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Full-deployed energy management system tested in a microgrid cluster

Author

Listed:
  • Rosero, D.G.
  • Sanabria, E.
  • Díaz, N.L.
  • Trujillo, C.L.
  • Luna, A.
  • Andrade, F.

Abstract

Under a hierarchical structure perspective, the integration of tools like the internet of things, cloud computing, and machine learning into a microgrid real-time energy management system involves superior capabilities like an autonomous and scalable design, massive storage capabilities, real-time information analysis and processing, and security issues control, to mention a few. This paper evaluates most of them using a cloud-based real-time energy management system integrated into a real-life hardware-in-the-loop testbed. The proposed cloud-based system is tested by solving an economic power dispatch problem including the equalization of the multiple battery-based energy storage systems interacting within a multi-microgrid environment. The test assessment combined reviewing and running microgrid models, incorporating these models into a real-life power-hardware-in-the-loop unit, linking the testbed to a cloud server, and merging the energy management system with on-demand computing tools, primarily machine learning and the internet of things. As established by the experimental evidence, this paper cites the benefits of combining machine learning techniques and internet of things tools with a scalable and autonomous real-life cloud-based energy management system architecture to improve the framework's functionality, enhance the energy forecasting for generation and usage, and cut down the price paid to the service provider.

Suggested Citation

  • Rosero, D.G. & Sanabria, E. & Díaz, N.L. & Trujillo, C.L. & Luna, A. & Andrade, F., 2023. "Full-deployed energy management system tested in a microgrid cluster," Applied Energy, Elsevier, vol. 334(C).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261923000387
    DOI: 10.1016/j.apenergy.2023.120674
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    References listed on IDEAS

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    1. Kaur, Amandeep & Kaushal, Jitender & Basak, Prasenjit, 2016. "A review on microgrid central controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 338-345.
    2. Yael Parag & Benjamin K. Sovacool, 2016. "Electricity market design for the prosumer era," Nature Energy, Nature, vol. 1(4), pages 1-6, April.
    3. Rosero, D.G. & Díaz, N.L. & Trujillo, C.L., 2021. "Cloud and machine learning experiments applied to the energy management in a microgrid cluster," Applied Energy, Elsevier, vol. 304(C).
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