Simulating wind energy resources with mesoscale models: Intercomparison of stateof-the-art
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1 Downloaded from orbit.dtu.dk on: Nov 01, 2018 Simulating wind energy resources with mesoscale models: Intercomparison of stateof-the-art models Olsen, Bjarke Tobias; Hahmann, Andrea N.; Sempreviva, Anna Maria; Badger, Jake; Ejsing Jørgensen, Hans Publication date: 2015 Document Version Peer reviewed version Link back to DTU Orbit Citation (APA): Olsen, B. T. (Author), Hahmann, A. N. (Author), Sempreviva, A. M. (Author), Badger, J. (Author), & Ejsing Jørgensen, H. (Author). (2015). Simulating wind energy resources with mesoscale models: Intercomparison of state-of-the-art models. Sound/Visual production (digital), European Wind Energy Association (EWEA). General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
2 Simulating wind energy resources with mesoscale models: Intercomparison of state-of-the-art models Bjarke Tobias Olsen Andrea N. Hahmann Anna Maria Sempreviva Jake Badger Hans E. Jørgensen Acknowledgement Lorenzo Morselli
3 Overview Introduction Objective Question to be answered EWEA mesoscale models benchmarking exercise Intercomparison study of mesoscale models Wind energy community submits their model output To highlight common issues with mesoscale models To identify weaknesses and strengths for further evaluation of uncertainties How good are simulated time series from mesoscale models? How is the model setup related to performance?
4 Overview Data submission facilitated by EWEA ensuring anonymity of participants Raw mesoscale output for 6 sites in Northern Europe Hourly data for all of 2011 Wind speed, direction, temperature, and humidity, surface fluxes. Many vertical levels m. A lot of metadata: o Model name o Model version o Grid spacing (horizontal and vertical) o Forcing (Boundary data) o Surface roughness o and more
5 Participants: 3E (Belgium) Anemos GmbH (Germany) ATM PRO (Belgium) CENER (Spain) CIEMAT (Spain) DEWI (Germany) DTU Wind Energy (Denmark) DX Wind Technologies (China) EMD International (Denmark) ISAC-CNR (Italy) KNMI (The Netherlands) Met Office (United Kingdom) Noveltis (France) RES Ltd. (United Kingdom) Statoil ASA (Norway) University Oldenburg (Germany) Vestas (Denmark) Vortex (Spain) Models: Harmonie37h1.1 HIRLAM, v6.4.2 Met Office v8.4 MM5 RAMS 6.0 SKIRON 6.9 WRF v3.0.1 WRF v3.1 WRF v3.2.1 WRF v3.3.1 WRF v3.4 WRF v3.5.1 WRF v3.6 WRF v3.6.1
6 First impressions wind speed at Cabauw (80 meters)
7 Smaller error 1st order statistics mean wind speed Smaller errors offshore and aloft, larger inland and near the surface Same pattern for inter-model spread Smaller error
8 1st order statistics standard deviation Underprediction offshore Near surface: overprediction at Høvsøre, underprediction at Cabauw Greater inter-model variance inland σ = 1 N N t t=0 U t U 2
9 Direction wind rose 24 direction sectors Wind directions are well captured
10 2nd order statistics - Correlation Generally high correlation > 0.85 Higher correlation offshore and aloft, lower onshore and near the ground Higher correlation Higher correlation
11 Applying mesoscale data for wind energy FINO3 90 m Power curve: Vestas V80 (2 MW) Wind farm: Horns Rev 1 Wake calculation using WAsP
12 Applying mesoscale data for wind energy FINO3 90 m 1 Outlier 3 Outliers
13 Can we link the model setup to performance? Number of vertical level Forcing data Surface roughness PBL scheme Resolution: Yes some evidence Inconclusive too little data
14 So? How good are simulated time series from mesoscale models? How is the model setup related to performance? Summary Distribution of wind directions (Wind rose) well captured Smaller mean wind speed errors and higher correlation offshore and aloft Overprediction of mean wind speed near the surface inland Misrepresentation of surface characteristics? Variance of wind speed underpredicted offshore Some evidence that higher resolution is linked to a lower mean wind speed errors Inconclusive evidence for others factors too little data Thank you for your attention! btol@dtu.dk Some early results from this exercise was presented in Juli, see:
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