Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage

Edoardo Di Mattia, Agostino Gambarotta, Emanuela Marzi, Mirko Morini () and Costanza Saletti
Additional contact information
Edoardo Di Mattia: Interdepartmental Research Center on Food Safety, Technology and Innovation (SITEIA.PARMA), University of Parma, Tecnopolo Padiglione 33, Campus Universitario, 43124 Parma, Italy
Agostino Gambarotta: Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
Emanuela Marzi: Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
Mirko Morini: Interdepartmental Research Center on Food Safety, Technology and Innovation (SITEIA.PARMA), University of Parma, Tecnopolo Padiglione 33, Campus Universitario, 43124 Parma, Italy
Costanza Saletti: Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy

Energies, 2022, vol. 15, issue 19, 1-22

Abstract: The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab ® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation.

Keywords: Model Predictive Control; Dynamic Programming; vapor compression refrigeration system; renewable energy; freezing food (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (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/1996-1073/15/19/7125/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/19/7125/ (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:jeners:v:15:y:2022:i:19:p:7125-:d:927816

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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