The Power-SPRINT project investigated the cybersecurity risks posed by the growing integration of IoT-enabled smart-home appliances on power grid operations (e.g., smart electric vehicle charging stations, heat pumps, etc.). The vulnerabilities in these devices can be used to launch large-scale load-altering attacks (LAAs) that can disrupt the grid's power balance. These threats are nationally significant for the UK (and worldwide) as evidenced by the recent consultation launched by the UK's Department of Energy Security and Net Zero (DESNZ). The project provided a comprehensive framework for LAAs including (i) attack impact analysis, (ii) attack detection, and (iii) attack mitigation. In the following, I summarize the key results and publications from the project.
Whitepaper on Demand-Side Threats to Power Grid Operations
S. Lakshminarayana, C. Maple, A. Larkins, D. Flack, C. Few, and A.K. Srivastava, "Demand-Side Threats to Power Grid Operations from IoT-Enabled Edge", https://arxiv.org/pdf/2310.18820.pdf
Attack Impact Analysis
• The first research direction was aimed toward risk analysis and identifying the "weak points" of the grid. We used the theory of second-order dynamical systems to analyse the power system dynamics when subjected to LAAs. We derived the eigenvalue/vector sensitivities as a function of the attack parameters to identify the victim nodes from which that attacker can launch the most impactful attacks. The result was published in the following paper:
[J1] S. Lakshminarayana, S. Adhikari, and C. Maple, “Analysis of IoT-Enabled Load-Altering Attacks Using the Theory of Second-Order Systems,” IEEE Trans. on Smart Grid, 2021, vol. 12, no. 5, pp. 4415-4425, Sept. 2021.
• Next, we extended the analysis to understand the vulnerability of power systems to LAAs in low-inertia conditions caused due to the increasing penetration of renewable energy resources. We used data from the COVID-19 demand period which provided representative data for low-demand high-renewable energy penetration conditions. This work was a joint effort with colleagues from KAUST, Saudi Arabia, and Los-Alamos National Lab, USA.
[J2] S. Lakshminarayana, J. Ospina, and C. Konstantinou, “Load-Altering Attacks Against Power Grids under COVID-19 Low-Inertia Conditions”, IEEE Open Access Journal of Power and Energy, 2022, vol.9, pp. 226-240, 2022.
• We generalized these results to analyse higher-order models of power systems, which provide a more accurate representation of the real system. We applied the theory of rare-event sampling to identify the distribution of the LAAs (over the victim nodes) that can lead to the activation of emergency responses such as under-frequency load shedding and cascading failures. The results are summarized in the following publications:
[C1] M. P. Goodridge, S. Lakshminarayana and C. Few, "Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach," International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2022, pp. 1-6.
[C2] M.P. Goodridge, A. Zocca, and S. Lakshminarayana, "Analysis of Cascading Failures Due to Dynamic Load-Altering Attacks", IEEE Smartgridcomm 2023, Glasgow, UK.
[J3] M. P. Goodridge, S. Lakshminarayana, and A. Zocca, "Uncovering Load-Altering Attacks Against N-1 Secure Power Grids: A Rare-Event Sampling Approach", https://arxiv.org/abs/2307.08788v1
Attack Detection and Localization
• The second research direction was aimed at detecting and localizing LAAs by monitoring the power grid dynamics using data collected from advanced sensing devices (such as phasor measurement units) and inferring the attack parameters using machine learning algorithms. We used two different approaches – (i) physics-informed neural network (PINN) approach, and (ii) multi-dimensional convolutional neural networks. The latter provided superior localization performances and was robust to data quality issues that are inherent in any monitoring setup. The results are summarized in the following publications:
[J4] S. Lakshminarayana, S. Sthapit, H. Jahangir, C. Maple, and H.V. Poor, “Data-Driven Detection and Identification of IoT-Enabled Load-Altering Attacks in Power Grids”, IET Smart Grid Journal, 5(3), 203 – 218, 2022.
[J5] H. Jahangir, S. Lakshminarayana, C. Maple and G. Epiphaniou, "A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices," in IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10687-10697, June 2023.
Attack Mitigation
• The third research direction aimed at developing a framework to mitigate the destabilising effects of LAAs. To this end, we formulated a framework to compute the optimal dispatch from fast-acting inverter-based resources. This reactive mitigation strategy was devised based on the theory of a distributionally robust optimization approach that accounts for the uncertainty associated with attack detection. This work was a collaborative effort with colleagues from Imperial College, London.
[J6] Z. Chu, S. Lakshminarayana, B. Chaudhuri and F. Teng, "Mitigating Load-Altering Attacks Against Power Grids Using Cyber-Resilient Economic Dispatch," in IEEE Transactions on Smart Grid, vol. 14, no. 4, pp. 3164-3175, July 2023.
Key Conclusions
• Spatial and temporal factors of LAAs are important aspects that determine the attack impact. Efforts to regulate load-controlling organizations (e.g., bring them under the NIS regulations) must take these factors into account.
• Sophisticated attacks such as ramp and oscillatory load variations can have a more grave impact as compared to step change in the load, with a potential to induce power grid failures by manipulating a lower fraction of loads.
• Ongoing efforts to decarbonize the power grid by integrating renewable energy resources on a large scale can result in extremely low-inertia conditions, further exacerbating the vulnerability of grids to LAAs. The silver lining though is that if these resources are used intelligently (e.g., fast-frequency response, provision of synthetic inertia), they may increase the system's uptime when faced with LAAs (see the final conclusion).
• Power grid physical signals can be an important source of information to detect and localize the attacks (in addition to the cyber information). However, more research is needed to address the associated practical issues.
• Given that fully preventing cyber attacks can be extremely challenging, operators must prepare reactive strategies to counter the effects of LAAs. Fast-acting inverter-based resources can be an effective source to mitigate the destabilizing effects of LAAs and increase the system's uptime.