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High Resolution Load Profiles for Simulation and Analysis of Small Scale Energy Systems

: Kallert, A.; Egelkamp, R.; Schmidt, D.

Volltext ()

Energy Procedia 149 (2018), S.122-131
ISSN: 1876-6102
International Symposium on District Heating and Cooling (DHC) <16, 2018, Hamburg>
Bundesministerium für Wirtschaft und Technologie BMWi
EnEff:Wärme; 03ET1234B; EE+Hyg@TWI
Energieeffizienz und Hygiene in der Trinkwasser-Installation im Kontext: DHC Annex TS1 ”Low Temperature District Heating for Future Energy Systems“
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IEE ()
Randomized user profiles; Demand side modelling; Low Temperature District Heating Supply; Assessment; planning issues

In the analysis of energy systems such as local low temperature district heating (LTDH) systems, not only the properties of the supply system but also the characteristics of the buildings and the behavior of its inhabitants, have an influence on the overall energy demand. In particular in the case of decreasing supply temperatures, it is becoming increasingly important to consider the different energy demands in residential buildings, which differ in terms of their quantity and occurrence over time.In this context detailed energy demand profiles for different demands in buildings can be used for the analysis of demand and supply. Hence, as part of this paper an approach for the creation of stochastic user profiles is presented using a VBA tool, which provides the main basis for the generation of heating load profiles (HLP). The tool generates randomized profiles of electricity and DHW demand as well as body heat profiles depending on the number and behavior of the inhabitants. Using a simulation software the profiles are then used to create HLP for different building energy classes. The outcome of this work is a generally applicable approach for the generation of scale able HLP, which can be used for e.g. the analysis or design of complex energy systems such as LTDH supply schemes.To demonstrate the application of the developed method and the influences of random user profiles on the overall energy demand, examples of applications are shown. Furthermore, possible applications in future research projects are indicated.