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Operation Algorithms and Computational Simulation of Physical Cooling and Heat Recovery for Indoor Space Conditioning. A Case Study for a Hydro Power Plant in Lugano, Switzerland

Author

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  • Dimitris Katsaprakakis

    (Wind Energy and Power Plants Synthesis Laboratory, Department of Mechanical Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion Crete, Greece)

  • Vasilis Kagiamis

    (Xenikakis S.A., Knossos Av. Heraklion Crete, 71409 Crete, Greece)

  • George Zidianakis

    (Wind Energy and Power Plants Synthesis Laboratory, Department of Mechanical Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion Crete, Greece)

  • Luca Ambrosini

    (Azienda Elettrica di Massagno (AEM) SA, 6900 Massagno, Switzerland)

Abstract

This article presents the computational simulation process and the operation algorithms of the VAV and VRV systems, for indoor space conditioning, with extensive physical cooling and heat recovery. Through the introduction of appropriate operation algorithms, the article aims to highlight the high energy saving potential on indoor space conditioning, by exploiting physical cooling and heat recovery processes. The proposed algorithms are evaluated with a case study for a hydro power plant building located in the area of Lugano, Switzerland, with significant cooling needs for the whole year, due to high internal heat gains from indoor electrical equipment. This fact enables physical cooling during winter, for the cooling load coverage, and heat recovery, for the concurrent heating load coverage in different thermal zones of the building. Analytical operation algorithms are developed for a VAV and a VRV system. Both algorithms are computationally simulated. With the VAV system, 86.1% and 63.7% of the annual cooling and heating demand, respectively are covered by physical cooling and heat recovery. With the VRV system, 58.5% of the annual heating demand is covered by heat recovery. The set-up cost of the VAV system is almost twice higher than the set-up cost of the VRV system.

Suggested Citation

  • Dimitris Katsaprakakis & Vasilis Kagiamis & George Zidianakis & Luca Ambrosini, 2019. "Operation Algorithms and Computational Simulation of Physical Cooling and Heat Recovery for Indoor Space Conditioning. A Case Study for a Hydro Power Plant in Lugano, Switzerland," Sustainability, MDPI, vol. 11(17), pages 1-36, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4574-:d:260153
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    References listed on IDEAS

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    1. Pantaleo, Antonio M. & Fordham, Julia & Oyewunmi, Oyeniyi A. & De Palma, Pietro & Markides, Christos N., 2018. "Integrating cogeneration and intermittent waste-heat recovery in food processing: Microturbines vs. ORC systems in the coffee roasting industry," Applied Energy, Elsevier, vol. 225(C), pages 782-796.
    2. Hyo-Jun Kim & Young-Hum Cho, 2017. "A Study on a Control Method with a Ventilation Requirement of a VAV System in Multi-Zone," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
    3. Jiyuan Kuang & Chenghui Zhang & Fan Li & Bo Sun, 2018. "Dynamic Optimization of Combined Cooling, Heating, and Power Systems with Energy Storage Units," Energies, MDPI, vol. 11(9), pages 1-16, August.
    4. Li, Fan & Sun, Bo & Zhang, Chenghui & Zhang, Lizhi, 2018. "Operation optimization for combined cooling, heating, and power system with condensation heat recovery," Applied Energy, Elsevier, vol. 230(C), pages 305-316.
    5. Katsaprakakis, Dimitris Al., 2015. "Comparison of swimming pools alternative passive and active heating systems based on renewable energy sources in Southern Europe," Energy, Elsevier, vol. 81(C), pages 738-753.
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