An evaluation of domestic electric water heaters for frequency control
Bibliographic record
Abstract
Maintaining the frequency of a power system close to its nominal value (50 Hz or 60 Hz) is critical, which comes mainly from generators and flexible loads in traditional power systems. Direct load control (DLC) is a method to control controllable loads for power system optimization. In general, it is used to reduce or shave peak demand. Nonetheless, DLC also can be used to provide frequency control services. Domestic electric water heater (DEWH) is an important kind of controllable load, which takes a high percentage of domestic electric power consumption and has a large thermal energy storage capacity. Hence, DEWH can be a prime candidate for DLC. This study proposes a framework to provide frequency control service with DEWHs. A virtual battery pack system (VBPS) is introduced to be equivalent to the capacity of DEWHs, and a series of measurable indicators are proposed to show the capacity of the VBPS when providing frequency control services. An adaptive criterion is applied to classify controllable DEWHs, which helps to maintain end-user comfort. The performances of the proposed frequency control method during normal and contingency conditions are verified through case studies in CYME.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".