Comparative Resource and Energy Yield Assessment Procedures (CREYAP) Pt. II
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1 Comparative Resource and Energy Yield Assessment Procedures (CREYAP) Pt. II Niels G. Mortensen and Hans E. Jørgensen DTU Wind Energy, Risø Campus EWEA Technology Workshop: Resource Assessment 2013 Dublin, Ireland, 26 June 2013
2 Acknowledgements The data pack used for the comparison was made available by Renewable Energy Systems Ltd. (RES); thanks to Mike Anderson and Euan George. The 60 sets of results were submitted by 56 organisations from 17 countries; thanks to all of the teams for making the comparison and this presentation possible! Thanks to Tim Robinson and his team for arranging the 2013 comparison exercise and wind resource workshop. 2 DTU Wind Energy, Technical University of Denmark
3 Outline Purpose and participants Case study wind farm Wind farm and turbine data Wind-climatological inputs Topographical inputs Comparisons of results & methods The prediction process Long-term wind climate Wind farm energy yields Comparison to observed AEP Mast strategy and site results Summary and conclusions Appendices Team results and statistics 3 DTU Wind Energy, Technical University of Denmark
4 Purpose and participants Reliable energy yield predictions are obtained when the bias and the uncertainty are both low. Note, that the true value is often measured with some uncertainty... CREYAP Pt. II 60 teams from 56 organisations in 17 countries submitted results! consultancy (41) developer (7) R&D/university (5) wind turbine manufacturer (3) electricity generator/utility (2) certification body (1) service provider (1) Visit for more info on the CREYAP comparison exercises. 4 DTU Wind Energy, Technical University of Denmark
5 What s different compared to CREYAP Pt. I? General Complete case study Operating wind farm Production data available (5y) Data and info not scrambled Modelling Air density correction needed Larger terrain effects Larger wake effects These effects are all of order 10% Input data Seven measurement locations One reference, six auxiliary Two types of long-term data Ground-based MERRA reanalysis Roughness data for site Wind farm site only Obstacle data for site 5 DTU Wind Energy, Technical University of Denmark
6 Case study wind farm 22 wind turbines (28.6 MW) Rated power: 1.3 MW Hub height: 47 m Rotor diameter: 62 m Spacing: irregular, 4-5 D between neighbouring WTG Air density: kg m 3 Primary site meteorological mast Wind 50 and 40 m Std. 50 and 40 m Wind 48.5 m a.g.l. Two 50-m site assessment masts Same levels as primary mast 6 DTU Wind Energy, Technical University of Denmark
7 Copyright Walter Baxter, Jim Barton, Poljes and Panoramio. 7 DTU Wind Energy, Technical University of Denmark
8 Wind-climatological inputs site measured data M49 site data (5y) to Recovery rate 94% Statistics: U = 8.3 ms 1 P = 649 Wm 2 A = 9.4 ms 1 k = DTU Wind Energy, Technical University of Denmark
9 Wind-climatological inputs reference data Ground-based 5 years of hourly mean data 16+ years of monthly mean data 11-y historic wind data statistic MERRA reanalysis 16+ years of hourly mean data 9 DTU Wind Energy, Technical University of Denmark
10 Topographical inputs elevation 50-m DEM, km 2 Wind farm sites Elevation m a.s.l m a.s.l Vertical exaggeration 3 RIX index 1-3% 10 DTU Wind Energy, Technical University of Denmark
11 Data analysis & presentation Data material Results spreadsheets from 60 teams Data analysis Quality control and reformatting Consistent results (loss factors) Calculation of missing numbers no comprehensive reanalysis! Data presentation Comparison of results and methods Non-parametric box-whisker plot Statistics (median, quartiles, IQR) Overall distribution of all results Normal distribution fitted to the results Statistics (mean, standard deviation, coefficient of variation) Team results for each parameter (see appendix) 11 DTU Wind Energy, Technical University of Denmark
