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A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

Author

Listed:
  • Ana Cabrera-Tobar

    (Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy)

  • Alessandro Massi Pavan

    (Department of Engineering and Architecture, Center for Energy, Environment and Transport Giacomo Ciamician, University of Trieste, 34127 Trieste, Italy)

  • Giovanni Petrone

    (Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy)

  • Giovanni Spagnuolo

    (Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy)

Abstract
This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed.

Suggested Citation

  • Ana Cabrera-Tobar & Alessandro Massi Pavan & Giovanni Petrone & Giovanni Spagnuolo, 2022. "A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids," Energies, MDPI, vol. 15(23), pages 1-38, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9114-:d:990613
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    Cited by:

    1. D’Amore, G. & Cabrera-Tobar, A. & Petrone, G. & Pavan, A. Massi & Spagnuolo, G., 2024. "Integrating model predictive control and deep learning for the management of an EV charging station," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 33-48.
    2. Alexander Micallef & Josep M. Guerrero & Juan C. Vasquez, 2023. "New Horizons for Microgrids: From Rural Electrification to Space Applications," Energies, MDPI, vol. 16(4), pages 1-25, February.
    3. Ana Cabrera-Tobar & Francesco Grimaccia & Sonia Leva, 2023. "Energy Resilience in Telecommunication Networks: A Comprehensive Review of Strategies and Challenges," Energies, MDPI, vol. 16(18), pages 1-23, September.

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