Dataset for DESTinE

Citation Author(s):
Khandaker Akramul
Haque
Texas A&M University
SHINING
SUN
Texas A&M University
XIANG
HUO
Texas A&M University
ANA
GOULART
Texas A&M University
KATHERINE
DAVIS
Texas A&M University
Submitted by:
Khandaker Akram...
Last updated:
Sun, 03/02/2025 - 12:29
DOI:
10.21227/m1qf-f385
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Abstract 

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 fasterthan-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 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. 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.

Instructions: 

 

The dataset is available in both NetworkX GML and Python pickle formats and can be utilized with the Discrete Event Simulation Tool for Analysis of Energy Systems (DESTinE) to evaluate its performance in simulating large-scale cyber-physical systems.

Funding Agency: 
DOE and NSF
Grant Number: 
DE-CR0000018, 2220347

Comments

Dataset for DESTinE have been added to IEEE dataport on 2 March 2025.

Submitted by Khandaker Akram... on Sun, 03/02/2025 - 12:30

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