Evaluating Representative Days Selection for Capacity Planning with Variable Renewable Options
Bibliographic record
Abstract
Capacity planning models are necessary to study pathways to meeting decarbonization goals and meeting load growth. As these models are computationally costly, representative days are frequently used to reduce the problem size. The choice of representative days becomes even more important as variable renewable energy sources like wind and solar are increasingly becoming a part of the electricity mix. Ultimately, the set of selected days will vary based on the method used and number of days chosen. In turn, the outputs of the planning model can vary based on the set of days used - this work focuses on quantifying this variation. First, this paper introduces some existing methods for set selection, weighing additional considerations required when addressing variable renewables and regional variations. A series of metrics is then proposed for comparing the outputs of the planning models from different representative day sets. Finally, they are applied, in PyPSA, to Canada’s Maritime provinces as a $\mathbf{2 5}$-year grid planning problem and compared to a 365-day approach. Results indicate that there are a wide range of near-optimal solutions with contrasting generation mixes, and that capacity planning would be better informed by generating results over multiple sets of representative days, rather than a single set.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".