County Clustering with Bioenergy as Flexible Power Unit in a Renewable Energy System

Laura Stößel, Leila Poddie, Tobias Spratte, Ralf Schelenz and Georg Jacobs
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Laura Stößel: Chair for Wind Power Drives, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
Leila Poddie: Chair for Wind Power Drives, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
Tobias Spratte: Chair for Wind Power Drives, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
Ralf Schelenz: Chair for Wind Power Drives, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
Georg Jacobs: Institute for Machine Elements and System Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany

Energies, 2021, vol. 14, issue 17, 1-14

Abstract: The pressure on the energy sector to reduce greenhouse gas emissions is increasing. In the light of current greenhouse gas emissions in the energy sector, further expansion of renewable energy sources (RES) is inevitable to reduce emissions and reach the climate goals. This study aims at investigating structural characteristics of German counties regarding advantages for self-sufficient power systems based on RES. The modelling of the power sector based on RES is coupled with a cluster analysis in order to draw a large-scale conclusion on structural characteristics beneficial or obstructive for municipal energy systems. Ten clusters are identified with the Ward algorithm in a hierarchical-agglomerative method. The results underline a further need for RES expansion projects in order to close the gap between supply and demand. Only then, bioenergy can effectively balance the offset and support a truly self-sufficient local energy system. While the model results indicate that the majority of the counties are suitable for further expansion, this suitability is to be questioned in cluster 10. High population density is a critical characteristic, because with it come both a high demand and limited sites for further RES expansion projects.

Keywords: energy systems analysis; bioenergy; power demand; energy transition; renewable energy potentials; energy system model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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