12 Comparisons of results and methods 1. LT 50 m (mast) = Measured wind ± [long-term adjustment] comparison of long-term adjustment methods 2. LT 47 m (hub height)= LT 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 (P 50 ) = Potential AEP [technical losses] comparison of technical losses estimates 6. Net AEP (P 90 ) = Net AEP (P 50 ) [uncertainty estimate] comparison of uncertainty estimates 7. Comparison to observed AEP spread and bias 12 DTU Wind Energy, Technical University of Denmark
13 Long-term wind at the meteorological mast LT 50 m = Measured wind ± [long-term adjustment] 13 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
14 Comparison of LT adjustment methods Maximum value Q3 Median value, Q2 Q1 Minimum value 14 DTU Wind Energy, Technical University of Denmark
15 Long-term wind at hub height at the met. mast LT 47 m (hub height) = LT 50 m + [profile effects] 15 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
16 Wind profile and shear exponent Data points used = 55 (of 60) Mean shear exponent = Standard deviation = Coefficient of variation = 10% Range = to DTU Wind Energy, Technical University of Denmark
17 Comparison of vertical extrapolation methods 17 DTU Wind Energy, Technical University of Denmark
18 Gross energy yield of wind farm Gross AEP = Reference AEP ± [terrain effects] 18 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
19 Comparison of flow models 19 DTU Wind Energy, Technical University of Denmark
20 Potential energy yield of wind farm Potential AEP = Gross AEP [wake losses] 20 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
21 Comparison of wake models 21 DTU Wind Energy, Technical University of Denmark
22 Net energy yield of wind farm, P 50 Net AEP (P50) = Potential AEP [technical losses] where [technical losses] = AEP f 1 f 2 f n and f 1, f 2,, f n are the individual loss factors. 22 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
23 Technical losses by type Overall availability given as 96.8% (first 4 columns) Electrical loss given as 1.2% (first column) 23 DTU Wind Energy, Technical University of Denmark
24 Net energy yield (P 50 ) Data points used = 58 (of 60) Mean net yield = 75.7 GWh Standard deviation = 4.4 GWh Coefficient of variation = 5.8% Range = 64 to 91 GWh 24 DTU Wind Energy, Technical University of Denmark
25 Net energy yield of wind farm, P 90 Net AEP (P90) = Net AEP (P50) [uncertainty estimate] 25 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
26 Uncertainty estimates by type 26 DTU Wind Energy, Technical University of Denmark
27 Wind farm key figures Mean σ CV* Min Max Reference yield GWh Topographic effects % Gross energy yield GWh Wake loss % Potential yield GWh Technical losses % Net energy yield P 50 GWh Uncertainty % Net energy yield P 90 GWh * Coefficient of Variation in per cent. 27 DTU Wind Energy, Technical University of Denmark
28 Spread for different steps in the prediction process 28 DTU Wind Energy, Technical University of Denmark
29 Comparison to observed AEP spread and bias Observed long-term energy yield based on 5 years of production data; corrected for windiness, as well as an overall plant availability of 96.8%. This produces an observed yield of GWh/year. 29 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
30 How do the predictions compare to the observed AEP? 30 DTU Wind Energy, Technical University of Denmark
31 The six teams closest to the observed AEP Long-term adjustment None, unknown daily, Merra hourly or monthly, wind index monthly, wind index Weibull scale. Vertical profile log law, power law, modelled, CFD, linearised model Flow modelling Linearised model, CFD model Park modelling Eddy viscosity, Jensen-type Strategy All masts, M49 only (50/50) These teams are close to the overall median every step of the way 31 DTU Wind Energy, Technical University of Denmark
32 The six teams furthest away from the observed AEP Long-term adjustment NWP hourly ERA Interim, NWP hourly, Merra 7-day, NWP ERA-1, MCP hourly matrix + index, MCP unspecified Vertical profile not used, power law, log law, modelled, NWP Flow modelling Mesoscale model, mass-consistent model, CFD model, WRF, linearised model Park modelling Frandsen-type, CFD actuator disk, eddy viscosity, Jensen-type, proprietary, Jensen model + GCL (Larsen) Strategy not used, all masts, m49 only 32 DTU Wind Energy, Technical University of Denmark
