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A robust design of nearly zero energy building systems considering performance degradation and maintenance

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  • Huang, Pei
  • Huang, Gongsheng
  • Sun, Yongjun

Abstract

Nearly zero energy buildings (nZEBs) are considered as a promising solution to mitigate the energy and environmental problems. A proper sizing of the nZEB systems (e.g. HVAC systems, PV panels, wind turbines and batteries) is essential for achieving the desirable level of thermal comfort, energy balance and grid dependence. Parameter uncertainty, component degradation and maintenance are three crucial factors affecting the nZEB system performances and should be systematically considered in system sizing. Until now, there are some uncertainty-based design methods been developed, but most of the existing studies neglect component degradation and maintenance. Due to the complex impacts of degradation and maintenance, proper sizing of nZEB systems considering multiple criteria (i.e. thermal comfort, energy balance and grid dependence) is still a great challenge. This paper, therefore, proposes a robust design method of nZEB systems using genetic algorithm (GA) which takes into account the parameter uncertainty, component degradation and maintenance. The nZEB life-cycle cost is used as the fitness function, and the user’ performance requirements on thermal comfort, energy balance and grid dependence are defined as three constraints. This study can help improve the designers’ understanding of the impacts of uncertainty, degradation, and maintenance on the nZEB life-cycle performances. The proposed method is effective in minimizing the nZEB life-cycle cost through designing the robust optimal nZEB systems sizes and planning the optimal maintenance scheme, meanwhile satisfying the user specified constraints on thermal comfort, energy balance, and grid dependence during the whole service life.

Suggested Citation

  • Huang, Pei & Huang, Gongsheng & Sun, Yongjun, 2018. "A robust design of nearly zero energy building systems considering performance degradation and maintenance," Energy, Elsevier, vol. 163(C), pages 905-919.
    • Handle: RePEc:eee:energy:v:163:y:2018:i:c:p:905-919
    DOI: 10.1016/j.energy.2018.08.183
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    Cited by:

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    2. Pei Huang & Xingxing Zhang & Benedetta Copertaro & Puneet Kumar Saini & Da Yan & Yi Wu & Xiangjie Chen, 2020. "A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
    3. Huang, Pei & Sun, Yongjun, 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level," Energy, Elsevier, vol. 174(C), pages 911-921.
    4. Zinzi, Michele & Mattoni, Benedetta, 2019. "Assessment of construction cost reduction of nearly zero energy dwellings in a life cycle perspective," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. Walker, Linus & Hischier, Illias & Schlueter, Arno, 2022. "Scenario-based robustness assessment of building system life cycle performance," Applied Energy, Elsevier, vol. 311(C).
    6. Yuehong Lu & Mohammed Alghassab & Manuel S. Alvarez-Alvarado & Hasan Gunduz & Zafar A. Khan & Muhammad Imran, 2020. "Optimal Distribution of Renewable Energy Systems Considering Aging and Long-Term Weather Effect in Net-Zero Energy Building Design," Sustainability, MDPI, vol. 12(14), pages 1-20, July.
    7. Huang, Pei & Sun, Yongjun & Lovati, Marco & Zhang, Xingxing, 2021. "Solar-photovoltaic-power-sharing-based design optimization of distributed energy storage systems for performance improvements," Energy, Elsevier, vol. 222(C).
    8. Li, Hangxin & Wang, Shengwei, 2019. "Coordinated optimal design of zero/low energy buildings and their energy systems based on multi-stage design optimization," Energy, Elsevier, vol. 189(C).

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