A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model

Annette Steingrube, Keyu Bao, Stefan Wieland, Andrés Lalama, Pithon M. Kabiro, Volker Coors and Bastian Schröter
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Annette Steingrube: Fraunhofer Institute for Solar Energy Systems, Heidenhofstr. 2, 79110 Freiburg, Germany
Keyu Bao: Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany
Stefan Wieland: Fraunhofer Institute for Solar Energy Systems, Heidenhofstr. 2, 79110 Freiburg, Germany
Andrés Lalama: Institute for Visualization and Interactive Systems, Faculty of Computer Science, Electrical Engineering and Information Technology, University of Stuttgart, Keplerstraße 7, 70174 Stuttgart, Germany
Pithon M. Kabiro: Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany
Volker Coors: Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany
Bastian Schröter: Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany

Resources, 2021, vol. 10, issue 5, 1-19

Abstract: District heating is seen as an important concept to decarbonize heating systems and meet climate mitigation goals. However, the decision related to where central heating is most viable is dependent on many different aspects, like heating densities or current heating structures. An urban energy simulation platform based on 3D building objects can improve the accuracy of energy demand calculation on building level, but lacks a system perspective. Energy system models help to find economically optimal solutions for entire energy systems, including the optimal amount of centrally supplied heat, but do not usually provide information on building level. Coupling both methods through a novel heating grid disaggregation algorithm, we propose a framework that does three things simultaneously: optimize energy systems that can comprise all demand sectors as well as sector coupling, assess the role of centralized heating in such optimized energy systems, and determine the layouts of supplying district heating grids with a spatial resolution on the street level. The algorithm is tested on two case studies; one, an urban city quarter, and the other, a rural town. In the urban city quarter, district heating is economically feasible in all scenarios. Using heat pumps in addition to CHPs increases the optimal amount of centrally supplied heat. In the rural quarter, central heat pumps guarantee the feasibility of district heating, while standalone CHPs are more expensive than decentral heating technologies.

Keywords: energy system optimization; district heating; energy system modelling; 3D building model; urban energy simulation platform (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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