Research and I nnovation at DTU W ind Energy Presentation at the - PowerPoint PPT Presentation

Research and I nnovation at DTU W ind Energy Presentation at the Japanese-Danish Joint Workshop Future Green technology 10-12 December 2012, Hakata Japan Peter Hauge Madsen Head of Department, DTU Wind Energy, Technical University of Denmark

Outline • DTU Wind Energy • Context • Research & research infrastructure • Innovation and industry Poul la Cour at Askov 1891-1903 cooperation • International cooperation The new 6 MW offshore wind turbine by Siemens, from http: / / www.siemens.com/ press/ en/ presspicture 2 DTU W ind Energy, Technical University of Denm ark 20 December 2012

W ind technology expertise Composites and Materials Mechanics Materials Science and Characterisation Wind Energy Division Fluid Mechanics Test and Measurements Materials Research Division Wind Turbines Structures > 2 4 0 staff m em bers Including 150 academic staff members and 50 PhD Aeroelastic Design students Fluid Dynamics Meteorology Wind Energy Systems Composite Mechanics DTU W ind Energy, Technical University of Denm ark

DTU W ind Energy - 2 0 1 2 Quality Relevance I m pact Scientific excellence Strategic research programmes On society Wind Energy Systems Wind resources and siting Wind power integration and control Offshore wind energy Wind Turbine Technology Aero-elastic design Structural design and reliability Remote sensing and measurement tech. Wind Energy Basics Aero and hydro dynamics Boundary layer meteorology and turbulence Light, strong materials 4 DTU W ind Energy, Technical University of Denm ark 20 December 2012

Experim ents, Validation and Test Research and test facilities 105m/ s, Test section 2.2 x 3.3m DTU W ind Energy, Technical University of Denm ark

Wind Energy Education Programmes • Int. M.Sc in Wind Energy: - Mechanics: 30 students per year - Electronics: 10 students per year - About 50 thesis work per year • Nordic Master’s programme in Sustainable Energy • European Eramus Mundus Wind Master • PhD research school (DAWE): - about 50 PhD students at DTU • European Academy of Wind Energy 6 DTU W ind Energy, Technical University of Denm ark 20 December 2012

Electricity production and used fuel 200 150 PJ 100 50 0 1994 '00 '05 '11 Kul Olie Naturgas Vindkraft Anden vedvarende energi m.m. DTU W ind Energy, Technical University of Denm ark 7

W ind Pow er in Denm ark MW 4000 30% 25% 3000 20% 2000 15% 10% 1000 5% 0 0% 1990 '95 '00 '05 '11 Kapacitet, havvindm øller [ MW] Kapacitet, landvindm øller [ MW] Vindkraft i pct. af indenlandsk elforsyning DTU W ind Energy, Technical University of Denm ark 8

Danish Energy Policy Goals & I ndustry • 100 pct. renewable energy in 2050 • 100 pct. renewable energy in to 2 0 1 1 I ndustry statistics electricity and heat supply in 2035 • No coal and oil from 2030 • Employment 25.550 – 45 % manufacturing • Wind power covers 9 pct. of gross – 13 % test and product energy consumption in 2020. development • Wind Power covers 49,5 pct. of electricity consumption in 2020. • Turnover in Denmark 51.8 billion DKK • EU target for DK: • Export 38.8 billion DKK • Renewable energy covers 30% in • Global turnover 102.8 2020, with 10 % i transport (DK mia DKK expects 35% in 2020) 9 DTU W ind Energy, Technical University of Denm ark 20.12.2012

W ind Pow er Meteorology tools and maps WAsP – the Wind Atlas Analysis and Application Program W ind Atlas for Egypt ( 2 0 0 6 ) WAsP Engineering DTU W ind Energy, Technical University of Denm ark

Offshore W ind Conditions • Ocean winds • Lidar observations and modelling • Wind resource mapping using satellite data • Mesoscale modelling Lidar wind data and • Meteorological mast model from Horn’s observations Reef offshore • Wind farms shadow effect Satellite winds • Satellite observations showing the wake at Horn Reef wind farm. Mean wind speed map using satellite Envisat ASAR. DTU W ind Energy, Technical University of Denm ark

W ind Atlas update Fino 3 at 100m Obs Model 10 m QuikSCAT comparsion Wind atlas for South Baltic 5 km WRF simulations Novel features: • Verification against high (100 m) offshore measurements • Comparison over large spatial extent against QuikScat winds • Climatologies can be calculated for arbitrary periods by applying a wind classification weighting system DTU W ind Energy, Technical University of Denm ark

W ind conditions in com plex terrain Bolund experiment • Well-defined inflow conditions • Roughness change • Steep escarpment / “complex” • Intercomparison study of numerical micro scale flow models Mast Positions CFD were used to find the 10 positions 20 December 2012 13 DTU W ind Energy, Technical University of Denm ark

