Cost optimization via rescheduling in smart grids — A linear programming approach
Bibliographic record
Abstract
The smart grid opens the possibility for Demand Response (DR) programs. In order for end-users to obtain the maximal benefit from DR programs, low priority load should be shifted from the high energy price periods or should be operated at reduced power levels. To that end, this paper proposes a linear programming model which reschedules the household appliances at relatively lower energy price periods to minimize the total energy cost in a day. Since the rescheduling of a task creates inconvenience to the users, the proposed model considers this inconvenience as disutility and models it as a function of delay. The constraints related to different power consumption patterns of different loads and the constraints imposed by the utility have been considered to effectively model the appliance power consumption. An analysis is presented to evaluate the influence of different parameters on the model. Finally, a comparison to previous research is provided which shows that our model is as effective as others with a simple model description and extended features.
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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".