Developing a municipality typology for modelling decentralised energy systems - Jann Weinand; Russell McKenna; Wolf Fichtner (Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), 2018)
The recent rapid expansion of renewable energy capacities in Germany has been dominated by decentralised wind, photovoltaic (PV) and bioenergy plants. The spatially disperse and partly unpredictable nature of these resources necessitates an increasing exploitation of integration measures such as curtailment, supply and demand side flexibilities, network strengthening and storage capacities. Indeed, one solution to the large-scale integration of renewable energies could be decentralised autonomous municipal energy systems. The achievement of grid parity for some renewable energy technologies has strengthened the desire of some communities to become independent from central markets. Whilst many communities in Germany already strive for socalled energy autonomy, the vast majority do so only on an annual basis. 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. The large number (11,131) of German municipalities means that a national analysis at this resolution is not feasible. Hence, this study employs a cluster analysis to develop a municipality typology in order to analyse the techno-economic suitability of these 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. The first step is to scale the indicator values and reduce their number by using a factor analysis. Several alternative methods are weighed against each other, and the most suitable methods for the factor analysis are chosen. Secondly, 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. For example, one cluster contains all major German cities and has a low potential for renewable energies. Another cluster, on the other hand, contains the municipalities with a higher potential for renewable energies due to their high hydrothermal potential for geothermal power. 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.
urn:nbn:de:swb:90-798056
Keywords: 330