Comparing Photovoltaic Capacity Value Metrics: A Case Study for the City of Toronto
Bibliographic record
Abstract
Abstract Hourly electric power demand data in Toronto from 2000 to 2006 was analyzed along with coincident, simulated hourly photovoltaic (PV) power generation to quantify PV capacity value on a year‐round basis. Three different methods commonly employed by electric utilities were used to assess PV capacity value, and their results were compared. The first method is the Garver approximation to effective load carrying capability (ELCC), which served as a benchmark for capacity value. The other two methods equate PV capacity value with the capacity factor during “peak demand intervals”: for method 2 the interval includes all hours with loads within a given per cent deviation from the peak load; for method 3, a fixed “on‐peak” interval of 11–17 h in June–August is used. Methods 2 and 3 yielded PV capacity values of about 40%, in agreement with the results of the Garver approximation at low grid penetration. This is considerably higher than the yearly PV capacity factor of about 12%, and is in good agreement with previous studies. Capacity value varies significantly from year to year: for instance, values from method 1 at low grid penetration levels range from 30% (year 2000) to 44% (year 2006). Yearly variations in capacity value appear correlated with variations in the demand summer to winter peak ratio, reflecting the fact that PV capacity value is strongly linked to its capacity to reduce peak demand (“peak shaving”) during the summer. Copyright © 2008 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".