Distributed energy storage unit-based active demand response for residential loads
Bibliographic record
Abstract
This paper presents the development and testing of an active demand response (DR) for residential loads (RLs). The proposed DR is developed to mimic the industrial DR, which is based on injecting power to the grid during peak-demand times. The power injection in the proposed DR is achieved by discharging distributed energy storage units (DESUs) that are charged during off-peak-demand times. The desired DESUs are interconnected at distribution transformers that feed RLs. The injection of power during peak-demand times is aimed to reduce the grid power delivery during peak-demand hours. The DESU-based DR is implemented for performance evaluation using sets of data collected from several RLs during different seasons. Performance results show that the developed DR can be operated to offset the grid power delivery by more than 70% of RL power demands during peak-demand times. In addition, performance results demonstrate that the DESU-based DR is independent from the patterns of RL power consumption and/or number of customers participating in energy saving programs. The encouraging performance of the DESU-based DR supports its application to implement smart grid functions for RLs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".