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Distribution system resilience enhancement via mobile emergency generators
Taheri, B ; Sharif University of Technology | 2021
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- Type of Document: Article
- DOI: 10.1109/TPWRD.2020.3007762
- Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
- Abstract:
- Natural calamities always have been a serious threat to energy systems. In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. In particular, the proposed model is developed to enhance both survivability and restoration capability of distribution systems. In this regard, to increase the preparedness of the power distribution system, the system operator reconfigures the network by utilizing remote-control switches (RCSs), manual switches (MSs), and distributed generations (DGs) before the natural calamity hits. The proposed model contemplates the traveling time of crew teams to the MS sites in the transportation system in order to achieve the best switching sequence. In addition, it pre-positions the crews and mobile emergency generators (MEGs) in staging locations to hasten the likely post-disturbance operations. To do so, likely post-event operations are included in the model using the scenario generation technique. To validate the performance of the developed model a distribution system is employed. The results of simulations confirm the effectiveness of the proposed approach in declining the interruption of electric energy for customers. © 1986-2012 IEEE
- Keywords:
- Disasters ; Integer programming ; Remote control ; Stochastic systems ; Distributed generations (DGs) ; Distribution systems ; Electric energies ; Mixed integer linear programming ; Power distribution system ; Scenario generation ; Switching sequence ; Transportation system ; Stochastic models
- Source: IEEE Transactions on Power Delivery ; Volume 36, Issue 4 , 2021 , Pages 2308-2319 ; 08858977 (ISSN)
- URL: https://ieeexplore.ieee.org/document/9136725