136362 20260217205547.0 doi 10.3390/en17133085 sideral 139264 ART-2024-139264 eng De Souza, Ronelly José Multi-objective optimization of an energy community powered by a distributed polygeneration system 2024 Access copy available to the general public Unrestricted This paper presents a multi-objective optimization model for the integration of polygeneration systems into energy communities (ECs), by analyzing a case study. The concept of ECs is increasingly seen as beneficial for reducing global energy consumption and greenhouse gas emissions. Polygeneration systems have the potential to play a crucial role in this context, since they are known for producing multiple energy services from a single energy resource, besides the possibility of being fed also by renewable energy sources. However, optimizing the configuration and operation of these systems within ECs presents complex challenges due to the variety of technologies involved, their interactions, and the dynamic behavior of buildings. Therefore, the aim of this work is developing a mathematical model using a mixed integer linear programming (MILP) algorithm to optimally design and operate polygeneration systems integrated into ECs. The model is applied to a case study of an EC comprising nine buildings in a small city in the northeast of Italy. The work rests on the single- and multi-objective optimization of the polygeneration systems taking into account the sharing of electricity among the buildings (both self-produced and/or the purchased from the grid), as well as the sharing of heating and cooling between the buildings through a district heating and cooling network (DHCN). The main results from the EC case study show the possibility of reducing the total annual CO2 emissions by around 24.3% (about 1.72 kt CO2/year) while increasing the total annual costs by 1.9% (about 0.09 M€/year) or reducing the total annual costs by 31.9% (about 1.47 M€/year) while increasing the total annual CO2 emissions by 2.2% (about 0.16 kt CO2/year). The work developed within this research can be adapted to different case studies, such as in the residential–commercial buildings and industrial sectors. Therefore, the model resulting from this work constitutes an effective tool to optimally design and operate polygeneration systems integrated into ECs. info:eu-repo/grantAgreement/ES/DGA/T55-20R info:eu-repo/grantAgreement/ES/MINECO-PID2020-115500RB-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 3.2 2024 ENERGY & FUELS 112 / 182 = 0.615 2024 Q3 T2 0.713 2024 Engineering (miscellaneous) 2024 Q1 Electrical and Electronic Engineering 2024 Q2 Energy (miscellaneous) 2024 Q2 Renewable Energy, Sustainability and the Environment 2024 Q2 Fuel Technology 2024 Q2 Control and Optimization 2024 Q2 Energy Engineering and Power Technology 2024 Q2 7.3 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Reini, Mauro Serra, Luis M. Lozano, Miguel A. Universidad de Zaragoza (orcid)0000-0002-4411-9834 Nadalon, Emanuele Casisi, Melchiorre 5004 590 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Máquinas y Motores Térmi. 17, 13 (2024), 3085 [36 pp.] ENERGIES Energies 1996-1073 8959913 http://zaguan.unizar.es/record/136362/files/texto_completo.pdf Versión publicada 2720608 http://zaguan.unizar.es/record/136362/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:136362 articulos driver 2026-02-17-20:38:44 ARTICLE