Planning Smart Grid Functions in Residential Loads Using a Virtual Equivalent Battery Storage Unit
Bibliographic record
Abstract
This article develops a method for planning smart grid functions (peak-demand management, direct load control, and demand response) for residential loads. The proposed planning method is developed based on modeling the thermal energy stored in thermostatically controlled appliances (TCAs) as energy charged into a virtual (fictitious) equivalent battery storage unit (VE-BSU) at the distribution transformer that feeds these TCAs. The thermal energy stored in TCAs can reduce their power demands during peak-demand times. These reductions in power demands of TCAs can be modeled as discharging energy from the VE-BSU. The energy charged and discharged by the VE-BSU can be used to plan smart grid functions to maximize storing thermal energy in TCAs during off-peak-demand times (short time horizons). This feature is due to the ability to store thermal energy in TCAs, and completely use it over short time intervals. The VE-BSU-based planning method is implemented and tested for residential loads fed by five different distribution transformers. Test results demonstrate the ability of the proposed method to plan effective and stable actions of smart grid functions, with minor sensitivity to the number of residential loads and/or seasonal variations in their power demands.
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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) | 0.001 | 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".