On-line loss minimization by HVDC dispatch using OPF in EMS
Bibliographic record
Abstract
In Alberta, two HVDC lines went in service in 2015. While enhancing the flexible control of power flow for system operations, they also introduced new operational challenges at the AESO. One of the challenges is to optimize the MW flow on the two HVDC lines to minimize the MW losses in Alberta power grid. A tool was developed at the AESO to provide HVDC power orders for loss minimization based on off-line studies. The tool calculates the HVDC set points using weighted summation of MW flow of selected AC lines. Different pre-calculated weight sets are selected under different real-time outage conditions. The limitation of this approach is that there is no security validation to ensure the change in HVDC MW flows won't cause additional violations in the Alberta power grid. To overcome the limitation, this paper proposed a new approach of using on-line Optimal Power Flow (OPF) in EMS to determine the HVDC power orders for MW loss minimization and ensure the system operates within security limits. A case study demonstrated the advantages of the proposed on-line OPF approach on loss minimization and mitigation of limit violations. The results from the existing tool are also provided for comparison purposes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".