Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial Intelligence
This study introduces a resilience-oriented framework to enhance the performance of energy distribution networks under partial high workload conditions with a particular emphasis on natural gas systems. Given the critical role of gas delivery in economic stability and societal well-being, ensuring t...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807227/ |
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| author | Mehrdad Agha Mohammad Ali Kermani Negar Mohammadi Hasan Ghasemi Hadi Sahebi Hani Gilani |
| author_facet | Mehrdad Agha Mohammad Ali Kermani Negar Mohammadi Hasan Ghasemi Hadi Sahebi Hani Gilani |
| author_sort | Mehrdad Agha Mohammad Ali Kermani |
| collection | DOAJ |
| description | This study introduces a resilience-oriented framework to enhance the performance of energy distribution networks under partial high workload conditions with a particular emphasis on natural gas systems. Given the critical role of gas delivery in economic stability and societal well-being, ensuring the reliability and robustness of its network design is imperative. This research adopts a Social Network Analysis (SNA) methodology, augmented by an artificial intelligence-based approach, to appraise the dynamics and interdependencies of gas distribution networks under varying operational conditions. The proposed framework integrates SNA with simulation-based network analysis to enhance resilience by identifying critical nodes and edges susceptible to disruption, especially during demand fluctuations periods. A key advantage of this approach lies in its applicability as a consumer vulnerability mitigation strategy during high-demand scenarios, thereby reinforcing network robustness. The findings offer significant insights and methodological contributions applicable across gas networks, ultimately promoting smarter and more resilient energy solutions. Overall, this research provides a comprehensive strategy for strengthening network resilience, supporting the broader objectives of energy security and sustainability. |
| format | Article |
| id | doaj-art-35a3f5902d31490da7c3edf834ebd085 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-35a3f5902d31490da7c3edf834ebd0852025-01-15T00:02:44ZengIEEEIEEE Access2169-35362025-01-01136924694410.1109/ACCESS.2024.352021610807227Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial IntelligenceMehrdad Agha Mohammad Ali Kermani0https://orcid.org/0000-0002-2972-5852Negar Mohammadi1Hasan Ghasemi2Hadi Sahebi3https://orcid.org/0000-0002-0153-0420Hani Gilani4https://orcid.org/0000-0002-4849-7920School of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranThis study introduces a resilience-oriented framework to enhance the performance of energy distribution networks under partial high workload conditions with a particular emphasis on natural gas systems. Given the critical role of gas delivery in economic stability and societal well-being, ensuring the reliability and robustness of its network design is imperative. This research adopts a Social Network Analysis (SNA) methodology, augmented by an artificial intelligence-based approach, to appraise the dynamics and interdependencies of gas distribution networks under varying operational conditions. The proposed framework integrates SNA with simulation-based network analysis to enhance resilience by identifying critical nodes and edges susceptible to disruption, especially during demand fluctuations periods. A key advantage of this approach lies in its applicability as a consumer vulnerability mitigation strategy during high-demand scenarios, thereby reinforcing network robustness. The findings offer significant insights and methodological contributions applicable across gas networks, ultimately promoting smarter and more resilient energy solutions. Overall, this research provides a comprehensive strategy for strengthening network resilience, supporting the broader objectives of energy security and sustainability.https://ieeexplore.ieee.org/document/10807227/Social network analysis (AI)gas industryresilience networksimulation |
| spellingShingle | Mehrdad Agha Mohammad Ali Kermani Negar Mohammadi Hasan Ghasemi Hadi Sahebi Hani Gilani Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial Intelligence IEEE Access Social network analysis (AI) gas industry resilience network simulation |
| title | Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial Intelligence |
| title_full | Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial Intelligence |
| title_fullStr | Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial Intelligence |
| title_full_unstemmed | Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial Intelligence |
| title_short | Enhancing Gas Distribution Network Resilience Utilizing a Mixed Social Network Analysis-Simulation Approach: Application of Artificial Intelligence |
| title_sort | enhancing gas distribution network resilience utilizing a mixed social network analysis simulation approach application of artificial intelligence |
| topic | Social network analysis (AI) gas industry resilience network simulation |
| url | https://ieeexplore.ieee.org/document/10807227/ |
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