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The Role of Spatial Scale in Electricity System Optimisation Models

Frysztacki, Martha Maria ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract:

To investigate possible pathways to reduce greenhouse gas emissions in the electricity sector, researchers build optimisation models that typically minimise the total system costs such that all technical and physical constraints are met. For systems based on renewable energy, whose greatest expansion potentials are found for wind and solar generation, the chief challenge is dealing with their variability. To tackle this challenge, the optimisation models typically include large transmission networks to smooth renewable feed-in in space or storage technologies to smooth the variability in time. However, all aspects of the energy system at all levels of detail cannot currently be contained in a single model because of computational constraints. Instead, one must make simplifications and compromises that affect the optimality of the result from the point of view of the complete system. While reductions on the temporal scale and linearisation approaches of the model formulation have been previously analysed, in this thesis we focus on the quantification of the impact of the spatial scale. This is important because it is scientific practice to simplify models spatially while only little is known on the error made by the aggregation.
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Volltext (Version 2) §
DOI: 10.5445/IR/1000157620/v2
Veröffentlicht am 05.07.2023
Volltext (Version 1) §
DOI: 10.5445/IR/1000157620
Veröffentlicht am 06.04.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Hochschulschrift
Publikationsdatum 06.04.2023
Sprache Englisch
Identifikator KITopen-ID: 1000157620
HGF-Programm 37.12.02 (POF IV, LK 01) Design,Operation & Digitalization of the Future Energy Grids
Weitere HGF-Programme 37.06.01 (POF III, LK 01) Networks and Storage Integration
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xviii, 114, LIX S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Automation und angewandte Informatik (IAI)
Prüfungsdatum 17.02.2023
Projektinformation HGF, HGF IVF, VH-NG-1352
Externe Relationen Siehe auch
Forschungsdaten/Software
Forschungsdaten/Software
Schlagwörter energy transition, energy system modelling, renewable energy, spatial clustering, inverse methods, disaggregation methods
Relationen in KITopen
Referent/Betreuer Hagenmeyer, Veit
Brown, Thomas William
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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