Electrical grid peak reduction with efficient and flexible automated demand response scheduling
Bibliographic record
Abstract
As part of the smart grid, demand response (DR) mechanisms can be used by energy providers to encourage consumers to modify their electricity consumption in response to time-varying prices or other incentives. If many homes use automated appliance schedulers to respond to DR signals, consumption peaks can result during times with the lowest electricity prices. The effectiveness of strategies designed to prevent this phenomenon depends on what kind of automated appliance schedulers are being used in homes. In this paper, we assume homes are using a highly flexible appliance scheduler and explore how well two different strategies of reducing these consumption peaks perform. Namely, we consider setting electricity prices to be constant during off-peak times and using prices that increase when the total consumption of the home increases. We discuss how these two peak reduction strategies interact with the scheduler and propose modifications to the scheduler to improve or accommodate the strategies. We then present numerical simulations to show that the daily peak to average ratio (PAR) of the energy consumption of a group of homes using the flexible, sub-optimal scheduler can be reduced significantly by both strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".