33 Mast strategy impact on gross AEP What is the consequence of using a single mast (49) vs. multiple masts? For all teams: Single-mast predictions +2% higher than multiple mast do. Single- and multiple-mast predictions are different! Try now with one model only to see if pattern persists. Say, for WAsP teams only: Single-mast predictions +2% higher than multiple mast do. Single- and multiple-mast predictions are different! Rather clear signal, and significant. 33 DTU Wind Energy, Technical University of Denmark
34 Mast strategy impact on net AEP P 50 Does mast strategy have an impact on the final estimate of the net AEP? For all teams: Single-mast predictions +1% higher than multiple mast do. Single- and multiple-mast predictions are not different! Multiple-mast prediction is closer to the observed AEP. For WAsP teams only: Single-mast predictions are almost equal to multiple mast. Multiple-mast prediction is closer to the observed AEP. Less clear signal, not significant. 34 DTU Wind Energy, Technical University of Denmark
35 Predicted turbine site mean wind speeds 35 DTU Wind Energy, Technical University of Denmark
36 Predicted turbine site mean wind speeds 36 DTU Wind Energy, Technical University of Denmark
37 Predicted turbine site wake effects 37 DTU Wind Energy, Technical University of Denmark
38 Predicted turbine site wake effects 38 DTU Wind Energy, Technical University of Denmark
39 Turbine AEP contribution predicted vs. observed 39 DTU Wind Energy, Technical University of Denmark
40 Turbine energy yields predicted vs. observed 40 DTU Wind Energy, Technical University of Denmark
41 Turbine energy yields predicted vs. observed 41 DTU Wind Energy, Technical University of Denmark
42 Turbine energy yields predicted vs. observed 42 DTU Wind Energy, Technical University of Denmark
43 Summary and some conclusions... Wind resource assessment works if you do it right... Wind farm AEP predictions Mean bias is very small P 50 standard deviation is 6% Reported Uncertainty is 8% Mesoscale and NWP models are powerful, but not sufficient (give lower AEP) Mast strategy not quite clear? Single-site predictions work well The prediction process is complex and it is different to isolate effects What about the human factor!?! Steps that add little to the spread Vertical extrapolation Wake modelling Technical loss estimation Which steps could be improved Long-term correlation Flow and terrain modelling Uncertainty estimation What else could be improved? Definition and usage of concepts (e.g. reference yield and topographical effects) Standards and guidelines Engineering best practices Guidelines for reporting 43 DTU Wind Energy, Technical University of Denmark
44 Future comparisons After CREYAP Part I and II, one could step up the challenge, e.g.: Wind farm site where vertical extrapolation is very important Wind farm site where stability effects are important (coastal site) Offshore wind farm site Forested wind farm site Complex terrain wind farm site Wind farm with user-provided topographical inputs Future comparison exercises could thus be more focussed in order to highlight specific topics and should preferably be Real wind farm(s) with production data Thank you for your attention! 44 DTU Wind Energy, Technical University of Denmark
45 Appendices Team results, statistics and additional information 45 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
46 Contents Input data List of participants Wind farm photographs OS topographical map Domain and roughness map Long-term wind at the met. mast Long-term adjustment effect LT mean wind 50 m Turbulence 50 m LT hub height wind at met. mast Wind profile shear exponent LT mean wind 47 m Turbulence 47 m Energy yield of wind farm Reference energy yield Topographical effects Gross energy yield Wake losses Potential energy yield Technical losses Net energy yield (P 50 ) Capacity factor Uncertainty estimates Net energy yield (P 90 ) Wind farm energy yields Turbine site terrain effects Legend and references 46 DTU Wind Energy, Technical University of Denmark
