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ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation

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

Abstract

Energy simulation tools have a major role in the assessment of building energy retrofit (BER) measures. Exergoeconomic analysis and optimisation is a common practice in sectors such as the power generation and chemical processes, aiding engineers to obtain more energy-efficient and cost-effective energy systems designs. ExRET-Opt, a retrofit-oriented modular-based dynamic simulation framework has been developed by embedding a comprehensive exergy/exergoeconomic calculation method into a typical open-source building energy simulation tool (EnergyPlus). The aim of this paper is to show the decomposition of ExRET-Opt by presenting modules, submodules and subroutines used for the framework’s development as well as verify the outputs with existing research data. In addition, the possibility to perform multi-objective optimisation analysis based on genetic-algorithms combined with multi-criteria decision making methods was included within the simulation framework. This addition could potentiate BER design teams to perform quick exergy/exergoeconomic optimisation, in order to find opportunities for thermodynamic improvements along the building’s active and passive energy systems. The enhanced simulation framework is tested using a primary school building as a case study. Results demonstrate that the proposed simulation framework provide users with thermodynamic efficient and cost-effective designs, even under tight thermodynamic and economic constraints, suggesting its use in everyday BER practice.

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  • García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation," Applied Energy, Elsevier, vol. 192(C), pages 33-58.
  • Handle: RePEc:eee:appene:v:192:y:2017:i:c:p:33-58
    DOI: 10.1016/j.apenergy.2017.02.006
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    2. Jie, Pengfei & Zhang, Fenghe & Fang, Zhou & Wang, Hongbo & Zhao, Yunfeng, 2018. "Optimizing the insulation thickness of walls and roofs of existing buildings based on primary energy consumption, global cost and pollutant emissions," Energy, Elsevier, vol. 159(C), pages 1132-1147.
    3. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    4. Mohamed Hamdy & Gerardo Maria Mauro, 2017. "Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO 2 -eq vs. Discounted Payback Time," Energies, MDPI, vol. 10(7), pages 1-26, July.
    5. Ghaebi, Hadi & Parikhani, Towhid & Rostamzadeh, Hadi, 2018. "A novel trigeneration system using geothermal heat source and liquefied natural gas cold energy recovery: Energy, exergy and exergoeconomic analysis," Renewable Energy, Elsevier, vol. 119(C), pages 513-527.
    6. García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "The role of an exergy-based building stock model for exploration of future decarbonisation scenarios and policy making," Energy Policy, Elsevier, vol. 105(C), pages 467-483.
    7. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Napolitano, Davide Ferdinando, 2019. "Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness," Applied Energy, Elsevier, vol. 254(C).
    8. 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).
    9. Cascone, Ylenia & Capozzoli, Alfonso & Perino, Marco, 2018. "Optimisation analysis of PCM-enhanced opaque building envelope components for the energy retrofitting of office buildings in Mediterranean climates," Applied Energy, Elsevier, vol. 211(C), pages 929-953.
    10. Shadram, Farshid & Bhattacharjee, Shimantika & Lidelöw, Sofia & Mukkavaara, Jani & Olofsson, Thomas, 2020. "Exploring the trade-off in life cycle energy of building retrofit through optimization," Applied Energy, Elsevier, vol. 269(C).
    11. 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.
    12. Yoon, Sungmin & Yu, Yuebin, 2018. "Hidden factors and handling strategies on virtual in-situ sensor calibration in building energy systems: Prior information and cancellation effect," Applied Energy, Elsevier, vol. 212(C), pages 1069-1082.
    13. Ali Sadollah & Mohammad Nasir & Zong Woo Geem, 2020. "Sustainability and Optimization: From Conceptual Fundamentals to Applications," Sustainability, MDPI, vol. 12(5), pages 1-34, March.
    14. Joana Fernandes & Maria Catarina Santos & Rui Castro, 2021. "Introductory Review of Energy Efficiency in Buildings Retrofits," Energies, MDPI, vol. 14(23), pages 1-18, December.
    15. Paola Marrone & Paola Gori & Francesco Asdrubali & Luca Evangelisti & Laura Calcagnini & Gianluca Grazieschi, 2018. "Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting," Energies, MDPI, vol. 11(3), pages 1-20, March.

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