2026-04-13T16:52:55Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/715452024-12-05T12:09:11Zcom_2072_3622col_2072_479130

Systematic review on model predictive control strategies applied to active thermal energy storage systems Tarragona Roig, Joan Pisello, Anna Laura Fernàndez Camon, César Gracia Cuesta, Alvaro de Cabeza, Luisa F. Thermal energy storage Model predictive control Systematic review Energy management Renewable energy Active systems This paper presents a review of the application of model predictive control strategies to active thermal energy storage systems. To date, model predictive control has been used to manage such energy systems as heating, ventilation and air conditioning equipment or power generation plants. In all cases, the aim of the strategy has been to anticipate both production and consumption decisions to optimize the system performance, reducing the final energy cost. This ability of the strategy to forecast weather conditions and predict demand requirements in advance exceeds the performance of conventional control methods and made the strategy a very effective option to be coupled with active thermal energy storage systems. In this regard, this review paper presents the progress and results of the combination of these two technologies. The key contributions consist of a summary of the technical parameters employed, such as the prediction horizon length, the computational architecture approaches, the thermal energy storage material used and the influence of renewables in this kind of system. Additionally, the review summarises the latest enhancements to overcome computational issues and an analysis of the objective functions employed in each study, which were mainly focused to minimize the energy cost, the peak power and CO2 emissions. A discussion about the strengths and weaknesses of this technology is provided, highlighting the difficulty of the strategy to operate with complicated physical models as the key limitation to overcome. Finally, some future guidelines to enhance the application of this strategy to control different sort of systems are detailed. This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31 - MCIU/AEI/FEDER, UE) and the Agencia Estatal de Investigación (AEI) (RED2018-102431-T). The authors would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is a certified TECNIO agent in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme. A.L. Pisello's acknowledgements are due to the National Ministry of Research for supporting NEXT.COM project under the framework of PRIN 2017 (cod. 20172FSCH4_002) and the IEA EBC Annex 79 “Occupant-centric building design and operation” for inspiring human-centric research. 2024-12-05T12:09:11Z 2024-12-05T12:09:11Z 2024-12-05T12:09:11Z 2021-07-02T08:21:06Z 2021 2021-07-02T08:21:06Z info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion http://hdl.handle.net/10459.1/71545 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093849-B-C31/ES/METODOLOGIA PARA EL ANALISIS DE TECNOLOGIAS DE ALMACENAMIENTO DE ENERGIA TERMICA HACIA UNA ECONOMIA CIRCULAR/ MINECO/PN2013-2016/RED2018-102431-T Versió postprint del document publicat a: https://doi.org/10.1016/j.rser.2021.111385 Renewable & Sustainable Energy Reviews, 2021, vol. 149, p. 111385-1-111385-14 cc-by-nc-nd (c) Elsevier, 2021 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Elsevier