Bibliographic record
Abstract
In order to address the issue of climate change, our energy systems need to incorporate higher percentages of renewable energy generation. There are, however, unique challenges associated with this task. Although the marginal cost of renewable energy sources is at or near zero, renewables introduce a level of variability into the supply of electricity that is forcing electricity system operators to become more sophisticated in how they operate the electricity system. By more actively managing the demand for electricity through what is called demand response, some of the increased variability in the electricity supply due to higher percentages of renewables might be effectively mitigated by decreasing the variability in the demand for electricity. While various forms of demand response have existed for decades, technological improvements are increasing the usefulness and amount of demand response available to grid operators. This paper models Ontario’s electricity market for the year of 2014 and examines the effect of demand response at various levels of wind penetration in the electricity supply mix. The inclusion of demand response into the model introduces cost savings over the case with no demand response. Demand response decreases the need for conventional peaking sources and reduces the amount of excess electricity generation, making it easier for grid operators to incorporate a higher penetration of renewable energy sources.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".