An Efficient Data-Driven Model for Generation Expansion Planning with Short-Term Operational Constraints
Bibliographic record
Abstract
Generation expansion planning models have been useful aids for long-term planning in electricity markets. Recent growth in intermittent renewable generation has increased the need to model non-renewable responses to rapid changes in daily loads, leading research to incorporate unit commitment features into generation expansion models. Such combined models usually contain discrete variables which, along with many details, create long computation times that may take days or weeks. This is impractical for analysts who need to develop, debug, modify and use the model for many alternative runs. We propose a novel data-driven generation capacity planning model, in which generation is aggregated by technology type using clustering-based machine learning techniques. The model includes only the minimal unit commitment constraints necessary to integrate the operational requirements and long-term capacity planning. The variations in generation required to respond to rapid demand fluctuations are estimated from historical data, specifically as maximum rates of change for each generation type. We develop our data-driven model using the data from the province of Ontario, Canada. The developed model is a large-scale linear program that can solve problems in less than one hour on modest computing equipment with credible results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".