Data-Enabled Modeling and PMU-Based Real-Time Localization of EV-Based Load-Altering Attacks
Bibliographic record
Abstract
Recently, the integration of electric vehicles (EVs) and their associated electric vehicle supply equipment (EVSE) has increased significantly in smart grids. Due to the cyber vulnerabilities of this EV ecosystem, such integration makes the entire grid prone to cyberattacks, which can result in the outage of generators or even blackouts. On this basis, this paper leverages a data-enabled predictive attack model (DeeP-AM) to design an EV-based dynamic load-altering attack (EV-DLAA) that targets the frequency stability of the grid. Subsequently, a robust localization framework for the developed EV-DLAAs is proposed using power system measurements obtained from phasor measurement units (PMUs). First, frequency measurements in the transmission grid are used to develop an optimal EV-DLAA without having perfect knowledge of the grid’s topology. Such an optimal attack model ensures that the targeted frequency deviation is reached by utilizing the least number of EVs and a minimized time of instability (ToI). Then, a PMU-based real-time localization framework is developed based on the sparse identification of nonlinear dynamics (SINDy) method, which simultaneously estimates the magnitude and location of EV-DLAAs. The equations obtained from the SINDy method are effectively solved using a modified basis pursuit de-noising (MBPDN) approach. This approach enhances the accuracy and robustness of the localization framework, particularly when confronted with noise. The attack implications and the localization performance are evaluated using the New England 39-bus and Australian power systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".