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@PHDTHESIS{Chen:1043292,
      author       = {Chen, Shuying},
      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     = {FZJ-2025-02818},
      isbn         = {978-3-95806-822-3},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {XIII, 141},
      year         = {2025},
      note         = {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 (ICONNWP)
                      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
                      energybased 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          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2507140951235.367442698699},
      doi          = {10.34734/FZJ-2025-02818},
      url          = {https://juser.fz-juelich.de/record/1043292},
}