Modeling The Revenue of Wind Farms and Applications to Optimizing the Location of Wind Energy Development
Bibliographic record
Abstract
The goal of this thesis is to model wind farm revenue in Alberta and apply this model to inform optimal wind energy development decisions. The analysis begins by constructing a revenue model for existing wind farms using historical data from Alberta’s electricity market. A linear regression is then used to relate wind farm revenue to weather resource variables. This relationship is embedded within a bi-level optimization framework, which is used to study theoretical capacity allocation examples and examine the effect of spatial correlation on revenue outcomes. In the final part of the thesis, a potential mean field game approach is introduced to identify optimal locations for future wind farms. This model is based on historical data from AESO and ACIS and is used to evaluate the effect of policy on wind development. The results show that a high capacity factor increases revenue, while high covariance between sites reduces it. The bi-level framework highlights how correlation can impact capacity placement. The mean field game model identifies optimal locations under policy constraints and reveals how current policy affects revenue, emissions, and the overall path to decarbonization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".