Incorporating Well-Being Considerations in Generating Systems Using Energy Storage
Bibliographic record
Abstract
The increasing utilization of wind and solar energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. Most utilities use deterministic criteria in the planning and design of these systems. The main disadvantage of deterministic criteria is that they do not recognize and reflect the inherent random nature of the site resources, the system behavior, and the customer demands etc. Probabilistic techniques can be used to overcome this drawback and incorporate the inherent uncertainty in these factors. Power system planners and designers sometimes experience difficulties in interpreting and using probabilistic reliability indices. This difficulty can be alleviated by incorporating deterministic considerations into a probabilistic evaluation using the well-being concept. A simulation technique is presented in this paper which extends the conventional well-being approach to generating systems using energy storage. The proposed technique is illustrated and applied in this paper to several small stand-alone power systems. The effects on the system well-being of some of the major system parameters and the deterministic criteria are illustrated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".