Electric Grid Hardening and Resiliency: Part I, Resiliency and Safety

Rastgoufard, P.; Leevongwat, Ittiphong; Rastgoufard, Rastin

doi:10.1049/etr.2015.0085

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the sensitivity of this equipment to power, the power outages can a ect the quality of patient care. Therefore, planners should invest in electricity supply and outage prevention when they construct hospitals and other health care centers, such as clinics, laboratories, and nursing homes [16]. For this purpose, di erent types of power generators can be used to supply part of the electricity consumed by speci c departments in hospitals, such as the emergency rooms in an accidental power outage.…”

Section: Related Studiesmentioning

confidence: 99%

Resilient Network Design in a Location-Allocation Problem with Multi-Level Facility Hardening

2018

View full text Add to dashboard Cite

Many sources of risk a ect network elements, which may lead to network failure; thus, planners need to consider them in the network design. One of the most important strategies for disruption risk management is the static resilience. In this strategy, the network functionality is maintained after the disruption event by the prevention and hardening actions. In this paper, a resilient capacitated xed-charge location-allocation model is proposed. Both facility hardening and equipping of the network with backup facilities for disrupted elements are considered together to avoid supply network failure due to random disruption. Facilities are decided to be hardened in multiple levels before disruption events. The problem is formulated as a non-linear integer programming model; then, its equivalent linear form is presented. A Lagrangian Decomposition Algorithm (LDA) is developed to solve large-scale instances. Computational results con rm the high e cacy of the proposed solution approach, compared to classical solution approaches, in dealing with large-scale problems. Moreover, the superiority of the proposed model is con rmed in comparison to the classical models.

show abstract

Section: Related Studiesmentioning

confidence: 99%