EV Assisted Robust Security Constrained ACOPF for N-1 Line and Generator Contingencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it