h1

h2

h3

h4

h5
h6
% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@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},
}