Optimal Power Allocation for Hybrid Energy Harvesting and Power Grid Coexisting System With Power Upper Bounded Constraints
Bibliographic record
Abstract
As one of the green energy resources, the technique of energy harvesting harnesses energy from its surrounding environment. In this setting, a power grid is also utilized to serve as a supplementary source to regulate the not-so-stable harvested energy supply of the system. The power allocated to the user(s) from the sum of the harvested energy and the power grid is subject to peak power constraints. The background of these constraints comes from field requirements, such as avoiding the saturation of power allocated to the user(s), avoiding system level out-of-band power leakage, and reducing interference with other transmitter(s) due to the nonlinearity generated via the transmitting mechanisms to the user(s). The proposed problem considers simultaneously 1) the hybrid paradigm of both energy harvesting and grid power supplies, and 2) the peak power constraints in such systems. For our proposed problem, the most efficient known-to-date and popular convex optimization method of primal-dual interior method (PD-IPM) only computes an € solution, not an optimal solution, even with more computations. The novelty of the proposed algorithms is that they compute the exact solutions with the low degree polynomial computational complexity. To the best of the authors' knowledge, under the same assumptions, no prior publication, including PD-IPM, can arrive at such results. Numerical examples also illustrate efficiency of the proposed algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".