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A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design

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  • García Kerdan, Iván
  • Raslan, Rokia
  • Ruyssevelt, Paul
  • Morillón Gálvez, David

Abstract

This study presents a comparison of the optimisation of building energy retrofit strategies from two different perspectives: an energy/economic-based analysis and an exergy/exergoeconomic-based analysis. A recently retrofitted community centre is used as a case study. ExRET-Opt, a novel building energy/exergy simulation tool with multi-objective optimisation capabilities based on NSGA-II is used to run both analysis. The first analysis, based on the 1st Law only, simultaneously optimises building energy use and design's Net Present Value (NPV). The second analysis, based on the 1st and the 2nd Laws, simultaneously optimises exergy destructions and the exergoeconomic cost-benefit index. Occupant thermal comfort is considered as a common objective function for both approaches. The aim is to assess the difference between the methods and calculate the performance among main indicators, considering the same decision variables and constraints. Outputs show that the inclusion of exergy/exergoeconomics as objective functions into the optimisation procedure has resulted in similar 1st Law and thermal comfort outputs, while providing solutions with less environmental impact under similar capital investments. This outputs demonstrate how the 1st Law is only a necessary calculation while the utilisation of the 1st and 2nd Laws becomes a sufficient condition for the analysis and design of low carbon buildings.

Suggested Citation

  • García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design," Energy, Elsevier, vol. 128(C), pages 244-263.
    • Handle: RePEc:eee:energy:v:128:y:2017:i:c:p:244-263
    DOI: 10.1016/j.energy.2017.03.142
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    Cited by:

    1. García Kerdan, Iván & Morillón Gálvez, David, 2020. "Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building," Applied Energy, Elsevier, vol. 280(C).
    2. Olfati, Mohammad & Bahiraei, Mehdi & Heidari, Setareh & Veysi, Farzad, 2018. "A comprehensive analysis of energy and exergy characteristics for a natural gas city gate station considering seasonal variations," Energy, Elsevier, vol. 155(C), pages 721-733.
    3. Bjelland, David & Brozovsky, Johannes & Hrynyszyn, Bozena Dorota, 2024. "Systematic review: Upscaling energy retrofitting to the multi-building level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    4. Zhan, Jin & He, Wenjing & Huang, Jianxiang, 2024. "Comfort, carbon emissions, and cost of building envelope and photovoltaic arrangement optimization through a two-stage model," Applied Energy, Elsevier, vol. 356(C).
    5. Prince, & Hati, Ananda Shankar & Kumar, Prashant, 2023. "An adaptive neural fuzzy interface structure optimisation for prediction of energy consumption and airflow of a ventilation system," Applied Energy, Elsevier, vol. 337(C).
    6. 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.

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