Review of residential time-varying rate pilot projects and programs: effects in winter
Bibliographic record
Abstract
With the advent of smart meters that record electricity demand and use at intervals over each day, it is now possible for utilities to apply different rates for electricity used at different times of day. These are called time-varying rates (TVR) or time-of-day (TOD) rates. The purpose of charging different rates is to shift electricity use from times with high demand to those with lower demand, as a way to reduce peaks in demand, provide grid stability, and reduce costs. This report summarizes the public literature concerning the effects of TVR on electricity demand and consumption in winter, using reports from Canada and around the world. The general trends are that TVR rates almost always result in a reduction in peak demand and sometimes a small conservation effect. The higher the ratio between the peak and off-peak rates, the higher the peak demand reduction. Protection might be needed for low-income customers for whom the risk of a TVR-related bill increase might result in electricity charges that are a heavy burden. Program design, customer education, and feedback all influence the success of TVY implementation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".