Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future-Reference-Cited by-同舟云学术

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Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future

Published:2023 Issue: Volume: Page:357-387
ISSN:1612-1287
Container-title:Smart Grid 3.0
language:
Short-container-title:

Author:

Altin Necmi,Eyimaya Süleyman Emre

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-38506-3_14

Reference76 articles.

1. Altin N, Eyimaya SE, Nasiri A (2023) Multi-agent-based controller for microgrids: An overview and case study. Energies, 16(5):2445

2. Bacha S, Picault D, Burger B, Etxeberria-Otadui I, Martins J (2015) Photovoltaics in microgrids: an overview of grid integration and energy management aspects. IEEE Ind Electron Mag 9(1):33–46

3. Lidula NWA, Rajapakse AD (2011) Microgrids research: a review of experimental microgrids and test systems. Renew Sustain Energy Rev 15(1):186–202

4. Siano P, Cecati C, Yu H, Kolbusz J (2012) Real time operation of smart grids via FCN networks and optimal power flow. IEEE Trans Industr Inf 8(4):944–952

5. Sandelic M, Peyghami S, Sangwongwanich A, Blaabjerg F (2022) Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges. Renew Sustain Energy Rev 159:112127

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1. A Review on Multi-Agent Systems and JADE Applications in Microgrids;2024 12th International Conference on Smart Grid (icSmartGrid);2024-05-27

2. Machine Learning Techniques Applied to Improve High-Speed Spectrum Management in Fiber Optic Communications;2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS);2023-12-28

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