Evaluating future standards and codes with a focus on high penetration photovoltaic (HPPV) system deployment
Bibliographic record
Abstract
High-penetration photovoltaic (PV) system deployment is becoming a reality in several regions of the United States and the trend toward high penetration levels will continue to rise due to decreasing PV system costs in concert with increasing electric utility rates and societal deliberations. New standards and codes for high-penetration PV deployment must be developed, while some existing standards and codes will need to be revised to accommodate increasing levels of PV deployment. According to a recent industry report, cumulative grid-tied PV capacity in the U.S. grew to 792 MW by the end of year 2008, with a growth rate of 81% for new grid-tied PV installations in 2008 over 2007 and 53% in 2007 over 2006. These rapid growth rates are expected to continue and will be further spurred by the President's energy plan to double renewable capacity in the next three years to help the U.S. concurrently meet its economic, energy security, and environmental challenges. On May 20, 2010, in Denver, Colorado, the National Renewable Energy Laboratory, in conjunction with the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE), held a workshop to examine the key technical issues and barriers associated with high PV penetration levels. Addressing these standards and codes was a major finding of the High Penetration of PV Systems into the Distribution Grid Workshop held in Ontario, California, in February 2009.
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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 | 0.019 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".