Num erical results Speed-up along line A Speed-up at M1 & M2 5 m 2 m Mean Error: 2 6 % Linearized: 3 5 % LES: 2 6 % RANS 1 eqn.: 2 5 % RANS 2 eqn.: 2 0 % 20 December 2012 14 DTU W ind Energy, Technical University of Denm ark

DTU W ind Energy, Technical University of Denm ark

LI DAR Scanning of Bolund DTU W ind Energy, Technical University of Denm ark

W ake effects – a com plex flow essential for perform ance and loads CFD – Large eddy simulation DTU W ind Energy, Technical University of Denm ark

Fuga – a new w ake m odel • Linearised CFD • 10 6 times faster than conventional CFD • Supported by Carbon Trust • It Works! Lillgrund Farm Efficiency W ind Direction DTU W ind Energy, Technical University of Denm ark

Validation: Horns Rev data. 8 m / s 222 deg. WT01 WT11 WT21 WT31 WT41 WT51 WT61 WT71 WT81 WT91 WT02 WT12 WT22 WT32 WT42 WT52 WT62 WT72 WT82 WT92 WT03 WT13 WT23 WT33 WT43 WT53 WT63 WT73 WT83 WT93 WT04 WT14 WT24 WT34 WT44 WT54 WT64 WT74 WT84 WT94 270 deg. WT05 WT15 WT25 WT35 WT45 WT55 WT65 WT75 WT85 WT95 WT06 WT16 WT26 WT36 WT46 WT56 WT66 WT76 WT86 WT96 WT07 WT17 WT27 WT37 WT47 WT57 WT67 WT77 WT87 WT97 WT08 WT18 WT28 WT38 WT48 WT58 WT68 WT78 WT88 WT98 Simple closure: ν t = κ u * z No adjustable parameters! 19 DTU W ind Energy, Technical University of Denm ark GL Hassan - offshore workshop

EER EERA-DT DTOC I ntegrated design tool Meteorological data / Cluster layout / Turbine data • Integrate existing atmospheric and wake models from single Grid data wind farm to cluster scale. Wake models • Predict energy yield precisely through simulation. Grid Yield • Interconnection optimization models models for grid and offshore wind power plant system service. • Validation of the newly System Energy services yield integrated existing models based on wind farm Optimised Cluster Design observations. DTU W ind Energy, Technical University of Denm ark

The W alney Offshore W ind ( W OW ) Project •Comprehensive loads validation on a state of the art 3.6MW wind turbine •Collaboration with Siemens Wind Energy and DONG energy Key Measurements  Nacelle mounted LIDAR for wind measurements  Wave sonar and Buoy at turbine  Accelerometers, strain gauges on  Blade root, drive train, tower and foundation •Scientific Objectives  Validation of the dependencies of design loads  Prediction of turbine net damping Instrumented Turbine  Advanced wind/ wave correlation studies  Wake effects on loads 21 DTU W ind Energy, Technical University of Denm ark

HAW C2 – Risø DTU’s code for w ind turbine load and response • A tool for sim ulation of w ind turbine load & response in tim e dom ain. • Normal onshore turbines; 3B, 2B, pitch control, (active) stall • Offshore turbines (monopiles, tripods, jackets) • Floating turbines (HYWIND, Sway, Poseidon). • Based on a multibody formulation, which gives great flexibility • I t is a know ledge platform ! • New research/ models are continuously implemented and updated. • Core is closed source. E.g. Structure, aerodynamics, hydrodynamics, solver… • Submodels are open-source. E.g. water kinematics, standard controllers, generator models. DTU W ind Energy, Technical University of Denm ark

Topfarm w ind farm optim ization approach - loads and pow er Example: A 20 WT wind farm • Optimum wind turbines position for the 0 lowest cost of energy 330 30 Wind rose • Wake modeling using DWM (Dynamic 300 60 Wake Meandering) Tower base lifetime 0.025 0.02 0.015 0.01 0.005 fatigue loads in • Quick lookup for power and fatigue loads 270 90 wind farm in a database based on HAWC2 240 120 aeroelastic simulations 4 6 Tower base flange, Mx x 10 x 10 • Cost function including: Annual energy 2.2 210 150 6.182 180 Turbines in wake production and costs of: Turbines, Grid, 2.15 have higher 6.181 Foundation and O&M loads produce 2.1 6.18 less energy!! 6 7 Electrical power x 10 x 10 2.05 6.179 1.68 6.182 2 6.178 6.181 1.675 1.95 6.177 6.18 1.67 6.176 1.9 6.179 7.29 7.3 7.31 7.32 1.665 6.178 5 x 10 6.177 1.66 Annual energy yield for each turbine 6.176 1.655 7.29 7.3 7.31 7.32 5 DTU W ind Energy, Technical University of Denm ark x 10

New concepts offshore Floating turbines Life cycle costs offshore O&M O&M Wind Wind turbine turbine Sub- Sub- structure structure Combined wind and wave Grid Grid energy converters DTU W ind Energy, Technical University of Denm ark 21-aug-2008 24