Wind assessment: tackling uncertainty
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State of the art methodologies for wind assessment and how to minimise
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Wind assessment: tackling uncertainty
- 1. Rethinking Energy Worldwide Assessing the wind potential: tackling uncertainty Miguel de Vasconcelos Ferreira
- 2. Assessing the wind potential – tackling uncertainty Index about MEGAJOULE The importance of wind assessment Sources of uncertainty Annual variability Wind flow modelling Uncertainty implications Final remarks Miguel de Vasconcelos Ferreira 2
- 3. About MEGAJOULE 3
- 4. Assessing the wind potential – tackling uncertainty About MEGAJOULE Founded in February 4, 2004 founding partners having more than 10 years experience in wind energy consultancy Renewable Energy consultancy with focus in wind resource assessment Leading wind energy consultancy in Portugal On the way for global expansion Projects in Portugal, Spain, France, Italy, Slovakia, Poland, Romania, Bulgaria, Croatia, Bosnia, Moldova, Ukraine, Greece, Turkey, Israel, Brazil, Uruguay, USA, Mexico, Angola, Cape Verde, South Africa, Australia and East Timor Miguel de Vasconcelos Ferreira 4
- 5. Assessing the wind potential – tackling uncertainty About MEGAJOULE 0,8m 12 0° 2m 1m 1,5m Wind Energy 1,5m 0,2m 1,5m Mesoscale Wind Flow Simulation 0,2m 61m er er gg gg Lo Lo a a at at D D 30m Wind measurement campaigns 12m 10,5m Layout definition and energy calculation Site Assessment (IEC) Wind resource assessment (bankable) Wind farm project due diligence Warranty verification and power curve measurement Miguel de Vasconcelos Ferreira 5
- 6. Assessing the wind potential – tackling uncertainty About MEGAJOULE Solar Energy Solar Resource Mapping Layout definition Solar Farm Due-Diligence Site Survey and Assessment Energy Yield calculation
- 7. Assessing the wind potential – tackling uncertainty About MEGAJOULE Acciona Energía Energiekontor Neoenergia (Brazil) ABO Wind ENERSIS Networkx African Development Bank FINERGE (Endesa) Norvento Astrum Energy Fomentinvest Pacific Hydro Banco BPI Fundação Oriente Petrobrás European Investment Bank GALP Power PROEF BES Investimento GDF-Suez PSW Caixa BI GE Wind Energy REpower Catavento GENERG RP Global Continental Wind Partners GESFINU SEE EBRD GESTAMP SGE EDF EN Green Energy Group Siemens EDP Renováveis IBERWIND SSE Renewables EFACEC INFRACO Suzlon Eletrosul INFUSION TP ENEOP2 International Power Tractebel ENERCON Jaguar Capital Ventinveste ENERGI E2 (eoN) Jaime Ribeiro & Filhos Voltalia ENERGIX MARTIFER Renewables ZORLU Enerji Miguel de Vasconcelos Ferreira 7
- 8. Assessing the wind potential – tackling uncertainty About MEGAJOULE ..... .………………………………….…………………………… .......... .......... .......... .......... .......... .......... .......... MEGAJOULE SA MEGAJOULE MEGAJOULE MEGAJOULE MEGAJOULE MEGAJOULE MEGAJOULE Polska Romania Do Brasil Adria Türkie South Africa Western Europe North and South-East Brazil and Latin Adriatic Turkey South Africa North America Central Europe Europe America Africa Asia-Pacific Miguel de Vasconcelos Ferreira 8
- 9. The importance of wind assessment beyond the obvious… 9
- 10. Assessing the wind potential – tackling uncertainty The importance of wind assessment Power density varies with the cube of wind speed Being so, slight differences in annual wind speed, not noticeable for a human being, may result in significant differences in annual energy yield. Wind variability Wind characteristics vary along the year, as well as from one year to another. Failure in taking into account the effects of seasonality and annual variability may lead to important errors in annual energy production estimation. Also important is the spatial variability of the wind characteristics. In complex terrain, like mountainous regions, the wind characteristics can vary significantly even in a few hundred meters. Miguel de Vasconcelos Ferreira 10
- 11. Assessing the wind potential – tackling uncertainty The importance of wind assessment Complexity of physical phenomena Due to the complexity of the physical phenomena present at wind flow, local effects, such as orography, roughness or obstacles, lead to important local variations of the wind regime and, therefore, to the need of using simulation models. Simulation models, however, carry a significant uncertainty. Among the available models, different approaches exist, leading to a trade-off between uncertainty and calculation time (sometimes very large). Miguel de Vasconcelos Ferreira 11
- 12. Sources of uncertainty 12
- 13. Assessing the wind potential – tackling uncertainty Sources of uncertainty • Wind measurements Quality of the instruments Adequacy of mast and mounting Data checking and validation Minimise data losses • Annual wind variability Availability/Quality of long term wind data Correlation methodologies • Spatial wind variability Terrain characteristics: orography, forest Met mast siting/Number of met masts Wind flow models Miguel de Vasconcelos Ferreira 13
