An analysis of peak demand reductions due to elasticity of domestic appliances
Bibliographic record
Abstract
Unlike prior work on demand management, which typically requires industrial loads to be turned off during peak times, this paper studies the potential to carry out demand response by modifying the elastic load components of common household appliances. Such a component can decrease its instantaneous power draw at the expense of increasing its duration of operation with no impact on the appliance's lifetime. We identify the elastic components of ten common household appliances. Assuming separate control of an appliance's elastic component, we quantify the relationship between the potential reduction in aggregate peak and the duration required to complete the operation of appliances in four geographic regions: Ontario, Quebec, France and India. We find that even with a small extension to the operation duration of appliances, peak demand can be significantly reduced in all four regions both during winter and summer. For example, during winter in Quebec, a nearly 125 MW reduction in peak demand can be obtained with just a 10% increase in appliance operation duration. We conclude that exploiting appliance elasticity to reduce peak power demand should be an important consideration for appliance manufacturers. From a policy perspective, our study gives regulators the ability to quantitatively assess the impact of requiring manufacturers to conform to "smart appliance" standards.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".