Modeling and optimizing recovery strategies for power distribution system resilience

Arjomandi Nezhad, A ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/JSYST.2020.3020058
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. Both frequency and intensity of natural disasters have intensified in recent years. It is, therefore, essential to design effective strategies to minimize their catastrophic consequences. Optimizing recovery tasks, including distribution system reconfiguration (DSR) and repair sequence optimization (RSO), are the key to enhance the agility of disaster recovery. This article aims to develop a resilience-oriented DSR and RSO optimization model and a mechanism to quantify the recovery agility. In doing so, a new metric is developed to quantify the recovery agility and to identify the optimal resilience enhancement strategies. The metric is defined as 'the number of recovered customers divided by the average outage time of the interrupted customers.' A Monte-Carlo-based methodology to quantify the recovery agility of different DSR plans is developed. It will be shown that if the total number of interrupted customers over the recovery horizon is minimized, the metric will be maximized. Accordingly, the DSR and RSO optimization models are modified to maximize the introduced metric. The proposed optimization model is formulated as a mixed-integer linear programming model that can be solved via commercial off-the-shelf solvers. Finally, the proposed methodology is applied to several case studies to examine its effectiveness. It will be also shown how the proposed methodology can be utilized for distributed generator (DG) and tie-line placement problems in planning for enhanced structural resilience. © 2007-2012 IEEE
  6. Keywords:
  7. Disasters ; Integer programming ; Repair ; Sales ; Disaster-response ; Distribution system reconfiguration ; Optimization models ; Power-distribution system ; Recovery metric ; Recovery strategies ; Repair sequence optimization ; Resilience ; Sequence optimization ; Recovery
  8. Source: IEEE Systems Journal ; Volume 15, Issue 4 , 2021 , Pages 4725-4734 ; 19328184 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9195497