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

  • 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% decarbonizationThe 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.show moreshow less

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Metadaten
Author:Martin KüppersORCiD, Christian Perau, Marco FrankenORCiD, Hans Jörg HegerORCiD, Matthias Huber, Michael MetzgerORCiD, Stefan NiessenORCiD
Language:English
Document Type:Article
Year of first Publication:2020
published in (English):Energies
Publisher:MDPI
Place of publication:Basel
ISSN:1996-1073
Volume:13
Issue:16
Pages:15
Article Number:4076
Review:peer-review
Open Access:ja
Version:published
Tag:GIS; South Africa; energy system model; energy transition; optimization; spatial clustering
URN:urn:nbn:de:bvb:573-31469
Related Identifier:https://doi.org/10.3390/en13164076
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2023/03/02