Power factor‐based scheduling of distributed battery energy storage units optimally allocated in bulk power systems for mitigating marginal losses
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Bibliographic record
Abstract
This study addresses the problem of multi‐objective optimal allocation and management of multiple battery energy storage (BES) units. The multi‐objective genetic algorithm is first used to find the so‐called Pareto front while minimisation of power losses and the total installed capacity of the BES units are simultaneous objective functions. A number of solutions are chosen and developed over one year to find the best schedule for BES utilisation taking into account the power factor (PF) of the charge and discharge modes. Results of studies on the IEEE reliability test system 1996 confirm the existence of an optimal solution for loss reduction. Optimal tuning of the charge and discharge PFs has also proven effective for marginal loss reduction and saving energy every day of the year. Finally, it is shown that the power loss would decrease, even during charge hours, if PFs of BESs are optimally tuned.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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