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Analysis of thermal energy storage tanks and PV panels combinations in different buildings controlled through model predictive control

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  • Tarragona, Joan
  • Pisello, Anna Laura
  • Fernández, Cèsar
  • Cabeza, Luisa F.
  • Payá, Jorge
  • Marchante-Avellaneda, Javier
  • de Gracia, Alvaro

Abstract

The present study analyses the performance of a heating system controlled by a model predictive control strategy, where the impact of different combinations of thermal energy storage tank volumes and installed PV power capacities are analysed. The novelty of the paper lies in studying both economic and energy impacts of each equipment combination in different locations, buildings, and indoor occupancy schedules. The payback period, the reduction of the electricity grid consumption, and the behaviour of the coefficient of performance of the heat pump are studied in detail for all cases. Results point out that from an economic point of view, to invest in a thermal energy storage tank provides shorter payback periods in comparison to scenarios with PV panels, due to the high price of the solar elements. However, the energy performance analysis highlighted that the use of PV panels contributes to achieve up to 34%, 54%, and 90% of reduction of the electricity grid consumption in Helsinki, Strasbourg, and Athens, respectively. Finally, it is worth noting that the increase of the thermal energy storage volume improves the coefficient of performance of the heat pump.

Suggested Citation

  • Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & Cabeza, Luisa F. & Payá, Jorge & Marchante-Avellaneda, Javier & de Gracia, Alvaro, 2022. "Analysis of thermal energy storage tanks and PV panels combinations in different buildings controlled through model predictive control," Energy, Elsevier, vol. 239(PC).
  • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s036054422102449x
    DOI: 10.1016/j.energy.2021.122201
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    References listed on IDEAS

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    1. Arce, Pablo & Medrano, Marc & Gil, Antoni & Oró, Eduard & Cabeza, Luisa F., 2011. "Overview of thermal energy storage (TES) potential energy savings and climate change mitigation in Spain and Europe," Applied Energy, Elsevier, vol. 88(8), pages 2764-2774, August.
    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. Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
    4. Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.
    5. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    6. Jung, Wooyoung & Jazizadeh, Farrokh, 2019. "Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions," Applied Energy, Elsevier, vol. 239(C), pages 1471-1508.
    7. Yu, Min Gyung & Pavlak, Gregory S., 2021. "Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems," Applied Energy, Elsevier, vol. 282(PB).
    8. Tarragona, Joan & Fernández, Cèsar & de Gracia, Alvaro, 2020. "Model predictive control applied to a heating system with PV panels and thermal energy storage," Energy, Elsevier, vol. 197(C).
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    Cited by:

    1. Tang, Hong & Wang, Shengwei, 2023. "Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets," Energy, Elsevier, vol. 264(C).
    2. Mulu Bayray Kahsay & Johan Lauwaert, 2022. "Excess Energy from PV-Battery System Installations: A Case of Rural Health Center in Tigray, Ethiopia," Energies, MDPI, vol. 15(12), pages 1-11, June.
    3. Sohani, Ali & Cornaro, Cristina & Shahverdian, Mohammad Hassan & Moser, David & Pierro, Marco & Olabi, Abdul Ghani & Karimi, Nader & Nižetić, Sandro & Li, Larry K.B. & Doranehgard, Mohammad Hossein, 2023. "Techno-economic evaluation of a hybrid photovoltaic system with hot/cold water storage for poly-generation in a residential building," Applied Energy, Elsevier, vol. 331(C).

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