Modeling the Optimal Transition of an Urban Neighborhood towards an Energy Community and a Positive Energy District

Diego Viesi (), Gregorio Borelli, Silvia Ricciuti, Giovanni Pernigotto and Md Shahriar Mahbub
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Diego Viesi: Center for Sustainable Energy, Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123 Trento, Italy
Gregorio Borelli: Center for Sustainable Energy, Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123 Trento, Italy
Silvia Ricciuti: Center for Sustainable Energy, Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123 Trento, Italy
Giovanni Pernigotto: Faculty of Engineering, Free University of Bozen-Bolzano (UNIBZ), Piazza Università 5, 39100 Bolzano, Italy
Md Shahriar Mahbub: Center for Sustainable Energy, Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123 Trento, Italy

Energies, 2024, vol. 17, issue 16, 1-30

Abstract: Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO 2 emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO 2 emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 “all sectors and EC incentive” for the year 2024, a large reduction of 55% of CO 2 emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front.

Keywords: urban modeling interface; EnergyPLAN; multi-objective evolutionary algorithm; energy community; positive energy district (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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