High-resolution atmospheric modelling reveals lower costs for renewable energy systems in Southern Africa

This is the authors' abstract. We don't add key points for this paper.

Expanding renewable energy in Africa is crucial for increasing electricity access and combating climate change, but scarce or coarse-resolution meteorological data often hinders accurate model-based renewable energy systems planning. Current assessments of the renewable energy potential and, subsequently, the design of renewable energy systems are typically based on coarse resolution reanalysis products such as ERA5 or MERRA-2. In this interdisciplinary study, we used the high-resolution validated meteorological data ICON-LAM from 2017 to 2019, based on physically consistent, atmospheric simulations with the ICON model, as an input to design cost-optimised renewable energy systems over southern Africa, and compare it to results obtained with ERA5 data. Higher wind energy potentials, averaging about 50 %, were found derived from ICON-LAM compared to ERA5, while solar energy potentials were similar. The cost-optimised energy system design using ICON-LAM inputs leads to a cost reduction of about 14 %, driven mainly by a 13 % lower battery capacity requirement, compared to using ERA5 – a result rooted in the cheaper wind energy revealed by ICON-LAM. This suggests that the cost of renewable energy systems may have been overestimated in the past, potentially hindering their efficient deployment. Hence, the study emphasises the importance of using high-resolution atmospheric modelling data sets as a decisive input for energy system planning.