Urban energy consumptions growth has become an urgent topic that requires solutions for significantly reduce carbon emission in the next decades. This paper aims in exploring the integration of building performance improvement and low carbon district heat technological choices by considering the upgrade of conversion technologies, efficiency and the exploitation of local resources. The paper is based on a GIS-based model that spatially characterize the space heating demand of urban buildings. Starting from clustering buildings with similar thermo-physical characteristics, the total energy use of buildings can be depicted and compared with the energy balance data of the city in order to scale the bottom-up results for matching the total load. Reasonable energy efficiency measures are further proposed by considering three different scenarios up to 2050. Long-term building scenarios are applied to a district heating simulation model for investigating how the reduction of building heat demand will impact the district heating production and operations. In particular, the combination of the building model and the district heating model aims at exploring the effects of district network expansion or new low carbon investments from an economic and environmental perspective. The model has been successfully applied to the city of Turin, Italy and the city of Stockholm, Sweden. The flexibility of the approach may allow it to be easily adjusted to different urban areas for providing indications on cost-effective strategies for efficient, low-carbon heat solutions in integrated energy systems. Results highlight that finding synergies between the demand and supply sector will lead to environmental and economic benefits, in particular for district-heated cities.

Modeling Building Energy Demand Profiles and District Heating Networks for Low Carbon Urban Areas / Delmastro, Chiara; Martinsson, Fredrik; Mutani, Guglielmina; Corgnati, STEFANO PAOLO. - In: PROCEDIA ENGINEERING. - ISSN 1877-7058. - 198:(2017), pp. 386-397. [10.1016/j.proeng.2017.07.094]

Modeling Building Energy Demand Profiles and District Heating Networks for Low Carbon Urban Areas

DELMASTRO, CHIARA;MUTANI, GUGLIELMINA;CORGNATI, STEFANO PAOLO
2017

Abstract

Urban energy consumptions growth has become an urgent topic that requires solutions for significantly reduce carbon emission in the next decades. This paper aims in exploring the integration of building performance improvement and low carbon district heat technological choices by considering the upgrade of conversion technologies, efficiency and the exploitation of local resources. The paper is based on a GIS-based model that spatially characterize the space heating demand of urban buildings. Starting from clustering buildings with similar thermo-physical characteristics, the total energy use of buildings can be depicted and compared with the energy balance data of the city in order to scale the bottom-up results for matching the total load. Reasonable energy efficiency measures are further proposed by considering three different scenarios up to 2050. Long-term building scenarios are applied to a district heating simulation model for investigating how the reduction of building heat demand will impact the district heating production and operations. In particular, the combination of the building model and the district heating model aims at exploring the effects of district network expansion or new low carbon investments from an economic and environmental perspective. The model has been successfully applied to the city of Turin, Italy and the city of Stockholm, Sweden. The flexibility of the approach may allow it to be easily adjusted to different urban areas for providing indications on cost-effective strategies for efficient, low-carbon heat solutions in integrated energy systems. Results highlight that finding synergies between the demand and supply sector will lead to environmental and economic benefits, in particular for district-heated cities.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2680532
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