Optimizing Short-Term Hydrothermal Dispatch: A Robust Solution via Artificial Protozoa Optimizer
Bibliographic record
Abstract
Hydrothermal scheduling is a crucial problem in the field of power system aims to minimizing overall generation costs by efficiently distributing the hourly output between hydro and thermal units. However, the presence of uncertainties in both generation types introduces considerable complexity, making the task a challenging non-linear optimization problem. This article presents the Artificial Protozoa Optimizer (APO) to address the hydrothermal scheduling problem. The performance of APO is evaluated using three test systems comprises different hydro and thermal units. The results demonstrate that APO achieves cost reductions of $5.36 \%$, $0.85 \%$, and $0.44 \%$ for test systems 1,2, and 3, respectively, compared to the next best method, the Arithmetic Optimization Algorithm (AOA). Additionally, APO requires less computation time than several existing algorithms in the literature, including the cuckoo search algorithm, Arithmetic optimization algorithm, Evolutionary algorithm and sine cosine algorithm.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 teacher head, 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".