Demand response potential of water heaters to mitigate minimum generation conditions
Bibliographic record
Abstract
During periods of low electricity demand, particularly when demand drops below baseload supply levels, a system operator can encounter difficulties in efficiently dispatching its generating units. Referred to as `minimum generation conditions (MGC),' these states are troublesome because they can lead to increased greenhouse gas emissions, depressed electricity prices, or additional barriers to renewables integration. This work explores the potential of using electric water heaters (EWHs), in a demand response (DR) role, to mitigate the number and severity of these MGCs. A detection method for finding MGCs is first applied to the system in Ontario, Canada. At 2018 renewables target levels, it was found that most MGCs would occur in the early morning of spring and fall. To significantly address this issue next generation EWHs employing DR would need a deadband of 10°C to enable the 800+MW required.
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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) | 0.001 | 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 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".