A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration

•A resilient microgrid-forming model is set up considering master-slave DG operation.•The topology reconfiguration and microgrid-forming are coordinated in the model.•A mixed-integer second-order cone programming is employed to solve the model. Recent severe power outages caused by extreme weather h...

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Bibliographic Details
Published in:Applied energy Vol. 199; pp. 205 - 216
Main Authors: Ding, Tao, Lin, Yanling, Bie, Zhaohong, Chen, Chen
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2017
Subjects:
critical load
disasters
electricity
Master-slave control
Microgrid
Mixed-integer second-order cone programming
modernization
Resilient distribution network
topology
Topology reconfiguration
weather
Master-slave control
Topology reconfiguration
Resilient distribution network
Microgrid
Mixed-integer second-order cone programming
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:•A resilient microgrid-forming model is set up considering master-slave DG operation.•The topology reconfiguration and microgrid-forming are coordinated in the model.•A mixed-integer second-order cone programming is employed to solve the model. Recent severe power outages caused by extreme weather hazards have highlighted the importance and urgency of improving the resilience of electric distribution grids. Microgrids with various types of distributed generators (DGs) have the potential to enhance the electricity supply continuity and thus facilitate resilient distribution grids under natural disasters. In this paper, a novel load restoration optimization model is proposed to coordinate topology reconfiguration and microgrid formation while satisfying a variety of operational constraints. The proposed method exploits benefits of operational flexibility provided by grid modernization to enable more critical load pickup. Specifically, a mixed-integer second order cone programming is employed to reduce the computational complexity of the proposed optimization with optimality guaranteed. Finally, the effectiveness of the proposed method has been verified on an IEEE 33-bus test case and a modified 615-bus test system.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2017.05.012