Independent Power Supply Through Off-Grid Microgrids in South Africa: Potentials of AI Enhanced Business Models

Robyn Blake-Rath (), Anne Christin Dyck (), Gerrit Schumann () and Nils Wenninghoff ()
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Robyn Blake-Rath: Leibniz University Hannover
Anne Christin Dyck: Bonava Deutschland GmbH
Gerrit Schumann: University of Oldenburg
Nils Wenninghoff: R & D Division Energy, OFFIS—Institute for Information Technology Oldenburg (Old.)

A chapter in Digital Transformation for Sustainability, 2022, pp 119-137 from Springer

Abstract: Abstract Reliable, affordable, and sustainable electricity access is crucial for positive economic and social development. A large share of the population is, however, still not connected to the power grid. Moreover, the reliability cannot be ensured by the current electricity production and management within the South African power grid, resulting in outages. Furthermore, the current fossil fuel-based power generation, together with additionally installed backup diesel generators counteracts global climate change targets. This leads to a power grid transformation necessity, addressing the existing sustainability challenges. This paper therefore analyzes AI supported off-grid microgrids possibilities, using South Africa as a case study. For this purpose, technical and sustainable aspects of the current power grid status are analyzed. These issues are then addressed holistically, using current smart grids research results, together with AI enhanced forecasting technologies from the field of deep reinforcement learning, resulting in a feasible business model opportunity. The identified ICT opportunities have, however, the ability to contribute to a future, where the integration of renewable energies in interconnected, decentralized microgrids, together with preventing supply and consumption mismatches, provides the foundation for an affordable, reliable, and sustainable access to electricity for all.

Keywords: Smart grid; Sustainability; Decentralized electricity production; Deep reinforcement learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-031-15420-1_6

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