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%0 Thesis
%A Chen, Shuying
%T Variable renewable energy potential estimates based on high-resolution regional atmospheric modelling over southern Africa
%V 662
%I RWTH Aachen University
%V Dissertation
%C Jülich
%M RWTH-2025-04529
%@ 978-3-95806-822-3
%B Schriften des Forschungszentrums Jülich Reihe Energie & Umwelt = Energy & environment
%P 1 Online-Ressource (XIII, 141 Seiten) : Diagramme, Karten
%D 2025
%Z Druckausgabe: 2025. - Onlineausgabe: 2025. - Auch veröffentlicht auf dem Publikationsserver der RWTH Aachen University
%Z Dissertation, RWTH Aachen University, 2025
%X Africa is the world’s least electrified continent, home to three-quarters of the global population without electricity. Electricity generation in African countries today relies heavily on fossil fuels and hydropower, despite the continent’s abundant potential for the most widely accessible renewable energy sources—wind and solar, as Africa is the sunniest continent in the world and has many windy sites. Africa is also very vulnerable to climate change due to relatively low levels of local socio-economic development. Renewable energy is recognized as an important solution for Africa to address both climate change mitigation and electricity access. Reliable and highly resolved information on Renewable Energy Potential (REP) is imperative to support renewable power plant expansion. However, existing meteorological data sets over Africa used for REP estimates are often characterized by relatively coarse spatial resolution, data gaps in space and time, and general data quality issues. This challenges the reliability and accuracy of existing REP estimates, as well as the modelling of energy systems that include renewable energy. To overcome the existing meteorological data set challenges for renewable energy applications in Africa, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON-NWP) model in its Limited Area Mode (ICON-LAM) is implemented and run over southern Africa as a prototype for the continent. The ICON model is configured in a hindcast dynamical downscaling setup at a convection-permitting 3.3 km spatial resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in-situ observations, satellite, and composite data products. ICON-LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s-1 over land. Likewise the simulated rsds with an ME of 50 W m-2 well resembles the observations. In this work, the simulated 60 m wind speeds (ws60m) from the ICON-LAM simulation and the often-used 31 km-resolution ERA5 reanalysis are also evaluated against measurements at 18 weather masts. The wind power calculated from these simulated wind speed data sets is also compared with measurements at existing wind farms in South Africa. The estimated wind energy potential (WEP) based on ICON-LAM and ERA5 are finally compared using an innovative approach with 1.8 million eligible wind turbine placements over southern Africa. Results show ERA5 underestimates ws60m with a Mean Error (ME) of -1.8 m s-1 (-27
%F PUB:(DE-HGF)11 ; PUB:(DE-HGF)3
%9 Dissertation / PhD ThesisBook
%R 10.18154/RWTH-2025-04529
%U https://publications.rwth-aachen.de/record/1011082