EV Assisted Robust Security Constrained ACOPF for N-1 Line and Generator Contingencies
Bibliographic record
Abstract
The optimal power flow provides an economically optimal solution under normal conditions. However, when subject to disturbances, this system may fail to operate securely leading to serious consequences. Security constrained optimal power flow (SCOPF) ensures the reliable operation of the power grid following an outage. Corrective SCOPF uses fast response generating units to ensure feasibility of postcontingency scenarios. Future power grids with high integration of electric vehicles will have huge flexible energy capacities which could be used for ancillary services. Modelling EV behaviour can be challenging due to several uncertain factors affecting its operation and availability. This thesis investigates the potential of using EVs for corrective SCOPF of N-1 contingencies using a robust optimization model. The results show the techno-economic benefit of EV-based post-contingency real and reactive power control. There is cost savings up to 5% and the set level of robustness guides the performance of the model under uncertainty.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".