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Networked Microgrid Energy Management Based on Supervised and Unsupervised Learning Clustering

Author

Listed:
  • Navid Salehi

    (Electronic Engineering Department, Universitat Politècnica de Catalunya—BarcelonaTech (UPC), 08019 Barcelona, Spain)

  • Herminio Martínez-García

    (Electronic Engineering Department, Universitat Politècnica de Catalunya—BarcelonaTech (UPC), 08019 Barcelona, Spain)

  • Guillermo Velasco-Quesada

    (Electronic Engineering Department, Universitat Politècnica de Catalunya—BarcelonaTech (UPC), 08019 Barcelona, Spain)

Abstract

Networked microgrid (NMG) is a novel conceptual paradigm that can bring multiple advantages to the distributed system. Increasing renewable energy utilization, reliability and efficiency of system operation and flexibility of energy sharing amongst several microgrids (MGs) are some specific privileges of NMG. In this paper, residential MGs, commercial MGs, and industrial MGs are considered as a community of NMG. The loads’ profiles are split into multiple sections to evaluate the maximum load demand (MLD). Based on the optimal operation of each MG, the operating reserve (OR) of the MGs is calculated for each section. Then, the self-organizing map as a supervised and a k-means algorithm as an unsupervised learning clustering method is utilized to cluster the MGs and effective energy-sharing. The clustering is based on the maximum load demand of MGs and the operating reserve of dispatchable energy sources, and the goal is to provide a more efficient system with high reliability. Eventually, the performance of this energy management and its benefits to the whole system is surveyed effectively. The proposed energy management system offers a more reliable system due to the possibility of reserved energy for MGs in case of power outage variation or shortage of power.

Suggested Citation

  • Navid Salehi & Herminio Martínez-García & Guillermo Velasco-Quesada, 2022. "Networked Microgrid Energy Management Based on Supervised and Unsupervised Learning Clustering," Energies, MDPI, vol. 15(13), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4915-:d:856299
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    References listed on IDEAS

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    Cited by:

    1. Bassam A. Hemad & Nader M. A. Ibrahim & Shereen A. Fayad & Hossam E. A. Talaat, 2022. "Hierarchical Clustering-Based Framework for Interconnected Power System Contingency Analysis," Energies, MDPI, vol. 15(15), pages 1-12, August.
    2. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Aditya Sundararajan & Mohammed Olama & Yang Chen, 2023. "Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation," Energies, MDPI, vol. 16(7), pages 1-27, March.
    3. Alizadeh, Ali & Kamwa, Innocent & Moeini, Ali & Mohseni-Bonab, Seyed Masoud, 2023. "Energy management in microgrids using transactive energy control concept under high penetration of Renewables; A survey and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).

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