One-Node Method to Implement Smart Grid Functions Using a Battery Storage System
Bibliographic record
Abstract
This paper develops and tests the performance of the one-node method for implementing aggregated smart grid functions to operate residential loads. The developed method is based on utilizing a battery storage system (BSS) at the point-of-supply feeding the target residential loads. The power ratings of the BSS can be selected based on the required reduction in the load power demands during the peak-demand hours. Furthermore, the charging and discharging of the BSS are set based on peak-demand and off-peak demand hours and stability constraints for voltage and frequency at the point-of-supply. The proposed operation of the BSS is set to have it charge energy during off-peak-demand hours, and discharge energy during peak-demand hours. The energy charge into the BSS can be viewed as the equivalent thermal energy storage in thermostatically controlled appliances (TCAs), when operated using smart grid functions. The one-node with a BSS method is implemented and tested for a university campus that has 45 buildings, which are fed from one substation (point-of-supply) through a distribution system. Tests are conducted for the Summer and Winter seasons to demonstrate the efficacy of the developed method. Test results demonstrate accurate adjustments of power demands during peak-demand and off-peak-demand hours, along with simple structure to operate the BSS. Moreover, test results show the ability of the one-node method to reduce power losses and improve the voltage at the point-of-supply during peak-demand hours.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".