A Distributed Demand Response Control Strategy Using Lyapunov Optimization
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Bibliographic record
Abstract
Motivated by the potential ability of heating ventilation and air-conditioning (HVAC) systems in demand response (DR), we propose a distributed DR control strategy to dispatch the HVAC loads considering the current aggregated power supply (including the intermittent renewable power supply). The control objective is to reduce the variation of nonrenewable power demand without affecting the user-perceived quality of experience. To solve the problem, first, a queueing model is built for the thermal dynamics of the HVAC unit based on the equivalent thermal parameters (ETP) model. Second, optimization problems are formulated. Based on an extended Lyapunov optimization approach, a control algorithm is proposed to approximately solve the problems. Third, a DR control strategy with a low communication requirement is proposed to implement the control algorithm in a distributed way. Finally, practical data sets are used to evaluate and demonstrate the effectiveness and efficiency of the proposed control algorithm.
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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 | 0.000 | 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 it