Anderson, Mike

Ejsing Jørgensen, Hans

Hutton, Karen-Anne

Mortensen, Niels Gylling

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Comparison of resource and energy yield assessment proceduresMortensen, Niels Gylling; Ejsing Jørgensen, Hans; Anderson, Mike ; Hutton, Karen-AnnePublished in:Proceedings of EWEA 2012 - European Wind Energy Conference &amp; ExhibitionPublication date:2012Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Mortensen, N. G., Ejsing Jørgensen, H., Anderson, M., & Hutton, K-A. (2012). Comparison of resource andenergy yield assessment procedures. In Proceedings of EWEA 2012 - European Wind Energy Conference &Exhibition European Wind Energy Association (EWEA).Comparison of Resource and Energy Yield Assessment Procedures Niels G. Mortensen and Hans E. Jørgensen Technical University of Denmark (DTU)  Mike Anderson and Karen-Anne Hutton RES   Thanks to EWEA (Tim Robinson, Event Manager) for arranging the 2011 comparison exercise and wind resource workshop – and to the 40 teams for making this comparison possible!  F DTU Wind Energy, Technical University of Denmark Sample 28-MW onshore wind farm in Scotland 18 Apr 2012 EWEA 2012: Resource assessment 2 One reference climate  14y One wind farm layout One terrain specification        z0=3 cm RIX<2% One wind turbine model 40 different teams: Resulting AEP and σAEP? One site wind climate   4y (92%) DTU Wind Energy, Technical University of Denmark Wind farm characteristics • 28-MW wind farm – Fourteen 2-MW turbines – Hub height: 60 m – Rotor diameter: 80 m – Spacing: irregular, 3.7–4.8D  – Sketch electrical design given • Site meteorological mast (4y) – Wind speed @ 50 and 35 m – Std. deviation @ 50 and 35 m – Wind direction @ 35 m a.g.l. • Reference met. station (14y) – Wind speed and direction – Observed Wind Climate 18 Apr 2012 EWEA 2012: Resource assessment 3 DTU Wind Energy, Technical University of Denmark Case study limitations and assumptions  • Site wind mast coordinates offset • Site wind data offset a bit too • Uniform standard air density • Simplified terrain description – Uniform roughness, z0 = 0.03 m – No roughness changes – No stability changes – No shelter effects – No forest effects • No production data • Incomplete result submissions 18 Apr 2012 EWEA 2012: Resource assessment 4 DTU Wind Energy, Technical University of Denmark Data and results – prediction process in 6 steps 1. LT wind @ 50 m = Measured wind ± [long-term correlation effects] – comparison of long-term correlation methods 2. LT wind @ 60 m = LT wind @ 50 m + [wind profile effects] – comparison of vertical extrapolation methods 3. Gross AEP = Reference AEP ± [terrain effects] – comparison of flow models 4. Potential AEP = Gross AEP – [wake losses] – comparison of wake models 5. Net AEP (P50) = Potential AEP – [technical losses] – comparison of technical losses estimates 6. Net AEP (P90) = Net AEP (P50) – 1.282×[uncertainty estimate] – comparison of uncertainty estimates 18 Apr 2012 EWEA 2012: Resource assessment 5 DTU Wind Energy, Technical University of Denmark 1. Long-term wind on mast site @ anemometer height  LT wind @ 50 m = Measured wind ± [long-term corr. effects] 18 Apr 2012 EWEA 2012: Resource assessment 6 Categories not  very well defined! Maximum value Minimum value Q1 Median value, Q2 Q3 +2.4pp   DTU Wind Energy, Technical University of Denmark 2. Long-term wind on mast site @ hub height   LT wind @ 60 m = LT wind @ 50 m + [wind profile effects] 18 Apr 2012 EWEA 2012: Resource assessment 7 +0.0pp DTU Wind Energy, Technical University of Denmark 3. Gross energy yield of wind farm  Gross AEP = Reference AEP ± [terrain effects] 18 Apr 2012 EWEA 2012: Resource assessment 8 +0.4pp DTU Wind Energy, Technical University of Denmark 4. Potential energy yield of wind farm  Potential AEP = Gross AEP – [wake losses] 18 Apr 2012 EWEA 2012: Resource assessment 9 +0.3pp DTU Wind Energy, Technical University of Denmark 5. Net energy yield of wind farm (P50)  Net AEP (P50) = Potential AEP – [technical losses] 18 Apr 2012 EWEA 2012: Resource assessment 10 +1.3pp DTU Wind Energy, Technical University of Denmark 6. Net energy yield of wind farm (P90)  Net AEP (P90) = Net AEP (P50) – 1.282×[uncertainty] 18 Apr 2012 EWEA 2012: Resource assessment 11 +2.5pp Categories not well defined!!! DTU Wind Energy, Technical University of Denmark Steps in the prediction process  Which steps add to the spread of the predictions? 18 Apr 2012 EWEA 2012: Resource assessment 12 DTU Wind Energy, Technical University of Denmark Summary and conclusions – for this sample wind farm 18 Apr 2012 EWEA 2012: Resource assessment 13 P90 = 89 GWh/y, σP = 7% Range from 73 to 99 GWh/y (29%) • 3 steps add little to the spread: – Vertical extrapolation ... 0% – Flow modelling ... 6% – Wake modelling ... 5% • 3 steps add significantly to spread: – Long-term correlation ... 32% – Technical loss estimation ... 20% – Uncertainty estimation ... 38% • What else could be improved? – Definition and usage of concepts – Engineering best practices – Guidelines for reporting DTU Wind Energy, Technical University of Denmark How does this compare to TPWind 2030? 18 Apr 2012 EWEA 2012: Resource assessment 14 Assuming no bias! – and for this site    only, of course... DTU Wind Energy, Technical University of Denmark Comparison exercises – or verification data sets in public domain – could help improve our procedures! • Say, wind farm sites with significant –Roughness effects and roughness changes –Vertical extrapolation effects –Atmospheric stability effects (coastal site) • Or, wind farm sites located –Offshore –Forested terrain –Complex terrain • Real wind farm(s) with production data ⇒ more transparent, standardised, verified and reliable procedures! — Thank you for your attention! 15 18 Apr 2012 EWEA 2012: Resource assessment 

Comparison of resource and energy yield assessment procedures

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Comparison of resource and energy yield assessment procedures

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