Electric Vehicle-Based Load-Altering Attacks and Their Impacts on Power Grids Operations
Bibliographic record
Abstract
Broad deployment of electric vehicles (EVs) in power grids necessitates the integration of information and communication technologies (ICTs) into the cyber layers of EV ecosystems. However, such integration makes the power grid prone to cyberattacks originating from these ecosystems. This article investigates potential vulnerabilities in the physical and cyber layers that adversaries can exploit to impact the operation of power grids. Several threat models are developed to introduce EV-based load-altering attacks (EV-LAAs) and observe the impact of such attacks on the operation of the two-area Kundur benchmark. To cope with EV-LAAs, model-based and data-driven approaches can be suggested. In data-driven approaches, adequate data are obtained from smart meters and equipment in power grids to train machine learning models to distinguish between healthy and under-attack scenarios. In model-based approaches, estimators and observers are usually developed based on accurate models of understudied systems to estimate attack vectors. The accuracy of the proposed detection methods is not precisely 100%, and several attacks may not be identified due to overlooking the low-probability attack vectors posed by residential EVs. As such, mitigation techniques, e.g., wide-area damping controllers (WADCs), are suggested to ensure that oscillations are controlled following successful EV-LAAs. Finally, dynamic thermal rating (DTR), as a real-time strategy that uses actual conditions, can be integrated into developed detection methods. After identifying cyberattacks originating from EV ecosystems, the DTR can take preemptive actions, e.g., rerouting power, adjusting line ratings, or disconnecting infected feeders in distribution networks, to mitigate the impact of such attacks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".