Operation: cost/year, longevity, Risks: price/year of electricity, demand, reliability, …Risks: price/year of electricity, demand, reliability, …
ConstraintsConstraints Grid: Ave & surge capacity, max power storage, …Grid: Ave & surge capacity, max power storage, … Physical: area, height, topography, atmospherics, …Physical: area, height, topography, atmospherics, … Financial: capital raising, timing, NPV discounts, …Financial: capital raising, timing, NPV discounts, … Regulatory: environmental, permits, safety, …Regulatory: environmental, permits, safety, … Supply chain: availability & timing of turbines, …Supply chain: availability & timing of turbines, …
Energy StorageEnergy Storage
Compressed-air storage Surprisingly viable Efficiency ~50%
Pumped hydroelectric Cheap & scalable Efficiency < 50%
Advanced battery Cost prohibitive
Flywheel arrays (unviable) Superconducting capacitors (missing
technology)
Compressed-Air Storage Compressed-Air Storage SystemSystem
Wind farm:PWF = 2 PT (4000 MW)
Spacing = 50 D2
vrated = 1.4 vavg Transmission:PT = 2000 MW
Comp Gen
PC = 0.85 PT (1700 MW)
Underground storage
Wind resource:k = 3, vavg = 9.6 m/s,
Pwind = 550 W/m2 (Class 5)hA = 5 hrs.
Eo/Ei = 1.30
PG = 0.50 PT
(1000 MW)
hS = 10 hrs.(at PC)
1
0 1
CF = 81%CF = 81%CF = 76%CF = 76%
CF = 68%CF = 68%CF = 72%CF = 72%
Slope ~ 1.7
0.5
0.5
1.5
1.5
Optimization To DateOptimization To Date
Turbine blade designTurbine blade design Huge literatureHuge literature
GeneratorsGenerators Already near optimalAlready near optimal
Wind farm layoutWind farm layout Mostly offshoreMostly offshore Integer programmingInteger programming
TopographyTopography Multi-siteMulti-site + Transmission+ Transmission + Storage+ Storage
new new challengchallengee
Need Wind DataNeed Wind Data Prevalent Direction, Speed, seasonalityPrevalent Direction, Speed, seasonality Measurement tower position & duration Measurement tower position & duration
optimization too…optimization too…
US Investment in Wind US Investment in Wind PowerPower
2008 Investment: $16.4B 2008 Investment: $16.4B (private + (private + public)public)
Total since 1980: $45+BTotal since 1980: $45+B Estimate for 2009-2018: $300B-$700BEstimate for 2009-2018: $300B-$700B
Optimization can have a huge Optimization can have a huge impactimpact
San Goronio Pass, CA
Trusted Third PartyTrusted Third Party
Wind power industry now generates studies Wind power industry now generates studies for public utilities for public utilities Every industry provider (Vestas, GE, Siemens, Every industry provider (Vestas, GE, Siemens,
…) shows their wind-generators are the best …) shows their wind-generators are the best no true comparison, no site/context sensitivity.no true comparison, no site/context sensitivity.
No global optimization across designs, etc.No global optimization across designs, etc. Modeling, optimization, assessment is Modeling, optimization, assessment is
complex, requires expertisecomplex, requires expertise Room for a non-profit expertise pool and modelsRoom for a non-profit expertise pool and models Track evolving technologiesTrack evolving technologies
ReferencesReferences Schmidt, Michael, Schmidt, Michael, “The Economic Optimization of Wind The Economic Optimization of Wind
Turbine Design” MS Thesis, Georgia Tech, Mech E. Nov, Turbine Design” MS Thesis, Georgia Tech, Mech E. Nov, 2007.2007.
Donovan, S. “Wind Farm Optimization” University of Donovan, S. “Wind Farm Optimization” University of Auckland Report, 2005.Auckland Report, 2005.
Elikinton, C. N. “Offshore Wind Farm Layout Optimization”, Elikinton, C. N. “Offshore Wind Farm Layout Optimization”, PhD Dissertation, UMass, 2007.PhD Dissertation, UMass, 2007.
Lackner MA, Elkinton CN. An Analytical Framework for Lackner MA, Elkinton CN. An Analytical Framework for Offshore Wind Farm Layout Optimization. Offshore Wind Farm Layout Optimization. Wind Engineering Wind Engineering 2007; 2007; 3131: 17-31. : 17-31.
Elkinton CN, Manwell JF, McGowan JG. Optimization Elkinton CN, Manwell JF, McGowan JG. Optimization Algorithms for Offshore Wind Farm Micrositing, Algorithms for Offshore Wind Farm Micrositing, Proc. Proc. WINDPOWER 2007 Conference and ExhibitionWINDPOWER 2007 Conference and Exhibition, American American Wind Energy Association, Los Angeles, CA, 2007. Wind Energy Association, Los Angeles, CA, 2007.
Zaaijer, M.B. et al, “Optimization Through Conceptial Zaaijer, M.B. et al, “Optimization Through Conceptial Varation of a Baseline Wind Farm”, Delft University of Varation of a Baseline Wind Farm”, Delft University of Technology Report, 2004.Technology Report, 2004.
First First Wind Energy Optimization Summit, Wind Energy Optimization Summit, Hamburg, Feb 2009.Hamburg, Feb 2009.
THANK YOU!THANK YOU!
Supplementary MaterialSupplementary Material
US Electrical Power in 2008 US Electrical Power in 2008
Other (4.1%) = Biomass (2%) + Wind (1%) + Solar + Geothermal + …
A Second Opinion…A Second Opinion…
Power Class
Wind Power(W/m2)
Speed*(m/s)
1 <200 <5.6
2 200-300 5.6-6.4
3 300-400 6.4-7.0
4 400-500 7.0-7.5
5 500-600 7.5-8.0
6 600-800 8.0-8.8
7 >800 >8.8
From Battelle Wind Energy Resource Atlas
Viable Class 3 or above
Good Class 4 or above