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Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

Author

Listed:
  • Tobias Heidrich

    (Department for Sustainable Products and Processes (upp), University of Kassel, 34125 Kassel, Germany)

  • Jonathan Grobe

    (Department for Sustainable Products and Processes (upp), University of Kassel, 34125 Kassel, Germany)

  • Henning Meschede

    (Department for Sustainable Products and Processes (upp), University of Kassel, 34125 Kassel, Germany)

  • Jens Hesselbach

    (Department for Sustainable Products and Processes (upp), University of Kassel, 34125 Kassel, Germany)

Abstract

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible Renewable Energies levy (EEG levy) was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering the electricity price and weather forecasts as well as an internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

Suggested Citation

  • Tobias Heidrich & Jonathan Grobe & Henning Meschede & Jens Hesselbach, 2018. "Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany," Energies, MDPI, vol. 11(12), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3461-:d:189684
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    References listed on IDEAS

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    1. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9780521182935.
    2. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9781107005198.
    3. Sharifi, R. & Fathi, S.H. & Vahidinasab, V., 2017. "A review on Demand-side tools in electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 565-572.
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    Cited by:

    1. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    2. Seferlis, Panos & Varbanov, Petar Sabev & Papadopoulos, Athanasios I. & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2021. "Sustainable design, integration, and operation for energy high-performance process systems," Energy, Elsevier, vol. 224(C).
    3. Jan Kaczmarczyk & Joanna Ferdyn-Grygierek, 2020. "Thermal Comfort and Energy Use with Local Heaters," Energies, MDPI, vol. 13(11), pages 1-14, June.
    4. Wolf, Isabel & Holzapfel, Peter K.R. & Meschede, Henning & Finkbeiner, Matthias, 2023. "On the potential of temporally resolved GHG emission factors for load shifting: A case study on electrified steam generation," Applied Energy, Elsevier, vol. 348(C).
    5. Walmsley, Timothy Gordon & Philipp, Matthias & Picón-Núñez, Martín & Meschede, Henning & Taylor, Matthew Thomas & Schlosser, Florian & Atkins, Martin John, 2023. "Hybrid renewable energy utility systems for industrial sites: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    6. Eva Lucas Segarra & Hu Du & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models," Energies, MDPI, vol. 12(7), pages 1-16, April.

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