Optimum generation scheduling incorporating wind energy using HHO–IGWO algorithm
Bibliographic record
Abstract
Abstract Recently, renewable energy participation is gaining importance in the existing power system. However, the large penetration of these renewable energy sources into the existing power system network may cause an imbalance in supply and demand response. Unit commitment is the decision-making process in which generating units are turned ON and OFF at the hourly interval as per the load demand under certain constraints to provide economic scheduling. Thus, an advanced intelligent approach is needed to cope with this combined unit commitment problem with a large penetration of intermittent sources. This paper offers the solution to optimal scheduling by implementing the hybrid Harris Hawks optimizer algorithm (HHO–IGWO). Standard IEEE systems with 10-, 19-, 20-, and 40 units are simulated. Further, to test the feasibility and effectiveness of the proposed method, a comparative analysis for a 10-, 20-, and 40-unit system has also been performed with penetration. The comparative analysis reveals that proposed is more efficient in tackling unit commitment problem in the presence of wind as renewable energy source.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".