PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks
Smart grid power networks are essential for addressing the global energy crisis and combating climate change. In the past few decades, information and communication infrastructure have greatly improved. As a result, studying the characteristics of smart grids has become important. To accurately repr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10456903/ |
| _version_ | 1797217395688865792 |
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| author | Muhammad Irfan Abdelrahman B. M. Eldaly Rizwan Qureshi Muhammad Bilal Muhammad Shehzad Hanif |
| author_facet | Muhammad Irfan Abdelrahman B. M. Eldaly Rizwan Qureshi Muhammad Bilal Muhammad Shehzad Hanif |
| author_sort | Muhammad Irfan |
| collection | DOAJ |
| description | Smart grid power networks are essential for addressing the global energy crisis and combating climate change. In the past few decades, information and communication infrastructure have greatly improved. As a result, studying the characteristics of smart grids has become important. To accurately represent the connectivity of different components in power networks, we need precise models. In this study, we introduce a new growth model called PowerX. This model is designed to capture the characteristics of real-world power networks. PowerX is a growth model that is designed to capture the characteristics of real-world power networks by incorporating both random and ordered elements. Specifically, it is designed to accurately capture power networks’ degree distribution and clustering coefficient. To assess the effectiveness of PowerX, we compared it with existing growth models such as Watts Strogatz Small World model, Henneberg’s model, and Modified Henneberg’s model, using the US Western States Power Grid dataset consisting of 4789 nodes and 5571 edges. Our results show that PowerX precisely captures the degree distribution of the real dataset, and its clustering coefficient is close to the actual dataset, outperforming the other comparable models. In addition, we used Gephi to demonstrate the features of the Western States power grid, including identifying the most important node of the network, community structure, and the strongest and weakest nodes. This research provides valuable insights into the characteristics of power networks and demonstrates the effectiveness of PowerX in accurately modeling them. The datasets and codes are publicly available for further research at: github.com/irfan2inform/powerX. |
| first_indexed | 2024-04-24T12:01:11Z |
| format | Article |
| id | doaj.art-be7c658648394b3aa330f634ad694ac8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T12:01:11Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-be7c658648394b3aa330f634ad694ac82024-04-08T23:00:49ZengIEEEIEEE Access2169-35362024-01-0112487254873610.1109/ACCESS.2024.337241410456903PowerX: A Probabilistic Graph Model for Complex Smart Grid NetworksMuhammad Irfan0https://orcid.org/0000-0001-9821-7467Abdelrahman B. M. Eldaly1Rizwan Qureshi2Muhammad Bilal3https://orcid.org/0000-0002-6446-8687Muhammad Shehzad Hanif4Faculty of Electrical Engineering, Ghulam Ishaq Khan Institute (GIKI) of Engineering Sciences and Technology, Swabi, PakistanDepartment of Electrical Engineering, City University of Hong Kong, Hong Kong, SAR, ChinaCenter for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, ChinaDepartment of Electrical and Computer Engineering, Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Electrical and Computer Engineering, Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi ArabiaSmart grid power networks are essential for addressing the global energy crisis and combating climate change. In the past few decades, information and communication infrastructure have greatly improved. As a result, studying the characteristics of smart grids has become important. To accurately represent the connectivity of different components in power networks, we need precise models. In this study, we introduce a new growth model called PowerX. This model is designed to capture the characteristics of real-world power networks. PowerX is a growth model that is designed to capture the characteristics of real-world power networks by incorporating both random and ordered elements. Specifically, it is designed to accurately capture power networks’ degree distribution and clustering coefficient. To assess the effectiveness of PowerX, we compared it with existing growth models such as Watts Strogatz Small World model, Henneberg’s model, and Modified Henneberg’s model, using the US Western States Power Grid dataset consisting of 4789 nodes and 5571 edges. Our results show that PowerX precisely captures the degree distribution of the real dataset, and its clustering coefficient is close to the actual dataset, outperforming the other comparable models. In addition, we used Gephi to demonstrate the features of the Western States power grid, including identifying the most important node of the network, community structure, and the strongest and weakest nodes. This research provides valuable insights into the characteristics of power networks and demonstrates the effectiveness of PowerX in accurately modeling them. The datasets and codes are publicly available for further research at: github.com/irfan2inform/powerX.https://ieeexplore.ieee.org/document/10456903/Complex networksHenneberg’s modelgraph modelingpower networkspower systemssmart grids |
| spellingShingle | Muhammad Irfan Abdelrahman B. M. Eldaly Rizwan Qureshi Muhammad Bilal Muhammad Shehzad Hanif PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks IEEE Access Complex networks Henneberg’s model graph modeling power networks power systems smart grids |
| title | PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks |
| title_full | PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks |
| title_fullStr | PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks |
| title_full_unstemmed | PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks |
| title_short | PowerX: A Probabilistic Graph Model for Complex Smart Grid Networks |
| title_sort | powerx a probabilistic graph model for complex smart grid networks |
| topic | Complex networks Henneberg’s model graph modeling power networks power systems smart grids |
| url | https://ieeexplore.ieee.org/document/10456903/ |
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