Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and Scalability
Modern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment environment for...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11030558/ |
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| author | Khandaker Akramul Haque Shining Sun Xiang Huo Ana E. Goulart Katherine R. Davis |
| author_facet | Khandaker Akramul Haque Shining Sun Xiang Huo Ana E. Goulart Katherine R. Davis |
| author_sort | Khandaker Akramul Haque |
| collection | DOAJ |
| description | Modern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment environment for cyber-physical analysis including cyber-informed transmission planning, decision-making, and intrusion response. Hence, we present a scalable discrete event simulation tool for analysis of energy systems, called DESTinE. The tool is tailored for large-scale cyber-physical systems, with a focus on power systems. It supports faster-than-real-time traffic generation and models packet flow and congestion under both normal and adversarial conditions. Using three well-established power system synthetic cases with 500, 2000, and 10,000 buses, we overlay a constructed cyber network employing star and radial topologies. Experiments are conducted to identify critical nodes within a communication network in response to a disturbance. The findings are incorporated into a constrained optimization problem to assess the impact of the disturbance on a specific node and its cascading effects on the overall network. Based on the solution of the optimization problem, a new hybrid network topology is also derived, combining the strengths of star and radial structures to improve network resilience. Furthermore, DESTinE is integrated with a virtual server and a hardware-in-the-loop (HIL) system using Raspberry Pi 5. The performance of star, radial, and hybrid topologies is quantified under standalone operation, virtual server integration, and HIL setup to evaluate scalability and network performance. Results are compared for accuracy with the Common Open Research Emulator (CORE). The results show that DESTinE is efficient and scalable for large-scale test cases. These findings highlight DESTinE’s potential for real-time applications in large-scale cyber-physical systems. |
| format | Article |
| id | doaj-art-dff04dfa7e4d4d10a982f9a9786f9dee |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dff04dfa7e4d4d10a982f9a9786f9dee2025-08-20T03:31:21ZengIEEEIEEE Access2169-35362025-01-011310190010192110.1109/ACCESS.2025.357894811030558Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and ScalabilityKhandaker Akramul Haque0https://orcid.org/0000-0002-0111-1568Shining Sun1https://orcid.org/0009-0007-7012-4219Xiang Huo2https://orcid.org/0000-0003-3997-2547Ana E. Goulart3https://orcid.org/0000-0001-7184-7485Katherine R. Davis4https://orcid.org/0000-0002-1603-1122Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USADepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USADepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USADepartment of Engineering Technology and Industrial Distribution, College Station, Texas A&M University, College Station, TX, USADepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USAModern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment environment for cyber-physical analysis including cyber-informed transmission planning, decision-making, and intrusion response. Hence, we present a scalable discrete event simulation tool for analysis of energy systems, called DESTinE. The tool is tailored for large-scale cyber-physical systems, with a focus on power systems. It supports faster-than-real-time traffic generation and models packet flow and congestion under both normal and adversarial conditions. Using three well-established power system synthetic cases with 500, 2000, and 10,000 buses, we overlay a constructed cyber network employing star and radial topologies. Experiments are conducted to identify critical nodes within a communication network in response to a disturbance. The findings are incorporated into a constrained optimization problem to assess the impact of the disturbance on a specific node and its cascading effects on the overall network. Based on the solution of the optimization problem, a new hybrid network topology is also derived, combining the strengths of star and radial structures to improve network resilience. Furthermore, DESTinE is integrated with a virtual server and a hardware-in-the-loop (HIL) system using Raspberry Pi 5. The performance of star, radial, and hybrid topologies is quantified under standalone operation, virtual server integration, and HIL setup to evaluate scalability and network performance. Results are compared for accuracy with the Common Open Research Emulator (CORE). The results show that DESTinE is efficient and scalable for large-scale test cases. These findings highlight DESTinE’s potential for real-time applications in large-scale cyber-physical systems.https://ieeexplore.ieee.org/document/11030558/Scalabilitycyber-physical power systemslarge-scale communication networkdiscrete event simulationgrid resilience |
| spellingShingle | Khandaker Akramul Haque Shining Sun Xiang Huo Ana E. Goulart Katherine R. Davis Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and Scalability IEEE Access Scalability cyber-physical power systems large-scale communication network discrete event simulation grid resilience |
| title | Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and Scalability |
| title_full | Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and Scalability |
| title_fullStr | Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and Scalability |
| title_full_unstemmed | Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and Scalability |
| title_short | Scalable Discrete Event Simulation Tool for Large-Scale Cyber-Physical Energy Systems: Advancing System Efficiency and Scalability |
| title_sort | scalable discrete event simulation tool for large scale cyber physical energy systems advancing system efficiency and scalability |
| topic | Scalability cyber-physical power systems large-scale communication network discrete event simulation grid resilience |
| url | https://ieeexplore.ieee.org/document/11030558/ |
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