Multi‐objective stochastic optimal power flow considering voltage stability and demand response with significant wind penetration
Bibliographic record
Abstract
In this study, a multi‐objective stochastic optimal power flow (SOPF) problem with the presence of uncertain wind power generations is introduced. In particular, this study has two main contributions. First, it proposes a multi‐objective SOPF which consists of the operating cost, voltage stability and emission effects as the objective functions. The wind uncertainty is formulated as a scenario‐based technique. Demand response program is considered in this study, which is one of the most efficient control ways to reduce the risk of voltage instability after a contingency occurrence or a stressed loading condition. In addition, the proposed approach uses the technique of fuzzification to normalise all objective functions and to find the optimal solution. The second contribution proposes a line voltage stability index (LVSI). The proposed LVSI can detect precisely the voltage collapse in comparison with other LVSIs, especially after the occurrence of a given contingency due to the dynamic elements of the system. The proposed multi‐objective SOPF is also carried out with different existing LVSIs as the objective functions. These approaches are tested and validated by the modified WECC test system, the IEEE 39‐bus.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".