A fractional derivative approach to modelling a smart grid-off cluster of houses in an isolated area
Bibliographic record
Abstract
This paper presents an operational model of an electrical power supply in order to meet the load of a cluster of houses in a remote mountainous area.In outlying areas, an isolated power network represents the most economical solution.However, the implementation of a cluster of houses in an electrical microgrid requires optimal management of the power supply-demand in order to reach the users' requirements.Our case study is located in the "Cirque de Mafate" in Reunion Island.To build the model, the different types of individual consumption and the available energy production in situ are described.Energy management is achieved through a large mixed integer linear programming system.The model allows the production to fit the consumption by minimizing losses.Numerical calculations have been performed in order to determine an optimal solution that minimizes the use of the battery energy storage system and also satisfies the comfort of the inhabitants.The use of fractional derivative is introduced in the battery storage model.Simulations show that this emerging technology may lead to a technical solution that meets the above requirements of battery energy use and consumer satisfaction.It is also shown that a most effective and efficient use of energy resources is required in order to achieve sustainable management of electrical energy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".