Reliability evaluation of integrated wind/diesel/storage systems for remote locations
Bibliographic record
Abstract
One of the most promising applications for wind energy is its use in electric power systems for remote isolated locations. Currently most remote and isolated communities depend on conventional diesel fuel for their electricity supply. Diesel generations in these locations are expensive mainly due to the escalating fuel costs. The associated maintenance costs and transportation costs are also relatively high in many areas. On the other hand, the wind speed in those remote areas is usually fairly high and hence wind energy has huge potential. Wind energy based systems have no fuel cost and can, therefore, be included in these conventional small isolated systems in order to replace the costly diesel fuel by renewable energy. Wind power generation is, however, intermittent in nature, and therefore, energy storage systems are often considered to smooth out the fluctuations and improve the supply continuity. The energy available for storage, and the stored energy that can be used at any time is highly dependent on the system operating constraints. In this paper, some of these important constraints are incorporated in a sequential Monte Carlo Simulation technique for the adequacy evaluation of integrated wind/diesel/storage systems for remote locations. The impact of wind and energy storage on integrated wind/diesel/storage system reliability performance is examined. Potential problems associated with utilization of wind energy and energy storage in small isolated systems are also discussed.
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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) | 0.000 | 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) | 0.001 | 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".