47 Who submitted results? 60 teams from 56 organisations in 17 countries submitted results! consultancy (41), developer (7), R&D/university (5), wind turbine manufacturer (3), electricity generator/utility (2), certification body (1), service provider (1) Names of the organisations 3E (Belgium); 3TIER (USA); ALTRAN (Spain); ATM-PRO (Belgium); AWS Truepower (USA); Barlovento Recursos Naturales (Spain); BBB Umwelttechnik (Germany); Casa dos Ventos (Brazil); CENER (Spain); China Wind Power Center / CEPRI (China); CIRCE (Spain); CRES (Greece); Deutsche WindGuard (Germany); Digital Engineering (UK); DTU Wind Energy (Denmark); EDF Renewable Energy (USA); Edison (Italy); EMD International (Denmark); ENALLAKTIKI ENERGIAKI (Greece); Enerpark (Poland); EREDA (Spain); ESB International (Ireland); Estia (Greece); Etha (Finland); European Weather Consult (Germany); Fichtner (Germany); Fujian Hydro Power (China); GAMESA (Spain); GDF SUEZ (France); IMPSA (Brazil); INOVA Energy (Brazil); International Wind Engineering (Greece); Istos Renewables (Greece); ITOCHU Techno-Solutions (Japan); Kjeller Vindteknikk (Norway); Lahmeyer (Germany); Mainstream (USA); Megajoule (Portugal); Meteodyn (France); Mott MacDonald (UK); MS Techno (China); NREL (USA); Natural Power (UK); North China Electric Power University (China); Prevailing (UK); REpower Systems (Germany); RES Ltd. (UK); RSE S.p.A. (Italy); SgurrEnergy (UK); The Wind Consultancy Service (UK); Tractebel Engineering (Belgium); Wind Energy Corporation (Japan); Wind Prospect (UK); WIND-consult (Germany); WindSim (Norway); Winwind (Finland). 47 DTU Wind Energy, Technical University of Denmark
48 Case study wind farm Copyright Walter Baxter and licensed for reuse under this Creative Commons Licence. 48 DTU Wind Energy, Technical University of Denmark
49 Case study wind farm Copyright Jim Barton and licensed for reuse under this Creative Commons Licence. 49 DTU Wind Energy, Technical University of Denmark
50 Case study wind farm Copyright Poljes and Panoramio. 50 DTU Wind Energy, Technical University of Denmark
51 Case study wind farm Copyright Poljes and Panoramio. 51 DTU Wind Energy, Technical University of Denmark
52 52 DTU Wind Energy, Technical University of Denmark
53 Topographical inputs land cover 53 DTU Wind Energy, Technical University of Denmark
54 Long-term wind at the meteorological mast LT 50 m = Measured wind ± [long-term correlation effect] 54 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
55 Long-term adjustment effect Data points used = 57 (of 60) B45, 53 and 58 report no results Mean long-term effect = 0% Standard deviation = 2.2% Coefficient of variation = n/a Range = 9 to 6.5% (observed U 50 of 8.3 ms 1 assumed) 55 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
56 LT mean wind 50 m Data points used = 57 (of 60) B45, 53 and 58 report no results Mean wind speed = 8.3 ms -1 Standard deviation = 0.2 ms -1 Coefficient of variation = 2.2% Range = 7.6 to 8.9 ms DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
57 Turbulence 50 m Data points used = 55 (of 60) B11, 27, 37, 45, 58 report no results Mean turbulence intensity = 10% Standard deviation = 1.4% Coefficient of variation = 14% Range = 9 to 16% 57 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
58 Long-term wind at hub height at the met. mast LT 47 m = LT 50 m + [wind profile effects] 58 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
59 Wind profile shear exponent Data points used = 55 (of 60) B27, 45, 53, 58, 60 report no results B2, 11, 46, and 57 inferred by DTU Mean shear exponent = Standard deviation = Coefficient of variation = 10% Range = to DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
60 LT mean wind 47 m Data points used = 52 (of 60) B5, 10, 27, 37, 49, 53, 58 and 60 report no results. Mean wind speed = 8.3 ms -1 Standard deviation = 0.2 ms -1 Coefficient of variation = 2.4% Range = 7.5 to 8.8 ms DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
61 Turbulence 47 m Data points used = 49 (of 60) B5, 10, 11, 27, 31, 37, 49, 55, 56, 58, 60 report no results. Mean turbulence intensity = 10% Standard deviation = 1.2% Coefficient of variation = 12% Range = 9 to 15% 61 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
