Fuzzy Optimization Model of an Incremental Capacity Auction Formulation with GHG Consideration
Bibliographic record
Abstract
An incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure future generation capacity that reduces GHG emissions. In this thesis, we propose two ICA models incorporating energy-limited generation (renewables and storage) and a GHG emission constraint. All offers are converted into unforced capacity, negating any effect of energy limitations of generation offers. The first ICA model uses classical optimization and considers GHG emission limits and maximizes social welfare (SW). The second ICA model uses a fuzzy optimization technique to simultaneously optimize the objectives of SW maximization and GHG emission minimization. Both ICA models are tested on two datasets, a 10 and 338 capacity supply offer case which are constructed using Ontario data. While both models control GHG emissions as desired, the ICA model with fuzzy optimization is shown to find a better balance between maximizing net SW and minimizing GHG emissions. This is seen by the superior reductions in GHG emissions ranging from 33.36% up to 484.8% at the expense of minor decreases in SW ranging from 0.745% to 6.98%. Results demonstrate how GHG emission reduction results in increased selection of low carbon generation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".