Extended Model Predictive Controller to Develop Energy Management Systems in Renewable Source-Based Smart Microgrids with Hydrogen as Backup. Theoretical Foundation and Case Study

Francisco J. Vivas Fernández, Francisca Segura Manzano, José Manuel Andújar Márquez and Antonio J. Calderón Godoy
Additional contact information
Francisco J. Vivas Fernández: Grupo de Control y Robótica TEP-192, Centro de Investigación en Tecnología, Energía y Sostenibilidad, Universidad de Huelva, 21004 Huelva, Spain
Francisca Segura Manzano: Grupo de Control y Robótica TEP-192, Centro de Investigación en Tecnología, Energía y Sostenibilidad, Universidad de Huelva, 21004 Huelva, Spain
José Manuel Andújar Márquez: Grupo de Control y Robótica TEP-192, Centro de Investigación en Tecnología, Energía y Sostenibilidad, Universidad de Huelva, 21004 Huelva, Spain
Antonio J. Calderón Godoy: Grupo de Robótica Automática y Sistemas de Producción, Universidad de Extremadura, 10003 Caceres, Spain

Sustainability, 2020, vol. 12, issue 21, 1-28

Abstract: This article presents a methodological foundation to design and experimentally test a Model Predictive Controller (MPC) to be applied in renewable source-based microgrids with hydrogen as backup. The Model Predictive Controller has been developed with the aim to guarantee the best energy distribution while the microgrid operation is optimized considering both technical and economic parameters. As a differentiating element, this proposal provides a solution to the problem of energy management in real systems, addressing technological challenges such as charge management in topologies with direct battery connection, or loss of performance associated with equipment degradation or the required dynamics in the operation of hydrogen systems. That is, the proposed Model Predictive Controller achieves the optimization of microgrid operation both in the short and in the long-term basis. For this purpose, a generalized multi-objective function has been defined that considers the energy demand, operating costs, system performance as well as the suffered and accumulated degradation by microgrid elements throughout their lifespan. The generality in the definition of the model and cost function, allows multi-objective optimization problems to be raised depending on the application, topology or design criteria to be considered. For this purpose, a heuristic methodology based on artificial intelligence techniques is presented for the tuning of the controller parameters. The Model Predictive Controller has been validated by simulation and experimental tests in a case study, where the performance of the microgrid under energy excess and deficit situations has been tested, considering the constrains defined by the degradation of the systems that make up the microgrid. The designed controller always made it possible to guarantee both the power balance and the optimal energy distribution between systems according to the predefined priority and accumulated degradation, while guaranteeing the maximum operating voltage of the system with a margin of error less than 1%. The simulation and experimental results for the case study showed the validity of the controller and the design methodology used.

Keywords: model predictive control; energy management system; renewable source-based smart microgrid; hydrogen backup systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/21/8969/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/21/8969/ (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:jsusta:v:12:y:2020:i:21:p:8969-:d:436364

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

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