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Multi-Objective Optimization of an Energy Community Powered by a Distributed Polygeneration System

Author

Listed:
  • Ronelly José De Souza

    (Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy
    GITSE-I3A, Department of Mechanical Engineering, University of Zaragoza, 50009 Zaragoza, Spain)

  • Mauro Reini

    (Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy)

  • Luis M. Serra

    (GITSE-I3A, Department of Mechanical Engineering, University of Zaragoza, 50009 Zaragoza, Spain)

  • Miguel A. Lozano

    (GITSE-I3A, Department of Mechanical Engineering, University of Zaragoza, 50009 Zaragoza, Spain)

  • Emanuele Nadalon

    (Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy)

  • Melchiorre Casisi

    (Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy)

Abstract

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 CO 2 emissions by around 24.3% (about 1.72 kt CO 2 /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 CO 2 emissions by 2.2% (about 0.16 kt CO 2 /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.

Suggested Citation

  • Ronelly José De Souza & Mauro Reini & Luis M. Serra & Miguel A. Lozano & Emanuele Nadalon & Melchiorre Casisi, 2024. "Multi-Objective Optimization of an Energy Community Powered by a Distributed Polygeneration System," Energies, MDPI, vol. 17(13), pages 1-36, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3085-:d:1420193
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    References listed on IDEAS

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    3. Herenčić, Lin & Kirac, Mislav & Keko, Hrvoje & Kuzle, Igor & Rajšl, Ivan, 2022. "Automated energy sharing in MV and LV distribution grids within an energy community: A case for Croatian city of Križevci with a hybrid renewable system," Renewable Energy, Elsevier, vol. 191(C), pages 176-194.
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    5. Joeri Rogelj & Gunnar Luderer & Robert C. Pietzcker & Elmar Kriegler & Michiel Schaeffer & Volker Krey & Keywan Riahi, 2015. "Energy system transformations for limiting end-of-century warming to below 1.5 °C," Nature Climate Change, Nature, vol. 5(6), pages 519-527, June.
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