Survey of 53 Fifth‐Generation District Heating and Cooling (5GDHC) Networks in Germany
Fifth‐generation district heating and cooling (5GDHC) networks are a promising technology to decarbonize the heating and cooling supply of buildings and districts. Up to today, only a small number of 5GDHC networks have been built, mostly in Switzerland and Germany. As a result, there are substantia...
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Veröffentlicht in: | Energy technology (Weinheim, Germany) Germany), 2022-11, Vol.10 (11), p.n/a |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Fifth‐generation district heating and cooling (5GDHC) networks are a promising technology to decarbonize the heating and cooling supply of buildings and districts. Up to today, only a small number of 5GDHC networks have been built, mostly in Switzerland and Germany. As a result, there are substantial uncertainties and knowledge gaps in the planning and operation of 5GDHC networks. Herein, a survey is conducted among utility companies and engineering offices to collect data on 53 5GDHC systems in Germany, including technical, economic, and political key figures and design decisions. Results show that 5GDHC networks are mostly planned for small new build districts (less than 100 buildings). The most frequent heat sources are horizontal geothermal collectors (used in 23 networks) and geothermal probes (17 networks). 74% of the surveyed networks provide not only heat but also cold. Typical network temperatures are in the range of −5–20 °C. The survey results show that the price models as well as political design decisions, such as the obligation to connect to the network, vary strongly among the districts.
This article presents data for 53 fifth‐generation district heating and cooling (5GDHC) networks in Germany. The data is collected in a comprehensive survey among utility companies and engineering offices. The survey data includes technical information, such as network temperatures, heat sources, network lengths, and primary energy factors, as well as economic and political design decisions like price models. |
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ISSN: | 2194-4288 2194-4296 |
DOI: | 10.1002/ente.202200749 |