Loads Mismatch and Network Voltage Behavior for Future Planning of Demand Response With Customer Satisfaction
Bibliographic record
Abstract
Thermostatically controlled loads (TCLs) aggregated into a generalized virtual battery (VB) offer a systematic approach to optimally manage such devices. However, challenges arise when operational conditions are oversimplified or overlooked, leading to a mismatch between expected and actual outcomes. This paper delves into the demand response mismatch (DRM) challenge within the context of peak load management, exploring the implications of power reductions stemming from external management and aggregator control. A customer satisfaction index has been introduced to assess the impact of such reductions on customer comfort. A comprehensive VB model is employed to govern the aggregator, ensuring adherence to all operational constraints. The DRM percentage is evaluated under both standard operating conditions and peak shaving strategies. The study also investigates the different types of customer discomfort according to device operation and cost. A predictive analysis of the percentages of load DRM serves as a valuable tool for future peak shaving planning. This research further analyzes the impacts of voltage variation on a distribution network that ends with customer loads.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".