Adaptive Feasibility Area Estimation to Enhance Cybersecurity of Electrolysis-Based Hydrogen Refueling Stations Integrated With Power Distribution Systems
Bibliographic record
Abstract
This paper introduces a novel cyberattack-resilient model designed for the optimal operation management of electrolysis-based hydrogen refueling stations (eHRSs) integrated with electric power systems. The optimization model aims to coordinate the scheduling of eHRSs to concurrently support both the transportation sector and the electric power utility. This includes fulfilling the hydrogen demand of electric mobility systems (e-Mobility) and enhancing the resilience of the electric grid by following ancillary service signals issued by the grid operator. Adaptive feasibility areas (FAs) are estimated using the operating parameters of the integrated transportation and power system to identify potential cyberattacks. A framework is developed wherein dispersed eHRSs are managed by an eHRS chain aggregator. The operating parameters of eHRSs are communicated between the individual stations and the eHRS chain aggregator. Additionally, the eHRS chain aggregator interfaces with the electric power utility operator to address the utility’s requirements. Various scenarios are modeled to assess the technical and financial impacts of cyberattacks on eHRS. The proposed model is employed to detect false data injection attacks and mitigate the adverse effects of cyberattacks on the integrated transportation and power system. Simulation studies are conducted to evaluate the effectiveness and practicality of the proposed model. The performance of the FA-based method is compared with traditional deep neural network models and data-driven methods, demonstrating 13.5% and 8.68% improvements, respectively in detection accuracy. In addition, the proposed model achieves a 19% reduction in training time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".