Optimal Resource Allocation to Enhance Power Grid Resilience Against Hurricanes

Optimal resource allocation is critical when maximizing the resilience of the electrical power distribution network against natural disasters. This paper presents a two-step optimization strategy that integrates a pre-disaster preparedness plan and a post-disaster resource re-allocation procedure to...

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Bibliographic Details
Published in:IEEE transactions on power systems 2023-05, Vol.38 (3), p.2621-2629
Main Authors: Yang, Zejun, Marti, Andrea, Chen, Ying, Marti, Jose R.
Format: Article
Language:English
Subjects:
Disaster management
Disaster response
Electric power distribution
Emergency procedures
Hurricanes
interdependencies
Minimization
Mixed integer
Natural disasters
Nonlinear programming
optimal resource allocation
Optimization
Resilience
Resource allocation
Resource management
Risk management
system restoration
Wind speed
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Summary:Optimal resource allocation is critical when maximizing the resilience of the electrical power distribution network against natural disasters. This paper presents a two-step optimization strategy that integrates a pre-disaster preparedness plan and a post-disaster resource re-allocation procedure to optimize the resilience of the power distribution network against hurricanes. Emergency resources are operationally interdependent, and it is these interdependencies that determine how the resources should be distributed to the critical loads in the network. This work uses the concept of the Human Readable Table (HRT) to relate the interdependencies among these resources. The resource allocation optimization is then formulated into a Mixed-Integer Nonlinear Programming (MINP) problem. The proposed method is tested on the IEEE 70-node system. The results show that this two-step procedure decreases the probability of failure for the critical nodes during the pre-hurricane stage and increases the system's ability to recover during the post-hurricane stage.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2022.3193133