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 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".