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Model predictive control applied to a heating system with PV panels and thermal energy storage

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  • Tarragona, Joan
  • Fernández, Cèsar
  • de Gracia, Alvaro

Abstract

Two thirds of the total buildings final energy are used for heating purposes, specifically during the peak period. There is a mismatch between the power generation from renewable energy resources and demand. Thermal energy storage systems have been used not only to fill the gap between supply and demand, but also to take advantage of the time-of-use tariff structures. Nowadays, the application of smart controls to regulate heating systems is growing in popularity. Within this context, a model predictive control strategy to improve the operation of a space-heating system coupled with renewable resources is proposed. This model uses a dynamic approach based on forecasting all energy inputs into the system over a given period of time in advance, before taking any operational decisions. The model predictive control strategy was applied to minimize annual energy costs of the heating system of a detached house located in Puigverd de Lleida (Spain) and, based on a heat pump coupled to a thermal energy storage unit and photovoltaic panels. The results show the potential of the model predictive control with a 24-h horizon. In that case, energy cost savings of 58% can be achieved, compared to the same heating system without smart control.

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  • 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).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220303364
    DOI: 10.1016/j.energy.2020.117229
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    Cited by:

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    2. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    3. Bastida, Hector & De la Cruz-Loredo, Ivan & Ugalde-Loo, Carlos E., 2023. "Effective estimation of the state-of-charge of latent heat thermal energy storage for heating and cooling systems using non-linear state observers," Applied Energy, Elsevier, vol. 331(C).
    4. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Zhou, Yuan & Wang, Jiangjiang & Liu, Yi & Yan, Rujing & Ma, Yanpeng, 2021. "Incorporating deep learning of load predictions to enhance the optimal active energy management of combined cooling, heating and power system," Energy, Elsevier, vol. 233(C).
    6. Skandalos, Nikolaos & Karamanis, Dimitris, 2021. "An optimization approach to photovoltaic building integration towards low energy buildings in different climate zones," Applied Energy, Elsevier, vol. 295(C).
    7. Chiara Bersani & Ahmed Ouammi & Roberto Sacile & Enrico Zero, 2020. "Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption," Energies, MDPI, vol. 13(14), pages 1-17, July.
    8. de Gracia, Alvaro & Tarragona, Joan & Crespo, Alicia & Fernández, Cèsar, 2020. "Smart control of dynamic phase change material wall system," Applied Energy, Elsevier, vol. 279(C).
    9. 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).
    10. 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.

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