1Weidinger, T., 2Costa, A. A., 3Lajos, T., 4Kiss, Á., Estimation of wind energy potential in the Equatorial Costal Area of Brazil based on measurements and mesoscale numerical model results 1Weidinger, T., 2Costa, A. A., 3Lajos, T., 4Kiss, Á., 1Gyöngyösi, A. Z., 4Papp, B. 1Department of Meteorology, 4Department of Atomic Physics, Eötvös Loránd University, Budapest 3Department of Fluid Mechanics, Budapest University of Technology and Economics 2Universidade Estadual do Ceará, Campus do Itapery - Fortaleza

Data sets and model approaches Contents Problem formulation, motivation Climate of the Coastal region of Ceará Data sets and model approaches Interannual variation of wind speed Methodology of group at CEARÁ (based on RAMS) Methodology at the Hungarian group (based on ETA) Conclusion, remarks

Problem formulation, motivation The wind power generation costs in Brazil (39-84 US$/MWh) is already comparable to thermoelectric, nuclear and even new hydroelectric plants. Most focus has been put on the state of Ceará precise and reliable wind data collection resource assessment programs Collaboration between the Brazil and Hungarian group Development of methodology for wind energy potential based on mesoscale model calculation and statistical analysis: exchange experiences, cross checking the results.

Climate diagram of Fortaleza State Ceará Climate diagram of Fortaleza Temperature [oC] Precipitation [mm] 1 2 3 4 5 6 7 8 9 10 11 12 Month Wind climate of Brazil

Mesoscale weather forecast models Data sets Synoptic measurements from 2001, Tower measurements 2004-2006. Mesoscale weather forecast models RAMS Regional Atmospheric Modeling System (Developed at Colorado State University), ETA-model (NCEP, former NMC, Washington).

Variability of the wind speed Synoptic weather station Fortalesa Airport (u [m/s]) Year 2001 2002 2003 2004 2005 2006 Sum Average 3.9 4.1 4.4 4.8 4.7 Dispersion 1.9 2.1 2.0 Wind tower data for January 2005 and 2006 Paracuru 2005 8.4 2.3 8.55 0.07 0.15 2006 10.2 1.55 10.4 1.6 0.04 Camocim 2005 8,0 3,4 8,25 3,3 0,15 0,18 2006 9,1 9,55 0,21 0,16

Wind power estimation – Methodology University of Ceará Initial data set (GCM output, measurements) Climatological dataset, statistics Mesoscale Model (RAMS) Model Output Correction Local wind field < 90m resolution (WasP) Interpolation, ~ 1 km x 1 km grid cells Post processing, model output Local surface parameters (elevation, type of vegetation, surface roughness, albedo, etc.) Microscale modelling Micrositing Fine topography and land cover data set

Nested models for the planned Batoque wind farm: grid resolution: 20 km, 4 km, 0,8 km and Local model Local model

Wind power estimation for Batoque, 2005 [kW] 900 800 700 600 Generator 500 400 300 200 100 Wind power [kW] 1 3 5 7 9 11 Month

Wind power estimation for Batoque, 2005 January 40 Sector: All A:8.7 m/s K:4.05 U:7.89 m/s P:369 W/m2 f [%] 20 Wind rose u [m/s] 5 10 15 20

ETA model calculation Mesoscale non-hydrostatic meteorological model calculation for the NE costal region of Ceará, Brasil. Fine grid, horizontal resolution 2 km x 2 km Centre of model domain: S 4o, W 38o Initial and boundary conditions from NCEP Global model for 2006. Model integration performed automatically.

ETA Model domain

Wind profile calculation 975 hPa Linear profil 1000 hPa Power law profil

Mean wind speed at 10 m 2006. Jan. 1-10 Cross section 3.2S 3.4S Batoque 3.6S 3.8S 4.0S

Mean wind speed at 1000 hPa 2006. Jan. 1-10

Mean wind speed at 975 hPa 2006. Jan. 1-10

Mean wind speed at 950 hPa 2006. Jan. 1-10

Cross section of the wind speed, a case study u [m/s] H [m] u(975 hPa) Uncertainty: ~0.5 m/s u(1000 hPa) u(10 m) Surface

Measured and modeled wind, 2006. January 1 - 10, Tower Model u(60) FL1500 [kW] 9,4 954 8.1 611 u(975hPa) 9.2 905

BATOQE Modeled wind, 2006. January Wind direction 2006 Wind [m/s] FL1500 [kW] 60 m 8.2 623 80 m 8.5 707 975 hPa 9.6 997

Conclusion, remarks Wind power estimations were done using mesoscale model approaches for the coastal region of Ceará, Brazil. Interannual variation of the trade wind is not negligible for the long term wind power estimation. Uncertainty due to choice of the grid cell is higher than 0.5 m/s for mean wind speed calculation. Mean and daily variations of the near surface wind speed are underestimated on the coastal region using the ETA model.