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Experimental and numerical analysis of hybrid solar heating and cooling system for a residential user

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  • Figaj, Rafał
  • Żołądek, Maciej

Abstract

The paper presents an experimental investigation of a solar dish concentrating system with thermal collectors, along with a numerical investigation of a solar cooling installation based on such system by means of dynamic simulation. The main aim of this study is the analysis of heat generation from the system for heating and cooling purposes, with the use of sorption chillers. A computer simulation of the dynamic operation of the proposed solar heating and cooling system was developed within TRNSYS software. The experimental data were used to validate the model of the concentrator and solar collectors. Successively, a solar heating and cooling installation for a residential application, integrating the different configurations of solar energy collecting devices and thermally driven chillers, was proposed and dynamically simulated. Flat-plate, evacuated tube and photovoltaic-thermal collectors were considered along with absorption and adsorption chiller units. In the present study, different system configurations, localities and time bases were considered to investigate the energy and economic performance of the system.

Suggested Citation

  • Figaj, Rafał & Żołądek, Maciej, 2021. "Experimental and numerical analysis of hybrid solar heating and cooling system for a residential user," Renewable Energy, Elsevier, vol. 172(C), pages 955-967.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:955-967
    DOI: 10.1016/j.renene.2021.03.091
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    References listed on IDEAS

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    Cited by:

    1. Figaj, Rafał, 2021. "Performance assessment of a renewable micro-scale trigeneration system based on biomass steam cycle, wind turbine, photovoltaic field," Renewable Energy, Elsevier, vol. 177(C), pages 193-208.
    2. Bilardo, Matteo & Ferrara, Maria & Fabrizio, Enrico, 2022. "The role of solar cooling for nearly zero energy multifamily buildings: Performance analysis across different climates," Renewable Energy, Elsevier, vol. 194(C), pages 1343-1353.
    3. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).

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