Data-driven distributionally robust optimization approach for the coordinated dispatching of the power system considering the correlation of wind power
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
With the increasing penetration of large-scale wind power into the power grid, it is crucial to develop a precise model to accurately depict the stochasticity and correlation among wind farm outputs, which is highly important for ensuring the safe and efficient utilization of wind energy in grid dispatching. In this article, a data-driven distributionally robust optimization (DDRO) dispatching approach that accounts for spatial correlations among outputs from multiple wind farms is proposed. The proposed approach is applied to a source-network-load-storage grid system to ascertain unit start-stop schedules and resource allocation effectively. First, a truncated spatial correlation model is proposed, enabling a comprehensive representation of spatial correlations and output constraints between distinct wind farms . Second, the ISODATA clustering algorithm is employed to generate typical scenarios, reduce model complexity, and expedite the computation process. Third, a unit commitment model considering the demand response is constructed and solved using the DDRO approach. Finally, the proposed model is applied to the IEEE 30-bus system to test its robustness and cost-effectiveness compared to the traditional robust optimization model. Additionally, it is applied to the IEEE 118-bus system to demonstrate its scalability and stability.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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