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Optimal resilience-oriented microgrid formation considering failure probability of distribution feeders

Jahromi, S. N ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.epsr.2022.108012
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. After a natural disaster, there is an urgent need to supply critical loads such as hospitals as soon as possible. Microgrid (MG) formation is one of the quickest ways to achieve this goal. However, in MG formation studies, there is a trade-off between maximizing the amount of restored loads and minimizing their risk of interruption due to the following aftershocks. For the former objective, the minimum number of MGs should be formed, whereas, for the latter objective, the maximum number of MGs should be configured. This paper tackles this contradictory situation by considering the failure risk of distribution feeders in its proposed optimization framework. In this paper, at first, a novel objective function is proposed to model the impact of feeders’ failure probability on the survivability of MGs. Then, a two-stage master/slave optimization is presented to optimize the number and configuration of MGs. In this optimization framework, a heuristic algorithm will determine the open/close status of feeders, a graph search method will find the formed MGs, and finally, an optimal power flow study will be run to maximize the amount of supplied loads. This paper shows that the proposed methodology will result in an optimal solution, which establishes an appropriate balance between the amount of supplied loads and their risk of interruption. IEEE 33-bus test system is employed to investigate the effectiveness of the proposed methodology. © 2022 Elsevier B.V
  6. Keywords:
  7. Distribution system ; Failure probability ; Disasters ; Economic and social effects ; Electric load flow ; Feeding ; Flow graphs ; Heuristic algorithms ; Heuristic methods ; Microgrids ; Outages ; Probability distributions ; Systems engineering ; Critical load ; Distribution feeders ; Distribution systems ; Microgrid ; Natural disasters ; Optimal resilience ; Optimization framework ; Resiliency ; Trade off ; Optimization
  8. Source: Electric Power Systems Research ; Volume 209 , 2022 ; 03787796 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0378779622002383