Integrating the Energy Markets in North-America: Conditions Helping Large-scale Integration of Wind Power? By
Bibliographic record
Abstract
The intermittency of wind resources may appear as an important handicap for integrating large-scale wind power in existing grids. However, in Canada and the United States, studies 1,2,3,4,5,6 show that coordination between interconnected power systems can be economically beneficial for both the wind power and electric utility industries if some power system management and market rules are applied. In order to avoid power shortage, our results show that the primary condition helping large-scale integration of wind power is related to changes in market structure rules. If the system is operated in an optimal coordinated manner, on a daily basis for example, the addition of wind power can better optimize the economic operation of the overall system, thereby hopefully increasing revenues from wind power projects, and decreasing costs for the utility and its rate payers. This paper first presents the main elements of the generation optimization model used to compute the benefits of integrating wind power in: a) a hydro-based system (using the largest power system in Canada as a test case; and b) a thermal-based system (using Vermont as a test case). Further, the authors discuss the conditions and rules needed in the generation model to alleviate operational limitations and constraints frequently associated with the intermittent nature of wind. These conditions include market rules, utility management practices, physical constraints like transmission capacities, load profiles, storage characteristics, wind resource patterns, wind power forecasting, and many other aspects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".