Water-Filling Exact Solutions for Load Balancing of Smart Power Grid Systems
Bibliographic record
Abstract
For the demand side management, the elastic power loads can be scheduled to achieve load balancing and to minimize the fluctuation of the overall load. In a process of power supply, the inelastic power loads can be regarded as a group of parameters. Then the demand response can be utilized to realize the optimal allocation of the elastic power loads. Importantly, the power load balancing can reduce the cost from power generations since no cost is spent on the requirement of frequency control. This paper focuses on the optimal allocation of the elastic load for load balancing. A load balancing problem, with the node and the group power upper bound constraints, is investigated in this paper. Water-filling algorithm is proposed for exactly and efficiently computing the optimal solutions to the power load balancing optimization problem with lower degree polynomial computational complexity. The proposed algorithm can be applied to solve the target optimization problems with large-scale due to utilization of non-derivative water-filling method. To the best of the authors' knowledge, there is no existing algorithm reported in the open literature that can compute the exact solution to the target problem.
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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".