Managing Uncertainty of Wind Energy With Wind Generators Cooperative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Power systems around the world have set ambitious targets for renewable energy integration. Several jurisdictions incentivize renewables such as the Feed-in Tariff in Ontario, Canada. These incentives shall eventually run out and renewables would have to competitively sell energy into electricity markets, overcoming uncertainty and variability in their output. This paper proposes a Wind Generators Cooperative (WGC) model for competitive integration of renewables into energy markets, in the future, overcoming challenges posed by their uncertain and variable nature. The proposed model minimizes the effect of uncertainty and maximizes returns for wind generators. In the proposed WGC model, uncertainty of the total wind power output is reduced by the smoothing effect and using pumped-hydro facilities. Using these pumped-hydro facilities, WGC stores wind energy produced during low marginal price hours and releases it during high marginal price hours. In this paper, a case study with actual data from Ontario, Canada is presented with detailed sensitivity analyses. Analyses clearly demonstrate that the WGC increases returns to wind generators and reduces their exposure to uncertainty. The study and sensitivity analyses show that the WGC model is financially viable and minimizes output uncertainty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it