Optimal Planning Framework for Mitigating Cyber-Induced Cascading Failures in Power Grids

The growing digitization of power grids has increased their vulnerability to cyberattacks, potentially leading to widespread power outages. While many studies address cyber-induced outages, resulting cascading failures are often overlooked. This paper introduces a planning framework that employs cyber-physical measures to mitigate these disruptions. It generates a range of cyberattack scenarios, including denial of service (DoS) attack and load alter attacks (LAA) on substations while accounting for uncertainties. These cyberattacks can shift the grid's stable operating point, potentially triggering protection mechanisms and causing cascading failures. The impact of these failures is quantified using a cascading failure model that quantifies the demand not served. The framework integrates cyber-physical measures, including the optimal allocation of cyber reinforcement units (which may include components like phasor measurement units and other cyber-defense mechanisms) and energy storage systems to reduce cascading propagation. The framework's effectiveness in reducing cascading impacts is evaluated using the IEEE 39-bus system.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-26
DOI
https://doi.org/10.5281/zenodo.15089405
Primary Topic
Advanced Clustering Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimal Planning Framework for Mitigating Cyber-Induced Cascading Failures in Power Grids

Zenodo (CERN European Organization for Nuclear Research)
Advanced Clustering Algorithms Research
article

Optimal Planning Framework for Mitigating Cyber-Induced Cascading Failures in Power Grids

article en

Abstract

The growing digitization of power grids has increased their vulnerability to cyberattacks, potentially leading to widespread power outages. While many studies address cyber-induced outages, resulting cascading failures are often overlooked. This paper introduces a planning framework that employs cyber-physical measures to mitigate these disruptions. It generates a range of cyberattack scenarios, including denial of service (DoS) attack and load alter attacks (LAA) on substations while accounting for uncertainties. These cyberattacks can shift the grid's stable operating point, potentially triggering protection mechanisms and causing cascading failures. The impact of these failures is quantified using a cascading failure model that quantifies the demand not served. The framework integrates cyber-physical measures, including the optimal allocation of cyber reinforcement units (which may include components like phasor measurement units and other cyber-defense mechanisms) and energy storage systems to reduce cascading propagation. The framework's effectiveness in reducing cascading impacts is evaluated using the IEEE 39-bus system.

Zenodo (CERN European Organization for Nuclear Research)
Defense Logistics Agency (US), Johns Hopkins University (US), George Mason University (US), Woxsen School of Business (IN), University of Cyprus (CY), University of Petroleum and Energy Studies (IN), Colorado State University (US)
Affordable and clean energy
Openalex Percentile: Top 75%
Advanced Clustering Algorithms Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Optimal Planning Framework for Mitigating Cyber-Induced Cascading Failures in Power Grids · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS