Energy Management of Refrigeration Systems with Thermal Energy Storage Based on Non-Linear Model Predictive Control

Guillermo Bejarano (), João M. Lemos, Javier Rico-Azagra, Francisco R. Rubio and Manuel G. Ortega
Additional contact information
Guillermo Bejarano: Departamento de Ingeniería, Escuela Técnica Superior de Ingeniería, Universidad Loyola Andalucía, Avda. de las Universidades s/n, 41704 Dos Hermanas, Spain
João M. Lemos: INESC-ID—Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa, Instituto Superior Técnico, University Lisboa, Rua Alves Redol, 9, 1000-029 Lisbon, Portugal
Javier Rico-Azagra: Departamento de Ingeniería Eléctrica, Universidad de la Rioja, Calle San José de Calasanz, 31, 26004 Logroño, Spain
Francisco R. Rubio: Departamento de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain
Manuel G. Ortega: Departamento de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain

Mathematics, 2022, vol. 10, issue 17, 1-27

Abstract: This work addresses the energy management of a combined system consisting of a refrigeration cycle and a thermal energy storage tank based on phase change materials. The storage tank is used as a cold-energy buffer, thus decoupling cooling demand and production, which leads to cost reduction and satisfaction of peak demand that would be infeasible for the original cycle. A layered scheduling and control strategy is proposed, where a non-linear predictive scheduler computes the references of the main powers involved (storage tank charging/discharging powers and direct cooling production), while a low-level controller ensures that the requested powers are actually achieved. A simplified model retaining the dominant dynamics is proposed as the prediction model for the scheduler. Economic, efficiency, and feasibility criteria are considered, seeking operating cost reduction while ensuring demand satisfaction. The performance of the proposed strategy for the system with energy storage is compared in simulation with that of a cycle without energy storage, where the former is shown to satisfy challenging demands while reducing the operating cost by up to 28%. The proposed approach also shows suitable robustness when significant uncertainty in the prediction model is considered.

Keywords: refrigeration system; thermal energy storage; phase change materials; non-linear model predictive control; scheduling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/17/3167/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/17/3167/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:17:p:3167-:d:905621

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().