Bruno Hadengue
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland / ETH Zurich, Institute of Environmental Engineering, 8093 Zürich, Switzerland
Andreas Scheidegger
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Eberhard Morgenroth
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland / ETH Zurich, Institute of Environmental Engineering, 8093 Zürich, Switzerland
Tove A. Larsen
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
Download articlehttp://dx.doi.org/10.3384/ecp19157639Published in: Proceedings of the 13th International Modelica Conference, Regensburg, Germany, March 4–6, 2019
Linköping Electronic Conference Proceedings 157:65, p. 8
Published: 2019-02-01
ISBN: 978-91-7685-122-7
ISSN: 1650-3686 (print), 1650-3740 (online)
Domestic Hot Water (DHW) systems are large energy consumers in newly-built residential buildings. Mitigation measures involve more efficient hot water appliances and distribution systems, waste heat recovery systems, or changes in consumer habits. However, the implementation of these measures must be investigated carefully, as combinations may lead to unforeseen systemic interactions limiting their potential. In this article, we present tools to identify and optimize these interactions. The WaterHub modules were developed for Material and Energy Flow Analyses (MEFA) of domestic hot water systems. Two modules are available: (i) the WaterHub Modelica library includes models for MEFA system definition, and (ii) The HydroGen Python module provides methods for the stochastic generation of appliance-specific hydrographs, used as input data for the simulation of the system energy and water flows. First, we describe the technical aspects of these modules. Second, we provide an example of how they may be used in a didactic scenario analysis of a heat recovery device.
Domestic Hot Water Systems, Material and Energy Flow Analysis, Modelica Library, stochastic demand modeling