scholarly journals A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters

Author(s):  
Hassan Tavakol-Davani ◽  
Michael Violante ◽  
Saeed Manshadi

Abstract In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed-integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC; Leadville, CO; and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.

2013 ◽  
Vol 23 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Angelo Sifaleras

We present a wide range of problems concerning minimum cost network flows, and give an overview of the classic linear single-commodity Minimum Cost Network Flow Problem (MCNFP) and some other closely related problems, either tractable or intractable. We also discuss state-of-the-art algorithmic approaches and recent advances in the solution methods for the MCNFP. Finally, optimization software packages for the MCNFP are presented.


2001 ◽  
Vol 33 (3) ◽  
pp. 591-604
Author(s):  
Carlos E. Testuri ◽  
Richard L. Kilmer ◽  
Thomas Spreen

AbstractThis study provides insight into the seasonality of Class I price differentials in the southeastern dairy industry. This is accomplished by analyzing monthly estimates of Class I price differentials obtained from the imputed price solution or dual solution of a generalized capacitated minimum cost network flow model of the dairy industry. A smooth seasonal pattern emerges through the monthly sequence with the lowest and highest estimated Class I price differentials occurring in April and September respectively. Miami and Jacksonville areas reach $ 5.40 and $ 4.36 per hundredweight in April and $ 6.79 and $ 5.53 per hundredweight in September.


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