- 14. Assessing the wind potential – tackling uncertainty The annual wind variability issue • Lack of long term data No long term wind energy specific data series Limited access to meteorological stations data • Lack of quality of long term data Inadequate siting of met stations for wind projects purpose Long operation without anemometer calibration Location change over time Measurement height change over time Miguel de Vasconcelos Ferreira 14
- 15. Assessing the wind potential – tackling uncertainty The annual wind variability issue • Using mesoscale virtual wind data series Enables to derive a long term data series for the required site Better control of uncertainty factors that are present in met masts Represents the pattern even when having a systematic deviation 100% NCEP NCAR (1000 mBar) Jasionka Airport (Poland) Local Mast #1 Local Mast #2 Virtual Wind Index Monthly WindIndex [%] 50% 0% Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 -50% -100% Miguel de Vasconcelos Ferreira 15
- 16. Assessing the wind potential – tackling uncertainty The modelling issue - Linear models Advantages • Reference tools, industry recognized • Fast and easy to use • Use of measurement results as an input Problems • Not adequate for complex terrain • Do not take into account recirculation or turbulence effects • Do not consider vertical component of wind speed Miguel de Vasconcelos Ferreira 16
- 17. Assessing the wind potential – tackling uncertainty The modelling issue - CFD models Advantages: • Adequate for complex terrain • Simulate recirculation areas • Calculate vertical component of wind speed • Consider stratification effects (temperature) • Consider time variation phenomena Problems • Heavy calculation load and more expertise required •“Driven” by boundary conditions (using mesoscale virtual data series as an input can minimise this problem) Miguel de Vasconcelos Ferreira 17
- 18. Assessing the wind potential – tackling uncertainty The modelling issue - CFD models When is CFD needed :: Possible criteria • Obstacles • Forests • Higher elevations nearby Terrain • High orographic complexity (RIX > 40%) • Measured Turbulence Intensity at Met masts > 15% • Deviations in Cross-predictions between masts > 10% Measured data • Ratio between anemometer height and hub height < 2/3 Measurement • Large distances between met masts and turbine positions campaign • Deviations between predicted and measured wind profiles > 5% Linear model performance Miguel de Vasconcelos Ferreira 18
- 19. Assessing the wind potential – tackling uncertainty CFD performance • Feasible :: speed and cost • Repeatability & reliability • Better results Miguel de Vasconcelos Ferreira 19
- 20. Assessing the wind potential – tackling uncertainty CFD performance 140 120 100 Height [m] a.g.l. 80 60 40 20 SODAR2 WINDIE WAsP SODAR1 WINDIE WAsP 0 0.9 1.0 1.1 1.2 1.3 1.4 Non-Dimensional Wind Speed Miguel de Vasconcelos Ferreira 20
- 21. Assessing the wind potential – tackling uncertainty Uncertainty minimisation using CFD Case Study - 35 MW wind farm in complex terrain i) First wind assessment using data from one met mast Uncertainty using traditional linear models = 19.1% Uncertainty using WINDIE CFD model = 15.4% Decision to install a second met mast and perform new wind assessment ii) Uncertainty using traditional linear models = 12.7% Uncertainty using WINDIE CFD model = 11.6% Miguel de Vasconcelos Ferreira 21
- 22. Assessing the wind potential – tackling uncertainty Uncertainty minimisation using CFD When the local measurements coverage is low, the use of the CFD can enhance the accuracy by reaching lower uncertainty values Importance of local wind data is still present by permitting the CFD model to achieve better results The difference in accuracy between linear models and CFD tends to decrease when more local measurements are available A case by case analysis should always be made as some atypical cases can always occur, leading to different results Miguel de Vasconcelos Ferreira 22
- 23. Uncertainty implications 23
- 24. Assessing the wind potential – tackling uncertainty Uncertainty implications Example: P50 = 79.5 GWh/y Unc. 10% => P90 = 69 GWh/y (-13%) Unc. 20% => P90 = 59 GWh/y (-26%) On a 100€/MWh tariff, it means 1 M€ annual difference! Miguel de Vasconcelos Ferreira 24
- 25. Final remarks 25
- 26. Assessing the wind potential – tackling uncertainty Final remarks Issues related with wind assessment have impact in every stage of the project development, from site procurement to wind farm operation. Wind assessment must be carefully planned, since the start, as sometimes it might not be possible to recover from previous inaccuracies, penalising the project in its latter stages. The costs associated to the use of state of the art wind assessment methodologies are very low, when compared with the total investment costs. Accurate wind assessment adds value to the project, by enabling uncertainty minimisation, lowering project risk and, therefore, permitting better financing conditions. Miguel de Vasconcelos Ferreira 26
- 27. Thank you for your attention! Rethinking Energy Worldwide miguel.ferreira@megajoule.pt Tel: +351 220 915 480 Fax: +351 229 488 166 www.megajoule.pt