Robust optimisation framework for SCED problem in mixed AC‐HVDC power systems with wind uncertainty
Bibliographic record
Abstract
Wind power uncertainties have made the large integration of wind power generating units in the power system highly challenging. One promising solution to overcome the challenges associated with the intermittency of the renewable energy resources (RESs) is to connect areas with diverse renewable energy portfolios via high voltage direct current (HVDC) transmission lines with controllable power transfer capability. The installation of HVDC transmission lines in the power system has resulted in the evolution of conventional alternating current (AC) networks to mixed AC‐HVDC power systems. In this study, to address wind power uncertainties in mixed AC‐HVDC multi‐area power systems, a modified robust optimisation (RO) model for the security‐constrained economic dispatch (SCED) problem is proposed. The proposed RO model is used to minimise the generation cost and wind power curtailment under the worst‐case scenario of actual wind power. Unlike the existing RO models, the proposed RO model considers a modified uncertainty set based on the wind power admissibility and addresses the budget of uncertainty more accurately to adjust the solution's level of conservatism. Extensive numerical studies demonstrate the economic and operational advantages of the proposed RO model for solving the SCED problem in mixed AC‐HVDC power systems with high penetration of RESs.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".