Optimal Demand Response for Distribution Feeders With Existing Smart Loads
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Load characteristics play an important role in distribution systems, which are traditionally designed to supply peak load; hence, decreasing this peak can considerably reduce overall grid costs. Basic components of smart grids such as smart meters allow two-way communication between the utilities and customers; in this context, controllable smart loads are being introduced, which allow developing and implementing energy management systems for customers and distribution feeders. Therefore, this paper studies the impact of existing smart loads, in particular Peaksaver PLUS (PS+) loads in ON, Canada, to reduce summer peak loads for distribution feeders. A neural network model of controllable loads is developed and integrated into an unbalanced distribution optimal power flow (DOPF) model to optimally control tap changers and switched capacitors, as well as sent signals to programmable thermostats of air conditioners in residential buildings, in particular those associated with the PS+ program. The developed integrated DOPF is tested and validated using a practical system, demonstrating the benefits of using existing controllable loads to optimally operate distribution feeders.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it