Load-Altering Attacks Against Power Grids: A Case Study Using the GB-36 Bus System Open Dataset

2025-08-12

AI Summary

The proliferation of high-power IoT devices—such as electric vehicle charging stations—exacerbates cyber-physical attack risks to power grids, particularly load alteration attacks (LAAs) that threaten frequency stability and trigger cascading failures. Method: Leveraging the GB-36 real-world transmission network model published by the UK’s National Electricity System Operator (NESO), this work systematically investigates LAA-induced instability using DIgSILENT PowerFactory simulations integrated with realistic dynamic load models. It quantifies system tolerance thresholds under varying attack locations, time delays, and magnitudes. Contribution/Results: We propose and validate a co-design defense strategy combining optimal battery energy storage system (BESS) placement with fast frequency response (FFR), which significantly mitigates frequency deviations and delays or prevents cascading outages. This study bridges the gap between academic attack modeling and practical grid resilience, delivering actionable, implementation-ready guidance for system operators to enhance cyber-physical security and operational robustness.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

The growing digitalization and the rapid adoption of high-powered Internet-of-Things (IoT)-enabled devices (e.g., EV charging stations) have increased the vulnerability of power grids to cyber threats. In particular, the so-called Load Altering Attacks (LAAs) can trigger rapid frequency fluctuations and potentially destabilize the power grid. This paper aims to bridge the gap between academic research and practical application by using open-source datasets released by grid operators. It investigates various LAA scenarios on a real-world transmission network, namely the Great Britain (GB)-36 Zone model released by the UK's National Electricity System Operator (NESO). It evaluates the threshold of LAA severity that the grid can tolerate before triggering cascading effects. Additionally, it explores how Battery Energy Storage Systems (BESS) based fast frequency response services can mitigate or prevent such impacts. Simulations are conducted using DIgSILENT PowerFactory to ensure realistic system representation. The analysis provides several useful insights to grid operators on the LAA impact, such as the influence of the relative locations of BESS and LAA, as well as how delays in attack execution can influence the overall system response.

Problem

• Investigates Load-Altering Attacks (LAAs) on real-world power grids
• Evaluates grid tolerance thresholds for LAAs before cascading effects
• Explores Battery Energy Storage Systems (BESS) for LAA mitigation

Innovation

• Uses GB-36 bus system open dataset
• Evaluates LAA severity threshold
• Explores BESS for frequency response