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Multi-objective optimization framework for the selection of configuration and equipment sizing of solar thermal combisystems

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  • Rey, Anthony
  • Zmeureanu, Radu

Abstract

Solar combisystems supplying thermal energy for both domestic hot water and space heating can reduce primary energy consumption for residential buildings. As their overall performance depends on their design, this paper presents the development and use of a multi-objective optimization framework for the selection of configuration and equipment sizing of a residential solar combisystem in Montreal, Quebec, Canada. A generic solar combisystem model, which enables different configurations to be chosen, is first developed, and then coupled with a micro-time variant multi-objective particle swarm optimization, which was developed and used to find the non-dominated combisystem design alternatives. The life cycle cost (LCC), energy use (LCE), and exergy destroyed (LCX) of the solar combisystem are used as objective functions to find the best feasible designs. For the minimum LCC, only one flat-plate collector is required to store energy within one thermal storage tank, whereas seven evacuated-tube collectors and two thermal storage tanks are used for the minimum LCE value. The micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) algorithm used found a non-dominated solution that reduced the LLC by 29% compared with the initial design solution, with the increase of LCE by 72%. Since the LCC and LCE objective functions are conflicting, another non-dominated solution increased the LCC by 36% and reduced the LCE by 27%. The proposed multi-objective optimization framework can therefore be used to get the most out of solar thermal combisystems given specific economic and environmental conditions.

Suggested Citation

  • Rey, Anthony & Zmeureanu, Radu, 2018. "Multi-objective optimization framework for the selection of configuration and equipment sizing of solar thermal combisystems," Energy, Elsevier, vol. 145(C), pages 182-194.
    • Handle: RePEc:eee:energy:v:145:y:2018:i:c:p:182-194
    DOI: 10.1016/j.energy.2017.10.125
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    Cited by:

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    2. Yu, Min Gyung & Pavlak, Gregory S., 2022. "Extracting interpretable building control rules from multi-objective model predictive control data sets," Energy, Elsevier, vol. 240(C).
    3. Liu, Yanfeng & Zhao, Yiting & Chen, Yaowen & Wang, Dengjia & Li, Yong & Yuan, Xipeng, 2022. "Design optimization of the solar heating system for office buildings based on life cycle cost in Qinghai-Tibet plateau of China," Energy, Elsevier, vol. 246(C).
    4. Frangopoulos, Christos A., 2018. "Recent developments and trends in optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 1011-1020.
    5. Cabral, Charlette & Andiappan, Viknesh & Aviso, Kathleen & Tan, Raymond, 2021. "Equipment size selection for optimizing polygeneration systems with reliability aspects," Energy, Elsevier, vol. 234(C).

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