An Appropriate Wind Model for Wind Integrated Power Systems Reliability Evaluation Considering Wind Speed Correlations
Why this work is in the frame
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Bibliographic record
Abstract
Adverse environmental impacts of carbon emissions are causing increasing concerns to the general public throughout the world. Electric energy generation from conventional energy sources is considered to be a major contributor to these harmful emissions. High emphasis is therefore being given to green alternatives of energy, such as wind and solar. Wind energy is being perceived as a promising alternative. This source of energy technology and its applications have undergone significant research and development over the past decade. As a result, many modern power systems include a significant portion of power generation from wind energy sources. The impact of wind generation on the overall system performance increases substantially as wind penetration in power systems continues to increase to relatively high levels. It becomes increasingly important to accurately model the wind behavior, the interaction with other wind sources and conventional sources, and incorporate the characteristics of the energy demand in order to carry out a realistic evaluation of system reliability. Power systems with high wind penetrations are often connected to multiple wind farms at different geographic locations. Wind speed correlations between the different wind farms largely affect the total wind power generation characteristics of such systems, and therefore should be an important parameter in the wind modeling process. This paper evaluates the effect of the correlation between multiple wind farms on the adequacy indices of wind-integrated systems. The paper also proposes a simple and appropriate probabilistic analytical model that incorporates wind correlations, and can be used for adequacy evaluation of multiple wind-integrated systems.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it