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Cloud and machine learning experiments applied to the energy management in a microgrid cluster

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  • Rosero, D.G.
  • Díaz, N.L.
  • Trujillo, C.L.

Abstract

The way to organize the generation, storage, and management of renewable energy and energy consumption features has taken relevance in recent years due to demands that define the social welfare of this century. Like demand increases, other factors require grid infrastructure improvement, updates, and opening to other technologies that assuage the final customer needs. Precisely, the interest in renewable energy sources, the constant evolution of energy storage technologies, the continuous research involving microgrid management systems, and the evolution of cloud computing technologies and machine learning strategies motivate the development of this article. Tasks associated with a microgrid cluster like the integration of a considerable number of heterogeneous devices, real-time support, information processing, massive storage capabilities, security considerations, and advanced optimization techniques usage could take place in an autonomous and scalable energy management system architecture under a machine learning perspective running in real-time and using Cloud resources. This paper focuses on identifying the elements considered by different authors to define a cloud-based architecture and ensure the appropriately supervised learning functionality under a microgrids cluster environment. Namely, it was necessary to revise and run microgrid simulations, real-time simulation platforms usage, connection to a virtual server for microgrid control and set the energy management system using cloud computing and machine learning. Based on the review and considering the scenarios mentioned, this article presents a scalable and autonomous cloud-based architecture that allows power generation forecast, energy consumption prediction, a real-time energy management system using machine learning techniques.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011090
    DOI: 10.1016/j.apenergy.2021.117770
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    References listed on IDEAS

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    1. Ziba Rostami & Sajad Najafi Ravadanegh & Navid Taghizadegan Kalantari & Josep M. Guerrero & Juan C. Vasquez, 2020. "Dynamic Modeling of Multiple Microgrid Clusters Using Regional Demand Response Programs," Energies, MDPI, vol. 13(16), pages 1-19, August.
    2. Sang-Ji Lee & Jin-Young Choi & Hyung-Joo Lee & Dong-Jun Won, 2017. "Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm," Energies, MDPI, vol. 10(7), pages 1-16, July.
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    Cited by:

    1. Ronaldo Silveira Junior, Jose & Conrado, Bruna R.P. & Matheus dos Santos Alonso, Augusto & Iglesias Brandao, Danilo, 2023. "Interoperability of single-controllable clusters: Aggregate response of low-voltage microgrids," Applied Energy, Elsevier, vol. 340(C).
    2. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    3. Hugo Gaspar Hernandez-Palma & Jonny Rafael Plaza Alvarado & Jesús Enrique García Guiliany & Guilherme Luiz Dotto & Claudete Gindri Ramos, 2024. "Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 1-10, March.
    4. Sana Qaiyum & Martin Margala & Pravin R. Kshirsagar & Prasun Chakrabarti & Kashif Irshad, 2023. "Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    5. Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    6. Qiang Wang & Dong Yu & Jinyu Zhou & Chaowu Jin, 2023. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    7. Tostado-Véliz, Marcos & Hasanien, Hany M. & Jordehi, Ahmad Rezaee & Turky, Rania A. & Jurado, Francisco, 2023. "Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue," Applied Energy, Elsevier, vol. 339(C).
    8. 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).
    9. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.
    10. Muqing Wu & Qingsu He & Yuping Liu & Ziqiang Zhang & Zhongwen Shi & Yifan He, 2022. "Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids," Sustainability, MDPI, vol. 14(21), pages 1-13, October.

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