Reliability Focused and Market Driven Growth of Wind Power in Electric Power Systems
Bibliographic record
Abstract
Electric power generation from renewable sources has received considerable attention due to environmental concerns. Recent technological developments in wind turbines have resulted in large scale applications in power systems. The rapid growth of wind power has been backed by different forms of financial incentives throughout the world. Long-term growth of wind power should, however, be driven by sustainable market mechanisms. Environmental benefits can be used to the advantage of the renewables to compete with the less costly conventional power sources. Assigning monetary value to the environmental benefits and specifying targets for their growth have been recognized as a potential solution. This paper presents a probabilistic method to evaluate the renewable energy credit and its impact on wind penetration and adequacy of power generating systems. The technique incorporates reliability and economic analyses and is applied to published test systems to illustrate the results and their influence on key system variables. The paper provides useful information to system planners and policy makers of wind power generation.
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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 | 0.000 | 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) | 0.000 | 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 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".