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@PHDTHESIS{Chen:1011082,
author = {Chen, Shuying},
othercontributors = {Hendricks Franssen, Harrie-Jan and Leuchner, Michael and
van Lipzig, Nicole and Quoilin, Sylvain},
title = {{V}ariable renewable energy potential estimates based on
high-resolution regional atmospheric modelling over southern
{A}frica},
volume = {662},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag},
reportid = {RWTH-2025-04529},
isbn = {978-3-95806-822-3},
series = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
Umwelt = Energy $\&$ environment},
pages = {1 Online-Ressource (XIII, 141 Seiten) : Diagramme, Karten},
year = {2025},
note = {Druckausgabe: 2025. - Onlineausgabe: 2025. - Auch
veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2025},
abstract = {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
$(-27\%).$ In contrast, ICON-LAM shows a ME of -0.1 m s-1
$(-1.8\%),$ resulting in a much higher average WEP by $48\%$
compared to ERA5. A combined Global Wind Atlas-ERA5 product
reduces the ws60m underestimation of ERA5 to -0.3 m s-1
$(-4.7\%),$ but shows a similar average WEP compared to ERA5
resulting from the WEP spatial heterogeneity. ICON-LAM also
reproduces the observed wind power better than the others,
further consolidating the reliability of its derived WEP.
Underestimating wind energy yields may hinder the expansion
of wind energy, as less economic performance is expected,
which underlines the importance of highly resolved
meteorological data. Increasing the share of renewable
energy in African energy systems is imperative and urgent to
address climate change mitigation and access to electricity.
This thesis also investigates the impact of the
high-resolution ICON-LAM simulations on energy system
modelling for southern Africa. An energy system design,
encompassing wind energy, solar energy, and battery storage,
is derived exemplarily to meet $100\%$ of the local
electricity demand, cost-optimized, for each administrative
province in southern Africa. Different meteorological data
sets, including ICON-LAM as well as the commonly used ERA5
and its variant, are utilized and compared to derive
cost-optimized energy systems. The results show significant
differences in the wind energy potentials derived from
different meteorological data sets, while similar solar
energy potentials are found. Cost-optimized energy systems
when using ICON-LAM meteorological inputs require less total
annual cost (approx. $14\%)$ and battery capacity (approx.
$13\%)$ compared to the other energy system solutions using
different meteorological input datasets. This suggests that
the cost of renewable energy systems may have been
overestimated in the past, potentially also hindering its
local development. The study further emphasizes the
importance of using high-resolution, alternative,
atmospheric modelling data sets as a decisive input for
energy system modelling. Overall, our results show that the
ICON model is able to reproduce the renewable energy related
variables and basic atmospheric flows in southern Africa.
Compared to other commonly used data sets, the ICON
simulations reveal higher wind energy potentials, and
cost-optimized energy systems based on these simulations
require lower total annual costs and battery capacity. These
findings are critical for local renewable energy
development, as renewable energy potentials may have long
been underestimated and the costs of building renewable
energy-based energy systems overestimated in southern
Africa. Further tuning of physical parameterization schemes
specifically for southern Africa may improve the performance
of the ICON simulation. Adapting a more sophisticated energy
system that includes the real-world power grid and various
energy-using sectors may also improve the accuracy of the
energy system modelling performed in this study.},
cin = {532820 / 530000},
ddc = {550},
cid = {$I:(DE-82)532820_20140620$ / $I:(DE-82)530000_20140620$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
urn = {urn:nbn:de:hbz:5:2-1474685},
doi = {10.18154/RWTH-2025-04529},
url = {https://publications.rwth-aachen.de/record/1011082},
}
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