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This paper presents a survey of the literature on the strategies to enhance the resilience of power systems while shedding lights on the research gaps. Using a deductive methodology on the literature covering the resilience of power systems, we reviewed more than two hundred peer-reviewed articles spanning the 2010–2019 decade. We find that there is vacuum on the level of integration that considers the interdependence of local or decentralized decision making in an adaptive power system. This gap is widened by the absence of policies to enhance resilience in power networks. While there is significant coverage and convergence of research on algorithms for solving the multi-objective problem in optimization routines, there are still uncharted territories on how to incorporate system degradation while designing these self-restoration systems. We posit that a shift to a smarter, cleaner and more resilient power network requires sustained investments rather than disaster-induced responses.
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Optimizing microgrid deployment for community resilience
The ability to (re)establish basic community infrastructure and governmental functions, such as medical and communication systems, after the occurrence of a natural disaster rests on a continuous supply of electricity. Traditional energy-generation systems consisting of power plants, transmission lines, and distribution feeders are becoming more vulnerable, given the increasing magnitude and frequency of climate-related natural disasters. We investigate the role that fuel cells, along with other distributed energy resources, play in post-disaster recovery efforts. We present a mixed-integer, non-linear optimization model that takes load and power-technology data as inputs and determines a cost-minimizing design and dispatch strategy while considering operational constraints. The model fails to achieve gaps of less than 15%, on average, after two hours for realistic instances encompassing five technologies and a year-long time horizon at hourly fidelity. Therefore, we devise a multi-phase methodology to expedite solutions, resulting in run times to obtain the best solution in fewer than two minutes; after two hours, we provide proof of near-optimality, i.e., gaps averaging 5%. Solutions obtained from this methodology yield, on average, an 8% decrease in objective function value and utilize fuel cells three times more often than solutions obtained with a straight-forward implementation employing a commercial solver.
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- Award ID(s):
- 2053856
- PAR ID:
- 10481150
- Publisher / Repository:
- Optimization and Engineering
- Date Published:
- Journal Name:
- Optimization and Engineering
- ISSN:
- 1389-4420
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11081-023-09844-6
