IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v240y2019icp372-385.html
   My bibliography  Save this article

Economic model predictive control of combined thermal and electric residential building energy systems

Author

Listed:
  • Kuboth, Sebastian
  • Heberle, Florian
  • König-Haagen, Andreas
  • Brüggemann, Dieter

Abstract

This article investigates the potential of economic model predictive control of complex residential energy systems with electric coupling to the public grid. The examined system includes a battery energy storage system, photovoltaic power generation, an air-to-water heat pump, thermal energy storage and a building model. The said power generation provides energy for electric loads as well as domestic hot water and space heating. Model predictive control algorithms manage the energy system by nonlinear global optimization. Within this optimization, a time-varying state space model, which is derived from the energy system simulation model, reflects the system dynamics. Owing to the resulting high complexity, two algorithms for distributed model predictive control are developed. In addition, the developed approaches are compared to a common reference control concept as well as centralized model predictive control. For the comparison of annual operational costs, current German energy prices and subsidies are implemented into the economic calculation. Results show an improved performance of the developed approaches with 11.6% cost reduction in comparison to the reference. This is achieved through an increase of the heat pump seasonal performance factor by 3.4%, reduced curtailment of electrical photovoltaic energy to 21.5% of the reference value and prevention of auxiliary heater operation. Furthermore, increased photovoltaic self-consumption by the heat pump results in a slight reduction of battery storage operation. In conjunction with monetary and energetic advantages, model predictive control increases the comfort in regards to the violation of minimum limits for the comfort criteria.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:240:y:2019:i:c:p:372-385
    DOI: 10.1016/j.apenergy.2019.01.097
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919300996
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.097?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
    3. Alimohammadisagvand, Behrang & Jokisalo, Juha & Kilpeläinen, Simo & Ali, Mubbashir & Sirén, Kai, 2016. "Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control," Applied Energy, Elsevier, vol. 174(C), pages 275-287.
    4. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    5. Baeten, Brecht & Rogiers, Frederik & Helsen, Lieve, 2017. "Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response," Applied Energy, Elsevier, vol. 195(C), pages 184-195.
    6. Ummenhofer, C.D. & Heyer, G. & Roediger, T. & Olsen, J. & Page, J., 2017. "Improved system control logic for an MCHP system incorporating electric storage," Applied Energy, Elsevier, vol. 203(C), pages 737-751.
    7. 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.
    8. 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.
    9. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    10. Wanjiru, Evan M. & Sichilalu, Sam M. & Xia, Xiaohua, 2017. "Model predictive control of heat pump water heater-instantaneous shower powered with integrated renewable-grid energy systems," Applied Energy, Elsevier, vol. 204(C), pages 1333-1346.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.
    2. Clauß, John & Stinner, Sebastian & Sartori, Igor & Georges, Laurent, 2019. "Predictive rule-based control to activate the energy flexibility of Norwegian residential buildings: Case of an air-source heat pump and direct electric heating," Applied Energy, Elsevier, vol. 237(C), pages 500-518.
    3. Nordgård-Hansen, Ellen & Kishor, Nand & Midttømme, Kirsti & Risinggård, Vetle Kjær & Kocbach, Jan, 2022. "Case study on optimal design and operation of detached house energy system: Solar, battery, and ground source heat pump," Applied Energy, Elsevier, vol. 308(C).
