Kueppers, Martin ; Perau, Christian ; Franken, Marco ; Heger, Hans Joerg ; Huber, Matthias ; Metzger, Michael ; Niessen, Stefan (2024)
Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization.
In: Energies, 2020, 13 (16)
doi: 10.26083/tuprints-00017018
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Kueppers, Martin ; Perau, Christian ; Franken, Marco ; Heger, Hans Joerg ; Huber, Matthias ; Metzger, Michael ; Niessen, Stefan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization |
| Sprache: | Englisch |
| Publikationsjahr: | 15 Januar 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Energies |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| (Heft-)Nummer: | 16 |
| Kollation: | 15 Seiten |
| DOI: | 10.26083/tuprints-00017018 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/17018 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | spatial clustering, energy system model, optimization, GIS, South Africa, energy transition |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-170181 |
| Zusätzliche Informationen: | This article belongs to the Section F: Electrical Engineering |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Technik und Ökonomie Multimodaler Energiesysteme (MMES) |
| Hinterlegungsdatum: | 15 Jan 2024 14:01 |
| Letzte Änderung: | 26 Feb 2024 16:20 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization. (deposited 15 Jan 2024 14:01) [Gegenwärtig angezeigt]
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