A Novel Distributed Cloud-Fog Based Framework for Energy Management of Networked Microgrids

Power grid resilience, reliability, and sustainability can be improved by decomposing the large-scale grids into the Networked Microgrids (NMGs). However, different MGs may have different roles and policies. Hence, in comparison with conventional networks, optimal energy management, as well as the g...

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Bibliographic Details
Published in:IEEE transactions on power systems 2020-07, Vol.35 (4), p.2847-2862
Main Authors: Dabbaghjamanesh, Morteza, Kavousi-Fard, Abdollah, Dong, Zhao Yang
Format: Article
Language:English
Subjects:
Benders decomposition
Cloud computing
Constraint modelling
Contingency
distributed cloud-fog computing
Distributed generation
Distributed power generation
dynamic thermal line rating (DLR)
Economic models
Electric power grids
Electric power systems
Energy management
Energy management systems
Feeders
Integer programming
Linear programming
Microgrids
Network reliability
Networked microgird
Optimization
Power generation economics
Power generation reliability
Reconfiguration
Topology
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Summary:Power grid resilience, reliability, and sustainability can be improved by decomposing the large-scale grids into the Networked Microgrids (NMGs). However, different MGs may have different roles and policies. Hence, in comparison with conventional networks, optimal energy management, as well as the grid reconfiguration of the NMGs is more completed and challenging. This article develops a three-layer cloud-fog computing architecture for energy management of reconfigurable NMGs considering dynamic thermal line rating (DLR) constraint. DLR can potentially affect the ampacity of feeders, especially in the islanding mode, when lines approach their maximum capacity. In order to avoid any feeder contingency in the off-grid (islanded) mode, the reconfiguration technique as well as the cloud fogs framework are employed to change the topology of the NMG network swiftly and release the line capacity. Finally, the proposed problem is formulated as a mixed-integer linear optimization problem considering DLR constraint. The proposed model is examined on a modified IEEE 69 bus test network. Results demonstrate the high performance and effectiveness of the developed model and also validate its reliability and economic assets.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2019.2957704