    4. Marszal-Pomianowska, Anna & Widén, Joakim & Le Dréau, Jérôme & Heiselberg, Per & Bak-Jensen, Birgitte & de Cerio Mendaza, Iker Diaz, 2020. "Operation of power distribution networks with new and flexible loads: A case of existing residential low voltage network," Energy, Elsevier, vol. 202(C).
    5. Maria Pinamonti & Alessandro Prada & Paolo Baggio, 2020. "Rule-Based Control Strategy to Increase Photovoltaic Self-Consumption of a Modulating Heat Pump Using Water Storages and Building Mass Activation," Energies, MDPI, vol. 13(23), pages 1-21, November.
    6. Emmanouil Psimopoulos & Fatemeh Johari & Chris Bales & Joakim Widén, 2020. "Impact of Boundary Conditions on the Performance Enhancement of Advanced Control Strategies for a Residential Building with a Heat Pump and PV System with Energy Storage," Energies, MDPI, vol. 13(6), pages 1-25, March.
    7. Shukhobodskiy, Alexander Alexandrovich & Colantuono, Giuseppe, 2020. "RED WoLF: Combining a battery and thermal energy reservoirs as a hybrid storage system," Applied Energy, Elsevier, vol. 274(C).
    8. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
    9. Dengiz, Thomas & Jochem, Patrick & Fichtner, Wolf, 2019. "Demand response with heuristic control strategies for modulating heat pumps," Applied Energy, Elsevier, vol. 238(C), pages 1346-1360.
    10. Pospíšil, Jiří & Špiláček, Michal & Kudela, Libor, 2018. "Potential of predictive control for improvement of seasonal coefficient of performance of air source heat pump in Central European climate zone," Energy, Elsevier, vol. 154(C), pages 415-423.
    11. Wiesheu, Michael & Rutešić, Luka & Shukhobodskiy, Alexander Alexandrovich & Pogarskaia, Tatiana & Zaitcev, Aleksandr & Colantuono, Giuseppe, 2021. "RED WoLF hybrid storage system: Adaptation of algorithm and analysis of performance in residential dwellings," Renewable Energy, Elsevier, vol. 179(C), pages 1036-1048.
    12. 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.
    13. Cristian Sánchez & Lionel Bloch & Jordan Holweger & Christophe Ballif & Nicolas Wyrsch, 2019. "Optimised Heat Pump Management for Increasing Photovoltaic Penetration into the Electricity Grid," Energies, MDPI, vol. 12(8), pages 1-22, April.
    14. Vladimir Z. Gjorgievski & Nikolas G. Chatzigeorgiou & Venizelos Venizelou & Georgios C. Christoforidis & George E. Georghiou & Grigoris K. Papagiannis, 2020. "Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus," Energies, MDPI, vol. 13(8), pages 1-18, April.
    15. Finck, Christian & Li, Rongling & Kramer, Rick & Zeiler, Wim, 2018. "Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems," Applied Energy, Elsevier, vol. 209(C), pages 409-425.
    16. Joshua M. Pearce & Nelson Sommerfeldt, 2021. "Economics of Grid-Tied Solar Photovoltaic Systems Coupled to Heat Pumps: The Case of Northern Climates of the U.S. and Canada," Energies, MDPI, vol. 14(4), pages 1-17, February.
    17. Castellini, Marta & Menoncin, Francesco & Moretto, Michele & Vergalli, Sergio, 2021. "Photovoltaic Smart Grids in the prosumers investment decisions: a real option model," Journal of Economic Dynamics and Control, Elsevier, vol. 126(C).
    18. Psimopoulos, Emmanouil & Bee, Elena & Widén, Joakim & Bales, Chris, 2019. "Techno-economic analysis of control algorithms for an exhaust air heat pump system for detached houses coupled to a photovoltaic system," Applied Energy, Elsevier, vol. 249(C), pages 355-367.
    19. John Clauß & Sebastian Stinner & Christian Solli & Karen Byskov Lindberg & Henrik Madsen & Laurent Georges, 2019. "Evaluation Method for the Hourly Average CO 2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating," Energies, MDPI, vol. 12(7), pages 1-25, April.
    20. Balvís, Eduardo & Sampedro, Óscar & Zaragoza, Sonia & Paredes, Angel & Michinel, Humberto, 2016. "A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings," Applied Energy, Elsevier, vol. 177(C), pages 60-70.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:240:y:2019:i:c:p:372-385. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.