Generation reliability assessment of stand-alone hybrid power system — A case study
Bibliographic record
Abstract
Many rural communities are not able to access the main power grid supply due to high cost associated with grid extension. The stand-alone renewable energy based hybrid power system is a significant adaptation to cope with increasing power demand along with environmental consideration in rural communities. In such a system, determining the correct size and capacity of renewable generating units to supply continuous and reliable electricity is of great importance. For this purpose, the reliability of a standalone renewable energy based hybrid generation system is evaluated in this paper to meet the load demand of a residential building located in St. John's, Newfoundland and Labrador (NL), Canada. A probabilistic reliability evaluation approach, Monte Carlo simulation technique is adopted to compute the reliability index, Loss of Load Probability (LOLP) utilizing the generation models, renewable resources data and load demand data for a whole year. The main advantage of this technique over the deterministic approaches is its ability to provide quantitative reliability assessment taking the actual system behavior into consideration. The system reliability is evaluated based on LOLP values, which are computed considering different combinations of renewable energy mixtures in the generation system. In this paper, three dominant renewable energy resources, solar, wind and hydro, are taken into consideration, and the most reliable energy mixture is determined. Furthermore, system reliability is assessed considering variation in total generation capacity. This type of analyses will be useful for the system designers to determine total capacity and optimum sizing of the system before installation.
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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) | 0.000 | 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 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".