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The effect of climate change on wind resources in South Africa

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

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  1. 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

  2. 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

  3. 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?

  4. 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

  5. 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

  6. Methods contd. • Shuttle Radar Topography Mission (SRTM) elevation data employed in generation of WAsP compatible contour maps

  7. 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

  8. 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):

  9. 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

  10. 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

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