One-Node Approach to Implement Smart Grid Functions without Storage Units
Bibliographic record
Abstract
This paper presents and tests a one-node method for implementing smart grid functions to operate residential loads. The proposed method is developed based on adjusting the power demands of residential loads to achieve a desired load-demand profile at the supply node. A desired load-demand profile is set based on operating thermostatically controlled appliances (TCAs) in the target residential loads. Smart grid functions are implemented to operate TCAs so that thermal energy is stored during the daily off-peak-demand hours. This stored thermal energy is discharged during daily peak-demand hours in order to reduce power demands of residential loads during these hours. The command power assigned to each TCA controller (set to implement smart grid functions) is initiated using a modified-profile for residential load hosting these TCAs. The one-node method is implemented and tested for a university campus that has 45 buildings. Each building has central central heating units and water heaters, and some buildings have central air conditioner units. Tests are performed for different seasons, where power demands of campus buildings are controlled by peak-demand management (as a smart grid function). Test results show the accuracy and simplicity of the one-node method to assign command values for each building to ensure reduced power losses and improved voltage.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".