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Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

Author

Listed:
  • Felix Garcia-Torres

    (Application Unit, Centro Nacional del Hidrogeno, 13500 Puertollano, Ciudad Real, Spain)

  • Ascension Zafra-Cabeza

    (Department of Systems Engineering and Automatic Control, Universidad de Sevilla, 41092 Sevilla, Spain)

  • Carlos Silva

    (IN+ Center for Innovation, Technology and Policy Research, Mechanical Engineering Department, Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisbon, Portugal)

  • Stephane Grieu

    (Processes, Materials and Solar Energy (PROMES-CNRS) Laboratory, University of Perpignan Via Domitia, 66100 Perpignan, France)

  • Tejaswinee Darure

    (Processes, Materials and Solar Energy (PROMES-CNRS) Laboratory, University of Perpignan Via Domitia, 66100 Perpignan, France)

  • Ana Estanqueiro

    (Unidade de Energias Renováveis e Eficiência Energética, Laboratorio Nacional de Energia e Geologia, 1649-038 Lisbon, Portugal)

Abstract

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

Suggested Citation

  • Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1296-:d:506805
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    4. Karan Singh Joshal & Neeraj Gupta, 2023. "Microgrids with Model Predictive Control: A Critical Review," Energies, MDPI, vol. 16(13), pages 1-26, June.
    5. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    6. Tobias Kull & Bernd Zeilmann & Gerhard Fischerauer, 2021. "Field-Ready Implementation of Linear Economic Model Predictive Control for Microgrid Dispatch in Small and Medium Enterprises," Energies, MDPI, vol. 14(13), pages 1-23, June.
    7. Muhammad Nauman & Wajiha Shireen & Amir Hussain, 2022. "Model-Free Predictive Control and Its Applications," Energies, MDPI, vol. 15(14), pages 1-24, July.
    8. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    9. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
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