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Buffer Tank Discharge Strategies in the Case of a Centrifugal Water Chiller

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  • Attila Kostyák

    (Department of Building Services and Building Engineering, Faculty of Engineering, University of Debrecen, Ótemető Str. 2-4, 4028 Debrecen, Hungary)

  • Csaba Béres

    (Department of Building Services and Building Engineering, Faculty of Engineering, University of Debrecen, Ótemető Str. 2-4, 4028 Debrecen, Hungary)

  • Szabolcs Szekeres

    (Department of Building Services and Building Engineering, Faculty of Engineering, University of Debrecen, Ótemető Str. 2-4, 4028 Debrecen, Hungary)

  • Imre Csáky

    (Department of Building Services and Building Engineering, Faculty of Engineering, University of Debrecen, Ótemető Str. 2-4, 4028 Debrecen, Hungary)

Abstract

In this article, energy optimization of the cooling system of IKEA Budaörs is carried out. The cooling system is served by a centrifugal water chiller and includes a large-volume cooling buffer tank. The facility operates the hydraulic system of the buffer storage tank only during the transitional period. The main goal is to reduce energy consumption by changing the operating strategy of the existing system. To test the operating strategies, the operation and the thermal load of the shopping center during the summer season had to be simulated to find the best operation strategy. A hybrid method (real data and calculated values) was used in the simulation. The three operating scenarios examined show that the annual energy consumption and the number of operating hours of the chiller can be reduced by using the buffer tank with the right strategy. In the examined scenarios, a 30% energy improvement was achieved. The possibility of using a buffer tank is significantly limited by the fact that the heat exchangers were sized for low forward water temperatures. By re-sizing the heat exchangers, the utilization of the buffer tank could be considerably improved in conditions close to peak heat load.

Suggested Citation

  • Attila Kostyák & Csaba Béres & Szabolcs Szekeres & Imre Csáky, 2022. "Buffer Tank Discharge Strategies in the Case of a Centrifugal Water Chiller," Energies, MDPI, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:188-:d:1013744
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    References listed on IDEAS

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    1. Huang, Kuo-Tsang & Hwang, Ruey-Lung, 2016. "Future trends of residential building cooling energy and passive adaptation measures to counteract climate change: The case of Taiwan," Applied Energy, Elsevier, vol. 184(C), pages 1230-1240.
    2. Szodrai, Ferenc & Lakatos, Ákos & Kalmár, Ferenc, 2016. "Analysis of the change of the specific heat loss coefficient of buildings resulted by the variation of the geometry and the moisture load," Energy, Elsevier, vol. 115(P1), pages 820-829.
    3. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    4. Tiwari, G.N. & Mishra, R.K. & Solanki, S.C., 2011. "Photovoltaic modules and their applications: A review on thermal modelling," Applied Energy, Elsevier, vol. 88(7), pages 2287-2304, July.
    5. Djuric, Natasa & Novakovic, Vojislav, 2009. "Review of possibilities and necessities for building lifetime commissioning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 486-492, February.
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