Stochastic unit commitment in smart grid communications
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
There is growing interest in renewable energy resources and smart grid. Since most renewable sources are highly intermittent, they can induce significan fluctuation on the supply side of the power grid. On the other hand, the use of smart meters and smart appliances in the smart grid can cause significan uncertainties on the demand side as well. Unit commitment scheduling of power generation systems is an important issue in smart grid communications to coordinate energy demand and generation. In this paper, we study the stochastic unit commitment problem in smart grid communications. Hidden Markov models (HMMs) are used for renewable energy resources. The stochastic power demand loads are modeled by a Markov-modulated Poisson process (MMPP). We show that, under reasonable conditions on the smart grid, structural results can be derived for the unit commit problem, which make the solution practically useful. Simulation results are presented to show the effectiveness of the proposed schemes.
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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