Probabilistic reliability evaluation for power systems with high penetration of renewable power generation
Bibliographic record
Abstract
In this paper, a probabilistic analytical approach for reliability evaluation of power systems with high penetration of wind and solar photovoltaic (PV) renewable power generation is presented. Due to intermittent nature of wind and solar power, the traditional deterministic method cannot properly address such uncertainties, probabilistic methods need to be utilized. In this paper, the loss of load method, one of the most effective probabilistic analytical methods, is adopted. The generation model is represented by the capacity outage probability table (COPT). The load model is presented by the load duration curve (LDC). Both generation and load models are used to obtain the system reliability considering wind and PV sources with certain forced outage rate (FOR). Simulation results are obtained using MATLAB. It is found that renewable energy sources can significantly improve the system reliability, but not as good as conventional power generators with the same rated capacity due to the actual reduced capacity value of renewable energy sources caused by intermittency. This analytical analysis is useful for the power system planner to quantify reliability improvements by installing grid-connected hybrid renewable power generation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".