Influence of wind power on hourly electricity prices and GHG emissions: Evidence that congestion matters from Ontario zonal data
Bibliographic record
Abstract
With the growing share of wind production, understanding its impacts on electricity price and greenhouse gas (GHG) emissions becomes increasingly relevant, especially to design better wind-supporting policies. Internal grid congestion is usually not taken into account when assessing the price impact of fluctuating wind output. Using 2006-2011 hourly data from Ontario (Canada) , we establish that the impact of wind output, both on price level and marginal GHG emissions, greatly differs depending on the congestion level. Indeed, from a 3.3% price reduction when wind production doubles, the reduction jumps to 5.5% during uncongested hours, but is only 0.8% when congestion prevails. Similarly, avoided GHG emissions due to wind are estimated to 331.93 kilograms per megawatt-hour (kg/MWh) using all data, while for uncongested and congested hours, estimates are respectively 283.49 and 393.68 kg/MWh. These empirical estimates, being based on 2006-2011 Ontario data, cannot be generalized to other contexts. The main contribution of this paper is to underscore the importance of congestion in assessing the price and GHG impacts of wind. We also contribute by developing an approach to create clusters of data according to the congestion status and location. Finally, we compare different approaches to estimate avoided GHG emissions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".