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Wind Power: Optimization at All Levels

This article delves into the optimization of wind power at all levels, from turbine selection to economic considerations. Discover the potential of wind energy, cost comparisons with other power sources, and top wind power producers. Learn about optimization opportunities such as site selection, turbine design, and grid upgrades. Explore the challenges in economic optimization, energy storage options, and the importance of wind data for effective planning. Uncover the significant investments in wind power and the need for a trusted third party for unbiased assessments. References provide further insights into wind turbine design and optimization strategies.

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Wind Power: Optimization at All Levels

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  1. Wind Power: Optimization at All Levels Jaime Carbonell www.cs.cmu.edu/~jgc 11-September-2009

  2. Wind Turbines (that work) HAWT: Horizontal Axis VAWT: Vertical Axis

  3. Wind Turbines (flights of fancy)

  4. Wind Power Factoids • Potential: 10X to 40X total US electrical power • .01X in 2009 • Cost of wind: $.02 – $.06/kWh • Cost of coal $.02 – $.03 (other fossils are more) • Cost of solar $.25/kWh – Photon Consulting • “may reach $.10 by 2010” Photon Consulting • State with largest existing wind generation • Texas (7.9 MW) – Greatest capacity: Dakotas • Wind farm construction is semi recession proof • Duke Energy to build wind farm in Wyoming – Reuters Sept 1, 2009 • Government accelerating R&D, keeping tax credits • Grid requires upgrade to support scalable wind 

  5. Top Wind Power Producersin TWh for Q2 2008

  6. Sustained Wind-Energy Density From: National Renewable Energy Laboratory, public domain, 2009

  7. Yet Another Wind Map

  8. US Wind Farms in 2006

  9. Inside a Wind Turbine GE Wind Energy's 3.6 megawatt wind turbine From Wikipedia

  10. Power Calculation • Wind kinetic energy: • Wind power: • Electrical power: • Cb  .35 (<.593 “Betz limit”) • Max value of • Ng  .75 generator efficiency • Nt  .95 transmission efficiency

  11. Wind v & E match Weibull Dist. Weibull Distribution: Red = Weibulldistribution of wind speed over time Blue = Wind energy (P = dE/dt) Data from Lee Ranch, Colorado wind farm

  12. Optimization Opportunities • Site selection • Altitude, wind strength, constancy, grid access, … • Turbine selection • Design (HAWTs vs VAWTs), vendor, size, quantity, • Turbine Height: “7th root law” • Greater precision for local conditions • Local topography (hills, ridges, …) • Turbulence caused by other turbines • Prevailing wind strengths, direction, variance • Ground stability (support massive turbines) • Grid upgrades: extensions, surge capacity, … • Non-power constraints/preferences • Environmental (birds, aesthetics, power lines, …) • Cause radar clutter (e.g. near airports, air bases) World’s Largest Wind Turbine (7+Megawatts, 400+ feet tall)

  13. Oops... • What’s wrong with this picture? • Proximity of turbines • Orientation w.r.t. prevaling winds • Ignoring local topography • … Near Palm Springs, CA

  14. $1M-3M/MW capacity $3M-20M/turbine Questions Economy of scale? NPV & longevity? Interest rate? Operational costs? Price of Electricity 8% improvement in 25B invested = $2B Price of storage vs upgrade of grid transmission vs both Economic Optimization

  15. Penultimate Optimization Challenge • Objective Function • Construction: cost, time, risk, capacity, … • Grid: access & upgrade cost, • Operation: cost/year, longevity, • Risks: price/year of electricity, demand, reliability, … • Constraints • Grid: Ave & surge capacity, max power storage, … • Physical: area, height, topography, atmospherics, … • Financial: capital raising, timing, NPV discounts, … • Regulatory: environmental, permits, safety, … • Supply chain: availability & timing of turbines, …

  16. Energy 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)

  17. Compressed-Air Storage System Wind resource: k = 3, vavg = 9.6 m/s, Pwind = 550 W/m2 (Class 5) hA = 5 hrs. 1.5 Slope ~ 1.7 1 CF = 81% 0.5 CF = 76% PC = 0.85 PT (1700 MW) PG = 0.50 PT (1000 MW) CF = 72% CF = 68% 0 0.5 1 1.5 Comp Gen hS = 10 hrs. (at PC) Wind farm: PWF = 2 PT (4000 MW) Spacing = 50 D2 vrated = 1.4 vavg Eo/Ei = 1.30 Underground storage Transmission: PT = 2000 MW

  18. Optimization To Date • Turbine blade design • Huge literature • Generators • Already near optimal • Wind farm layout • Mostly offshore • Integer programming • Topography • Multi-site • + Transmission • + Storage new challenge

  19. Need Wind Data • Prevalent Direction, Speed, seasonality • Measurement tower position & duration optimization too…

  20. US Investment in Wind Power • 2008 Investment: $16.4B (private + public) • Total since 1980: $45+B • Estimate for 2009-2018: $300B-$700B  Optimization can have a huge impact San Goronio Pass, CA

  21. Trusted Third Party • Wind power industry now generates studies for public utilities • Every industry provider (Vestas, GE, Siemens, …) shows their wind-generators are the best  no true comparison, no site/context sensitivity. • No global optimization across designs, etc. • Modeling, optimization, assessment is complex, requires expertise • Room for a non-profit expertise pool and models • Track evolving technologies

  22. References • Schmidt, Michael, “The Economic Optimization of Wind Turbine Design” MS Thesis, Georgia Tech, Mech E. Nov, 2007. • Donovan, S. “Wind Farm Optimization” University of Auckland Report, 2005. • Elikinton, C. N. “Offshore Wind Farm Layout Optimization”, PhD Dissertation, UMass, 2007. • Lackner MA, Elkinton CN. An Analytical Framework for Offshore Wind Farm Layout Optimization. Wind Engineering 2007; 31: 17-31. • Elkinton CN, Manwell JF, McGowan JG. Optimization Algorithms for Offshore Wind Farm Micrositing, Proc. WINDPOWER 2007 Conference and Exhibition, American Wind Energy Association, Los Angeles, CA, 2007. • Zaaijer, M.B. et al, “Optimization Through Conceptial Varation of a Baseline Wind Farm”, Delft University of Technology Report, 2004. • First Wind Energy Optimization Summit, Hamburg, Feb 2009.

  23. THANK YOU!

  24. Supplementary Material

  25. US Electrical Power in 2008 Other (4.1%) = Biomass (2%) + Wind (1%) + Solar + Geothermal + …

  26. A Second Opinion… From Battelle Wind Energy Resource Atlas Viable  Class 3 or above Good  Class 4 or above

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