Resource planning and a depot location model for electric power restoration

Wang, Shaojun; Sarker, Bhaba R.; Mann, Lawrence; Triantaphyllou, Evangelos

doi:10.1016/s0377-2217(02)00803-2

Cited by 34 publications

(8 citation statements)

References 19 publications

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“…There are two research streams in the existing literature focusing on network restoration problems. One of the two streams defines restoration optimization as an optimal resource allocation problem, which determines which network components to repair [6,7]. For example, Zhang et al employ an inoperability 2 of 16 input-output model for allocating restoration resources of a disrupted infrastructure network [8].…”

Section: Introductionmentioning

confidence: 99%

Resilience-Based Restoration Model for Supply Chain Networks

Mao

Lou²,

Cao

et al. 2020

Mathematics

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An optimal restoration strategy for supply chain networks can efficiently schedule the repair activities under resource limits. However, a wide range of previous studies solve this problem from the perspective of cost-effectiveness instead of a resilient manner. This research formulates the problem as a network maximum-resilience decision. We develop two metrics to measure the resilience of the supply chain networks, i.e., the resilience of cumulative performance loss and the resilience of restoration rapidity. Then, we propose a bi-objective nonlinear programming model, which aims to maximize the network resilience under the budget and manpower constraints. A modified simulated annealing algorithm is employed to solve the model. Finally, a testing supply chain network is utilized to illustrate the effectiveness of the proposed method framework. The results show that the optimal restoration schedule generated by the proposed model is a tradeoff between the cumulative performance loss and the restoration rapidity. Additionally, the sensitivity analysis of parameters indicates that decision-maker’s preference, tolerance factor of delivery time, number of work crews, and availability of budget all have significant impacts on the restoration schedule.

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Section: Introductionmentioning

confidence: 99%

Resilience-Based Restoration Model for Supply Chain Networks

Mao

Lou²,

Cao

et al. 2020

Mathematics

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“…The model allows system operators to make a trade‐off between the cost of repair resources allocation and the cost of load interruption. Depot location models to optimally store the resources needed for efficient and economic power restoration in the face of extreme events is proposed in [72, 73 ]. It should be noted that [71–73 ] all formulate the problem as stochastic mixed‐integer programming, which could be efficiently solved by commercial solvers.…”

Section: Enhancing Resiliencementioning

confidence: 99%

Resilient distribution system leveraging distributed generation and microgrids: a review

Liu

Jiang

Ollis

et al. 2020

IET Energy Systems Integration

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“…The reconfiguration and the resource dispatching issues were considered in a systematic way for deriving the optimal time sequence in every step of the restoration plan. In [34], a decision-making model to manage the required resources for economic power restoration operation was proposed. The optimal number of depots, the optimal location of depots, and the optimal number of repair crews were determined by their model in order to minimize the transportation cost associated with restoration operation.…”

Section: ) Resource Allocationmentioning

confidence: 99%

Proactive Recovery of Electric Power Assets for Resiliency Enhancement

et al. 2015

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This paper presents a significant change in current electric power grid response and recovery schemes by developing a framework for proactive recovery of electric power assets with the primary objective of resiliency enhancement. Within the proposed framework, which can potentially present the next generation decision-making tool for proactive recovery, several coordinated models will be developed including: 1) the outage models to indicate the impact of hurricanes on power system components; 2) a stochastic pre-hurricane crew mobilization model for managing resources before the event; and 3) a deterministic post-hurricane recovery model for managing resources after the event. Proposed models will be extended to ensure applicability to a variety of electric power grids with different technologies and regulatory issues. The theoretical and practical implications of the developed models will push the research frontier of proactive response and recovery schemes in electric power grids, while its flexibility will support application to a variety of infrastructures, in response to a wide range of extreme weather events and natural disasters.

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Resource planning and a depot location model for electric power restoration

Cited by 34 publications

References 19 publications

Resilience-Based Restoration Model for Supply Chain Networks

Resilience-Based Restoration Model for Supply Chain Networks

Resilient distribution system leveraging distributed generation and microgrids: a review

Proactive Recovery of Electric Power Assets for Resiliency Enhancement

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