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

Author

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  • Martin Kueppers

    (Siemens AG, Corporate Technology, Otto-Hahn Ring 6, 81739 Munich, Germany
    Technology and Economics of Multimodal Energy Systems, Technical University of Darmstadt, Landgraf-Georg-Str. 4, 64283 Darmstadt, Germany)

  • Christian Perau

    (Siemens AG, Corporate Technology, Otto-Hahn Ring 6, 81739 Munich, Germany
    Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW), RWTH Aachen University, Schinkelstraße 6, 52062 Aachen, 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
    Siemens AG, Corporate Technology, Guenther-Scharowsky-Str. 1, 91050 Erlangen, Germany)

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.

Suggested Citation

  • Martin Kueppers & Christian Perau & Marco Franken & Hans Joerg Heger & Matthias Huber & Michael Metzger & Stefan Niessen, 2020. "Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization," Energies, MDPI, vol. 13(16), pages 1-15, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4076-:d:395572
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    References listed on IDEAS

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