Fluid Approximation of Smart Grid Systems: Optimal Control of Energy Storage Unit
Bibliographic record
Abstract
A model of the smart grid system with two different energy sources -the main grid and the energy storage units -is considered.The arriving power demands can be activated by either type of energy sources, with differing rates and costs.Finding an optimal policy that minimizes the expected long-run operational cost of the system is the main interest of this work.The problem is considered in a so called heavy traffic regime, and is solved using fluid approximation techniques.The formal scaling limit of the problem leads to a simple deterministic optimization problem, whose solution is shown to be an achievable lower bound on the limiting cost of the stochastic problem.Three different scenarios are considered according to whether the batteries are disposable or rechargeable and whether the arrival rates are homogeneous or nonhomogeneous.The solution method provides a good alternative to numerical methods such as Markov Decision Processes. RES modelThe smart grid system in which rechargeable energy storage units (batteries) are used.x
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".