A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters
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 geot...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/16/2483 |
| _version_ | 1797441300286406656 |
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| author | Michael Violante Hassan Davani Saeed D. Manshadi |
| author_facet | Michael Violante Hassan Davani Saeed D. Manshadi |
| author_sort | Michael Violante |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:22:01Z |
| format | Article |
| id | doaj.art-a462e21134434bb18634d5bd729f5eb8 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T12:22:01Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a462e21134434bb18634d5bd729f5eb82023-11-30T22:40:15ZengMDPI AGWater2073-44412022-08-011416248310.3390/w14162483A Decision Support System to Enhance Electricity Grid Resilience against Flooding DisastersMichael Violante0Hassan Davani1Saeed D. Manshadi2Department of Civil, Construction & Environmental Engineering, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182, USADepartment of Civil, Construction & Environmental Engineering, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182, USADepartment of Electrical & Computer Engineering, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182, USAIn 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.https://www.mdpi.com/2073-4441/14/16/2483decision support systemflood controlutility polesmathematical programmingpower systemresilience |
| spellingShingle | Michael Violante Hassan Davani Saeed D. Manshadi A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters Water decision support system flood control utility poles mathematical programming power system resilience |
| title | A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters |
| title_full | A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters |
| title_fullStr | A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters |
| title_full_unstemmed | A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters |
| title_short | A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters |
| title_sort | decision support system to enhance electricity grid resilience against flooding disasters |
| topic | decision support system flood control utility poles mathematical programming power system resilience |
| url | https://www.mdpi.com/2073-4441/14/16/2483 |
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