62 Gross energy yield of wind farm Gross AEP = Reference AEP ± [terrain effects] 62 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
63 Reference energy yield Data points used = 52 (of 60) Mean reference yield = 97.8 GWh Standard deviation = 5.7 GWh Coefficient of variation = 5.8% Range = 79.3 to 106 GWh 63 DTU Wind Energy, Technical University of Denmark
64 Topographical effects Data points used = 51 (of 60) Mean terrain effect = 7.5% Standard deviation = 4.4% Coefficient of variation = n/a Range = 19 to 1% 64 DTU Wind Energy, Technical University of Denmark
65 Gross energy yield Data points used = 58 (of 60) Mean gross yield = 91.5 GWh Standard deviation = 4.3 GWh Coefficient of variation = 4.7% Range = 76.4 to 113 GWh 65 DTU Wind Energy, Technical University of Denmark
66 Potential energy yield of wind farm Potential AEP = Gross AEP [wake losses] 66 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
67 Wake losses Data points used = 58 (of 60) Mean wake loss = 10.3% Standard deviation = 1.8% Coefficient of variation = 18% Range = 3.9% to 17% 67 DTU Wind Energy, Technical University of Denmark
68 Potential energy yield Data points used = 58 (of 60) Mean potential yield = 82.2 GWh Standard deviation = 4.6 GWh Coefficient of variation = 5.6% Range = 67.2 to 102 GWh 68 DTU Wind Energy, Technical University of Denmark
69 Net energy yield of wind farm, P 50 Net AEP (P50) = Potential AEP [technical losses] where [technical losses] = AEP f 1 f 2 f n and f 1, f 2,, f n are the individual loss factors. 69 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
70 Technical losses Data points used = 59 (of 60) Mean technical loss = 8.0% Standard deviation = 2.7% Coefficient of variation = 34% Range = 4.4 to 20% 70 DTU Wind Energy, Technical University of Denmark
71 Net energy yield (P 50 ) Data points used = 58 (of 60) Mean net yield = 75.7 GWh Standard deviation = 4.4 GWh Coefficient of variation = 5.8% Range = 64 to 91 GWh 71 DTU Wind Energy, Technical University of Denmark
72 Capacity factor Data points used = 58 (of 60) Mean capacity factor = 30.2% Std. deviation = 1.8% Coefficient of variation = 5.8% Range = 26 to 36% 72 DTU Wind Energy, Technical University of Denmark
73 Net energy yield of wind farm, P 90 Net AEP (P90) = Net AEP (P50) [uncertainty estimate] 73 DTU Wind Energy, Technical University of Denmark EWEA CREYAP II 26 Jun 2013
74 Uncertainty estimates Data points used = 46 (of 60) Mean uncertainty = 8% Standard deviation = 2.2% Coefficient of variation = 28% Range = 3.6 to 12% 74 DTU Wind Energy, Technical University of Denmark
75 Net energy yield (P 90 ) Data points used = 53 (of 60) Mean net yield = 66 GWh Standard deviation = 4.7 GWh Coefficient of variation = 7.1% Range = 56 to 79 GWh 75 DTU Wind Energy, Technical University of Denmark
76 Wind farm energy yields 76 DTU Wind Energy, Technical University of Denmark
77 Predicted turbine site terrain effects 77 DTU Wind Energy, Technical University of Denmark
78 Legend and references Legend to graphs Distribution graphs: histograms + fitted normal distribution. Statistics given next to graph. Team result graphs: mean value is base value for histogram, y-axis covers a range of ±2 standard deviations, x-axis covers teams No team number means result not submitted. Box-whisker plots: whiskers defined by the lowest datum still within 1.5 IQR of the lower quartile (Q1), and the highest datum still within 1.5 IQR of the upper quartile (Q3). For more information on CREYAP Pt. I Mortensen, NG & Ejsing Jørgensen, H 2011, 'Comparison of resource and energy yield assessment procedures'. in: Proceedings. EWEA. Mortensen, NG, Ejsing Jørgensen, H, Anderson, M & Hutton, K-A 2012, 'Comparison of resource and energy yield assessment procedures'. in: Proceedings of EWEA European Wind Energy Conference & Exhibition. EWEA - The European Wind Energy Association. 78 DTU Wind Energy, Technical University of Denmark
Comparison of Resource and Energy Yield Assessment Procedures
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