000136362 001__ 136362
000136362 005__ 20240731105612.0
000136362 0247_ $$2doi$$a10.3390/en17133085
000136362 0248_ $$2sideral$$a139264
000136362 037__ $$aART-2024-139264
000136362 041__ $$aeng
000136362 100__ $$aDe Souza, Ronelly José
000136362 245__ $$aMulti-objective optimization of an energy community powered by a distributed polygeneration system
000136362 260__ $$c2024
000136362 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136362 5203_ $$aThis 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.
000136362 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T55-20R$$9info:eu-repo/grantAgreement/ES/MINECO-PID2020-115500RB-I00
000136362 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000136362 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000136362 700__ $$aReini, Mauro
000136362 700__ $$aSerra, Luis M.
000136362 700__ $$0(orcid)0000-0002-4411-9834$$aLozano, Miguel A.
000136362 700__ $$aNadalon, Emanuele
000136362 700__ $$aCasisi, Melchiorre
000136362 773__ $$g17, 13 (2024), 3085 [36 pp.]$$pENERGIES$$tEnergies$$x1996-1073
000136362 8564_ $$s8959913$$uhttps://zaguan.unizar.es/record/136362/files/texto_completo.pdf$$yVersión publicada
000136362 8564_ $$s2720608$$uhttps://zaguan.unizar.es/record/136362/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000136362 909CO $$ooai:zaguan.unizar.es:136362$$particulos$$pdriver
000136362 951__ $$a2024-07-31-09:23:43
000136362 980__ $$aARTICLE