Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization

Martin Kueppers, Christian Perau, Marco Franken, Hans Joerg Heger, Matthias Huber, Michael Metzger and Stefan Niessen
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Martin Kueppers: Siemens AG, Corporate Technology, Otto-Hahn Ring 6, 81739 Munich, Germany
Christian Perau: Siemens AG, Corporate Technology, Otto-Hahn Ring 6, 81739 Munich, Germany
Marco Franken: Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW), RWTH Aachen University, Schinkelstraße 6, 52062 Aachen, Germany
Hans Joerg Heger: Siemens AG, Corporate Technology, Otto-Hahn Ring 6, 81739 Munich, Germany
Matthias Huber: Siemens AG, Corporate Technology, Otto-Hahn Ring 6, 81739 Munich, Germany
Michael Metzger: Siemens AG, Corporate Technology, Otto-Hahn Ring 6, 81739 Munich, Germany
Stefan Niessen: Technology and Economics of Multimodal Energy Systems, Technical University of Darmstadt, Landgraf-Georg-Str. 4, 64283 Darmstadt, Germany

Energies, 2020, vol. 13, issue 16, 1-15

Abstract: The decarbonization of energy systems has led to a fundamental change in their topology since generation is shifted to locations with favorable renewable conditions. In planning, this change is reflected by applying optimization models to regions within a country to optimize the distribution of generation units and to evaluate the resulting impact on the grid topology. This paper proposes a globally applicable framework to find a suitable regionalization for energy system models with a data-driven approach. Based on a global, spatially resolved database of demand, generation, and renewable profiles, hierarchical clustering with fine-tuning is performed. This regionalization approach is applied by modeling the resulting regions in an optimization model including a synthesized grid. In an exemplary case study, South Africa’s energy system is examined. The results show that the data-driven regionalization is beneficial compared to the common approach of using political regions. Furthermore, the results of a modeled 80% decarbonization until 2045 demonstrate that the integration of renewable energy sources fundamentally changes the role of regions within South Africa’s energy system. Thereby, the electricity exchange between regions is also impacted, leading to a different grid topology. Using clustered regions improves the understanding and analysis of regional transformations in the decarbonization process.

Keywords: spatial clustering; energy system model; optimization; GIS; South Africa; energy transition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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