Managing Uncertainty of Wind Energy With Wind Generators Cooperative
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".