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

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.