A methodological framework for identifying District Heating Networks in Germany by utilizing the census data

Smart Energy, vol. 21, pp. 100218

Abstract

In this study, we present a methodological framework to identify the spatial position and heat demand of District Heating Networks (DHNs) for residential buildings in Germany by utilizing the census dataset for 2022. A clustering approach is used to locate DHNs and further datasets are included to derive and evaluate additional characteristics. A top-down calibration is applied to match the heat demand of the identified networks with validation data for 2022 from the Federal Statistical Office of Germany. In total, 8,684 areas of presumed DHNs could be identified in Germany. 90 % of the demand is covered by about 10.87 % of the DHNs. The resulting dataset is published under the name District Heating Networks Dataset from Clustered Census Data (AixDHN) 1 1 https://github.com/RWTH-EBC/AixDHN . on GitHub. To demonstrate the application of the AixDHN dataset, a use case is presented in which the potential of Shallow Geothermal Collectors (SGCs) for supplying the identified district heating grids is evaluated. The use case reveals a technical potential of approximately 15.31 TWh/a for SGCs in Germany, supplying the heat for domestic buildings connected to the DHNs. The AixDHN dataset provides a spatially detailed, transparent basis and enables the evaluation of the potential of further renewable heat sources for integration into DHNs. The methodology’s strengths lie in its robust, generalized, and systematic approach, which makes it applicable throughout Germany without any further research on a regional level. It may act as a tool to support the planning and transformation of the heating sector towards renewable sources as well as municipal heat planning on a broad scope. When cooling applications are addressed alongside heating, the economic viability of cold DHNs can be significantly improved, particularly enabling new applications in mixed-use areas that remain attractive even with lower heat densities. • Germany’s district heating networks are mapped by fusing 2022 census, Hotmaps, and federal statistics. • A robust clustering method is developed that works nationwide without region-specific tuning. • 90 % of district heating demand is served by only 10.87 % of networks, indicating strong concentration. • The dataset spans 8,146 km 2 and represents 50.16 TWh/a of heat demand. • A technical potential of 15.31 TWh/a is identified for integrating shallow geothermal collectors.

Authors 4

1. Kai Droste corresponding Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, North Rhine-Westphalia, Germany

2. Luca Döring Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, North Rhine-Westphalia, Germany

3. Jonas Klingebiel Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, North Rhine-Westphalia, Germany

4. Rahul Karuvingal Aachen Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, North Rhine-Westphalia, Germany