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The effect of climate change on wind resources in South Africa. University of Pretoria Graduate School of Technology Management (Energy Institutional Research Theme) Lynette Herbst. Energy Postgraduate Conference 2013. Rationale.
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The effect of climate change on wind resources in South Africa University of Pretoria Graduate School of Technology Management (Energy Institutional Research Theme) Lynette Herbst Energy Postgraduate Conference 2013
Rationale • South Atlantic and South Indian Ocean high pressure systems have been reported to increase (Jury, 2013) • Poleward shift of westerly wind system in Southern Hemisphere observed (Rouault et al., 2009) • These changes may affect mean wind velocity over South Africa • Possible implications for wind energy industry
Aim & Objective • Aim: To determine whether energy production from wind resources will change in the 21st century • Objective: To establish the change in annual energy production and power density under the influence of climatic variability in two locations in South Africa. How will a change in the mean wind velocity affect energy production in Alexander Bay and Calvinia?
Methods • Two locations (Alexander Bay and Calvinia) within the WASA (Wind Atlas of South Africa) domain was selected for analysis in WAsP 11 • WAsP (Wind Atlas Analysis and Application Program): wind power industry standard PC-software for wind resource assessment and siting of wind farms • Areas selected • close to proposed sites for wind farm development
Methods contd. • Roughness/contour map, • Observed wind climate and • Wind turbine generator files combined in WAsP 11 to calculate wind resource Process carried out for Alexander Bay and Calvinia with observed and ‘future’ data sets
Methods contd. • Shuttle Radar Topography Mission (SRTM) elevation data employed in generation of WAsP compatible contour maps
Methods contd. • ‘Roughness’ refers to land cover/friction wind encounters as it moves over surface • Google Earth image used as background image to map roughness • Areas of different roughness demarcated, and roughness lengths (z0 (m)) specified
Methods contd. • Monthly observed climate data downloaded from WASA website for Alexander Bay and Calvinia • MS Excel files concatenated to create single files containing whole year data • Future data created by assuming 10% increase in 10m mean wind velocity in 2081-2100 based on work of McInneset al. (2011) • 10m mean wind velocities were converted to 60m winds with Hellman’s exponential law (Bañuelos-Ruedas, 2011):
Results: Calvinia • Tested for significant difference with Mann-Whitney U test at a 95% confidence level • U > critical value (42) • Therefore, no significant difference in annual energy production and power density between current and future scenarios 2081-2100 2010-10 to 2012-09
Results: Alexander Bay • U > critical value (42) • Therefore, no significant difference in annual energy production and power density between current and future scenarios 2010-10 to 2012-09 2081-2100