Abstract
The district heating (DH) demand of various systems has been simulated in several studies. Most studies focus on the temporal aspects rather than the spatial component. In this study, the DH demand for a medium-sized DH network in a city in southern Germany is simulated and analyzed in a spatially explicit approach. Initially, buildings are geo-located and attributes obtained from various sources including building type, ground area, and number of stories are merged. Thereafter, the annual primary energy demand for heating and domestic hot water is calculated for individual buildings. Subsequently, the energy demand is aggregated on the segment level of an existing DH network and the water flow is routed through the system. The simulation results show that the distribution losses are overall the highest at the end segments (given in percentage terms). However, centrally located pipes with a low throughflow are also simulated to have high losses. The spatial analyses are not only useful when addressing the current demand. Based on a scenario taking into account the refurbishment of buildings and a decentralization of energy production, the future demand was also addressed. Due to lower demand, the distribution losses given in percentage increase under such conditions.
Highlights
A district heating (DH) network allows the distribution of heat from energy producers to consumers
The energy demand is aggregated on the segment level of an existing DH network and the water flow is routed through the system
Once we classified the buildings according to the TABULA typology and derived their total primary energy demand for heating and domestic hot water, we analyzed the spatial variation within the DH network
Summary
A district heating (DH) network allows the distribution of heat from energy producers to consumers. A common practice within the field of navigation, this technique is not commonly applied for the routing within DH networks, as it does not take into account specific heating network properties such as fluctuating demand and supply, decentralized production, flow and throughput rates, or quickly changing weather conditions It offers the opportunity of estimating the optimal dimension of DH pipelines and identifying important nodes and edges within the network. A spatial redistribution (decentralization) of the energy production may lead to shorter transport distances and lower distribution losses Another measure to improve the efficiency of the system is utilizing excess heat from industrial processes [24], which otherwise would remain unused. Detailed building types Nr. persons and flats per building Geocoding of NEXIGA and casaGeo addresses Total primary energy demand for heating and domestic hot water 14 land use classes DH network and DH supply area. The building heat demand, as well as the routing within the DH network, were conducted according to Sections 2.2–2.4
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
