A Practical Real-Time OPF Method Using New Triangular Approximate Model of Wind Electric Generators
Bibliographic record
Abstract
Near-term output forecast of wind electric generators (WEG) has uncertainties. Optimal power flow (OPF) schedules that consider forecasted output of WEGs carry risk due to these uncertainties. This risk can be quantified as expected energy not served (EENS). Traditional methods such as Monte Carlo simulation (MCS) with OPF can capture the stochastic nature of WEG output while considering ac transmission system constraints to simultaneously minimize risks from EENS and total operating costs, analyze influence of wind variability on total costs, correlate reserves and wind variability, etc. However, the MCS technique is extremely time consuming and computationally burdensome. This paper proposes a new triangular approximate distribution (TAD) model that very closely represents the normal probabilistic distribution function of forecasted wind speed to capture stochastic information of WEG output forecast and quantify EENS. This TAD model is used to formulate the proposed OPF method considering ac transmission systems to: 1) simultaneously minimize risk due to EENS and total operating costs and 2) analyze the impact of wind variability on system parameters such as EENS, operating costs, location marginal prices, and reserve costs. Tests on IEEE test systems reveal that the proposed method is accurate, fast, and suitable for real-time use.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".