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Experimental Long-Term Investigation of Model Predictive Heat Pump Control in Residential Buildings with Photovoltaic Power Generation

Author

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  • Sebastian Kuboth

    (Chair of Engineering Thermodynamics and Transport Processes (LTTT), Center of Energy Technology (ZET), University of Bayreuth, 95440 Bayreuth, Germany)

  • Theresa Weith

    (Chair of Engineering Thermodynamics and Transport Processes (LTTT), Center of Energy Technology (ZET), University of Bayreuth, 95440 Bayreuth, Germany)

  • Florian Heberle

    (Chair of Engineering Thermodynamics and Transport Processes (LTTT), Center of Energy Technology (ZET), University of Bayreuth, 95440 Bayreuth, Germany)

  • Matthias Welzl

    (Chair of Engineering Thermodynamics and Transport Processes (LTTT), Center of Energy Technology (ZET), University of Bayreuth, 95440 Bayreuth, Germany)

  • Dieter Brüggemann

    (Chair of Engineering Thermodynamics and Transport Processes (LTTT), Center of Energy Technology (ZET), University of Bayreuth, 95440 Bayreuth, Germany)

Abstract

This article presents a 125-day experiment to investigate model predictive heat pump control. The experiment was performed in two parallel operated systems with identical components during the heating season. One of the systems was operated by a standard controller and thus represented a reference to evaluate the model predictive control. Both test rigs were heated by an air-source heat pump which is influenced by real weather conditions. A single-family house model depending on weather measurement data ensured a realistic heat consumption in the test rigs. The adapted model predictive control algorithm aimed to minimize the operational costs of the heat pump. The evaluation of the measurement results showed that the electrical energy demand of the heat pump can be reduced and the coefficient of performance can be increased by applying the model predictive controller. Furthermore, the self-consumption of photovoltaic electricity, which is calculated by means of a photovoltaic model and global radiation measurement data, was more than doubled. Consequently, the energy costs of heat pump operation were reduced by 9.0% in comparison to the reference and assuming German energy prices. The results were further compared to the scientific literature and short-term measurements were performed with the same experimental setup. The dependence of the measurement results on the weather conditions and the weather forecasting quality are shown. It was found that the duration of experiments should be as long as possible for a comprehensive evaluation of the model predictive control potential.

Suggested Citation

  • Sebastian Kuboth & Theresa Weith & Florian Heberle & Matthias Welzl & Dieter Brüggemann, 2020. "Experimental Long-Term Investigation of Model Predictive Heat Pump Control in Residential Buildings with Photovoltaic Power Generation," Energies, MDPI, vol. 13(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6016-:d:446813
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    References listed on IDEAS

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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Kuboth, Sebastian & Heberle, Florian & König-Haagen, Andreas & Brüggemann, Dieter, 2019. "Economic model predictive control of combined thermal and electric residential building energy systems," Applied Energy, Elsevier, vol. 240(C), pages 372-385.
    3. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
    4. Ronny Gelleschus & Michael Böttiger & Thilo Bocklisch, 2019. "Optimization-Based Control Concept with Feed-in and Demand Peak Shaving for a PV Battery Heat Pump Heat Storage System," Energies, MDPI, vol. 12(11), pages 1-16, June.
    5. Franco, Alessandro & Fantozzi, Fabio, 2016. "Experimental analysis of a self consumption strategy for residential building: The integration of PV system and geothermal heat pump," Renewable Energy, Elsevier, vol. 86(C), pages 1075-1085.
    6. Salpakari, Jyri & Lund, Peter, 2016. "Optimal and rule-based control strategies for energy flexibility in buildings with PV," Applied Energy, Elsevier, vol. 161(C), pages 425-436.
    7. Fischer, David & Bernhardt, Josef & Madani, Hatef & Wittwer, Christof, 2017. "Comparison of control approaches for variable speed air source heat pumps considering time variable electricity prices and PV," Applied Energy, Elsevier, vol. 204(C), pages 93-105.
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    1. Krzysztof Księżopolski & Mirosław Drygas & Kamila Pronińska & Iwona Nurzyńska, 2020. "The Economic Effects of New Patterns of Energy Efficiency and Heat Sources in Rural Single-Family Houses in Poland," Energies, MDPI, vol. 13(23), pages 1-19, December.
    2. Parantapa Sawant & Oscar Villegas Mier & Michael Schmidt & Jens Pfafferott, 2021. "Demonstration of Optimal Scheduling for a Building Heat Pump System Using Economic-MPC," Energies, MDPI, vol. 14(23), pages 1-15, November.

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