Accommodating large amounts of variable generation in North America
Bibliographic record
Abstract
To accommodate higher penetrations level of variable generation, changes will be required to the traditional methods used by system planners and operators in order to maintain ongoing bulk power system reliability. While the focus of this paper is on the integration of wind generation, the conclusions and recommended actions may also apply to the integration of all types of variable generation technologies. In 2006, natural gas-fired generation produced 20% of the electricity in the United States while representing 41% of the installed summer generating capacity. Coal-fired generation produced 49% of the electrical energy in North America and represented 32% of the installed summer capacity. Heavy and light oil is primarily used as a back-up fuel for natural gas. Fossil fuels are nonrenewable, that is, they draw on finite resources. In contrast, renewable energy resources - such as wind, solar, ocean, biomass, hydro, etc. can be replenished at a generally predictable rate. Government policy is the key driver for renewable energy expansion in the US and Canada. For example, over 50% of (non-hydro) renewable capacity additions in the US from the late 1990s through 2007 have occurred in states with mandatory Renewable Portfolio Standards. The proposed level of commitment to renewables offers many benefits as well as certain challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".