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,Pithon M Kabiro,Bastian Schröter,Stefan Wieland,Andrés Lalama,Volker Coors

doi:10.3390/resources10050052

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.

Highlights

Space heating accounted for 26% of the total end energy demand in 2019, and for 68% of the end energy demand in the residential sector in Germany [1]
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

Summary

Introduction

Space heating accounted for 26% of the total end energy demand in 2019, and for 68% of the end energy demand in the residential sector in Germany [1]. To achieve Germany’s goals of reducing 2030 GHG emissions by 65% compared to 1990, and to netzero by 2045, both the technologies and energy carriers employed for procuring space heating need to undergo fundamental shifts. To facilitate this shift as efficiently as possible, it is highly important to have a detailed understanding of the most economically viable solution to provide space heating for a given configuration of buildings or city quarters [3]. Different aspects have to be taken into account on both the building and district level to assess where heating grids are the most viable option, such as the heating density in a quarter, the current heating structure and possible extensions of existing heating stations. 3D building models can help to assess heating demand of single buildings by using information such as the size, form and orientation of buildings, the buildings’ usage types (e.g., residential or commercial), their number of inhabitants, the buildings’ physical properties such as U-values of surfaces, and their geographic location, and by extension, the local climate [6]

Methods

Results

Conclusion