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Sensitivity analysis for robust design of building energy systems

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  • Ashouri, Araz
  • Petrini, Flavio
  • Bornatico, Raffaele
  • Benz, Michael J.

Abstract

The comprehensive design of building systems incorporates the tasks of selection, sizing and control of devices. A simultaneous acquirement of these tasks is a necessity to achieve an overall optimal design. However, such mutual optimizations become a complex problem, implying a high computational effort. A greater challenge appears once the uncertainties of boundary conditions such as weather conditions, user demands and energy costs are taken into account. A common approach to protect the suggested system configuration against the possible uncertainties is a stochastic optimization which results in a robust design.

Suggested Citation

  • Ashouri, Araz & Petrini, Flavio & Bornatico, Raffaele & Benz, Michael J., 2014. "Sensitivity analysis for robust design of building energy systems," Energy, Elsevier, vol. 76(C), pages 264-275.
  • Handle: RePEc:eee:energy:v:76:y:2014:i:c:p:264-275
    DOI: 10.1016/j.energy.2014.07.095
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    1. Arcuri, P. & Florio, G. & Fragiacomo, P., 2007. "A mixed integer programming model for optimal design of trigeneration in a hospital complex," Energy, Elsevier, vol. 32(8), pages 1430-1447.
    2. Hakimi, S.M. & Moghaddas-Tafreshi, S.M., 2009. "Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran," Renewable Energy, Elsevier, vol. 34(7), pages 1855-1862.
    3. Ashouri, Araz & Fux, Samuel S. & Benz, Michael J. & Guzzella, Lino, 2013. "Optimal design and operation of building services using mixed-integer linear programming techniques," Energy, Elsevier, vol. 59(C), pages 365-376.
    4. Andersson, Malin & Dillen, Hans & Sellin, Peter, 2006. "Monetary policy signaling and movements in the term structure of interest rates," Journal of Monetary Economics, Elsevier, vol. 53(8), pages 1815-1855, November.
    5. Lozano, Miguel A. & Ramos, Jose C. & Serra, Luis M., 2010. "Cost optimization of the design of CHCP (combined heat, cooling and power) systems under legal constraints," Energy, Elsevier, vol. 35(2), pages 794-805.
    6. Fux, Samuel F. & Benz, Michael J. & Guzzella, Lino, 2013. "Economic and environmental aspects of the component sizing for a stand-alone building energy system: A case study," Renewable Energy, Elsevier, vol. 55(C), pages 438-447.
    7. Bornatico, Raffaele & Pfeiffer, Michael & Witzig, Andreas & Guzzella, Lino, 2012. "Optimal sizing of a solar thermal building installation using particle swarm optimization," Energy, Elsevier, vol. 41(1), pages 31-37.
    8. Fazlollahi, Samira & Mandel, Pierre & Becker, Gwenaelle & Maréchal, Francois, 2012. "Methods for multi-objective investment and operating optimization of complex energy systems," Energy, Elsevier, vol. 45(1), pages 12-22.
    9. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    10. Fabrizio, Enrico & Corrado, Vincenzo & Filippi, Marco, 2010. "A model to design and optimize multi-energy systems in buildings at the design concept stage," Renewable Energy, Elsevier, vol. 35(3), pages 644-655.
    11. Ai, B. & Yang, H. & Shen, H. & Liao, X., 2003. "Computer-aided design of PV/wind hybrid system," Renewable Energy, Elsevier, vol. 28(10), pages 1491-1512.
    12. Lai, Sau Man & Hui, Chi Wai, 2009. "Feasibility and flexibility for a trigeneration system," Energy, Elsevier, vol. 34(10), pages 1693-1704.
    13. Rezvan, A. Taghipour & Gharneh, N. Shams & Gharehpetian, G.B., 2012. "Robust optimization of distributed generation investment in buildings," Energy, Elsevier, vol. 48(1), pages 455-463.
    14. Ren, Hongbo & Zhou, Weisheng & Gao, Weijun, 2012. "Optimal option of distributed energy systems for building complexes in different climate zones in China," Applied Energy, Elsevier, vol. 91(1), pages 156-165.
    15. Nemet, Andreja & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Kravanja, Zdravko, 2012. "Methodology for maximising the use of renewables with variable availability," Energy, Elsevier, vol. 44(1), pages 29-37.
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