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Optimal renovation of buildings towards the nearly Zero Energy Building standard

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  • Iturriaga, E.
  • Aldasoro, U.
  • Terés-Zubiaga, J.
  • Campos-Celador, A.

Abstract

In this paper, a Mixed-Integer Linear Programming model is proposed for the design of the energy renovation of existing buildings, considering both Energy Supply Systems and the adoption of Energy Saving Measures to reduce the demand of buildings in retrofitting towards the nearly Zero Energy Building standard. The method is applied to an existing building located in Bilbao (northern Spain), getting the optimal design, i.e. lower annual net cost, for different limits of non-renewable primary energy consumption. The demand reduction produced by the Energy Saving Measures is included as an input from previously validated dynamic simulations and a simple method is presented for its specific distribution in reference days. This simple method, based on degree-days, allows reference days to be generated that, through an Energy Saving Measure based base temperature, consider the weather, the use and the thermal properties' dependency on the distribution of the demand. The optimization method is used to provide the design selection and operation strategy of the renovation of buildings to meet different non-renewable primary energy consumption limits and to provide designs for different constraints: economic, space availability, etc.

Suggested Citation

  • Iturriaga, E. & Aldasoro, U. & Terés-Zubiaga, J. & Campos-Celador, A., 2018. "Optimal renovation of buildings towards the nearly Zero Energy Building standard," Energy, Elsevier, vol. 160(C), pages 1101-1114.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:1101-1114
    DOI: 10.1016/j.energy.2018.07.023
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    References listed on IDEAS

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    3. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Olkkonen, Ville & Hirvonen, Janne & Heljo, Juhani & Syri, Sanna, 2021. "Effectiveness of building stock sustainability measures in a low-carbon energy system: A scenario analysis for Finland until 2050," Energy, Elsevier, vol. 235(C).
    5. Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof & Allan, James & Hoffmann, Volker H., 2022. "MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits," Applied Energy, Elsevier, vol. 314(C).
    6. Richarz, Jan & Henn, Sarah & Osterhage, Tanja & Müller, Dirk, 2022. "Optimal scheduling of modernization measures for typical non-residential buildings," Energy, Elsevier, vol. 238(PA).
    7. Ali Bagheri & Konstantinos N. Genikomsakis & Véronique Feldheim & Christos S. Ioakimidis, 2021. "Sensitivity Analysis of 4R3C Model Parameters with Respect to Structure and Geometric Characteristics of Buildings," Energies, MDPI, vol. 14(3), pages 1-20, January.
    8. Santos-Herrero, J.M. & Lopez-Guede, J.M. & Flores-Abascal, I., 2021. "Modeling, simulation and control tools for nZEB: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    9. Hong, Taehoon & Kim, Jimin & Lee, Minhyun, 2019. "A multi-objective optimization model for determining the building design and occupant behaviors based on energy, economic, and environmental performance," Energy, Elsevier, vol. 174(C), pages 823-834.
    10. Davor Končalović & Jelena Nikolic & Vladimir Vukasinovic & Dušan Gordić & Dubravka Živković, 2022. "Possibilities for Deep Renovation in Multi-Apartment Buildings in Different Economic Conditions in Europe," Energies, MDPI, vol. 15(8), pages 1-15, April.
    11. Francesco Calise & Mário Costa & Qiuwang Wang & Xiliang Zhang & Neven Duić, 2018. "Recent Advances in the Analysis of Sustainable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-30, September.

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