Risk‐adjustable stochastic schedule based on Sobol augmented Latin hypercube sampling considering correlation of wind power uncertainties
Bibliographic record
Abstract
Abstract The risks associated with wind power forecast (WF) deviations are of paramount importance to many power system participants (PSPs). However, traditional sampling approaches are computationally prohibitive to model these deviations. Additionally, setting a risk level for satisfying different PSPs receives little attention. This paper constructs a risk‐adjustable stochastic day‐ahead scheduling (RSDS) model to balance the risk requirements of PSPs, and proposes a Sobol‐augmented Latin Hypercube Sampling (SaLHS) approach to improve sampling efficiency for scenario generation process in RSDS. At first, SaLHS and D‐vine copula are combined to generate WF error scenarios for RSDS considering correlations of wind farms. Specifically, SaLHS improves the uniformity and removes the correlation of random samples. Then, a Glue‐VaR‐based generation adequacy index (GVGAI) is proposed to measure operational risk. By adjusting the parameters of GVGAI, a desirable risk level can be obtained considering requirements of different PSPs. Furthermore, a multi‐objective RSDS model is constructed considering operational cost and GVGAI. At last, an entropy‐Weighted Aggregated Sum Product Assessment method is proposed to find the best compromise solution for RSDS model based on the Pareto front obtained by an ε‐constraint method. A modified IEEE‐RTS system is used to validate the effectiveness of proposed method via numerical simulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".