Developing a municipality typology for modelling decentralised energy systems

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One solution to the large-scale integration of renewable energies could be decentralised autonomous municipal energy systems. Several studies have already analysed the technical and economic implications of the mainly decentralised future energy system, but most are restricted in their insights by limited temporal and spatial resolution. This study employs a cluster analysis to develop a municipality typology in order to analyse the techno-economic suitability of municipalities for autonomous energy systems. A total of 34 socio-technical indicators are employed at the municipal level, with a particular focus on the sectors of Private Households and Transport, and the potentials for decentralised renewable energies. Selected quantitative cluster validation methods are employed alongside qualitative criteria to determine the optimal number of clusters. This results in a total of ten clusters, which show a large variation as well as some overlap with respect to specific indicators. An analysis of the municipalities from three German renewable energy projects “Energy Municipalities” “Bioenergy Villages” and “100% Renewable Energy Regions” shows that in eight of the ten clusters municipalities are aiming for energy autonomy (in varying degrees). It is challenging to differentiate between the clusters regarding readiness for energy autonomy projects, however, especially if the degree of social acceptance and engagement for such projects is to be considered. To answer the more techno-economical part of this question, future work will employ the developed clusters in the context of an energy system optimisation. Insights gained at the municipal level will then be qualitatively transferred to the national context to assess the implications for the whole energy system.

Original languageEnglish
JournalUtilities Policy
Volume57
Pages (from-to)75-96
ISSN0957-1787
DOIs
Publication statusPublished - 1 Apr 2019
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Cluster analysis, Decentralised energy system, Energy autonomy, Socio-energetic indicators

ID: 170743119