Multiagent Stochastic Simulation of Minute-to-Minute Grid Operations and Control to Integrate Wind Generation Under AC Power Flow Constraints
Bibliographic record
Abstract
The variability and uncertainty inherent to wind generation, which is rapidly increasing, could significantly impact operations efficiency in the future, particularly frequency regulation reserves. This paper addresses these issues from both analytical and curative standpoints through operational impact studies which combine a transmission grid representation with a distributed agent-based control architecture that mimics industry organization charts and follows NERC reliability management rules. As simulating a control area over many years of recorded historical operating conditions is a massive computation problem, the new scheme uses distributed computing with 228 computing nodes to maintain a reasonable simulation time. The simulator-derived automatic generation control and load following generated with a data set characterizing 3000 MW of wind generation integrated in the Québec interconnection were compared with statistical analysis-based results. The results of the simulation of a full year of minute-by-minute operations suggest that wind integration will increase the number of generating unit start-ups and shut-downs by approximately 5%. The simulator was also able to estimate import/export opportunity losses, as well as several impacts related to voltages and reactive power attributable to increased wind penetration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".