The feasibility of energy autonomy for municipalities: local energy system optimisation and upscaling with cluster and regression analyses

Jann Michael Weinand (), Russell McKenna and Wolf Fichtner
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Jann Michael Weinand: Karlsruhe Institute for Technology
Russell McKenna: University of Aberdeen
Wolf Fichtner: Karlsruhe Institute for Technology

Sustainability Nexus Forum, 2021, vol. 29, issue 2, No 9, 153-159

Abstract: Abstract The sheer number of alternative technologies and measures make the optimal planning of energy system transformations highly complex, requiring decision support from mathematical optimisation models. Due to the high computational expenses of these models, only individual case studies are usually examined. In this article, the approach from the author’s PhD thesis to transfer the optimisation results from individual case studies to many other energy systems is summarised. In the first step, a typology of the energy systems to be investigated was created. Based on this typology, representative energy systems were selected and analysed in an energy system optimisation model. In the third step, the results of the representative case studies were transferred to all other energy systems. This approach was applied to a case study for determining the minimum costs of energy system transformation for all 11,131 German municipalities from 2015 to 2035 in the completely energy autonomous case. While a technical potential to achieve energy autonomy is present in 56% of the German municipalities, energy autonomy shows only low economic potential under current energy-political conditions. However, energy system costs in the autonomous case can be greatly reduced by the installation and operation of base-load technologies like deep-geothermal plants combined with district heating networks. The developed approach can be applied to any type of energy system and should help decision makers, policy makers and researchers to estimate optimal results for a variety of energy systems using practical computational expenses.

Keywords: Energy system optimisation; Cluster analysis; Regression analysis; Computational complexity reduction; Decentralised energy; Energy autonomy; Off-grid systems (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00550-021-00514-8

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