Aleatory and Epistemic Uncertainty Considerations in Power System Reliability Evaluation
Bibliographic record
Abstract
There are two fundamentally different forms of uncertainty in power system reliability assessment. Aleatory uncertainty arises because the study system can potentially behave in many different ways. The component failure and repair processes are random and create variability known as aleatory uncertainty. There are also limitations in assessing the actual parameters of the key elements in a reliability assessment. This is known as epistemic uncertainty and is knowledge based and therefore can be reduced by better information. Load forecast uncertainty belongs in this category. Load forecast uncertainty is an important factor in long range system planning and has been shown to have a significant impact on the calculated reliability indices in power system reliability evaluation. Generally, a higher capacity reserve is required in order to maintain a specified level of reliability for an uncertainty load than for a known load. It is important to recognize the differences in aleatory and epistemic uncertainty and appropriately incorporate and appreciate the implications of these uncertainties in system analyses. Two developed Monte Carlo simulation programs including aleatory and epistemic uncertainties are applied in this paper to a study system and the impacts of load forecast uncertainty, wind power and their interactive effects on the system reliability are examined. The basic indices of loss of load expectation (LOLE), loss of energy expectation (LOEE) and the index probability distributions are used to illustrate the effects.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".